Reconstructing brain functional networks through identifiability and Deep Learning

Author:

Zanin Massimiliano1ORCID,Aktürk Tuba23,Yıldırım Ebru2,Yerlikaya Deniz4,Yener Görsev456,Güntekin Bahar37

Affiliation:

1. Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, 07122 Palma de Mallorca, Spain

2. Program of Electroneurophysiology, Vocational School, Istanbul Medipol University, Istanbul, Turkey

3. Health Sciences and Technology Research Institute (SABITA), Istanbul Medipol University, Istanbul, Turkey

4. Department of Neurosciences, Health Sciences Institute, Dokuz Eylül University, Izmir, Turkey

5. School of Medicine, Izmir University of Economics, Izmir, Turkey

6. Brain Dynamics Multidisciplinary Research Center, Dokuz Eylül University, Izmir, Turkey

7. Department of Biophysics, School of Medicine, Istanbul Medipol University, Turkey

Abstract

Abstract We propose a novel approach for the reconstruction of functional networks representing brain dynamics based on the idea that the co-participation of two brain regions in a common cognitive task should result in a drop in their identifiability, or in the uniqueness of their dynamics. This identifiability is estimated through the score obtained by Deep Learning models in supervised classification tasks; and therefore requires no a priori assumptions about the nature of such co-participation. The method is tested on EEG recordings obtained from Alzheimer‘s and Parkinson‘s Disease patients, and matched healthy volunteers, for eyes-open and eyes-closed resting state conditions; and the resulting functional networks are analysed through standard topological metrics. Both groups of patients are characterised by a reduction in the identifiability of the corresponding EEG signals, and by differences in the patterns that support such identifiability. Resulting functional networks are similar, but not identical to those reconstructed by using a correlation metric. Differences between control subjects and patients can be observed in network metrics like the clustering coefficient and the assortativity, in different frequency bands. Differences are also observed between eyes-open and closed conditions, especially for Parkinson‘s Disease patients.

Funder

H2020 European Research Council

TÜBITAK

Agencia Estatal de Investigación

Publisher

MIT Press

Subject

Applied Mathematics,Artificial Intelligence,Computer Science Applications,General Neuroscience

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