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Personalization of hybrid brain models from neuroimaging and electrophysiology data

R. Sanchez-Todo, R. Salvador, View ORCID ProfileE. Santarnecchi, View ORCID ProfileF. Wendling, View ORCID ProfileG. Deco, View ORCID ProfileG. Ruffini
doi: https://doi.org/10.1101/461350
R. Sanchez-Todo
aNeuroelectrics Barcelona, Av. Tibidabo 47bis, 08035, Barcelona, Spain
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R. Salvador
aNeuroelectrics Barcelona, Av. Tibidabo 47bis, 08035, Barcelona, Spain
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E. Santarnecchi
bBerenson-Allen Center for Non-Invasive Brain Stimulation, Beth Israel Medical Center, Harvard Medical School, Boston, MA, USA
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  • ORCID record for E. Santarnecchi
F. Wendling
cUniv Rennes, INSERM, LTSI-UMR1099, F-35000 Rennes, France
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G. Deco
dInstitució Catalana de Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Computational Neuroscience, Plaça de la Mercè, 10–12, 08002 Barcelona, Spain
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G. Ruffini
aNeuroelectrics Barcelona, Av. Tibidabo 47bis, 08035, Barcelona, Spain
eNeuroelectrics Corporation, Cambridge, 02139 MA, USA
fStarlab Barcelona, Av. Tibidabo 47bis, 08035, Barcelona, Spain
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  • For correspondence: giulio.ruffini@neuroelectrics.com
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Abstract

Personalization is rapidly becoming standard practice in medical diagnosis and treatment. This study is part of an ambitious program towards computational personalization of neuromodulatory interventions in neuropsychiatry. We propose to model the individual human brain as a network of neural masses embedded in a realistic physical matrix capable of representing measurable electrical brain activity. We call this a hybrid brain model (HBM) to highlight that it encodes both biophysical and physiological characteristics of an individual brain. Although the framework is general, we provide here a pipeline for the integration of anatomical, structural and functional connectivity data obtained from magnetic resonance imaging (MRI), diffuse tensor imaging (DTI connectome) and electroencephalography (EEG). We personalize model parameters through a comparison of simulated cortical functional connectivity with functional connectivity profiles derived from cortically-mapped, subject-specific EEG. We show that individual information can be represented in model space through the proper adjustment of two parameters (global coupling strength and conduction velocity), and that the underlying structural information has a strong impact on the functional outcome of the model. These findings provide a proof of concept and open the door for further advances, including the model-driven design of non-invasive brain-stimulation protocols.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted November 04, 2018.
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Personalization of hybrid brain models from neuroimaging and electrophysiology data
R. Sanchez-Todo, R. Salvador, E. Santarnecchi, F. Wendling, G. Deco, G. Ruffini
bioRxiv 461350; doi: https://doi.org/10.1101/461350
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Personalization of hybrid brain models from neuroimaging and electrophysiology data
R. Sanchez-Todo, R. Salvador, E. Santarnecchi, F. Wendling, G. Deco, G. Ruffini
bioRxiv 461350; doi: https://doi.org/10.1101/461350

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