Leonardo Bonetti

@leonardobonetti.bsky.social

Associate Professor, Center for Music in the Brain, Department of Clinical Medicine, Aarhus University Senior Research Fellow, Centre for Eudaimonia and Human Flourishing, University of Oxford

2/n 🧘‍♂️ During rest, FREQ-NESS reliably separates well-known resting state brain networks — the Default Mode Network, alpha-band parieto-occipital, and motor-beta sensorimotor topographies. Textbook configurations emerge directly from the data and based on frequency, without predefining regions.

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3/n 🎧 Auditory stimulation reshapes the entire frequency-resolved network landscape: • EMERGENCE: Attunement to the 2.4 Hz stimulation • RE-ARRANGEMENT: Spatial shift of alpha from occipital to sensorimotor, spectral shift to high alpha activity • INVARIANCE: Beta networks remain unchanged

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4/ Provided the network separation, we also tracked cross-frequency coupling (CFC) between networks. During passive listening to the metronome, the phase of low-freq (2.4 Hz) auditory networks selectively modulates the gamma band amplitude in more distributed medial temporal networks.

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5/ 🛠️ FREQ-NESS has been a long time in the making. If you’re interested in exploring frequency-resolved brain networks in your own data — check out the toolbox and documentation here: 👉 shorturl.at/mOVKF Feel free to reach out for clarification or collaborations #OpenScience #Toolbox

GitHub - mattiaRosso92/Frequency-resolved_brain_network_estimation_via_source_separation_FREQ-NESS

Contribute to mattiaRosso92/Frequency-resolved_brain_network_estimation_via_source_separation_FREQ-NESS development by creating an account on GitHub.

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