Fig. 1 | Scientific Reports

Fig. 1

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

Fig. 1

Illustration of the proposed VAE-VIENT framework. A VAE learns 2D latent representations \(z=(z1_i,z2_i)\) from dynamic functional connectivity matrices (dFCs), leading to (1) evaluation of the proposed model against other generative models implementing different latent dimensions, (2) exploration of latent space with the ability to view discrete or continuous representations (here we observe how brain patterns are organized in latent space), and (3) two simulation paradigms, including a receptive field analysis that generates tensor representations to study the effect of perturbing input dFCs, and an ablation study of Global Neuronal Workspace (GNW) connections to study the transition from wakefulness to unconsciousness.

Back to article page