Predicting anxiety from wholebrain activity patterns to emotional faces in young adults: a machine learning approach.

In the distressed sample we obtained similar results as the ones obtained when considering the whole sample with almost significant p-values after correcting for multiple comparison, potentially due to the smaller sample size (Table S01 – Supplementary Material).

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It is interesting to note that the models decoding MASQ-D almost reached significance for both cross-validations strategies, suggesting a weak association between the self-report depression measure and the patterns of brain activity during dynamic emotional face processing.

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