ArticleFrontiers in human neuroscience2022
The music of the hemispheres: Cortical eigenmodes as a physical basis for large-scale brain activity and connectivity patterns.
Article in Frontiers in human neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Modeling the influences of non-local connectomic projections on geometrically constrained cortical dynamics.PLoS computational biology · 2026Article
- Classification of major depressive disorder using vertex-wise brain sulcal depth, curvature, and thickness with a deep and a shallow learning model.Molecular psychiatry · 2026Article
- Brain modes of resonance estimated by a biophysical multi-compartment finite elements model.Scientific reports · 2025Article
- Geometric constraints on the architecture of mammalian cortical connectomes.bioRxiv : the preprint server for biology · 2025Article
- Extracting interpretable signatures of whole-brain dynamics through systematic comparison.PLoS computational biology · 2024Article
- Integrative, segregative, and degenerate harmonics of the structural connectome.Communications biology · 2024Article
- Extracting interpretable signatures of whole-brain dynamics through systematic comparison.bioRxiv : the preprint server for biology · 2024Article
- Neural Field Continuum Limits and the Structure-Function Partitioning of Cognitive-Emotional Brain Networks.Biology · 2023Review
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5 authors.
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Abstract
Neuroscience has had access to high-resolution recordings of large-scale cortical activity and structure for decades, but still lacks a generally adopted basis to analyze and interrelate results from different individuals and experiments. Here it is argued that the natural oscillatory modes of the cortex-cortical eigenmodes-provide a physically preferred framework for systematic comparisons across experimental conditions and imaging modalities. In this framework, eigenmodes are analogous to notes of a musical instrument, while commonly used statistical patterns parallel frequently played chords. This intuitive perspective avoids problems that often arise in neuroimaging analyses, and connects to underlying mechanisms of brain activity. We envisage this approach will lead to novel insights into whole-brain function, both in existing and prospective datasets, and facilitate a unification of empirical findings across presently disparate analysis paradigms and measurement modalities.
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