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Generative Embedding for Model-Based Classification of fMRI Data

Figure 10

Discriminative features.

A support vector machine with a sparsity-inducing regularizer (capped -regularizer) was trained and tested in a leave-one-out cross-validation scheme, resulting in subsets of selected features. The figure summarizes these subsets by visualizing how often each feature (printed along the y-axis) was selected across the repetitions (given as a fraction on the x-axis). Error bars represent central 95% posterior probability intervals of a Beta distribution with a flat prior over the interval [0, 1]. A group of 9 features was consistently found jointly informative for discriminating between aphasic patients and healthy controls (see main text). An additional figure showing which features were selected in each cross-validation fold can be found in the Supplementary Material (Figure S3). Crucially, since each feature corresponds to a model parameter that describes one particular interregional connection strength, the group of informative features can be directly related back to the underlying dynamic causal model (see highlighted connections in Figure 3).

doi: https://doi.org/10.1371/journal.pcbi.1002079.g010