PT  - JOURNAL ARTICLE
AU  - A. Grigis
AU  - J. Tasserie
AU  - V. Frouin
AU  - B. Jarraya
AU  - L. Uhrig
TI  - Predicting Cortical Signatures of Consciousness using Dynamic Functional Connectivity Graph-Convolutional Neural Networks
AID  - 10.1101/2020.05.11.078535
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.05.11.078535
4099  - http://biorxiv.org/content/early/2020/05/12/2020.05.11.078535.short
4100  - http://biorxiv.org/content/early/2020/05/12/2020.05.11.078535.full
AB  - Decoding the levels of consciousness from cortical activity recording is a major challenge in neuroscience. Using clustering algorithms, we previously demonstrated that resting-state functional MRI (rsfMRI) data can be split into several clusters also called “brain states” corresponding to “functional configurations” of the brain. Here, we propose to use a supervised machine learning method based on artificial neural networks to predict functional brain states across levels of consciousness from rsfMRI. Because it is key to consider the topology of brain regions used to build the dynamical functional connectivity matrices describing the brain state at a given time, we applied BrainNetCNN, a graph-convolutional neural network (CNN), to predict the brain states in awake and anesthetized non-human primate rsfMRI data. BrainNetCNN achieved a high prediction accuracy that lies in [0.674, 0.765] depending on the experimental settings. We propose to derive the set of connections found to be important for predicting a brain state, reflecting the level of consciousness. The results demonstrate that deep learning methods can be used not only to predict brain states but also to provide additional insight on cortical signatures of consciousness with potential clinical consequences for the monitoring of anesthesia and the diagnosis of disorders of consciousness.Competing Interest StatementThe authors have declared no competing interest.