Intrinsic Neural Connectiity of ACT-R ROIs

Intrinsic Neural Connectiity of ACT-R ROIs

Yulin Qin1, 2, Haiyan Zhou1, Zhijiang Wang1, Jain Yang1, Ning Zhong1, and John R. Anderson2

1. International WIC Institute, Beijing University of Technology, Beijing, China2. Department of Psychology, Carnegie Mellon University, Pittsburgh, PA 15213

Outline

• Introduction • Methods• Results• Discussion

• Introduction – Why resting brain– A basic question– Functional connection of cognitive and metacognitive

regions

• Methods• Results• Discussion

• Why resting brain

Goal of cognitive psychology:

Cognitive psychology is the science of how the mind is organized to produce intelligent thought and how it is realized in the brain

(John R. Anderson (2010) . Cognitive Psychology and its implications (7th edition). New York, NY: Worth Publishers)

Collins and Quillian (1969) Retrieval time from semantic memory. Journal of Verbal Learning and Verbal Behavior, 8, 240-247

Organization example 1: Semantic Network

John R. Anderson, Michael D. Byrne, Scott Douglass, Christian Lebiere, and Yulin Qin. (2004). An integrated theory of the mind . Psychological Review. 111(4): 1036-1060

Organization example 2: ACT-R

Fundamental Hypothesis:

Organization + Stimulus

=> Task evoked activation

inferStimulus + Task evoked activation ===>

Organization(Task)

Why resting brain?

Synchronized spontaneous fluctuation in resting brain

===> Organization(Resting) infer

A basic Question●

Is Organization(Task) consistent with

Organization(Resting) ?

Stimulus + Task evoked activation ===> Organization(Task)

Synchronized spontaneous fluctuation in resting brain ===> Organization(Resting)

• Functional connection of cognitive and metacognitive regions

Pyramid problemsRegular Problems: 4$3=x

(4$3=4+3+2=9 => x=9)Exception Problems x$4=x

(2$4=2+1+0+(-1)=2 => x=2)Cognitive pattern:

(1) Equal activity for exception and regular problems;(2) Much stronger activation when solving the problem than when

reflecting on the problem’s solution after solving the problem Metacognitive pattern:

(1) Stronger activity for exception than regular problems;(2) Equal activity when solving the problem and when reflecting on the

problem’s solution after solving the problem

Samuel Wintermute, Shawn Betts, Jennifer L. Ferris, Jon M. Fincham, John R. Anderson (submitted). Networks supporting execution of mathematical skill versus acquisition of new competence. Cognitive, Affective, and Behavior Neuroscience.

Cognitive Correlation-1 0 1

Met

cogn

itive

Cor

rela

tion 1

-1

0

(b) Cognitive(a) Metacognitive

Metacognitive

Cognitive

Mixed

Anti-Cognitive

Anti-MetacognitiveNegative

(c) (d)

2. More than 20% of brain significantly(p<0.01) correlated with one or both of them

1. Involving two basic brain activation patterns

Outline

• Introduction • Methods (resting brain)

– Participants – Procedure – Data acquisition– Data preprocessing– Functional connectivity analysis

• Results• Discussion

Participants

• 21 healthy students from BJUT• 10 female, 24.1±1.9 years old • Signed an informed consent

• Data from all subjects were used for further data processing since their head shifted less than 1.5 mm or the head rotated less than 1.5°

Procedure

• Eye closed resting state scanning • 307 images• Totally 10’20’’ in one session

Data Acquisition• 3.0 Tesla Siemens MRI scanner with a standard whole-head 12

channel coil• TR = 2 s• TE = 31 ms• Flip angle = 90• FOV = 200 mm × 200 mm• Matrix =64 × 64• Thickness = 3.2 mm• Gap = 0 mm• Axial slices = 32 (with AC-PC through the 23rd slice from the top of

the brain• Voxel size = 3.125 mm × 3.125 mm × 3.2 mm

Data Preprocessing

• NIS (NeuroImaging Software, http://kraepelin.wpic.pitt.edu/nis/).

• First 7 images deleted for magnetization equilibrium• Motion correction• Spatially normalized to a standard brain

Functional Connectivity Analysis• REST (Resting-State fMRI Data Analysis Toolkit,

http://www.restfmri.net/forum/index.php )• Ideal band pass filter

– Time serials in each voxel filtered into the frequency range of 0.01–0.08 Hz (period: 100s – 12.5s)

• Regression analysis– Several sources of spurious variances from 6 head motion

parameters, global mean signal and white matter signal removed• Seeds definition

– Predefined (see below)• Voxel wised connectivity in whole brain

– r map– r to Z map– Group t-test: p<0.01(uncorrected), cluster size>4

Predefined 12 Seeds

Motor

PSPL

ACC

HIPS

PPC

ANG

BA 10

Anterior Insula

Caudate

Middle Insula

PSPL: Posterior superior parietal lobule; ACC: Anterior cingulate cortex; HIPS: Horizontal intraparietal sulcus; PPC: Posterior parietal cortex; LIPFC: Lateral inferior prefrontal cortex; ANG: Angular gyrus

LIPFC

Fusiform is 5 slices below to the last slice, not shown here.

Outline

• Introduction • Methods• Results

– Metacognitive network– Cognitive network– Mixted network– Control network

• Discussion

1. Metacognitive network

Seed: BA 10R L L RFunctional Connectivity in Resting State

Seed: ANGR L L R

Functional Connectivity in Resting State

Functional Connectivity in Resting State

Seed: FusiformL RR L

2. Cognitive network

R L

RLFunctional Connectivity in Resting State

Seed: PPC2.1

Functional Connectivity in Resting State

Seed: HIPSR L

L R2.2

R L

Functional Connectivity in Resting State

Seed: PSPLL R

2.3

R L

Functional Connectivity in Resting State

Seed: MotorL R

2.4

3. Mixed network

Functional Connectivity in Resting StateSeed: LIPFCR L L R

PPCHIPSANG LIPFC

CognitiveMixedMetacognitive

4. Control network

R L

Functional Connectivity in Resting State

Seed: ACCL R

1,32

1 – metacognitive (most, with 3)2 – cognitive3 - mixed

23

R L

Functional Connectivity in Resting State

Seed: CaudateL R

1

2

1 – metacognitive (most)2 – cognitive3 - mixed

3

R L

Functional Connectivity in Resting State

Seed: Insula-anteriorL R

1,3

2

1 – metacognitive (most)2 – cognitive3 - mixed

R L

1 – metacognitive (most)2 – cognitive3 - mixed

Functional Connectivity in Resting State

Seed: Insula-middleL R

1,32

Outline

• Introduction • Methods• Results• Discussion

2. Convergent evidence for four kinds of brain networks:(1) Metacognitive(2) Cognitive(3) Mixed(4) Control

1. High consistency between the brain activation patterns in task-on brain and the spontaneous fluctuation patterns in resting brain

3. Functional connectivity in the resting brain can help us to elaborate the picture of brain organization

3.1 Two kinds of cognitive connectivity in resting brainSeed: HIPSRL Seed: PPC

LL R

3.2. Separated patterns between PPC and LIPFC in functional connectivity in resting state Seed: LIPFCL RRL Seed: PPC

L

In many places, they are very close, but do not overlap