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Spectral Fingerprints of Neuronal Interactions

Final Report Summary - SPECFIN (Spectral Fingerprints of Neuronal Interactions)

The brain is a highly dynamic and distributed system. How do sophisticated cognitive processes such as perception, decision-making, and motor behavior emerge from dynamic interactions across the brain? Which neural mechanisms coordinate these interactions? Substantial progress has been made understanding individual neurons and brain regions, but we lack understanding of how they work together.

Brain activity exhibits oscillations, i.e. periodicity, at various different frequencies and spatial scales. These oscillations can be measured non-invasively in the human brain using electroencephalography (EEG) or magnetoencephalography (MEG) and may serve as highly informative markers, or ‘spectral fingerprints’ of the specific circuit interactions involved in different cognitive functions. Furthermore, these oscillations may also mediate and coordinate these interactions. The central goal of this project was to systematically characterize these oscillations non-invasively and to investigate the underlying neuronal circuit mechanisms. To address this goal, we applied an interdisciplinary scientific approach. We combined non-invasive recordings of neuronal population activity (EEG and MEG), novel analytical techniques, and large-scale cellular-level recordings of neuronal activity. The project yielded several key outcomes:

First, our results suggest that there are generic types of oscillations that, depending on the brain region at hand, can be dissociated in several subtypes of oscillations. This suggest a hierarchical taxonomy with network-specific subtypes of several generic oscillations. Second, our results show that local neuronal oscillations are coupled across the brain in different coupling modes. These coupling modes are expressed in different brain networks, show distinct temporal dynamics and provide complementary information about pathological brain processes. Third, we found that highly specific neuronal information can be extracted from non-invasive electrophysiological signals (M/EEG) and that this information is comparable to neuronal information encoded by the activity of individual neurons. Fourth, we found that several functionally distinct neuronal cell-types can be identified based on the temporal characteristics of their activity. This opens a powerful new window, to study cell-type specific circuit mechanisms of cognitive functions. Fifth, we found that specific local and large-scale oscillations are associated with the functional properties of neurons coupled to these oscillations.

The projects findings advance our understanding of neuronal oscillations – also as potential biomarkers for novel approaches in the study, diagnosis and therapy of various neuropsychiatric disorders.