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How Brain Structure-function Decoupling Supports Individual Cognition and Its Molecular Mechanism

Overview
Journal Hum Brain Mapp
Publisher Wiley
Specialty Neurology
Date 2024 Feb 10
PMID 38339909
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Abstract

Functional signals emerge from the structural network, supporting multiple cognitive processes through underlying molecular mechanism. The link between human brain structure and function is region-specific and hierarchical across the neocortex. However, the relationship between hierarchical structure-function decoupling and the manifestation of individual behavior and cognition, along with the significance of the functional systems involved, and the specific molecular mechanism underlying structure-function decoupling remain incompletely characterized. Here, we used the structural-decoupling index (SDI) to quantify the dependency of functional signals on the structural connectome using a significantly larger cohort of healthy subjects. Canonical correlation analysis (CCA) was utilized to assess the general multivariate correlation pattern between region-specific SDIs across the whole brain and multiple cognitive traits. Then, we predicted five composite cognitive scores resulting from multivariate analysis using SDIs in primary networks, association networks, and all networks, respectively. Finally, we explored the molecular mechanism related to SDI by investigating its genetic factors and relationship with neurotransmitter receptors/transporters. We demonstrated that structure-function decoupling is hierarchical across the neocortex, spanning from primary networks to association networks. We revealed better performance in cognition prediction is achieved by using high-level hierarchical SDIs, with varying significance of different brain regions in predicting cognitive processes. We found that the SDIs were associated with the gene expression level of several receptor-related terms, and we also found the spatial distributions of four receptors/transporters significantly correlated with SDIs, namely D2, NET, MOR, and mGluR5, which play an important role in the flexibility of neuronal function. Collectively, our findings corroborate the association between hierarchical macroscale structure-function decoupling and individual cognition and provide implications for comprehending the molecular mechanism of structure-function decoupling. PRACTITIONER POINTS: Structure-function decoupling is hierarchical across the neocortex, spanning from primary networks to association networks. High-level hierarchical structure-function decoupling contributes much more than low-level decoupling to individual cognition. Structure-function decoupling could be regulated by genes associated with pivotal receptors that are crucial for neuronal function flexibility.

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How brain structure-function decoupling supports individual cognition and its molecular mechanism.

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References
1.
Richiardi J, Altmann A, Milazzo A, Chang C, Chakravarty M, Banaschewski T . BRAIN NETWORKS. Correlated gene expression supports synchronous activity in brain networks. Science. 2015; 348(6240):1241-4. PMC: 4829082. DOI: 10.1126/science.1255905. View

2.
Arnatkeviciute A, Fulcher B, Fornito A . A practical guide to linking brain-wide gene expression and neuroimaging data. Neuroimage. 2019; 189:353-367. DOI: 10.1016/j.neuroimage.2019.01.011. View

3.
Arnatkeviciute A, Fulcher B, Oldham S, Tiego J, Paquola C, Gerring Z . Genetic influences on hub connectivity of the human connectome. Nat Commun. 2021; 12(1):4237. PMC: 8271018. DOI: 10.1038/s41467-021-24306-2. View

4.
Hawrylycz M, Lein E, Guillozet-Bongaarts A, Shen E, Ng L, Miller J . An anatomically comprehensive atlas of the adult human brain transcriptome. Nature. 2012; 489(7416):391-399. PMC: 4243026. DOI: 10.1038/nature11405. View

5.
Haufe S, Meinecke F, Gorgen K, Dahne S, Haynes J, Blankertz B . On the interpretation of weight vectors of linear models in multivariate neuroimaging. Neuroimage. 2013; 87:96-110. DOI: 10.1016/j.neuroimage.2013.10.067. View