intrinsic neural connectiity of act-r rois
DESCRIPTION
Intrinsic Neural Connectiity of ACT-R ROIs. Yulin Qin 1, 2 , Haiyan Zhou 1 , Zhijiang Wang 1 , Jain Yang 1 , Ning Zhong 1 , and John R. Anderson 2 1. International WIC Institute, Beijing University of Technology, Beijing, China - PowerPoint PPT PresentationTRANSCRIPT
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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
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Outline
• Introduction • Methods• Results• Discussion
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• Introduction – Why resting brain– A basic question– Functional connection of cognitive and metacognitive
regions
• Methods• Results• Discussion
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• 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)
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Collins and Quillian (1969) Retrieval time from semantic memory. Journal of Verbal Learning and Verbal Behavior, 8, 240-247
Organization example 1: Semantic Network
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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
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Fundamental Hypothesis:
Organization + Stimulus
=> Task evoked activation
inferStimulus + Task evoked activation ===>
Organization(Task)
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Why resting brain?
Synchronized spontaneous fluctuation in resting brain
===> Organization(Resting) infer
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A basic Question●
Is Organization(Task) consistent with
Organization(Resting) ?
Stimulus + Task evoked activation ===> Organization(Task)
Synchronized spontaneous fluctuation in resting brain ===> Organization(Resting)
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• 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.
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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
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Outline
• Introduction • Methods (resting brain)
– Participants – Procedure – Data acquisition– Data preprocessing– Functional connectivity analysis
• Results• Discussion
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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°
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Procedure
• Eye closed resting state scanning • 307 images• Totally 10’20’’ in one session
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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
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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
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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
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Predefined 12 Seeds
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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.
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Outline
• Introduction • Methods• Results
– Metacognitive network– Cognitive network– Mixted network– Control network
• Discussion
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1. Metacognitive network
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Seed: BA 10R L L RFunctional Connectivity in Resting State
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Seed: ANGR L L R
Functional Connectivity in Resting State
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Functional Connectivity in Resting State
Seed: FusiformL RR L
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2. Cognitive network
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R L
RLFunctional Connectivity in Resting State
Seed: PPC2.1
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Functional Connectivity in Resting State
Seed: HIPSR L
L R2.2
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R L
Functional Connectivity in Resting State
Seed: PSPLL R
2.3
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R L
Functional Connectivity in Resting State
Seed: MotorL R
2.4
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3. Mixed network
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Functional Connectivity in Resting StateSeed: LIPFCR L L R
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PPCHIPSANG LIPFC
CognitiveMixedMetacognitive
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4. Control network
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R L
Functional Connectivity in Resting State
Seed: ACCL R
1,32
1 – metacognitive (most, with 3)2 – cognitive3 - mixed
23
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R L
Functional Connectivity in Resting State
Seed: CaudateL R
1
2
1 – metacognitive (most)2 – cognitive3 - mixed
3
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R L
Functional Connectivity in Resting State
Seed: Insula-anteriorL R
1,3
2
1 – metacognitive (most)2 – cognitive3 - mixed
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R L
1 – metacognitive (most)2 – cognitive3 - mixed
Functional Connectivity in Resting State
Seed: Insula-middleL R
1,32
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Outline
• Introduction • Methods• Results• Discussion
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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
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3.1 Two kinds of cognitive connectivity in resting brainSeed: HIPSRL Seed: PPC
LL R
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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