Decoupling of brain function from structure reveals regional behavioral specialization in humans
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Abstract (arXiv)
The brain is an assembly of neuronal populations interconnected by structural pathways. Brain activity is expressed on and constrained by this substrate. Therefore, statistical dependencies between functional signals in directly connected areas can be expected higher. However, the degree to which brain function is bound by the underlying wiring diagram remains a complex question that has been only partially answered. Here, we introduce the structural-decoupling index to quantify the coupling strength between structure and function, and we reveal a macroscale gradient from brain regions more strongly coupled, to regions more strongly decoupled, than expected by realistic surrogate data. This gradient spans behavioral domains from lower-level sensory function to high-level cognitive ones and shows for the first time that the strength of structure-function coupling is spatially varying in line with evidence derived from other modalities, such as functional connectivity, gene expression, microstructural properties and temporal hierarchy.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 May 2019.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 4; 2025: 1; 2026: 3.
10 papers.
- 2024What to align in multimodal contrastive learning?cited by 41
- 2024NeuroPath: A Neural Pathway Transformer for Joining the Dots of Human Connectomescited by 6
- 2024Fine-Grained Domain Generalization with Feature Structuralizationcited by 3
- 2026NeuroMambaLLM: Dynamic Graph Learning of fMRI Functional Connectivity in Autistic Brains Using Mamba and Language Model Reasoningcited by 2
- 2024Learning Graph Filters for Structure-Function Coupling based Hub Node Identificationcited by 1
- 2026Stability of Flow Models for Graph Signalscited by 1
- 2021Graph-LDA: Graph Structure Priors to Improve the Accuracy in Few-Shot Classificationcited by 0
- 2022Pruning Graph Convolutional Networks to select meaningful graph frequencies for fMRI decodingcited by 0
- 2025Comparing and Scaling fMRI Features for Brain-Behavior Predictioncited by 0
- 2026Topological Signal Processing: An Application-Oriented Tutorialcited by 0
- 2026Stability of Flow Models for Graph Signals8 Jul 2026
- 2026Topological Signal Processing: An Application-Oriented Tutorial18 May 2026
- 2026NeuroMambaLLM: Dynamic Graph Learning of fMRI Functional Connectivity in Autistic Brains Using Mamba and Language Model Reasoning14 Feb 2026
- 2025Comparing and Scaling fMRI Features for Brain-Behavior Prediction28 Jul 2025
- 2024Learning Graph Filters for Structure-Function Coupling based Hub Node Identification22 Oct 2024
- 2024NeuroPath: A Neural Pathway Transformer for Joining the Dots of Human Connectomes26 Sep 2024
- 2024What to align in multimodal contrastive learning?11 Sep 2024
- 2024Fine-Grained Domain Generalization with Feature Structuralization13 Jun 2024
- 2022Pruning Graph Convolutional Networks to select meaningful graph frequencies for fMRI decoding9 Mar 2022
- 2021Graph-LDA: Graph Structure Priors to Improve the Accuracy in Few-Shot Classification23 Aug 2021