Spatiotemporal organization of brain areas’ spectral fingerprints informing a new generation of computational whole-brain models

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Cognition requires the coordinated interaction of structurally and functionally diverse brain areas. Neuronal oscillations have been linked to a multitude of cognitive processes, are disturbed in neuropsychiatric diseases and have been recently revealed, through non-invasive magnetoencephalography (MEG) recordings, to be unique to each area. However, the detailed spatio-temporal organization of these “spectral fingerprints”, their potential co-variation with structural properties of the brain, as well as their subject-specificity and individual differences have not been characterized. For that reason, I propose, first, to characterize these properties using a large existing MEG data set of a healthy population provided by the Human Connectome Project. Together, spectral, temporal, spatial and structural information, as well as their variation in healthy individuals, will provide an extensive characterization of area-specific neural processing and is a crucial step for establishing spectral fingerprints as biomarkers to be compared, in future work, with a diseased population. Moreover, spectral fingerprints will inform and improve computational whole-brain models. Specifically, established models, spatially resolved at the level of cortical areas, possess unrealistic power spectra. I therefore propose, second, to develop a new generation of whole-brain models with more realistic power spectra and to infer the underlying neural circuitry parameters by constraining these models with each area’s spectral fingerprint. Computational modeling in combination with non-invasively obtainable biomarkers can reveal disturbances in neural circuit parameters in diseased individuals and can thereby hint at neural circuit mechanisms of neuropsychiatric diseases which remain poorly understood.