bioRxivNeurosciencePreprint
Structural connectivity shapes a cross-modal gradient of brain flexibility
Matteo NeriiD, Marianna Angioielli, Elsa Bourges, Camille MazzaraiD, Emahnuel Troisi LopeziD, Matteo DemuruiD, Mario De LucaiD, Enrica GalloiD, Mario QuarantelliiD, Andrea BrovelliiD, Pierpaolo SorrentinoiD
Abstract
The brain must rapidly and dynamically recruit the appropriate brain regions to respond to environmental changes and sustain flexible cognition. An extensive body of literature views the ability to spontaneously explore a rich repertoire of activity patterns, even during rest, as a correlate of the capacity to respond rapidly to internal fluctuations and external stimuli. Moreover, brain flexibility at rest successfully indexes pathological conditions such as Parkinsons disease, multiple sclerosis, and amyotrophic lateral sclerosis. However, previous work provides no information about the contribution of each region to the flexibility of brain dynamics. Given the functional and structural heterogeneity of brain regions, we hypothesized their roles in sustaining flexibility to be heterogeneous and related to regional structural properties. To systematically investigate these hypotheses, we define the flexibility gradient (FG), a new measure, which quantifies regional contributions to flexibility. We employed three datasets spanning different recording techniques and a range of spatial and temporal scales: 47 MEG recordings, 11 EEG recordings, and 77 fMRI recordings along with the corresponding tractographies. We found that FG is not homogeneously distributed, with associative and occipital regions contributing the most. This gradient is symmetric across left and right hemispheres, and the structural properties of brain regions correlate with their contribution to flexibility. Our findings are stable across modalities and temporal scales, highlighting that brain flexibility is not only a global dynamical property but also a spatially structured feature shaped by the anatomical organization of the brain.
- organism: human
- assay: electronic health records
- research focus: dimensionality reduction
- approach: statistical model
- Citations
- 0
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