Abstract
This paper introduces a novel method that unifies structural connectivity and functional time series to form a signed coupling interaction network or “signed resting state structural connectome” (signed rs-SC) to describe neural excitation and inhibition. We employ an energy representation of neural activity based on the Ising model from statistical mechanics, hereby bypassing traditional BOLD correlations. The spin model is a function of a coupling interaction (traditionally positive or negative) and spin-states of paired brain regions. Observed functional time series represent brain states over time. A maximum pseudolikelihood with a constraint is used to estimate the coupling interaction. The constraint is introduced as a penalty function such that the learned interactions are scaled relative to structural connectivity; the sign of the interactions may infer inhibition or excitation over an underlying structure. We evaluate our method by comparing a group of otherwise healthy APOE-e4 carriers with a control group of non APOE-e4 subjects. Our results identify a global shift in the excitation-inhibition balance of the APOE e4 signed rs-SC compared to the control group, providing the first connectomics-based support for hyperexcitation related to APOE e4.
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This study is funded in part by NIA AG056782 and NSF-IIS 1837956.
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Fortel, I. et al. (2019). Brain Dynamics Through the Lens of Statistical Mechanics by Unifying Structure and Function. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11768. Springer, Cham. https://doi.org/10.1007/978-3-030-32254-0_56
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DOI: https://doi.org/10.1007/978-3-030-32254-0_56
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