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Generative Embedding for Model-Based Classification of fMRI Data

Figure 4

Practical implementation of generative embedding for fMRI.

This figure summarizes the three core steps involved in the practical implementation of generative embedding proposed in this paper. This procedure integrates the inversion of a generative model into cross-validation. In step 1, within a given repetition , the model is specified using all subjects except . This yields a set of time series for each subject . In step 2, the model is inverted independently for each subject, giving rise to a set of subject-specific posterior parameter means . In step 3, these parameter estimates are used to train a classifier on all subjects except and test it on subject , which yields a prediction about the class label of subject . After having repeated these three steps for all , the set of predicted labels can be compared with the true labels, which allows us to estimate the algorithm's generalization performance. In addition, parameters that proved jointly discriminative can be interpreted in the context of the underlying generative model. The sequence of steps shown here corresponds to the procedure shown in Figure 2c and 2f, where it is contrasted with alternative procedures that are simpler but risk an optimistic bias in estimating generalization performance.

doi: https://doi.org/10.1371/journal.pcbi.1002079.g004