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Hippocampal subfield volumes in mood disorders.

B CaoI C PassosB MwangiH Amaral-SilvaJonika TannousM-J WuG B Zunta-SoaresJ C Soares
Published in: Molecular psychiatry (2017)
Volume reduction and shape abnormality of the hippocampus have been associated with mood disorders. However, the hippocampus is not a uniform structure and consists of several subfields, such as the cornu ammonis (CA) subfields CA1-4, the dentate gyrus (DG) including a granule cell layer (GCL) and a molecular layer (ML) that continuously crosses adjacent subiculum (Sub) and CA fields. It is known that cellular and molecular mechanisms associated with mood disorders may be localized to specific hippocampal subfields. Thus, it is necessary to investigate the link between the in vivo hippocampal subfield volumes and specific mood disorders, such as bipolar disorder (BD) and major depressive disorder (MDD). In the present study, we used a state-of-the-art hippocampal segmentation approach, and we found that patients with BD had reduced volumes of hippocampal subfields, specifically in the left CA4, GCL, ML and both sides of the hippocampal tail, compared with healthy subjects and patients with MDD. The volume reduction was especially severe in patients with bipolar I disorder (BD-I). We also demonstrated that hippocampal subfield volume reduction was associated with the progression of the illness. For patients with BD-I, the volumes of the right CA1, ML and Sub decreased as the illness duration increased, and the volumes of both sides of the CA2/3, CA4 and hippocampal tail had negative correlations with the number of manic episodes. These results indicated that among the mood disorders the hippocampal subfields were more affected in BD-I compared with BD-II and MDD, and manic episodes had focused progressive effect on the CA2/3 and CA4 and hippocampal tail.
Keyphrases
  • bipolar disorder
  • major depressive disorder
  • cerebral ischemia
  • temporal lobe epilepsy
  • protein kinase
  • blood brain barrier
  • brain injury
  • machine learning
  • single cell
  • deep learning
  • sleep quality
  • depressive symptoms