Brain structural covariance networks associated with executive function in major depressive disorder
Poster No:
400
Submission Type:
Abstract Submission
Authors:
Xiqin LIU1, Yuanyuan Li1, Qiyong Gong1,2
Institutions:
1Department of Radiology and Huaxi MR Research Center, West China Hospital of Sichuan University, Chengdu, China, 2Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, China
First Author:
Xiqin LIU
Department of Radiology and Huaxi MR Research Center, West China Hospital of Sichuan University
Chengdu, China
Department of Radiology and Huaxi MR Research Center, West China Hospital of Sichuan University
Chengdu, China
Co-Author(s):
Yuanyuan Li
Department of Radiology and Huaxi MR Research Center, West China Hospital of Sichuan University
Chengdu, China
Department of Radiology and Huaxi MR Research Center, West China Hospital of Sichuan University
Chengdu, China
Qiyong Gong
Department of Radiology and Huaxi MR Research Center, West China Hospital of Sichuan University|Department of Radiology, West China Xiamen Hospital of Sichuan University
Chengdu, China|Xiamen, China
Department of Radiology and Huaxi MR Research Center, West China Hospital of Sichuan University|Department of Radiology, West China Xiamen Hospital of Sichuan University
Chengdu, China|Xiamen, China
Introduction:
Impairment of executive function (EF) is common in major depressive disorder (MDD) (Semkovska et al., 2019). However, there are no reliable brain-based biomarker of this cognitive impairment in MDD. Previous findings on brain regional alterations linked to EF in MDD have been inconsistent. Structural covariance networks (SCNs) represent the degree of similarity in morphological properties between two regions and may be a powerful approach to understand individual-variance in cognitive abilities (Seidlitz et al., 2018). The current study aimed to investigate the relationship between the patterns of large-scale SCNs and EF in subjects with and without MDD.
Methods:
Two hundred and eighty-three first-episode drug-naïve patients with MDD and 81 age- and sex-matched healthy controls (HC) were enrolled in this study. All patients underwent EF assessment (Stroop Color and World Test, Trail Making Test difference and Modified Wisconsin Card Sorting Test) and brain magnetic resonance imaging (MRI). We used confirmatory factor analysis to estimate a latent variable representing EF. Individual SCNs based on gray matter volume were constructed using a novel method combining probability density estimation and Kullback-Leibler divergence. Global and nodal properties of these constructed networks were subsequently estimated using graph theory analysis. We then applied partial correlations and partial least squares correlations to examine their associations with the EF latent variable.
Results:
In both MDD and HC groups, EF was positively related to global efficiency, local efficiency and small-worldness (Fig. 1). Furthermore, EF was positively linked to pronounced betweenness centrality (r = 0.55, ppermutated = 0.005, and explained 30.25% of the variance in EF) and degree centrality patterns (r = 0.49, ppermutated = 0.038, and explained 24.01% of the variance in EF) in MDD patients (Fig. 2), whereas no significant patterns emerged in the HC group. Bootstrap tests indicated that 10 nodes, encompassing bilateral middle frontal gyrus (MFG), precentral gyrus (PrG) and inferior temporal gyrus (ITG), had robust and reliable positive contributions to the betweenness centrality pattern, while 4 nodes, located in the right thalamus, cingulate gyrus (CG) and left inferior frontal gyrus (IFG) were robust and reliable in their negative contributions. For the degree centrality pattern, 18 nodes with positive weights encompassed bilateral PrG, parahippocampal gyrus (PhG), right ITG, left lateral occipital cortex (LOcC), bilateral orbital gyrus (OrG), right MFG and IFG; 5 nodes with negative weights were located in thalamus and left CG.
·Fig.1. The associations between global metrics of the gray matter covariance network and executive function in (A) major depressive disorder patients, and (B) healthy controls.
·Fig.2. The associations between nodal metrics of the gray matter covariance network and executive function in major depressive disorder patients.
Conclusions:
The current study is the first to investigate the patterns of gray matter covariance networks associated with EF in MDD and HC. EF was positively related to global efficiency, local efficiency and small-worldness of the SCNs in both MDD and HC groups, consistent with previous findings showing correlations of these measures with individual differences in cognitive control (Berlot et al., 2016; Reineberg and Banich, 2016). However, interindividual variability in EF was only positively associated with betweenness and degree centrality patterns of nodes located primarily in high-level cognitive network in MDD patients, which may represent a compensatory mechanism where the coordination of these regions facilitates executive function in MDD. This study highlights the importance of SCN measures in characterizing EF, and the observed patterns of network metrics may guide clinical decision-making for individualized treatments of executive dysfunction in MDD. Future research needs to explore the causal nature of this relationship.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems
Novel Imaging Acquisition Methods:
Anatomical MRI 2
Keywords:
STRUCTURAL MRI
Other - depression; executive function; structural covariance network
1|2Indicates the priority used for review
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Provide references using APA citation style.
Reineberg, A. E., & Banich, M. T. (2016). Functional connectivity at rest is sensitive to individual differences in executive function: A network analysis. Human brain mapping, 37(8), 2959-2975.
Seidlitz, J., Váša, F., Shinn, M., Romero-Garcia, R., Whitaker, K. J., Vértes, P. E., ... & Bullmore, E. T. (2018). Morphometric similarity networks detect microscale cortical organization and predict inter-individual cognitive variation. Neuron, 97(1), 231-247.
Semkovska, M., Quinlivan, L., O'Grady, T., Johnson, R., Collins, A., O'Connor, J., ... & Gload, T. (2019). Cognitive function following a major depressive episode: a systematic review and meta-analysis. The Lancet Psychiatry, 6(10), 851-861.