OBJECTIVE: A growing literature indicates that unipolar depression and bipolar 
depression are associated with alterations in grey matter volume. However, it is 
unclear to what degree these patterns of morphometric change reflect symptom 
dimensions. Here, we aimed to predict depressive symptoms and hypomanic symptoms 
based on patterns of grey matter volume using machine learning.
METHOD: We used machine learning methods combined with voxel-based morphometry 
to predict depressive and self-reported hypomanic symptoms from grey matter 
volume in a sample of 47 individuals with unmedicated unipolar and bipolar 
depression.
RESULTS: We were able to predict depressive severity from grey matter volume in 
the anteroventral bilateral insula in both unipolar depression and bipolar 
depression. Self-reported hypomanic symptoms did not predict grey matter loss 
with a significant degree of accuracy.
DISCUSSION: The results of this study suggest that patterns of grey matter 
volume alteration in the insula are associated with depressive symptom severity 
across unipolar and bipolar depression. Studies using other modalities and 
exploring other brain regions with a larger sample are warranted to identify 
other systems that may be associated with depressive and hypomanic symptoms 
across affective disorders.