LFP classification and inferring SDC
Neural network models were used to classify LFP features using PyTorchWe use the GCE frameworkIn the present work, the GCE was implemented using two separate networks: a feature compression network that maps the data from the feature space to the low-dimensional latent space and a feature reconstruction network that reconstructs the feature space data from the latent components (Fig. The trained feature compression network was used to infer discriminative components of the LFP collected during months 2–5. LFP spectral features, computed in 10-second segments, were minimum–maximum scaled to the training set (LFP features from months 1 and 6) and projected through the feature compression network to infer discriminative and non-discriminative components. The SDC was transformed to the probability of belonging to the ‘sick’ state which was determined as the ratio of the number of SDC values greater than or equal to SDC from the ‘sick’ state weeks to the total number of SDC values (equation below). This allowed the SDC to be compared directly against the face classifier output. The SDC was then averaged across the 10-second segments within a week:To map what features correspond to the SDC and SNDCs, the component values were varied in the latent space and passed through the feature reconstruction network. The resulting changes in the features were fit with second-order polynomials, and the magnitude of the coefficients served as an indicator of feature change between weeks 1–4 and weeks 21–24. As the slope term captured most of the change, it was used as a measure of the features underlying the SDC.