Specific Gray Matter Volume Changes of the Brain in Unipolar and Bipolar Depression.

Junyan Wang,Jizhi Wang,Aixia Zhang,Yong Xu,Ning Sun,Chunxia Yang,Penghong Liu,Sha Liu

doi:10.3389/fnhum.2020.592419

Abstract

To identify the common and specific structural basis of bipolar depression (BD) and unipolar depression (UD) is crucial for clinical diagnosis. In this study, a total of 85 participants, including 22 BD patients, 36 UD patients, and 27 healthy controls, were enrolled. A voxel-based morphology method was used to identify the common and specific changes of the gray matter volume (GMV) to determine the structural basis. Significant differences in GMV were found among the three groups. Compared with healthy controls, UD patients showed decreased GMV in the orbital part of the left inferior frontal gyrus, whereas BD patients showed decreased GMV in the orbital part of the left middle frontal gyrus. Compared with BD, UD patients have increased GMV in the left supramarginal gyrus and middle temporal gyrus. Our results revealed different structural changes in UD and BD patients suggesting BD and UD have different neurophysiological underpinnings. Our study contributes toward the biological determination of morphometric changes, which could help to discriminate between UD and BD.

Highlights

Bipolar and unipolar depression (BD and UD) are leading causes of disability worldwide in view of the often recurrent pattern of illness, which significantly impinges on the quality of life of affected individuals (Delvecchio et al, 2012)
The anxiety states were evaluated based on the Hamilton Anxiety Scale (HAMA) (Hamilton, 1959), and the manic states were evaluated based on the Young Mania Rating Scale (YMRS) (Young et al, 1978)
We reported that patients with mood disorders (UD and bipolar depression (BD)) showed specific gray matter volume (GMV) differences in the left middle temporal gyrus, orbital part of the middle frontal gyrus, inferior frontal gyrus, and supramarginal gyrus

Summary

Introduction

Bipolar and unipolar depression (BD and UD) are leading causes of disability worldwide in view of the often recurrent pattern of illness, which significantly impinges on the quality of life of affected individuals (Delvecchio et al, 2012). Mental illness is diagnosed primarily through a careful assessment of behavior and subjective reports of abnormal experiences to categorize patients (Phillips and Kupfer, 2013). This means that it is difficult for clinicians to distinguish the two disorders based solely on clinical manifestations (Chen et al, 2018). The development of neuroimaging techniques provides an opportunity to identify UD and BD and to deepen our understanding of the underlying mechanisms of affective disorders

Methods

Results

Conclusion