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Impairment of Cognitive Control Component Processes after Traumatic Brain Injury

Permanent Link: http://ufdc.ufl.edu/UFE0024450/00001

Material Information

Title: Impairment of Cognitive Control Component Processes after Traumatic Brain Injury An fMRI Study
Physical Description: 1 online resource (79 p.)
Language: english
Creator: Sozda, Christopher
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2009

Subjects

Subjects / Keywords: acc, brain, cognitive, dlpfc, fmri, tbi
Clinical and Health Psychology -- Dissertations, Academic -- UF
Genre: Psychology thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Traumatic Brain Injury (TBI), an insult to the brain that emerges from external contact or inertia (e.g., acceleration or deceleration forces) continues to remain a significant public health care issue in the US, and injury-related cognitive impairments are a major contributor to disability in patients with TBI. Whereas both the nature of brain injuries sustained and patterns of cognitive impairment following TBI are heterogeneous, deficits in 'cognitive control' are common. These impairments likely reflect disruption in one of two essential component processes implemented in a closely interactive, yet dissociable frontal neural network: a dorsolateral prefrontal cortex (dlPFC)-mediated regulative component supporting maintenance of task goals and implementation of control, and an anterior cingulate cortex (ACC)-mediated evaluative component that supports conflict processing, performance monitoring, and signals the need for strategic adjustments toward goal attainment. Functional magnetic resonance imaging (fMRI) and behavioral data were acquired while 10 severe TBI (sTBI) participants and 12 neurologically-healthy controls performed a task-switching cued-Stroop task to enable us to examine component process integrity in severe TBI. Behavioral data were analyzed using ANOVAs and fMRI data using a whole-brain voxel-wise general linear model and planned linear contrasts. Behaviorally, both controls and sTBI patients demonstrated standard Stroop interference RT effects, that is, significant slowing in the incongruent compared to congruent conditions. TBI patients committed significantly more errors than controls under incongruent color-naming conflict conditions, but not congruent conditions. fMRI data revealed that, compared to controls, TBI patients exhibited reduced dlPFC-mediated regulative activity, but intact ACC-mediated conflict-related activity. Additionally, controls but not sTBI patients, showed greater bilateral caudate activation under high 'conflict' conditions. Not surprisingly, neural networks mediating regulative-component processes are altered after TBI. In contrast to previous studies, however, cortical evaluative-mediated conflict processing-related activity was not reduced in our sample of sTBI patients; rather, this function was largely preserved. In contrast, sTBI patients demonstrated reductions in subcortical (e.g., fronto-striatal) activity, a finding not previously reported. Thus, cognitive impairments in sTBI may result from subcortical, rather than cortical dysfunction and may reflect impairment in dissociable components of cognitive control. These findings may have implications for the design of cognitive rehabilitation strategies.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Christopher Sozda.
Thesis: Thesis (M.S.)--University of Florida, 2009.
Local: Adviser: Perlstein, William.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2009
System ID: UFE0024450:00001

Permanent Link: http://ufdc.ufl.edu/UFE0024450/00001

Material Information

Title: Impairment of Cognitive Control Component Processes after Traumatic Brain Injury An fMRI Study
Physical Description: 1 online resource (79 p.)
Language: english
Creator: Sozda, Christopher
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2009

Subjects

Subjects / Keywords: acc, brain, cognitive, dlpfc, fmri, tbi
Clinical and Health Psychology -- Dissertations, Academic -- UF
Genre: Psychology thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Traumatic Brain Injury (TBI), an insult to the brain that emerges from external contact or inertia (e.g., acceleration or deceleration forces) continues to remain a significant public health care issue in the US, and injury-related cognitive impairments are a major contributor to disability in patients with TBI. Whereas both the nature of brain injuries sustained and patterns of cognitive impairment following TBI are heterogeneous, deficits in 'cognitive control' are common. These impairments likely reflect disruption in one of two essential component processes implemented in a closely interactive, yet dissociable frontal neural network: a dorsolateral prefrontal cortex (dlPFC)-mediated regulative component supporting maintenance of task goals and implementation of control, and an anterior cingulate cortex (ACC)-mediated evaluative component that supports conflict processing, performance monitoring, and signals the need for strategic adjustments toward goal attainment. Functional magnetic resonance imaging (fMRI) and behavioral data were acquired while 10 severe TBI (sTBI) participants and 12 neurologically-healthy controls performed a task-switching cued-Stroop task to enable us to examine component process integrity in severe TBI. Behavioral data were analyzed using ANOVAs and fMRI data using a whole-brain voxel-wise general linear model and planned linear contrasts. Behaviorally, both controls and sTBI patients demonstrated standard Stroop interference RT effects, that is, significant slowing in the incongruent compared to congruent conditions. TBI patients committed significantly more errors than controls under incongruent color-naming conflict conditions, but not congruent conditions. fMRI data revealed that, compared to controls, TBI patients exhibited reduced dlPFC-mediated regulative activity, but intact ACC-mediated conflict-related activity. Additionally, controls but not sTBI patients, showed greater bilateral caudate activation under high 'conflict' conditions. Not surprisingly, neural networks mediating regulative-component processes are altered after TBI. In contrast to previous studies, however, cortical evaluative-mediated conflict processing-related activity was not reduced in our sample of sTBI patients; rather, this function was largely preserved. In contrast, sTBI patients demonstrated reductions in subcortical (e.g., fronto-striatal) activity, a finding not previously reported. Thus, cognitive impairments in sTBI may result from subcortical, rather than cortical dysfunction and may reflect impairment in dissociable components of cognitive control. These findings may have implications for the design of cognitive rehabilitation strategies.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Christopher Sozda.
Thesis: Thesis (M.S.)--University of Florida, 2009.
Local: Adviser: Perlstein, William.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2009
System ID: UFE0024450:00001


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IMPAIRMENT OF COGNITIVE C ONTROL COMPONENT PROCESSES AFTER TRAUMATIC BRAIN INJURY: AN FMRI STUDY By CHRISTOPHER NICHOLAS SOZDA A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2009 1

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2009 Christopher Nicholas Sozda 2

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To m y parents and family, in love and gratitude, for their love, inspiration, encouragement, and unwavering support 3

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ACKNOWL EDGMENTS Foremost, I thank my advisor, Dr. William M. Perlstein, for his active interest, guidance and support during this project. I am grateful fo r his patience, motivati on, and enthusiasm that, taken together, make him a great mentor. I also thank Dr. Michael J. Larson, Dr. Neha Dixit, and David Stigge-Kaufmann, for assistance in data collection. Lastly, I wish to also thank Dr. David Janicke, Dr. Deidre Pereira, a nd Dr. Catherine Price, for servi ng on my thesis committee. This work was supported by NIH grants K01 MH01857 and R21 MH073076 (WMP). 4

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TABLE OF CONTENTS page ACKNOWLEDGMENTS ...............................................................................................................4 LIST OF TABLES ...........................................................................................................................7 LIST OF FIGURES .........................................................................................................................8 ABSTRACT .....................................................................................................................................9 CHAPTER 1 INTRODUCTION................................................................................................................. .11 Traumatic Brain Injury: Epidemiology and Sequelae ............................................................11 TBI and Cognitive Impairment: A Brief Review ...................................................................13 Cognitive Control Theory .......................................................................................................14 Regulative/Strategic Processes ........................................................................................15 Evaluative Processes .......................................................................................................17 Dissociation of Cognitive Control Component Processes Using fMRI and EEG ...........18 Cognitive Control and TBI ..............................................................................................20 Functional Neuroimaging .......................................................................................................21 BloodOxygen Level Dependent (BOLD) F unctional Magnetic Resonance Im aging (fMRI) ..........................................................................................................................22 The BOLD Response Hemodynamic Inverse Problem ................................................23 fMRI and TBI Methodological Considerations ............................................................23 Blocked vs. Event-Related Experim ental Task Designs .................................................24 Neuroimaging and Cognitive Control ....................................................................................25 Neuronal Correlates of Regulative Processes ..................................................................26 Neuronal Correlates of Evaluative Processes ..................................................................26 Predictions ..............................................................................................................................27 Behavioral Data ...............................................................................................................27 fMRI ................................................................................................................................27 Regulative Processes ................................................................................................28 Evaluative Processes ................................................................................................28 2 METHODS...................................................................................................................... .......29 Participants .............................................................................................................................29 Clinical and Symptom Assessment .................................................................................30 Materials and Procedures ........................................................................................................32 Cognitive Activation Task ...............................................................................................32 fMRI Acquisition a nd Data Reduction ...................................................................................33 MRI Scanning ..................................................................................................................33 Functional Image Data Reduction ...................................................................................34 Statistical Analyses .................................................................................................................35 5

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Behavioral Data Analyses ...............................................................................................35 Functional Imaging Data Analyses .................................................................................35 3 RESULTS...................................................................................................................... .........39 Behavioral Analyses ...............................................................................................................39 RT Analyses ....................................................................................................................39 Error Rate Analyses .........................................................................................................40 fMRI Data ...............................................................................................................................41 Head Movement ..............................................................................................................41 Cue-Related Regulative Activation .................................................................................42 Probe-Related Evaluative Processes ................................................................................42 4 DISCUSSION................................................................................................................... ......55 Behavioral Data ......................................................................................................................55 fMRI Data ...............................................................................................................................56 Regulative Processes .......................................................................................................56 Evaluative Processes .......................................................................................................58 Alternative Explanations and Limitations ..............................................................................61 Limitations of fMRI ........................................................................................................61 Sample Size Limitations ..................................................................................................62 Future Directions ....................................................................................................................63 Summary .................................................................................................................................65 LIST OF REFERENCES ...............................................................................................................66 BIOGRAPHICAL SKETCH .........................................................................................................79 6

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LIST OF TABLES Table page 2-1 Demographic data for contro ls and s evere TBI participants ............................................37 2-2 Injury characteristics for severe TBI participants .............................................................37 2-3 Emotional functioning data for controls and severe TBI participants ..............................37 3-1 Correlations of mean erro r rates and correct-trial RTs ( ms) in the cued-Stroop as a function of group ................................................................................................................43 3-2 Means and standard errors of correct-tri al RTs (m s) in the cued-Stroop as a function of group ..............................................................................................................................43 3-3 Means and standard errors of correct-tri al RT (m s) in the cued-Stroop as a function of congruency.....................................................................................................................44 3-4 Means and standard errors of correct-tri al RTs (m s) in the cued-Stroop as a function of task .................................................................................................................................45 3-5 Means and standard errors of correct-tri al RTs (m s) in the cued-Stroop as a function of blocking .........................................................................................................................45 3-6 Means and standard errors of error rates (pr oportion) in the cued -Stroop as a function of group ..............................................................................................................................46 3-7 Means and standard errors of error rates (pr oportion) in the cued -Stroop as a function of congruency.....................................................................................................................47 3-8 Means and standard errors of error rates (pr oportion) in the cued -Stroop as a function of task .................................................................................................................................48 3-9 Means and standard errors of error rates (pr oportion) in the cued -Stroop as a function of blocking .........................................................................................................................48 3-10 Statistical parametric mapping summary for within-and between-groups RFX-GLM analyses : fMRI activation for the color-naming > word-reading contrast .........................49 3-11 Statistical parametric mapping summar y for within-and between-groups RFX-GLM analyses: fMRI activation for the incongruent > congruent contrast .................................50 7

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LIST OF FI GURES Figure page 2-1 .............................................................................................38 Schematic of the task-switching cued Stroop task. As shown, task trials comprise an instructional cue followed after a delay by a stimulus probe to wh ich the participan t responds with a button press. 3-1 ...................................51 Box plot of mean translational (x, y, z) in ter-scan head m ovement during fMRI as a function of group. P-value indica tes that controls and pati ents with severe TBI did not significantly differ in head movement during fMRI scanning. 3-2 ............................51 Box plot of mean rotational (p itch, roll, yaw) inter-scan head movement during fMRI as a function of group. P-value indicates that controls and patients with severe TBI did not significantly differ in head movement during FMRI scanning. 3-3 ..........................................52 fMRI statistical overlay map illustrating signi f icant clusters of activity associated with the color-naming > word-reading contra st for controls (threshold: p = .021 and 7 contiguous voxels). dlPFC = dor solateral pre-frontal cortex 3-4 ...................................52 fMRI statistical overlay map illustrating signi f icant clusters of activity associated with the color-naming > word-reading group by task interaction (threshold: p = .023 and 6 contiguous voxels). dlPFC = dorsolateral pre-frontal cortex 3-5 .................................53 fMRI statistical overlay map illustrating si gnif icant clusters of activity associated with the incongruent > congrue nt contrast for patients with severe TBI (threshold: p = .035 and 14 contiguous voxels). ACC = anterior cingulate cortex 3-6 ............................................................................................................53 fMRI statistical overlay map illustrating si gnif icant clusters of activity associated with the incongruent > congruent contrast for controls (threshold: p = .035 and 14 contiguous voxels). 3-7 ......................................................................................54 fMRI statistical overlay map illustrating si gnif icant clusters of activity associated with the incongruent > c ongruent group by congruency in teraction (threshold: p =.050 and 18 contiguous voxels). 8

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Abstract of Thesis Presen ted to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science IMPAIRMENT OF COGNITIVE CONTROL COMPONENT PROCESSES AFTER TRAUMATIC BRAIN INJURY: AN FMRI STUDY By Christopher Nicholas Sozda May 2009 Chair: William M. Perlstein Major: Psychology Traumatic Brain Injury (TBI), an insult to the brain that emerges from external contact or inertia (e.g., acceleration or deceleration forces) c ontinues to remain a significant public health care issue in the US, and inju ry-related cognitive impairments are a major contributor to disability in patients with TBI. Whereas both th e nature of brain injuries sustained and patterns of cognitive impairment following TBI are heter ogeneous, deficits in cognitive control are common. These impairments likely reflect disruption in one of two essential component processes implemented in a closely interactive, yet dissociable frontal neural network: a dorsolateral prefrontal cortex (dlPFC)-mediated regulative component supporting maintenance of task goals and implementation of control, and an anterior cingulate cortex (ACC)-mediated evaluative component that supports conflict processi ng, performance monitoring, and signals the need for strategic adjustments toward goal at tainment. Functional magnetic resonance imaging (fMRI) and behavioral data were acquired while 10 severe TB I (sTBI) participants and 12 neurologically-healthy controls performed a task-switching cued-Stroop task to enable us to examine component process integr ity in severe TBI. Behavioral data were analyzed using ANOVAs and fMRI data using a whole-brain vo xel-wise general linear model and planned linear contrasts. Behaviorally, both controls and sTBI patients demonstrated standard Stroop 9

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10 interference RT effects, that is significant slowing in the inco ngruent compared to congruent conditions. TBI patients committed significantly more errors than controls under incongruent color-naming conflict conditions but not congruent conditions fMRI data revealed that, compared to controls, TBI patients exhibited reduced dlPFC-mediated regulative activity, but intact ACC-mediated conflict-related activity. Additionally, controls but not sTBI patients, showed greater bilate ral caudate activation under high c onflict conditions. Not surprisingly, neural networks mediating regulative-component pr ocesses are altered after TBI. In contrast to previous studies, however, cortical evaluative-mediated conflict pr oce ssing-related activity was not reduced in our sample of sT BI patients; rather, this functi on was largely preserved. In contrast, s TBI patients demonstrated reductions in subcortical (e.g., fronto-striatal) activity, a finding not previously reported. Thus, cognitive im pairments in sTBI may result from subcortical, rather than cort ical dysfunction and may reflect impairment in dissociable components of cognitive control. These findings may have implications for the design of cognitive rehabil itation strategies.

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CHAP TER 1 INTRODUCTION Traumatic Brain Injury: Epidemiology and Sequelae Traumatic Brain Injury (TBI), a non-penetra ting insult to the brain that results from external contact or inertia (e.g., acceleration or decel eration forces), is a si gnificant public health care issue in the United States a nd is the leading cause of deat h and disability for persons under 45 years of age (Coronado, Thomas, Sattin, & Johnson, 2005; NIH, 1998; Thurman, Alverson, Dunn, Guerrero, & Sniezek, 1999). Often resultin g in temporary or permanent cognitive, behavioral, emotional, psychosocial, or physical impairment, TBI will affect nearly 1-2 million individuals annually (Langloi s, Rutland-Brown, & Thomas, 2004; Thurman, 2001). However, national reports on the prevalen ce of brain injury (e.g., Langloi s et al., 2004; NIH, 1998) are likely underestimates of the true severity and societ al burden of TBI, as data often fail to account for individuals treated in outpatient settings, physic ians offices, military facilities, or who sustain a TBI but do not obtain medical attention (Finkelstein, Corso, & Miller, 2006). Regardless, the estimated annual rate of emergency department visits due to TBI (403 per 100 000; Langlois, Rutland-Brown, & Wald, 2006) continues to rise, a nd the estimated annual incidence for TBI (85 per 100,000; Langlois et al., 2006) is nearly 34 times greater than diagnosed HIV/AIDS cases (Lezak, Howieson, & Loring, 2004). Nearly 300,000 TBI cases are of enough severi ty to require hosp italization, where an estimated 52,000 individuals will die (Thurma n et al., 1999). Moreover, 80,000-90,000 patients will experience long-term disabilities, which have a profound impact on their ability to return to pre-morbid levels of functioning (Thurman & Guerrero, 1999). Whereas some patients will only experience transient symptoms, such as post-tr aumatic amnesia (PTA) or loss of consciousness (LOC), persistent disturbances of cognitive functioning can include difficulties in arousal, 11

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attention, concentration, m emory, language, re duced processing speed, and impairment in executive functions such as planning and or ganization (e.g., Levin, Eisenberg, & Benton, 1991; Rao & Lyketsos, 2000). Affective changes may manifest as depression, mania, anxiety, impulsiveness, agitation, or violent/aggressive behavior (e.g. Stewert & Hemsath, 1988; van Zomeren & van den Burg, 1985). Consequently, an estimated 5 million injured individuals will require long-term to lifelong rehabilitation therapy and physical as sistance to perform routine daily activities (Langlois et al., 2006; Thur man et al., 1999). Moreover, notwithstanding crippling physical debilitations to affected individuals, the ec onomic consequences to society attributable to TBI due to income loss and illn ess-related lifetime expenses exceed $55 billion per year (Finkelstein et al., 2006); while the emo tional cost of TBI on an afflicted individuals interpersonal relationships with coworkers, fr iends, and family members is immeasurable. Transportation accidents involving automobiles are the major cause of TBI related hospitalizations, whereas falls re sult in the highest rates of TBI among very young children and adults ages 75 years and over (Langlois et al., 20 06). Approximately 90% of all brain injuries result from TBI (Lezak et al., 2004), with m ild TBI accounting for nearly 80% of all cases (Levin et al., 1990). Whereas mild TBI ofte n does not require treatment intervention for functional recovery, func tional outcome following moderate to severe (M/S) TBI is worse, and long-term rehabilitation is often required to maxi mize recovery of function (Levin et al., 1990). However, heterogeneity within th e TBI population has often led to difficulties in treating patients who display similar clinical presentations (e .g., Doppenberg, Choi, & Bullock, 2004; Saatman et al., 2008). For example, brain damage after closed head injury is generally diffuse, and often acquired via injury mechanisms including but not limited to coup-c ontrecoup injuries, and diffuse axonal injury (DAI). In coup-contrecoup injuries, brain da mage is not limited to bruising 12

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at the im pact site (coup bruising), rather, injury to the brain oppos ite to the initia l impact location at which the head strikes an external object (contrecoup injury) has been commonly reported (Drew & Drew, 2004; Levin & Kraus, 1994). Likewi se, DAI, an injury mechanism characterized by axonal stretching and disruption leading to eventual separation of white matter nerve fibers and blood vessels due to sudden acceleration-deceleration and/or rotational forces (Adams, Graham, Murray, & Scott, 1982; Strich, 1961), has been identified as a leading cause of morbidity and mortality in patients with TBI (Murray, Gean, & Evans, 1996). Consequently, the heterogeneous nature of TBI is a major barrier to finding effective interventions to maximize functional outcome after TBI. TBI and Cognitive Impairment: A Brief Review Physical and neurobeha vioral impairments are common se quelae of brain injury (Horn & Sherer, 1999), however, even in patients with goo d neurological recovery, persistent cognitive deficits are often the most pronounced and common complaint of TBI survi vors (Cicerone et al., 2005; Lovell & Franzen, 1994). Moreover, postmorbid cognitive dysfunction often impedes eventual patient functional outcome in areas including vocational re-attainment, independent living, and psychosocial adaptati on (Ben-Yishay & Diller, 1993; Cicerone et al., 2000; Sherer, Madison, & Hannay, 2000). Specifically, widespread impairments of cognitive functioning after TBI have been observed across a broad range of domains including attent ion (Ziino & Ponsford, 2006), short-term memory and learning (Levin et al., 1987; Vakil, 2005) working memory (Perlstein, Cole, Dixit, & Demery, 2004; Va llat-Azouvi, Weber, Legrand, & Azouvi, 2007), speed of information processing (Felmi ngham, Baguley, & Green, 2004), and executive functions including but not limited to contex t-processing, conflict processing, and errormonitoring (Larson, Perlstein, Demery, & Sti gge-Kaufman, 2006; Lars on, Stigge-Kaufman, Schmulfass, & Perlstein, 2007). While debate continues regarding the precise nature of 13

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executive control functions (Duncan, Johnson, Swales, & Freer, 1997; Miyake, Friedm an, Emerson, Witzki, & Howerter, 2000 ; Stuss & Alexander, 2000), recent findings suggest that severity-related impairments in cognitive contro l, a set of higher-order executive processes supported by the frontal cortex and critical to execu tive function (Miller, 2000; Miller & Cohen, 2001; Lorist, Boksem, & Ridderinkhof, 2005), are re flective of enduring c ognitive deficits in TBI (Larson et al., 2007; Perl stein et al., 2004; Perlstein, Larson, Dotson, & Kelly, 2006; Scheibel et al., 2007; Seignourel et al., 2005; Soeda et al., 2005). Moreover, current theories of neurobehavioral dysfunction in TBI have been based on observed impairments in cognitive control component processes (Anderson, Levin, & Jacobs, 2002; Burgess & Robertson, 2002; Larson et al., 2007; Levine, Katz, Dade & Black, 2004; Perlst ein et al., 2006). Cognitive Control Theory In the past decade, cognitive neuroscientists have posited a conceptual model known as cognitive control, encompassing traditional high-level cognitive proc esses necessary for controlled information processi ng and coordinated actions su ch as executive functioning, working memory, planning, and attention (e.g., Botvinick, Carter, Braver, Barch, & Cohen, 2001; Miller & Cohen, 2001). Cognitive control, has been describe d as a system that guides voluntary, complex actions (MacDonald, Cohen, Stenger, & Carter, 2000, p.1835), and defined asthe ability to orchestrate thought and action in accord with internal goals (Miller & Cohen, 2001, p. 167). Specifically, cognitive control proc esses support the active maintenance and representation of task goals, and flexibly allo cate mental resources towards goal-directed behaviors (Derfuss, Brass, Neumann, & von Cr amon, 2005; Miller & Cohen, 2001). In addition, cognitive control processes are engaged duri ng novel, difficult, ambiguous, conflicting, and rapidly changing stimulus conditions, when stro ng prepotent response tendencies are to be inhibited, and when strategic adjustments towa rd goal attainment are needed (e.g., Botvinick, 14

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Cohen, & Carter, 2004; Kerns et al., 2004; MacD onald et al., 2000). Importantly, numerous cognitive neuroscience studies suggest cognitiv e control comprises two essential component processes implemented in a closely interactive, yet dissociable frontal neural network: a regulative component supporting maintenance of task goals and implementation of control, and an evaluative component responsible for conflict pr ocessing, performance monitoring, and signaling necessary strategic ad justments toward goal attainment (e.g., Kerns et al., 2004; MacDonald et al., 2000). Regulative/Strategic Processes Regulative processes of contro l are involved in the activati on and implementation of topdown control of cognition, and are engaged when be havior must be guided by internal states or intentions (Miller & Cohen, 2001). In addition, regulative processe s are used to override strong prepotent response tendencies a nd strategically allocate limite d mental resources towards goaldirected behavior (Cohen, Barc h, Carter, & Servan-Schreiber, 1999). Importantly, successful implementation of regulative control is depende nt upon the internal re presentation and active maintenance of context information (Brave r & Cohen, 2000; Braver & Barch, 2002). Context information has been defined to include any ta sk-relevant information th at can be internally represented and can bias processing of beha vioral responses for task performance (e.g., representation of task goals or in structions), but also liberally defined to include information and representations that can be rele vant to interpretive or attentio nal processes that occur during earlier stages of information processing (Bra ver & Cohen, 2000; Braver & Barch, 2002; Cohen et al., 1999). The construct of context representation shares similarities with traditional models of working memory (Baddeley, 1992; Baddeley & Hitch 1994), however, current theories of cognitive control postulate that context repres ents a subset of simultaneous working memory 15

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processes which is in contrast to standard m odels of working m emory that propose distinct representations for control (Braver & Cohen, 2000; Braver & Barch, 2002). One experimental paradigm that demonstrates the importance of regulative control for successful performance is the task-switching cued-Stroop, which was originally developed by Cohen et al. (1999) and modified from the original task de veloped by Stroop (1935). At the beginning of each trial, participants are presented with an initial context of one-of-two tasks to perform: either word-reading (WR) or color-na ming (CN). While versions of the card Stroop (e.g., Golden, 1978; MacLeod, 1991; Stroop, 1935) re quire minimal active maintenance of task goals for successful performance as participants are consistently reminded of proper task context, in the task-switching Stroop, trials are presented individually and task instructions (CN or WR) randomly vary throughout task completion. Conse quently, participants must rely heavily on the active representation and maintenance of the cont ext provided by the task instruction to bias allocation of mental resources and response sele ction for successful performance. Moreover, participants must actively maintain representa tion of context (CN or WR) provided by task instructions across a delay, and employ these co ntext representations to provide the correct response in the face of strong competition for re sponse selection. For example, in the Stroop task, words are printed in either the same co lor as the words semantic meaning (e.g., RED printed in red ink), or in a different color ink then the words semantic meaning (e.g., RED printed in blue ink). Importantly, the context prov ided by the task instruction must be used to inhibit and override the influence of a strong prepotent response tendency (e.g., to read the word), in favor of a less prepotent response (e.g., name the printed ink color). Consequently, the task-switching cued-Stroop is a prime example of a task that elicits cognitive processes that require the top-down implementation of cognitiv e control for the selective allocation of 16

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attentional resources to overcom e response conflict Such allocation of mental resources allows behavior to be guided towards th e selection of infrequent but a ppropriate task-relevant responses instead of strong competing prepot ent responses (Miller & Cohen, 2001). Evaluative Processes Whereas regulative processes of control are involved in the top-down control of cognition, evaluative processes of control monitor task pe rformance for the presence of response competition or conflict. Numerous researchers (e .g., Botvinick, et al., 2004; Carter & van Veen, 2007; Kerns et al., 2004; van Veen & Carter, 2006) have suggested that th e detection of conflict between simultaneously activated but mutually incompatible res ponses plays a crucial role in signaling when strategic adjustments in top-dow n control for successful adaptation to everchanging task demands need to be engaged. Such detection and monitori ng of response conflict occurs in many cognitive tasks including but not limited to the Eriksen flanker task (Eriksen & Eriksen, 1974), the Simon task (Simon, 1969), an d the Stroop task (MacLeod, 1991; Cohen et al., 1999; Stroop, 1935). Regarding th e latter, processing of incongruent Stroop stimuli (e.g., the word RED printed in blue ink) results in response conflict due to the simultaneous activation of incompatible responses associ ated with different stimulus properties (e.g., printed ink color and semantic meaning of the word), and active onlin e monitoring of this conflict is postulated to signal the need for adjustments in top-down control necessary for more efficient task performance (Carter & van Veen, 2007). Consequen tly, evaluative processes of cognitive control are involved in the initial detection of stimulus conflict, as well as in th e subsequent signaling for strategic adjustments of top-dow n control to reduce subsequent experience of conflict in performance (Botvinick et al., 2001; Botvinick et al., 2004; Carter & van Veen, 2007) 17

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Dissociation of Cognitive Control Comp onent Processes Using fMRI and EEG The task-sw itching cued-Stroop developed by Cohen et al. (1999) has been found to reliably elicit a high degree of cognitive contro l during task performance. The cued-Stroop is a modification of the original Stroop task (Str oop, 1935) and stimuli are comprised of the same three colors and color-words (red, green, blue) used in the card Stroop (e.g., Golden, 1978). In contrast to the card Stroop (e.g., Golden, 1978) where trials are bl ocked and participants read stimuli on a list of cards, in the cued-Stroop, participants are provided with an instructional cue prior to each trial to provide them a context of which task to perform; either word-reading (WR; a strong prepotent response) or color-naming (CN; a less prepotent response). Following a brief delay during which participants must maintain the task-instruction in memory, a Stroop stimuli is presented to participants in eith er the same color as its semantic meaning (congruent), or in a different color than its semantic meaning (inc ongruent). Importantly, th e context provided by the task instruction, must be used to override a strong prepotent re sponse tendency (e.g., to read the colored word), in favor of a less prepotent resp onse (e.g., to name the ink color of the colored word). Consequently, the cued-Stroop allows for the temporal separation of instruction-related regulative processes (e.g., mainte nance of task instructions) fr om response-related evaluative processes (e.g., conflict-detection and performance monitoring). Moreover, in an event-related functional magnetic resonance imaging (fMRI) study using the task-switch cued-Stroop paradigm descri bed above, MacDonald et al. (2000) found a double dissociation in the regulative a nd evaluative component processes of cognitive control. Results suggest that the left dorsolateral prefrontal cortex (dlPFC) was more active following instructions for the more demanding color-naming task, consiste nt with a role in the maintenance of task goals and top-down implantation of control, while the anterior cingulate cortex (ACC) was more active following presentation of inc ongruent than congruent stimuli, consistent with a role in 18

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conflict detection (MacD onald et al., 2000). Although Macdonald et al. (2000) were unable to further decompose evaluative processes of control (e.g., conflict processing and errormonitoring), as correct a nd incorrect trials were not differentia ted in analyses, findings from their study suggest that cognitive control consists of dissociable, yet in teractive component processes: a dlPFC-mediated regulative component suppor ting maintenance of task goals and implementation of control, and an ACC-mediat ed evaluative component that supports conflict processing, performance/error monitoring, and signaling for strategic adju stments in control. Whereas imaging methods such as fMRI can provide valuable information regarding the spatial localization of cognitive control component processes, event-related potentials (ERPs) have been used to examine the temporal dissociation of the neural-ele ctrical activity of cognitive control. For example, using a single-trial cued-Stroop, West (2003) found that regulative processes of control were associ ated with onset of an occipita l-parietal slow wave. Similarly, Larson et al. (2004), also using a single-trial cued-Stroop obser ved modulation of a slow-wave associated with regulative processes of contro l, however, observed a more frontal-lateral localization. Importantly, findings of greater modulation of a frontal-lateral slow wave by Larson et al. (2004) are consiste nt with neuroimaging results obtained by MacDonald et al. (2000) of greater activation in the dlPFC following the more attention-demanding color-naming instruction as compared to the word-reading instruction. Regarding the temporal nature of evaluative processes of control, researchers using the single-trial cued-Stroop have associated detectio n of conflict with onset of an N450 component (e.g., Larson et al., 2004; Perlstei n et al., 2006; West, 2003) In addition, Perl stein et al. (2006) observed that the N450 component in the incongruent color-naming condition was significantly larger than the N450 to the congruent condition. Sp atiotemporal source analysis suggests that the 19

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ACC contains the neural generator of the N450 com ponent (Liotti, Woldorff, Perez, & Mayberg., 2000), which is also consistent with fMRI results observed by MacDonald et al. (2000) of greater activation in the ACC associated with incongruent color-naming (conflict) stimulus conditions. Taken together, these results suggest that cognitive control comprises two component processes implemented in a closely interactive, yet dissociable frontal ne ural network: a dlPFC mediated regulative component supporting active maintenan ce of task goals and implementation of control, and an ACC-mediated evaluative component responsible for detection and processing of conflict, performance monito ring, and signaling nece ssary strategic adjustments toward goal attainment (e.g., Kerns et al., 2004; MacDonald et al., 2000). Cognitive Control and TBI Not surprisingly, recent theories of cogn itive dysfunction in TBI have been founded on observations of impairments of cognitive contro l component processes (e.g., Larson et al., 2007; Perlstein et al., 2006). Moreover, numerous studies have found that TBI patients show deficits in both regulative processes of control such as in the active maintenance of context representations (Seignourel et al., 2005; Larson et al., 2006), as well as evaluative aspects of control such as conflict monitoring/resolution and response selec tion (Soeda et al., 2005; Scheibel et al., 2003). In addition, patients with TBI, regardless of inju ry severity, have been found to perform less well on working memory tasks dependent upon cognitiv e control processes (Christodoulou et al., 2001; McAllister et al., 2001; Perlstein et al., 2004). Neuroimaging and electrophysiological studies also indicate that patients with TBI demonstrate neurophysiologic differences when e ngaging cognitive control. For example, using the task-switching cued-Stroop and ERPs to exam ine the temporal nature of cognitive control component processes, Perlstein et al. (2006) found that patients with TBI demonstrated impairments in both regulative (e.g., top-down implementation of control) and evaluative (e.g., 20

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processing of conflict) aspects of control. Rega rd ing the latter, particip ants with TBI failed to demonstrate an expected overt N450 response to incongruent-stimulus conflict conditions, suggesting impairment in the detection of respons e conflict (Perlstein et al., 2006). In addition, ERP results from a separate study demonstrate th at error-related activity was attenuated in participants with TBI, which al so suggests impairment in evaluative (e.g., control/performance monitoring) aspects of control (Larson et al., 2007). Given the heterogeneity of the TBI populati on, findings from research examining the neuronal activity of impairments of cognitive control processes ha ve also been contradictory. For example, using a blocked-design fMRI para digm, Soeda et al. (2005) observed reduction in the anterior cingulate cortex in patients with TBI, however, Scheibel et al. (2007) observed greater activation in the anterior cingulate cortex. Whereas both studies (Soeda et al. 2005; Scheibel et al., 2007) suggest alterati on in the neural networks mediating cognitive control and disruption in evaluative processe s of control, their findings underscore the current gap in knowledge regarding understanding the neural basis of cognitive co ntrol, and highlight the need for further examination into the spatial dissoci ation of regulative and evaluative processes of cognitive control in patients with TBI. Functional Neuroimaging Functional neuroimaging techniqu es have contributed greatly to understanding the neural bases of cognitive control, and include in vivo procedures of human brain mapping such as positron emission tomography (PET) and functio nal magnetic resonance imaging (fMRI). Moreover, rapidly evolving technological advances have facilitate d application of these methods towards better understanding neuronal functioning, as well as advanced the ability of researchers to spatially localize the neural mechanisms underl ying cognitive processes such as attention and memory (Stern & Silbersweig, 2001). Notabl y, blood-oxygen level-dependent (BOLD) fMRI 21

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was selected for use in the pres ent study due to seve ral advantages over PET including superior spatial resolution, cost efficiency, safety (e.g., fMRI does not require ra diation exposure), and flexibility in experimental de sign (e.g., Cabeza & Nyberg, 2000). BloodOxygen Level Dependent (BOLD) Functional Magnetic Resonance Imaging (fMRI) As a certain task is executed or performed, brain regions required for that task performance utilize oxygen, and it is on this simple principl e that fMRI was founded. Specifically, the BOLD contrast method (Ogawa et al., 1990) is depende nt upon the different magnetic properties of hemoglobin, which is the primary oxygen transp orting metallo-protein found within red blood cells (Matthews & Jezzard, 2004; Logothetis & Wandell, 2004). Specifically, deoxyhemoglobin (e.g., hemoglobin not carrying oxygen) is parama gnetic (Pauling & Coryel l, 1936), that is, it contains weak magnetic properties, and this property allows the molecule to serve as an indigenous contrast agent during scanning (Forster et al., 1998). During task execution, immediate oxygen consump tion causes an initial increase in the level of deoxyhemoglobin required by the ac tive tissue, and as oxygen levels decrease, an increase in blood-flow carrying oxyhemoglobin to the active tissue occurs to meet demands for continuing task performance (Kim & Ugur bil, 1997). However, oxygen consumption is disproportionately slow relativ e to the increase flow of blood and oxyhemoglobin to active tissue, causing a decrease in the level of deoxyhe moglobin in functionally active areas (Raichle, 2001). Consequently, as the paramagnetic pr operties of deoxyhemoglobin decrease and are removed from active tissue, a disruption in the ho mogeneity of the magnetic field occurs (Forster et al., 1998), causing an elevation in BOLD signal intensity necessary for acquisition of functional images (Raichle, 2001). 22

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The BOLD Response Hemodynamic Inverse Problem Num erous research studies (e.g., Heeger, Huk, Geisler, & Albrecht, 2000; Logothetis & Wandell, 2004) have postulated that the BOLD signal is a complex f unction reflective of changing levels of cerebral blood flow, blood vol ume, and oxygen metabolism; and researchers have coined the term hemodynamic response to re fer to changes in blood flow that accompany neuronal changes. However, as Boynton et al (1996) report, the hemodynamic response often lags greatly behind the neuronal activity that starts the event. For example, the hemodynamic response resulting from presentation of a brief se nsory stimulus has been reported to be delayed in onset approximately 2s after neuronal ac tivity, while the hemodynamic response following presentation of a stimulus lasting 1s is dela yed in onset approximatel y 4-6s after neuronal activity (Buckner, 1998; Rosen, Buckner, & Dale 1998; Stern & Silbersweig, 2001). In addition, the peak of the hemodynamic response does not occur until approximately 6s following onset, and return to baseline levels does not occur until about 10 s after initial stimulus presentation (Buckner, 1998; Rosen et al., 1998; Stern & Silb ersweig, 2001). Consequently, for a task that takes less than 1s to complete, the measured hemodynamic respons e for that event is measured seconds after the underlying neur onal activity trigger the event has subsided, a predicament referred to as the hemodynamic inverse problem (Buckner, 2003). Conseque ntly, results obtained using BOLD fMRI reflect an indirect measurement of neuronal activity. fMRI and TBI Methodological Considerations Whereas researchers have used fMRI to i nvestigate cognitive dysf unction following TBI in areas such as cognitive control (Scheibel et al., 2007), motor processing speed (Prigatano, Johnson, & Gale, 2004), problem-solving (Cazalis et al., 2006), and working memory (Chen et al., 2004; Christodoulou et al., 2001; McAllister et al., 1999; McAllister et al., 2001; Newsome et al., 2007; Perlstein et al., 2004; Scheibel et al., 2003; Tu rner & Levine, 2008), numerous 23

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m ethodological issues, while inherent to all imag ing research, are of particular concern to researchers who use functional neuroimaging in TBI research (Hillary et al., 2002). For example, signal detection in fMRI relies upon cerebral blood flow (CBF), however, both human and animals of TBI have demonstrated reduced baseline levels of CBF post-injury (Bouma, Muizelaar, Choi, Newlon, & Young, 1991; Salvant & Muizelaar, 1993; Kochanek et al., 2002), an effect observed in rats at 1 year post-injury (Kochanek et al., 2002). Moreover, blood flow abnormalities in patients with moderate to severe TBI relative to comparis on subjects have been observed during completion of working memory tasks, an effect particularly concerning due to abnormalities in the frontal lobes, an area exte nsively imaged by researchers (Christodoulou et al., 2001; Scheibel et al., 2003). Taken together with additional challenges of head motion and susceptibility artifact, researchers must be cogn izant of and address th ese challenges in there experimental designs (H illary et al., 2002). Blocked vs. Event-Related Ex perimental Task Designs Researchers using fMRI often employ cogniti ve tasks using one of two major types of experimental designs: blocked or event-related (Buckner et al., 1996; Dale & Buckner, 1997). In blocked designs, stimuli are presen ted to participants rapidly a nd repeatedly, with short interstimulus onsets between trials. Consequentl y, blocked designs elicit multiple overlapping hemodynamic responses that may be valuable in investigating subtle differences in BOLD signal intensity (Friston et al., 1999). In addition, due to the repeated presentation of stimuli, blocked designs do not require stringent randomization among variables, and are thus easier paradigms to implement in research experiments (Aguirre & D'Esposito, 1999; Donaldson & Buckner, 2001). Additional advantages of blocked designs include better signal-to-noise ratio, easier detection of non-physiological artifacts due to signal fluctuations, and increased statistical power (Friston et al., 1999). 24

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In contrast, event-related experim ental de signs generally employ longer inter-stimulus intervals (e.g., 10s) between s timulus onsets than blocked de signs (Buckner, 1998; Dale, 1999; Josephs, Turner, & Friston, 1997). Consequently, instead of examining multiple overlapping hemodynamic responses produced in a blocked de sign, in an event-related design, researchers can examine an averaged time-locked hemodynami c response that initiates following stimulus onset, peaks, and returns to baseline before subsequent stimulus onset (Donaldson & Buckner, 2001; Joseph et al., 1997; Miezin, Maccotta, Ollin ger, Petersen, & Buckner, 2000). Thus, eventrelated designs allow for randomized presentation of differing stimuli, as well as the examination of performance-accuracy and changes in pe rformance over time (Buckner, 1998; Dale, 1999; Josephs et al., 1997). Consequently, these methodological advantageous have made eventrelated designs a valuable tool in cognitive neuroscience research. Neuroimaging and Cognitive Control Given the sensitivity of fMRI in the examin ation and spatial localization of neuronal activity, it has been an important tool in the investigation of the underlying neural mechanisms of cognitive control. For example, since the public ation of Macdonald et al (2000), fMRI has been used to examine cognitive control processes in a plethora of experiment al designs as well as applied to the examination of disruptions in c ognitive control processes in clinical populations including but not limited to schizophrenia (e.g., Carter, Mac donald, Ross, & Stenger, 2001; Perlstein, Dixit, Carter, Noll, & Cohen, 2003), de pression (e.g., Harvey et al., 2005), attention deficit-hyperactivity disorder (R ubia et al., 2006; Vaidya et al., 2005) and TBI (Scheibel et al., 2007; Soeda et al., 2005). Whereas the majority of research studies using fMRI have examined evaluative processes of cognitive control, a brief re view of regulative processes is also provided 25

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Neuronal Correla tes of Regulative Processes Whereas neuroimaging research investigati on the active maintenan ce of task-relevant information is limited (e.g., MacDonald et al., 2000) a myriad of fMRI studies have investigated the neural mechanisms underlying the implementati on of control (Durston et al., 2003; Kerns et al., 2004; Milham et al., 2001; Milham et al., 2003). Findings suggest strong contribution and activation of the dlPFC during the top-down implem entation of cognitive control. In addition, Botvinick et al. (2001) postulated that as to p-down control and more efficient allocation of mental resources increased, that subsequent levels of response conflict would decrease. As predicted, several fMRI studies investigating this hypothesis have observed reduction of conflict following conflict stimulus conditions following increased activation of the dlPFC (Egner & Hirsch, 2005; Kerns, 2006; Kerns et al., 2004). Re garding task-switching, the successful ability to switch tasks has been associated with activa tion of the prefrontal cortex in both blocked (DiGirolamo et al., 2001; Dreher et al., 2002) and event-related fM RI (Dove et al., 2000; Sohn et al., 2000) designs. Neuronal Correlates of Evaluative Processes Regarding evaluative processes of control, numerous studies have suggested that the ACC is critically involved th e detection of conflict (B arch et al., 2001; Botvinick et al., 1999; Braver et al., 2001; MacDonald et al ., 2000; Van Veen & Carter, 200 2). Moreover, neuroimaging studies have consistently demonstrated that ACC activation is greates t to incongruent-conflict stimulus trials, an observation found across differe nt cognitive tasks including but not limited to the Stroop task (Egner & Hirsch, 2005; Kerns et al., 2004) and the Sim on task (Kerns, 2006). Imaging findings have also provided support for th e postulate that the A CC signals the dlPFC for needed strategic adjustments in control (Kerns, 2006; Kerns et al., 2004). Taken together, Carter and van Veen (2007) have proposed that the ACC pl ays a crucial role in th e detection of conflict 26

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between m utually incompatible stimuli, and s ubsequent signaling to the dlPFC for strategic adjustments in control. Predictions Given the sensitivity of fMRI in the examin ation and spatial localization of neuronal activity, one aim of the present study was to use the task-switching cued-Stroop (Cohen et al., 1999) to further characterize and assess the integrity and spatial dissociation of regulative and evaluative processes of cognitive control. In addition, the prese nt study sought to characterize and exam ine impairments of cognitive control in i ndividuals with severe TBI. Based on a review of research literature, th e following hypotheses are post ulated: Behavioral Data It is p redicted that both controls and pa rticipants with TBI w ill experience a Stroop interference effect for RT, that is, response slowing in the inc ongruent compared to congruent condition. In addition, it is predicte d that RT interference will be significantly greater for colornaming trials than w ord-reading trials. We do not predict th a t the two groups will differ in the extent to which they exhibit the Stroop RT effect. Regarding error-ra tes, it is predicted that both control participants and individuals with severe TBI will commit greater errors to incongruent color-naming and word-reading conditions than congruent conditions. In addition, it predicted that if patients with severe TBI are impaired in evaluative conflict processes, then behaviorally, they will demonstrate greater impairment than controls on incongruent color-naming trials. fMRI fMRI will be used to further ex amine th e spatial dissociation of cognitive control component processes including dlPFC mediated regulative processes (e .g., task-maintenance) and ACC mediated evaluative proc esses (e.g., conflic t detection). 27

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Regulative Processes 1) It is predicted that greater regulative activity in the left dlPFC (e.g., Brodmann's area 9) will be observed following the more attention-demanding color-naming instruction as compared to the word-reading instruction. 2) It is predicted that if TBI patients are impaired in regulative processes (e.g., implementation of control), then they will s how reduced task instruction-related left dlPFC activation compared to controls duri ng differentiation of color-naming and wordreading instructions. Evaluative Processes 1) It is predicted that evaluative conflict-related activity, that is activity associated with incongruent color-naming (conflict) stimulus conditions, will be greater in the ACC (e.g., Brodmann's areas 24 and 32) when compared to activity associ ated with congruent colornaming stimuli. 2) It is predicted that if TBI patients are impa ired in evaluative proce sses of control, then they will show reduced conf lict-related ACC activation compared to controls during incongruent color-naming (conflict) stimulus conditions. 28

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CHAP TER 2 METHODS Participants Twelve healthy participants and 10 patients with severe TBI were recruited from the local community via institutional review board (IRB) approved advertisements, the Florida Brain Injury Association, the Brain and Spinal Cord In jury Program of Florida, local brain injury association support groups, and Brooks Rehabilitation Hospital (Jacksonville). Participants received course credit or fi nancial compensation for their pa rticipation in the study, and all individuals provided written informed consent in accordance with procedures established by the University of Florida Health Science Center IRB. All participants in the TBI group sustained a TBI as described by Lezak et al. (2004). Determination of injury severity was made fo llowing medical record review of lowest postresuscitation Glasgow Coma Scale (GCS) score (Teasdale & Jennett, 1974); severe TBI was defined as a GCS score<9. Neurol ogical indices, including neurorad iological findings taken from acute computerized tomography (CT) scans, duration of loss of consciousness (LOC), and duration of post-traumatic amnesia (PTA) were also acquired from medical record review or, when LOC and PTA information were not ava ilable in medical records, from structured participant and significant ot her interview (King, et al., 1997; McMillan, Jongen, & Greenwood, 1996). Data for LOC and PTA indicated all TBI pa rticipants met criteria for severe TBI as traditionally defined by LOC>6 hours and/or PTA>7 days (Bigler, 1990; Bond, 1986). Only patients who did not exhibit current PTA were included. Exclusion criteria for the study included a hist ory of schizophrenia or bipolar disorder, substance abuse disorder, atten tion-deficit hyperactivit y disorder, learning di sability, inpatient psychiatric treatment predating br ain injury, clinically-significant depression or anxiety predating 29

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brain injury by no m ore than two years, or subs tance use within two w eeks of testing or of sustained abuse over the past year In addition, any individual with any type of prior TBI, penetrating head injury, neurologi cal disorder (e.g., stroke, seizure disorder) not directly related to the TBI, current anti-epilep tic medication use, or color-blin dness as measured by the Ishihara pseudo-isochromatic color plates (Clark, 1924) was excluded from st udy participation. Nonnative English speakers, individu als below 18 or above 55 years of age, patients with language comprehension deficits, hand or finger mobility imp airments, uncorrected visual impairment, or patients involved in current litigation were also excluded from study participation. Demographic characteristics for control and TB I participants are presented in Table 2-1, while injury characteristics (e.g., duration of LOC) of patients with TBI are presented in Table 22. Participants with TBI were at least 12 months post-injury, with the exception of one well functioning patient (two months post-injury) who desired to comp lete study participation early before returning to vocational duties. Median sc ores (ranges) for time since injury, LOC, and PTA in the TBI group were 54.5 months (2-60 mont hs), 21 days (1-60 days) and 24.5 days (1360 days), respectively. Gender distribution was not significantly different between groups [ 2(1, N = 22) = 0.22, p = .693 (TBI: 6 male/4 female; Control: 6 male/6 female)], and groups were matched for age, education, and pare ntal education (see Table 2-1). Clinical and Symptom Assessment All participants underwent a comprehensive screening of medical, psychiatric, and psychosocial history, including assessment of pre-and post-morbid functioning, and self-and significant-other reported symptomatology. Following study enrollment, self-and significantother reported clinical sympto matology was assessed using the 20-item Dysexecutive Questionnaire (DEX), part of the Behavioral Assessment of the Dysexecutive Syndrome (BADS) battery (Wilson, Alderman, Bu rgess, Emslie, & Evans, 1996), and the modified Neurobehavior 30

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Rating Scale (NRS; Mathias & Coats, 1999) derive d from the original NRS (Levin et al., 1987). Both the DEX and the modified NRS have self-a nd significant-other rating scales that assess cognitive, behavioral and affective changes th at may occur after TBI in domains including emotion, personality, motivation, behavior, and cognition. Estimation of pre-morbid intellectual f unctioning was determined using the North American Adult Reading Test (NAART; Blair & Spreen, 1989; Spreen & Strauss, 1991). Whereas the NAART has been criticized for ove restimating low IQ scores and underestimating high IQ scores (Johnstone, Callahan, Kapila, & Bouman, 1996), findings suggest utility in providing good estimates of WAIS-R and WAIS-III sc ores, especially when scores fall in the average range of intellectual functioning (J ohnstone et al., 1996). As shown in Table 2-3, compared to controls, participants with TBI committed significantly greater NAART errors, resulting in a significantly lowe r estimate of premorbid intellect ual functioning in participants with TBI. However, mean estimates of pr emorbid WAIS-R FSIQ scores (Controls = 109.43, TBI = 105.71; Spreen & Strauss, 1991), while di fferent between groups, both fell within the average range of intellectual functioning (Wechsler, 1981). All participants also completed the Beck Depression Inventory Second Edition, (BDI-II; Beck, Steer, & Brown, 1996) and St ate-Trait Anxiety Inventory (STAI; Spielberger, Gorsuch, & Lushene, 1970), to assess for current levels of depressive and anxiety symptomatology, respectively. As shown in Table 2-3, groups did no t differ in the extent to which they endorsed symptoms of either state anxiety or trait anxi ety; however, participants with TBI endorsed a significantly greater number of de pressive symptoms relative to the control group. Whereas mean BDI-II scores (Controls = 5.58, TBI = 13.10) were statistically significant between groups, both group means fell below clinical cut-off levels for depression (e.g., 14 for mild depression; 31

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Beck et al., 1996). However, one control particip ant and four TBI patients had BDI-II scores reflective of clinically significant depression (e.g., 14 for m ild depression; Beck et al., 1996). Materials and Procedures Cognitive Activation Task Participants completed a computer-based version of the task -switching cued-Stroop originally developed by Cohen et al. (1999), and used in the event-related fMRI context by MacDonald et al. (2000). The task switching cued -Stroop was modified from the original task developed by Stroop (1935), and uses the same th ree stimulus colors and color words (blue, red, and green) as in the card Stroop (Golden, 1978). In the present version of the task, participants were presented on a trial-by-trial basis with a ta sk instructional cue (e .g., the word color or word), followed after a delay by the Stroop stimulus, as shown in Figure 2-1. Specifically, the task to be performed varies on a trial-by-trial basis and includes a delay between task instruction for each trial and the stimulus to be responded to. Varying the task to be performed (word readingWR, color-namingCN) on a trial-wise ba sis increases demands for the representation and maintenance of context information inherent in the stimulus-preceding task instruction. Additionally, by introducing a delay between task instruction and the presentation of the Stroop stimulus to which subjects respond, one can examine the implementation of control, and temporally dissociate representation of the task context (regulative co mponent) from processes requiring conflict detection and re solution (evaluative component). Each trial lasted 22.5s, and began with the visual presentation of an instructional cue lasting 1.5s which provided participants with a context of which task to pe rform; either CN or WR If participants were presented with the cue Color, they were instru cted to manually respond to the printed ink color of the subsequent probe stimulus (while ignorin g the written word itself). If the cue Word was presented, participants were instructed to respo nd to the written meaning of the probe stimulus. 32

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Im portantly, participants had to use the context provided by the ta sk instruction to inhibit and override the influence of a strong prepotent response tendency (e.g., to read the word), in favor of a less prepotent response (e.g., name the prin ted ink color). Each cue was followed by a delay of 11s during which participants were presente d with a fixation cross. Subsequently, a probe (Stroop stimuli) lasting 1.5s was presented to pa rticipants in one of two congruency conditions. In the congruent condition, stimulus words were pr inted in the same color as the words semantic meaning (e.g., RED printed in red ink), and in the incongruent condition, stimulus words were printed in a different color ink then the word s semantic meaning (e.g., RED printed in blue ink). Prior to task completion, a ll participants were instructed to respond manually to the probe stimulus as quickly and accurately as possible us ing the index, middle, a nd ring fingers of their right hand to make a button press to one of thr ee color-coded response keys. In addition, trials were presented in either task-s witching blocks, that is, where both CN and WR trials could occur, or non-task-switching bloc ks, in which only one task instruction would occur. To achieve adequate signal-to-noise ration for statistical comparisons, tria ls were presented in 16 blocks of 12 trials each, for a total of 192 trials distributed equally across task conditions (CN, WR, congruent, and incongruent). Trials were pr esented pseudo-randomly for each participant, however, program requirements ensured that each condition occurred equa lly frequently during each trial block. All participants were trained in color-button mapping to at least 80% prior to scanner task performance, and E-Prime software Version 1.1 (Psychology Software Tools, Pittsburgh, PA) was used to generate stimuli and record behavioral responses and RTs. fMRI Acquisition and Data Reduction MRI Scanning MRI scanning was conducted at the University of Florida McKnight Brain Institute using a Siemens Allegra head 3 Tesla MRI head scanner equipped with a standard head RF coil, using 33

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gradient echo planar ima ging (EPI) pulse sequences. Task stimuli were projected onto a translucent screen above the participant's head with Integrated Functional Imaging System (IFIS; Psychology Software Tools) hardware. Functiona l images were acquired in 35 axial slices approximately 30 off the anterior commissure-p osterior commissure (AC-PC) line using a T2*weighted EPI pulse sequence (repetition time, TR=2500ms; echo time, TE=30ms; flip angle, FA=90; field of view, FOV=24cm; 64 x 64 voxels at 3.75mm3 with .4mm slice gap). The 30 AC-PC line offset was used to decrease signa l loss from the orbitofrontal cortex due to susceptibility artifacts (McClure, Laibson, Lo ewenstein, & Cohen, 2004), Prior to functional scanning, a high-resolution 3D an atomical image was acquired using a T1-weighted MP-RAGE protocol (176 1-mm thick; repetition time, TR = 2000ms; echo time, TE = 4.13ms; flip angle, FA = 8; matrix = 512 x 512 voxels; field-of-view, FOV = 24cm) for evaluation of structural abnormalities, and to enable transformation of functional data into standard reporting space (Talairach & Tournoux, 1988). Each of 16 12-tria l blocks was 4 min and 35s in duration, and total functional scanning time was approximately 80 min. Scan acquisition was time-locked to each trial-event onset (e.g., cue and probe) and lasted the entire duration of each 22.5s trial, allowing for acquisition of 108 total volumes pe r functional run (e.g., 9 images per trial). Functional Image Data Reduction All imaging data were processed us ing BrainVoyager QX version 1.10.4 (Brain Innovation, Maastricht, the Netherlands; http ://www.brainvoyager.com). Pre-processing of functional imaging data consisted of rigid-body 3-dimensional motion correction using trilinear interpolation, slice-scan time correction using sinc interpolation to account for potential timing differences across individual-slice acquisition spatial smoothing with a 3D 8-mm full-width at half m aximum (FWHM) Gaussian kernel to acco mmodate between-subject differences in brain anatomy, voxel-wise linear detren ding, and high-pass filtering of frequencies below 3 cycles per 34

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tim e course to remove low-frequency nonlinear drif ts. Initial co-registration of functional images to respective high resolution three-dimensi onal anatomical volumes was completed using standard BrainVoyager QX co-regist ration procedures, and subsequent manual alignment of data was conducted as needed based on visual inspec tion of alignment adequacy. To enable groupwise analyses of functional imaging data, all im ages were spatially normalized into standard stereotactic Talairach space (T alairach & Tournoux, 1988) was completed using the standard 9parameter landmark Talairach method defined on each individuals anatomical volume. Statistical Analyses Behavioral Data Analyses Stroop RT and error rate data were analyzed separately using JMP for Windows version 6.0.3 (SAS Institute Inc., Cary, NC, USA). For each condition and participant, median correcttrial RTs and mean error rates for correct respons es were calculated. RTs and error rates for each task (CN, WR) were analyzed separately using a 2-Group x 2-Condition (congruent, incongruent) repeated-measures analysis of variance (ANOVA). Results were Bonferronicorrected for multiple comparisons, and interaction effects were decomposed using least-square means contrasts. Functional Imaging Data Analyses Analyses of fMRI data were conducted us ing a two-step mixed-model general linear modeling (GLM) approach as described by Friston et al. (1995). For each pa rticipant, a separate fixed-effects GLM with separate predictors for each trial event (cue, probe), each task instruction (CN, WR), each blocking condition (task-swit ching, non-task switching), and each congruency condition (congruent, incongruent) was created, resulting in a tota l of 16 predictors that were used to examine task-relevant effects. To better account for inter-subject va riability and allow for more accurate population-level genera lization of results, functional data were then subjected to 35

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separate-subject predictors random-effects GLM analyses (z -transformed time course, in-brain functional image intensity threshold = 300). The hemodynamic response function (HRF) for each event was estimated by convolvi ng box-car time courses (e.g., the time intervals belonging to each pred ictor) with a standard gamma function (T wo Ga mma HRF; Onset = 0, Response undershoot ratio = 6, Time to response peak = 5s, Time to undershoot peak = 15s, Response dispersion = 1, undershoot dispersion = 1), including the first two functional images for instruction-locked images, to account for the su stained cue-related main tenance activity, and the first probe-locked image, to acc ount for probe-related transient act ivity. Planned linear statistical contrasts were then conduc ted to separately examine group-related differences in component processes of cognitive control reflective of cue-related regula tive activity (e.g., CN > WR) and probe-related evaluative activity (e .g., incongruent > congruent). Si gnificant regions of interest (ROIs) were then selected to investigate group differences. For each ROI, random effects contrasts of parameter estimates ( ) were computed to examin e within-and between-group differences in neural activity in these regions, and to determ ine the strength of covariance between the data and the HRF. All da ta clusters were thresholded at p <.05 and maintained threedimensional spatial contiguity extending at least 4-voxels. 36

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Table 2-1. D emographic data for controls and severe TBI partic ipants Controls ( n =12) Severe TBI ( n = 10) M SD M SD t -statistic Age (years) 22.92 6.42 25.10 7.28 -.75ns Education (years) 14.67 1.23 13.90 1.66 1.24ns Mothers education (years) 13.83 2.98 13.70 2.83 .11ns Fathers education (years) 14.75 4.07 14.20 2.20 .38ns Parental education (years) 14.29 3.08 13.95 2.35 .29ns nsp >.22 Table 2-2. Injury characteristic s for severe TBI participants M SD Range Time since injury (months) 46.80 21.84 2-69 Initial Glasgow Coma Scale Score 3.60 1.26 3-6 Loss of consciousness (days) 22.90 18.08 1-60 Post-traumatic amnesia (days) 28.30 14.89 13-60 Table 2-3. Emotional functioning data for controls and severe TBI participants Controls ( n =12) Severe TBI ( n = 10) M SD M SD t -statistic NAART (errors) 23.75 5.51 32.60 10.19 -2.59* BDI-II 5.58 5.37 13.10 8.50 -2.52* STAI-State 9.50 7.03 32.20 7.71 -0.86ns STAI-Trait 31.75 8.01 33.50 10.12 -0.45ns NAART, North American Adult Reading Test ; BDI-II, Beck Depression Inventory 2nd Edition; STAI, State-Trait Anxiety Inventory; nsp >.40, *p < .05. 37

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Figure 2-1. Schematic of the task-switching cued Stroop task. As shown, task trials comprise an instructional cue followed after a delay by a stimulus probe to wh ich the participant responds with a button press. 38

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CHAP TER 3 RESULTS Behavioral Analyses The possibility of a speed/accuracy trade-off was examined by correlating mean error-rates and RTs. Analyses revealed that, collapsed across block, RT and error-rate data were positively correlated for all conditions regardless of group (Table 3-1). In addition, as the directions of the correlations are positive, this would indicate the opposite of a speed-accuracy tradeoff, and suggest that performance accuracy of controls and participants with severe TBI was not negatively affected by incr eased speed of response. RT Analyses Means and standard errors of correct-trial me dian RTs are presented as a function of group (Table 3-2), congruency (Table 3-3), task (Tab le 3-4), and blocking (T able 3-5). An omnibus repeated measures analysis of variance (RM ANOVA) revealed a significant main effect of congruency, F (1,140) = 122.01, p < .001; however, no significant main effects of group, F (1,20) = .82, p > .05, task, F (1,140) = .035, p > .05, or blocking, F (1,140) = 1.10, p > .05, were observed. Subsequent RT analyses are presented collapsed across blocking, and separately for word-reading and color-naming tasks. In the word-reading task, signifi cant main effects of congruency, F (1, 60) = 52.38, p < .001, and blocking, F (1, 60) = 4.13, p < .05, were observed. Least-square means post-hoc comparisons revealed the presence of the standa rd Stroop error-rate in terference effect as evidenced by longer RTs in the incongruent, t (60) = -7.24, p < .001, than in the congruent condition, an effect present for both controls, t (60) = -4.25, p < .001, d = .56, and patients with TBI, t (60) = -5.92, p < .001, d = .76. Whereas a main effect of group was not observed, F (1, 20) = 0.99, p > .05, post-hoc analyses revealed that TB I patients demonstrated longer RTs than 39

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controls on incongruent trials, t (60) = -4.05, p < .01, d = .47. Lastly, post-hoc analyses revealed that RTs were longer in task-switching blocks, t (60) = -2.03, p < .05, than non-task-switching blocks. No significant interactions were observed am ong variables (all ps > .05). In the color-naming task, a signi ficant effect of congruency, F (1, 60) = 73.24 p < 0.001) was observed. Least-square means post-hoc compar isons revealed the presence of the standard Stroop error-rate interferen ce effect as evidenced by longer RTs in the incongruent, t (60) = -8.56, p < .001, than in the congruent condition, an effect present for both controls t (60) = -5.54, p < .001, d = .74, and patients with TBI t (60) = -6.53, p < .001, d = .78. Whereas a main effect of group was not observed, F (1, 20) = 0.64, p > .05, post-hoc analyses revealed that TBI patients demonstrated longer RTs than controls on incongruent trials, t (60) = -3.35, p < .01, d = .37. No significant interactions were observed among variables (all p s > .05). Error Rate Analyses Means and standard errors of error rates (proportions) are pr esented as a function of group (Table 3-6), congruency (Table 3-7), task (Tab le 3-8), and blocking (T able 3-9). An omnibus RMANOVA revealed significant main effects of group, F (1,20) = 6.97, p < .05, congruency, F (1,140) = 59.48, p < .001, and blocking, F (1,140) = 6.65, p < .05, but not task F (1,140) = 0.65, p > .05. In addition, significant intera ctions of group with congruency, F (1,140) = 11.06, p < .005, and group with blocking, F (1,140) = 8.55, p < .005, were observed. In order to decompose the significant interactions, we conducted separa te analyses for each task condition, collapsed across our blocking factor. In the word-reading tas k, main effects of group, F (1, 20) = 7.77, p < .05, and congruency, F (1, 60) = 30.11, p < .001, were observed. Least-square m eans post-hoc com parisons revealed the presence of the standard Stroop interference effect as evidenced by greater errors on incongruent than trials, an eff ect present in both controls, t (60) = -3.29, p < .005, d = 1.17, and 40

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patients with severe TB I, t (60) = -4.43, p < .001, d = .79. In addition, posthoc analyses revealed that TBI patients committed significantly more errors than controls on both congruent, t (60) = 2.96, p < .005, d = .93, and incongruent trials, t (60) = -4.46 p < .001, d = .83. No significant interactions among variables were observed. In the color-naming task, main effects of group, F (1, 20) = 5.13, p < .05, and congruency, F (1, 60) = 27.29, p < .001, were observed. In addition, significant interactions of group with congruency, F (1, 60) = 10.89, p < .01, and group with blocking, F (1, 60) = 6.52, p < .05, were observed. Least-square means post-hoc comparisons revealed that TBI patients committed significantly more e rrors on incongruent, t (60) = -5.77, p < .001, d = 1.06, than congruent trials, suggesting the presence of the sta ndard Stroop interference effect. In addition, post-hoc analyses revealed that TBI patients committed significantl y more errors than controls on incongruent trials, t (60) = -5.08, p < .001, d = .93, but not congruent trials, t (60) = -0.41, p > .05. Lastly, post-hoc analyses revealed that TBI patients committed significantly more errors than controls during completion of task-switching blocks, t (60) = -4.55 p < .001, d = .88. fMRI Data Head Movement Independent-samples t-tests revealed that patients with severe TBI did not exhibit significantly greater head motion than controls, either as a main e ffect or in any interaction of group with condition ( t s(20) 1.46, ps .16). Average estimated mean translational (Figure 3-1) and rotational (Figure 3-2) inter-scan displacement was less than 1 voxel dimension (3.8mm) and .1, respectively. Analysis of group and condition-related effects did not reveal any significant differences ( F s 2.81, ps .10). W hereas no group differences in movement within the scanner were observed, any scans during which movement in any of six dimensions (x, y, z, pitch, roll, 41

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yaw) for a given subject exceeded th e 99.5% qua ntile for movement parameters across the control group were excluded from subsequent analyses. Cue-Related Regulative Activation Table 3-10 lists all clusters within the brain that had a significantly higher activation intensity following the color-naming than word-read ing instruction for both controls and patients with severe TBI. As illustrated in Figure 3-3 (threshold: p <.021 and 7 contiguous voxels), within the left dlPFC, greater activity was observed in controls following the color-naming, compared to word-reading instruction. Examination of this cont rast in participants with severe TBI failed to demonstrate activation of the left dlPFC, or any other regions of intere st. A direct betweengroup interaction and examination of Beta weight s revealed a significant increase in dlPFC cuerelated activity (Figure 3-4; threshold: p =.023 and 6 contiguous voxels ) in controls, but not patients with severe TBI patients. Probe-Related Evaluative Processes Table 3-11 lists all clusters within the brain that had a significantly higher activation intensity for incongruent, compared to congrue nt, color-naming trials for both controls and patients with severe TBI. As illustrated in Figure 3-5 (threshold: p =.035 and 14 contiguous voxels) within the left ACC, greater activity was observed in TBI patients for incongruent, compared to congruent, color-naming trials. Th is same contrast using the same threshold revealed greater bilateral caudate activation in controls (Figur e 3-6) A direct between-group interaction and examination of Beta weights reveal ed greater activation of the left caudate head (Figure 3-7; threshold: p =.050 and 18 contiguous voxels) in controls, but not TBI patients. 42

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Table 3-1. C orrelations of mean error rates and co rrect-trial RTs (ms) in the cued-Stroop as a function of group Controls ( n =12) Severe TBI ( n = 10) Color-naming Congruent r = .064 r = .50* Incongruent r = .064 r = .49* Word-reading Congruent r = .46* r = .18 Incongruent r = .33 r = .49* *ps .027 Table 3-2. Means and standard errors of correct-t rial RTs (ms) in the cued-Stroop as a function of group Controls ( n =12) Severe TBI (n = 10) M SE M SE t -statistic Blocked (Non-task-switching) Color-naming Congruent 1048.88 93.21 1135.85 93.42 -1.40 Incongruent 1281.67 100.92 1481.60 174.56 -3.23* Word-reading Congruent 1030.17 83.71 1194.95 82.70 -2.66* Incongruent 1205.21 106.76 1369.35 90.34 -2.65* Mixed (Task-Switching) Color-naming Congruent 1053.67 79.48 1130.45 97.87 -1.06 Incongruent 1296.29 105.21 1398.00 124.48 -1.41 Word-reading Congruent 1115.04 86.61 1124.65 83.93 -0.13 Incongruent 1308.75 103.11 1512.55 172.40 -2.82* Collapsed across blocking Color-naming Congruent 1051.27 59.90 1133.15 65.85 -1.82 Incongruent 1288.97 71.30 1439.80 104.78 -3.35* Word-reading Congruent 1072.60 59.96 1159.80 57.91 -1.92 Incongruent 1256.98 73.38 1440.95 96.14 -4.05* *ps .0103 43

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Table 3-3. Means and standard erro rs of correct-trial RT (m s) in the cued-Stroop as a function of congruency Congruent Incongruent M SE M SE t -statistic Controls ( n =12) Blocked Color-naming 1048.88 93.21 1281.67 100.92 -3.94* Word-reading 1030.17 83.71 1205.21 106.76 -2.96* Mixed Color-naming 1053.67 79.48 1296.29 105.21 -3.52* Word-reading 1115.04 86.61 1308.75 103.11 -2.81* Collapsed Color-naming 1051.27 59.90 1288.98 71.31 -5.54* Word-reading 1072.60 59.96 1256.98 73.38 -4.25* Severe TBI (n = 10) Blocked Color-naming 1135.85 93.42 1481.60 174.56 -5.34* Word-reading 1194.95 82.70 1369.35 90.34 -2.70* Mixed Color-naming 1130.45 97.87 1398.00 124.48 -3.54* Word-reading 1124.65 83.93 1512.55 172.40 -5.14* Collapsed Color-naming 1133.15 65.85 1439.80 104.78 -6.53* Word-reading 1159.80 57.91 1440.95 96.14 -5.92* *ps < .05 44

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Table 3-4. Means and standard errors of correct-t rial RTs (m s) in the cued-Stroop as a function of task Color-naming Word-reading M SE M SE t -statistic Controls ( n =12) Blocked Congruent 1048.88 93.21 1030.17 83.71 0.32 Incongruent 1281.67 100.92 1205.21 106.76 1.29 Mixed Congruent 1053.67 79.48 1115.04 86.61 -0.89 Incongruent 1296.29 105.21 1308.75 103.11 -0.18 Collapsed Congruent 1051.27 59.90 1072.60 59.56 -0.49 Incongruent 1288.98 71.31 1256.98 73.38 0.73 Severe TBI (n = 10) Blocked Congruent 1135.85 93.42 1194.95 82.70 -0.91 Incongruent 1481.60 174.56 1369.35 90.34 1.74 Mixed Congruent 1130.45 97.87 1124.65 83.93 0.08 Incongruent 1398.00 124.48 1512.55 172.40 -1.52 Collapsed Congruent 1133.15 65.85 1159.80 57.91 -0.56 Incongruent 1439.80 104.78 1440.95 96.14 -0.02 Table 3-5. Means and standard errors of correct-t rial RTs (ms) in the cued-Stroop as a function of blocking Blocked Mixed M SE M SE t -statistic Controls ( n =12) Color-naming Congruent 1048.88 93.21 1053.67 79.48 -0.08 Incongruent 1281.67 100.92 1296.29 105.21 -0.24 Word-reading Congruent 1030.17 83.71 1115.04 86.61 -1.38 Incongruent 1205.21 106.76 1308.75 103.11 -1.68 Severe TBI (n = 10) Color-naming Congruent 1135.85 93.42 1130.45 97.87 0.08 Incongruent 1481.60 174.56 1398.00 124.48 1.24 Word-reading Congruent 1194.95 82.70 1124.65 83.93 1.04 Incongruent 1369.35 90.34 1512.55 172.40 -2.12* *p < .05 45

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Table 3-6. Means and standard erro rs of error rates (proportion) in the cued-Stroop as a function of group Controls ( n =12) Severe T BI ( n = 10) M SE M SE t -statistic Blocked (Non-task-switching) Color-naming Congruent .068 .014 .054 .020 0.46 Incongruent .117 .026 .186 .054 -2.09* Word-reading Congruent .034 .014 .102 .030 -2.06* Incongruent .100 .020 .162 .038 -1.84 Mixed (Task-switching) Color-naming Congruent .061 .017 .099 .025 -0.94 Incongruent .093 .025 .323 .078 -5.64* Word-reading Congruent .033 .008 .105 .037 -1.75 Incongruent .115 .027 .265 .056 -3.66* Collapsed across block Color-naming Congruent .065 .011 .077 .016 -0.41 Incongruent .105 .018 .255 .049 -5.08* Word-reading Congruent .034 .008 .104 .023 -2.96* Incongruent .108 .017 .213 .035 -4.46* *ps < .05 46

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Table 3-7. Means and standard erro rs of error rates (proportion) in the cued-Stroop as a function of congruency Congruent Incongruent M SE M SE t -statistic Controls ( n =12) Blocked Color-naming .068 .014 .117 .026 -1.52 Word-reading .034 .014 .100 .020 -2.09* Mixed Color-naming .061 .017 .093 .025 -0.82 Word-reading .033 .008 .115 .027 -2.10* Collapsed Color-naming .065 .011 .105 .018 -1.43 Word-reading .034 .008 .108 .017 -3.29* Severe TBI (n = 10) Blocked Color-naming .054 .020 .186 .054 -3.80* Word-reading .102 .030 .162 .038 -1.69 Mixed Color-naming .099 .025 .323 .078 -5.25* Word-reading .105 .037 .265 .056 -3.74* Collapsed Color-naming .077 .016 .255 .049 -5.77* Word-reading .104 .023 .213 .035 -4.43* *ps < .05 47

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Table 3-8. Means and standard erro rs of error rates (proportion) in the cued-Stroop as a function of task Color-nam ing Word-reading M SE M SE t -statistic Controls ( n =12) Blocked Congruent .068 .014 .034 .014 1.08 Incongruent .117 .026 .100 .020 0.28 Mixed Congruent .061 .017 .033 .008 0.67 Incongruent .093 .025 .115 .027 -0.58 Collapsed Congruent .065 .011 .034 .008 1.24 Incongruent .105 .018 .108 .017 -0.12 Severe TBI (n = 10) Blocked Congruent .054 .020 .102 .030 -1.39 Incongruent .186 .054 .162 .038 0.71 Mixed Congruent .099 .025 .105 .037 -0.14 Incongruent .323 .078 .265 .056 1.37 Collapsed Congruent .077 .016 .104 .023 -1.00 Incongruent .255 .049 .213 .035 1.58 Table 3-9. Means and standard erro rs of error rates (proportion) in the cued-Stroop as a function of blocking Blocked Mixed M SE M SE t -statistic Controls ( n =12) Color-naming Congruent .068 .014 .061 .017 0.28 Incongruent .117 .026 .093 .025 0.69 Word-reading Congruent .034 .014 .033 .008 0.02 Incongruent .100 .020 .115 .027 -0.42 Severe TBI (n = 10) Color-naming Congruent .054 .020 .099 .025 -1.16 Incongruent .186 .054 .323 .078 -3.56* Word-reading Congruent .102 .030 .105 .037 -0.05 Incongruent .162 .038 .265 .056 -2.86* *ps < .05 48

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Table 3-10. Statistical param etric mapping summary for within-and between-groups RFX-GLM analyses: fMRI activation for the color-naming > word-reading contrast Region of change Brodmann area(s) Talairach coordinates Controlsa: X Y Z R ITG 21 57 -16 -14 R FG 37 50 56 -2 R PrCG 9 39 7 33 R IPL 40 37 -32 45 R SOG 19 32 -70 27 R Insula 13 27 20 -5 L Caudate -15 15 6 L MFG 6 -29 -4 49 L IFG 47 -25 32 -2 L Claustrum -27 43 8 L MOG 19 -33 -75 11 L SbG-WM -34 -67 10 L MFG 10 -34 43 22 L STG 21 -48 -24 -4 L IFG 46 -47 38 5 L STG 38 -53 13 -19 TBIa: No significant regions of change Group X Task Interactionb R IPL 40 65 -30 29 R Insula 13 41 -22 25 R IPL 40 46 -29 42 R Insula 13 38 6 19 R Claustrum 22 25 -2 R PostCG 3 34 -33 48 L IFG 47 -21 35 -3 L MFG 10 -34 45 20 L Insula 13 -38 24 22 L MFG 46 -46 35 17 L SG 40 -48 -54 30 L STG 38 -52 20 -20 a(threshold: p=.021 and 7 contiguous voxels); b(threshold: p=.023 and 6 contiguous voxels); Note : R = right; L= left; FG = Fusiform gyrus; IF G = inferior frontal gyrus; IPL = inferior parietal lobule; ITG = inferior temporal gyrus; MFG = Middl e frontal gyrus; MOG = middle occipital gyrus; PostCG = post-central gyrus; PrCG = pre-central gyrus ; SbG-WM = Sub-gyral white matter; SG = supramarginal gyrus; SOG = superior occipital gyrus; STG = superior temporal gyrus; X Y and Z are coordinates in standard st ereotactic space (Talairach & Tournoux, 1988) in which positive values refer to regions of right ( X ), anterior to ( Y ), and superior to ( Z ) the anterior commissure. 49

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Table 3-11. Statistical param etric mapping su mmary for within-and between-groups RFX-GLM analyses: fMRI activation for the in congruent > congrue nt contrast Region of change Brodmann area(s) Talairach coordinates X Y Z Controlsa: R MFG 10 29 48 -1 R/L Caudate -4 3 9 R ISLL 4 -72 -37 -2 -9 -18 -7 -25 -38 L IPL 39 -35 -59 41 L MFG 6 -46 0 37 L Tuber -44 -59 -29 L MTG 37 -51 -57 -10 L PostCG 3 -64 -17 32 TBIa: R AG 39 46 -69 28 R Insula 37 17 0 L Cuneus 7 -2 -65 30 R Pons 10 -22 -33 L Nodule -1 -45 -29 L Thal -4 -26 12 L ACG 32 0 27 30 L SFG 6 -1 12 51 L Cuneus 8 -16 -99 11 -13 -18 -30 L IPL 7 -31 -54 40 L Insula 13 -38 14 8 L MFG 9 -47 9 34 Group X Congruency Interactionb L Caudate Head -6 18 -1 L STG 38 -32 18 -24 a(threshold: p =.035 and 14 contiguous voxels); b(threshold: p =.050 and 18 contiguous voxels) Note : R = right; L= left; AG = Angular Gyrus; ACG = Anterior cingulate gyrus; IPL = inferior parietal lobule; ISLL = Infe rior Semi-lunar Lobule; MFG = Middle frontal gyrus; MTG = Middle Temporal Gyrus; PostCG = post-central gyrus; SFG = Superior Frontal Gyrus; STG = superior temporal gyrus; Thal = Thalamus; X Y and Z are coordinates in st andard stereotactic space (Talairach & Tournoux, 1988) in which positiv e values refer to regions of right ( X ), anterior to ( Y ), and superior to ( Z ) the anterior commissure. 50

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Figure 3-1. Box plot of mean translational (x, y, z) inter-scan head movement during fMRI as a function of group. P-value indica tes that controls and pati ents with severe TBI did not significantly differ in head movement during fMRI scanning. Figure 3-2. Box plot of mean rotational (pitch, roll, yaw) in ter-scan head movement during fMRI as a function of group. P-value indicate s that controls and patients with severe TBI did not significantly differ in he ad movement during FMRI scanning. 51

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dlPFC Figure 3-3. f MRI statistical overlay map illustrating significant clusters of activity associated with the color-naming > word-reading contra st for controls (threshold: p = .021 and 7 contiguous voxels). dlPFC = dor solateral pre-frontal cortex dlPFC Figure 3-4. fMRI statistical overlay map illustrati ng significant clusters of activity associated with the color-naming > word-reading group by task interaction (threshold: p = .023 and 6 contiguous voxels). dlPFC = dorsolateral pre-frontal cortex 52

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ACC Figure 3-5. fMRI statistical overlay map illustrati ng significant clusters of activity associated with the incongruent > congrue nt contrast for patients with severe TBI (threshold: p = .035 and 14 contiguous voxels). ACC = anterior cingulate cortex Caudate Caudate Figure 3-6. fMRI statistical overlay map illustrati ng significant clusters of activity associated with the incongruent > congruent contrast for controls (threshold: p = .035 and 14 contiguous voxels). 53

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Caudate Figure 3-7. fMRI statistical overlay map illustrati ng significant clusters of activity associated with the incongruent > c ongruent group by congruency interaction (threshold: p =.050 and 18 contiguous voxels). 54

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CHAP TER 4 DISCUSSION Numerous cognitive neuroscience studies suggest that cognitive control comprises two essential component pro cesses implemented in a closely inte ractive, yet dissociable frontal neural network: a dlPFC-mediated regulative component supporting main tenance of task goals and implementation of control, and an ACC-mediated evaluative component responsible for conflict processing, and signaling necessary strate gic adjustments toward goal attainment (e.g., Kerns et al., 2004; Larson, Kauf man, & Perlstein, 2009; MacDonald et al., 2000). The current study utilized fMRI and a cued-Stroop task to fu rther assess the integrity and spatial dissociation of regulative and evaluative processes of control, as well as to examine impairments of cognitive control in severe TBI patients. Behavioral Data Examination of behavioral results of th e present study are consistent with initial expectations, and indicate that TBI patient s displayed generalized slowing during task performance, as evidenced by slower RTs versus controls on every task condition. Whereas a between-group inter action with congruency was not observe d, TBI patients were significantly slower than controls on incongrue nt word-reading trials, as well as significantly slower versus controls in the interf erence color-word naming conflict cond ition. These results are consistent with previous literature showing generalized slowing of TBI patients on the cued-Stroop task (e.g., Larson et al., 2007; Perlstei n et al., 2006; Seignourel et al., 2005), and may be reflective of broader impairments in reduced information processing abilities of even basic cognitive processes following braininjury (Ferraro, 1996). Examination of error rates revealed a signifi cant interaction of group with congruency for color-naming but not word-reading trials. Specifically, TBI patients committed significantly 55

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more errors than control part icipants on incongruent, but not congruent color-nam ing trials, a pattern of performance suggestive of inhibitory deficits. This pattern of results suggest error rates are specifically sensitive to inhibitory deficits of TBI, a finding consistent with previous literature showing inhibitory de ficits of TBI patient s on the cued-Stroop task (e.g., Larson et al., 2007; Perlstein et al., 2006; Sei gnourel et al., 2005). Thus, thes e results may suggest that TBI patients are impaired in their ability to use cont ext information provided by stimulus cues to inhibit and override the influence of a strong prepotent response tendency (e.g., to read the word), in favor of a less prepotent re sponse (e.g., name the printed ink color). fMRI Data Regulative Processes As predicted, when examining activation a ssociated with correct task performance, controls but not severe TBI patients demonstrated greater regulative-activity within the left dlPFC following the more attentionally dema nding color-naming, than word-reading task instruction. This finding is c onsistent with numerous studies (e.g., MacDonald et al., 2000) of healthy controls that have observed regulativemediated activation of the left dlPFC during completion of a similar modified cued-Stroop pa radigm. Additionally, a direct between-group interaction revealed greater activation within the dlPFC in controls but not TBI patients. Consequently, these results sugge st that TBI patients demonstrat ed reduced activation to the more attentionally demanding color-naming task, a result reflective of impairment in the topdown implementation of control. In addition, these results may suggest that during task preparation, our sample of severe TBI patient s were unable to properly allocate limited attentional resources necessary to override a strong prepotent res ponse tendency to read the word instead of name the ink color. 56

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Impairment of regulative processes of cont rols in our sample of TBI patients may be partially explained by disruption in one of five basal ganglia-thala mocortical circuits (Alexander & Crutcher, 1990; Alexander, DeLong, & Stric k, 1986). Specifically, Alex ander et al. (1986) described a series of five discre te, yet integrated and parallel neural circuits that subserve motor and cognitive functions: dorsolateral prefrontal, lateral orbitofront al, anterior cingulate, motor, and oculomotor. Importantly, each circuit was postul ated to involve differen t parts of the frontal cortex, basal ganglia, and thalamus (Alexa nder et al., 1986). For the present study and examination of regulative processes of control, the dorsolateral pref rontal circuit may be of most relevance. Specifically, the dorsola teral prefrontal circ uit postulated by Alexander et al. (1986) involves neural projections from the head and ta il of the caudate nucleus to select convexity areas of the dlPFC evaluated in the present study and implicated in studies of cognitive control (e.g., Brodmanns Area 9/10; Goldman & Nauta, 1977; Selemon & Goldman-Rakic, 1985; Yeterian & VanHoesen, 1978). Not surprisingly, the dorsolateral prefrontal circuit has been implicated to subserve a number of executive functioning abilities including but not limited to planning, organization, learning and memory, set shifting, and regulating actions (Duffy & Campbell, 1994). Moreover, lesions to prefrontal areas proposed to be involved in the dorsolateral prefrontal circuit have been obser ved to result in dysfunction of higher cognitive functions including goal selection, planning, sequencing, response set formation, set shifting, and spatial working memory (Cummings, 1993). Due to th e diffuse nature of closed-head injuries, it is possible that lesions along the dorsolateral prefrontal circuit in our sample of severe TBI patients may have resulted in impairment of regula tive processes of contro l, while leaving other circuits and cognitive func tions relatively intact. 57

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Additionally, im pairment of a dor solateral prefrontal circuit may also provide a framework for postulating the neurobiological underpinning s of impairment of regulative processes of cognitive control. For example, Braver & C ohen (2000) have postulated a relationship between regulative processes of contro l (e.g. context maintenance), dopaminergic neurotransmission, and the prefrontal cortex, a postulate supported by several neuroimaging studies that have implicated the contribution of dopaminergic neurotransmission to performance on cognitive control tasks (Braver et al., 2001; Braver et al ., 2003). Whereas much more futu re research is needed, taken together these findings suggest that disruption of dopaminergic neurotransmissions in the dlPFC may mediate disturbances of cognitive control following traumatic brain injury. Evaluative Processes In contrast to initial predictions, when examining activation leading to correct task performance, TBI patients demonstrated relatively intact activity within the ACC for incongruent, compared to congruent, color-nam ing trials. Consequently, these results may suggest that while our sample of TBI patients demonstrated greater errors than controls on conflict trials, the postulated neural underpinni ngs of conflict-detecti on are relatively intact. Regarding consistency with previous findings, this result is mixed when co mpared with previous studies that report alteration of activation within the ACC following TBI (e.g., Easdon, Levine, OConnor, Tisserand, & Hevenor, 2004; Soeda et al., 2005; Scheibel et al., 2007). For example, reduction in ACC activation has been reported in patients with TBI during completion of a gostop task (Easdon et al., 2004) and Stroop task (Soeda et al., 2005), a finding that may reflect injury-related disruption of neural networks. In contrast, Sc heibel et al. (2007) observed greater activation within the ACC in TBI patients compar ed to orthopedic injury control participants during completion of an incompatible portion of a stimulus-response compatibility task, a finding that may indicate alteration in the neural networks mediating cognitive control reflective 58

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of com pensation for inefficient cognitive proc esses. One possible explanation for these contradictory findings may be due to methodological differences in each study. For example, both Soeda et al. (2005) and Ea sdon et al. (2004) employed fixe d-effects analyses of their imaging data, which may bias there results due to the influence of single observations, while use of block design fMRI by Scheibel et al. (2007) potentially confounds examination of several task-relevant effects by including both correct and incorrect trials in their analyses. Methodological advantages in th e present study such as use of random-effects analyses and event-related fMRI may shed light on these issu es. For example, in the present study, activation outside of regions of interests was observed fo r trials during which part icipants performed the task correctly. This distinction is important, as findings of intact ACC activation by Scheibel et al. (2007) were collapsed across successful and incorrect trials. Consequently, the apparently greater magnitude and extent of ac tivation of brain regions outside our primary region of interest (e.g. ACC) in TBI patients may reflect plasticity following injury, as succe ssful task performance may be partially dependent on all active regions during task performance. Using the same contrast for evaluation of activity associated with incongruent, compared to congruent, color-naming trials revealed an unexpected finding in the present study: proberelated bilateral caudate nucleus activation in controls but not TBI patients Historically, the caudate nucleus has been divide d into three major regions: a he ad, body, and tail; and has been thought to play a primary role in motor func tion. However, recent reviews suggest that the caudate nucleus is highly involve d in cognitive processes that s ubserve goal-directed behaviors in learning and memory tasks (Grahn, Parkinson, & Owen, 2008; Packard & Knowlton, 2002). The caudate nucleus has also been observed to pl ay a role in the representation of anticipated reward (Gold, 2003), and Peterson et al. (2002), using event-related fM RI, observed increased 59

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signal intensity of the caudate nuc leus during the incongruent, as com pared to congruent events of Simon and Stroop tasks. Whereas studies investigating the role of the caudate in TBI are rare, lesions to the caudate nucleus in humans and primates have been reported to lead to impairments across numerous cognitive domains including pr oblem solving (Schmidtke, Manner, Kaufmann, & Schmolck, 2002), planning and sequencing (Mendez, Adams, & Lewandowski, 1989), attention span (Mendez et al ., 1989), short-and long-term memory (Fuh & Wang, 1995), and verbal fluency (Fuh & Wang, 1995). Conseque ntly, sub-cortical (e.g., caudate nucleus) contributions to higher level cognitive proces ses should not be overl ooked following braininjury. Additionally, these results may further su ggest disruption in a dorsolateral prefrontal subcortical circuit (e.g., Alexande r et al., 1986) following TBI. For example, the dorsolateral prefrontal circuit proposed by Alex ander et al. (1986) involves neur al projections from the head and tail of the caudate nucleus to select convexity areas of the dorsolateral prefrontal cortex. Not surprisingly, disruption in any frontal basal gang lia-thalamo-cortical circuit might result in cognitive impairment symptoms that resemble damage to the prefrontal cortex itself (e.g., Cummings, 1993), while other cognitive functions re main intact. Whereas much more research is needed to examine this postulate, the observed reduction of caudate-mediated evaluative activity and dlPFC-mediated regulative ac tivity and in the present st udy may suggest disruption of a prefrontal-subcortical circuit that represents an underlying stru ctural correlate reflective of behavioral impairment in dissociable components of cognitive control. In contrast to initial predictions, controls fa iled to demonstrate activity within the ACC for incongruent, compared to congruent, color-naming trials. Notably, these this finding represents a significant limitation in the present study as it dir ectly contrasts a plethor a of studies (e.g., Egner & Hirsch, 2005; Kerns et al., 2004 ; MacDonald et al., 2000) using he althy controls that implicate 60

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the role of the ACC in evaluative processes of control such as c onflict-detection, response inhibition, and response selection. Whereas this result cannot be fu lly explained, reasons for this finding m ay arise by examining aspects of data analysis procedures employed in the current study that differ from previous neuroimaging studies of cognitive control (e.g., MacDonald et al., 2000). For example, in contrast to the presen t study which used a referenced hemodynamic function, MacDonald et al. (2000) conducted ANOVAs without a reference function, and obtained their results using an inverse quadratic function. Cons equently, the results in the present study are less able to account for differences in vari ance than observed in previous studies of cognitive control (e.g., MacDonald et al., 2000). In addition, unlike the present study, MacDonald et al. (2000) did not di stinguish error trials from corre ct trials, which may potentially confound their results. Thus, taken together these methodological differences may partially explain lack of conflict-related ACC activation in our sa mple of controls. Nevertheless, our lack of replication represen ts a significant limitati on in the present study. Alternative Explanations and Limitations Several methodological limitations and alternative expl anations of findings of the current study warrant further discussion. For example, alt hough all TBI participants were classified as sustaining severe injuries, the heterogeneity of this population was evidenced by individual differences in injury mechanism, injury localiz ation, time since injury, PTA, and LOC (Lezak et al., 2004). Whereas studies using TBI participants cannot control for all th ese variables, studies of TBI must acknowledge and appreciate he terogeneity within the TBI population. Limitations of fMRI Numerous research studies (e.g., Heeger et al., 2000; Logothetis & Wandell, 2004) have postulated that the BOLD signal is a complex func tion reflective of changi ng levels of cerebral blood flow, blood volume, and oxygen metabolism that occur as a result of neural activity. 61

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However, as Boynton et al. ( 1996) report, the hemodyna mic res ponse often lags greatly behind the neuronal activity that starts the event. Mo reover, as the BOLD signal measured by fMRI is reflective of vascular changes correlated with ne ural activity, and not neur al activity directly, any observed injury-related differences may have re sulted from several factors outside of those hypothesized (Hillary et al., 2002). Specifically, differences in in jury-related activ ation patterns (e.g., lack of caudate activation) could reflect changes in vascular processes or changes due to cell atrophy, rather than neural processes. For example, both human and animal models of TBI have demonstrated reduced baseline levels of cerebral blood flow post-injury (e.g., Bouma et al., 1991). Moreover, blood flow abnormalities in patients with mode rate to severe TBI relative to comparison subjects have been observed during co mpletion of working memory tasks, an effect particularly concerning due to abnormalities in the frontal lobes, an area extensively imaged by researchers (e.g., Christodoulou et al., 2001). One possible way to examine some of these potential confounds is through examination of hi gh-resolution structural MRIs, which will allow for examination of structural abnormalities. Sample Size Limitations Not surprisingly, sample sizes in fMRI st udies are often influenced by real-world constraints such as financial costs and lengt h of scanning time. Consequently, researchers frequently contemplate how to achieve the most statistical power in an experiment while coping with a trade-off between research costs, sample size, and scanning time, an issue encountered in the present study. Regarding blocked designs, De smond and Glover (2002) estimated that to achieve 80% statistic al power using a liberal si gnificance threshold (e.g., p = .05), approximately 12 subjects per group were required; however, using conservative thresholds and correcting for multiple comparisons required approximately twi ce as many subjects. Of note to the current study, Murphy and Garavan (2004) estimated that fo r event-related fMRI designs, approximately 62

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20 participants per group were required for adequa te statistical power. Consequently, a potential lim itation of this study arises primarily from our limited sample of controls ( n = 12) and TBI patients (n = 10), which may have resulted in false posit ive and false negative activations in our data. Future Directions Despite its limitations, the current stu dy supports the continued neurophysiological examination of regulative (e.g., top-down implem entation of control) and evaluative (e.g., conflict-detection) processes of cognitive control. Future studies will aim to indentify specific component processes of control th at are reflective of wide-sprea d cognitive impairments in TBI utilizing both electrophysiological (e.g., ERP) and hemodynamic-based (e.g., fMRI) neuroimaging methods. Moreover, diffusion tensor imaging, an MRI technique that examines diffusion of water in tissue will be used to examine damage to white matter tracts, which is especially important given the pr evalence of diffuse axonal injuries in TBI (Murray et al., 1996). Future studies will also build upon these results and examine additional evaluative processes of control such as performance-monitoring. Moreov er, as electrophysiological differences have been observed during incorrect task performance in individuals with TBI (Larson et al., 2007), it will be useful to examine the magnitude and spatia l distribution of error-related stimulus activity in controls and patients with TBI using fMRI. Regarding translation of laborat ory research findings to clinic ally-practical use, we first plan to investigate how impairments of cognitive control component processes relate to measures of real-world functioning. Specifically, relationshi ps will be first formed between specific neural underpinnings of cognitive control impairments and measures of real-world functioning, including self-and significant other rating scal es of TBI-related symptomatology including the Dysexecutive Questionnaire (DEX ; Wilson, Alderman, Burgess, Emslie, & Evans, 1996) and a 63

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modified version of the Neur obehavioral Rating Scale (NRS-R Mathias & Coats, 1999). The DEX consists of a 20 Item self & significant-other ratings scales that assess cognitive, behavioral and affectiv e changes that may occur after TBI in four main areas: emotion or personality, motivation, behavior, and cogniti on (Levin et al., 1987; Wilson et al., 1996). The NRS-R has strongly been advocated for use as a secondary outcome measure in clinical trials of TBI, as evidenced by its high completion rate, ease of ad ministration, and clinical usefulness to obtain information in accordance with other measures of neurobehavioral and global functioning (McCauley et al., 2001). As demonstrated previo usly (e.g., Larson et al., 2006; Seignourel et al., 2005), mean selfand significant-other ratings of overall cognitive, behavioral and emotional functioning on the NRS-R were signifi cantly sensitive to TBI severity. Additionally, it may be possible to identify neuro-cognitive subtypes of TBI survivors on the basis of behavioral performance and brain ac tivity reflecting componen t-process deficits in cognitive control that will aid in tailoring treatment strategies. For example, classification on the basis of similarities or differences on vari ables between groups ma y result in a better understanding of the underlying etiology and treatment of a particular disorder (Morris & Fletcher, 1988). Interestingly, most studies of neuro-cognitive sequelae of TBI have relied on group averaging of data to arrive at genera l conclusions regarding TBI-related cognitive impairments; however, the TBI population is qu ite heterogeneous. Consequently, it may be possible to meaningfully exploit the heteroge neity within the TBI population by identifying neuro-cognitive subtypes of patients to determine if differen tial treatment response be predicted from neural activity reflective of primary compon ent process deficits in cognitive control. For example, a cognitive rehabilitation approach focused on improving successful attainment of goal-directed behavior is Goal Management Training (GMT), or iginally devised by Robertson 64

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(1996). It is possible that neural activity reflective of a prim ar y deficit in either regulative or evaluative component processes of cognitive contro l can be used to predict responsiveness to GMT, and subtypes of patients that differentia lly respond to treatment can be indentified. Summary The current study used fMRI to further assess the integrity and spatia l dissociation of two important components of cognitive control: a regulative component supporting maintenance of task goals and implementa tion of control, and an evaluative component responsible for conflict processing, and signaling necessary strategic adjustments toward goa l attainment. In addition, the present study sought to characterize and examine impairments of cognitive control in individuals with severe TBI. In conclusion, the results of our study suggest that: Neural networks mediating regulative-com ponent processes are altered after TBI. Cortical evaluative-mediated conflict proces sing-related activity was not reduced in our sam ple of TBI patients; rather, this function was largely preserved. In contrast, TBI patients demonstrated reductions in subcortical (e.g., frontostriatal) activity, a finding not previously reported. Consequently, cognitive im pairments fo llowing severe TBI may result from subcortical, rather than cortical dysfunction and may reflect impairment in a prefrontal-subcortical circuit that unde rlies dissociable components of cognitive control. These findings may have implications for the design of cognitive reha bilitation strategies, as our findings offer a framework for the study of the neural mechanisms responsible for the impairment of two dissociable components of cognitive control. 65

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LIST OF REFERE NCES Adams, J. H., Graham, D. I., Murray, L. S., & Sc ott, G. (1982). Diffuse axonal injury due to nonmissile head injury in huma ns: an analysis of 45 cases. Annals of Neurology, 12, 557563. Aguirre, G. K., & D'Esposito, M. (1998). Experime ntal design for brain fMRI. In: C. Moonen and P.A. Bandettini (Eds.), Functional MRI (pp. 369-380), Berlin: Springer-Verlag. Alexander, G. E., & Crutcher, M. D. (1990) Functional architecture of basal ganglia circuits: neural substrates of parallel processing. Trends in Neuroscience, 13, 266-271. Alexander, G. E., Delong, M. R., & Strick, P. L. (1986). Parallel organi zation of functionally segregated circuits linking basal ganglia and cortex. Annual Review of Neuroscience, 9, 357-381. Anderson, V., Levin, H. S., & Jacobs, R. (2002). Ex ecutive functions after fr ontal lobe injury: A developmental perspective. In D.T. Stuss & R.T. Knight (Eds.), Principles of Frontal Lobe Function (pp. 504-527), New York: Oxford University Press. Baddeley, A. (1992). Working memory, Science, 255, 556-559. Baddeley, A. D., & Hitch, G. I. (1994). Developments in the concept of working memory. Neuropsychology, 8, 485-493. Barch, D. M., Carter, C. S., Brav er, T. S., MacDonald, A., Sabb, F. W., Noll, D. C., & Cohen, J. D. (2001). Prefrontal cortex and context pr ocessing in medication naive first-episode patients with schizophrenia. Archives of General Psychiatry, 58, 280-288. Beck, A. T., Steer, R. A., & Brown, G. K. (1996). Beck Depression Inventory Second Edition (BDI-II) USA: The Psychological Corporation. Ben-Yishay, Y., & Diller, L. (1993). Cognitive remediation in trauma tic brain injury: update and issues. Archives of Physical Medicine and Rehabilitation, 74, 204-213. Bigler, E.D. (1990). Ne uropathology of traumatic brain in jury. In. E.D. Bigler (Ed.), Traumatic Brain Injury, Austin, TX: Pro-ed. Blair, J.R., & Spreen, O. (1989). Predicting pr emorbid IQ: A revision of the National Adult Reading Test. The Clinical Neuropsychologist 3, 129-136. Bond, M.R. (1986). Neurobehavioral sequelae of closed head injur y. In I. Grant & K. M. Adams (Eds.), Neuropsychological assessment of neuropsychological disorders (pp. 347-373), New York: Oxford University Press. Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., & Cohen, J. D. (2001). Conflict monitoring and cognitive control. Psychological Review, 108, 624-652. 66

PAGE 67

Botvinick, M. M., Cohen, J. D., & Carter, C. S. (2004). Conflict m onitoring and anterior cingulate cortex: an update. Trends in Cognitive Sciences, 8, 539-546. Botvinick, M., Nystrom, L. E., Fissell, K., Ca rter, C. S., & Cohen, J. D. (1999). Conflict monitoring versus selection-for-ac tion in anterior cingulate cortex. Nature, 402, 179-181. Bouma, G. J., Muizelaar, J. P., Choi, S. C., Newlon, P.G., & Young, H. F. (1991) Cerebral circulation and metabolism after severe trau matic brain injury: the elusive role of ischemia. Journal of Neurosurgery, 75, 685-693. Boynton, G. A., Engel, S. A., Glover, G., & Heeg er, D. (1996). Linear systems analysis of functional magnetic resonance imaging in human V1. Journal of Neuroscience, 16, 42074221. Braver, T. S., & Barch, D. M. (2002). A theory of cognitive control, aging cognition, and neuromodulation. Neuroscience and Biobehavioral Reviews, 26, 809-817. Braver, T. S., Barch, D. M., Gray, J. R., Molfese, D. L., & Snyder, A. (2001). Anterior cingulate cortex and response conflict: effects of frequency, inhibition and errors. Cerebral Cortex, 11, 825-836. Braver, T. S., & Cohen, J. D. (2000). On the c ontrol of control: the ro le of dopamine in regulating prefrontal function and working me mory. In S. Monsell & J. Driver (Eds.), Control of cognitive processe s: attention and performance (pp. 713-737), Cambridge, MA: MIT Press. Braver, T. S., Reynolds, J. R., & Donaldson, D. I. (2003). Neural mechanisms of transient and sustained cognitive control during task switching. Neuron, 39, 713-726. Buckner, R. L. (1998). Event-related fMRI and the hemodynamic response. Human Brain Mapping, 6, 373-377. Buckner, R. L. (2003). The hemodynamic invers e problem: making inferences about neural activity from MRI signals. Proceedings of the National Academy of Sciences, 100, 21772179. Buckner, R. L., Bandettini, P. A., OCraven, K. M., Savoy, R. L., Petersen, S. E., Raichle, M. E., & Rosen, B. R. (1996). Detection of cortical ac tivation during averaged single trials of a cognitive task using functiona l magnetic resonance imaging. Proceedings of the National Academy of Sciences, 96, 14878-14883. Burgess, P. W., & Robertson, I. H. (2002). Princi ples of the rehabilita tion of frontal lobe function. In D.T. Stuss & R.T. Knight (Eds.), Principles of Frontal Lobe Function (pp. 557-572), New York: Oxford University Press. 67

PAGE 68

Cabeza, R., & Nyberg, L. (2000). Im aging cogniti on II: An empirical review of 275 PET and fMRI studies. Journal of Cognitive Neuroscience, 12, 1-47. Carter, C. S., MacDonald III, A W., Ross, L. L ., & Stenger, V. A. ( 2001). Anterior cingulate cortex activity and impaired self-monito ring of performance in patients with schizophrenia: An event-related fMRI study. American Journal of Psychiatry, 158, 14231428. Carter, C. S., & van Veen, V. ( 2007). Anterior cingulate cortex and conflict detection: An update of theory and data. Cognitive, Affective, and Behavioral Neuroscience 7, 367-379. Cazalis, F., Feydy, A., Valabregue, R., Pelegrini-Issac, M., Pierot L., & Azouvi, P. (2006). fMRI study of problem solving after se vere traumatic brain injury. Brain Injury 20, 1019-1028. Chen, J., Johnston, K., Frey, S., Petrides, M ., Worsley, K., & Ptito, A. (2004). Functional abnormalities in symptomatic concussed athletes: an fMRI study. Neuroimage 22, 68-82. Christodoulou, C., DeLuca, J., Ricker, J. H ., Madigan, N. K., Bly, B. M., Lange, G., et al. (2001). Functional magnetic resonance im aging of working memory impairment following traumatic brain injury. Journal of Neurology, Neur osurgery, and Psychiatry, 71, 161-168. Cicerone, K. D., Dahlberg, C., Kalmar, K., Langenbahn, D. M., Malec, J. F., Bergquist, T. F., et al. (2000). Evidence-based cognitive rehabi litation: recommendations for clinical practice. Archives of Physical Me dicine and Rehabilitation, 81, 1596-1615. Cicerone, K. D., Dahlberg, C., Malec, J. F., Langenbahn, D. M., Felicetti, T., Kneipp, S., et al. (2005). Evidence-based cognitive rehabilitation: Updated review of the literature from 1998 through 2002. Archives of Physical Medicine and Rehabilitation, 86 1681-1692. Clark, J.H. (1924). The Ishihara Test for Color Blindness. American Journal of Physiological Optics, 5, 269-276. Cohen, J. D., Barch, D. M., Carter, C. S., & Servan-Schreiber, D. (1999). Context-processing deficits in schizophrenia: converging evid ence from three theoretically motivated cognitive tasks. Journal of Abnormal Psychology, 108, 120-133. Coronado, V., Thomas, K., Sattin, R., & Johnson, R. L. (2005). The CDC traumatic brain injury surveillance system: Characteristics of persons aged 65 years and ol der hospitalized with a TBI. Journal of Head Trauma Rehabilitation, 20, 215-228. Cumm ings, J. L. (1993). Fr ontal-subcortical circu its and human behavior. Archives of Neurology, 50, 873-880. Dale, A. M. (1999). Optimal experime ntal design for event-related fMRI. Human Brain Mapping, 8, 8560-8572. 68

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Dale, A. M., & Buckner, R. L. (1997). Selective av eraging of rapidly presen ted individual trials using fMRI. Human Brain Mapping, 5, 329-340. Derrfuss, J., Brass, M., Neum ann, J., & von Cramon, D. Y. (2005). Involvement of the inferior frontal junction in cognitive control: meta -analyses of switching and Stroop studies. Human Brain Mapping, 25, 22-34. Desmond, J. E., & Glover, G. H. (2002). Estimat ing sample size in functional MRI (fMRI) neuroimaging studies: statistical power analyses. Journal of Neuroscience Methods, 188, 115-128. DiGirolamo, G. J., Kramer, A. F., Barad, V., Cepe da, N. J., Weissman, D. H., Milham, M. P., et al. (2001). General and task-specific frontal lobe recruitment in older adults during executive processes: a fMRI i nvestigation of task-switching, Neuroreport, 12, 20652071. Donaldson, D. I., & Buckner, R. L. (2001). Eff ective paradigm design. In: P. Jezzard, P. M. Matthews, & S. M. Smith (Eds.), Functional magnetic re sonance imaging: an introduction to methods (pp. 177-196), Oxford University Press, Oxford. Doppenberg, E. M., Choi, S. C. and Bullock, R. (2004). Clinical trials in tr aumatic brain injury: lessons for the future. Journal of Neurosurgical Anesthesiology, 16, 87-94. Dove, A., Pollmann, S., Schubert, T., Wiggins, C. J., & von Cramon, D. Y. (2000). Prefrontal cortex activation in task switching: An event-related fMRI study. Cognitive Brain Research, 9, 103-109. Dreher, J.-C., Koechlin, E., Ali, S. O., & Grafman, J. (2002). The roles of timing and task order during task switching, Neuroimage 17, 95-109. Drew, L. B., & Drew, W. E. (2004). The cont recoup-coup phenomenon: a new understanding of the mechanism of closed head injury. Neurocritical Care, 1, 385-390. Duffy, J. D., & Campbell, J. J. (1994). The regi onal prefrontal syndromes: A theoretical and clinical overview. Journal of Neuropsychiatry an d Clinical Neuroscience, 6, 379-387. Duncan, J., Johnson, R., Swales, M., & Freer, C. (1997) Frontal lobe deficits after head injury: Unity and diversity of function. Cognitive Neuropsychology, 14, 713-741. Durston, S., Tottenham, N. T., Thomas, K. M., Davidson, M. C., Eigsti, I. M., Yang, Y., Ulug, A. M., & Casey, B. J. (2003). Differential patterns of striatal activ ation in young children with and without ADHD. Biological Psychiatry 53, 871-878. Easdon, C., Levine, B., OConnor, C., Tisserand, D., & Hevenor, S. (2004). Neural activity associated with response inhibition following traumatic brain injury : an event-related fMRI investigation. Brain and Cognition, 54, 136-138. 69

PAGE 70

Egner, T., & Hirsch, J. (2005). Cognitiv e control mechanisms resolve conflict through cortical amplification of task-relevant information. Nature Neuroscience, 8, 1784-1790. Eriksen, B. A., & Eriksen, C. W. (1974). Effect s of noise letters upon the identification of a target letter in a nonsearch task. Perception and Psychophysics, 16, 143-149. Felmingham, K. L., Baguley, I. J., & Green, A. M. (2004). Effects of diffuse axonal injury on speed of information processing follo wing severe traumatic brain injury. Neuropsychology, 18, 564-571. Ferraro, F. R. (1996). Cognitive slowing in closed-head injury. Brain and Cognition, 32, 429440. Finkelstein, E., Corso, P ., & Miller, T. (2006). The Incidence and Economic Burden of Injuries in the United States New York: Oxford University Press. Forster, B. B., MacKay, A. L., Whittall, K. P., Kiehl, K. A., Smith, A. M., Hare, R. D., & Liddle, P. F. (1998). Functional magnetic resonance imaging: the basics of blood-oxygen-level dependent (BOLD) imaging. Canadian Association of Radiologists Journal, 49, 320-329. Friston, K. J., Holmes, A. P., Price, C. J., Bu chel, C., & Worsley, K. J. (1999). Multisubject FMRI studies and conjunction analyses. Neuroimage, 10, 385-396. Friston, K. J., Holmes, A. P., Worsley, K. J., Poline, J. B., Frith, C. D., & Frackowiak, R. S. J. (1995). Statistical parametric ma ps in functional neuroimaging. Neuroimage 6, 218-229. Fuh, J. L, & Wang, S. J. (1995). Caudate hemorrh age: clinical features, neuropsychological assessments and radiological findings. Clinical Neurology and Neurosurgery, 97, 296299. Gold, J. I. (2003). Linking reward expectation to behavior in the basal ganglia. Trends in Neurosciences, 26, 12-14. Goldman, P. S., & Nauta, W. J. H. (1977). Anintricately patterned prefronto-caudate projection in the rhesus monkey. Journal of Comparative Neurology, 171, 369-386. Golden, C. J. (1978). Stroop Color and Word Test. Chicago: Stoelting. Grahn, J. A., Parkinson, J. A., & Owen, A. M. ( 2008). The role of the basal ganglia in learning and memory: neuropsychological studies. Behavioural Brain Research, in press. Harvey, P. O., Fossati, P., Pochon, J. B ., Levy, R., Lebastard, G., Lehericy, S., et al. (2005). Cognitive control and brain resources in major depression: An fMRI study using the nback task. Neuroimage, 26, 860-869. 70

PAGE 71

Heeger, D. J., Huk, A. C., Geisler, W S., & Albr echt, D. G. (2000). Spikes versus BOLD: What does neuroimaging tell us about neuronal activity?. Nature Neuroscience 3, 631-633. Hillary, F. G., Steffener, J., Biswal, B. B., Lange, G., DeLuca, J., & Ashburner, J. (2002). Functional magnetic resonance imaging t echnology and traumatic brain injury rehabilitation: guidelines for met hodological and conceptual pitfalls. Journal of Head Trauma Rehabilitation, 17, 411-430. Horn, L. J., & Sherer, M. (1999). Re habilitation of traumatic brain inju ry. In K. M. Grabois, S. J. Garrison, A. Hart, & L. D. Lehmkuhl (Eds.), Physical medicine and rehabilitation: the complete approach (pp. 1281-1304), Cambridge, MA : Blackwell Science. Johnstone, B., Callahan, C. D., Kapila, C. J., & Bouman, D. E. (1996). The comparability of the WRAT-R Reading Test and NAART as esti mates of premorbid intelligence in neurologically impaired patients. Archives of Clinical Neuropsychology, 11, 513-519. Josephs, O., Rees, G., Turner, R., & Fris ton, K. J. (1997). Event-related fMRI. Human Brain Mapping, 5, 243-248. Kerns, J. G. (2006). Anterior cingulate and prefront al cortex activity in an FMRI study of trialto-trial adjustments on the Simon task. Neuroimage, 33, 399-405. Kerns, J. G., Cohen, J. D., MacDonald, A. W., Ch o, R. Y., Stenger, V. A., & Carter, C. S. (2004). Anterior cingulate conflict mon itoring and adjustments in control. Science 303 1023-1026. Kim, S. G., & Ugurbil, K. (1997). Comparis on of blood oxygenation and cerebral blood flow effects in fMRI: estimation of relative oxygen consumption change. Magnetic Resonance in Medicine, 38, 59-65. King, N. S., Crawford, S., Wenden, F. J., Moss, N. E., Wade, D. T., & Caldwell, F. E. (1997). Measurement of post-traumatic amnesia: How reliable is it?. Journal of Neurology, Neurosurgery, and Psychiatry, 62, 38-42. Kochanek, P. M., Hendrich, K. S., Dixon, C. E., Schiding, J. K., Williams, D. S., & Ho, C. (2002). Cerebral blood flow at one year af ter controlled cortic al impact in rats: assessment by magnetic resonance imaging. Journal of Neurotrauma, 19, 1029-1037. Langlois, J. A., Rutland-Brown, W., & Thomas, K. E. (2004). Traumatic Brain Injury in the United States: Emergency Department Visits, Hospitalizations, and Deaths. Atlanta, GA: National Center for Injury Prevention and Control. Langlois, J. A., Rutland-Brown, W ., & Wald, M. (2006). The epidemiology and impact of traumatic brain injury: a brief overview. Journal of Head Trauma Rehabilitation, 21, 375-378. 71

PAGE 72

Larson, M. J., Jones, V., Kelly, K. G., & Perlst ein, W M. (2004). Dissociating components of cognitive control with high-density ERPs: Implementation of control, conflict processing, and error monitoring. Paper presented at the 32nd annual meeting of the international neuropsychological society, Baltimore, MD. Larson, M. J., Kaufman, D. A. S., & Perlstein, W. M. (2009). Neural time course of conflict adaptation effects on the Stroop task. Neuropsychologia, 47, 663-670. Larson, M. J., Perlstein, W. M., Demery, J. A ., & Stigge-Kaufman, D. A. (2006). Cognitive control impairments in traumatic brain injury. Journal of Clinical and Experimental Neuropsychology 28, 968-986. Larson, M. J., Stigge-Kaufman, D. A., Schmul fass, I. M., & Perlstein, W. M. (2007). Performance monitoring, error processing, and evaluative control following severe TBI. Journal of the International Neuropsychological Society 13, 961-971. Levin, H. S., Eisenberg, H. M., & Benton, A. L. (1991). Mild Head Injury. New York: Oxford University Press. Levin, H. S., Gary, H., Eisenberg, H., Ruff, R., Barth, J., Kreutzer, J., High, W., Portman, S., Foulkes, M., & Jane, J. (1990). Neurobehavioral outcome 1-year after severe head injury: Experience of the Traumatic Coma Data Bank. Journal of Neurosurgery 73, 699-709. Levin, H. S., High, W., Goethe, R., Sisson, R. A., Overall, J. E., Rhoades, H., Eisenberg, H. M., Kalisky, Z., & Gary, H. (1987). The neurobeha vior rating scale: Assessment of the behavioral sequelae of head injury by the clinician. Journal of Neurology, Neurosurgery, and Psychiatry, 50, 183-193. Levin, H., & Kraus, M. F. (1994). The fr ontal lobes and traumatic brain injury. Journal of Neuropsychiatry and Clinical Neurosciences, 6, 443-454. Levine, B., Katz, D. I., Dade, L., & Black, S. E. (2002). Novel approaches to the assessment of frontal damage and executive deficits in trau matic brain injury. In D. T. Stuss & R. T. Knight (Eds.), Principles of frontal lobe function (pp. 448-465), New York: Oxford University Press. Lezak, M. D., Howieson, D. B., & Loring, D. W. (2004). Neuropsychological Assessment (4th edition). New York: Oxford University Press. Liotti, M., Woldorff, M. G., Perez, R., & Mayber g, H. S. (2000). An ERP study of the temporal course of the Stroop color-word interference effect. Neuropsychologia 38, 701-711. Logothetis, N. K., & Wandell, B. A. (2004). Interpreting the BOLD signal. Annual Review of Physiology, 66, 735-769. Lorist, M. M., Boksem, M. A. S., & Ridderinkhof, K. R. (2005). Impaired cognitive control and 72

PAGE 73

reduced cin gulate activit y during mental fatigue. Cognitive Brain Research 24, 199-205. Lovell, M., & Franzen, M. (1994). Neuropsychological assessment. In: J. M. Silver, S. Yudofsky, & R. E. Hales (Eds), Neuropsychiatry of traum atic brain injury (pp. 133-160), Washington: American Psychiatric Press. MacDonald III, A. W., Cohen, J. D., Stenger, V. A., & Carter, C. S. (2000). Dissociating the role of the dorsolateral prefrontal and anterior cingulate cortex in cognitive control. Science 288, 1835-1838. MacLeod, C. M. (1991). Half a century of research on the Stroop effect: an integrative review. Psychological Bulletin, 109, 163-203. Mathias, J. L., & Coats, J. L. (1999). Emotional and cognitive sequelae to mild traumatic brain injury. Journal of Clinical and Experimental Neuropsychology 21, 200-215. Matthews, P. M., & Jezzard, P. (2004). Functional magnetic resonance imaging. Journal of Neurology, Neurosurgery, and Psychiatry 75, 6-12. McAllister, T. W., Saykin, A .J., Flashman, L. A., Sparling, M. B., Johnson, S. C., Guerin, S. J., Mamourian, A. C., Weaver, J. B., & Yanofsky, N. (1999). Brain activation during working memory 1 month after mild trau matic brain injury. A functional MRI study. Neurology 53, 1300-1308. McAllister, T. W., Sparling, M. B., Flashman, L. A., Guerin, S. J., Mamourian, A. C., & Saykin, A. J. (2001). Differential working memory load effects after mild tr aumatic brain injury. NeuroImage 14, 1004-1012. McCauley, S. R., Levin, H. S., Va nier, M., Mazaux, J. M., Boake, C., Goldfader, P. R., Rockers, D., Butters, M., Kareken, D.A., Lamber t, J., & Clifton, G. L. (2001). The Neurobehavioral Rating Scale Revised: sensitivity and validity in closed head injury assessment. Journal of Neurology, Neurosurgery, and Psychiatry, 71, 643-651. McClure, S. M., Laibson, D. I ., Loewenstein, G., & Cohen, J. D. (2004). Separate neural systems value immediate and delayed monetary rewards. Science 306 503-507. McMillan, T. M., Jongen, E. L., & Greenwood, R. J. (1996). Assessment of post-traumatic amnesia after severe closed head injury: retrospective or prospective?. Journal of Neurology, Neurosurgery, and Psychiatry, 60 422-427. Mendez, M. F., Adams, N. L., & Lewandowski, K. S. (1989). Neurobehavioral changes associated with caudate lesions. Neurology, 39, 349-54. Miezin, F. M., Maccotta, L., Ollinger, J. M., Petersen, S. E., & Buckner, R. L. (2000). Characterizing the hemodynamic response: e ffects of presentation rate, sampling procedure, and the possibility of orderi ng brain activity based on relative timing. 73

PAGE 74

Neuroimage, 11, 735-759. Milham M. P., Banich, M. T., Claus, E. D., & Cohen, N. J. (2003). Practice-related effects demonstrate complementary roles of anterior cingulate and prefr ontal cortices in attentional control. Neuroimage, 18, 483-493. Milham, M. P., Banich, M. T., Webb, A., Barad, V., Cohen, N. J., Wszalek, T., & Kramer, A. F. (2001). The relative involvement of anterior cingulate and prefr ontal cortex in attentional control depends on the nature of conflict. Brain Research. Cognitive Brain Research, 12, 467-473. Miller, E. K. (2000). The prefront al cortex and cognitive control. Nature Reviews: Neuroscience, 1, 59-66. Miller, E. K., & Cohen, J. D. (2001). An integr ative theory of prefr ontal cortex function. Annual Review of Neuroscience, 24 167-202. Miyake, A., Friedman, N. P., Emerson, M. J., Wi tzki, A. H., & Howerter, A. (2000). The unity and diversity of executive func tions and their contributions to complex frontal lobe tasks: a latent variable analysis. Cognitive Psychology 41, 49-100. Morris, R.D., & Fletcher, J.M. (1988). Classi fication in neuropsychology: A theoretical framework and research paradigm. Journal of Clinical and Experimental Neuropsychology 10, 640-658. Murphy, K., & Garavan, H. (2004). An empirical investigation into the number of subjects required for an event-related fMRI study. Neuroimage 22, 879-885. Murray, J. G., Gean, A. D., & Evans, S. J. (1996). Imaging of acute head injury. Seminars in Ultrasound, CT, and MR, 17, 185-205. National Institutes of Health (1998). NIH Consensu s Statement on Rehabilitation of Persons with Traumatic Brain Injury, Bethesda, MD. Newsome, M. R., Scheibel, R. S., Steinberg, J. L., Troyanskaya, M., Sharma, R. G., Rauch, R. A., Li, X., & Levin, H. S. (2007). Working memory brain activation following severe traumatic brain injury. Cortex 43, 95-111. Ogawa, S., Lee, T. M., Kay, A. R., & Tank, D. W. (1990). Brain m agnetic resonance imaging with contrast dependent on blood oxygenation. Proceedings of the National Academy of Sciences 87, 9868-9872. Packard, M. G., & Knowlton, B. J. (2002). Learning and m emory functions of the basal ganglia. Annual Review of Neuroscience, 25, 563-593. Pauling, L., & Coryell, C. (1936). The magnetic properties and structure of he moglobin, 74

PAGE 75

oxyhemoglobin and carbonmonoxyhemoglobin Proceedings of the National Academy of Sciences, 22, 210-216. Perlstein, W. M., Cole, M. A., Dixit, N. K., & De mery, J. A. (2004). Parametric manipulation of working memory load in chronic traumatic brain injury. Journal of the International Neuropsychological Society 10, 724-741. Perlstein, W. M., Dixit, N. K., Carter, C. S., Noll, D. C., & Cohen, J. D. (2003). Prefrontal cortex dysfunction mediates deficits in working memory and prepotent responding in schizophrenia. Biological Psychiatry, 53, 25-38. Perlstein, W. M., Larson, M. J., Dotson, V. M., & Kelly, K. G. (2006). Tem poral dissociation of components of cognitive control dysfunction in severe TBI: ERPs and the cued-Stroop task. Neuropsychologia, 44, 260-274. Peterson, B. S., Kane, M. J., Alexander, G. M., Lacadie, C., Skudlarski, P., Leung, H-C., May, J., & Gore, J. C. (2002). An event-related f unctional MRI study comp aring interference effects in the Simon and Stroop tasks. Brain Research. Cognitive Brain Research, 13, 427-440. Prigatano, G. P., Johnson, S. C., & Gale, S. D. (2004). Neuroimaging correlates of the Halstead Finger Tapping Test several year s post-traumatic brain injury. Brain Injury, 18, 661-669. Raichle, M. E. (2001). Cognitive neuroscience: Bold insights. Nature, 412, 128-130. Rao, V., & Lyketsos, C. G. (2000). Neuropsychi atric sequelae of trau m atic brain injury. Psychosomatics, 41, 95-103. Robertson, I.H. (1996). Goal Management Traini ng: A Clinical Manual. Cambridge: PsyConsult. Rosen, B. R., Buckner, R. L., & Dale, A. M. (1998) Event-related functional MRI: past, present, and future. Proceedings of the National Academy of Sciences, 95, 773-80. Rubia, K., Smith, A. B., Woolley, J., Nosarti, C., Heyman, I., Taylor, E., & Brammer, M. J. (2006). Progressive increase of fronto-st riatal brain activation from childhood to adulthood during event related tasks of cognitive control. Human Brain Mapping, 27, 973-993. Saatman, K. E., Duhaime, A. C., Bullock, R., Ma as, A. I. R., Valadka, A., & Manley. G. T. (2008). Journal of Neurotrauma, 25, 719-738. Salvant Jr., J. B., & Muizelaar, J. P (1993). Ch anges in cerebral blood flow and metabolism related to the presence of subdural hematoma. Neurosurgery 33, 387-393. Scheibel, R. S., Newsome, M. R., Steinberg, J. L., Pearson, D. A., Rauch, R. A., Mao, H., 75

PAGE 76

Troyanskaya, M., Sharm a, R. G., & Levin, H. S. (2007). Altered br ain activation during cognitive control in patients with moderate to severe traumatic brain injury. Neurorehabilitation and Neural Repair, 21, 36-45. Scheibel, R. S., Pearson, D. A., Faria, L. P., Kotrla, K. J., Aylward, E., Bachevalier, J., et al. (2003). An fMRI study of executive functioning after severe diffuse TBI. Brain Injury, 17, 919-930. Schmidtke, K., Manner, H., Kaufmann, R., & Schmolck, H. (2002). Cognitive procedural learning in patients with fronto-striatal lesions. Learning and Memory, 9, 419-429. Seignourel, P. J., Robins, D. L., Larson, M., Deme ry, J. A., Cole, M. A., & Perlstein, W. M. (2005). Cognitive control in closed head in jury: Context maintenance dysfunction or prepotent response inhibition deficit?. Neuropsychology, 19 578-590. Selemon, L. D., & Goldman-Rakic, P. S. ( 1985). Longitudinal topogra phy and interdigitation of cortico-striatal projecti ons in the rhesus monkey. Journal of Neuroscience, 5, 776-794. Sherer, M., Madison, C. F., & Hannay, H .J. (2000) A review of outcome after moderate and severe closed head injury with an introduction to life care planning. Journal of Head Trauma Rehabililitation, 15, 767-782. Simon, J. R. (1969). Reactions towa rds the source of stimulation. Journal of Experimental Psychology, 81, 174-176. Soeda, A., Nakashima, T., Okumura, A., Kuwata, K., Shinoda, J., & Iwama, T. (2005). Cognitive impairment after traumatic brain injury: a functional magnetic resonance imaging study using the Stroop task. Neuroradiology 47, 501-506. Sohn, M., Ursu, S., Anderson, J. R., Stenger, V. A ., & Carter, C. (2000). The role of prefrontal cortex and posterior parietal cortex in task switching. Proceedings of the National Academy of Sciences, 97, 13448-13453. Spreen, O., & Strauss, E. (1991). A compendium of neuropsychologi cal tests: Administration, norms, andcommentary New York: Oxford University Press. Stern, E., & Silbersweig, D. A. (2001). Advan ces in functional neuroimaging methodology for the study of brain systems underlying human neuropsychological function and dysfunction. Journal of Clinical and Ex perimental Neuropsychology, 23, 3-18. Stewert, J. T., & Hemsath, R. H. (1988). Bipol ar illness following traumatic brain injury: treatment with lithium and carbamazepine. Journal of Clinical Psychiatry, 49, 74-75. Strich, S. J. (1961). Shearing of ne rve fibres as a cause of brain damage due to head injury: a pathological study of twenty cases. Lancet, 2, 443-448. 76

PAGE 77

Stroop, J. R. (1935). Studies of interf erence in serial verbal reactions. Journal of Experimental Psychology, 18, 643-662. Stuss, D. T., & Alexander, M. P. (2000). Executive functions and the frontal lobes: a conceptual view. Psychological Research 63, 289-298. Talairach, J., & Tournoux, P. (1988). Co-planar Sterotaxic A tlas of the Human Brain New York: Thieme. Teasdale, G., & Jennett, B. (1974). Assessment of coma and impaired consciousness: a practical scale. Lancet, ii 81-84. Thurman, D. (2001). The epidemiology and economics of head trauma. In L. P. Miller, R. L. Hayes, & J. K. Newcomb (Eds.), Head Trauma: Basic, Preclinical, and Clinical Directions (pp. 327-348) New York: John Wiley & Sons, Inc. Thurm an, D. J., Alverson, C., Dunn, K., Guerrero, J., & Sniezek, J. (1999). Traumatic brain injury in the United States: a public health perspective. Journal of Head Trauma Rehabilitation, 14, 602-615. Thurman, D., & Guerrero, J. (1999). Trends in ho spitalization associated with traumatic brain injury. Journal of the American Medical Association, 282, 954-957. Turner, G. R., & Levine, B. (2008). Augmente d neural activity during executive control processing following diffuse axonal injury. Neurology, 71, 812-818. Vaidya, C. J., Bunge, S. A., Dudukovic, N. M., Zalecki, C. A., Elliott, G. R., & Gabrieli, J. D. (2005). Altered neural substrates of cogniti ve control in childhood ADHD: evidence from functional magnetic resonance imaging. American Journal of Psychiatry, 162, 16051613. Vakil, E. (2005). The effect of moderate to se vere traumatic brain inju ry (TBI) on different aspects of memory: a selective review. Journal of Experimental and Clinical Neuropsychology, 27, 977-1021. Vallat-Azouvi, C., Weber, T., Legrand, L., & Azouvi P. (2007). Working me mory after severe traumatic brain injury. Journal of the Inte rnational Neuropsyc hological Society, 13 770780. van Veen, V., & Carter, C. S. (2002). The anterior cingulate as a conflict monitor: fMRI and ERP studies. Physiology and Behavior, 77, 477-482. van Veen, V., & Carter, C.S. (2006). Conf lict and cognitive c ontrol in the brain. Current Directions in Psychological Science, 15, 237-240. van Zomeren, A. H., & van den Burg, W. (1985). Residual complaints of patients two years 77

PAGE 78

after severe head injury. Journal of N eurology, Neurosurgery and Psychiatry, 48, 21-28. Wechsler, D. (1981). WAIS-R Manual. New York: Psychological Corporation. West, R. (2003). Neural correlates of cognitive control and conflic t detection in the Stroop and digit-location tasks. Neuropsychologia, 41, 1122-1135. Wilson, B. A., Alderman, N., Burgess, P. W., Emslie, H., & Evans, J. J. (1996). Behavioural assessment of the dysexecutive syndrome Bury St. Edmunds, UK: Thames Valley Test Company. Yeterian, E. H., & vanHoesen, G. W. (1978). Cortic o-striate projections in the rhesus monkey: The organization of certain cortieo-caudate connections. Brain Research, 139, 43-63. Ziino, C., & Ponsford, J. (2006). Selective atten tion deficits and subjec tive fatigue following traumatic brain injury. Neuropsychology, 20, 383-389. 78

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79 BIOGRAPHICAL SKETCH Christopher Nicholas Sozda received his B achelor of Science degree in psychology in 2006 from the University of Pittsburgh, where he studied under the mentorship of Dr. Anthony E. Kline. Christopher received his Master of Sc ience degree from the University of Florida in 2009, and plans to continue his studies in clinical-cognitive neuroscience and neuropsychology.