Tasserie, J. | bioRxiv Science

bioRxiv2020

A collaborative resource platform for non-human primate neuroimaging

Neuroimaging non-human primates (NHPs) is a growing, yet highly specialized field of neuroscience. Resources that were primarily developed for human neuroimaging often need to be significantly adapted for use with NHPs or other animals, which has led to an abundance of custom, in-house solutions. In recent years, the global NHP neuroimaging community has made significant efforts to transform the field towards more open and collaborative practices. Here we present the PRIMatE Resource Exchange (PRIME-RE), a new collaborative online platform for NHP neuroimaging. PRIME-RE is a dynamic community-driven hub for the exchange of practical knowledge, specialized analytical tools, and open data repositories, specifically related to NHP neuroimaging. PRIME-RE caters to both researchers and developers who are either new to the field, looking to stay abreast of the latest developments, or seeking to collaboratively advance the field.

neuroscience↗

bioRxiv2020

Predicting Cortical Signatures of Consciousness using Dynamic Functional Connectivity Graph-Convolutional Neural Networks

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 selfsupervised machine learning method based on artificial neural networks to predict functional brain states across levels of consciousness from rsfMRI. The Functional Connectivity (FC) matrices reflect the brain-state dynamic at a given time. Because it is key to consider the FC topologies, a specific graph-Convolutional Neural Network (gCNN), namely BrainNetCNN, is considered to predict the brain states in awake and anesthetized nonhuman primates. To avoid the circularity that remains in the training stage, where the target is composed of pseudo-labels, recent self-supervised techniques are implemented. Using a linear probe for the prediction, the network achieves a prediction accuracy consistent with state-of-the-art methods lying in [0.655, 0.759] depending on the experimental settings. To put forward the interest of such a representation, the transition probabilities and the set of connections found to be important for predicting a brain state are computed. This latter is directly linked with the level of consciousness. The results demonstrate that deep learning methods are not only able to predict brain states but also provide additional insight into cortical signatures of consciousness with potential clinical consequences for the monitoring of anesthesia and the diagnosis of disorders of consciousness.

neuroscience↗