Heterogeneity Analysis on Multi-State Brain Functional Connectivity and Adolescent Neurocognition

Abstract

Brain functional connectivity or connectome, a unique measure for brain functional organization, provides a great potential to explain the neurobiological underpinning of behavioral profiles. Existing connectome-based analyses highly concentrate on brain activities under a single cognitive state, and fail to consider heterogeneity when attempting to characterize brain-to-behavior relationships. In this work, we study the complex impact of multi-state functional connectivity on behaviors by analyzing the data from a recent landmark brain development and child health study. We propose a nonparametric, Bayesian supervised heterogeneity analysis to uncover neurodevelopmental subtypes with distinct effect mechanisms. We impose stochastic block structures to identify network-based functional phenotypes and develop a variational expectation-maximization algorithm to facilitate an efficient posterior computation. Through integrating resting-state and task-related functional connectomes, we dissect heterogeneous effect mechanisms on children’s fluid intelligence from the functional network phenotypes, including Fronto-parietal Network and Default Mode Network, under different cognitive states. Based on extensive simulations, we further confirm the superior performance of our method on uncovering brain-to-behavior relationships. Supplementary materials for this article are available online including a standardized description of the materials available for reproducing the work.

Keywords:

• Bayesian modeling
• Functional connectivity
• Heterogeneity analysis
• Stochastic block model
• Variational algorithm

Supplementary Materials

The supplementary materials contain detailed derivation on the VEM algorithm and posterior computation steps, additional results of real data analysis, computational time, additional simulations, R codes to implement the method, and reproducibility materials.

Acknowledgments

The authors would like to thank the Editor, the Associate Editor and all the anonymous Reviewers for their constructive comments and suggestions which significantly helped improve this article. Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9–10 and follow them over 10 years into early adulthood. The ABCD Study is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium/_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This article reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.

Disclosure Statement

The authors declare no competing interests.

Additional information

Funding

Zhang’s research is partially supported by NIH grants R01HG010171 and R01MH116527 and NSF grant DMS-2112711. Zhao’s research is partially supported by NIH grants RF1AG081413, R01EB034720 and RF1AG068191.