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

TY - JOUR

T1 - A morphometric signature of depressive symptoms in unmedicated patients with mood disorders

AU - Wise, T.

AU - Marwood, L.

AU - Perkins, A. M.

AU - Herane Vives, A.

AU - Williams, S.C.R

AU - Young, A.

AU - Cleare, A.J.

AU - Arnone, D.

PY - 2018/7

Y1 - 2018/7

N2 - ObjectiveA 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.MethodWe 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.ResultsWe 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.DiscussionThe 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

AB - ObjectiveA 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.MethodWe 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.ResultsWe 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.DiscussionThe 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

KW - Depression, bipolar disorder, magnetic resonance imaging, MRI, Machine learning, VBM, DARTEL

UR - http://www.scopus.com/inward/record.url?scp=85045857350&partnerID=8YFLogxK

U2 - 10.1111/acps.12887

DO - 10.1111/acps.12887

M3 - Article

SN - 0001-690X

VL - 138

SP - 73

EP - 82

JO - Acta Psychiatrica Scandinavica

JF - Acta Psychiatrica Scandinavica

IS - 1

ER -