Deep learning models reveal the link between dynamic brain connectivity patterns and states of consciousness.

A low-dimensional variational autoencoder (VAE) can model dynamic functional connectivity from resting-state fMRI to capture brain patterns related to consciousness. The VAE balanced reconstruction and classification performance compared to other models. Its latent representations stratified brain patterns and experimental conditions. Receptive field analysis identified latent directions for transitioning between patterns, and an ablation study virtually inactivated brain areas. The model summarized consciousness-specific information in key inter-areal connections, consistent with the global neuronal workspace theory. This framework may support development of an interpretable computational brain model for disorders of consciousness.

Study at a glance

Abstract

Decoding states of consciousness from brain activity is a central challenge in neuroscience. Dynamic functional connectivity (dFC) allows the study of short-term temporal changes in functional connectivity (FC) between distributed brain areas. By clustering dFC matrices from resting-state fMRI, we previously described "brain patterns" that underlie different functional configurations of the brain at rest. The networks associated with these patterns have been extensively analyzed. However, the overall dynamic organization and how it relates to consciousness remains unclear. We hypothesized that deep learning networks would help to model this relationship. Recent studies have used low-dimensional variational autoencoders (VAE) to learn meaningful representations that can help explaining consciousness. Here, we investigated the complexity of selecting such a generative model to study brain dynamics, and extended the available methods for latent space characterization and modeling. Therefore, our contributions are threefold. First, compared with probabilistic principal component analysis and sparse VAE, we showed that the selected low-dimensional VAE exhibits balanced performance in reconstructing dFCs and classifying brain patterns. We then explored the organization of the obtained low-dimensional dFC latent representations. We showed how these representations stratify the dynamic organization of the brain patterns as well as the experimental conditions. Finally, we proposed to delve into the proposed brain computational model. We first applied a receptive field analysis to identify preferred directions in the latent space to move from one brain pattern to another. Then, an ablation study was achieved where we virtually inactivated specific brain areas. We demonstrated the model's efficiency in summarizing consciousness-specific information encoded in key inter-areal connections, as described in the global neuronal workspace theory of consciousness. The proposed framework advocates the possibility of developing an interpretable computational brain model of interest for disorders of consciousness, paving the way for a dynamic diagnostic support tool.