Poster No:
1822
Submission Type:
Abstract Submission
Authors:
Alexander Simon1, Xilin Shen1, Wenjing Luo1, Saloni Mehta1, Jagriti Arora1, Fuyuze Tokoglu1, Anja Samardzija1, Corey Horien1, R Todd Constable1
Institutions:
1Yale University, New Haven, CT
First Author:
Co-Author(s):
Introduction:
Uncovering how abnormalities in functional brain network organization relate to cognition and psychiatric symptoms is an important endeavor of brain- phenotyping. Although extensive work has been done to model how functional network architecture relates to various cognitive processes (Rosenberg, Finn et al. 2016) and symptoms (Xia, Ma et al. 2018), little has been done to examine how functional networks reconfigure to allow individuals with clinically meaningful impairments to maintain cognitive performance. In this study, we tested for evidence supporting the theory that network compensation strategies, detectable in functional connectivity models, are related to maintaining cognitive performance in individuals with clinically relevant impairments. We show that individuals with low levels of self-reported attentiveness and reduced cognitive control network connectivity were able to maintain normal levels of performance on neuropsychiatric evaluations via increased connectivity in a working memory network.
Methods:
The relationships between psychiatric symptoms, cognitive performance, and network compensation were analyzed in a transdiagnostic sample consisting of healthy controls and individuals with any combination of 14 different psychiatric diagnoses (n=190). Symptoms were evaluated using the attentiveness scale from the positive and negative affective schedule (PANAS) (Watson, Clark et al. 1988). Functional brain networks were defined for each of the six Research Domain Criteria (RDoC) cognitive constructs (Insel, Cuthbert et al. 2010) using Neurosynth (Yarkoni, Poldrack et al. 2011) to initially identify network nodes. The set of edges within each cognitive construct network was then expanded by including edges with the strongest connections to the nodes within the network constructs. To ensure maximal independence between network constructs, edges shared between networks were removed. This resulted in approximately 200 edges for each construct network. Network connectivity was normalized by performance on the Wechsler Adult Intelligence Scale (WAIS-IV) Symbol Search neuropsychiatric test (Wechsler 2008) by computing the linear relationship between network connectivity and test scores and calculating the distance of each individual's network connectivity from the regression line. Finally, the difference between the normalized working memory network and cognitive control network was correlated with PANAS attentiveness scores.
Results:
By calculating differences in connectivity relative to cognitive performance between cognitive construct networks, we developed an approach for evaluating network compensatory strategies to maintain cognitive performance in clinically impaired individuals. The difference between working memory network and cognitive control network connectivity was significantly negatively correlated with PANAS attentiveness scores (r = -0.184, p = 0.011), such that a larger difference between working memory and cognitive control network connectivity was associated with poorer attentiveness ratings. This finding provides evidence supporting the validity of using this approach to examine network compensation strategies.
Conclusions:
The findings suggest that individuals with lower self-reported attentiveness scores can maintain higher levels of neuropsychiatric test performance in part by increased reliance upon a working memory network to compensate for reduced connectivity in a cognitive control network. These results have the potential to advance the applications of neuroimaging in psychiatry by improving our understanding of how cognitive deficits can be compensated for by leveraging networks related to different cognitive constructs. Furthermore, the methodology in this study can be utilized to investigate how other network compensation strategies might contribute to cognitive maintenance in individuals with different psychiatric symptoms.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia)
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
fMRI Connectivity and Network Modeling 1
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Cognition
Data analysis
FUNCTIONAL MRI
Modeling
Psychiatric Disorders
Other - Functional networks
1|2Indicates the priority used for review
Provide references using author date format
Insel, T., B. Cuthbert, M. Garvey, R. Heinssen, D. S. Pine, K. Quinn, C. Sanislow and P. Wang (2010). "Research domain criteria (RDoC): toward a new classification framework for research on mental disorders." Am J Psychiatry 167(7): 748-751.
Rosenberg, M. D., E. S. Finn, D. Scheinost, X. Papademetris, X. Shen, R. T. Constable and M. M. Chun (2016). "A neuromarker of sustained attention from whole-brain functional connectivity." Nat Neurosci 19(1): 165-171.
Watson, D., L. A. Clark and A. Tellegen (1988). "Development and validation of brief measures of positive and negative affect: the PANAS scales." J Pers Soc Psychol 54(6): 1063-1070.
Wechsler, D. (2008). "Wechsler Adult Intelligence Scale--Fourth Edition." APA PsychTests.
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Yarkoni, T., R. A. Poldrack, T. E. Nichols, D. C. Van Essen and T. D. Wager (2011). "Large-scale automated synthesis of human functional neuroimaging data." Nat Methods 8(8): 665-670.