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  1. Center Overview Determinants of Executive Function & Dysfunction

  2. An Interdisciplinary Behavioral Science Center • “The purpose of these Centers is to support collaborative, hypothesis-driven basic research activities that will extend the most cutting-edge theories and approaches in basic behavioral science to incorporate current approaches in neuroscience. Center activities will be driven by a basic research question (or set of questions) that is framed at the behavioral level (e.g., cognition, emotion, personality, social interaction) and that is forging connection with neural-level processes. Ultimately, knowledge yielded by such connections will increase the explanatory power of behavioral science, and will enrich neuroscience by providing an ever-more-detailed understanding of behavioral and mental processes. The integration of knowledge that results will be in the service of the fullest understanding of the complex and reciprocal biobehavioral processes responsible for mental health and mental illness.”

  3. Determinants of Executive Function & Dysfunction • Aim 1: What types of cognitive representations and processes support and enable executive function? • Aim 2: How do neural systems, most notably those of prefrontal cortex (PFC), support and enable executive function? • Aim 3: How can our understanding of executive function be linked across computational, psychological, and neurobiological levels of analysis?

  4. Differing Theoretical Perspectives Three Component Model Active vs. Latent Representations Process Model

  5. 5 Year Goal • “We have therefore set as a major goal of the first five-year period of the center the creation of a short volume or series of articles that outlines the answers we have obtained to the question of the neural and cognitive substrates of executive function. • In particular, we will discuss a) how explanations of executive function can be integrated across levels of analysis and b) the ways in which disparate viewpoints (executive function as a unitary concept vs. executive function composed of distinct subfunctions) can be reconciled.

  6. 5 Year Goal • Not this…… • But this…… EUREKA!!!!!!

  7. 5 Year Plan • Start Date May, 2008 (Feb 1, 2008- Jan 31st,2013 • Year 1 – Getting to know you……better • Year 2 – Initial studies • Year 3 – Targeted experiments • Year 4 - Theoretical Synthesis • Year 5 – Major Output/Renewal

  8. Figure 1 The Organizational Structure of the Center Center Steering Committee Marie T. Banich (Center Director; PI, Administrative Core, Brain Mapping Core, Research Project 1) Yuko Munakata (Center Associate Director; PI, Research Project 5) Randy O'Reilly (PI, Research Project 2, Computational Core) Wendy Heller (PI, Research Project 3) Naomi Friedman (PI, Research Project 4) Administrative Core PI: Banich Co-Is: Munakata, Heller Brain Mapping Core PI: Banich Co-Is: Cordes, Curran, Du, Miller, Tanabe Computational Core PI: O'Reilly Co-Is: Munakata, Colunga, Kim Project 1: CONTROL PROCESSES PI: Banich Co-Is: Curran, Miller, O'Reilly Project 2: LEARNING PROCESSES PI: O'Reilly Co-Is: Colunga, Curran, Heller, Munakata Project 3: EMOTION PI: Heller Co-I: Banch,Friedman, Miller, Miyake, O'Reilly, Sutton Project 4: GENETICS PI: Friedman Co-Is: Hewitt, Haberstick, Miyake, O'Relly, Smolen, Willcutt, Young Project 5: REPRESENTATIONS AND DEVELOPMENT PI: Munakata Co-Is: Banich, Curran, Colunga, Miyake, O'Reilly Center Organization

  9. Center Approach • Examine issues at three levels • Neurobiological • Brain Mapping, Genetics • Psychological • Computational • Utilize as many of these methods as possible within each project • At least two

  10. P2: Learning processes P3: Emotional processes P4: Genetics P1: Control processes Computa-tional Psychological Brain region Neurotrans-mitter Computa-tional Psychological Brain region Neurotrans-mitter Computa-tional Psychological Brain region Neurotrans-mitter Computa-tional Psychological Brain region Neurotrans-mitter Computa-tional Psychological Brain region Neurotrans-mitter P5: Representations & development Cross Project Interactions • Explicitly designed for multi-level interaction

  11. Center Activities • Individual Projects • Weekly Meetings • Annual Conference • External Speaker Series

  12. Other Objectives • Multi-disciplinary training of junior scholars • Public Outreach • Linkage to Clinical Issues

  13. Project 1 Control Processes

  14. Project Specific Aims • Aim 1: To test the validity of our neural model of cognitive control using fMRI, ERPs, and computational modeling as a way to elucidate the organization of prefrontal cortex for executive function. • Aim 2: To investigate the hypothesis that the neural substrates involved in the inhibition or suppression of internal mental representations are distinct from those involved in amplification or maintenance of internal mental representations.

  15. Specific Aim 1 – Organization of Prefrontal Cortex for Control • Model derived from long series of study on the Stroop task • Predictions about relationship between different prefrontal brain regions • Examined using ERP data with source localization via fMRI from the Illinois group • fMRI data collected on a Cue-Stroop Study • Examine model with a new fMRI paradigm • Task switching and inhibition • Anson Whitmer poster

  16. Specific Aim 2 - Control Processes for Mental Representations • Think/No-Think paradigm • Mental equivalent of Go/No-Go Task • Follow up research with ERPs • New Task – “Thought Suppression” • Another paradigm to look at the selection of information in memory • fMRI study • Both will be discussed by Brendan Depue

  17. Cross Project Collaborations • Project 2 • Modifying current model in line with principles examined in this project • Project 3 – • Collaborative effort on source-modeling of Stroop data and integration with emotion • Integration with models of emotion processing • Project 4 • Modeling integrates information from Stroop data on shifting • Project 5 • Examination of issues of inhibition/selection and their neural substrates

  18. Project 2 Learning Processes

  19. Project Specific Aims • 2.1: Factors affecting learning of reps in PFC • 2.1.a: Effects of training params: blocked -> abstract? Tests Rougier et al (2005) model • 2.1.b: Effects of connectivity, learning cascades on emergent organization of PFC reps • 2.2: Factors affecting learning from +/- feedback • 2.2.a: DA drug and mood induction interactions • 2.2.b: Causes of differential feedback resp in kids

  20. Specific Aim 2.1.a • Hypothesis: blocked training -> more abstract representations of task-relevant rules (Rougier et al., 2005) • 90 college age participants • H,S,V dimensions, replicate Nosofsky & Palmeri (1996) -> insufficient learning • Now training with simpler featural stimuli

  21. Specific Aim 2.1.b • Hypothesis: “spiral” hierarchical connectivity = hierarchy of representations (rule complexity) • Results published in Reynolds & O’Reilly, 2009 • Connectivity drives representational differences: higher = “outer loop” higher-order

  22. Specific Aim 2.1.b • Question: rostrocaudal PFC axis best described by rule complexity, abstraction, or both? • Current studies confound these factors; we have a clean manipulation • Prelim results??

  23. Specific Aim 2.1.b • Hypothesis: Can we account for dorsal/ventral division of labor (Atallah et al, 2008) in coherent PFC framework? • First pass published in Pauli, Atallah, & O’Reilly, in press • What/How Instrumental Pavlovian (WHIP) framework (more later)

  24. Specific Aim 2.2 • Hypothesis: Does PVLV model (primary value, learned value; O’Reilly et al 2007) capture fMRI reward signals in anorexics and ctrls? • Collaborative work with Guido Frank, UCHSC • Comparing vs. TD predictions in simple CS-US conditioning paradigm with sugar water

  25. Cross-Project Collaborations.. • Project 1: cascade Stroop model building on Herd, Banich & O’Reilly (2006) • Project 3: planning on OFC/DA studies • Project 4: major work on PBWM application to “3 factor” individual differences data • Project 5: collab on 2.1.a, abstraction and learning, PFC/BG modeling

  26. Project 3: Effects of Emotionon Executive FunctionWendy Heller and Gregory A. Miller with Ph.D. students Laura Crocker, Jenika McDavitt, Christina Murdock-Jordan, Sarah Sass, Jeff Spielberg, Stacie Warren, former Ph.D. student Becky Levin Silton, post-doc Dave Towers, and BIC colleagues Brad Sutton, and Tracey WzalekBeckman Institute Biomedical Imaging Center and Depts. of Psychology and Bioengineering, University of Illinois at Urbana-Champaign

  27. Project 3: Overview • Emotion affects cognition • Strong evidence that (even mild) positive affect enhances some aspects of executive function (EF) • Weak and inconclusive evidence for effects of negative affect on EF • Most work used clinical populations – role of negative affect inferred, not directly manipulated • Multiple confounds • May not even be EF that is affected

  28. Project 3: Overview • Understand effects of both trait & state emotion on EF and associated brain activity • Use integrated fMRI/ERP methods to examine both regional & temporal dynamics • Leverage Miyake & Friedman’s model of separable but related EF domains to specify impact of emotion on EF components • Create a base from which to extend work to depression and anxiety • Contribute to building both psychological & neurobiological models of emotional processes

  29. Project 3: Specific Aim 1 • To examine effects of BOTH POSITIVE AND NEGATIVE affective STATES on the Color-Word Stroop task, which has been extensively modeled computationally (e.g., Herd et al., 2006), has relevant brain regions identified (e.g., Banich, 2009) and is a basis of Cascade-of-Control model • Hypotheses: • positive affect: improved performance, > activity in posterior DLPFC • negative affect: disrupted performance, < activity in posterior DLPFC

  30. Project 3: Specific Aim 1 Undergraduates at U of I undergo an affective context (mood) manipulation (ACM) before performing Color-Word Stroop task fMRI during Stroop Initial work: 42+ participants piloted in various task versions over the course of year 2, using Gur faces originally proposed; failed to find robust or lasting effects of ACM

  31. Project 3: Specific Aim 1 • Current approach: Adapted from Gilboa-Schectman, Revelle, & Gotlib, 2000 • Participants identify a personal emotional memory, which they’ll recall and mentally elaborate on in later session • Next, they generate a set of words that reference the autobiographical memory • Participant-supplied words used with standard ANEW words, all serving as cues interspersed among every few color words in the fMRI Stroop task • Cue words are designed to maintain affective manipulation throughout the task

  32. Project 3: Specific Aim 1 • 48 participants will have 3 ACMs per session • One positive, one negative, one neutral • Half positive first, half negative • Neutral always second • Between subjects design unless 2nd ACM is successful

  33. Project 3: Specific Aim 2 • Examine the impact of TRAIT positive or negative affect on executive functioning • A new and successfully piloted incentive task replaces previous plan for fMRI tasks • Inhibition, Shifting, and Updating represented by a series of tasks inspired by Friedman and Miyake • Use our well-established procedures to recruit people high on positive & low on negative affect & vice versa

  34. Project 3: Specific Aim 2 • Study 2: • Based on conversations with Center participants, we developed the incentive (i) Stroop task to: • Examine behavior/brain relationships between motivational systems that track reward/punishment (OFC, NAc) simultaneously with those associated with top-down control and emotional valence (e.g., DLPFC, per Cascade-of-Control model & Heller & Miller models of brain activity for emotion) • iStroop rationale and details presented this p.m. & see Spielberg et al. poster

  35. Project 3: Specific Aim 2 • Study 2: Each component represented by…a series of tasks inspired by Friedman and Miyake • Extensive task development in consultation with Friedman, Miyake, Banich, O’Reilly, and other Center members • A mix of standardized tests, tests used by Friedman et al., modified research measures, & one new test developed by Stacie Warren & Dave Towers in our lab • Rationale and details of EF subcomponent measures presented this p.m., & see Warren et al., poster

  36. Cross-Project Collaborations Project 1: Using combined ERP/fMRI technology to test Cascade of Control model; integrating Cascade of Control model with models of brain function for emotion to develop predictions regarding effects of emotion on specific EFs Project 2: Planning to incorporate hypothesized effects of positive and negative affect on EF in computational models of attentional control e.g., cascade Stroop model, building on Herd, Banich & O’Reilly (2006); planning to integrate models of OFC/DA mechanisms into analyses of fMRI data from incentive Stroop task Project 4: Applying 3-factor model to study effects of emotion on EFs; collecting genetic material to examine emotional disposition on EF components Project 5: Collaborating on anxiety/depression/selection studies

  37. Project 4 Genetic Mechanisms of Executive Functions

  38. Use computational models of EF tasks to both generate predictions and incorporate results for genetic analyses. Examine the influence of polymorphisms in a large set of DA-related genes on inhibiting, updating, and shifting abilities in a sample of normal young adults. Conduct parallel analyses on data from identical or comparable EF tasks collected as part of three ongoing studies of individuals selected for learning, conduct, and/or attention problems. Project Specific Aims

  39. Specific Aim 1 .73 .74 .40 Inhibiting Updating Shifting .44 .53 .42 .65 .66 .46 .66 .63 .74 Antisac Stop Stroop Keep Letter S2back Number Color Category .81 .72 .82 .58 .56 .79 .56 .60 .45 • Simulate the influence of genetic polymorphisms related to the DA system within a biologically plausible computational model. • Goal: develop more detailed models of how the DA system dynamically regulates 3 EFs:

  40. Specific Aim 1 Hidden Stimulus & Parietal Input Prefrontal Cortex Verbal & Manual Output Ventral Striatum (PVLV) Dorsal Striatum (Matrix & SNr) • Incorporate genetic effects into model Approach: PBWM model of 9 core EF tasks

  41. Specific Aim 1 • Models completed: • Updating (keep track, letter memory, & n-back) • Shifting (one shifting task so far) • Have begun exploring genetic manipulations • In the works: • Inhibiting (Stroop has been modeled previously) • Continue exploring other manipulations • To Do: • Integrate models

  42. Specific Aim 2 Test predictions from the computational models Population: ~800 twins from the Colorado Longitudinal Twin Study (LTS) who completed the 9 core EF tasks at age 17

  43. Specific Aim 2 gene Inhibiting Updating Shifting Antisac Stop Stroop Keep Letter S2back Number Color Category Approach: Use structural equation modeling to examine the genetic influences at the level of latent variables

  44. Specific Aim 2 Current stage: genotyping In the works: waiting for results of models to make a priori predictions. Testing some predictions for n-back with new data

  45. Specific Aim 3 • Similar analyses in selected samples who completed identical or similar EF tasks • Populations: • Community based sample of twins selected for conduct and attention problems • School-based sample selected for ADHD and reading disability • Young adults with and without ADHD • Analyses will be conducted after aims 1 and 2 are completed.

  46. Cross-Project Collaborations Project 1: Shifting models build on cue-Stroop model Project 2: Modeling incorporates 3-factor model into PBWM framework; PBWM framework incorporates 3-factors Project 3: Application of 3-factor model to study of effects of emotion on EFs Project 5: Potential for collaboration on trade-offs in maintenance vs. flexibility

  47. Project 5 Representations supporting executive control and its development during childhood

  48. Project 5 Specific Aims • 5.1 Investigating Relations among Abstract Representations, Active Representations and Executive Control During Early Development • 5.2 Investigating Effects of Manipulating Abstraction and Active Maintenance Abilities During Early Development • 5.3 Investigating Neural Components of Executive Control Representations

  49. Specific Aim 5.1 • Hypothesis: Increasingly active, abstract representations support development of cognitive control. • Children (3-8 year olds) and neural network models: Individual difference approach, testing relations among active and abstract representations and cognitive control, behavioral and pupilometric measures

  50. Specific Aim 5.1 • Found that children reactively retrieve information as needed in service of cognitive control, rather than proactively maintain. Chris Chatham's talk. • Found synergy between flexibly switching from one task to another (requires active task representations) and generalizing behavior to new exemplars (requires abstract representations). Maria Kharitonova’s poster.