1 FUNCTIONAL ROLES OF THETA AND ALPHA BAND NEURAL OSCILLATIONS IN MEMORY AND ATTENTION By KRISTOPHER LEE ANDERSON A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE RE QUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2011
2 2011 Kristopher Lee Anderson
3 To my family: Mom, Dad, and Brother
4 ACKNOWLEDGMENTS I would like to start by thanking my mentor, Dr. Mingzhou Ding, whose guidanc e has given me valuable insight into what it takes to be a successful researcher. His hard work and dedication is a great inspiration and I hope to one day live up to his example and provide similar guidance to future students. Extra special thanks to Dr. Keith Berg for introducing me to cognitive neuroscience research and congratulations on your retirement I sincerely thank Dr. Kimford Meador for hi s support and collaboration. Thanks to Dr. Charles Schroeder for sharing the primate data Thank you to my c ommittee members: Dr. Linda Hermer, Dr. Hans van Oostrom, and Dr. Bruce Wheeler for your support and advice. A very special thanks to Dr. Rajasimhan Rajagovindan for showing me the ropes and being my research big brother through graduate school. I am also very thankful to all of my colleague s in the lab, past and present: Yonghong Chen, Mukesh Dhamala, Yan Zhang, Xue Wang, Anil Bollimunta, Hariharan Nalatore, Sahng Min Han, Mo Ju e, Yuelu Liu, Xiaotong Wen, Chao Wang Haiqing Huang and Amy Trongnetrpunya fo r the fun and insightful conversations. I feel comfortable saying that the Ding Lab is the best lab in the BME department. (No offense to the other faculty, though! ) The BME staff : Kathryn W hitesides, Tifiny McDonald, Danielle Wise, Anide Pierre Louis, and April Derfinyak have been awesome Thank you guys for putting up wit h my paperwork procrastination. Also, thanks to Art Bautista Hardman for keeping the computers running smoothly. Thank you to all of the volunteers who participated in our studies and mad e this research possible, particularly the patients at Shands who gave their time and effort in order to help others during a very difficult time in their lives.
5 Above all, I save my most heartfelt appreciation for Carolyn and John Anderson, my mom and da d, for who m I owe everything I am and everything I will be When times are tough, I draw strength from your unconditional love When times are good, I look forward to sharing my happiness with you. You guys are literally the best parents in the universe A lso, thanks to my brother, Andy Anderson, for not pantsing me more than you did.
6 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 8 LIST OF FIGURES ................................ ................................ ................................ .......... 9 ABSTRACT ................................ ................................ ................................ ................... 10 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 13 Aim 1 ................................ ................................ ................................ ....................... 13 Aim 2 ................................ ................................ ................................ ....................... 14 Aim 3 ................................ ................................ ................................ ....................... 15 2 ANALYSIS OF CORTICAL THETA RHYTHMS DURING A MEMORY TASK ........ 17 2.1 Background and Significance ................................ ................................ ......... 17 2.1.1 Role of Prefrontal Cor tex and Medial Temporal Lobe in Memory Processes ................................ ................................ ............................ 17 2.1.2 Role of the Theta Rhythm in Memory Processes ............................... 18 2.2 Materials and Me thods ................................ ................................ ................... 19 2.2.1 Subjects and Electrode Placement ................................ ...................... 19 3.2.2 Experimental Paradigm ................................ ................................ ....... 20 3.2.3 Data Analysis and Hypothesis Testing ................................ ................ 21 2.3 Results ................................ ................................ ................................ ........... 25 2.3.1 Behavioral Results ................................ ................................ ............... 25 2.3.2 Power Results ................................ ................................ ..................... 25 2.3.3 Coherence Results ................................ ................................ .............. 26 2.3.4 Granger Causality Resu lts ................................ ................................ ... 27 2.4 Discussion ................................ ................................ ................................ ...... 28 2.4.1 Theta and PFC MTL Interaction ................................ .......................... 29 2.4.2 Theta and Neuronal Communication ................................ ................... 30 2.4.3 Generation and Propagation of Cortical Theta ................................ .... 31 3 ATTENTIONAL MODULATION OF THE SOMATOSENSORY MU RHYTHM IN HUMANS ................................ ................................ ................................ ................ 42 3.1 Background and Significance ................................ ................................ ......... 42 3.2 Materials and Methods ................................ ................................ ................... 45 3.2.1 Participants ................................ ................................ .......................... 45 3.2.2 Stimulation Device ................................ ................................ ............... 45 3.2.3 EEG Recording ................................ ................................ .................... 45
7 3.2.4 Experimental Design and Paradigm ................................ .................... 46 3.2.5 Source Estimation ................................ ................................ .............. 47 3. 2.6 Data Preprocessing ................................ ................................ ............. 49 3.2.7 Behavior and Evoked Potential Analysis ................................ ............. 50 3.2.8 Spectral Power Analyses ................................ ................................ ..... 50 3.2.9 Correlation Between Prestimulus Mu Power and Evoked Potential Amplitude ................................ ................................ ............................ 51 3.2.10 Time Frequency Analysis of Mu and Beta Activity in SI ...................... 52 3.3 Results ................................ ................................ ................................ ........... 53 3.3.1 Behavior ................................ ................................ .............................. 53 3.3.2 Somatosensory Evoked Po tential (SEP) ................................ ............. 54 3.3.3 Prestimulus Power in 8 12 Hz: Scalp Level ................................ ......... 55 3.3.4 Prestimulus Power in 8 12 Hz: Source Level ................................ ...... 56 3.3.5 Prestimulus Power in 15 35 Hz: Source Level ................................ .... 58 3.3.6 From Prestimulus Mu Power To Stimulus Evoked Activity .................. 59 3.3.7 Time Frequency Analysis of Mu and Beta Activity in SI ...................... 60 3.4 Discussion ................................ ................................ ................................ ...... 60 3.4.1 Mu and Attention ................................ ................................ ................. 61 3.4.2 Evoked Activity and Attention ................................ .............................. 66 3.4.3 Relationship Between Prestimulus Mu and Evoked Activ ity ................ 69 3.4.4 Summary ................................ ................................ ............................. 71 4 LAMINAR ANALYSIS OF ELECTROPHYSIOLOGICAL RECORDINGS FROM SI IN NONHUMAN PRIMATES ................................ ................................ .............. 80 4.1 Background and Significance ................................ ................................ ......... 80 4.2 Methods ................................ ................................ ................................ ......... 81 4.2.1 Experimental Task ................................ ................................ ............... 81 4.2.2 Data Collection ................................ ................................ .................... 82 4.2.3 Evoked Potential and Current Source Density Analysis ...................... 83 4.2.4 Correlation Between Prestimulus Mu Power and P20 Amplitude ........ 83 4.2.5 Phase Realignment and Averaging ................................ ..................... 84 4.3 Results ................................ ................................ ................................ ........... 85 4.4 Discussion ................................ ................................ ................................ ...... 86 5 CONCLUSION ................................ ................................ ................................ ........ 93 LIST O F REFERENCES ................................ ................................ ............................... 96 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 113
8 LIST OF TABLES Table page 2 1 Total number o f bipolar derivations in each area and total number of inter grid pairwise combinations of bipolar signals for each grid pair. ......................... 34 2 2 P values from Wilcoxon signed rank test for difference in theta coherence peak values between free recall and baseline. ................................ ................... 34
9 LIST OF FIGURES Figure page 2 1 Approximate placement of electrode grids for each of the three subjects. ......... 35 2 2 Schematic of the experimental paradigm. ................................ .......................... 36 2 3 Power spectra in each of the three areas for Subjec t 3.. ................................ .... 37 2 4 Inter grid coherence results for Subject 3. ................................ .......................... 38 2 5 Average percent of significantly coherent inter grid bipolar sig nal pairs ............. 39 2 6 M ean Granger causality values for PFC MTL in each subject. ........................... 40 2 7 R epresentation of the causal relationship for theta between PFC and MTL. ...... 41 3 1 Schematic of the experimental paradigm.. ................................ ......................... 72 3 2 Regional sources seeded for source space anal ysis. ................................ ......... 73 3 3 Somatosensory evoked potential comparison. ................................ ................... 74 3 4 Prestimulus power comparison in the sensor space. ................................ .......... 75 3 5 Power spectral analysis in the source space. ................................ ..................... 76 3 6 M ean mu 8 12 Hz band power and b eta 15 35 Hz band power ........................ 77 3 7 From prestimulus mu power to stimulus evoked response.. ............................... 78 3 8 Time course of mu and beta power in SI. ................................ ........................... 79 4 1 Stimulus evoked activity in SI.. ................................ ................................ ........... 90 4 2 From prestimulus mu power to evoked P20.. ................................ ..................... 91 4 3 10Hz ongoing activity in SI.. ................................ ................................ ............... 92
10 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy FUNCTIONAL ROLE S OF THETA AND ALPHA BAND NEURAL OSCILLATIONS IN MEMORY AND ATTENTION By Kristopher Lee Anderson May 2011 Chair: Mingzhou Ding Major: Biome d ical Engineering Synchronized neural activity involving widespread networks is common in the central nervous sys tem. This activity often manifests itself as oscillations, which at one point were considered to be background noise or an indication of an idling state of the brain. It is now generally accepted that these oscillations play a role in higher order cognitiv e processes, and these roles are currently under active investigation. In this dissertation, we study the roles of theta (4 8 Hz) and alpha (8 12 Hz) band oscillations in two higher order cognitive processes: memory and attention. First, we studied the rol e of theta (4 8 Hz) oscillations in the communication between two distant brain regions that are both involved in memory processes The medial temporal lobe (MTL) and the prefrontal cortex (PFC) are known to be critical structures for human memory processe s. Furthermore, it has been suggested that they are part of a memory network. While memory modulated interaction between PFC and MTL has been observed at the hemodynamic level, it remains unclear what the neuronal process is that mediates the communication between these two areas. Experiments in rodents suggest that field oscillations in the theta band (4 8 Hz) facilitate PFC MTL interaction. No such evidence has been reported in humans. To address this
11 problem, cortical electrical activity from MTL, PFC an d lateral temporal lobe was recorded from implanted electrode grids in three epilepsy patients performing a verbal free recall memory task. The data were analyzed using a parametric spectral method to obtain estimates of power, coherence, and Granger causa lity. A task modulated increase in coherence values between PFC and MTL was seen during free recall as opposed to a baseline condition. Concurrently, the number of coherent PFC MTL site pairs was significantly increased during recall. Granger causality ana lysis further revealed that the increased coherence is a consequence of higher bidirectional information flow between the two regions, with a generally greater driving from MTL to P FC, namely, (MTL PFC) > (PFC MTL). We then investigated the role of mu and alpha (8 12 Hz) oscillations in somatosensory spatial attention. Neural oscillations with a frequency of around 10 Hz are thought to be a ubiquitous phenomenon in sensory cortices, and it has been hypothesized that the level of 10 Hz activity is related to local cortical excitability. During spatial attention, the visual alpha rhythm has been found to be modulated according to the direction of attention. Specifically, a desynchronization (decrease in amplitude) of the alpha rhythm over visual cortex contral ateral to the direction of attention as well as a synchronization (increase in amplitude) over visual cortex ipsilateral to the direction of attention have been reported. These modulations have been associated with both a facilitation and an inhibition of sensory processing, respectively. It has been proposed that the somatosensory mu rhythm serves a similar function to the visual alpha rhythm, and the two rhythms have been found to have similar behaviors in cognitive tasks such as working memory. In this c hapter we investigate whether the somatosensory mu
12 rhythm is somatotopically modulated by spatial attention in a way similar to the visual alpha rhythm. 128 channel EEG was recorded while subjects performed a somatosensory spatial attention task. In addit ion to analyses on scalp recorded data, a spatial filtering method was utilized to investigate spatial attention effects in the source space. The direction of spatial attention was found to have an effect on the ongoing mu rhythm occurring in primary somat osensory cortex as well as stimulus evoked activity. Lastly, an analysis was performed to investigate the correlation between the level of prestimulus mu activity and subsequent evoked activity in primary somatosensory cortex. Finally, we further investiga ted the previous findings regarding the mu rhythm and its relationship with evoked activity by utilizing microelectrode recordings through the cortical laminae of Area 3b in the primary somatosensory cortex of a rhesus monkey during somatosensory stimulati on as well as during a baseline period. We were able to confirm that oscillatory activity in the mu band indeed occurs in primary somatosensory cortex. By examining the stimulus evoked P20 component, a homologue of the human P50 (also known as the P1) soma tosensory evoked component, we found evidence supporting the previous interpretations that the human P50 is associated with local inhibition.
13 CHAPTER 1 INTRODUCTION Since first being recorded by Hans Berger in 1929 (Berger 1929) n eural oscillations have been a major focus of neuroscience research and there has been much debate as to the functional significance of these oscillations in the brain. Early research led to the theory that prominent neuronal oscillations were merely an indicator state in the brain. This was due primarily to the fact that the amplitude of these oscillations, particularly the alpha or mu (8 10 Hz) rhythms, appeared to be higher during resting as opposed to active states (Kuhlman 1978; Pfurtscheller et al., 1997). Mo re detailed studies using controlled conditions ha ve led to a rethinking of this view. For example, alpha oscillations recorded over posterior electrodes (Jensen et al., 2002) and theta (4 7 Hz) oscillations recorded over frontal electrodes (Jensen and Tes che 2002 ; Meltzer et al., 2008 ) have both been shown to increase dur ing certain short term memory retention tasks raising the possibility that these rhythms might play an active part in higher order cognitive processes Currently, our understanding of ne ural oscillations, including both their functional roles as well as the mechanisms of their generation is quickly expanding and i t is now becoming clear that the y are critical to the proper functioning of the brain, not just epiphenomena of neural firing This dissertation explores the functional significance of theta and alpha band oscillations in two higher order cognitive processes; namely, memory and attention. This exploration is carried out along three specific aims: Aim 1 An investigation of the the ta rhythm as a facilitator of communication between the prefrontal cortex and medial temporal lobe during memory. The medial temporal lobe
14 (MTL) and the prefrontal cortex (PFC) are known to be critical structures for human memory processes. Furthermore, it has been suggested that together, they form part of a memory network. While memory modulated interaction between PFC and MTL has been observed at the hemodynamic level, what remains unclear is the neuronal process that mediates the communication between t hese two areas. Experiments in rodents suggest that field oscillations in the theta band (4 8 Hz) facilitate PFC MTL interaction. No such evidence has been reported in humans. We investigated this by recording intracortical EEG while subjects performed a v erbal free recall task and during a relaxed baseline period. Coherence and Granger causality were estimated between prefrontal and medial temporal areas to determine whether or not an interaction exists and, if so, the dynamics of this interaction. The res ults of this investigation (Chapter 2) have been published (Anderson et al., 2010). Aim 2 An investigation of the effects of directed spatial attention on the somatosensory mu rhythm. Directed attention is a mechanism through which brain resources are all ocated to increase detection and/or discrimination of relevant stimuli. Spatial directed attention in the visual domain has been shown to affect the ongoing 8 12 Hz alpha rhythm, specifically causing a suppression of alpha over parieto occipital sites cont ralateral to the direction of attention ( Sauseng et al., 2005; Thut et al., 2006; Worden et al., 2000; Wyart and Tallon Baudry, 2008 ). The somatosensory mu rhythm has traditionally been seen as being functionally similar to the visual alpha rhythm (Pfurtsc heller et al., 1996). However, the effect of spatial directed attention on the somatosensory mu rhythm has not yet been reported in the literature.
15 In order to investigate the effects of lateralized spatial attention on the somatosensory mu rhythm, we reco rded high density EEG while subjects performed a somatosensory directed attention task. In addition to analyses on scalp recorded data, a spatial filtering method was utilized to investigate spatial attention effects in the source space. Furthermore, we in vestigate whether mu band oscillatory activity exists in primary somatosensory cortex by performing CSD analysis on ongoing local field potentials recorded directly from Area 3b in an awake behaving monkey Aim 3 An investigation of the role of the somato sensory mu rhythm in stimulus processing. Directed spatial attention has been shown to have an effect on early and middle latency SEPs ( Eimer and Forster, 2003; Garcia Larrea et al., 1991, 1995; Jones et al., 2009, 2010; Mauguiere et al., 1997b ). Our findi ngs show that directed spatial attention also modulates ongoing somatosensory mu activity. Accordingly, one might suspect that these two phenomena are related in some way. This assumption is supported by experiments showing a relationship between prestimul us mu rhythm amplitude and somatosensory evoked responses. An inverted U relationship has been found between the amplitude of the ongoing prestimulus mu rhythm and the N1 amplitude (Zhang and Ding 2009). Additionally, mu rhythm amplitude has been shown to influence early evoked responses measured using magnetoencephalography (Nikouline et al., 2000 b; Jones et al. 2009, 2010 ). Using the EEG data recorded while subjects performed a somatosensory attention experiment, we investigated the effect of the amplitud e of the somatosensory mu rhythm immediately before stimulus arrival on evoked activity during different attentional states. We follow this up by performing a similar correlation between prestimulus mu
16 power and early evoked activity on laminar recordings from an awake behaving monkey. The results of the human EEG experiement (Chapter 3) have been published (Anderson and Ding, 2011).
17 CHAPTER 2 ANALYSIS OF CORTICAL THETA RHYTHMS DURING A MEMORY TASK 2 .1 Background and Significance 2 .1.1 Role of Prefrontal C ortex and Medial Temporal Lobe in Memory Processes Two brain areas consistently implicated in human memory are the medial temporal lobe (MTL) and the prefrontal cortex (PFC). The role of the MTL in memory processes was well established in Scoville and Miln MTL lesions led to anterograde amnesia. Subsequent functional neuroimaging (Cohen et al., 1999; Daselaar et al., 2001) and electrophysiological studies (Fell et al., 2001) ole in episodic encoding and retrieval. Similarly, lesions in the PFC are known to cause impaired memory functions, such as free recall (Shimamura 1995; Wheeler et al., 1995) and working memory (Petrides and Milner 1982; Bechara et al., 1998). PET and fMR I imaging studies have reported reproducible activations of the PFC in a broad array of memory processes (McIntosh et al., 1997; Fletcher and Henson 2001; Bunge et al., 2004; Dove et al., 2006). The hypothesis that both structures are part of a unified me mory network is supported by the direct anatomical pathways linking PFC and MTL in monkeys and rats (Goldman Rakic et al., 1984; Squire et al., 1989; Burwell et al., 1995; Suzuki 1996; Degenetais et al., 2003). In humans, functional connectivity analysis of fMRI data has revealed correlations between the lateral PFC and MTL in working memory (Gazzaley et al., 2004), episodic encoding (Grady et al., 2003), and episodic retrieval (Nee and Jonides 2008). These functional connectivity results suggest that not only are PFC and MTL both involved in memory, their task related activity is statistically correlated, which can be taken to imply interaction. However, analyses at the hemodynamic level do not provide direct insight
18 into how these interactions are media ted physiologically. Additional details of the interaction, including directions of information flow, remain not known. 2 .1.2 Role of the Theta Rhythm in Memory Processes What are the possible neuronal processes that could mediate the interaction between t hese two areas? Animal studies have long suggested that the theta rhythm, a prominent 4 to 8 Hz oscillatory phenomenon in the limbic system, is closely linked to the formation, storage and retrieval of memory (Miller 1991; Kahana et al., 2001; Buzsaki 20 02; Vertes 2005). More recent work postulates that during memory processes, theta oscillations mediate interactions between MTL and other cortical areas (Jensen and Lisman 2005), including PFC (Jones and Wilson 2005). In humans, theta activity has been ob served in MTL (Meador et al., 1991; Kahana et al., 1999; Tesche and Karhu 2000; Eckstrom et al., 2005) as well as in frontal areas (Asada et al., 1999; Jensen and Tesche 2002; Sederberg et al., 2003). Furthermore, these theta activities were shown to cor relate with memory performance. The issue of whether or not theta is a physiological process that mediates PFC MTL interaction in humans remains unresolved. A recent study by Raghavachari et al., (2006), utilizing human intracranial recordings, found a lac k of coherent theta activity between distant cortical sites during working memory. This finding suggests two implications: (1) there is a significant species difference in the processes that mediate cortical interaction during memory or (2) experimental or analytical issues have prevented the observation of the role of theta activity in human inter areal interaction. To examine this problem, three patients undergoing presurgical evaluation for intractable epilepsy were recruited to perform a free recall mem ory task. Intracranial electroencephalogram (iEEG), also known as electrocorticogram (ECoG), was recorded
19 from multiple implanted electrode grids. The subjects were first given a series of words to remember. Then, following a distraction period, the subjec ts were asked to recall the words from memory. Data from the period of recall was compared with that from a baseline period of eyes open fixation. Consistent with the hypothesis that theta acts to mediate memory related interaction between PFC and MTL, gr eater theta band coherence was found during recall between electrodes in the prefrontal and in the medial temporal areas. Granger causality analysis was used to estimate the directionality of this theta interaction, further defining the role played by each area. 2 .2 Materials and Methods 2 .2.1 Subjects and Electrode Placement Three epilepsy patients gave informed consent and participated in the study. The experimental and recording protocol was approved by the institutional review board of the University of Florida and the affiliated Shands Hospital at the University of Florida. Figure 2 1 illustrates the approximate positions of the implanted electrode grids in each of the three subjects. Two subjects had electrodes placed on the left hemisphere and the rem aining subject had electrodes placed on the right hemisphere. All three subjects had grids covering the lateral prefrontal cortex (PFC) and lateral temporal lobe (LTL), as well as strips of electrodes on the ventral surface of the temporal lobe. The two mo st medial electrodes on the ventral strips, henceforth referred to as subtemporal grids, were treated as proxies of medial temporal lobe (MTL) activity due to their proximity to the parahippocampal region. In each grid, the electrodes were 3mm in diameter with a spacing of 10mm between neighboring electrodes. Depending on the subject, additional grids were implanted, but were not included in the analyses for lack of corresponding coverage in all subjects.
20 3.2.2 Experimental Paradigm The experimental paradig m was a verbal free recall and recognition task. An LCD monitor placed three feet in front of the subject was used to present the stimuli. A fixation cross remained in the center of the screen throughout the experiment. As illustrated in Figure 2 2, the experiment consisted of multiple blocks, with each block starting with the sequential presentation of 20 words (encoding period) chosen from the Kucera and Francis word pool (Kucera and Francis 1967). Each word was displayed on the screen for 2 seconds wit h a delay between words randomly selected from between 1 and 2 seconds (mean 1.5 seconds). The subject was instructed to try to remember as many of the words as possible. Next, in order to minimize recency effects and to discourage verbal rehearsal, the s ubjects were asked to count aloud, backward by threes, starting at a random number for 30 seconds (distraction period). Following this distraction period, subjects were given 50 seconds to recall aloud as many of the previously presented words as they coul d remember (free recall period). After this free recall period, another set of twenty words was presented, which was followed by another distraction period and another recall period. Finally, an additional distraction period was given followed by a presen tation of 80 words sequentially on the screen, consisting of the 40 previously presented words along with 40 words which had not been seen previously (recognition period). The subjects were asked to respond by button presses to indicate whether or not they recognized the word as being a previously presented word or a new word. The word stayed on the screen until the subject responded. Each response was followed by a delay of between 1 and 2 seconds (mean 1.5 seconds) before the appearance of the next word. The subjects each performed three blocks. At the end of each block, a 1 minute baseline period
21 during which the subject maintained their gaze on the fixation cross was recorded. The subjects were then allowed a short break before beginning the next block The current analyses were focused on the data from two conditions: free recall and baseline fixation. 3.2.3 Data Analysis and Hypothesis T esting Data were sampled at 400 Hz by a Nicolet amplifier system, band pass filtered from 0.16 to 30 Hz, and downs ampled to 200 Hz. Data segments contaminated by artifacts were excluded from further analysis. The remaining artifact free data was divided into non overlapping epochs of 500 ms in length. Epochs from different blocks but within the same condition (free r ecall or fixation baseline), after a bipolar treatment (see below), were combined and treated as realizations of an underlying stochastic process to be characterized by power, coherence, and Granger causality spectra. Physiological differences in these var iables between the two conditions were then assessed. Bipolar derivations: The raw data in each implanted electrode grid were recorded against a common reference electrode fixed to the scalp of the subject contralateral to the hemisphere of grid placement. The reference electrode is not free of neural activity, which may potentially confound functional connectivity measures such as coherence and Granger causality, as the activity underlying this electrode will appear in all recorded channels. Volume condu ction presented a further complicating factor. To overcome these problems, the data were re referenced as bipolar signals. Specifically, for the two most medial electrodes in the subtemporal grid, their difference was treated as a representation of MTL ac tivity. For the frontal and lateral temporal grids, the differences between all pairwise combinations of horizontally, vertically, and diagonally neighboring electrodes were used to represent activity in the respective brain regions.
2 2 These difference signa ls will henceforth be referred to as bipolar signals or bipolar derivations. Table 2 1 gives the number of such signals in each of the three recording grids for all three subjects. For Inter grid analysis, three combinations are possible: PFC MTL, LTL MTL, and PFC LTL. Bipolar derivations from every grid were pairwise combined with bipolar derivations from another grid and coherence and Granger causality were computed for each such pair. The total number of such pairs is given in Table 2 1 for each of the t hree inter grid combinations in each subject. Spectral analysis: Each pair of bipolar signals was subjected to autoregressive (AR) spectral analysis ( Ding et al., 2000 ; Ding et al., 2006; Rajagovindan and Ding 2008). Briefly, l et the pair of bipolar signa ls at time t be denoted by X t = (x 1t x 2t ) T where T stands for matrix transposition. Assume that the data can be described by the following AR model: ( Eq. 2.2.3. 1) where E t is a temporally uncorrelated residual error series w ith covariance matrix k are 2 2 coefficient matrices to be estimated from data (Ding et al., 2000 ; Ding et al., 2006 ). The model order m was determined by the Akaike Information Criterion (AIC) (Akaike 1974) and was further verified by comparing the spectral estimates from the AR model with that from the Fourier method For the data analyzed in this study m = 17 was chosen as a tradeoff between sufficient spectral resolution and over parameterization. Once the model coefficients A k ated, the spectral matrix can be evaluated according to :
23 ( Eq. 2.2.3. 2) where the asterisk denotes matrix transposition and complex conjugation and is the transfer function. The power spectrum of ch annel l is given by S ll (f) (l = 1 or 2), which is the l th diagonal element of the spectral matrix S(f). The coherence spectrum between channel l and channel k is: ( Eq. 2.2.3. 3) The value of coherence ranges from one to zero, with one indicating maximum interdependence between the two bipolar signals at frequency f and zero indicating no interdependence. The Granger causality spectrum from x 2t to x 1t is defined as (Geweke 1982; Brovelli et al., 2004 ; Ding et al., 2006 ): ( Eq. 2.2.3. 4) which can be interpreted as the proportion of x 2t power of the x 1t series at frequency f. The logarithm is taken to preserve certain favorable statistical propert ies. Similarly, the causality spectrum from x 1t and x 2t can be obtained by switching the indices 1 and 2 in Eq. ( 2.2.3. 4). In the present work, Granger causality analysis was carried out for those pairs of bipolar signals whose coherence in the theta range was deemed statistically significant (see below). Interpretation of Granger causality: Statistically, for two simultaneously measured time series, one series can be called causal to the other if we can better predict the
24 second series by incorporating pas t knowledge of the first one (Wiener, 1956). This concept was later adopted and formalized by Granger (1969) in the context of linear regression models of stochastic processes (see Eq. ( 2.2.3. 1)). Specifically, if the variance of the prediction error for t he second time series at the present time is reduced by including past measurements from the first time series in the linear regression model, then the first time series can be said to have a causal (directional or driving) influence on the second time ser ies. Reversing the roles of the two time series, one repeats the process to address the question of causal influence in the opposite direction. Here directions of causal influence are equated with directions of synaptic transmission of neuronal activity ( Ding et al., 2006; Bollimunta et al., 2008) Random permutation test for statistical significance: To test the significance of inter grid coherence, the following procedure was followed (Brovelli et al., 2004), the aim of which is to create a null hypothes is distribution for the peak coherence values in the theta range for both conditions. (1) The 500ms epochs were numbered from 1 to N where N is the total number of epochs for a given condition (free recall or baseline). (2) The epoch index from each brain region was permuted randomly to create a synthetic data set where it is reasonable to assume that no interdependence exists between any pair of bipolar derivations. (3) All pairwise coherence was calculated for the synthetic data set and the single largest peak coherence value among all coherence values in the theta range (4 8 Hz) from all channel pairs was selected. (4) Steps (2) and (3) were repeated many times. (5) The null hypothesis distribution was estimated by fitting an extreme value distribution to the peak coherence values (Wang et al., 2007). Calculated coherence values from the experimental data were considered significant if they
25 exceeded the 99.9th percentile value of the maximum null hypothesis distribution between conditions (p<0.001). The an alysis of a frequency range as opposed to that of a single frequency point and the simultaneous comparison of many inter grid channel pairs present a multiple comparison problem. The traditional Bonferroni correction is not applicable here as the underlyin g variables may not be independent. Choosing the maximum value in Step (3) is a way to account for this problem (Nichols and Holmes 2002). 2 .3 Results 2 .3.1 Behavioral Results All subjects completed the task according to instructions. Subjects sustained at tention throughout the experiment and actively attempted to recall the presented words throughout the free recall periods. The mean percent of words correctly recalled was 27.84.7% which is in line with previously reported results in healthy subjects (Fer nandez et al., 1998). During the recognition phase of the task, subjects correctly recognized a mean of 92.75.5% of the words presented. 2 .3.2 Power Results Spectral power was estimated for all the bipolar signals in each of the three implanted grids usin g the parametric AR approach and the result from Subject 3 (see Figure 2 1 for approximate electrode locations) is shown in Figure 2 3. A small peak in the range between 4 and 8 Hz was seen in most bipolar derivations, indicating synchronized local theta o scillations (Kahana et al., 1999; Raghavachari et al., 2001; Sederberg et al., 2003; Canolty et al., 2006). The power spectra for the remainin g two subjects are similar. The average peak theta frequency between all subjects was: 7.41 Hz for MTL, 7.78 Hz fo r LTL and 7.35 Hz for PFC. The larger spectral peaks at the
26 lower frequencies (~2Hz) are an artifact of combining the band pass filtering (0.16 to 30 Hz) with the 1/f spectral characteristic of the EEG signal (Slutzky 1937; Pritchard 1992; Demanuele et al. 2007). The thick solid curves are the averages of the bipolar power in each grid for each condition. For all three subjects, no consistent difference in theta power between the two conditions was found in any of the three regions 2 .3.3 Coherence Results To investigate the level of interaction between the cortical areas, coherence spectra between all possible pairwise combinations of bipolar derivations between a given pair of grids were estimated, and the result for PFC MTL from Subject 3 is shown in Fig ure 2 4A. In contrast to the power spectra in Figure 2 3 where the theta peak is rather modest, coherence peaks in the theta range were much more prominent, indicating communication between the two brain regions via theta band oscillations. Moreover, the a verage theta coherence (thick solid curves) is higher for free recall than for baseline. This impression is confirmed in Figure 2 4B where peak coherence values in theta range are plotted for the two conditions. The height of the rectangular bars is the me an. A Wilcoxon signed rank test revealed that the interaction between PFC and MTL was significantly higher when the subject actively recalled words compared to baseline (p=0.00002). No systematic spatial patterns were seen in frontal electrodes that showed increased coherence with MTL. For PFC LTL the coherence is slightly higher during free recall (p = 0.0089). There is no significant difference between the two conditions for LTL MTL (p = 0.77). This pattern of interaction is found in all three subjects, a s summarized in Table 2 2 below. Another way of quantifying the task related modulation of inter areal theta synchrony is the number of pairwise bipolar combinations whose peak theta coherence
27 values exceed the estimated 99.9% confidence thresholds (horizo ntal lines in Figure 2 4B; see Methods) which corresponds to a significance level of p<0.001. Since the number of channels in each grid varied between subjects, the percentages of pairwise combinations above threshold were calculated and averaged across su bjects. As seen in Figure 2 5, there is a highly significant (p = 0.002) task related increase in the percentage of theta coherent bipolar pairs for PFC MTL. For PFC LTL a smaller increase in the number of coherent pairs is observed (p = 0.02). For LTL MTL while the percentage of pairs exhibiting significant theta coherence is high, the difference between the two conditions is not significant (p = 0.31). 2 .3.4 Granger Causality Results Coherence is a symmetric interdependence measure. Namely, when A is coh erent with B, B is equally coherent with A. To gain insight into the information flow pattern between the PFC and MTL during memory performance, Granger causality analysis was carried out on the pairs of bipolar signals that showed significant theta cohere nce during recall. The results for the three subjects are shown in Figure 2 6. The peak and both conditions. This suggests that MTL theta plays a greater role in driving PFC MTL synchrony than PFC theta. Reciprocal causal influence from PFC to MTL is also seen, indicating that the communication is bidirectional. Consistent with the task rela ted increase of coherence in Figures 2 4 and 2 5, Granger causality values in both directions show an increase for the free recall condition compared to the baseline fixation condition. For Subjects 1 (left panel) and 3 (right panel) the increase is signif icant at p<0.01. For Subject 2 (middle panel), the p value cannot be assessed, the
28 reason being that no Granger causality value is available during the baseline period due to the fact that the number of bipolar signal pairs exhibiting significant theta coh erence for this subject during baseline is zero. These results, schematically summarized in Figure 2 4B, suggest that the increased coherence between the PFC and MTL in Figure 2 2 and Figure 2 3 is a consequence of increased communication in the theta band in both the PFC MTL and MTL PFC directions. 2 .4 Discussion In this manuscript, we investigated whether theta oscillations play a role in mediating the interaction between the prefrontal cortex and the medial temporal lobe in human memory processes. Cortic al electrical activity from three brain areas (lateral prefrontal cortex (PFC), medial temporal lobe (MTL), and lateral temporal lobe (LTL)) were recorded from implanted electrode grids in three epilepsy patients performing a verbal free recall task. The m ulti electrode data, after a bipolar treatment, were analyzed using an autoregressive spectral method to obtain estimates of spectral power, coherence, and Granger causality. Coherent theta activity was found in all pairwise combinations of the three corti cal regions. When the free recall condition was compared to the baseline fixation condition, a large task modulated increase in the overall coherence values between PFC and MTL was seen. At the same time, the number of coherent site pairs between PFC and M TL was also significantly increased during recall. Granger causality analysis of the coherent site pairs further revealed that the increased coherence is a consequence of higher bidirectional information flow between the two brain regions, with a generally greater driving from MTL to PFC.
29 2 .4.1 Theta and PFC MTL I nteraction The importance of theta activity in the hippocampus and other limbic system structures is well recognized (Buzsaki 2002; Vertes et al., 2004). Recent experiments with rats have begun t o provide evidence in support of the notion that PFC MTL interaction is mediated through theta oscillations and that this interaction is relevant for memory. Data in rats demonstrate that medial PFC neuronal firings are phase locked to hippocampal theta an d this entrained firing is modulated by animal behavior (Hyman et al. 2005). In addition, increased LFP theta coherence between these two areas has been observed in rats during voluntary behaviors (Young and McNaughton 2009) and during decision making (J ones and Wilson 2005). In humans, in spite of extensive evidence implicating PFC and MTL in memory related functions, the questions of whether they work together as part of a network and what physiological processes might mediate their interaction remain unanswered. This lack of understanding could be in part attributable to the difficulty of noninvasive electrophysiological access to MTL structures. Recording of intracranial EEG (iEEG or ECOG) from patients undergoing presurgical monitoring to determine epileptic seizure foci partly overcomes this limitation. A recent study by Raghavachari et al. (2006) used this recording technique to investigate the coherence of theta oscillations between sites throughout the brain during a working memory task. It is re ported that significant levels of coherence occurred only between nearby (<20mm) sites, while distant sites very rarely showed coherent theta activity. Consequently, it was concluded that, in different brain areas, cortical theta oscillations are generated independently. In this regard, our study can be seen as the first to report PFC and MTL interaction in the theta band in humans. While the theta coherence values between the two brain areas are generally
30 low, and are not statistically significant in most site pairs (> 80%), these values, as well as the number of coherent pairs between PFC and MTL, are nevertheless significantly higher during free recall of remembered words as opposed to a baseline condition. This task related modulation provides the key ev idence for the role of PFC MTL interaction in memory performance and suggests that theta activity is an underlying physiological process that may mediate this interaction. It should be noted that the memory related increase in PFC MTL communication is obse rved for both hemispheres (see Figure 2 1). While hemispheric asymmetry of memory functions is commonly found (Tulving et al., 1994; Nyberg et al., 1996; Habib et al., 2003), functional imaging studies have shown PFC activation during verbal free recall in both the left and the right hemispheres (Petrides et al., 1995; Fletcher et al., 1998). Hippocampal activation during verbal episodic retrieval has been found bilaterally in multiple imaging studies as well (Lepage et al., 1998). 2 .4.2 Theta and N euronal C ommunication Neuronal ensembles interact and communicate with one another through the transmission of action potentials which carry information. Increased theta coherence during memory recall is a reflection of increased theta phase locking (Bressler and Kelso 2001). Siapas et al. (2005) hypothesize that, over short timescales, theta phase locking could be a mechanism for directing information flow between brain regions. They found that neurons constantly fire action potentials at restricted phases of lo cal theta (see also Lakatos et al. 2008). An offset in theta phase between two neurons would allow the neuron with the earlier phase preference to drive the neuron with the later phase preference. Over longer timescales, these consistent relationships wou ld strengthen synaptic connections through spike timing dependent plasticity. This could
31 also lead to the formation of resonant phase locked loops between regions with activation and transmission delays summing to around 150ms, which corresponds to the est imated delays between PFC and MTL in humans (Miller 1991). Along another line of reasoning, Jensen (2001) suggests that cortical theta activity that is synchronous, yet out of phase, with hippocampal theta could allow decoding of phase encoded hippocampal phase, yet entrained to the hippocampal theta rhythm. With respect to the present experiment, two other considerations are relevant. First, field potential oscillations are accompan ied by rhythmic bursts of action potentials. Lisman pointed out that bursting is a more reliable means of transmitting information over long distance than single action potentials (Lisman 1997). Second, Lengyel et al. (2005), using computational modeling, show that memory retrieval might occur in a theta rhythmic fashion. Thus, the idea of theta serving as information carrier during memory recall can be seen as grounded in both theoretical and empirical considerations. This is in further agreement with pro posals where the role of theta oscillations in facilitating communication between the cortical and MTL structures has been emphasized (Miller 1991; Jensen 2005; Johnson 2006). 2 .4.3 Generation and Propagation of C ortical T heta Extensive evidence in anim al studies as well as the evidence presented here for humans suggests that theta oscillations may facilitate communications between PFC and MTL. The question remains as to where these oscillations are generated and how they propagate. The hippocampal theta rhythm is generally believed to be caused by input from rhythmically bursting GABAergic and cholinergic neurons in the medial septum (Vertes and Kocsis 1997) as well as through recurrent connections within the
32 hippocampus proper (Kocsis et al., 1999). Th e mechanisms underlying the generation of cortical theta are less understood. Cortical neurons have the ability to generate theta activity through the action of GABAergic interneurons (Blatow et al., 2003). Cholinergic input from structures in the basal fo rebrain is another important contributor (Liljenstrom and Hasselmo 1995; Jones 2004). Using phase analysis in rats, Siapas et al. (2005) proposed that PFC theta is the result of unidirectional theta input from the hippocampus. This idea is supported by T ierney et al. (2004), who found that hippocampal activity directly influences prefrontal interneurons which, as mentioned above, can create rhythmic cortical theta activity. These results seem to imply a passive role for the PFC in memory performance, a vi ewpoint at variance with work postulating an active role of the PFC in both memory encoding (Fletcher et al., 1998a) and retrieval (Buckner and Wheeler 2001, Fletcher et al., 1998b). Our Granger causality analysis shows that the PFC MTL interaction is bid irectional and, during free recall of verbal information, the compared to a baseline condition, with a generally greater causal influence from MTL to TL). This result supports an active role for the PFC in the present experimental paradigm. It is also in agreement with the prevailing notion that, at the top of the executive control hierarchy, the PFC coordinates posterior brain areas for goal oriented b ehavior (Knight et al., 1999; Fuster 2001; Miller and Cohen 2001). It should be cautioned, however, that the bidirectional PFC MTL interaction inferred from Granger causality, while consistent with the recurrent anatomical pathways existing between the t wo areas, cannot rule out the possibility that a third structure drives both
33 PFC and MTL (Kaminski et al., 2001). Further investigations with more extensive spatial sampling are needed to elucidate the exact network mechanism.
34 Table 2 1. Total number of bipolar derivations in each area and total number of inter grid pairwise combinations of bipolar signals for each grid pair. Subject # Number of bipolar derivations Number of inter grid combinations PFC LTL MTL PFC MTL PFC LTL LTL MTL 1 110 55 1 110 6050 55 2 55 55 1 55 3025 55 3 55 20 2 110 1100 40 Table 2 2. P values from Wilcoxon signed rank test for difference in theta coherence peak values between free recall and baseline. Subject # Grid pairs PFC MTL PFC LTL LTL MTL 1 0.00009 0.0007 0.52 2 0.00019 0.0021 0.39 3 0.00002 0.0089 0.77
35 Figure 2 1. Approximate placement of electrode grids for each of the three subjects.
36 Figure 2 2. Schematic of the experimental paradigm.
37 Figure 2 3. Power spectra in each of the three areas for Subject 3. Gray curves: spectra from individual bipolar derivations. Thick curves: average spectra. The large peak seen at the low frequency range (~2Hz) is an artifact resulting from combining the high pass action of the band pass filter (0.16 to 30 Hz) with the 1/f type spectral characteristic of EEG data. Theta frequency range is indicated by shaded background.
38 Figure 2 4. Inter grid coherence results for Subject 3. A) Coherence spectra of all pairwise combinations of bipolar derivations between PFC and the posterior MTL electrodes (gray curves) (see Figure 1). Thick curves: averages of the gray curves. Theta frequency range is indicated by shaded background. B) Coherence peak values in the theta range. Individual peaks are plotted as black dots. The horizontal placements of the dots within each bar are random. From left to right: PFC MTL, PFC LTL, and LTL MTL. Each bar represents tal lines indicate the significance threshold corresponding to p=0.001 for different grid pairs.
39 Figure 2 5. Average percent of inter grid bipolar signal pairs whose theta coherence exceeds the significance threshold for both baseline (white bars) and fr ee recall (black bars) conditions. The standard errors are plotted as error bars. The one sided t test p values are included below each plot.
40 Figure 2 6. Mean Granger causality values for the coherent pairs of bipolar signals for PFC MTL in each subject The number of such pairs is given below each condition. White and black bars represent MTL PFC and PFC MTL respectively. Standard errors are plotted as err or bars. Conditions where MTL PFC is larger than PFC MTL at p=0.05 level using a one sided paired t test are marked by an asterisk. Such comparison was not done for the baseline condition for Subject 2 as no coherent bipolar pairs were found for that subject in the baseline condition. Results from both subtemporal grids are combined for Subject 3.
41 Fig ure 2 7. Schematic representation of the causal relationship for theta activity between PFC and MTL.
42 CHAPTER 3 ATTENTIONAL MODULATI ON OF THE SOMATOSENS ORY MU RHYTHM IN HUMANS 3 .1 Background and Significance Field oscillations in the 8 12 Hz range have b een observed in visual, somatosensory, and auditory cortices, and they are referred to as alpha, mu, and tau rhythms, respectively. Among these rhythms, visual alpha is the most extensively studied (Shaw, 2003). Typically, the amplitude of alpha is higher over visual areas that are not engaged in a task and lower over areas that are engaged. In particular, when attention is deployed to a location in visual space, decreased alpha amplitudes (desynchronization) have been found over visual areas contralateral to the direction of attention (Sauseng et al., 2005; Thut et al., 2006; Wyart and Tallon Baudry, 2008), a modulation which is thought to reflect the engagement of relevant cortical areas (Medendorp et al., 2007) through a local increase of cortical excitab ility (Klimesch et al., 2007; Romei et al., 2008; Worden et al., 2000). Conversely, an increase in alpha amplitude, known as synchronization, has been reported over visual cortex ipsilateral to the attended direction (Kelly et al., 2006; Rihs et al., 2007; Worden et al., 2000; mechanism, whereby processing of irrelevant stimuli is inhibited in order to better process relevant stimuli (Cooper et al., 2003; Jensen et al., 2002; Klimesch et al., 2007). It has been shown that alpha synchronization and desynchronization also play an important role in other high order cognitive processes such as memory and visual imagery (Jensen et al., 2002; Klimesch et al., 1999; Medendorp et al., 2007; Tuladhar et al., 2007).
43 Are these functional properties of visual alpha shared by similar oscillations in other sensory cortices? The mu rhythm over somatosensory cortex (Gastaut, 1952) is known to behave similarly to the visual alpha rhythm in some respects. For example, a decrease in amplitude of mu, known as an event related desynchronization (ERD), has been noted following somatosensory stimulation (Pfurtscheller, 1989; Nikouline et al., 2000a; Della Penna et al., 2004), which is akin to the pari eto occipital alpha ERD that occurs after visual stimulation (Pfurtscheller et al., 1979; Vijn et al., 1991; Pfurtscheller pathway) areas have been found to increase in ampli tude during a visual working 2007). A similar effect has been seen during a somatosensory delayed match to sample task, where mu power was higher over areas hypothesized to b e not engaged by the task, such as somatosensory cortex ipsilateral to the sample stimulus (Haegens et al., 2010). Despite these similarities, whether and how the ongoing somatosensory mu rhythm responds topographically to spatial attention, a hallmark of visual alpha reactivity, has only recently been investigated. Jones et al. (2010), using magnetoencephalography (MEG), found that cued spatial attention to the hand decreased mu power in the hand area of SI while attention to the foot on the same side of t he body was accompanied by a mu power increase in the hand area. Also utilizing MEG, van Ede at al. (2010) found that attentive as well as non attentive expectation of a somatosensory stimulus modulated, in a lateralized manner, the beta rhythm, a 15 35 Hz somatomotor rhythm with some functional similarities to the mu rhythm (Salenius et
44 al ., 1997; Pfurtscheller and Lopes da Silva 1999; Ritter et al., 2009). No such expectation effect was seen for the mu rhythm. While in the MEG modality, beta band activit y is often analyzed along with or in lieu of mu activity (Jones et al., 2009; Jones et al., 2010; van Ede et al. 2010), the beta rhythm is often not very prominent in EEG recordings (Zhang and Ding 2010). The effect of lateralized spatial attention on th e somatosensory mu rhythm currently remains uninvestigated. We recorded high density EEG while subjects performed a somatosensory spatial attention task in which sustained attention was directed to either the right or the left hand. Oscillatory activity in the 8 12 Hz (mu) and 15 35 Hz (beta) frequency range during a prestimulus time period when somatosensory attention was deployed to either direction was measured and compared with a baseline period. A spatial filter was applied to the scalp recorded data i n order to investigate the effects of somatosensory spatial attention on cortical areas such as primary somatosensory cortex (SI), secondary somatosensory cortex (SII), posterior parietal cortex, lateral and medial frontal areas, and occipital cortex. The inclusion of the occipital cortex (a) removed a potential source of volume conduction that might negatively impact the estimation of somatosensory mu activity which is smaller in magnitude than visual alpha and (b) allowed the examination of possible cross modal attention effects. Finally, the effect of the mu rhythm prior to stimulus onset on stimulus processing during different states of attention was investigated by correlating prestimulus mu power with evoked activity in SI. Although the relationship be tween prestimulus mu power and evoked potentials has been investigated previously (Nikouline et al., 2000b; Jones et al., 2009, 2010; Reinacher et al., 2009; Zhang and
45 Ding 2010), the current analyses extend these findings by revealing the impact of sourc e localized estimates of high, low, and intermediate amplitudes of prestimulus mu on early and late evoked activity during different attentional states 3 .2 Materials and Methods 3 .2.1 Participants A total of 15 healthy right handed subjects (Aged 19 27 ye ars, 8 female) participated in the experiment. All participants provided written informed consent and were paid in accordance with the guidelines of the Institutional Review Board (IRB 02) at the University of Florida. All subjects performed the task acco rding to instructions and were included in the following analyses. 3 .2.2 Stimulation Device Somatosensory stimuli were delivered using a two channel, custom built, computer controlled, constant current stimulation device. The device was optically isolated from the stimulus presentation computer and battery powered to ensure the Stimulus amplitude was adjustable from 0 to 5 mA in ~0.02 mA steps and stimulus duration was fixed at 0.5 ms. Event triggers sent to the EEG recording amplifier were precise to the sub millisecond level. 3 .2.3 EEG Recording The experiment took place in a dimly lit, acoustically and electrically shielded booth. Subjects sat comfortably in a chair with their arms apart and resting on a table in front of them. They were instructed to keep their eyes open and fixated on a small cross on a computer monitor 1.5 m in front of them throughout the experiment.
46 The EEG data were acquired using a 128 channel BioSemi ActiveTwo System (www.biosemi.com) with a sampling rate of 2048 Hz. Four channels of electrooculogram (EOG) were recorded in addition to the 128 scalp channels. Statistical analyses of scalp recorded data were performed on electrodes CP3 and CP4. T hese electrodes were chosen to represent activity over primary somatosensory cortex because they are where the largest early (~50ms post stimulus) evoked activity was measured. 3 .2.4 Experimental Design and Paradigm The task was a somatosensory oddball tas k involving directed spatial attention. A block design was used. Subjects were instructed to fixate on a cross in the center of a computer screen and direct their attention to either their right (attend right or ATTR), left (attend left or ATTL), or both ( attend both or ATTB) hands during a block (Figure 3 1) Each block consisted of 70 electrical stimuli being delivered over either the right or left median nerve with equal probability. The inter stimulus interval was uniformly distributed between 2.5 and 3 .5 seconds. Each stimulus could either be a standard (low amplitude, 80 92% probability) or a target (higher amplitude, 8 20% probability). The amplitude of the standard stimulus was held fixed throughout the experiment at twice the detection threshold for each hand. Here, the detection threshold for each hand was determined using an up down staircase procedure (Leek, 2001) to find the amplitude at which the subject detected the stimulus 50% of the time. Amplitudes for the target stimuli were initially set during a short practice run before the experiment to achieve a target detection error rate of around 25% for both the attend left and attend right conditions. During the experiment, target amplitudes were adjusted at every third block to keep the error rat e for the attend left and attend right conditions around 25%. As the target
47 stimuli were not held constant throughout the experiment, only data from standard stimuli were used for the analyses in this paper. Before each block, the subjects were instructed to mentally count the number of target stimuli delivered to the attended hand and to ignore stimuli delivered to the unattended hand. In the attend both condition, the subjects were instructed to count the total number of targets delivered to both hands. T he subjects verbally reported the number of detected targets at the end of each block. A fourth, baseline, condition without any stimuli was recorded at the beginning of the experiment and every six blocks, in which the subject was instructed to relax and stare at the fixation cross (as in the other blocks) for 3 minutes. The order of the blocks, in groups of three, alternated between ATTB ATTR ATTL and ATTB ATTL ATTR. In total, the experiment consisted of between 15 18 blocks of stimuli (5 6 each o f ATTB, ATTR, ATTL) and 3 4 baseline blocks, resulting in 175 210 stimuli per condition, per hand (1050 1260 total). For the current study, only the attend left, attend right, and baseline conditions were analyzed. 3 .2.5 Source Estimation Electrode locatio ns, as well as three fiducial landmarks, were digitized by means of a Polhemus spatial digitizer. Regional dipole source analysis (Scherg, 1992) was used to create a spatial filter using the Brain Electrical Source Analysis (BESA) software package which im plements a least squares algorithm to solve the overdetermined problem and estimate the activity contributed by each source to the scalp recorded data. Based on findings from previous research, relevant fixed regional sources were seeded into a 4 shell ell ipsoidal head model (brain, CSF, skull, and skin conductivities
48 of .33, 1.0, .0042, and .33 mohm/m, respectively) and source activity was estimated As illustrated in Figure 3 2, 11 sources wer e seeded in relevant brain areas: Bilateral primary somatosensory (SI) sources were seeded near the postcentral gyrus, consistent with the hand area found in previous studies (Valeriani et al., 1997; Waberski et al., 2002; Bowsher et al., 2004; Della Penna et al., 2004; Gaetz and Cheyne, 2006; Ritter et al., 2009). Bilateral secondary somatosensory (SII) sources were seeded near the parietal operculum. Coordinates were chosen based on a meta analysis (Eickhoff et al., 2006). Bilateral posterior parietal (P P) sources were seeded near the superior parietal lobule, a location indicated as being involved in maintaining spatial attention (Corbetta et al., 1998; Kastner and Ungerleider, 2000). Bilateral lateral frontal (LF) sources were seeded near the middle fr ontal gyrus. Source sensitivity maps (not shown) for these dipoles indicated contributions from both dorsolateral prefrontal cortex and frontal eye fields. A medial frontal source (MF) was seeded near the inter hemispheric space between the left and right anterior cingulate gyri. Bilateral occipital (O) sources were seeded near the foveal confluence, an area where V1,V2, and V3 are thought to converge (Dougherty et al., 2003; Schira et al., 2009). In addition to the above 11 task relevant sources, 5 more so urces were seeded to minimize contamination of estimated source activity from other brain areas. Bilateral sources were seeded near the frontal poles. These sources were used to account for ocular activity that was below the rejection threshold. Central an d parietal midline sources were seeded to minimize lateral source sensitivity overlap. A deep midline source was seeded to account for additional brain activity. The source sensitivity map (not shown) indicated mostly local (subcortical) and inferior tempo ral lobe contributions to this source. For each of the 16 regional sources seeded, magnitudes of ERPs and spectral estimates of ongoing neuronal activities from the three dipolar components were used
49 to obtain orientation independent measures. Note that, a s no structural MRI was recorded from the participants, source locations should be considered approximate. However, estimated source waveforms are relatively insensitive to variations in dipole location. 3 .2.6 Data Preprocessing Two sets of data, sensor le vel and source level, were analyzed in this study. Preprocessing steps were similar for both data sets, and any differences will be noted below. As the exact locations of the recording electrodes were slightly different for each subject, spherical spline i channels of data into a standard 81 channel 10 10 montage. This spatially interpolated data set was used for all sensor level analyses. Channels with poor signal quality for each individual subject we re not included in this transformation. All sensor level analyses were performed on the average referenced data. First, the signals were band pass filtered between 0.3 and 85 Hz and down sampled to 256 Hz for subsequent analyses. The data were then epoched around each standard stimulus from 700 ms to 500 ms. For baseline data, artificial triggers were inserted into the continuous recordings every 600 to 800 ms, and epoched as above 500 ms to 0 ms, with 0 ms de noting the onset of an artificial trigger, was used for baseline analyses). After this, the DC component was subtracted from each epoch. Any epoch with activity in the EOG channels exceeding 75 uV, or with activity exceeding 50 uV in any scalp channel, was excluded from further analysis. This procedure resulted in between ~15% to ~30% of epochs being rejected from each subject.
50 3 .2.7 Behavior and Evoked Potential Analysis Behavioral performance for each block was measured as (targets reported actual targets )/(actual targets). The amplitude of target stimuli was adjusted throughout the experiment to obtain consistent behavioral results. The mean of the prestimulus baseline period from 100 to 0 ms was subtracted from each epoch before averaging. The ERPs for each subject were weighted equally to compute the grand average. The source space ERPs were computed as the square root of the sum of each of the three dipole components squared. A Wilcoxon signed rank test was used at each time point to test whether the d ifference between conditions was statistically significant. If the tests on at least three consecutive sample points (~12 ms) resulted in p values less than 0.05, the effect in that time period was considered significant. 3 .2.8 Spectral Power Analyses Spec tral power analyses were performed on a time period immediately preceding all artifact free standard stimuli in the attend right and attend left conditions. For the baseline condition, the analyses were performed on the same time window preceding the artif icially inserted triggers described in Section 3.2. 6. This time window was defined to be from 500 ms to 0 ms relative to each stimulus/trigger. This prestimulus window was chosen to be short enough as to minimize the effect of the neuronal response to the previous stimulus and to capture a stable state of the brain at the time of stimulus onset while at the same time long enough to allow for sufficient frequency resolution. For all channels and source dipoles, power spectral densities (PSDs) were estimated for each prestimulus epoch using a multitaper FFT approach with 3 DPSS tapers over this time window, resulting in 4 Hz smoothing. In order to obtain an orientation
51 independent measure in the source space, magnitudes of the PSDs for each source at each ep och were obtained by taking the square root of the sum of the squares of each interpolation was used to obtain a more smooth curve. We used the estimated spectral power between 8 and 12 Hz to compute the average mu band power and 15 to 35 Hz to compute the average beta band power. To facilitate averaging across subjects, the average band power was normalized by finding the ratio between the power of each condition and the mean pow er of all three conditions for that subject. As an example, the normalized band power for the ignore condition in a single subject would be calculated as: (ignore)/(ignore+attend+baseline)/3. A Wilcoxon signed rank test was used to test whether the differe nce between two conditions was statistically significant. 3 .2.9 Correlation Between Prestimulus Mu Power and Evoked Potential Amplitude To analyze the correlation between prestimulus mu power and evoked potential amplitude in SI, trials for each subject an d each condition were divided into two groups: right stimuli and left stimuli. The trials in each group were then rank ordered by the amplitude of the prestimulus mu power estimated from the SI source contralateral to stimulation, and sorted into 5 bins of equal size with an overlap of 50%. Each bin contained about 33% of the total available trials in each group. The power bins were indexed from 1 to 5 where Bin 1 has the smallest mu power and Bin 5 has the largest. For each subject, the trials within a po wer bin were used to calculate the evoked activity in source SI in the same way as described in the above section on behavior and evoked potential analysis. The mean amplitudes of the evoked activity in two time ranges: 45 to 55 ms and 140 to 160 ms, cente red on the peaks of evoked activity in the
52 SI source, and where significant differences between the amplitudes of the sensor level SEP were found, were then computed for each bin. To minimize the effect of inter subject variability in evoked activity ampli tude on population averaging, the following procedure was adopted to normalize the data from each subject. Let the mean amplitude for Subject K in Power Bin J be denoted as A(K,J). The mean evoked amplitude for this subject will be calculated as mean_A(K) A(K, 5 )]/5. The normalized evoked amplitude was calculated as the percent change against this mean, namely, norm_A(K,J) = (A(K,J) mean_A(K))/mean_A(K). This normalized evoked amplitude was then averaged across subjects to obtain t he mean normalized evoked amplitude for each power bin. The results were then combined correlation coefficients were computed to test for statistical dependence. A quadratic regression was performed on the late component (140 to 160 ms). Similar analyses were performed on the other bilateral sources (SII, PP, LF, and O). For each source, the time intervals chosen for analysis were defined according to where a significant diffe rence was found between the grand average evoked activity to attended and unattended contralateral somatosensory stimuli. 3 .2.10 Time Frequency Analysis of Mu and Beta Activity in SI The temporal evolution of mu and beta power was compared between differen t attention conditions for the SI sources. First, the data were epoched from 1500 to 2500 ms around each standard stimulus or, for the baseline condition, each artificially inserted trigger (see Section 3.2. 6). Epochs containing artifacts during this time period were rejected from further analysis. The data from each epoch were then time frequency decomposed by convolving with Morlet wavelets to obtain power estimates
53 from 8 12 Hz and 15 35 Hz with center frequencies at 1 Hz intervals. The time course of t he mean band power was then calculated for each trial and averaged within each condition. As with the prestimulus power analysis described in Section 3.2. 8, an orientation independent measure was obtained by calculating the magnitude of the power estimates from the three dipoles in each regional source for each time point. Results were then combined across hemispheres in a way similar to that described in Section 3.2. 8, with the additional step of using the temporal mean of each condition (as opposed to the power at each time point) as the normalization value. As an example, the normalized mu power time course for the ignore condition in a single subject would be calculated as: Normalized_ignore(t) = ignore(t)/(temporal_mean(ignore)+temporal_mean(attend)+tem poral_mean(baseline))/3 A Wilcoxon signed rank test was used to test whether the difference between two conditions was statistically significant at each time point. 3 3 Results 3 .3.1 Behavior All 15 subjects performed the task according to instructions. T he error rate in target detection averaged across subjects for each condition was: 25.6% (2.3%) for attend left and 25.5% (1.9%) for attend right. This rate was maintained throughout the experiment by adjusting the amplitude of the target stimuli to ensu re consistent task difficulty in both the attend left and attend right conditions. The amplitudes of standard stimuli, which were held fixed at twice the detection threshold for each subject throughout the experiment, averaged across subjects were 2.71 mA (0.17 mA) for right standard stimuli and 2.58 mA (0.14 mA) for left standard stimuli. The generally higher detection threshold for the right hand, consistent with
54 previous reports (Friedli et al., 1987; Meador et al., 1998), reflects handedness related t hreshold asymmetry (all subjects in the current study were right handed). 3 .3.2 Somatosensory Evoked Potential (SEP) The grand average SEP waveforms for stimuli delivered contralaterally and ipsilaterally to recording electrodes over somatosensory cortex ( CP3 and CP4) under attend and ignore conditions are shown in Figure 3 2 A and B. Data from the two hemispheres have been combined. The P1 component, sometimes also referred to as the P45, P50, or P60 component, peaks around 50 ms and is only seen in the hem isphere contralateral to stimulation. This component is significantly larger for the ignore condition compared with the attend condition. The N1 component, a bilateral negative component peaking around 150 ms and sometimes referred to as the N140, shows th e opposite effect; a greater amplitude for attended stimuli than ignored stimuli. For contralateral stimuli, this negativity extends from 150 ms to 200 ms, overlapping a central positive component that peaks around 200 ms. Figure 3 2 C shows the grand avera ge SEP for all stimuli (right and left) under attend and ignore conditions which emphasizes the bilateral N1 attention effect. The N1 effect is slightly larger in the right hemisphere as opposed to the left, as seen in Figure 3 2 D, which is a topographic p lot of the mean difference between all attended stimuli (right stimulus, attend right; left stimulus, attend left) and all ignored stimuli (right stimulus, attend left; left stimulus, attend right) in the time period from 140 to 160 ms. This effect is cons istent with a right hemispheric dominance in the parietal lobes during spatial attention (Heilman and Abell, 1980; Heilman et al., 1985; Mesulam, 1999; Meador et al., 2002).
55 3 .3.3 Prestimulus Power in 8 12 Hz: Scalp Level In the period prior to stimulus on set, oscillatory activity can be used as a measure to give insight into the state of the brain and how directed attention modulates this state to facilitate information processing. Figure 3 3 shows the effects of somatosensory attention on oscillations in the mu band (8 12 Hz) recorded at the scalp level. Normalized prestimulus power spectra for sensors CP3 and CP4, averaged over all subjects, are plotted in Figure 3 3 A. A peak in the mu band (8 to 12 Hz) exists in both sensors for all conditions. It can al so be seen in both sensors that the average prestimulus mu power measured over somatosensory cortex contralateral to the direction of attention is lower than the power over cortex ipsilateral to the direction of attention. A one sided Wilcoxon signed rank test of the difference in average mu power between attend ipsilateral and attend contralateral resulted in p=0.11 and p=0.06 for CP3 and CP4, respectively. Figure 3 3 B shows the average percent difference in 8 12 Hz band power between the attend right and attend left conditions over the entire scalp. Consistent with Figure 3 3 A, it can be seen that when somatosensory attention is directed to the right side, mu power over the contralateral (left) somatosensory cortex is lower than the mu power in the ipsilat eral (right) somatosensory cortex. The effect appears to be localized to sensors lying over somatosensory cortex. Similar patterns of alpha power reduction have been observed over visual cortex with visual spatial attention (Thut et al., 2006; Rajagovindan and Ding, 2010). It is worth noting that while an attention related decrease in mu power was seen in Figure 3 3 A, the difference was not highly significant (p=0.11 for CP3 electrode and p=0.06 for CP4 electrode). It is likely that, given the large visual alpha activity, the mu
56 power estimation is adversely affected by volume conduction from the occipital cortex, which may also explain the broad increase in 8 12 Hz power during somatosensory attention compared with baseline in brain areas outside the somato sensory cortex in Figure 3 3 C. This problem is overcome below by carrying out spectral power analysis in the source space. 3 .3.4 Prestimulus Power in 8 12 Hz: Source Level All power results in the following sections are obtained from the magnitude of the P SDs of the three components of each regional source dipole (see Methods). Figure 3 5 shows the mean power spectra for the regional sources collapsed across conditions and hemispheres. Spectral power peaks in the 8 12 Hz range can be seen in the somatosenso ry (SI, SII), posterior parietal (PP), and occipital (O) sources for all 15 subjects. A slight peak in the beta band (~20 Hz) can be seen in the SI and SII sources for 2 subjects and 3 subjects, respectively. The frontal sources, lateral frontal (LF) and m edial frontal (MF), do not show peaks in the mu frequency range, though a slight bump in the theta range can be seen in some subjects. Spectral power estimates from 0 to 3 Hz are not plotted, as combining the high pass action of the bandpass filtering with the 1/f spectral characteristic of the electroencephalography signal can create an artificial spectral peak in this frequency range (Slutzky, 1937; Demanuele et al., 2007). The largest oscillations in the 8 12 Hz frequency band occur in the occipital sou rces, where the grand average peak is 6.5 V2/Hz, compared with peaks of 2.9 V2/Hz, 2.3 V2/Hz, and 2.1 V2/Hz in SI, SII, and PP sources, respectively. If the somatosensory 8 12 Hz oscillations were due to voltage propagation from the occipital cortex, one w ould expect the amplitude of the oscillations measured from the posterior parietal sources (located between somatosensory and visual cortices) to lie between
57 the amplitudes of the somatosensory and occipital oscillations. This is not the case, as the peak amplitude in the posterior parietal sources is less than the somatosensory sources as well as the occipital sources. Therefore somatosensory mu oscillations appear to be generated in local cortices. The 9.0 Hz peak seen in SII could represent rhythm, a 7 9 Hz rhythm recorded in SII that is responsive to somatosensory stimulation (Narici et al., 2001), though voltage propagation from SI cannot be ruled out. One phenomenon to note is that mu activity was found in the SI sources of all 15 subjects This finding contrasts with earlier reports where the incidence of observable mu oscillations in scalp EEG varied from 4% to 60% (Shaw, 2003). However, healthy adolescents a et al., (2000) report that mu activity can be detected in most normal adults through spectral analysis of EEG activity. Figure 3 6 (top) shows the results of a comparison of mu band power between conditions in the source space. For a given source, the condition where attention is directed contralaterally to the source hemisphere is designated as the attend condition, and the condition where attention is directed ipsilaterally to the source hemisphere is designated as the ignore cond ition. The results are combined across hemispheres and band power values for each subject were normalized according to the procedure described in Section 3.2. 8. For the primary somatosensory (SI) sources, a significant decrease in mu power is seen in the attend condition when compared with the ignore condition (p=0.002) and baseline (p=0.031). No significant difference is seen between the ignore and baseline
58 conditions in SI (p=0.35). In addition, 8 12 Hz (possibly sigma) oscillations in SII do not appear to be modulated by the current spatial attention task (p>0.1 between all conditions), although past work has shown that attention modulates stimulus evoked responses in SII (Hsiao et al., 1993; Mima et al., 1998; Steinmetz et al., 2000; Fujiwara et al., 20 02; Hoechstetter et al., 2002). No significant differences are found between conditions in the posterior parietal sources. Power in the 8 12 Hz band is not significantly modulated in lateral frontal cortex by the current spatial attention task (p>0.1 betwe en all conditions). The medial frontal source. In occipital cortex, a significant po wer increase is seen from baseline to somatosensory attention conditions (p=0.000027 for baseline vs ignore and p=0.000047 10 Hz oscillations occurs over cortical areas that are irrelevant to the task (Foxe et al., 1998; Jensen et al., 2002; Cooper et al., 2003; Rihs et al., 2007; Klimesch et al., 2007). 3 .3.5 Prestimulus Power in 15 35 Hz: Source Level According to a previous MEG study (van Ede et al., 2010) beta b and is defined to be 15 35 Hz. As shown in Figure 3 6 (bottom) prestimulus beta power during the attend condition is significantly less than during the ignore condition (p = 0.048) in the primary somatosensory (SI) cortex. Attention does not appear to sign ificantly modulate beta oscillations in any of the other cortical areas. The larger error bars (standard errors of the mean) are likely due to the lack of precision in estimating beta power in the absence
59 of consistent spectral peaks in this frequency rang e. Further analyses below will focus on the mu band, where the attention effects are more robust. 3 .3.6 From Prestimulus Mu Power To Stimulus Evoked Activity The above results showed that, over primary somatosensory cortex contralateral to the attended dir ection, attention reduced mu power prior to stimulus onset and at the same time modulated the stimulus evoked response. Presumably, the prestimulus mu power reduction contributed to the subsequently improved stimulus processing. In order to investigate the relationship between pre and post stimulus activity, we estimated the magnitude of the evoked potential in SI for two time periods: 45 ms to 55 ms (early), and 140 ms to 160 ms (late) as a function of different levels of prestimulus mu power in the same hemisphere under the attend condition (attend contralaterally to source hemisphere) and the ignore condition (attend ipsilaterally to source hemisphere) (see Section 3.2. 9 for details). Stimuli delivered contralateral to each SI source were included and re sults were combined across hemispheres. As seen in Figure 3 7, a significant positive linear relationship was found between prestimulus mu power and the early evoked component for both attended and ignored stimuli (Spearman rank correlation rho=0.26, p=0.0 37 for attended stimuli and rho=0.29, p=0.019 for ignored stimuli). The later component followed a nonlinear quadratic relationship with prestimulus mu power in both conditions. For attended stimuli, the relationship was of an inverted U type, with a p val ue for the F statistic of 0.041. The relationship for ignored stimuli followed an upright U shape with p=0.091. The spatial specificity of the relationship between prestimulus mu power and evoked response was investigated by employing a similar analysis fo r the remaining sources. For each source, the time intervals chosen for analysis were defined according
60 to where a significant difference was found between the grand average evoked activity to attended and unattended contralateral somatosensory stimuli. Th ese sources and the intervals are as follows: SII from140 ms to 180 ms, PP from 85 ms to 110 ms and from 180 ms to 200 ms, and LF from 135 ms to 160 ms. No significant linear or quadratic relationships were seen between prestimulus 8 12 Hz power and evoked activity during these latencies to attended or unattended contralateral somatosensory stimuli in any of these regional sources. 3 .3.7 Time Frequency Analysis of Mu and Beta Activity in SI Figure 3 8 (top) shows the mu power at the SI source as a function of time in the period from 1000 ms to 2000 ms where 0 ms denotes the onset of the standard stimulus or the artificially inserted trigger for the baseline condition. It can be seen that the prestimulus mu power is significantly different between the attent ion conditions and this difference is diminished following stimulus input. This result demonstrates that the task related effect seen in Figure 3 6 is not due to bottom up stimulus processing but top down attention to the upcoming stimulus. A similar resul t is seen with beta power in Figure 3 8 (bottom) but the effect is less significant. 3 4 Discussion In this study, we investigated the effects of spatial somatosensory attention on stimulus processing and on prestimulus somatosensory mu (8 12 Hz) and visua l alpha (8 12 Hz) band oscillations. For the two components of the somatosensory evoked potential investigated, the P1 was reduced with attention, while the N1 was enhanced with attention. At the sensor level, the power of the mu oscillations over somatose nsory cortex contralateral to the attended direction and prior to stimulus onset was reduced by spatial attention in a manner similar to the reduction of alpha oscillations in visual cortex
61 by visual spatial attention, though this effect did not reach sign ificance. Interestingly, the occipital alpha rhythm exhibited an intermodal attention effect, in that it was greatly elevated above baseline level during somatosensory attention. To more precisely localize attention effects, a spatial filtering method was used to estimate activity from multiple cortical sources, including bilateral SI, bilateral SII, bilateral posterior parietal, bilateral occipital, bilateral frontal, and medial frontal areas. A significant modulation of the mu rhythm according to directio n of attention was observed in SI cortex, with a desynchronization occurring over SI contralateral to the direction of attention. A smaller, yet also significant, lateralized attention effect was also seen in the beta band (15 to 35 Hz). Additionally, a so matosensory attention related increase of visual alpha was seen in occipital sources. Lastly, a comparison of prestimulus mu power and evoked activity in SI revealed a positive linear relationship between mu and early (~50 ms) evoked activity for both atte nded and ignored stimuli, while a quadratic relationship was found between mu and later (~150ms) evoked activity. This relationship between prestimulus mu and the later evoked component was dependent upon whether the stimulus was attended or ignored, havin g an inverted U shape for attended stimuli and an upright U shape for ignored stimuli. 3 .4.1 Mu and Attention It has been postulated that field oscillations in the 10 Hz range should be characteristic of ongoing neuronal activity in every sensory cortex (S haw, 2003). To date, the visual alpha rhythm has been the most extensively studied, and its active role in sensory processing as well as in higher order cognitive processes such as memory and attention has been firmly established (Klimesch et al., 2007; Pa lva and Palva, 2007; Rajagovindan and Ding, 2010). One hallmark of visual alpha reactivity is its modulation
62 by spatial attention, where an increase or decrease in the amplitude of alpha over visual cortex has been attributed to inhibition or facilitation, respectively, of visual stimulus processing (Klimesch et al., 2007; Romei et al., 2008). Physiologically, alpha is considered to be a local reflection of the level of cortical excitability, with a smaller alpha amplitude being associated with greater exci tability (Foxe et al., 1998; Jones et al., 2000; Worden et al., 2000; Bastiaansen and Brunia, 2001; Klimesch et al., 2007; Neuper et al., 2006; Jones et al., 2009). This hypothesis is supported by evidence from transcranial magnetic stimulation (TMS), whic h has found an inverse relationship between posterior alpha power and stimulation threshold for inducing illusory phosphenes (Romei et al., 2008). Further support for this hypothesis can be found in a recent study by Lee et al. (2010), which utilized optog enetics to determine a positive correlation between local neuronal excitation and blood oxygenation level dependent (BOLD) signals detected with functional magnetic resonance imaging (fMRI). This finding, combined with negative correlations between local B OLD and alpha/mu band power from simultaneous recordings of EEG and fMRI (Goldman et al., 2002; Feige et al., 2005; Moosmann et al., 2003; de Munck et al., 2009; Ritter et al., 2009), is strong evidence for the inverse relationship between local 10 Hz powe r and cortical excitability. Relative to visual alpha, the mu rhythm, measured over somatosensory cortex, is less well understood. Traditionally, the mu rhythm has been investigated in relation to its event related synchronization and desynchronization pro perties with respect to movement and stimulation. More recent work has begun to associate changes in the ongoing mu rhythm with higher order cognitive processes such as working memory
63 (Haegens et al., 2010) and anticipation (Babiloni et al., 2004, 2008). J ones et al., (2010), utilizing MEG, addressed the question of whether and how spatial attention modulates somatosensory mu and beta oscillations. They reported that spatial attention to the hand led to a decrease in mu power below baseline in the hand area of SI while spatial attention to the foot on the same side of the body was accompanied by a mu power increase above baseline in the same hand area. A similar, yet weaker effect was also seen in the beta band. Our study, utilizing EEG, confirms and extends this finding by showing that, prior to sensory input, sustained lateralized somatosensory spatial attention decreased the mu rhythm over somatosensory cortex contralateral to the direction of attention. We did not observe, however, a significance increase of mu power above baseline in somatosensory cortex ipsilateral to the direction of attention. This discrepancy could be explained by the difference in task requirements. In our task, attention is directed either to the left hand or right hand, while in th e task of Jones et al. (2010), attention is directed to either the left hand or left foot. It is possible that directing somatosensory attention away from the hand, where somatosensory input is often consciously processed, to the foot, where conscious proc essing of input occurs less often, would require active inhibition of the hand area as well as facilitation of the foot area. It is thus conceivable that the mu activity in the foot area of SI would more closely match our results. In the MEG modality, beta band activity is often analyzed along with mu activity in the 8 to 12 Hz band (Jones et al., 2010; van Ede et al., 2010). In EEG recordings, however, the beta rhythm is often not very prominent (Zhang and Ding 2010). In the present work spectral peaks in the beta band were only observed in a small number of
64 subjects. In the primary somatosensory cortex, a significant lateralized attention effect was found for prestimulus beta, with smaller beta over SI contralateral to the direction of attention compared w ith that over SI ipsilateral to the direction of attention. This result is similar to that of van Ede et al. ( 2010 ) who found a lateralized modulation of beta band activity in SI during expectation of a lateralized somatosensory stimulus. This effect was stronger during attentive expectation as compared with non attentive expectation. No such effect was seen in the mu band. In the visual domain, how alpha band oscillations are modulated in visual areas representing ignored visual locations is also debated with some groups reporting predominantly an increase in alpha power in these areas, while other groups report only a decrease in alpha power over cortex that represents attended locations. Still others have reported both effects simultaneously. This lead s to a question of whether spatial attention is achieved through a suppression of irrelevant cortical processing, an enhancement of relevant cortical processing, or a combination of both. The answer appears to be that the relative contribution of enhanceme nt and inhibition depends on the task. Three reports in the visual modality with findings similar to ours are: Sauseng et al. (2005), Thut et al. (2006), and Wyart and Tallon Baudry (2008). All three used modified versions of the Posner cuing paradigm (Pos ner, Nissen, and Ogden, 1978). These tasks, as with ours, involved only two directions of attention (left versus right) and there were no simultaneously presented competing stimuli within a trial which would require active inhibition. In contrast to our fi ndings, Worden et al. (2000) reported only an alpha increase ipsilateral to the direction of cued spatial attention, though this was not compared with a precue baseline. In fact, it appears that during the period
65 immediately before the cue when attention h as not yet been deployed, alpha power is higher bilaterally than during either attention condition. Contrary to the above mentioned reports, three papers which found, compared with a baseline period, primarily an increase in alpha power ipsilateral to the direction of attention are: Yamagishi et al. (2003), Kelly et al. (2006), and Rihs et al. (2007). It appears that the discrepancy between their and our findings can be attributed to differences in the experimental tasks. Both Kelly et al. (2006) and Yamagi shi et al. (2003) used tasks where stimuli to be attended and ignored were presented simultaneously within a trial, requiring active suppression of the ignored stimuli. Rihs et al. (2007) employed a more complicated cued spatial attention paradigm, where a ttention needed to be deployed to one of 8 spatial locations around a fixation point. The authors suggested that the predominance of alpha increase could be due to the number of behaviorally relevant locations increasing the need for active inhibition. It is worth noting that in our study, concurrent with the modulation of somatosensory mu, there is an intermodal effect in the visual domain where the occipital alpha rhythm is increased above baseline during somatosensory task conditions. This finding is con sistent with the notion that an increase of alpha power reflects an active inhibition of visual processing (Klimesch et al., 2007). Such an increase in visual alpha power during attention to non visual modalities has been reported previously (Foxe et al., 1998; Fu et al., 2001). Additionally, Pfurtscheller (1992) found an inverse relationship between somatomotor mu and visual alpha rhythms during both finger movement and reading tasks. During the reading task, a decrease in 10 12 Hz activity was seen over v isual areas while an increase in 10 12 Hz activity (also
66 known as event related synchronization or ERS) was seen over bilateral somatomotor areas. The reverse was found during the finger movement task. Further support for this idea comes from Haegens et al (2010) who showed that occipital alpha power during the retention period in a somatosensory delayed match to sample task is positively correlated with working memory performance as well as from Bollimunta et al. (2008) who found that higher levels of alp ha activity recorded from early visual cortex in monkeys led to better reaction times to auditory stimuli. We have interpreted the modulation of prestimulus mu power as being a result of the direction of spatial attention in anticipation of the upcoming st imulus (a top down process). Due to the fact that the attention conditions were manipulated block wise, as opposed to using a cued design, it is possible that this effect is due to an attentional modulation of the response to the previous stimulus (a botto m up process). In order to investigate this, we compared the time courses of mu power in SI between conditions and found that while the prestimulus mu power is significantly different between the attention conditions, this difference is diminished followin g stimulus input. This result demonstrates that the task related effect seen in Figure 3 6 is not due to bottom up stimulus processing but top down attention to the upcoming stimulus. 3 .4.2 Evoked Activity and Attention ERP analyses showed two effects due to spatial attention: a suppression of the P1 (~50 ms) component and an enhancement of the N1 (~150 ms) component with attended stimuli. Nomenclature for these components in the literature varies with differing peak latencies due to task, stimulation site, and recording modality; we will refer to the initial positive peak occurring over contralateral somatosensory cortex around 50 ms after the stimulus as the P1 component and the large bilateral negative
67 component peaking over posterior parietal cortex arou nd 150 ms after the stimulus as the N1 component. A greater stimulus evoked response in the N1 time window has been previously associated with attention (Michie, 1984; Garcia Larrea et al., 1995; Forss et al., 1996; Eimer and Forster, 2003; Zopf et al., 2 004) as well as stimulus detection and awareness (Libet et al., 1967; Schubert et al., 2006; Zhang and Ding, 2010). The topography of this effect varies between reports. In the current study, the N1 attention effect is most prominent over parietal electrod es and spatial filtering/source modeling indicates a contribution from SI (not shown). The N1 component measured in primary somatosensory cortex is thought to be generated in part due to excitatory feedback from higher order areas to the superficial layers of SI (Cauller and Kulics, 1991; Cauller et al., 1998). This feedback could be a key process in the conscious perception of stimuli and a larger N1 could indicate a greater level of higher order stimulus processing occurring for attended as opposed to ign ored stimuli. The evoked somatosensory P1 component has been found to vary in amplitude with stimulus intensity. However, previous research has also found it to be affected by endogenous factors (Tomberg and Desmedt, 1996; Schubert et al., 2008). In the cu rrent study, the intensity of standard stimuli was kept constant within each subject, so modulations of evoked activity can be attributed to cognitive processes such as attention. Our finding of an enhanced P1 component for ignored stimuli is consistent wi th Jones et al. (2010), who reported a positive SEF component (M50) peaking at 50 ms to be of a greater amplitude following ignored as opposed to attended vibrotactile stimuli. This result conflicts with the findings of Schubert et al. (2008), however, who
68 reported an attentional enhancement of this component during a cued somatosensory spatial attention task. Others have reported no difference in P1 amplitude due to spatial attention (Eimer and Forster, 2003; Zopf et al., 2004). The conflicting results cou ld be due to a difference in task design, as ours was a sustained as opposed to a cued spatial attention task show a trend toward a suppression of P1 with attention, thou gh it was not reported as being statistically significant. In the current experiment, the significant effect of sustained attention on the evoked P1 component, which is considered to be generated in SI by purely feed forward mechanisms, supports the theory of sensory gain control occurring at early stages of cortical processing (Hillyard et al., 1998). A suppression of early evoked activity by attention may seem counterintuitive. One possible explanation is that some aspect of the P1 component could represe nt local inhibition. With a mean latency of around 50 ms, the P1 is not the earliest cortical evoked response; the N20/P20 complex is the first cortically generated activity recorded on the scalp in humans, and is generated by the initial excitatory input to area 3b from the thalamus (Wood et al., 1985; Lee and Seyal, 1998). The P20 in monkeys, which is analogous to the human somatosensory P1 (Allison et al., 1992; Arezzo et al., 1981), is associated with increased neural activity in middle cortical layers (Kulics and Cauller, 1986), and simultaneous excitatory and inhibitory activity (Peterson et al., 1995). Wikstrm et al. (1996) hypothesized that SI activity in humans between 45 and 60 ms could correspond with local inhibitory post synaptic potentials (IP SPs) occurring after the initial thalamocortical volley. This is supported by the neural model of Jones et al.
69 (2009), which predicts a larger 50 ms evoked response in SI to be associated with an increased activation of excitatory neurons which subsequentl y activate inhibitory inverse relationship between early activity (P1) generated in middle layers and later activity (N1) generated in superficial layers could possibly be related to acetylcholine release during sustained attention (Himmelheber et al., 2000), which has been found to have a hyperpolarizing effect on Layer IV stellate cells while depolarizing pyramidal neurons in Layers II/III and Layer V in rat SI cortex (Egg ermann and Feldmeyer, 2009). 3 .4.3 Relationship Between Prestimulus Mu and Evoked Activity It is reasonable to speculate that the prestimulus mu desynchronization due to attention contributed to the subsequently improved stimulus processing by attention. T he relationship between prestimulus mu oscillations and stimulus processing has been investigated previously. Linkenkaer Hansen et al., (2004) and Zhang and Ding (2010) both found that the amplitude of prestimulus mu oscillations predicts subsequent percep tion of a threshold level somatosensory stimulus, with an intermediate level of mu leading to better stimulus detection. Further evidence of the relationship between ongoing mu activity and subsequent stimulus processing can be seen in the results of Nikou line et al. (2000b), Reinacher et al. (2009), Zhang and Ding (2010), and Jones et al. (2009, 2010), who all reported correlations between prestimulus mu power and the amplitude of stimulus evoked activity. Zhang and Ding (2010), using EEG, found an inverte d U relationship between mu power and the somatosensory evoked N1 component. A similar finding has also been made in the visual domain between alpha power and the visual evoked P1 component (Rajagovindan and Ding, 2010). In contrast, Reinacher et al. (2009 ) reported a larger negative frontal midline component
70 occurring 140 ms after suprathreshold stimuli delivered during periods of high mu activity, as compared with the same stimuli delivered without mu triggering. Both Nikouline et al. (2000b) and Jones et al. (2009) found a positive linear relationship between mu and early evoked components occurring around 50 to 60 ms measured with MEG. The positive correlation found by Jones et al. (2009) was predicted by a neural model developed in the same study. This model predicted an inverse relationship between mu and later evoked activity occurring 135 ms post stimulus, though this result was not found in their experimental data. In the current experiment, we found a positive linear relationship between prestimulus mu power and the magnitude of early evoked activity (~50 ms) in SI, in agreement with the findings of Nikouline et al. (2000b) and Jones et al. (2009) mentioned above. Interestingly, the relationship between early evoked activity and prestimulus mu was th e same for both attended and ignored stimuli. This might be an indication of a direct physiological correlation between the level of mu activity and the generation of the somatosensory P1 component. For the later evoked activity (~150 ms) after attended st imuli, its magnitude follows an inverted U function in relation to prestimulus mu power. This is consistent with the findings of Zhang and Ding (2010) and suggests that, in the attentive state, the most effective information processing occurs with an inter mediate level of mu activity in the somatosensory cortex (Linkenkaer Hansen et al., 2004; Zhang and Ding 2010). A theory has been proposed by Rajagovindan and Ding (2010) to explain a similar relationship between occipital alpha oscillations and visually e voked P1 responses. However, for ignored stimuli during the current task, both high and low amplitudes of
71 prestimulus mu corresponded to a larger evoked response, leading to an upright U relationship between prestimulus mu and later evoked activity. To our knowledge, this effect has not been reported and does not appear to fit well with existing models. It is possible that the smaller amplitude of the N1 component evoked by ignored stimuli, compared to attended stimuli, affected its proper estimation, as th e p value of the quadratic fit for this condition was not quite significant (p = 0.091). The fact that no significant linear or quadratic relationships between prestimulus 8 12 Hz power and evoked response to contralateral somatosensory stimuli were found in the remaining regional sources suggests that these pre and post stimulus relationships are specific to primary somatosensory cortex. 3 .4.4 Summary Our analyses support the view that ~10 Hz oscillations are a ubiquitous phenomenon in sensory cortex, and that these oscillations are involved in higher cognitive functions such as attention. Specifically, we found that during sustained lateralized somatosensory spatial attention, the mu rhythm is somatotopically modulated in a way similar to the visual alpha rhythm during spatial attention in the visual domain. The increase in visual alpha activity during attention to the somatosensory domain suggests that these rhythms are involved with suppressing irrelevant input in addition to facilitating relevant input. Finally, our finding that early (P1) and later (N1) evoked activity are both influenced by, yet follow different relationships with, the level of prestimulus mu power indicates that these oscillations might be working at multiple levels to impact sensory processing. Further work is necessary to understand the neural mechanisms underlying the relationship between oscillatory activity and stimulus processing.
72 Figure 3 1. Schematic of the experimental paradigm. The top section illustrates three experimenta l blocks and a baseline period. The subject is instructed before each block which hand to attend to. The lower section illustrates the stimulus sequence in an experimental block. Abbreviations for stimuli are: LS: left standard, RS: right standard, LT: le ft target, RT: right target. Stimuli are randomly delivered to the left and right median nerves with interstimulus intervals of between 2.5 and 3.5 seconds. Subjects are instructed to mentally count the number of target stimuli to the attended hand(s). At the end of each block, subjects are asked to report the number of targets detected.
73 Figure 3 2. Regional sources seeded for source space analysis. Coordinates for the sources are in Talairach space. The 11 sources analyzed in this study are in black an d labeled in the upper left schematic.
74 Figure 3 3. Somatosensory evoked potential comparison. (A) Grand average SEP from Channels CP3 and CP4 to contralateral stimuli under attend and ignore conditions (left stimuli for CP4 and right stimuli for CP3). Three major SEP components, positivities at 50 and 100 ms and a negativity at 150 ms, are seen. Significant differences (p<0.05, Wilcoxon signed rank test) between the two conditions, marked by the horizontal yellow bars, are found in the range of the P1 ( ~50 ms) component and following the N1 (~150) component. (B) Grand average SEP to ipsilateral stimuli (left stimuli for CP3 and right stimuli for CP4). The activation is smaller for ipsilateral stimuli compared with contralateral stimuli, and no clear comp onents are visible before 100 ms. A significant difference between conditions is seen in the range of the N150 component. (C) Grand average SEP computed using all stimuli (left and right for both CP3 and CP4). Significant differences are seen in the ranges of the P1 and N1 components. The difference between attend and ignore conditions in the 150 ms range is more prominent in this plot, due to the bilateral nature of the N1 component. (D) Topographic map of the voltage difference between the SEP to all atte nded stimuli and the SEP to all ignored stimuli in the time period from 140 to 160 ms. While an attention effect (greater negativity) can be seen in both left and right parietal areas, the effect is more pronounced in the right hemisphere. A greater fronta l positivity in this time period for attended stimuli can also be seen.
75 Figure 3 4. Prestimulus power comparison in the sensor space. (A) Normalized power spectra for each condition estimated for CP3 (left) and CP4 (right). At each channel, the power sp ectra for each condition in each subject were normalized by dividing the power at all frequencies by the average mu (8 12 Hz) band power of all three conditions. These two electrodes are represented by black dots on the topographic plots (B and C). Spectra l power estimates from 0 to 3 Hz are contaminated due to high pass filtering combined with 1/f spectral characteristics, and are not shown. (B) The percent difference in prestimulus power in the 8 12 Hz band between attend right and attend left was compute d according to the formula: (Attend Right Attend Left)/((Attend Right + Attend Left)/2). (C) The prestimulus power in the 8 to 12 Hz band from both somatosensory attention conditions is compared with the baseline condition. The percent difference between conditions was computed for each scalp sensor using the formula: ((Attend Left + Attend Right)/2 Baseline)/Baseline. The two bars in the center of the plot indicate the scaling functi on (Delorme & Makeig, 2004).
76 Figure 3 5. Power spectral analysis in the source space. Gray curves represent power spectra from individual subjects and black curves are grand averages. Source abbreviations are: SI: primary somatosensory, SII: secondary somatosensory, PP: posterior parietal, O: occipital, LF: lateral frontal, MF: medial frontal. These power spectra were computed as the mean of the attend left, attend right, and baseline conditions, and have been combined across hemispheres (except for MF which is a single, medial source). Peak amplitude and frequency in the mu band range (8 12 Hz) has been marked for the sources where a peak exists. Note that the Y axis scale is different for the occipital source, while the scale is the same for all othe r sources.
77 Figure 3 6. Mean mu 8 12 Hz band power (top) and beta 15 35 Hz band power (bottom) for all conditions normalized and averaged across subjects. Values for each regional source in both hemispheres have been combined. Source abbreviations are th e same as in previous figures; condition abbreviations conditions where attention is directed either contralaterally or ipsilaterally to the source hemisphere. Error bars represent plus or minus one standard error of the mean. Lines between bars indicate a significant difference between conditions with 1, and 2 stars signifying p values less than 0.05, and 0.01, respectively, as measured by a Wilcoxon signed rank test.
78 Figure 3 7. From prestimulus mu power to stimulus evoked response. For the SI source, the mean magnitude of evoked activity in two time periods: 45 55 ms (top left and right) and 140 160 ms (bottom left and right) is plotted as a function of prestimulus ongoing mu po wer. Evoked amplitudes have been normalized for each subject and averaged across subjects as described in Section 3.2. 9. Bin 1 represents the lowest prestimulus mu power while Bin 5 represents the highest prestimulus mu power. Error bars represent the stan dard error of the mean for each bin. Gray curves represent the fit regression curves, either linear or quadratic.
79 Figure 3 8. Time course of mu (top) and beta (bottom) power in SI normalized and averaged across hemispheres and subjects. Shaded areas ind icate one attention is directed either contralaterally or ipsilaterally to the source hemisphere. The significance level of the difference between the ignore and attend conditi ons at each time point (as measured by a Wilcoxon signed rank test) is plotted at the bottom of the figure. The red line indicates p = 0.05.
80 CHAPTER 4 LAMINAR ANALYSIS OF ELECTROPHYSIOLOGICAL RECORDINGS FROM SI IN NONHUMAN PRIMATES 4.1 Background and Sign ificance In the previous chapter, scalp recorded EEG was utilized to study the effects of spatial attention in the somatosensory domain on neural oscillations and evoked activity. A spatial filtering method was employed to localize activity to primary soma tosensory cortex (SI) ; however, the spatial resolution of this method is limited. In this chapter, we further investigate some of the findings in the previous chapter utilizing multielectrode recordings from the cortical laminae of SI (Area 3b) in nonhuman primates. First we look for further evidence that the P1 component of the somatosensory evoked potential (SEP) which peaks around 50 ms in humans is related to inhibitory activity. In Chapter 3, two lines of evidence led to this hypothesis: 1) ignored s omatosensory stimuli evoked a larger P1 than attended somatosensory stimuli and 2) a higher level of prestimulus mu, which has previously been associated with cortical inhibition, led to a larger P1. Previous research has suggested that a positive SRP comp onent occurring around 20ms is a homologue of the human P1 (Allison et al., 1992; Arezzo et al., 1981). By analyzing local field potential as well as multiunit activity recordings from all layers of Area 3b of SI in monkeys during somatosensory stimulation we are able to gain further insight into the nature of the human P1 SEP. Next, we investigate whether a ~10 Hz oscillatory rhythm is present in SI. In the previous chapter, we made the assumption that the spatial filtering method was accurate enough to l ocalize activity to primary somatosensory cortex. However, as no structural MRI was obtained from the subjects, the location of the dipoles must be
81 considered approximate. While mu oscillations are commonly attributed to SI ( Haegens et al., 2010; Jones et al., 2010 ), and have been recorded from electrodes placed directly over SI in humans ( Crone et al., 1998 ), direct evidence of mu band activity in the primary somatosensory cortex of behaving nonhuman primates would provide further support in favor of the e xistence of mu in human SI. If mu oscillations indeed occur in SI, it would be less likely that the oscillations detected using our spatial filtering method originate d in another area, such as primary motor cortex or SII. 4.2 Methods Intracortical multie lectrode recordings were performed in two female rhesus macaques ( Macaca Mulatta ) at the Nathan S. Kline Institute for Psychiatric Research in Orangeburg, New York. All animal experimentation was reviewed, approved, and monitored by the local Institutional Animal Care and Use Committee and complied with United States Public Health Service guidelines for animal research. For a more detailed description of recording methods, see Lipton et al. (2010). 4.2.1 Experimental Task After recovery from surgery, animal s were accustomed to a primate chair and head restraint. They were not required to attend to or discriminate any of the stimuli, and were habituated to electrical somatosensory stimulation. Constant current electrical stimuli were delivered to two gold cup electrodes positioned over the median nerve just proximal to the wrist. A GRASS S8 stimulator (Astro Med) was used to deliver the stimuli, which consisted of a 200 s duration square wave pulse with an amplitude which was just subthreshold for the adducto r pollicis brevis (APB) muscle to twitch. Trains of s timuli were delivered every 2 seconds to either wrist Electrophysiological
82 r ecordings were made during trains of somatosensory stimuli as well as during a resting period with no stimuli. 4.2.2 Data Coll ection Data were collected during multiple penetrations of the hand representation in area 3b with 0.34 mm diameter linear array multicontact electrodes (24 contacts; 0.1 0.3 M ohm impedance ; Neurotrak) that record from all cortical layers simultaneously. T he multielectrodes used in this study had an intercontact spacing of 200 m, which allowed concurrent sampling over a 5 mm span of brain tissue. Classification of Area 3b was possible by relating the location of the recording site and response patterns to previously published characteristics. After preamplification (10x) at the electrode headstage, signals from each channel were amplified (1000x), bandpass filtered (0.1 Hz to 3 kHz), and processed separately to extract field potential and multi unit activit y measures. Local field potentials (LFPs) were obtained from the signal by bandpass filtering from 0.1 to 500 Hz. MUA was obtained from the signal at each contact by high pass filtering the amplifier output at 500 Hz to isolate action potential frequency a ctivity, full wave rectifying the high frequency activity, and then integra ting the activity down to 1 kHz This measure yields an estimate of the envelope firing pattern in loca l neurons (Legatt et al., 1980 ), measured in microvolts. Larger values represe nt greater activity Four penetrations from one monkey were identified as Area 3b and included in the current investigation For each penetration, blocks of stimuli delivered to the wrist contralateral to the electrodes as well as resting period data were analyzed. For the stimulation blocks, data were epoched from 100 to 400ms. Resting state data were divided into arbitrary 500ms epochs. For artifact removal, the mean and standard deviation of the data points from each channel in each epoch were calculate d for both
83 LFP and MUA data. Any epoch containing a data point from any channel that was further than 3.5 standard deviations from the computed mean was removed from further analysis. 4.2.3 Evoked Potential and Current Source Density Analysis Average e voke d LFP and MUA due to contralateral stimuli was comput ed for each penetration. Before averaging, the mean of the prestimulus baseline period from 100 to 0 ms was subtracted from the LFP as well as the MUA in each epoch. The spatial current source density ( CSD) was calculated from the averaged LFP data using a 3 point second spatial derivative. A grand average of the evoked LFP, CSD, and MUA for the four penetrations was then computed. 4.2.4 Correlation Between Prestimulus Mu Power and P20 Amplitude To analy ze the correlation between prestimulus mu power and evoked P20 amplitude in Area 3b of primary somatosensory cortex, the prestimulus mu power was first estimated for each epoch. The prestimulus period chosen for analysis was from 250 to 0 ms before each s timulus. In order to represent the level of local cortical mu, the signal from which power was estimated was a bipolar derivation of Electrode 1 (most s uperficial) subtracted from Electrode 23 (deepest). Power was estimated for each prestimulus epoch using a multitaper FFT approach with 3 DP SS tapers over this time window The epochs in each penetration were rank ordered by the ampli tude of the prestimulus mu power and sorted into 3 bins of equal size with no overlap The power bins were indexed from 1 to 5 where Bin 1 has the smallest mu power and Bin 5 has the largest.
84 For each penetration the trials within a power bin were used to calculate the evoked activity in the same way as described in the above section on behavior and evoked potential analysis. The mean amplitudes of the evoked activity in the time range from 17 20 m s, centered on the peak of the P20 component in the grand average evoked potential w as then computed for each bin. The evoked activity for this analysis comes from Electrode 2, the e lectrode with the largest P20 peak in the grand average data. T he following procedure was adopted to normalize the data from each penetration. Let the mean amplitude for Penetration K in Power Bin J be denoted as A(K,J). The mean evoked amplitude for this penetration will be calculated as m ean_A(K) = [A(K,1) + A(K,2) + A(K, 3)]/3 The normalized evoked amplitude was calculated as the percent change against this mean, namely, norm_A(K,J) = (A(K,J) mean_A(K))/mean_A(K). This normalized evoked amplitude was then averaged across penetration s to obtain the coefficients were computed to test for statistical dependence. 4.2.5 Phase Realignment and A veraging The CSD of ongoing neural activity is more difficult to estimate than for trial averaged activity as traditional averaging cannot be performed due to the lack of a stimulus related trigger. Estimates of CSD from single epochs can be noisy (Shah et al. 2004; Lakatos et al. 2005, 2007). In order to analyze the CSD of ongoing oscillatory mu activity, a phase realigned averaging technique (Bollimunta et al, 2008) was performed. First, the phase of the dominant LFP mu rhythm in each epoch was estimated by fitting a sinusoid to the d ata from a given electrode with the highest power in the 8 12 Hz band Then The LFP and MUA data from all contacts were shifted to align the mu
85 phase between all epoch s. As there was no pre epoch baseline period to subtract, the temporal mean was subtracte d for each epoch to account for any baseline differences. The 3 point spatial CSD was then computed using the averaged LFP data. Phase aligned grand averages of LFP, CSD, and MUA were then computed using all four penetrations. 4.3 Results Figure 4 1 shows the grand average stimulus evoked LFP, CSD, and MUA in Area 3b or primary somatosensory cortex The onset of afferent activity as seen in the MUA around 6 7 ms. This activity is peaks concurrently a current sink located in the granular layers (Electrodes 1 3 and 14) as seen in the CSD and a slight positive peak in granular and infragranular LFP around 10ms. Following this, a source in the supragranular layers peaks at 1 8 1 9 ms. This source is associated with a positive LFP peak in the first 4 electrodes. The increased multiunit activity brought on the initial afferent volley d rops at the same time as th e peak of this supragranular source Note that the MUA does not go below baseline levels, but it does sharply drop from the high level following the stimulus b efore rebounding. A granular sink (Electrodes 7 and 8) and source (Electrode 11) is then seen peaking at 21 22 ms. Finally, a granular sink peaks at 75 ms followed by a supragranular source at 85ms. We next investigated the relationship between prestimulus mu power and the amplitude of the evoked P20. As seen in Figure 4 2 a significant positive linear relationship exists between the two. The Spearman rank correlation coefficient for this relationship was 0.887 with a p value of 0.0001. Figure 4 3 shows th e grand average phase aligned average ongoing mu activity. In t he trace of raw data (Figure 4 3 A), a clear mu oscillation is seen in the granular and
86 infragranular layers. Aft er phase realignment (Figure 4 3 B), CSD analysis reveals a pattern of sinks and s ources in these layers. The phase realigned MUA average reveals the high and low excitability phases of the local mu rhythm, with a more negative extracellular LFP corresponding to a higher excitability. 4.4 Discus sion In this study, we investigated ongoin g as well as stimulus evoked activity recorded from primary somatosensory cortex in nonhuman primates. First, we determined the location of the generator of the P20 SEP component. The P20 is thought to be a homologue of the human P1, which peaks around 50 ms following a somatosensory stimulus. A source pea king around 18 19 ms, corresponding to the latency of the monkey P20, was found in the supragranular layers at the same time as a relative drop in multiunit activity In agreement with the results of the p revious chapter, a positive linear relationship was found between this early SEP component and prestimulus mu power. We then confirmed that oscillatory activity in the mu band indeed occurs in primary somatosensory cortex. Using a phase realigned averaging technique, we found current sources and sinks in SI corresponding to the phase of the ongoing mu We also found a modulation of multiunit activity according to mu phase. These findings provide evidence in support of the interpretations of our findings mad e in the previous chapter. Electrophysiological recordings from nonhuman primates are frequently used to gain insight into how the human brain functions. This is because it is difficult and often not possible to perform certain types of recordings in human subjects. O n e particular recording modality multielectrode recordings through cortical layers, is often carried out in monkeys in order to shed light on the intra cortical generators of ERPs (Schroeder et al, 1995). In this study, we are concerned with g aining a better understanding of the
87 generation of the human P1 component of the SEP, which peaks around 50 ms. Prior research has fou nd an early positive component with a latency of around 20 ms (P20) in monkeys to be analogous to the human P1 (Allison et al., 1992; Arezzo et al., 1981) thus, an investigation of the properties of this component will allow for a better understanding of the human P1 The effects of attention on the human somatosensory P1 are less well studied than later evoked activity such as the N1 (peaking around 140 ms). This component is thought to be generated entirely in primary somatosensory cortex due to feed forward mechanisms so any attention effect found with this component supports the hypothesis of attention acting at an early level of cortical processing ( Hillyard et al., 1998). While s ome studies have indicated that attention does not have an effect on this component ( Eimer and Forster, 2003; Zopf et al., 2004 ), others do show an attention effect ( Jones et al., 2009; Schubert et al., 2008 ). In the previous chapter, we found that, following ignored somatosensory stimuli, the amplitude of the P1 evoked component was significantly larger than for attended stimuli. This is a notable result, as later evoked components, such as the N1, tend to be smaller following ignored as compared to attended stimuli (Michie, 1984; Garcia Larrea et al., 1995; Forss et al., 1996; Eimer and Forster, 2003; Zopf et al., 2004) As the N1 is thought to be generated by top down feedback processes ( Caulle r and Kulics, 1991; Cauller et al., 1998 ), a larger N1 is associated with better stimulus processing ( Libet et al., 1967; Schubert et al., 2006; Zhang and Ding, 2010 ). T he opposite relationship was found for the P1 component during attention, so i t is reas onable to assume that some aspect of this component could represent local inhibition.
88 In our analysis of four penetrations of Area 3b in primary somatosensory cortex in a single monkey, we found the P20 component to be localized to the supragranular layer s. A current source corresponding in time with the P20 was also located in these layers Coincident with this source was a decrease in multiunit activity (spiking) in all layers indicating that this component is of an inhibitory nature. In additional sup port of the inhibitory nature of the P20, we found that the level of ongoing mu activity immediately preceding a stimulus was positively correlated with P1 amplitude. H igher levels of ~10 Hz activity have been associated with local cortical inhibition ( Kli mesch et al., 2007 ). This relationship has also been found by Jones et al. (2009), who used model analysis to hypothesize that the P1 is associated with a greater amount of inhibitory as well as excitatory activity, and that a larger P1 leads to smal ler su bsequent evoked activity. In the previous chapter, we used a spatial filtering method to estimate SI activity from scalp recordings in humans. Our finding of a decrease of mu in primary somatosensory cortex contralateral to the attended hand relies on the assumption that the mu activity we measured was in fact from SI. One possible reason that this assumption might be incorrect would be if mu oscillations did not exist in SI. While research utilizing source localization techniques with scalp recorded data ( Gaetz and Cheyne, 2006; Nikouline et al., 2000 ) as well as recordings from the cortical surface ( Crone et al., 1998 ) have detected mu activity over primary somatosensory cortex clear evidence of the generation of mu oscillations in primate SI has not yet been shown. In order to test whether mu band oscillations might occur in primary somatosensory cortex we performed current source density analysis from laminar recordings of ongoing
89 activity in monkey Area 3b. CSD analysis is very susceptible to noise and unreliable in single trials, so special steps must be taken when applying CSD analysis to ongoing data Normally, stimulus based averaging is first performed In ongoing data, there are no stimuli to be used for averaging, so another method must be used. We used a phase realigned averaging technique to overcome this obstacle. After calculating our phase realigned averages, we performed traditional spatial CSD and found a pattern of current sources and sinks in granular and infragranular layers correspondin g with mu phase. If the mu oscillations seen in the raw recordings from SI were due to volume conduction from an outside source, a clear pattern of sources and sinks would not be discernible This finding lends support to the existence of mu oscillations i n Area 3b in primary somatosensory cortex
90 Figure 4 1. Stimulus evoked activity in SI. Left) Mean evoked laminar local field potentials (LFPs) to contralateral stimuli. Right) Mean evoked multi unit activity (MUA) to contralateral stimuli. The evoked LF P s and MUA are plotted as black curv es one for each contact with lower curves representing deeper contacts The evoked responses are plotted over current source densities (colored background) calculated from the evoked LFP s Blue indicates a source while red indicates a sink. The image to the far left is an illustration of the 24 contact linear array microelectrode inserted into the cortex.
91 Figure 4 2. From prestimulus mu power to evoked P20. For Electrode 2 (the second contact from the surface), the me an magnitude of evoked activity from 17 20 ms is plotted as a function of prestimulus ongoing mu power measured across all cortical laminae by subtracting the signal from Electrode 1 from Electrode 23. Evoked amplitudes have been normalized for each penetr ation and averaged acoss penetrations. Bin 1 represents the lowest prestimulus mu power and Bin 3 represents the highest mu power. Error bars represent the standard error of the mean for each bin. The gray curve represents the fit linear regression line.
92 Figure 4 3 10Hz ongoing activity in SI. A) Sample of raw laminar LFP recorded from SI. B) Phase aligned LFP (black sinusoidal curves) plotted over current source density calculated from the phase aligned data (left) and mean MUA activity (right) On the se plots, deeper contacts are lower and more superficial contacts are higher. The image to the far left is an illustration of the 24 contact linear array microelectrode inserted into the cortex.
93 CHAPTER 5 CONCLUSION This dissertation investigated the funct ional role of theta and alpha band neural oscillations in stimulus processing and the higher order cognitive processes of spatial attention and episodic memory retrieval. While originally considered background noise or an idling state of the brain, it is n ow generally accepted that neural oscillations play an active role in cognition. The findings presented in this dissertation provide evidence as to the functions of oscillatory activity in neural communication and cortical excitability. In the first study we investigated whether theta oscillations play a role in mediating the interaction between the prefrontal cortex and the medial temporal lobe in human memory processes. We measured the coherence and Granger causality between bipolar pairs of electrodes in grids covering lateral prefrontal cortex, medial temporal lobe, and lateral temporal lobe. A large increase was found in the coherence between the prefrontal cortex and medial temporal lobe during memory recall as opposed to baseline. This confirmed our hypothesis that these two areas are communicating during memory processes and provides evidence that the theta rhythm acts as a facilitator for such communication. The flow of information, revealed through Granger causality analysis, was bidirectional bet ween the prefrontal cortex and the medial temporal lobe, with a generally higher driving from the medial temporal lobe to the prefrontal cortex. In the second stud y, we investigated the effects of spatial somatosensory attention on stimulus processing and on prestimulus mu (8 12 Hz) and visual alpha (8 12 Hz) band oscillations. Using a spatial filtering method, we were able to estimate activity from multiple brain areas including bilateral primary somatosensory (SI), secondary somatosensory (SII), prefront al, lateral temporal, and occipital cortices. During the
94 somatosensory attention task, the amplitude of the visual alpha rhythm was found to be greatly elevated above baseline, indicating active suppression of visual processing. Conversely, during the soma tosensory attention task, the amplitude of the mu rhythm in primary somatosensory cortex contralateral to the direction of attention was found to decrease compared to baseline. This desynchronization has been associated with the active facilitation of sens ory processing. We then compared the amplitude of the ongoing mu rhythm in SI immediately before a stimulus to the evoked activity in SI following the stimulus. For an evoked component occurring around 150 ms after th e stimulus, known as the N1, a quadrati c relationship was found between prestimulus mu power and N1 amplitude. This relationship depended on attentional state. In SI contralateral to the stimulus side, an inverted U type relationship occurred to attended stimuli. In other words, an intermediate value of mu activity leads to a higher evoked N1 component. An upright U relationship was found with unattended stimuli. For an earlier component occurring around 50 ms after stimulus and known as the P1, a linear relationship was found between prestimulu s mu power a nd P1 amplitude. This relationship was unchanged according to attentional state. In the third study, we analyzed multielectrode recordings t hrough all cortical laminae in Area 3b of primary somatosensory cortex in a rhesus monkey during somato sensory stimulation as well as during a baseline period. Through this analysis, we were able to gain further insight into our findings in the second study. First, we examined the stimulus evoked P20 component, a homologue of the human P1. A source coincidi ng with the latency of the P20 was found in the supragranular layers, and
95 this source coincided with a drop in multiunit activity in all layers. This result supports the possibility that this source has in hibitory properties. We also determined that a posi tive linear relationship exists between the level of prestimulus mu activity and the amplitude of the P20. This relationship matches the one found in humans between prestimulus mu and the P1 and lends support to the inhibitory nature of the P1 and P20 comp onents. We then confirmed that oscillatory activity in the mu band indeed occurs in primary somatosensory cortex.
96 LIST OF REFERENCES Akaike H. 1974. A new look at the statistical model identification. IEEE Trans Auto Contr. 19:716 723. Allison T, McCar thy G, Wood CC. 1992. The relationship between human long latency somatosensory evoked potentials recorded from the cortical surface and from the scalp. Electroencephalogr Clin Neurophysiol 8 4 :301 314. Anderson KL, Ding M. 2011. Attentional modulation of the somatosensory mu rhythm. Neurosci. 180:165 180. Anderson KL, Rajagovindan R, Ghacibeh GA, Meador KJ, Ding M. 2010. Theta oscillations mediate interaction between prefrontal cortex and medial temporal lobe in human memory. Cereb Cortex. 20:1604 1612. Ar ezzo JC, Vaughan HG, Legatt AD. 1981. Topography and intracranial sources of somatosensory evoked potentials in the monkey. II. Cortical components. Electroencephalogr Clin Neurophysiol 5 1 :1 18. Armstrong James M, Welker E, Callahan C. 1993. The contribut ion of NMDA and non NMDA receptors to fast and slow transmission of sensory information in the rat SI barrel cortex. J Neurosci. 13:2149 2160. Asada H, Fukuda Y, Tsunoda S, Yamaguchi M, Tonoike M. 1999. Frontal midline theta rhythms reflect alternative act ivation of prefrontal cortex and anterior cingulate cortex in humans. Neurosci Lett. 274:29 32. Babiloni C, Brancucci A, Arendt Nielsen L, Babiloni F, Capotosto P, Carducci F, Cincotti F, Del Percio C, Petrini L, Rossini PM, Chen AC. 2004. Attentional proc esses and cognitive performance during expectancy of painful galvanic stimulations: a high resolution EEG study. Behav Brain Res 1 52 :137 147. Babiloni C, Capotosto P, Brancucci A, Del Percio C, Petrini L, Buttiglione M, Cibelli G, Luca Romani G, Maria Ros sini P, Arendt Nielsen L 2008 Cortical alpha rhythms are related to the anticipation of sensorimotor interaction between painful stimuli and movements: a high res olution EEG study. J Pain, 9 902 911. Bastiaansen MCM., Brunia CHM. 2001. Anticipatory atte ntion: an event related desynchronization approach. Int J Psychophysiol 4 3 :91 107. Bechara A, Damasio H, Tranel D, Anderson SW. 1998. Dissociation of working memory from decision making within the human prefrontal cortex. J Neurosci. 18:428 437. Blatow M, Rozov A, Katona I, Hormuzdi SG, Meyer AH, Whittington MA, Caputi A, Monyer H. 2003. A novel network of multipolar bursting interneurons generates theta frequency oscillations in neocortex. Neuron. 38:805 817.
97 Bollimunta A, Chen Y, Schroeder CE, Ding M. 20 08. Neuronal mechanisms of cortical alpha oscillations in awake behaving macaques. J Neurosci 2 8 :9976 9988. Bowsher D, Brooks J, Enevoldson P. 2004. Central representation of somatic sensations in the parietal operculum (SII) and insula. Eur Neurol 5 2 :21 1 225. Braun C, Haug M, Wiech K, Birbaumer N, Elbert T, Roberts L. 2002. Functional organization of primary somatosensory cortex depends on the focus of attention. Neuroimage. 17:1451 1458. Bressler SL, Kelso JAS. 2001. Cortical coordination dynamics and c ognition. Trends Cogn Sci. 5:26 36. Brovelli A, Ding M, Ledberg A, Chen Y, Nakamura R, Bressler SL. 2004. Beta oscillations in a large scale sensorimotor cortical network: directional influences revealed by granger causality. Proc Natl Acad Sci. 101:9849 9 854. Buckner RL, Wheeler ME. 2001. The cognitive neuroscience of remembering. Nat Rev Neurosci. 2:624 634. Bunge SA, Burrows B, Wagner AD. 2004. Prefrontal and hippocampal contributions to visual associative recognition: interactions between cognitive cont rol and episodic retrieval. Brain Cogn. 56:141 152. Burton H, Sinclair R. 2000. Tactile spatial and cross modal attention effects in the primary somatosensory cortical areas 3b and 1 2 of rhesus monkeys. Somatosens Mot Res. 17:213 228. Burwell RD, Witter M P, Amaral DG. 1995. Perirhinal and postrhinal cortices of the rat: a review of the neuroanatomical literature and comparison with findings from the monkey brain. Hippocampus. 5:390 408. Buzsaki G. 2002. Theta oscillations in the hippocampus. Neuron. 33:325 340. Canolty RT, Edwards E, Dalal SS, Soltani M, Nagarajan SS, Kirsch HE, Berger MS, Barbaro NM, Knight RT. 2006. High gamma power is phase locked to theta oscillations in human neocortex. Science. 313:1626 1628. Cauller LJ, Kulics AT. 1991. The neural ba sis of the behaviorally relevant N1 component of the somatosensory evoked potential in SI cortex of awake monkeys: evidence that backward cortical projections signal conscious touch sensation. Exp Brain Res 8 4 :607 619. Cauller LJ, Clancy B, Connors BW. 19 98. Backward cortical projections to primary somatosensory cortex in rats extend long horizontal axons in layer I. J Comp Neurol 3 90 :297 310.
98 control? Nat Neurosci. 3 :421 423. Cohen NJ, Ryan J, Hunt C, Romine L, Wszalek T, Nash C. 1999. Hippocampal system and declarative (relational) memory: summarizing the data from functional neuroimaging studies. Hippocampus. 9:83 98. Cooper NR, Croft RJ, Dominey SJJ, Burgess AP, Gr uzelier JH. 2003. Paradox lost? Exploring the role of alpha oscillations during externally vs. internally directed attention and the implications for idling and inhibition hypotheses. Int J Psychophysiol 4 7 :65 74. Corbetta M, Akbudak E, Conturo T, Snyder A, Ollinger J, Drury H, Linenweber M, Petersen SE, Raichle ME, Van Essen DC, Shulman GL. 1998. A common network of functional areas for attention and eye movements. Neuron 2 1 :761 773. Crone NE, Miglioretti DL, Gordon B, Sieracki JM, Wilson MT, Uematsu S, Lesser RP. 1998. Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis I. Alpha and beta event related desynchronization. Brain. 121:2271 2299. Daselaar SM, Rombouts SA, Veltman DJ, Raaijmakers JG, Lazeron RH, Jonker C. 2001. Parahippocampal activation during successful recognition of words: a self paced event related fMRI study. Neuroimage. 13:1113 1120. Degenetais E, Thierry AM, Glowinski J, Gioanni Y. 2003. Synaptic influence of hippocampus on pyramidal cells of the rat prefrontal cortex: an in vivo intracellular recording study. Cereb Cortex. 13:782 792. Della Penna S, Torquati K, Pizzella V, Babiloni C, Franciotti R, Rossini PM, Romani GL. 2004. Temporal dynamics of alpha and beta rhythms in human SI and SII after galvanic median nerve stimulation. A MEG study. Neuroimage 2 2 :1438 1446. Delorme A, Makeig S. 2004. EEGLAB: an open source toolbox for analysis of single trial EEG dynamics including independent component analysis. J Neurosci Methods 1 34 :9 21. Demanuele C, James CJ, Sonuga Barke EJ. 2007. Distinguishing low frequency oscillations within the 1/f spectral behaviour of electromagnetic brain signals. Behav Brain Funct 3:62. de Munck JC, Gonalves SI, Mammoliti R, Heethaar RM, Lopes da Silva FH. 2009. Interac tions between different EEG frequency bands and their effect on alpha fMRI correlations. Neuroimage 4 7 :69 76.
99 Devinsky O, Morrell M, Vogt B. 1995. Contributions of anterior cingulate cortex to behavior. Brain. 118:279 306. Dhamala M, Rangarajan G, Ding M 2008. Estimating Granger causality from Fourier and wavelet transforms of time series data. Phys Rev Lett. 100:018701. Ding M, Bressler SL, Yang W, Liang H. 2000. Short window spectral analysis of cortical event related potentials by adaptive multivariat e autoregressive modeling: data preprocessing, model validation, and variability assessment. Biol Cyber. 83:35 45. Ding M, Chen Y, Bressler SL. 2006. Granger causality: basic theory and application to neuroscience. In: Schelter B, Winderhalder M, Timmer J, editors. Handbook of Time Series Analysis. Berlin (Germany):Wiley VCH. p. 437 460. Dougherty RF, Koch VM, Brewer AA, Fischer B, Modersitzki J, Wandell BA. 2003. Visual field representations and locations of visual areas V1/2/3 in human visual cortex. J Vis 3:586 598. Dove A, Brett M, Cusak R, Owen AM. 2006. Dissociable contributions of the mid ventrolateral frontal cortex and the medial temporal lobe system to human memory. Neuroimage. 31:1790 1801. Eggermann E, Feldmeyer D. 2009. Cholinergic filtering in the recurrent excitatory microcircuit of cortical layer 4. Proc Natl Acad Sci U S A 1 06 :11753 11758. Eickhoff SB, Amunts K, Mohlberg H, Zilles K. 2006. The human parietal operculum. II. Stereotaxic maps and correlation with functional imaging results. Cereb Cortex 1 6 :268 279. Eimer M, Forster B. 2003. Modulations of early somatosensory ERP components by transient and sustained spatial attention. Exp Brain Res 1 51 :24 31. Ekstrom AD, Caplan JB, Ho E, Shattuck K, Fried I, Kahana MJ. 2005. Human hippocamp al theta activity during virtual navigation. Hippocampus. 15:881 889. Fell J, Fernandez G, Klaver P, Elger C, Fries, P. 2003. Is synchronized neuronal gamma activity relevant for selective attention? Brain Res Rev. 42:265 272. Fell J, Klaver P, Lehnertz K, Grunwald T, Schaller C, Elger CE, Fernandez G. 2001. Human memory formation is accompanied by rhinal hippocampal coupling and decoupling. Nat Neurosci. 4:1259 1264. Fernandez G, Weyerts H, Schrader Bolsche M, Tendolkar I, Smid HGOM, Tempelmann C, Hinrichs H, Scheich H, Elger CE, Mangun GR, Heinze HJ. 1998. Successful verbal encoding into episodic memory engages the posterior hippocampus: a parametrically analyzed functional magnetic resonance imaging study. J Neurosci. 18:1841 1847.
100 Feige B, Scheffler K, E sposito F, Di Salle F, Hennig J, Seifritz E. 2005. Cortical and subcortical correlates of electroencephalographic alpha rhythm modulation. J Neurophysiol 9 3 :2864 2872. Fletcher PC, Shallice T, Dolan RJ. 1998a. The functional roles of prefrontal cortex in episodic memory I: encoding. Brain. 121:1239 1248. Fletcher PC, Shallice T, Frith CD, Frackowiak RS, Dolan RJ. 1998b. The functional roles of prefrontal cortex in episodic memory II: retrieval. Brain. 121:1249 1256. Fletcher PC, Henson RNA. 2001. Frontal l obes and human memory: insights from functional neuroimaging. Brain. 124:849 881. Flint A, Connors B. 1996. Two types of network oscillations in neocortex mediated by distinct glutamate receptor subtypes and neuronal populations. J Neurophysiol. 75:951 956 Forss N, Merlet I, Vanni S, Hamalainen M, Mauguiere F, Hari R. 1996. Activation of human mesial cortex during somatosensory target detection task. Brain Res 7 34 :229 235. Frot M, Mauguiere F. 1999. Timing and spatial distribution of somatosensory respons es recorded in the upper bank of the sylvian fissure (SII area) in humans. Cereb Cortex. 9:854 863. Foxe JJ, Simpson GV, Ahlfors SP. 1998. Parieto occipital approximately 10 Hz activity reflects anticipatory state of visual attention mechanisms. Neurorepor t 9:3929 3933. Friedli WG, Fuhr P, Wiget W. 1987. Detection threshold for percutaneous electrical stimuli: asymmetry with respect to handedness. J Neurol Neurosurg Psychiatry 5 0 :870 876. Fu KG, Foxe JJ, Murray MM, Higgins BA, Javitt DC, Schroeder CE. 200 1. Attention dependent suppression of distracter visual input can be cross modally cued as indexed by anticipatory parieto occipital alpha band oscillations. Brain Res Cogn Brain Res 1 2 :145 152. Fujiwara N, Imai M, Nagamine T, Mima T, Oga T, Takeshi ta K, Toma K, Shibasaki H. 2002 Second somatosensory area (SII) plays a significant role in selective somatosensory attention. Brain Res Cogn Brain Res, 14 389 397. Fuster JM. 2001. The prefrontal cortex An update: time is of the essence. Neuron. 30:319 333. Gaetz W, Cheyne D. 2006. Localization of sensorimotor cortical rhythms induced by tactile stimulation using spatially filtered MEG. Neuroimage 3 0 :899 908.
101 Garcia Larrea L, Lukaszewicz A, Maguiere F. 1995. Somatosensory responses during selective spatial attention: The N120 to N140 transition. Psychophysiology 3 2 :526 537. Garcia Larrea L, Bastuji H, Mauguiere F. 1991. Mapping study of somatosensory evoked potentials during selective spatial attention. Electroencephalogr Clin Neurophysiol. 80:201 214. Gast aut, H.. 1952. Electrocorticographic study of the reactivity of rolandic rhythm. Revue Neurologique. 87:176 182. memory maintenance. Cogn Affect Behav Neurosci. 4:580 599. Ge weke J. 1982. Measurement of linear dependence and feedback between multiple time series. Amer Statist Assoc. 77:304 313. Giesbrecht B, Woldorff M, Song A, Mangun G. 2003. Neural mechanisms of top down control during spatial and feature attention. Neuroima ge. 19:496 512. Gitelman D, Nobre A, Parrish T, LaBar K, Kim Y, Meyer J, Mesulam M. 1999. A large scale distributed network for covert spatial attention: further anatomical delineation based on stringent behavioral and cognitive controls. Brain. 122:1093 1 106. Goldman RI, Stern JM, Engel J, Cohen MS. 2002. Simultaneous EEG and fMRI of the alpha rhythm. Neuroreport 1 3 :2487 2492. Goldman Rakic PS, Selemon LD, Schwartz ML. 1984. Dual pathways connecting the dorsolateral prefrontal cortex with the hippocampal formation and parahippocampal cortex in the rhesus monkey. Neuroscience. 12:719 743. Grady CL, McIntosh AR, Craik FIM. 2003. Age related differences in the functional connectivity of the hippocampus during memory encoding. Hippocampus. 13:572 586. Granger CWJ. 1969. Investigating causal relations by econometric models and cross spectral methods. Econometrics. 37:424 438. Habib R, Nyberg L, Tulving E. 2003. Hemispheric asymmetries of memory: the HERA model revisited. Trends Cogn Sci. 7:241 245. Haegens S, Os ipova D, Oostenveld R, Jensen O. 2010. Somatosensory working memory performance in humans depends on both engagement and disengagement of regions in a distributed network. Hum Brain Mapp 3 1 :26 35. Hasselmo ME, Eichenbaum H. 2005. Hippocampal mechanisms fo r the context dependent retrieval of episodes. Neural Netw. 18:1172 1190.
102 Heilman KM, Watson RT, Valenstein E. 1985. Neglect and related disorders. In: KM Heilman and E Valenstein, editors. Clin Neuropsychology 2nd ed. New York (NY): Oxford University Pre ss. p 268 307. Heilman KM, Abell TVD. 1980. Right hemisphere dominance for attention: The mechanism underlying hemispheric asymmetries of inattention (neglect). Neurology 3 0 :327. Hilgetag C, Theoret H, Pascual Leone A. 2001. Enhanced visual spatial attent ion ipsilateral to rTMS induced virtual lesions of human parietal cortex. Nat Neurosci. 4:953 957. Hillyard SA, Vogel EK, Luck SJ. 1998. Sensory gain control (amplification) as a mechanism of selective attention: electrophysiological and neuroimaging evide nce. Philos Trans R Soc Lond B Biol Sci. 3 53 :1257 1270. Himmelheber AM, Sarter M, Bruno JP. 2000. Increases in cortical acetylcholine release during sustained attention performance in rats. Brain Res Cogn Brain Res 9:313 325. Hoechstetter K, Rupp A, Meinc k H, Weckesser D, Bornfleth H, Stippich C, Berg P, Scherg M. 2000. Magnetic source imaging of tactile input shows task independent attention effects in SII. Neuroreport. 11:2461 2465. Hopfinger J, Buonocore M, Mangun G. 2000. The neural mechanisms of top d own attentional control. Nat Neurosci. 3:284 291. y DM, Johnson KO. 1993 Effects of selective attention on spatial form processing in monkey primary and secondary somatosenso ry cortex. J Neurophysiol, 70 444 447. Hyman JM, Zilli EA, Paley AM, Hasselmo ME. 2005. Medial prefrontal cortex cells show dynamic modulation with the hippocampal theta rhythm dependent on behavior. Hippocampus. 15:739 749. Jensen O. 2001. Information transfer between rhythmically coupled networks: reading the h ippocampal phase code. Neural Comput. 13:2743 2761. Jensen O. 2005. Reading the hippocampal code by theta phase locking. Trends Cogn Sci. 9:551 553. Jensen O, Gelfand J, Kounios J, Lisman JE. 2002. Oscillations in the alpha band (9 12 Hz) increase with mem ory load during retention in a short term memory task. Cereb Cortex 1 2 :877 882. Jensen O, Lisman JE. 2005. Hippocampal sequence encoding driven by a cortical multi item working memory buffer. Trends Neurosci. 28:67 72.
103 Jensen O, Tesche CD. 2002. Frontal t heta activity in humans increases with memory load in a working memory task. Eur J Neurosci. 15:1395 1399. Johnson JD. 2006. The conversational brain: fronto hippocampal interaction and disconnection. Med Hypotheses. 67:759 764. Jokisch D, Jensen O. 2007. Modulation of gamma and alpha activity during a working memory task engaging the dorsal or ventral stream. J Neurosci 2 7 :3244 3251. Jones BE. 2004. Activity, modulation and role of basal forebrain cholinergic neurons innervating the cerebral cortex. Prog Brain Res. 145:157 170. Jones MW, Wilson MA. 2005. Theta rhythms coordinate hippocampal prefrontal interactions in a spatial memory task. PLoS Biol. 3:e402. Jones SR, Pinto DJ, Kaper TJ, Kopell N. 2000. Alpha frequency rhythms desynchronize over long corti cal distances: A modeling study. J Comput Neurosci 9:271 291. Jones SR, Pritchett DL, Sikora MA, Stufflebeam SM, Hamalainen M, Moore CI. 2009 Quantitative analysis and biophysically realistic neural modeling of the MEG mu rhythm: rhythmogenesis and modul ation of sensory evoked responses. J Neurophysiol 1 02 :3554 3572. Kahana MJ, Seelig D, Madsen JR. 2001. Theta returns. Curr Opin Neurobiol. 11:739 744. Kahana MJ, Sekuler R, Caplan JB, Kirschen M, Madsen JR. 1999. Human theta oscillations exhibit task depe ndence during virtual maze navigation. Nature. 399:781 784. Kaminski M, Ding M, Truccolo W, Bressler SL. 2001. Evaluating causal relations in neural systems: Granger causality, directed transfer function (DTF) and statistical assessment of significance. Bi ol Cyber. 85:147 157. Kastner S, Ungerleider LG. 2000. Mechanisms of visual attention in the human cortex. Annu Rev Neurosci 2 3 :315 341. Kelly SP, Lalor EC, Reilly RB, Foxe JJ. 2006. Increases in alpha oscillatory power reflect an active retinotopic mecha nism for distracter suppression during sustained visuospatial attention. J Neurophysiol 9 5 :3844 3851. Klimesch W, Doppelmayr M, Schwaiger J, Auinger P, Winkler T. 1999. Paradoxical alpha synchronization in a memory task. Brain Res Cogn Brain Res 7:493 50 1. Klimesch W, Sauseng P, Hanslmayr S. 2007. EEG alpha oscillations: The inhibition timing hypothesis. Brain Res Rev 5 3 :63 88.
104 Knight RT, Richard Staines W, Swick D, Chao LL. 1999. Prefrontal cortex regulates inhibition and excitation in distributed neura l networks. Acta Psychologica. 101:159 178. Kocsis B, Bragin A, Buzsaki G. 1999. Interdependence of multiple theta generators in the hippocampus: a partial coherence analysis. J Neurosci. 19:6200 6212. Koene RA, Gorchetchnikov A, Cannon RC, Hasselmo ME. 20 03. Modeling goal directed spatial navigation in the rat based on physiological data from the hippocampal formation. Neural Netw. 16:577 584. Kopell N, Ermentrout G, Whittington M, Traub R. 2000. Gamma rhythms and beta rhythms have different synchronizatio n properties. Proc Natl Acad Sci. 97:1867 1872. Kucera H, Francis WN. 1967. Computational analysis of present day American English. Providence (RI): Brown University Press. Kuhlman W. 1978. Functional topography of the human mu rhythm. Electroenceph Clin N europhysiol. 44:83 93. Kulics AT, Cauller LJ. 1986. Cerebral cortical somatosensory evoked responses, multiple unit activity and current source densities: their interrelationships and significance to somatic sensation as revealed by stimulation of the awak e monkey's hand. Exp Brain Res 6 2 :46 60. LaBerge D. 1997. Attention, awareness, and the triangular circuit. Conscious Cogn. 6:149 181. and multisensory interaction in pri mary auditory cortex. Neuron. 53:279 292. Lakatos P, Karmos G, Mehta AD, Ulbert I, Schroeder CE. 2008. Entrainment of neuronal oscillations as a mechanism of attentional selection. Science. 320:110:113. Lakatos P, Shah AS, Knuth KH, Ulbert I, Karmos G, Sch roeder CE. 2005. An oscillatory hierarchy controlling neuronal excitability and stimulus processing in the auditory cortex. J Neurophysiol. 94:1904 1911. Lee EK, Seyal M. 1998. Generators of short latency human somatosensory evoked potentials recorded over the spine and scalp. J Clin Neurophysiol 1 5 :227 234. Lee JH, Durand R, Gradinaru V, Zhang F, Goshen I, Kim D, Fenno LE, Ramakrishnan C, Deisseroth K. 2010. Global and local fMRI signals driven by neurons defined optogenetically by type and wiring. Nature 4 65 :788 792.
105 Leek MR. 2001. Adaptive procedures in psychophysical research. Percept Psychophys 6 3 :1279 1292. Legatt AD, Arezzo J, Vaughan HG. 1980. Averaged multiple unit activity as an estimate of phasic changes in local neuronal activity: effects of v olume conducted potentials. J Neurosci Methods 2:203 217. Lengyel M, Kwag J, Paulsen O, Dayan P. 2005. Matching storage and recall: hippocampal spike timing dependent plasticity and phase response curves. Nat Neurosci. 8:1677 1683. Lepage M, Habib R, Tulvi ng E. 1998. Hippocampal PET activations of memory encoding and retrieval: the HIPER model. Hippocampus. 8:313 322. Levy DA, Bayley PJ, Squire LR. 2004. The anatomy of semantic knowledge: medial vs lateral temporal lobe. Proc Natl Acad Sci. 101:6710 6715. L ibet B, Alberts WW, Wright EW, Feinstein B. 1967. Responses of human somatosensory cortex to stimuli below threshold for conscious sensation. Science 1 58 :1597 1600. Liljenstrom H, Hasselmo ME. 1995. Cholinergic modulation of cortical oscillatory dynamics. J Neurophysiol 74:288 297. Linkenkaer Hansen K, Nikulin VV, Palva S, Ilmoniemi RJ, Palva JM. 2004. Prestimulus oscillations enhance psychophysical performance in humans. J Neurosci 2 4 :10186 10190. Branch CA, Isler JR, Schroeder CE. 2010. Interactions within the hand representation in primary somatosensory cortex of primates. J Neurosci. 30:15895 15903. MacDonald A, Cohen J, Stenger V, Carter C. 2000. Dissociating the role of the dorsolateral prefr ontal and anterior cingulate cortex in cognitive control. Science. 288:1835 1838. Mauguiere F, Merlet I, Forss N, Vanni S, Jousmaki V, Adeleine P, Hari R. 1997a. Activation of a distributed somatosensory cortical network in the human brain: a dipole modeli ng study of magnetic fields evoked by median nerve stimulation Part I: location and activation timing of SEP sources. Electroencephalogr Clin Neurophysiol. 104:281 289. Mauguiere F, Merlet I, Forss N, Vanni S, Jousmaki V, Adeleine P, Hari R. 1997b. Activat ion of a distributed somatosensory cortical network in the human brain: a dipole modeling study of magnetic fields evoked by median nerve stimulation Part II: effects of stimulus rate, attention and stimulus detection. Electroencephalogr Clin Neurophysiol. 104:290 295.
106 McFarland D, Miner L, Vaughan T, Wolpaw J. 2000. Mu and Beta rhythm topographies during motor imagery and actual movements. Brain Topogr 1 2 :177 186. McIntosh AR, Nyberg L, Bookstein FL, Tulving E. 1997. Differential functional connectivity o f prefrontal and medial temporal cortices during episodic memory retrieval. Hum Brain Mapp. 5:323 327. Meador KJ, Allison J, Loring D, Lavin T, Pillai J. 2002. Topography of Somatosensory Processing: Cerebral Lateralization and Focused Attention. J Int Neu ropsychol Soc 8:349 359. Meador KJ, Ray PG, Day L, Ghelani H, Loring DW. 1998. Physiology of somatosensory perception: Cerebral lateralization and extinction. Neurology 5 1 :721 727. Meador KJ, Thompson JL, Loring DW, Murro AM, King DW, Gallagher BB, Lee G P, Smith JR, Flanigin HF. 1991. Behavioral state specific changes in human hippocampal theta activity. Neurology. 41:869 872. Medendorp WP, Kramer GF, Jensen O, Oostenveld R, Schoffelen J, Fries P. 2007. Oscillatory activity in human parietal and occipital cortex shows hemispheric lateralization and memory effects in a delayed double step saccade task. Cereb Cortex 1 7 :2364 2374. Meltzer JA, Zaveri HP, Goncharova II, Distasio MM, Papademetris X, Spencer SS, Spencer DD, Constable RT. 2008. Effects of working memory load on oscillatory power in human intracranial EEG. Cereb Cortex. 18:1843 1855. Mesulam M. 1999. Spatial attention and neglect: parietal, frontal and cingulate contributions to the mental representation and attentional targeting of salient extrape rsonal events. Philos Trans R Soc Lond B Biol Sci. 354:1325 1346. Michie PT. 1984. Selective attention effects on somatosensory event related potentials. Ann N Y Acad Sci 4 25 :250 255. Miller EK, Cohen JD. 2001. An integrative theory of prefrontal cortex f unction. Ann Rev Neurosci. 24:167 202. Miller R. 1991. Cortico hippocampal interplay and the representation of contexts in the brain. Berlin (Germany): Springer. Mima T, Nagamine T Nakamura K, Shibasaki H. 1998 Attention modulates both primary and second somatosensory cortical activities in humans: a magnetoencephalograp hic study. J Neurophysiol, 80 2215 2221. Moosmann M, Ritter P, Krastel I, Brink A, Thees S, Blankenburg F, Taskin B, Obrig H, Villringer A. 2003. Correlates of alpha rhythm in functional magnetic resonance imaging and near infrared spectroscopy. Neuroimage 2 0 :145 158.
107 Narici L, Forss N, Jousmki V, Peresson M, Hari R. 2001. Evidence for a 7 to 9 Hz "Sigma" rhythm in the human SII cortex. Neuroimage 1 3 :662 668. Nee DE, Jonides J. 2008. N eural correlates of access to short term memory. Proc Natl Acad Sci. 105:14228 14233. Neuper C, Wrtz M, Pfurtscheller G. 2006. ERD/ERS patterns reflecting sensorimotor activation and deactivation. Prog Brain Res. 159:211 222. Niedermeyer E. 1997. Alpha rh ythms as physiological and abnormal phenomena. Int J Psychophysiol 2 6 :31 49. Nikouline VV, Linkenkaer Hansen K, Wikstrm H, Kesniemi M, Antonova EV, Ilmoniemi RJ, Huttunen J. 2000. Dynamics of mu rhythm suppression caused by median nerve stimulation: a m agnetoencephalographic study in human subjects. Neurosci Lett 2 94 :163 166. Nikouline VV, Wikstrm H, Linkenkaer Hansen K, Kesniemi M, Ilmoniemi RJ, Huttunen J. 2000. Somatosensory evoked magnetic fields: relation to pre stimulus mu rhythm. Clin Neurophys iol 1 11 :1227 1233. Nyberg L, Cabeza R, Tulving E. 1996. PET studies of encoding and retrieval: the HERA model. Psychon Bull Rev. 3:135 148. Nichols TE, Holmes AP. 2002. Nonparametric permutation tests for functional neuroimaging: a primer with examples. H um Brain Mapp. 15:1 25. Palva S, Palva JM. 2007. New vistas for alpha frequency band oscillations. Trends Neurosci 3 0 :150 158. Palvides C, Greenstein Y, Grudman M, Winston J. 1988. Long term potentiation in the dentate gyrus is induced preferentially on t he positive phase of theta rhythm. Brain Res. 439:383 387. Pascual Marqui R, Michel C, Lehmann D. 1994. Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. Int J Psychophys. 18:49 65. Peterson NN, Schroe der CE, Arezzo JC. 1995. Neural generators of early cortical somatosensory evoked potentials in the awake monkey. Electroencephalogr Clin Neurophysiol/Evoked Potentials Section 9 6 :248 260. Petrides M, Milner B. 1982. Deficits on subject ordered tasks afte r frontal and temporal lobe lesions in man. Neuropsychologia. 20:249 262. Petrides M, Alivisatos B, Evans AC. 1995. Functional activation of the human Ventrolateral frontal cortex during mnemonic retrieval of verbal information. Proc Natl Acad Sci. 92:5803 5807.
108 Pfurtscheller G. 1989. Functional topography during sensorimotor activation studied with event related desynchronization mapping. J Clin Neurophysiol 6:75 84. Pfurtscheller G 1992 Event related synchronization (ERS): an electrophysiological corre late of cortical areas at rest. Electroencephalogr Clin Neurophysiol, 83, 62 69 Pfurtscheller G, Aranibar A, Maresch H. 1979. Amplitude of evoked potentials and degree of event related desynchronization (ERD) during photic stimulation. Electroencephalogr Clin Neurophysiol 4 7 :21 30. Pfurtscheller G, Neuper C, Mohl W. 1994. Event related desynchronization (ERD) during visual processing. Int J Psychophysiol 1 6 :147 153. Posner MI, Nissen MJ, Ogden WC. 1978. Attended and unattended processing modes: The role of set for spatial location In: HJ Pick and IJ Saltzman, editors. Modes of Perception. Hillsdale (NJ): Erlbaum. p 137 157. Pritchard WS. The brain in fractal time: 1/f like power spectrum scaling of the human electroencephalogram. Int J Neurosci. 66:119 1 29. Raghavachari S, Kahana MJ, Rizzuto DS, Caplan JB, Kirschen MP, Bourgeois B, Madsen JR, Lisman JE. 2001. Gating of human theta oscillations by a working memory task. J Neurosci. 21:3175 3183. Raghavachari S, Lisman JE, Tully M, Madsen JR, Bromfield EB, Kahana MJ. 2006. Theta oscillations in human cortex during a working memory task: evidence for local generators. J Neurophysiol. 95:1630 1638. Rajagovindan R, Ding M. 2008. Decomposing neural synchrony: toward an explanation for near zero phase lag in cort ical oscillatory networks. PLoS ONE. 3:e3649. Rajagovindan R, Ding M. In press From prestimulus alpha oscillation to visual evoked response: an inverted U function and its attentional modulation. J Cogn Neurosci doi:10.1162/jocn.2010.21478. Reinacher M, Becker R, Villringer A, Ritter P. 2009. Oscillatory brain states interact with late cognitive components of the somatosensory evoked potential. J Neurosci Methods 1 83 :49 56. Ruben J, Schwiemann J, Deuchert M, Meyer R, Krause T, Curio G, Villringer K, Kurt h R, Villringer A. 2001. Somatotopic organization of human secondary somatosensory cortex. Cereb Cortex. 11:463 473. Rihs TA, Michel CM, Thut G. 2007. Mechanisms of selective inhibition in visual spatial attention are indexed by alpha band EEG synchronizat ion. Eur J Neurosci 2 5 :603 610.
109 Ritter P, Moosmann M, Villringer A. 2009. Rolandic alpha and beta EEG rhythms' strengths are inversely related to fMRI BOLD signal in primary somatosensory and motor cortex. Hum Brain Mapp 3 0 :1168 1187. da Rocha A, Pereira A, Coutinho F. 2001. N methyl D aspartate channel and consciousness: from signal coincidence detection to quantum computing. Prog Neurobiol. 64:555 573. Romei V, Rihs T, Brodbeck V, Thut G. 2008. Resting electroencephalogram alpha power over posterior sit es indexes baseline visual cortex excitability. Neuroreport 1 9 :203 208. Salenius S, Schnitzler A, Salmelin R, Jousmaki V, Hari R. 1997. Modulation of human cortical rolandic rhythms during natural sensorimotor tasks. Neuroimage. 5:221 228. Sauseng P, Klim esch W, Stadler W, Schabus M, Doppelmayr M, Hanslmayr S, Gruber WR, Birbaumer N. 2 005. A shift of visual spatial attention is selectively associated with human EEG alpha activity. Eur J Neurosci 2 2 :2917 2926. Scherg M. 1992. Functional imaging and localiz ation of electromagnetic brain activity. Brain Topogr 5:103 111. Schira MM, Tyler CW, Breakspear M, Spehar B. 2009. The foveal confluence in human visual cortex. J Neurosci 2 9 :9050 9058. Schroeder CE, Steinschneider M, Javitt DC, Tenke CE, Givre SJ, Meht a AD, Simpson GV, Arezzo JC, Vaughan HG. 1995. Localization of ERP generators and identification of underlying neural processes. Electroencephalogr. 44:55 75. Schubert R, Blankenburg F, Lemm S, Villringer A, Curio G. 2006. Now you feel it, now you don't: E RP correlates of somatosensory awareness. Psychophysiology 4 3 :31 40. Schubert R, Ritter P, Wustenberg T, Preuschhof C, Curio G, Sommer W, Villringer A. 2008. Spatial Attention related SEP amplitude modulations covary with BOLD signal in S1 A simultaneou s EEG -fMRI study. Cereb Cortex 1 8 :2686 2700. Scoville WB, Milner B. 1957. Loss of recent memory after bilateral hippocampal lesions. J Neuropsychiatry Clin Neurosci. 12:103 113. Sederberg PB, Kahana MJ, Howard MW, Donner EL, Madsen JR. 2003. Theta and ga mma oscillations during encoding predict subsequent recall. J Neurosci. 23:10809 10814. Shah AS, Bressler SL, Knuth KH, Ding M, Mehta AD, Ulbert I, Schroeder CE. 2004. Neural dynamics and the fundamental mechanisms of event related brain potentials. Cereb Cortex. 14:476 483.
110 Shaw JC. 2003. modern studies of the alpha rhythm component of the electroencephalogram with commentaries on associated neuroscience and neuropsychology Amsterdam (Nethe rlands): Elsevier Science Ltd. Shimamura AP. 1995. Memory and the prefrontal cortex. Ann N Y Acad Sci. 769:151 159. Siapas AG, Lubenov EV, Wilson MA. 2005. Prefrontal phase locking to hippocampal theta oscillations. Neuron. 46:141 151. Simoes C, Jensen O, Parkkonen L, Hari R. 2003. Phase locking between human primary and secondary somatosensory cortices. Proc Natl Acad Sci. 100:2691:2694. Slutzky E. 1937. The summation of random causes as the source of cyclic processes. Econometrica 5:105 146. Squire L, Sh imamura AP, Amaral DG. 1989. Memory and the hippocampus. In: Byrne JH, Berry WO, editors. Neural models of plasticity: experimental and theoretical approaches. New York (NY): Academic Press. p 208 239. Steinmetz PN, Roy A, Fitzgerald PJ, Hsiao SS, Johnson KO, Niebur E. 2000 Attention modulates synchronized neuronal firing in primate somatosensory cortex. Nature, 404(6774), 180 190. Stuss DT, Alexander MP. 2005. Does damage to the frontal lobes produce impairment in memory. Curr Dir Psychol Sci. 14:84 88. S uzuki WA. 1996. Neuroanatomy of the monkey entorhinal, perirhinal and parahippocampal cortices: organization of cortical inputs and interconnections with amygdala and striatum. Semin Neurosci. 8:3 12. Tesche CD, Karhu J. 2000. Theta oscillations index huma n hippocampal activation during a working memory task. Proc Natl Acad Sci. 97:919 924. Thut G, Nietzel A, Brandt SA, Pascual Leone A. 2006. Alpha band electroencephalographic activity over occipital cortex indexes visuospatial attention bias and predicts v isual target detection. J Neurosci 2 6 :9494 9502. Tierney PL, Degenetais E, Thierry AM, Glowinsky J, Gioanni Y. 2004. Influence of the hippocampus on interneurons of the rat prefrontal cortex. Eur J Neurosci. 20:514 524. Tomberg C, Desmedt JE. 1996. Non av eraged human brain potentials in somatic attention: the short latency cognition related P40 component. J Physiol 4 96 :559 574.
111 Tuladhar AM, Huurne NT, Schoffelen J, Maris E, Oostenveld R, Jensen O. 2007. Parieto occipital sources account for the increase i n alpha activity with working memory load. Hum Brain Mapp 2 8 :785 792. Tulving E, Kapur S, Craik FIM, Moscovitch M, Houle S. Hemispheric encoding/retrieval asymmetry in episodic memory: positron emission tomography findings. Proc Natl Acad Sci. 91:2016 202 0. Valeriani M, Restuccia D, Di Lazzaro V, Le Pera D, Tonali P. 1997. The pathophysiology of giant SEPs in cortical myoclonus: a scalp topography and dipolar source modelling study. Electroencephalogr Clin Neurophysiol/Evoked Potentials Section 1 04 :122 13 1. Vertes RP, Kocsis B. 1997. Brainstem diencephalo septohippocampal systems controlling the theta rhythm of the hippocampus. Neuroscience. 81:893 926. Vertes RP. 2001. Analysis of projections from the medial prefrontal cortex to the thalamus in the rat, w ith emphasis on nucleus reunions. J Comp Neurol. 442:163 187. Vertes RP. 2005. Hippocampal theta rhythm: a tag for short term memory. Hippocampus. 15:923 935. Vertes RP, Hoover W, Viana Di Prisco GV. 2004. Theta rhythm of the hippocampus: subcortical contr ol and functional significance. Behav Cogn Neurosci Rev. 3:173 200. Vijn PC, van Dijk BW, Spekreijse H. 1991. Visual stimulation reduces EEG activity in man. Brain Res 5 50 :49 53. Von Stein A, Sarnthein J. 2000. Different frequencies for different scales o f cortical integration: from local gamma to long range alpha/theta synchronization. Int J Psychophys. 38:301 313. Waberski T, Gobbele R, Darvas F, Schmitz S, Buchner H. 2002. Spatiotemporal imaging of electrical activity related to attention to somatosenso ry stimulation. Neuroimage. 17:1347 1357. Wang X, Chen Y, Ding M. 2007. Testing for statistical significance in bispectra: A Biomedical Engineering. 54:1974 1982 Wheeler MA, Stu ss DT, Tulving E. 1995. Frontal lobe damage produces episodic memory impairment. J Int Neurophysol Soc. 1:525 536. Wiener N. 1956. The theory of prediction. In: Beckenham E, editor. Modern Mathematics for Engineers. New York (NY): McGraw Hill.
112 Wikstrm H, Huttunen J, Korvenoja A, Virtanen J, Salonen O, Aronen H, Ilmoniemi RJ. 1996. Effects of interstimulus interval on somatosensory evoked magnetic fields (SEFs): a hypothesis concerning SEF generation at the primary sensorimotor cortex. Electroencephalogr Cl in Neurophysiol/Evoked Potentials Section 1 00 :479 487. Wood C, Cohen D, Cuffin B, Yarita M, Allison T. 1985. Electrical sources in human somatosensory cortex: identification by combined magnetic and potential recordings. Science 2 27 :1051 1053. Worden MS, Foxe JJ, Wang N, Simpson GV. 2000. Anticipatory biasing of visuospatial attention indexed by retinotopically specific alpha band electroencephalography increases over occipital cortex. J Neurosci 2 0 :RC63. Wyart V, Tallon Baudry C. 2008. Neural dissociat ion between visual awareness and spatial attention. J Neurosci 2 8 :2667 2679. Xu L, Stoica P, Li J, Bressler S, Shao X, Ding M. 2009. ASEO: A method for the simultaneous estimation of single trial event related potentials and ongoing b rain activities. IEEE Trans Biomed Eng. 56:111 121. Yamagishi N, Callan DE, Goda N, Anderson SJ, Yoshida Y, Kawato M. 2003. Attentional modulation of oscillatory activity in human visual cortex. Neuroimage 2 0 :98 113. Young CK, McNaughton N. 2009. Coupling of theta oscillations between anterior and posterior midline cortex and with the hippocampus in freely behaving rats. Cereb Cortex. 19:24 40. Zhang Y, Ding M. 2010. Detection of a weak somatosensory stimulus: role of the prestimulus mu rhythm and its top down modulation. J Cogn Neurosci 2 2 :307 322. Zopf R, Giabbiconi CM, Gruber T, Mller MM. 2004. Attentional modulation of the human somatosensory evoked potential in a trial by trial spatial cueing and sustained spatial attention task measured with high de nsity 128 channels EEG. Brain Res Cogn Brain Res 2 0 :491 509.
113 BIOGRAPHICAL SKETCH Kristopher Anderson grew up in Lake Worth, Florida attending Highland Elementary School and Lake Worth Middle School One time, he was out on a 9.5 horsepower aluminum b oat with his good friends Joseph Liberman and Jared Jacob when he fell into the water while trying to start the engine. After graduating from Lake Worth High S chool in 1999, he enrolled at the University of Florida in Gainesville. Kristopher received his B achelor of Science in computer engineering in 2005. Kristopher then spent some time working in a developmental psychology lab under Dr. W. Keith Berg a before returning to UF to work towards a PhD in biomedical engineering specializing in cognitive neuros cience under the mentorship of Dr. Mingzhou Ding.