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PVLV Model of Phasic Dopamine Learning. R. O’Reilly T. Hazy, J. Reynolds, G. Frank. Temporal Difference Dopamine Reward Model. Brain Dopamine Reward Response = Reward Occurred – Reward Predicted. Schultz, Dayan & Montague 1997. DA ↑. Unexpected Reward. DA ↑. DA ↔. Expected Reward.

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Pvlv model of phasic dopamine learning l.jpg

PVLV Model ofPhasic Dopamine Learning

R. O’Reilly

T. Hazy, J. Reynolds, G. Frank


Temporal difference dopamine reward model l.jpg
Temporal Difference Dopamine Reward Model

Brain Dopamine Reward Response = Reward Occurred – Reward Predicted

Schultz, Dayan & Montague 1997

DA ↑

Unexpected Reward

DA ↑

DA↔

Expected Reward

DA ↑

DA ↓

Unexpected No-Reward


Phasic dopamine firing l.jpg
Phasic Dopamine Firing

  • Transition from Reward/US to CS onset (Schultz et al)

  • TD model says why, but not how (neural mechanisms)

  • And TD not a good fit for growing set of data..


Pvlv model l.jpg
PVLV Model

  • Central Nucleus of the Amygdala (CNA) = CS -> DA

  • Ventral Striatum (NAc, patch) = US -> no DA


Pvlv model5 l.jpg
PVLV Model

  • PV = Cancelling US burst

  • LV = Driving CS burst

  • No chaining: just simpler delta-rule/Rescorla Wagner


Pvlv predictions l.jpg
PVLV Predictions

  • CS – driven DA is dissociable from US – driven:

    • Conditioned orienting & autoshaping to a CS = CNA, but not to US

    • CS DA is not subject to blocking effect

    • CS DA cannot drive further CS learning: 1st order (CNA) is dissociable from 2nd order (BLA), and no 3rd order

  • Time is just another input; no temporal chaining as in TD



Slide8 l.jpg

ROI Activation Analysis after Full Brain Statistical Analysis

Guido K.W. Frank, M.D.

The Children’s Hospital,

University of Colorado Denver


Slide9 l.jpg

Receiving Sucrose (US) Analysis

Unexpected

AN greater than CW

p=0.005

5vox

uncorr.

SVC p<0.02, FEW, FDR, Cluster


Slide10 l.jpg

Receiving Sucrose (US) Analysis

Unexpected

 Time Activity Curves

Anorexia

Nervosa

VTA

CNA-L

CNA-R

AVS-L

AVS-R

VTA

CNA-L

CNA-R

AVS-L

AVS-R

Control

Women


Influence of emotion reward punishment on ef incentive stroop task l.jpg

Influence of Emotion/Reward & Punishment on EF Analysis:Incentive Stroop Task

Jeff Spielberg, with Wendy Heller, Gregory A. Miller, Laura Crocker, Stacie Warren, Christina Murdock-Jordan, post-doc Dave Towers, Brad Sutton & Tracey Wzalek at BIC, and Center colleagues Marie Banich & Randy O’Reilly

Department of Psychology and Beckman Biomedical Imaging Center, University of Illinois at Urbana-Champaign


Influence of emotion reward punishment on ef l.jpg
Influence of Emotion/Reward & Punishment on EF Analysis

  • Multiple ways to conceptualize emotional processes psychologically

  • Circumplex models, valence, bipolarity

    • Valence/arousal, positive affect/negative affect

    • Valence-based dichotomies prevalent (pos. vs. neg. emotionality/temperament/extraversion)

  • Fundamental motive systems that underlie behavior

    • Appetitive/approach/behavioral activation/incentive motivation

    • Defensive/withdrawal/behavioral inhibition/aversive motivation


Influence of emotion reward punishment on ef13 l.jpg
Influence of Emotion/Reward & Punishment on EF Analysis

  • No comprehensive model(s) of brain function or structure integrating roles or mechanisms of brain regions implicated in emotion:

    • Left & Right DLPFC: positive/negative affect

    • dACC: anxiety

    • Amygdala: fear

    • Nucleus accumbens: reward/punishment

    • Posterior ACC: emotional autobiographical memory

    • Orbital frontal cortex: reward punishment

  • Distinct lines of research, most not integrated with each other


Influence of emotion reward punishment on ef14 l.jpg
Influence of Emotion/Reward & Punishment on EF Analysis

  • Lateralization a predominant feature of models of PFC organization for emotion

  • Divided according to superior/inferior lines

    • Davidson motivation model, also Harmon-Jones, Coan, & Allen, others

      • Approach/withdrawal mapping onto left vs. right DLPFC

    • Heller, Miller, Banich, and others’ valence/arousal model

      • Positive/negative valence mapping onto left vs. right DLPFC

      • Adds emotional arousal & right parietal activity


Slide15 l.jpg

Prefrontal Lateralization for Emotional Processes Analysis

Approach

Motivation

Withdrawal

Motivation

L R

Positive Emotion

Negative Emotion


Slide16 l.jpg

Influence of Emotion/Reward & Punishment on EF Analysis

Positive valence

Approach Motivation

Attachment

Trait Anger

Anger Out

Anxious Apprehension

Anxious Arousal

Anhedonic Depression

Distinguishable, lateralized prefrontal areas sensitive to…

L

R

L

R

L

R

(From Engels et al., 2007; Herrington et al., 2005; Herrington et al., 2009; Spielberg et al., 2008; Stewart et al., 2008;

all regions depicted replicated in at least two studies)


Slide17 l.jpg

Patterns of lateralization for emotion tend to be reversed for orbital frontal cortex (OFC)

Positive/negative valence mapping onto right vs. left OFC

Note LDLPFC spot!

Influence of Emotion/Reward & Punishment on EF

deAraujo, Rolls, Kringelbach, McGlone, & Phillips, 2003


Influence of emotion reward punishment on ef18 l.jpg
Influence of Emotion/Reward & Punishment on EF for orbital frontal cortex (OFC)

  • Incentive Stroop Task:

    • Engages motivational systems, emotional systems, and executive functions simultaneously

    • TASK

      • Press a button ASAP when a word appears

    • AFFECTIVE CONTEXT COMPONENT OF THE TASK

      • IGNORE the meaning of the word, which can be positive, negative, or neutral

    • Design allows us to examine the effect of the emotional content of the irrelevant information (affective context) on the ability to ignore that information


Influence of emotion reward punishment on ef19 l.jpg
Influence of Emotion/Reward & Punishment on EF for orbital frontal cortex (OFC)

  • MOTIVATIONAL COMPONENT OF THE TASK

    • Before each word is shown, participants see a cue which tells them whether they will win and/or lose money depending on how fast they push the button

    • If they push the button fast enough they get the positive outcome on that trial (i.e., win money or avoid losing money)

    • If they don’t push the button fast enough they get the negative outcome on that trial (i.e., lose money or miss winning money)

    • On some trials, they neither win nor lose money

  • Allows us to examine the effect of anticipating rewards and punishments on the ability to ignore irrelevant information


Slide20 l.jpg

For dollar sign on left: for orbital frontal cortex (OFC)

if it’s green, can win money if push button fast enough

if it’s grey, can’t win money on that trial

For dollar sign on right:

if it’s red, can lose money if don’t push button fast enough

if it’s grey, can’t lose money on that trial


Slide21 l.jpg

Fast enough = win money for orbital frontal cortex (OFC)

Too slow = do not win money

Fast enough = win money

Too slow = lose money

Fast enough = do not lose money

Too slow = lose money

Do not win or lose money

regardless if they are fast or slow


Incentive stroop task l.jpg
Incentive Stroop Task for orbital frontal cortex (OFC)

Cue

ISI

Emotional word

ISI

Feedback

Time within trial


Influence of emotion reward punishment on ef28 l.jpg
Influence of Emotion/Reward & Punishment on EF for orbital frontal cortex (OFC)

  • Recruiting 3 groups of subjects according to PANAS scores

    • High positive, low negative affect

    • High negative, low positive affect

    • Low negative, low positive affect

  • Participants are run in counterbalanced EEG/fMRI sessions

  • SCIDs & assessment of EF components via neuropsychological tests


Influence of emotion reward punishment on ef29 l.jpg
Influence of Emotion/Reward & Punishment on EF for orbital frontal cortex (OFC)

  • Preliminary behavioral findings:

    • Emotional content of the irrelevant information affects ability to ignore that information

      • Pleasant or unpleasant words elicit slower RTs than do neutral words

      • Emotional words thus harder to ignore

    • Motivational context also affects the ability to ignore irrelevant information

      • RT faster on trials in which reward or punishment is possible

      • Thus, possible rewards and punishments make it easier to ignore irrelevant information

    • Findings indicate effects of both affective context and motivation on executive function (as measured by RT)


Influence of emotion reward punishment on ef30 l.jpg
Influence of Emotion/Reward & Punishment on EF for orbital frontal cortex (OFC)

  • Activation in left DLPFC (yellow) when viewing cues signaling the potential for reward (associated with faster RTs)

  • Activation in right OFC (green) when receiving rewards (associated with faster RT)

  • Activation in left OFC (red) when receiving punishments (also associated with faster RT)


Slide31 l.jpg

Influence of Emotion/Reward & Punishment on EF for orbital frontal cortex (OFC)

  • Preliminary results thus:

    • Confirm effects of both affective and motivational contexts on executive function

    • Replicate opposing patterns of lateralization for DLPFC and OFC

    • Allow us to examine timing of regional activity, connectivity, relationships of regional and temporal dynamics to emotional disposition

    • Can be extended to examine dysfunctional relationships in depression & anxiety


Effects of anxiety on selection among competing options l.jpg
Effects of Anxiety on Selection Among Competing Options for orbital frontal cortex (OFC)

Hannah R. Snyder & Yuko Munakata (Project 5) in collaboration with:

Marie T. Banich (Project 1)

Tim Curran & Erika Nyhus (Imaging Core)

With consultation from Project 3


Anxiety and uncertainty l.jpg
Anxiety and Uncertainty for orbital frontal cortex (OFC)

  • Anxious apprehension (worry) is linked to intolerance of uncertainty(e.g. Ladouceur, Talbot & Dugas, 1997), decision-making problems, and indecisiveness(e.g. Sachdev & Malhi, 2005).

  • Prominent symptoms of anxiety disorders including GAD and OCD.

  • Why?

  • Approach this question using our framework for understanding one aspect of EF: selection among competing options.


Selection among competing options l.jpg
Selection Among Competing Options for orbital frontal cortex (OFC)

  • We constantly face the need to choose one option from among multiple valid choices.

    • e.g. grocery shopping, selecting a retirement plan, or choosing a word to express a thought.

  • Selecting between multiple options is time-consuming and effortful (e.g. Iyenger & Lepper, 2000; Sethi-Iyenger et al., 2004; Snyder & Munakata, 2008).

  • Left ventrolateral prefrontal cortex (VLPFC) is involved in selection (e.g. Thompson-Schill et al., 1997,1998; Schnur et al., 2009).

  • Illinois center colleagues Engels, Heller, & Miller have shown that left VLPFC is involved in anxious apprehension.

  • What specific mechanisms might support selection, and how might they be affected by anxiety?


Selection among competing options35 l.jpg
Selection Among Competing Options for orbital frontal cortex (OFC)

  • Test neural network predictions about selection using a well-controlled language-production task: verb generation.


Neural network model l.jpg

Pyramidal for orbital frontal cortex (OFC)

Cell

Pyramidal

Cell

-

+

-

+

GABAergic

Interneurons

Neural Network Model

  • Demonstrates that competitive, inhibitory dynamics among neurons in prefrontal cortical networks support selection among competing alternatives.

    • Amplify activity of most active representation and suppress activity of competing representations, via inhibitory, GABAergic interneurons.


Neural network model37 l.jpg
Neural Network Model for orbital frontal cortex (OFC)

  • Suggest that prefrontal GABA function plays key role in selection and breakdown of this process.

  • Makes sense of findings which were previously disconnected from each other, linking anxiety to:

    • Reduced GABA(e.g. Kalueff & Nutt., 2007)

    • VLPFC dysfunction(e.g. Engles et al., 2007).


Competitive inhibition selection l.jpg
Competitive Inhibition & Selection for orbital frontal cortex (OFC)

  • Neural network predictions:

    • Anxiety (reduced neural inhibition) impairs selection and associated VLPFC activity, even in a simple, non-affective language-production task.

    • The GABA agonist midazolam (increased neural inhibition) improves selection.

    • Retrieval from semantic memory is unaffected.

  • These predictions were supported in 3 studies.


Anxiety decreased inhibition impairs selection l.jpg
Anxiety: for orbital frontal cortex (OFC)Decreased Inhibition Impairs Selection

Network Predictions

Participants (RTs)

  • Reduced competitive inhibition in the VLPFC layer impairs selection.

  • High anxious apprehension participants have impaired selection.

  • No effect on retrieval.


Slide40 l.jpg

Anxiety: Decreased Inhibition Impairs VLPFC Function During Selection

  • Anxious apprehension correlates negatively with VLPFC activity during selection (when retrieval demands are low).

  • No correlation during retrieval.

VLPFC ROI


Midazolam increased inhibition improves selection l.jpg
Midazolam: SelectionIncreased Inhibition Improves Selection

Network Predictions

Participants (RTs)

  • Increased competitive inhibition in the VLPFC layer improves selection when retrieval demands are low.

  • Midazolam improves selection when retrieval demands are low.

  • No effect on retrieval.


Conclusions l.jpg
Conclusions Selection

  • Neural network model suggests that competitive inhibitory dynamics in prefrontal networks are critical for selection.

  • As predicted by model, participants high in anxious apprehension (linked to reduced GABAergic function) show impaired selection but not retrieval.

  • Consistent with clinical evidence for decision-making problems and intolerance of uncertainty in anxiety disorders.

  • Participants high in anxious apprehension show reduced left VLPFC recruitment during selection.

  • Could represent failure to activate inhibitory interneurons.


Conclusions cont l.jpg
Conclusions (cont.) Selection

  • As predicted by model, midazolam (GABA agonist) improves selection when retrieval demands are low.

  • Suggests GABA agonists may be beneficial in treating cognitive, in addition to affective, symptoms of anxiety disorders.


Ongoing and future directions l.jpg
Ongoing and Future Directions Selection

  • Study with selected high and low anxiety participants across multiple selection tasks.

  • Comparing underdetermined to prepotent competition (behavioral and fMRI studies).

  • Effects of depression on controlled retrieval.


Thanks l.jpg
Thanks! Selection

  • Professional research assistants: Paula Villar, Kirsten Orcutt, and Luka Ruzic

  • Undergraduate honors thesis students: Natalie Hutchison and Teesa Dutta

  • Clinical collaborators: Rosi Kaiser and Mark Whisman

  • All DEFD members for helpful input.


Major component processes involved in executive function l.jpg

Major Component Processes Involved in Executive Function Selection

Friedman, Hewitt, Willcutt, Young, Smolen, Miyake, O’Reilly, Hazy, Herd, Brant, Chatham


Three components of efs l.jpg
Three Components of EFs Selection

  • Inhibition

    • Stopping prepotent (dominant or automatic) responses (e.g., stop-signal)

  • Updating

    • Monitoring and rapid addition/deletion of the contents of working memory (e.g., n-back)

  • Shifting

    • Switching flexibly between tasks or mental sets (e.g., number-letter)


Unity and diversity l.jpg
Unity and Diversity Selection

.59

Plus-Minus

.57

Shifting

Number-Letter

.46

Local-Global

.56

.46

Keep Track

.45

Updating

.42

Tone Monitoring

.63

Letter Memory

.63

.40

Stroop

.33

Inhibition

Stop Signal

.57

Antisaccade

Miyake et al. (2000), Cognitive Psychology


Unity and diversity of efs l.jpg
Unity and Diversity of EFs Selection

Unity

Diversity

Common EF

=

+

UpdatingAbility

Updating-Specific

=

+

ShiftingAbility

Shifting-Specific

=

+

InhibitionAbility

Inhibition-Specific


Nested factor model l.jpg

Common Selection

EF

Updating specific

Shifting specific

Keep

Letter

S2ba

Num

Col

Cat

Anti

Stop

Stroop

Nested Factor Model

.54

.53

.22

.49

.46

.58

.46

.58

.43

.41

.44

.37

.47

.42

.46


Twin study of efs l.jpg
Twin Study of EFs Selection

Colorado Longitudinal Twin Study (LTS)

159 MZ (identical twin) pairs & 134 DZ (fraternal twin) pairs

9 EF tasks to construct latent variables

Compare MZ and DZ twin data to estimate:

A: Additive genetic (heritability)

C: Shared environment

E: Nonshared environment


Genetic unity and diversity l.jpg
Genetic Unity and Diversity Selection

A

A

A

C

C

C

E

E

E

Keep

Letter

S2ba

Num

Col

Cat

98%

0%

2%

100%

0%

0%

76%

0%

24%

Common

EF

Updating specific

Shifting specific

Anti

Stop

Stroop

Friedman et al. (2008), Journal of Experimental Psychology: General


Translational implications l.jpg
Translational Implications Selection

Components show different relations to a range of behavioral and psychological problems:

Depression

Behavioral disinhibition

Attention problems

Early (toddler-age) self-restraint

Sleep problems

More precision about EF profiles


Biological basis of unity and diversity l.jpg
Biological Basis of Unity and Diversity Selection

Emerges from involvement of multiple brain areas

Different brain areas suited to different operations

PFC for active maintenance

Basal ganglia for updating PFC

Different influences of genes in these areas

COMT in PFC (Val158Met in COMT gene)

D2 receptors in striatum (C957T in DRD2 gene)


Slide55 l.jpg

Hidden Selection

Stimulus & Parietal Input

Prefrontal Cortex

Verbal & Manual Output

Ventral Striatum (PVLV)

Dorsal Striatum (Matrix & SNr)

Example Model: N-Back

Inputs: serial order & item information. Outputs: verbal & manual output

Leabra framework

(O’Reilly, 2001)

PBWM architecture

(Hazy, Frank & O’Reilly, 2006)


Slide56 l.jpg

Striatal Matrix Selection

Decides when to maintain info in PFC; trained with RL on predicted reward (PVLV)

Prefrontal

Maintains informationwith intrinsic & recurrent maintenance currents

Example Model: N-Back


Modeling genetic influences l.jpg
Modeling Genetic Influences Selection

  • Use a number of polymorphisms known to affect DA.

  • e.g.

    • COMT val/met → affects levels of tonic DA in PFC

    • DRD2 TAQ1A SNP → affects density of D2 receptors in striatum

  • Simulate those effects within model

57


Slide58 l.jpg

Example Manipulation: COMT Selection

  • COMT: val/met polymorphism

  • COMT removes DA in PFC

  • Met/met have higher tonic DA vs. relatively low (val/val) or middle (val/met) levels

  • Met/met individuals perform better on a range of cognitive tasks (Savitz et al., 2006)


Slide59 l.jpg

Modeling COMT effects Selection

  • Met variant

    • increased DA in PFC

    • excites active neurons, inhibits less active

    • enhances recurrent NMDA channels effects

    • Thought to increase signal-to-noise ratio in PFC

  • Modeled as increased gain of PFC neurons





Gain curves for pfc neurons l.jpg
Gain curves for PFC neurons Selection

01/05/10


Slide64 l.jpg

PFC Gain Affects Performance Selection

  • Gain manipulations replicate observed inverse U-shaped curve for DA effects

    • COMT polymorphisms plus amphetamine (e.g., Mattay et al., 2003)

01/05/10


Slide65 l.jpg

Goal maintenance Selection

(PFC)

Specificity of Gating

(BG)

Slipperiness of Reps

(PFC)

Keep

Letter

S2ba

Num

Col

Cat

Models Test the Simple Story

Common

EF

Updating specific

Shifting specific

Anti

Stop

Stroop

Modeling can reveal nonlinear effects, interactions between systems, and divisions of labor over learning


Slide66 l.jpg

Planned Work Selection

  • Model the rest of the tasks:

    • Inhibition: Stroop, antisaccade, stop signal

    • Updating: Keep Track, n-Back, Letter Memory

    • Shifting: color-shape, letter-number, vowel-cons

  • Unify models

  • Predict (and explain) effects of specific genes on components

    • Gene effects on brain measures:activation by area (BOLD), latency by area (ERP)


Slide67 l.jpg

Major component processes involved in executive function: SelectionAssessment of Executive Function Components

Stacie Warren, with Wendy Heller and Gregory A. Miller, post-doc Dave Towers, and Center colleagues Marie Banich, Naomi Friedman, Akira MiyakePsychology Department, University of Illinois at Urbana-Champaign


Framework for test selection l.jpg
Framework for Test Selection Selection

  • Goals

    • Target 3 EF domains: shifting, inhibition, and updating

  • Sensitivity

    • Detect effects of personality (e.g., positive/negative trait affect) and psychopathology (depression/anxiety) on EF

    • Selective enough to engage prefrontal regions such as DLPFC

    • Level of difficulty

      • Floor/ceiling effects

  • Task Simplicity

    • Isolate EF components we are interested in

      • Task impurity problem


Framework for test selection69 l.jpg
Framework for Test Selection Selection

  • Comparable nonverbal analogues to verbal tasks

  • Multiple measures for each domain

    • Helps alleviate task impurity problem & low reliability

  • Tolerability and practicality

    • Longer the battery the more reliable

    • Increases likelihood for boredom, dropping out

  • IQ and Processing Speed measures

    • Differential deficit


Approach to test selection l.jpg
Approach to Test Selection Selection

  • Comprehensive sampling of EF performance

  • Tasks identified as critically dependent on one of the subcomponents of EF?

    • “Executive Function” tasks

  • Empirically supported EF component tasks

    • Miyake, Friedman, and colleagues

  • Clinical Measures

    • D-KEFS (Delis-Kaplan Executive Functioning System)


D kefs l.jpg
D-KEFS Selection

  • A relatively new measure that attempts to isolate component processes necessary for EF task performance.

  • Consists of tests that are adaptations of tests currently used for assessing EF

  • Greatly improved on earlier versions of these tasks by providing process scores that offer insight into performance scores

  • Normed on a sample of 1,700 across US, ages 8-89


Ef tasks l.jpg
EF Tasks Selection

  • Response Inhibition

    • DKEFS Stroop

    • Stop Signal Task

    • TOL (updated computerized version)

  • Switching

    • DKEFS

      • Trails, Category Fluency, Design Fluency, Stroop

    • Plus-Minus

RED BLUE GREEN YELLOW


Ef tasks73 l.jpg
EF Tasks Selection

  • Updating

    • Keep Track

    • Letter Memory

    • Spatial Updating Task (Heller/Miller lab developed)

      • Visuospatial updating task

      • More details in a bit


Additional tasks l.jpg
Additional Tasks Selection

  • Processing Speed

    • WAIS Coding & Symbol Search

  • IQ

    • WTAR (VIQ)

    • WAIS Block Design

  • PASAT-100

    • Attentional control, divided attention, working memory

  • Subjective Reports of EF in Everyday Life

    • Behavior Rating Inventory of Executive Function (BRIEF): self and informant reports


Spatial updating task l.jpg
Spatial Updating Task Selection

  • Why?

    • Lack of visuospatial tasks that target updating

      • WMC, dual-task components, too many operations, etc.

    • “Gold standard” is n-back

      • Requires significant attentional control

    • Spatial task without verbal tags

      • Assisted n-back

  • Demo task


Slide76 l.jpg

90 degrees Selection

135 degrees

45 degrees

0.1

0.2

180 degrees

0 / 360 degrees

0.3

0.4

0.5

0.6

0.7

0.8

0.9

225 degrees

315 degrees

270 degrees


Spatial updating task77 l.jpg

How? Selection

Used Letter Memory as a template

Matlab randomly generated box locations

Circular grid used to avoid verbal tags & reduce effects of saccades

“Real” trial sequence lengths of 9, 11, & 13

Randomly generated targets within a sequence length

Avoided recognizable spatial patterns

Spatial Updating Task


Slide78 l.jpg

90 degrees Selection

135 degrees

45 degrees

0.1

0.2

180 degrees

0 / 360 degrees

0.3

0.4

0.5

0.6

0.7

0.8

0.9

225 degrees

315 degrees

270 degrees


Task piloting l.jpg
Task Piloting Selection

  • What are we measuring?

    • Errors within a sequence

    • Time

      • Time it takes to respond from response cue (“???”) to first mouse click

      • Total time it takes to respond within a step

    • Distance

    • Velocity


Pilot data l.jpg
Pilot Data Selection

  • First two pilot rounds N=19 (informal, lab members, friends)

    • 3 vs. 4 back

    • Some sequences revised

  • Third round of piloting

  • N=13; 18-20 years, 11 female

  • Reliability: .96




Slide83 l.jpg

The Nature of Inhibitory Processes: per subjectIs stopping or monitoring the crucial executive component to inhibitory control?

Chris Chatham & Yuko Munakata (Project 5) in collaboration with Marie Banich, Tim Curran, Albert Kim


Fractionating inhibitory control l.jpg
Fractionating Inhibitory Control per subject

  • Inhibitory control requires multiple subprocesses. Among them:

    • Vigilance

    • Detection of the need for stopping or suppression, often as cued by infrequent or unusual stimuli

    • Stopping and/or suppression

  • Most theories emphasize #3;

    • but Context-monitoring may account for some of the variance thought to be explained by #3

      • E.g., the involvement of the right inferior frontal gyrus in inhibitory control

“Context

Monitoring”


Empirical approach chatham claus munakata in prep chatham banich curran kim munakata in prep l.jpg
Empirical Approach per subject(Chatham, Claus & Munakata, in prep; Chatham, Banich, Curran, Kim & Munakata, in prep)

Task Stimuli:

No Signal; 75% of trials

Stop Signal or Oddball; 25% of trials

X = {100, 150, 250, 300} ms

Rest of TR

200 ms

Rest of TR

200 ms

time

time

2x2 Task Design:

Fixations both intermixed & blocked: a hybrid fMRI design


Unique predictions of the context monitoring account l.jpg
Unique predictions of the Context Monitoring Account per subject

  • Temporal dynamics:

    • monitoring predicts both sustained and transient components

  • Same parts of rIFG should be active in both tasks

    • despite their different stopping demands

  • rIFG may be more active in oddball task

    • Oddball task presented first

    • Thus signal stimulus is most unusual/infrequent then

  • Also: individual differences, pupillometry


Context monitoring better accounts for bold in rifg than stopping n 18 thresholded at 2 58 l.jpg
Context monitoring better accounts for BOLD in RIFG than stoppingn=18, thresholded at 2.58

Task > Fixation

(sustained act)

Signal Trials > No Signal Trials(transient act)

Signal Trials > No Signal Trials(transient act)

Similar results achieved w/ ERP: a shared principal component above the right frontal lobe

(a second sample of 38 subjects)

Blue: Stop Task

Red: Oddball Task

Blue: Stop > Oddball (empty map)

Red: Oddball > Stop (cluster in rIFG)

Blue: Stop Task

Red: Oddball Task

Oddball task

Stop task


Slide88 l.jpg
The Nature of Inhibitory Processes: stoppingMonitoring may be the crucial executive component to inhibitory control

  • BOLD & ERPs in rIFG

    • do not show unique patterns in a task that demands stopping, relative to one that only demands context monitoring

      • In fact, rIFG is more strongly recruited by the latter task

  • Individual differences… (in a third sample of 96 subjects)

    • are not uniquely captured by a task that demands stopping, relative to one that only demands context monitoring

      • In fact, more variance is explained by the latter task

  • Temporal dynamics of rIFG

    • Have both sustained and transient components, consistent with a context monitoring function

    • Time course of activity is highly similar across tasks (ERP temporal PCA)


Future directions l.jpg
Future Directions stopping

  • ROI analyses (44 vs 45 vs 47)

  • Functional connectivity/PPI

  • Neural network modeling (w/ Project 2)


The nature of inhibitory processing l.jpg

The Nature of Inhibitory Processing stopping

Determinants of Executive Function and Dysfunction

B. Depue, M. Banich, K. Mackiewicz, G. Burgess, T. Curran, R. O’Reilly, Y. Munakata, C. Chatham, H. Snyder


Current implications l.jpg
Current Implications stopping

  • Our studies examining inhibitory function have suggested:

    • That areas of right LPFC appear to be involved in inhibitory control across multiple domains

      • Motor (well studied)

      • Memory/Thought

      • Emotional

    • Inhibitory control appears to down-regulate cortices that support representations of material involved in the specific task at hand


Think no think task l.jpg
Think/No-think Task stopping

  • Do inhibitory mechanisms act on pictorial and emotional memory representations?

  • Three phases:

    • Training

    • Experimental

    • Testing


Training phase 40 negative pairs l.jpg
Training Phase: 40 Negative Pairs stopping

Blocked Condition

+


Training phase practice until recognition 95 l.jpg
Training Phase: stoppingPractice Until Recognition >95%

or

+

or


Most importantly l.jpg
Most Importantly stopping

  • From this point on, no external representation of the target is shown

  • Individuals can only manipulate the internal components of memory representation


Experimental phase 240 trials think no think l.jpg
Experimental Phase: 240 Trials stoppingThinkNo-Think

Do not let previous associated picture enter consciousness

Think of previous associated picture

+

+

Cue

Target

Cue

Target


Repetition manipulation l.jpg

Repetition Manipulation stopping

0 “Baseline”

12x

Training

Training

+

+

Experimental

Experimental

+

+

+

+

+

Randomly

distributed

+

+

+

+

+

+

+

Testing

Testing

+

+

Short desc.

Short desc.


Testing phase cued recall l.jpg
Testing Phase: Cued Recall stopping

+ _____

+ _____

Short description

Short description

Cue

Target

Cue

Target


Behavioral results tnt l.jpg
Behavioral Results - TNT stopping

m (Base) = 62.5 %

m (T) = 71.1 %

m (NT) = 53.2 %


Imaging results tnt l.jpg
Imaging Results - TNT stopping

  • Sources of cognitive control/inhibitory control

  • Sites of where that control is directed


Sources of cognitive control l.jpg
Sources of Cognitive Control stopping

y=22

z=29

z=3

rMFG

rSFG

rIFG

rMFG

rIFG

NT>T

  • Right PFC

    • Involved in executive functions/cognitive control

    • Increased activity for NT trials suggests rLPFC increased involvement during inhibition


1 sites of cognitive control l.jpg
1. stoppingSites of Cognitive Control

z=5

y=-57

y=-90

z=-16

BA18

Pul

FG

BA17

BA17

NT>T

  • Pulvinar

    • Controlling the flow of visual information to cortex

  • Visual cortex

    • Visual areas and fusiform gyrus

      • Known to process visual representations, selective for objects/faces


2 memory processes and emotional components l.jpg
2. Memory Processes and Emotional Components stopping

y=-22

y=-14

y=1

y=5

  • Hippocampus/Parahippocampal Gyrus

    • Highly involved in encoding, consolidation, and retrieval

    • Binds associative components of episodic/semantic memory

  • Amygdala

    • Responsible for generating emotional responses

    • Bidirectional connectivity for modulation of learning and memory

Amy

Amy

Hip

Hip

Hip

Hip

Amy

Amy

Hip

NT>T


Important l.jpg
Important ! stopping

  • Looking at signal change analyses shows decreased activity below baseline for NT trials in

    • Sensory cortex (Pulvinar, Fusiform gyrus)

    • Emotion and Memory (Hippocampus, Amygdala)

  • People appear to inhibit/down-regulate brain areas underlying sensory gating, sensory representation, emotional components and memory processes of memory representation


Functional connectivity analysis l.jpg
Functional Connectivity Analysis stopping

  • Looking at the functional connections of brain regions over time

  • Examining NT trials>baseline

  • Two networks were identified:

    • rIFG functionally connected with the Pulvinar and Fusiform Gyrus

    • rMFG functionally connected with the Hippocampus and Amygdala


Functional connectivity analysis106 l.jpg
Functional Connectivity Analysis stopping

+

+

#

+

+

#

* = p<.05

+ = p<.01

# = p<.001


Summary tnt l.jpg
Summary - TNT stopping

  • Components of visual information in No-Think trials appear to be inhibited

    • This mechanism is invoked from earliest attempts at inhibition

  • Inhibition also involves decreasing activity in regions involved in memory and emotion

    • These mechanisms appear to require repeated attempts at inhibition


Thought suppression l.jpg
Thought Suppression stopping

  • To examine whether we get similar right LPFC regions involved in the control over memories as found with TNT

  • To determine whether the activity of these regions are specific to the inhibition of thoughts or the manipulation of thoughts more generally


Paradigm l.jpg
Paradigm stopping

  • 32 total stimuli

    • 8 neutral color pictures (e.g., peacock)

    • 8 neutral black & white pictures (e.g., penguin)

    • 16 neutral melodies with words (e.g, happy birthday)

  • 4 conditions

    • Maintain

    • Switch

    • Suppress

    • Clear


Paradigm110 l.jpg
Paradigm stopping

Maintain

Maintain

+

+

Switch

Zebra

+

Image

4 seconds

Fixation

2 – 16 seconds

Cognitive Manipulation

4 seconds

Fixation

2 – 16 seconds

Image

4 seconds

Cognitive Manipulation

4 seconds

Fixation

2 – 16 seconds


Thought suppression111 l.jpg
Thought Suppression stopping

Pattern of activation in the occipital cortex and other visual regions suggests that participants are complying with the task demands

Maintain>+++++

Maintain>Clear

Maintain>Suppress

Maintain>Switch

X=48

Z=-20


Linear regression l.jpg
Linear Regression stopping

  • Main > Switch > Clear > Suppress (visual areas)

  • Linear pattern suggests that there is increased right LPFC as a representation or supporting cortices must be inhibited/manipulated

X=48

Z=-20


Future studies l.jpg
Future Studies stopping

  • ERP and inhibition over memory retrieval (connection with Imaging Core)

  • Examining the TNT with PTSD (connection with Project 3)



Slide115 l.jpg

ERP and Memory Inhibition stopping

  • Parietal areas show differential processing for NT and T items

    • Such that NT items show reduced or possible blocked retrieval

  • Continue to exam results with source localization and seeding regions with fMRI data


Ptsd and memory inhibition l.jpg
PTSD and Memory Inhibition stopping

  • Collaboration with Denver VA

  • Examining the integrity of structure/volume of hippocampus

  • Examining the feasibility of using the TNT with war veterans


The influence of learning development on executive function l.jpg

The influence of learning & development on executive function:

Temporal dynamics in cognitive control

Chris Chatham & Yuko Munakata (Project 5) in collaboration with Michael Frank


A large developmental change in ef the temporal dynamics of control l.jpg
A Large Developmental Change in EF: function:The temporal dynamics of control

  • Age-related change in EF widely thought to reflect changes in the speed or strength of EFs

    • in goal maintenance or active inhibition of irrelevant information

    • Original DEFD studies built on this assumption of quantitative change

  • We have evidence for a more drastic qualitativeshift:

    • age-related change in when control is engaged

      (Chatham, Frank, & Munakata, 2009; Chatham & Munakata, in progress)


Cog control dynamics in ax cpt l.jpg
Cog Control Dynamics in AX-CPT function:

  • Adults maintain the informative context info provided by cues

    • “A” predicts target (87.5% of the time)

      • Might encourage AY errors!

    • “B” predicts nontarget (100% of the time!)

      • Might reduce BX errors!

  • Expected: some modulation of children’s behavior due to the maintained context

    • Observed: No maintained context; retrieval only when necessary

      • RT slowing, individual differences, sequence effects, speed-accuracy tradeoffs, pupillometry


Two examples of reactive proactive transition chatham frank munakata 2009 l.jpg
Two Examples of Reactive -> Proactive Transition function:(Chatham, Frank, & Munakata, 2009)

  • Mental Effort (pupil diameter):

  • Probe-period effort in 3.5 year olds

  • Delay-period effort in 8-year-olds

  • Individual Differences in RT:

  • Probe-Driven in 3-yr-olds,

  • Cue-Driven in 8-yr-olds


Slide121 l.jpg
When does the reactive -> proactive transition occur? function:Around 5 years of age. (Chatham & Munakata, in progress)

  • 6 year olds:

Cue

Delay

Probe

  • 5 year olds:


The influence of distraction on proactive control l.jpg
The Influence of Distraction on Proactive Control function:

  • Delay-period distractors disrupt proactive control (as in 6-year-olds)

  • Distractors have less effect on reactive control (as in 5-year-olds)


Learning development of efs l.jpg
Learning & Development of EFs function:

  • Development involves not only the strengthening of EFs, but also a change in how they are deployed:

    Reactive - EFs engaged only as needed in the moment

    Proactive - EFs engaged to meet an anticipated demand

  • The reactive to proactive shift may be graded

    • e.g., 5-year-olds have both cue & probe-driven relationships in RT


Future directions124 l.jpg
Future Directions function:

  • What advantages might a reactive mode confer to learning?

    • Neural network modeling

  • How task-dependent is the use of reactive and proactive mechanisms?

    • Convergent measures of reactive control

  • What are the neural correlates of reactive control?


Slide125 l.jpg

Developmental Differences in Toddler’s function:Behavioral Restraint Predict Executive Control Abilities 14 Years Later

Naomi P. Friedman, Akira Miyake, & John Hewitt

University of Colorado at Boulder


Self regulation and executive functions l.jpg
Self-Regulation and Executive Functions function:

  • Individual differences in lab-based EF tasks can capture variation in self-regulation

    • EF abilities are substantially related to:

      • Attention problems at school during adolescence (Friedman et al., 2007, Psychological Science)

      • Externalizing behavior problems in late adolescence (Young et al., 2009, Journal of Abnormal Psychology)

  • Emergence of self-regulatory abilities?


Self regulation in early childhood beyond l.jpg
Self-Regulation in Early Childhood & Beyond function:

  • Systematic variation in behavioral restraint exists in early childhood

    • Delay of gratification (Michel’s work)

    • Prohibition (Kochanska’s work)

  • It is developmentally stable and is predictive of success later in life

    • Academic achievement and social functioning (Duckworth & Seligman, 2006; Michel, Shoda, & Peake, 1988; Shoda, Michel, & Peake, 1990)


Main questions for the study l.jpg
Main Questions for the Study function:

  • Are individual differences in behavioral self-restraint during early childhood related to individual differences in EF abilities observed later in life?

  • If so, which aspects of EF abilities are most closely related to early self-restraint?

  • To what extent is the longitudinal relationship genetically mediated?


Unity and diversity of efs129 l.jpg

.65 function:

Keep Track

.66

Updating

Letter Memory

.46

Spatial 2-Back

.40

.66

Number-Letter

.63

Shifting

.74

Color-Shape

.74

Category Switch

.73

.42

Stroop

.53

Inhibition

Stop Signal

.44

Antisaccade

Unity and Diversity of EFs

Friedman et al. (2008) JEP:General, N = 582 (Longitudinal Twin Sample)


Slide130 l.jpg

Unity and Diversity of function:EFs

Unity

Diversity

Common EF

=

+

UpdatingAbility

Updating-Specific

=

+

ShiftingAbility

Shifting-Specific

Active maintenance of goals and goal-related information?

=

+

InhibitionAbility

Inhibition-Specific


Main questions for the study131 l.jpg
Main Questions for the Study function:

Are individual differences in behavioral self-restraint during early childhood related to individual differences in EF abilities observed later in life?

If so, which aspects of EF abilities are most closely related to early self-restraint?

To what extent is the longitudinal relationship genetically mediated?


Slide132 l.jpg

Unity and Diversity of Genetic Influences function:

Unity

Diversity

Common EF

=

+

UpdatingAbility

Updating-Specific

A

C

E

100%

0%

0%

=

+

ShiftingAbility

Shifting-Specific

A

C

E

76%

0%

24%

A

C

E

=

InhibitionAbility

98%

0%

2%

Friedman et al. (2008) JEP:General, N = 582 (Longitudinal Twin Sample)


The sample and tasks l.jpg
The Sample and Tasks function:

  • 822 individual twins from the Colorado Longitudinal Twin Study sample

    • All from same-sex twin pairs raised together

    • Normally distributed IQ

  • Task administration

    • Prohibition task: Ages 14, 20, 24, & 36 months

    • WAIS IQ: Age 16

    • EF test battery: Age 17


Prohibition task l.jpg
Prohibition Task function:

  • Procedure:

    • The experimenter draws attention to an attractive toy (a glitter wand)

    • “Now, don’t touch”

  • Dependent measure:

    • Whether the child touched the toy within 30 s





Slide138 l.jpg

Group Differences in EF (Age 17) children

Unity

Diversity

Common EF

.02 SDs below

=

+

UpdatingAbility

Updating-Specific

.34 SDs below

=

+

ShiftingAbility

Shifting-Specific

.45 SDs above

WAIS IQ

=

.24 SDs above

InhibitionAbility


Slide139 l.jpg

Genetic and Environmental Correlations children

Unity

Diversity

=

+

UpdatingAbility

Common EF

Updating-Specific

A

C

E

98%

0%

2%

=

+

ShiftingAbility

Shifting-Specific

A

E

C

80%

0%

20%

.29

C

A

E

=

−.66*

InhibitionAbility

−.03

94%

0%

6%

A

E

C

Group Membership

.54*

28%

32%

40%


Summary of the main results l.jpg
Summary of the Main Results children

  • Developmental differences in toddler’s behavioral self-restraint predict EF Abilities in early adulthood

  • Early prohibition performance is related:

    • positively to Common EF

    • negativelyto Shifting-Specific

  • This longitudinal relationship is due to common genetic influences:

    • Common EF = .54

    • Shifting-Specific = −.66


Slide141 l.jpg

Another Example of Opposing Effects Observed for Common EF and Shifting-Specific Factors

Unity

Diversity

=

+

UpdatingAbility

Common EF

Updating-Specific

A

C

E

100%

0%

0%

=

+

ShiftingAbility

Shifting-Specific

A

E

C

78%

22%

0%

−.05

A

=

−.20

C

E

InhibitionAbility

.56

99%

0%

1%

A

E

C

WAIS IQ

.57

75%

15%

10%


Discussion and conclusion l.jpg
Discussion and Conclusion and Shifting-Specific Factors

  • Shifting ability (measured as switch costs) may better be viewed as a mixture of two opposing forces

    • Common EF = stability (goal maintenance)

    • Shifting-specific = flexibility

  • Early behavioral restraint is a precursor of later executive functioning

  • Genetic factors contribute in part to this developmental stability


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