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Center Overview. Determinants of Executive Function & Dysfunction. An Interdisciplinary Behavioral Science Center.

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Center overview l.jpg

Center Overview

Determinants of Executive Function & Dysfunction


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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.”


Determinants of executive function dysfunction l.jpg
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?


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Differing Theoretical Perspectives

Three Component Model

Active vs. Latent Representations

Process Model


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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.


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5 Year Goal

  • Not this……

  • But this……

EUREKA!!!!!!


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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


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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


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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


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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


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Center Activities

  • Individual Projects

  • Weekly Meetings

  • Annual Conference

  • External Speaker Series


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Other Objectives

  • Multi-disciplinary training of junior scholars

  • Public Outreach

  • Linkage to Clinical Issues


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Project 1

Control Processes


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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.


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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


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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


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Cross Project Collaborations Representations

  • 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


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Project 2 Representations

Learning Processes


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Project Specific Aims Representations

  • 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


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Specific Aim 2.1.a Representations

  • 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


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Specific Aim 2.1.b Representations

  • 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


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Specific Aim 2.1.b Representations

  • Question: rostrocaudal PFC axis best described by rule complexity, abstraction, or both?

  • Current studies confound these factors; we have a clean manipulation

  • Prelim results??


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Specific Aim 2.1.b Representations

  • 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)


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Specific Aim 2.2 Representations

  • 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


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Cross-Project Collaborations.. Representations

  • 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


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Project 3: Effects of Emotion Representationson 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


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Project 3: Overview Representations

  • 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


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Project 3: Overview Representations

  • 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


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Project 3: Specific Aim 1 Representations

  • 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


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Project 3: Specific Aim 1 Representations

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


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Project 3: Specific Aim 1 Representations

  • 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


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Project 3: Specific Aim 1 Representations

  • 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


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Project 3: Specific Aim 2 Representations

  • 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


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Project 3: Specific Aim 2 Representations

  • 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


Project 3 specific aim 235 l.jpg
Project 3: Specific Aim 2 Representations

  • 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


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Cross-Project Collaborations Representations

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


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Project 4 Representations

Genetic Mechanisms of Executive Functions


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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


Specific aim 1 l.jpg
Specific Aim 1 predictions and incorporate results for genetic analyses.

.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:


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Specific Aim 1 predictions and incorporate results for genetic analyses.

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


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Specific Aim 1 predictions and incorporate results for genetic analyses.

  • 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


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Specific Aim 2 predictions and incorporate results for genetic analyses.

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


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Specific Aim 2 predictions and incorporate results for genetic analyses.

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


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Specific Aim 2 predictions and incorporate results for genetic analyses.

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


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Specific Aim 3 predictions and incorporate results for genetic analyses.

  • 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.


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Cross-Project Collaborations predictions and incorporate results for genetic analyses.

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


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Project 5 predictions and incorporate results for genetic analyses.

Representations supporting executive

control and its development during childhood


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Project 5 Specific Aims predictions and incorporate results for genetic analyses.

  • 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


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Specific Aim 5.1 predictions and incorporate results for genetic analyses.

  • 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


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Specific Aim 5.1 predictions and incorporate results for genetic analyses.

  • 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.


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Specific Aim 5.1 predictions and incorporate results for genetic analyses.

Next steps:

  • Test how transitions in reactive and proactive control relate to executive function more generally.

  • Collaborate on developmental fMRI studies of proactive and reactive control w/Silvia Bunge.

  • Test basis for predictive power of simplest abstraction measures.


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Specific Aim 5.2 predictions and incorporate results for genetic analyses.

  • Manipulate active/abstract reps.

  • Hyp: Children increasingly susceptible to distraction w/ emerging proactive control across development.

  • Hyp: Children develop more abstract reps of task rules w/ blocked vs. interleaved training.

  • Children (5-6 yrs): Manipulate presence of distractors in cog control task, behav & pupilometric measures. Chris Chatham’s talk.


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Specific Aim 5.3 predictions and incorporate results for genetic analyses.

  • Investigate neural components of EF reps.

  • Hypothesis: Inhibition at neural level takes diffuse rather than directed form, with possible exception of global inhibitory signal.

  • Children (6 yrs) and adults: Experimental (including neuropharmacological) & individual diff approaches (including anxiety/depression), behavioral, fMRI, and ERP measures, marker tasks.


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Specific Aim 5.3 predictions and incorporate results for genetic analyses.

  • Found effects of diffuse neural inhibition on selection. Hannah Snyder’s talk.

  • Found role of rIFG (putatively for directed inhibition) in monitoring. Chris Chatham's talk.

  • Found developmental trade-off between selective and global stopping. Katye Blackwell's poster.


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Specific Aim 5.3 predictions and incorporate results for genetic analyses.

Next steps:

  • Extend to clinical populations.

  • More targeted marker tasks.

  • Collaboration on developmental Think/No-Think w/Silvia Bunge.


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Cross-Project Collaborations.. predictions and incorporate results for genetic analyses.

  • Project 1: Inhibition/monitoring studies, Inhibition/selection studies

  • Project 2: Abstraction/learning studies

  • Project 3: Anxiety/depression/selection studies

  • Project 4: Potential for collaboration on trade-offs between EF components


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Administrative Core predictions and incorporate results for genetic analyses.

Determinants of Executive Function & Dysfunction


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Coordinate Center Activities predictions and incorporate results for genetic analyses.

  • Weekly Meetings

    • Remote attendance via Skype

  • Center website/wiki

  • Annual Conference

    • Organization of Prefrontal Cortex for Executive Function

    • Genetic and Experiential Influences on Executive Function


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Outside Speakers predictions and incorporate results for genetic analyses.

  • Julie Fiez

  • Joy Hirsch

  • Stephen Monsell

  • Morris Moscovitch

  • Tomas Paus

  • Patricia Reuter-Lorenz

  • Sharon Thompson-Shill

  • Masamichi Sakagami

  • Anthony Wagner


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Other Objectives predictions and incorporate results for genetic analyses.

  • Public Outreach

    • Open House

    • Targeted presentations

      • Café Scientifique - Denver

      • Beet Street – Fort Collins

  • Linkage to Clinical Issues & Investigations


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DEFD Computing Infrastructure predictions and incorporate results for genetic analyses.

A Shared High Performance Computing (HPC) Cluster for the Computational and Brain Mapping Cores


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What is an HPC Cluster? predictions and incorporate results for genetic analyses.

  • “A closely coupled, scalable collection of interconnected computer systems, sharing common hardware and software infrastructure, providing a parallel set of resources to services or applications for improved performance” – Robert W. Lucke


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It’s not an iMac predictions and incorporate results for genetic analyses.


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Realizing a cluster predictions and incorporate results for genetic analyses.

1. Requirements Capture

2. Plant Preparation

3. Technology Benchmarking

4. Purchasing

5. Receiving

6. Configuration


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Core i7 Performance predictions and incorporate results for genetic analyses.

http://www.intel.com/performance/server/xeon/hpc.htm


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DEFD Cluster Hardware predictions and incorporate results for genetic analyses.

  • 26 computational nodes + RAID data storage array

  • 208 Core i7 processing cores, 624 GB RAM

  • 20 TB high-speed, fault-tolerant storage

  • InfiniBand 20 Gb/s high-speed interconnect

  • Dual 1 Gb/s high-speed Ethernet networks

  • UPS protection of cluster and storage

  • 20 TB offsite storage for backup and archiving


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DEFD Cluster Software predictions and incorporate results for genetic analyses.

  • GridEngine-6.2

  • Torque/Maui-3.2.6

  • OpenMPI-1.3.2

  • MVAPICH-2.1.2

  • Python, Perl, Ruby

  • MATLAB-R2009a with 64 DCS workers

  • R-2.8.1, |stat

  • Emergent-5.0.1

  • FSL-4.1.4

  • SPM-5, SPM-8

  • MRICron, DCMTK

  • EEGLAB-7.1.3/4

  • FreeSurfer-4.5.0

  • GIFT, EEGIFT, FIT


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Computational Modeling in Computational Core predictions and incorporate results for genetic analyses.

R. O’Reilly


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Models predictions and incorporate results for genetic analyses.

  • PVLV = Primary Value, Learned Value = phasic dopamine from VTA, SNc under control of CNA, NAc..

  • PBWM = Prefrontal Cortex Basal-ganglia Working Memory = PVLV + BG + PFC -> BG provides dynamic gating over PFC active maintenance, can learn complex WM tasks

  • WHIP = What-How Instrumental/Pavlovian = PBWM model with multiple PFC areas = large-scale organization of PFC


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PBWM predictions and incorporate results for genetic analyses.


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WHIP predictions and incorporate results for genetic analyses.


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Use By Projects predictions and incorporate results for genetic analyses.

  • Proj 1: PBWM model of Cue Stroop & Cascade model

  • Proj 2: PBWM & WHIP models of pfc org, learning

  • Proj 3: PVLV and WHIP OFC model inform tasks

  • Proj 4: Extensive PBWM models for genetic factor exploration

  • Proj 5: PBWM mechanisms for selection, proactive vs. reactive, endogenous vs. exogenous


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Modeling Training predictions and incorporate results for genetic analyses.

  • Spring 2009 meetings on computational modeling

  • Computational Cognitive Neuroscience course

  • Hands on learning by trainees


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Brain Mapping Core predictions and incorporate results for genetic analyses.

Determinants of Executive Function & Dysfunction


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Data Collection Facilities predictions and incorporate results for genetic analyses.

  • Colorado

    • GE 3 T (Dept. of Psychiatry – UC, Denver), Yiping Du, physicist

    • High density array recordings (Boulder)

  • Illinois

    • Siemens Trio (Beckman Institute), Brad Sutton, physicist

    • ERP with source modeling


Data analysis l.jpg
Data Analysis predictions and incorporate results for genetic analyses.

  • Computational Cluster

  • 2-person programming staff

    • Supports all data analysis at Colorado

    • Is allowing for expansion of tools and techniques

      • On-going: SPM (PPI) Freesurfer

      • Planned: Connectivity, genetics

    • Training and resources for new users


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Experimental Design and Analysis predictions and incorporate results for genetic analyses.

  • fMRI Working Group

    • Presentation and Review of Study Design

      • Iterative Process

    • Presentation and Review of Results

  • Data collection

    • Coordinated with help of Center Staff


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Progress to Date predictions and incorporate results for genetic analyses.

  • Familiarization of all center personnel with fMRI methods

    • Fall 2009

  • Transfer of data processing stream to new cluster with parallelization

  • Increased documentation

  • Creation of new custom-made tools

  • Addition of new tools into data processing stream

  • Linkage of fMRI and ERPs – Source modeling


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Organization of Prefrontal Cortex for Executive Function predictions and incorporate results for genetic analyses.

Cascade-of-Control Model


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A cascade of control model predictions and incorporate results for genetic analyses.

  • Series of points at which control can be exerted

  • If they aren’t exerted at the first way station, it requires extra control at the next

  • Based on our prior studies


Our cascade of control model l.jpg

word reading predictions and incorporate results for genetic analyses.

w

2

Color identification

c

2. Bias to task-relevant representations

Our “cascade-of-control” model

Anterior Cingulate Cortex

Dorsolateral Prefrontal Cortex

BLUE

1. Bias to task-relevant processes

You’re not doing well. Increase control!!

3. Select information on which to base the response

1

3

“blue”

“blue”

4

4. Evaluate the response

“I feel that “green” was incorrect!”

Banich, Banich et al.., 2000a, 2000b, 2007 Milham et al. 2001, 2002, Compton et al., 2003; Milham, Banich & Barad, 2003; Milham, Banich, Claus & Cohen, 2003; Milham & Banich, 2005; Liu et al. 2004, 2006


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Methods: predictions and incorporate results for genetic analyses.n = 16

Cue: 1.5 secs

“name the

word”

“name the

ink color”

OR

50%

50%

Blank: 10.5 secs

  • In this task, participants were either cued to “name the ink color” or “name the word” of an upcoming stroop stimuli

Stimulus: 1.5 secs

- Incongruent vs. Neutral

BLUE

BLUE

BLUE

CAR

OR

OR

Blank: 10.5 secs

25%

25%

25%

25%

NEXT TRIAL


Predictions l.jpg
Predictions predictions and incorporate results for genetic analyses.

  • If pDLPFC activates appropriate sensory areas to facilitate future processing of task relevant information, then it should be heavily activated in time period before the stimulus (the cue period)

  • In contrast, if mid-DLPFC selects specific task relevant information (e.g. ink color red), then should be more activated during the stimulus.

  • If pACC is where the “buck stops” it should be the most activated during the stimulus for the most difficult trial types (Incongruent trials) because the conflict created in such trials is less likely to be resolved earlier in the cascade


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Reasonable Time Course predictions and incorporate results for genetic analyses.


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Results – Color trials (I&N) predictions and incorporate results for genetic analyses.

  • Red: Cue + Precue> Stimulus

  • Blue: Stimulus > Cue + Precue


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word reading predictions and incorporate results for genetic analyses.

w

2

Color identification

c

2a. Biasing of more specific information in WM

Revised“cascade-of-control” model

Anterior Cingulate Cortex

Dorsolateral Prefrontal Cortex

BLUE

1a. Biasing of relevant sensory information (abstract)

1a. Bias channel on which to base the response

You’re not doing well. Increase control!!

1

2b. Select information channel on which to base the response

1

“blue”

2

“blue”

3

4. Evaluate the response

“I feel that “green” was incorrect!”

Banich, Banich et al.., 2000a, 2000b, 2007 Milham et al. 2001, 2002, Compton et al., 2003; Milham, Banich & Barad, 2003; Milham, Banich, Claus & Cohen, 2003; Milham & Banich, 2005; Liu et al. 2004, 2006


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Organization of Prefrontal Cortex for Executive Function: predictions and incorporate results for genetic analyses.The time course of activity in dorsolateral prefrontal cortex and anterior cingulate cortex during top-down attentional control

Wendy Heller1, Rebecca Levin Silton1, David N. Towers1, Anna S. Engels1, Jeffrey M. Spielberg1, J.Christopher Edgar1, Sarah M. Sass1, Jennifer L. Stewart1, Bradley P. Sutton1, Marie T. Banich2, Gregory A. Miller1

1Department of Psychology and Beckman Institute Biomedical Imaging Center at the University of Illinois at Urbana-Champaign

2Institute of Cognitive Science, and Department of Psychology and Neuroscience, University of Colorado, Boulder, CO


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Cascade-of-Control Model predictions and incorporate results for genetic analyses.

W = word-processing area For word “blue” presented in green ink

C = color identification Cascade is per arrows

WB = representation of word “blue” RB = representation of response associated with “blue”

CG = representation of color “green” RG = representation of response associated with “green”

Banich, 2009, Current Directions in Psychological Science

Banich, Mackiewicz, Depue, Whitmer, Miller, & Heller (2009). Neuroscience and Biobehavioral Reviews


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Timing is key in the Cascade-of-Control Model predictions and incorporate results for genetic analyses.

LDLPFC activity should precede dACC activity

Late, but not early, dACC activity should distinguish between incongruent and congruent conditions

We combined the spatial resolution of fMRI with the temporal resolution of EEG/ERP methodologies to evaluate time course of activity in these regions using classic Color-Word Stroop task


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LDLPFC predictions and incorporate results for genetic analyses.

dACC

Dense-array scalp ERPs

+

Whole-head sMRI

+

Whole-head fMRI

N=89, Silton, Heller, Towers, Engels, Spielberg, Edgar, Sass, Stewart, Sutton, Banich, & Miller, in press, NeuroImage


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LDLPFC predictions and incorporate results for genetic analyses.

dACC

Dense-array scalp ERPs

+

Whole-head sMRI

+

Whole-head fMRI

ERP Source Model

N=89, Silton, Heller, Towers, Engels, Spielberg, Edgar, Sass, Stewart, Sutton, Banich, & Miller, in press, NeuroImage


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LDLPFC predictions and incorporate results for genetic analyses.

DLPFC

dACC

Dense-array scalp ERPs

+

Whole-head sMRI

+

Whole-head fMRI

ERP Source Model

ERP Source

Waveforms

n.s.

n.s.

*

*

r=.22, p=.02

N=89, Silton et al., in press, NeuroImage


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General Mediation Model predictions and incorporate results for genetic analyses.

Mediator

(M)

a

b

Independent

Variable

(X)

Dependent

Variable

(Y)

c'

Independent

Variable

(X)

Dependent

Variable

(Y)

c


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Testing the Cascade-of-Control Model predictions and incorporate results for genetic analyses.

Late dACC

(a path)

β =.34*

(b path)

β = 1.86*

LDLPFC

Stroop

Interference

β = -1.41 (c path)

β = -2.00* (c' path)

Regression Model Summary:

r 2 = .09 ; p = .02


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Testing Alternative Model One predictions and incorporate results for genetic analyses.

LDLPFC

(b path)

β = .41

(a path)

β =.05

“Early”

dACC

Stroop

Interference

β = -.69 (c path)

β = -.71 (c' path)

Regression Model Summary:

r 2 = .01


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Testing Alternative Model Two predictions and incorporate results for genetic analyses.

“Late”

LDLPFC

520-680

msecs

(a path)

β =.13

(b path)

β = -1.24

“Early” dACC

300-440

msecs

Stroop

Interference

β = -.75 (c path)

β = -.60 (c' path)

Regression Model Summary:

r 2 = .03


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Does LDLPFC really matter? predictions and incorporate results for genetic analyses.

  • LDLPFC added significant variance above and beyond dACC

  • LDLPFC and dACC interact rather than operating additively


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LDLPFC x dACC interaction predictions and incorporate results for genetic analyses.

  • For Stroop interference:

    At low levels of LDLPFC activity, more active dACC was associated with slower performance

  • For Stroop errors:

    At low levels of LDLPFC activity, more active dACC associated with fewer errors

n.s.

n.s.

Take home: dACC activity only relevant to

performance if LDLPFC activity low. Then

more dACC activity produces more accurate,

though slower, performance


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Supporting Cascade-of-Control Model predictions… predictions and incorporate results for genetic analyses.

Only late dACC differentiated incongruent and congruent conditions

Only incongruent late dACC amplitude was positively correlated with Stroop Interference

dACC activity did not impact performance provided that LDLPFC was sufficiently active. If not, dACC picked up the slack.


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Translational Implications: Regional & temporal dynamics in depression and anxiety

Normally, DLPFC acts through dACC within-trial to improve performance

A dynamic network of attentional control…

N=100, Silton et al., under review


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Translational Implications: Regional & temporal dynamics in depression and anxiety

Normally, DLPFC acts through dACC within-trial to improve performance

In Depression

DLPFC and dACC become uncoupled

DLPFC bypasses dACC to improve performance

A dynamic network of attentional control…

disrupted in psychopathology

N=100, Silton et al., under review


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Translational Implications: Regional & temporal dynamics in depression and anxiety

Normally, DLPFC acts through dACC within-trial to improve performance

In Depression

DLPFC and dACC become uncoupled

DLPFC bypasses dACC to improve performance

In Anxiety

DLPFC and dACC still coupled

dACC linked to more interference

A dynamic network of attentional control…

disrupted differentially in depression and anxiety

N=100, Silton et al., under review-b


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Future directions depression and anxiety

  • Work to clarify timing of subregions within LDLPFC and dACC as delineated by the Cascade of Control model

  • Extend findings of regional and temporal brain activity in depression and anxiety to performance on a broader range of executive function tasks, including domains of shifting, updating, and inhibiting


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Organization of Prefrontal Cortex for Executive Function: What/How/Abstraction Model (WHAM)

O’Reilly, Reynolds, Pauli, Hazy, Herd, Munakata, et al.


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What/How/Abstraction What/How/Abstraction Model (WHAM)(O’Reilly, TINS, submitted; How = Goodale & Milner, 1992)


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What/How Anatomy What/How/Abstraction Model (WHAM)(from Romanski, 2004)


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What/How in Wager & Smith ‘03 What/How/Abstraction Model (WHAM)

VLPFC = Object/Verbal (“What”)

DLPFC = Manipulations

(Effectively just a relabeling of Petrides: simpler WM vs. “processing”)


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WHIP Model: What/How Instrumental / Pavlovian What/How/Abstraction Model (WHAM)

Pauli, Atallah & O’Reilly, in press; Accounts for increasing range of behavioral neuroscience animal learning data


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Valence System: What/How/Abstraction Model (WHAM)PVLV + OFC/BLA

CNA = Global CS -> DA BLA = Specific CS -> VS & OFC


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Behavioral Goals What/How/Abstraction Model (WHAM)


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Selective Attention What/How/Abstraction Model (WHAM)


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Motor What/How/Abstraction Model (WHAM)


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The WHIP model What/How/Abstraction Model (WHAM)


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The WHIP Model (v1) What/How/Abstraction Model (WHAM)


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Abstraction What/How/Abstraction Model (WHAM)

  • Lots of consistent data (Badre & Wagner; Christoff et al), but also confounds with rule complexity (currently trying to disambiguate)

  • Development of abstraction & PFC are linked: Kharitonova, Munakata et al.

  • Increasing active maintenance = more abstraction (Rougier et al, 2005; testing..)


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Banich et al Cascade What/How/Abstraction Model (WHAM)

  • ~Same representations and localization..

  • Different dynamics, and some predictions..


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