Table 1 The accuracy of classification on the three datasets and cross predictions in the different cases of employing separate and joint features. The last column is the total data size in the pooled dataset. The FC dataset has a larger sample size because each rat subject had two fMRI scans for each experiment. Dimension reduction using PCA did not improve the classification accuracy in most cases except for FC-based classification on the Early dataset and the late-to-early cross prediction where the new accuracies and the optimal numbers of PCA components were indicated. In FC-based classification, only connections with significant group differences were retained except for cross predictions where all FC connections were used. For pooled, early, and late timepoints, the classification accuracy is the average over 1000 random data splits into 70% training and 30% testing. For early-to-late, the training set was all early datasets and the test set all late datasets (and vice versa for late-to-early)

From: Subject classification and cross-time prediction based on functional connectivity and white matter microstructure features in a rat model of Alzheimer’s using machine learning