Voxel-based asymmetry of the regional gray matter over the inferior temporal gyrus correlates with depressive symptoms in medicated patients with major depressive disorder.

The number of patients suffering from major depressive disorder (MDD) is increasing worldwide. Imbalanced hemispherical brain activity may be an underlying factor of MDD; however, whether structural asymmetry also contributes to the symptoms experienced in MDD has been scarcely investigated. In this study, we aimed to examine cortical asymmetry in association with the severity of depressive and cognitive symptoms observed in MDD during stable medication. The association between the affective and cognitive symptoms and gray matter asymmetry was evaluated in 17 MDD patients using voxel-wise gray matter asymmetry analysis on high-resolution T1-weighted MR images. Asymmetry index values in the inferior temporal gyrus (ITG) correlated with the scores of the 17-item Hamilton Depression Rating Scale (HDRS), but no association was found with the Beck Hopelessness Scale, and performance on the 1-, 2- and 3-back task. Our results indicate that the asymmetry of gray matter content in the ITG might be associated with higher depression severity. Our findings might help to better understand how structural changes contribute to depression severity in patients with MDD.

Two Classes of T1 Hypointense Lesions in Multiple Sclerosis With Different Clinical Relevance.

Background: Hypointense lesions on T1-weighted images have important clinical relevance in multiple sclerosis patients. Traditionally, spin-echo (SE) sequences are used to assess these lesions (termed black holes), but Fast Spoiled Gradient-Echo (FSPGR) sequences provide an excellent alternative. Objective: To determine whether the contrast difference between T1 hypointense lesions and the surrounding normal white matter is similar on the two sequences, whether different lesion types could be identified, and whether the clinical relevance of these lesions types are different. Methods: Seventy-nine multiple sclerosis patients' lesions were manually segmented, then registered to T1 sequences. Median intensity values of lesions were identified on all sequences, then K-means clustering was applied to assess whether distinct clusters of lesions can be defined based on intensity values on SE, FSPGR, and FLAIR sequences. The standardized intensity of the lesions in each cluster was compared to the intensity of the normal appearing white matter in order to see if lesions stand out from the white matter on a given sequence. Results: 100% of lesions on FSPGR images and 69% on SE sequence in cluster #1 exceeded a standardized lesion distance of Z = 2.3 (p < 0.05). In cluster #2, 78.7% of lesions on FSPGR and only 17.7% of lesions on SE sequence were above this cutoff value, meaning that these lesions were not easily seen on SE images. Lesion count in the second cluster (lesions less identifiable on SE) significantly correlated with the Expanded Disability Status Scale (EDSS) (R: 0.30, p ≤ 0.006) and with disease duration (R: 0.33, p ≤ 0.002). Conclusion: We showed that black holes can be separated into two distinct clusters based on their intensity values on various sequences, only one of which is related to clinical parameters. This emphasizes the joint role of FSPGR and SE sequences in the monitoring of MS patients and provides insight into the role of black holes in MS.