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Structure–function coupling in macroscale human brain networks

Nature Reviews Neuroscience (2024)Cite this article

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Abstract

Precisely how the anatomical structure of the brain gives rise to a repertoire of complex functions remains incompletely understood. A promising manifestation of this mapping from structure to function is the dependency of the functional activity of a brain region on the underlying white matter architecture. Here, we review the literature examining the macroscale coupling between structural and functional connectivity, and we establish how this structure–function coupling (SFC) can provide more information about the underlying workings of the brain than either feature alone. We begin by defining SFC and describing the computational methods used to quantify it. We then review empirical studies that examine the heterogeneous expression of SFC across different brain regions, among individuals, in the context of the cognitive task being performed, and over time, as well as its role in fostering flexible cognition. Last, we investigate how the coupling between structure and function is affected in neurological and psychiatric conditions, and we report how aberrant SFC is associated with disease duration and disease-specific cognitive impairment. By elucidating how the dynamic relationship between the structure and function of the brain is altered in the presence of neurological and psychiatric conditions, we aim to not only further our understanding of their aetiology but also establish SFC as a new and sensitive marker of disease symptomatology and cognitive performance. Overall, this Review collates the current knowledge regarding the regional interdependency between the macroscale structure and function of the human brain in both neurotypical and neuroatypical individuals.

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Fig. 1: Computing the structure–function coupling of a brain region.
Fig. 2: Axes of variation in normative structure–function coupling.
Fig. 3: Structure–function coupling in neurotypical and neuroatypical individuals.

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Acknowledgements

This work was supported by the National Institutes of Health grants T32-EB020087 (P.F.), R01MH113550 (D.S.B. and T.D.S.), RF1MH116920 (D.S.B.), R21MH106799 (D.S.B.), R01MH112847 (R.T.S. and T.D.S.), R01EB022573 (T.D.S.), R01MH120482 (T.D.S.), R37MH125829 (T.D.S.), R01MH112847 (R.T.S.), R01MH123550 (R.T.S.), R01NS112274 (R.T.S.), RF1MH116920 (D.S.B. and T.D.S.) and K99MH127296 (L.P.), the Swartz Foundation (D.S.B.), the AE Foundation (T.D.S.), the Brain & Behaviour Research Foundation (2020 NARSAD Young Investigator Grant to L.P.) and the John D. and Catherine T. MacArthur Foundation (D.S.B.).

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Authors and Affiliations

  1. Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Panagiotis Fotiadis

  2. Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA

    Panagiotis Fotiadis, Linden Parkes & Dani S. Bassett

  3. Department of Anaesthesiology, University of Michigan, Ann Arbor, MI, USA

    Panagiotis Fotiadis

  4. Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Kathryn A. Davis & Dani S. Bassett

  5. Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Theodore D. Satterthwaite & Dani S. Bassett

  6. Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA

    Theodore D. Satterthwaite & Russell T. Shinohara

  7. Center for Biomedical Image Computing & Analytics, University of Pennsylvania, Philadelphia, PA, USA

    Theodore D. Satterthwaite & Russell T. Shinohara

  8. Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA

    Theodore D. Satterthwaite

  9. Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA

    Dani S. Bassett

  10. Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, USA

    Dani S. Bassett

  11. Santa Fe Institute, Santa Fe, NM, USA

    Dani S. Bassett

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  1. Panagiotis Fotiadis
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Contributions

P.F. researched data for the article and wrote the article. All authors reviewed and/or edited the manuscript before submission.

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Correspondence to Panagiotis Fotiadis or Dani S. Bassett.

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Glossary

Coefficient of variation

A statistical metric quantifying the dispersion of a set of data points in relation to their mean value and defined as the ratio between the standard deviation and mean value of the data points.

Discriminant function analyses

Statistical analyses used in classification problems to determine the discriminant functions — weighted combinations of the provided independent variables — that maximize separability among two or more categories within the outcome (dependent) variable; accuracy of classification is also evaluated.

Global efficiency

The average of the inverse shortest-path distances from one node to all other nodes, capturing network efficiency in transferring information.

Heteromodal association cortices

Regions receiving convergent inputs from unimodal areas from more than one sensory modality.

Hub regions

Brain regions characterized by a relatively large number of connections to other regions (more commonly referred to as high-degree nodes).

Ictal periods

The periods between seizure onset and seizure termination, as defined electrographically.

Laminar differentiation

Brain regions with high levels of laminar differentiation (also referred to as granularization) are characterized by well-defined, highly developed cortical layers II and IV.

Limbic regions

Cortices including the hippocampal complex, the amygdaloid complex, the prepiriform olfactory cortex, the septal area and the substantia innominata.

Local connections

Connections linking non-rich club nodes to other non-rich club nodes.

Modules

A group of densely interconnected brain regions that are sparsely connected to the rest of the brain (also known as a community).

Paralimbic regions

Cortices including the caudal orbitofrontal cortex, the insula, the temporal pole, the parahippocampal gyrus and the retrosplenial–cingulate–parolfactory complex.

Path length

The minimum number of edges (the shortest path) required to traverse from one node to another.

Primary sensory and motor cortices

The cortical regions wherein sensory information from the external environment is first projected into the brain; the primary motor cortex relays motor programmes into spinal motor neurons to initiate further action.

Rich club

A subset of high-degree brain regions (nodes) that are densely interconnected; connections between rich club nodes are referred to as rich club connections.

Transmodal regions

The combined set of heteromodal association cortices and paralimbic and limbic regions; transmodal areas receive and integrate input from multiple sensory modalities to give rise to complex conscious perception and flexible cognition, and often exert a ‘top–down’ influence on unimodal association cortices.

Unimodal association cortices

Regions receiving projections from primary sensory areas and other unimodal association areas from the same sensory modality.

Unimodal sensory regions

The combined set of primary sensory and unimodal association cortical regions.

Variance

A statistical metric quantifying the dispersion of a set of data points around their mean.

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Fotiadis, P., Parkes, L., Davis, K.A. et al. Structure–function coupling in macroscale human brain networks. Nat. Rev. Neurosci. (2024). https://doi.org/10.1038/s41583-024-00846-6

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