FIGURE 1.

General procedure. Time series and volumes from hub brain regions of interest (ROI) of three major resting state networks were extracted: the fronto‐parietal network (FPN), the default mode network (DMN), and the sensorimotor network (SMN) during resting state functional MRI (fMRI). Global brain volume (GMV, WMV, CSF, and TIV), as well as regional GMV of ROIs were estimated for each individual (feature set 1: VOL). For the extracted time series, features relating to functional connectivity (feature set 2: rsFC) and nonlinear resting state dynamics (feature set 3: rsDyn) were assessed. Network dynamics were inferred subject‐wise via recurrent neural networks (RNNs). Features were then assessed based on the inferred RNNs illustrated here in terms of a flow field containing stable (open circles) and unstable (filled circles) fixed points, and nullclines (blue and red lines). Feature sets were used to build random forest classifiers classifying group membership (ALS vs. HC) based on the unimodal feature sets, as well as based on two and three (i.e., multimodal) feature sets. BOLD, blood‐oxygen level dependent signal; CSF, cerebrospinal fluid; GMV, gray matter volume; TIV, total intracranial volume; WMV, white matter volume