Extended Data Fig. 3: Support Vector Machine classification.

We ran a support vector machine (SVM) classification algorithm to test whether the multimodal independent components (ICs) outperformed the unimodal ICs in discriminating autistic from nonautistic individuals. a) Data are presented as the area under the receiver operating characteristic curve (AUC) along with the 95% confidence interval (CI) as a function of different thresholds between 85% to 99% that define whether an IC is multimodal or unimodal. b) Data are presented as the AUC along with the 95% CI when forcing uni- and multimodal features to have the same number of ICs in each fold. In the beginning (up to six ICs) there are no differences, which become apparent when increasing the number of ICs included as features. Abbreviations: IC=independent component; AUC= area under the receiver operating characteristic curve; CI=confidence interval.