Understanding the Relationship Between Human Brain Structure and Function by Predicting the Structural Connectivity From Functional Connectivity
| Autor: | Weifeng Liu, Xue Chen, Bao-Di Liu, Yanjiang Wang, Richard M. Shiffrin |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
General Computer Science medicine.diagnostic_test Computer science Mechanism (biology) Functional connectivity brain connectivity General Engineering Human brain TK1-9971 Euclidean distance 03 medical and health sciences network deconvolution 030104 developmental biology 0302 clinical medicine medicine.anatomical_structure structure-function relation medicine Connectome General Materials Science Electrical engineering. Electronics. Nuclear engineering Functional magnetic resonance imaging Human brain mapping Algorithm 030217 neurology & neurosurgery |
| Zdroj: | IEEE Access, Vol 8, Pp 209926-209938 (2020) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2020.3039837 |
| Popis: | Over the past decade, a growing number of studies have investigated the relationship between the structure and function of human brain by predicting the resting-state functional connectivity (rsFC) from structural connectivity (SC). Yet how the whole-brain patterns of FC emerge from SC still remains incompletely understood. Unlike previous studies, here we propose an alternative approach for addressing this issue by predicting SC from rsFC. We first hypothesize that the functional couplings among brain areas at rest are shaped at least in three phases temporally: the initial direct interplay between brain areas, the communications within and between network modules, and followed by the indirect interactions ascribed to indirect structural pathways. We then introduce a network deconvolution (ND) algorithm inspired from the mechanism of cell differentiation, named CDA, to distinguish the direct dependencies from the functional network followed by a weight trimming algorithm based on Euclidean distance kernel function for shrinking the modular effects. Finally, we keep those region pairs with shorter shortest path length (SPL) together with shorter Euclidean distance as the structural connections. We apply the model and the algorithms to three intensively studied group averaged empirical connectome datasets with different parcellation resolutions and the results demonstrate that the predicted intrahemispheric structural connections and the weights distribution are highly consistent with the empirical SC derived from diffusion magnetic resonance imaging (dMRI) and probabilistic tractography, thus strongly supporting the model and algorithms proposed. |
| Databáze: | OpenAIRE |
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