Evidence map›Paper›PMID 38339909›Full record

ArticleHuman brain mapping2024

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

Xiaoxi Dong, Qiongling Li, Xuetong Wang, Yirong He, Debin Zeng, Lei Chu, Kun Zhao, Shuyu Li

Abstract read
In one paragraph

Article in Human brain mapping, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Xiaoxi DongState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Qiongling LiState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Xuetong WangState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Yirong HeState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Debin ZengBeijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing, China.
Lei ChuBeijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing, China.
Kun ZhaoSchool of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
Shuyu LiState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.ORCID 0000-0002-3459-6821

Funding

National Natural Science Foundation of China 32271146National Natural Science Foundation of China 81972160National Natural Science Foundation of China 82202245Startup Funds for Top-Notch Talents at Beijing Normal University
6 · The paper itself

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.

Indexed as

ConnectomeNeocortexNervous System Physiological PhenomenaBrainCognitionHumansMagnetic Resonance Imagingcognitioncortical hierarchygenesneurotransmittersstructure-function decoupling

Identifiers

PMID38339909
PMCPMC10826895

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.