King's College London

Research portal

A morphometric signature of depressive symptoms in unmedicated patients with mood disorders

Research output: Contribution to journalArticlepeer-review

Original languageEnglish
Pages (from-to)73-82
JournalActa Psychiatrica Scandinavica
Issue number1
Early online date22 Apr 2018
Accepted/In press9 Mar 2018
E-pub ahead of print22 Apr 2018
PublishedJul 2018


King's Authors


A growing literature indicates that unipolar 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.
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 un-medicated unipolar and bipolar depression.

We were able to predict depressive severity from grey matter volume in the antero-ventral bilateral insula in both unipolar and bipolar depression. Self-reported hypomanic symptoms did not predict grey matter loss with a significant degree of accuracy.
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

Download statistics

No data available

View graph of relations

© 2020 King's College London | Strand | London WC2R 2LS | England | United Kingdom | Tel +44 (0)20 7836 5454