Abstract

Objective: Unipolar depression and depressive episodes of bipolar disorder have similar symptoms and their differential diagnosis is crucial because each disorder has different prognostic and therapeutic characteristics. The main aim of the current study was to investigate white matter alterations as measured by fractional anisotropy in individuals with bipolar disorder (BD) and unipolar depression (UD) using tract-based spatial statistics and to find out if these alterations can help to make a differential diagnosis between these two disorders.
Methods: Tract-based spatial statistics is a sensitive method of whole-brain analysis that relies on the voxel-based comparison. It uses nonlinear image transformation and permutation tests with correction for multiple comparisons. The study consisted of total number of 107 subjects; whom were diagnosed clinically at least by two different psychiatrists and their data were reviewed by another psychiatrist retrospectively. Whole-brain diffusion tensor images of 41 patients with bipolar disorder type 1, 43 patients with unipolar depression and 23 healthy controls were acquired using a 1.5 Tesla magnetic resonance imaging scanner. The results were analyzed with 1. Whole brain analysis, 2. Region of Interests (ROI) analysis followed by machine learning methods: Genetic Algorithm and Kernel Logistic Regression.
Results: Compared to controls, UD and BD subjects showed reduced FA in several white matter tracts (p

Key words: bipolar disorder, unipolar depression, tract-based spatial statistics, white matter