Major depressive disorder (MDD) and bipolar disorder (BD) are common severe affective diseases. Although previous neuroimaging studies have investigated brain abnormalities in MDD or BD, the structural and functional differences between these two disorders remain unclear. In this study, we adopted a multimodal approach, combining voxel-based morphometry (VBM) and functional connectivity (FC), to study the common and distinct structural and functional alterations in unmedicated MDD and BD patients. The VBM analysis revealed that both the MDD and BD patients showed decreased gray matter volume (GMV) in the left anterior cingulate cortex (ACC_L) and right hippocampus (HIP_R) compared with the healthy controls, and the MDD patients showed decreased GMV in the left superior frontal gyrus (SFG_L) and ACC_L compared with the BD patients. Furthermore, we took these clusters as seed regions to analyze the abnormal resting-state functional connectivity (RSFC) in the patients. We found that both the MDD and BD groups had decreased RSFC between the ACC_L and the left orbitofrontal cortex (OFC_L) and that the MDD group had decreased RSFC between the SFG_L and the HIP_L, compared with the healthy controls. Our results revealed that the MDD and BD patients were more similar than different in GMV and RSFC. These findings indicate that investigating the frontal-limbic system could be useful for understanding the underlying mechanisms of these two disorders.

No abstract

Our results may reflect the disrupted white matter topological organization in the whole-brain, and abnormal regional connectivity supporting cognitive and affective functioning in depressed BD, which, in part, be due to impaired rich club connectivity.

Brain white matter (WM) could be generally categorized into two types, deep and superficial WM. Studies combining these two types WM are important for a better understanding of brain plasticity induced by motor training. In this study, we applied both univariate and multivariate approaches to study gymnastic training-induced plasticity in brain WM. Specifically, we acquired diffusion tensor imaging data from 13 world class gymnasts and 14 non-athlete normal controls, reconstructed brain deep and superficial WM tracts, estimated and compared their fractional anisotropy (FA) difference between the two groups. Taking FA values as the features, we applied logistic regression and support vector machine to distinguish the gymnasts from the controls. Compared to the controls, the gymnasts showed lower FA in four regional deep WM tracts, including the occipital lobe portion of left inferior fronto-occipital fasciculus (IFOF.L), occipital and temporal lobe portion of right inferior longitudinal fasciculus (ILF.R), insular cortex portion of right uncinate fasciculus (UF.R), and parietal lobe portion of right arcuate fasciculus (AF.R). Meanwhile, we found lower FA in the superficial U-shaped tracts within the frontal lobe in the gymnasts compared to the controls. In addition, we detected that mean FA in either the AF.R or the U-shaped tracts connecting the left pars triangularis and superior frontal gyrus was negatively correlated with years of training in the gymnasts. Classification analyses indicated FA in deep WM hold higher potential to distinguish the gymnasts from the controls. Overall, our findings provide a more complete picture of training-induced plasticity in brain WM.

Long-term intensive gymnastic training can induce brain structural and functional reorganization. Previous studies have identified structural and functional network differences between world class gymnasts (WCGs) and non-athletes at the whole-brain level. However, it is still unclear how interactions within and between functional networks are affected by long-term intensive gymnastic training. We examined both intra- and inter-network functional connectivity of gymnasts relative to non-athletes using resting-state fMRI (R-fMRI). R-fMRI data were acquired from 13 WCGs and 14 non-athlete controls. Group-independent component analysis (ICA) was adopted to decompose the R-fMRI data into spatial independent components and associated time courses. An automatic component identification method was used to identify components of interest associated with resting-state networks (RSNs). We identified nine RSNs, the basal ganglia network (BG), sensorimotor network (SMN), cerebellum (CB), anterior and posterior default mode networks (aDMN/pDMN), left and right fronto-parietal networks (lFPN/rFPN), primary visual network (PVN), and extrastriate visual network (EVN). Statistical analyses revealed that the intra-network functional connectivity was significantly decreased within the BG, aDMN, lFPN, and rFPN, but increased within the EVN in the WCGs compared to the controls. In addition, the WCGs showed uniformly decreased inter-network functional connectivity between SMN and BG, CB, and PVN, BG and PVN, and pDMN and rFPN compared to the controls. We interpret this generally weaker intra- and inter-network functional connectivity in WCGs during the resting state as a result of greater efficiency in the WCGs' brain associated with long-term motor skill training.