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Topographic Factor Analysis: A Bayesian Model for Inferring Brain Networks from Neural Data

Figure 6

Predicting the covariance structure of an fMRI dataset.

A. Each dot reflects the covariance between a pair of images from a single participant (-axis: observed, -axis: estimated) using sources. The correlation reported in the panel is between entries in the two covariance matrices. B. We also used TFA to estimate the covariance structure of held-out data, using a 6-fold cross validation procedure. The panel displays the median correlations ( bootstrap-estimated 95% confidence intervals) between the observed and estimated covariance matrices (of held out data), as a function of the number of sources we fit. The medians are taken across the 6 folds and 9 participants, and the error bars reflect across-participant variability.

Figure 6

doi: https://doi.org/10.1371/journal.pone.0094914.g006