Comparing VBM and ROI analyses for detection of gray matter abnormalities in patients with bipolar disorder using MRI

Abstract

Background With the increasing efforts to a better understanding of psychiatric diseases, detection of brain morphological alterations is necessary. This study compared two methods—voxel-based morphometry (VBM) and region of interest (ROI) analyses—to identify significant gray matter changes of patients with bipolar disorder type I (BP I). Results The VBM findings suggested gray matter reductions in the left precentral gyrus and right precuneus of the patients compared to healthy subjects (α = 0.0005, uncorrected). However, no regions reached the level of significance in ROI analysis using the three atlases, i.e., hammers, lpba40, and neuromorphometrics atlases (α = 0.0005). Conclusion It can be concluded that VBM analysis seems to be more sensitive to partial changes in this study. If ROI analysis is employed in studies to detect structural brain alterations between groups, it is highly recommended to use VBM analysis besides.

Altered striatal functional connectivity and structural dysconnectivity in individuals with bipolar disorder: A resting state magnetic resonance imaging study

“… The processing framework of voxel-based morphometry (VBM) analysis using the CAT12 toolbox of SPM12 software as depicted by Seyedi et al ( 67 ). …”

“…The 100 voxel extent threshold was chosen and finally we used xjview (66) toolbox for MATLAB to record voxel brain area (represented with pseudo color),with significant differences, activation volume (cluster), activation intensity (statistically analyzed with t-test and expressed as T value; T value is proportional to the intensity). Figure 3 (49,67) depicts the VBM analysis processing framework.…”

“…A permutation test is a kind of statistical significance test in which all potential values of the test statistic under rearrangements of the labels on the observed data points are calculated to obtain the distribution of the test statistic under the null hypothesis (68). The regions that made it The processing framework of voxel-based morphometry (VBM) analysis using the CAT12 toolbox of SPM12 software as depicted by Seyedi et al (67).…”

Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging

“…In contrast, voxel-based approaches can detect statistically significant tissue density differences between the two groups ( Seyedi et al, 2020 ). It is more appropriate given the lack of consensus on which brain areas are important in ASD ( Eslami et al, 2021 ).…”

Neurobiological Features of Posttraumatic Stress Disorder (PTSD) and Their Role in Understanding Adaptive Behavior and Stress Resilience

“…Voxel-based morphometry is an automated neuroimaging method that was first presented by Ashburner and Friston in 2000 to inquire about macroscopic gray matter changes with high spatial resolution using T1-weighted MRI images, which were then statistically examined on all voxels, determining differences in the volumes of brain structures between groups [ 16 , 17 ]. Two studies have also applied ReHo, a neuroimaging method that allows the estimation of regional activation patterns in the encephalon based on the sum of the functional connectivity (FC) between a specific node in a network and surrounding areas [ 18 , 19 ].…”