Dynamic Reorganization of the Frontal-Parietal Network during
Cognitive Control and Episodic Memory
Kimberly L Ray1, Angus W MacDonald2, J Daniel Ragland1, James M Gold3, Steven M Silverstein4, Deanna M Barch5, Cameron S Carter1,6
1Imaging
Research Center, UC Davis, Sacramento, CA; 2Department of Psychology, University of Minnesota, Minneapolis, MN; 3Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore,
MD; 4Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers, NJ; 5Department of Psychiatry, Washington University, St Louis, MO; 6Department of Psychology, UC Davis, Davis, CA
RESULTS
INTRODUCTION
•  We used a novel graph theory approach to examine context-dependent
network reorganization fMRI tasks from 2 distinct cognitive domains that
varied in demands for cognitive control (CC): the DPX2 goal
maintenance task and the RiSE3 episodic memory task.
•  Individuals with schizophrenia (SZ) exhibit functional deficits in the
frontal-parietal network (FPN) during cognitive control processing4. Thus,
we examine differences in context-dependent network reorganization in
healthy adults and in schizophrenia.
1.  Modularity (Q): the ratio of within/between
module edges.
•  HC and SZ network organization is not random.
•  We hypothesize that the FPN will uniquely integrate with other highercognitive networks that will manifest as differential modular organizations
across task conditions.
•  We hypothesize that schizophrenia patients with exhibit reduced
functional network integration compared to healthy adults.
2.
Modularity
0.6
Healthy Controls
Schizophrenia
0.55
HC & SZ > null (500 randomizations), F(1,95)=5594.23, p<0.001.
0.5
•  HC network organization is more integrated than SZ.
0.45
HC < SZ, F(1,95)=11.330, p<0.001.
•  High CC requires more network integration than low.
High CC < Low CC, F(1,95) = 12.994, p<0.001.
2.  Mutual Information (MI): How stable is module
composition between high and low CC?
•  HC module composition is more stable than SZ.
HC > SZ, F(1,95) = 9.935, p<0.002.
•  DPX module composition is most stable, RiSE
encoding is least stable.
Hypotheses:
1.
Modularity (Q)
•  Cognitive processing occurs in the brain through dynamic integration
and segregation of functional brain networks1
0.4
Healthy Controls
Schizoprenia
0.35
Null
0.3
0.25
0.2
0.15
A Cue
B Cue
Item-Enc
Rel-Enc
Item-Rec
Rel-Rec
DPX > RiSE Recognition > RiSE Encoding, F(2,190) = 117.417, p < 0.001.
•  FPN segregation is related to network stability (MI).
FPN clustering coefficient is correlated with MI across all tasks. DPX: rACue=0.336 (p=0.001), rBCue=0.315 (p=0.002).
RiSE Encoding: rItem=0.443 (p=0.000), rRelation=0.346 (p=0.000). RiSE Recognition: rItem=0.240 (p=0.012), rRelation=0.309 (p=0.001).
3.  Resting-state “intrinsic” networks re-organize according to task context during higher cognition.
•  Low-level perceptual functioning nodes (i.e. sensorimotor, auditory, cerebellar networks) displayed
Stable modular organization across tasks and subject groups.
•  High-level cognitive functioning nodes (i.e. FPN, salience, DMN) varied in their module assignment and
Dynamically reorganized across tasks. These modules also display different aspects of network
integration and segregation between groups.
METHODS
fMRI Data Acquisition
Multi-site fMRI data (3T) were obtained from 56 healthy adults and 52
schizophrenia patients performing the RiSE and DPX tasks as part of the
CNTRACs project (http://cntracs.ucdavis.edu).
3.
Healthy Control Modules
Schizophrenia Modules
Cognitive Network
Legend6
•  The DPX task, engages proactive CC in the FPN via a sequence of cue-probe
stimuli where cues indicate the need for high (B-cue) or low (A-cue) CC.
•  The RiSE episodic memory task manipulates CC demands via different forms
of encoding encoding and retrieval, and context-dependently engages FPN.
fMRI Data Processing
Step 1: Standard fMRI preprocessing in FSL
with additional movement scrubbing.
Step 2: Beta series regression analysis was
performed to capture trial specific BOLD
effect for each condition5.
Step 3: Beta images representing similar trial
types were concatenated, resulting with a 4D
dataset for each condition type.
Step 4: Beta-series pairwise correlations for
245 nodes6 were extracted resulting with a
245x245 connectivity matrix.
CONCLUSIONS
Network Analysis of fMRI Data
Louvain Modularity (Q), and module partitions
of each task condition were extracted7.
•  The FPN exhibits task appropriate responses through two different mechanisms:
•  Enhanced within network connectivity to support proactive CC during the DPX (also reported in RiSE9).
•  Between network integration with DMN and salience networks to support different forms of encoding and
retrieval in the RiSE task.
•  Changes in modular composition across
tasks were quantified using mutual
information(MI)8.
•  Segregation of the FPN was assessed
using the Clustering-Coefficient(T)7, the
ratio of triangles to triplets in a network.
(Ray et al., 2017)
T=
∑ a a a = ∑ trianglespresent
∑ k (k −1) ∑ allpossibletriangles
ij ih
i
i
jh
•  SZ patients have previously been shown to have reduced FPN connectivity across the RiSE
and DPX tasks9.
•  In the present study, SZ also shows decreased network integration of task induced networks
as control demands are increased.
•  FPN connectivity is correlated with network stability (MI) suggesting that the FPN plays an
important role in maintaining stable networks as control demands change.
REFERENCES
1.Fornito A, et al. (2012). Competitive and cooperative dynamics of large-scale brain functional networks supporting
recollection. PNAS.
2.Lopez-Garcia P, et al. (2015) The neural circuitry supporting goal maintenance during cognitive control: a comparison
of expectancy AX-CPT and dot probe expectancy paradigms. CABN.
3.Ragland JD, et al.(2015) Functional and Neuroanatomic Specificity of Episodic Memory Dysfunction in Schizophrenia:
A Functional Magnetic Resonance Imaging Study of the Relational and Item-Specific Encoding Task. JAMA Psychiatry.
4.Lesh T, et al., (2010). Cognitive control deficits in schizophrenia: mechanisms and meaning.
Neurospychopharmacology.
5.Rissman J, et al., (2004) Measuring functional connectivity during distinct stages of a cognitive task. NeuroImage.
6.Power J, et al., (2011) Functional Network organization of the human brain. Neuron.
7.Rubinov M, Sporns O. (2010) Complex network measures of brain connectivity: Uses and interpretations.
NeuroImage.
8.Rubinov M, Sporns O.(2011) Weight-conserving characterization of complex functional brain networks. NeuroImage.
9.Ray K, et al. (2017) Functional network changes and cognitive control in schizophrenia. NeuroImage: Clinical.
Supported by:NIMH-5R01MH059883
Contact: [email protected]