Heritability of the Effective Connectivity in the Resting-State Default Mode Network

• Published in: Cerebral cortex (New York, N.Y. 1991) Vol. 27; no. 12; pp. 5626 - 5634
• Main Authors: Xu, Junhai, Yin, Xuntao, Ge, Haitao, Han, Yan, Pang, Zengchang, Liu, Baolin, Liu, Shuwei, Friston, Karl
• Format: Journal Article
• Language: English
• Published: United States 01.12.2017
• Subjects: Adolescent; Brain - diagnostic imaging; Brain - physiology; Brain Mapping; Female; Gene-Environment Interaction; Humans; Inheritance Patterns; Magnetic Resonance Imaging; Male; Models, Genetic; Models, Statistical; Neural Pathways - diagnostic imaging; Neural Pathways - physiology; Rest; Twins, Dizygotic; Twins, Monozygotic; Young Adult; default mode network; resting-state fMRI; effective connectivity; DCM; heritability
• ISSN: 1047-3211; 1460-2199
• DOI: 10.1093/cercor/bhw332

The default mode network (DMN) is thought to reflect endogenous neural activity, which is considered as one of the most intriguing phenomena in cognitive neuroscience. Previous studies have found that key regions within the DMN are highly interconnected. Here, we characterized the genetic influences on causal or directed information flow within the DMN during the resting state. In this study, we recruited 46 pairs of twins and collected fMRI imaging data using a 3.0 T scanner. Dynamic causal modeling was conducted for each participant, and a structural equation model was used to calculate the heritability of DMN in terms of its effective connectivity. Model comparison favored a full-connected model. Structural equal modeling was used to estimate the additive genetics (A), common environment (C) and unique environment (E) contributions to variance for the DMN effective connectivity. The ACE model was preferred in the comparison of structural equation models. Heritability of DMN effective connectivity was 0.54, suggesting that the genetic made a greater contribution to the effective connectivity within DMN. Establishing the heritability of default-mode effective connectivity endorses the use of resting-state networks as endophenotypes or intermediate phenotypes in the search for the genetic basis of psychiatric or neurological illnesses.