Treatment Response Prediction for Major Depressive Disorder Patients via Multivariate Pattern Analysis of Thalamic Features.

Antidepressant treatment, as an important method in clinical practice, is not suitable for all major depressive disorder (MDD) patients. Although magnetic resonance imaging (MRI) studies have found thalamic abnormalities in MDD patients, it is not clear whether the features of the thalamus are suitable to serve as predictive aids for treatment responses at the individual level. Here, we tested the predictive value of gray matter density (GMD), gray matter volume (GMV), amplitude of low-frequency fluctuations (ALFF), and fractional ALFF (fALFF) of the thalamus using multivariate pattern analysis (MVPA). A total of 74 MDD patients and 44 healthy control (HC) subjects were recruited. Thirty-nine MDD patients and 35 HC subjects underwent scanning twice. Between the two scanning sessions, patients in the MDD group received selective serotonin reuptake inhibitor (SSRI) treatment for 3-month, and HC group did not receive any treatment. Gaussian process regression (GPR) was trained to predict the percentage decrease in the Hamilton Depression Scale (HAMD) score after treatment. The percentage decrease in HAMD score after SSRI treatment was predicted by building GPRs trained with baseline thalamic data. The results showed significant correlations between the true percentage of HAMD score decreases and predictions (p < 0.01, r 2 = 0.11) in GPRs trained with GMD. We did not find significant correlations between the true percentage of HAMD score decreases and predictions in GMV (p = 0.16, r 2 = 0.00), ALFF (p = 0.125, r 2 = 0.00), and fALFF (p = 0.485, r 2 = 0.10). Our results suggest that GMD of the thalamus has good potential as an aid in individualized treatment response predictions of MDD patients.

Individualized diagnosis of major depressive disorder via multivariate pattern analysis of thalamic sMRI features.

BACKGROUND: Magnetic resonance imaging (MRI) studies have found thalamic abnormalities in major depressive disorder (MDD). Although there are significant differences in the structure and function of the thalamus between MDD patients and healthy controls (HCs) at the group level, it is not clear whether the structural and functional features of the thalamus are suitable for use as diagnostic prediction aids at the individual level. Here, we were to test the predictive value of gray matter density (GMD), gray matter volume (GMV), amplitude of low-frequency fluctuations (ALFF), and fractional amplitude of low-frequency fluctuations (fALFF) in the thalamus using multivariate pattern analysis (MVPA). METHODS: Seventy-four MDD patients and 44 HC subjects were recruited. The Gaussian process classifier (GPC) was trained to separate MDD patients from HCs, Gaussian process regression (GPR) was trained to predict depression scores, and Multiple Kernel Learning (MKL) was applied to explore the contribution of each subregion of the thalamus. RESULTS: The primary findings were as follows: [1] The balanced accuracy of the GPC trained with thalamic GMD was 96.59% (P < 0.001). The accuracy of the GPC trained with thalamic GMV was 93.18% (P < 0.001). The correlation between Hamilton Depression Scale (HAMD) score targets and predictions in the GPR trained with GMD was 0.90 (P < 0.001, r2 = 0.82), and in the GPR trained with GMV, the correlation between HAMD score targets and predictions was 0.89 (P < 0.001, r2 = 0.79). [2] The models trained with ALFF and fALFF in the thalamus failed to discriminate MDD patients from HC participants. [3] The MKL model showed that the left lateral prefrontal thalamus, the right caudal temporal thalamus, and the right sensory thalamus contribute more to the diagnostic classification. CONCLUSIONS: The results suggested that GMD and GMV, but not functional indicators of the thalamus, have good potential for the individualized diagnosis of MDD. Furthermore, the thalamus shows the heterogeneity in the structural features of thalamic subregions for predicting MDD. To our knowledge, this is the first study to focus on the thalamus for the prediction of MDD using machine learning methods at the individual level.

Aberrant state-related dynamic amplitude of low-frequency fluctuations of the emotion network in major depressive disorder.

Major depressive disorder (MDD) is a highly prevalent mental disorder that is typically characterized by pervasive and persistent low mood. This durable emotional disturbance may represent a key aspect of the neuropathology of MDD, typified by the wide-ranging distribution of brain alterations involved in emotion processing. However, little is known about whether these alterations are represented as the state properties of dynamic amplitude of low-frequency fluctuation (dALFF) variability in the emotion network. To address this question, we investigated the time-varying intrinsic brain activity derived from resting-state functional magnetic resonance imaging (R-fMRI). Data were obtained from 50 MDD patients and 37 sex- and age-matched healthy controls; a sliding-window method was used to assess dALFF in the emotion network, and two reoccurring dALFF states throughout the entire R-fMRI scan were then identified using a k-means clustering method. The results showed that MDD patients had a significant decrease in dALFF variability in the emotion network and its three modules located in the lateral paralimbic, media posterior, and visual association regions. Altered state-wise dALFF was also observed in MDD patients. Specifically, we found that these altered dALFF measurements in the emotion network were related to scores on the Hamilton Rating Scale for Depression (HAMD) among patients with MDD. The detection and estimation of these temporal dynamic alterations could advance our knowledge about the brain mechanisms underlying emotional dysfunction in MDD.

Disrupted Brain Entropy And Functional Connectivity Patterns Of Thalamic Subregions In Major Depressive Disorder.

PURPOSE: Entropy analysis of resting-state functional magnetic resonance imaging (R-fMRI) has recently been adopted to characterize brain temporal dynamics in some neuropsychological or psychiatric diseases. Thalamus-related dysfunction might be a potential trait marker of major depressive disorder (MDD), but the abnormal changes in the thalamus based on R-fMRI are still unclear from the perspective of brain temporal dynamics. The aim of this study was to identify local entropy changes and subregional connectivity patterns of the thalamus in MDD patients. PATIENTS AND METHODS: We measured the sample entropy of the R-fMRI data from 46 MDD patients and 32 matched healthy controls. We employed the Louvain method for the module detection algorithm to automatically identify a functional parcellation of the thalamus and then examined the whole-brain subregional connectivity patterns. RESULTS: The results indicated that the MDD patients had decreased entropy in the bilateral thalami compared with healthy controls. Increased functional connectivity between the thalamic subregions and the medial part of the superior frontal gyrus (mSFG) was found in MDD patients. CONCLUSION: This study showed new evidence about sample entropy changes in MDD patients. The functional connectivity alterations that were widely distributed across almost all the thalamic subregions with the mSFG in MDD suggest a general involvement independent of the location and function of the subregions.

Altered hypothalamic functional connectivity patterns in major depressive disorder.

The hypothalamus is a limbic structure involved in the emergence and persistence of major depressive disorder symptoms. Previous studies have indicated that major depressive disorder patients exhibited dysregulation between the hypothalamus and cerebral regions. However, it is still unclear about the exact hypothalamic functional connectivity patterns with other brain regions based on resting-state functional MRI in major depressive disorder. Here, we investigated the whole-brain voxel-based hypothalamic resting-state functional connectivity in 55 patients with major depressive disorder and 40 age sex-matched healthy controls. The results showed that major depressive disorder patients had a significant decrease in resting-state functional connectivity of the bilateral hypothalamus with the right insula, superior temporal gyrus, inferior frontal gyrus, and Rolandic operculum compared with healthy controls. This study suggests that the pathophysiology of major depressive disorder might be associated with the abnormal hypothalamic resting-state functional connectivity.

Synergistic effect of bone marrow-derived mesenchymal stem cells and platelet-rich plasma in streptozotocin-induced diabetic rats.

BACKGROUND: Diabetic wounds are a major clinical challenge, because minor skin wounds can lead to chronic, unhealed ulcers and ultimately result in infection, gangrene, or even amputation. Studies on bone marrow derived mesenchymal stem cells (BMSCs) and a series of growth factors have revealed their many benefits for wound healing and regeneration. Platelet-rich plasma (PRP) may improve the environment for BMSC development and differentiation. However, whether combined use of BMSCs and PRP may be more effective for accelerating diabetic ulcer healing remains unclear. OBJECTIVE: We investigated the efficacy of BMSCs and PRP for the repair of refractory wound healing in a diabetic rat model. METHODS: Forty-eight rats with diabetes mellitus induced by streptozotocin were divided into four groups: treatment with BMSCs plus PRP, BMSCs alone, PRP alone, phosphate buffered saline. The rate of wound closure was quantified. A histopathological study was conducted regarding wound depth and the skin edge at 7, 14, and 28 days after surgery. RESULTS: Wound healing rates were significantly higher in the BMSC plus PRP group than in the other groups. The immunohistochemistry results showed that the expression of platelet/endothelial cell adhesion molecule 1, proliferating cell nuclear antigen, and transforming growth factor-ß1 increased significantly in the BMSC plus PRP group compared to the other treatment groups. On day 7, CD68 expression increased significantly in the wounds of the BMSC plus PRP group, but decreased markedly at day 14 compared to the controls. CONCLUSION: The combination of BMSCs and PRP aids diabetic wound repair and regeneration.

Effects of exercise on AMPK signaling and downstream components to PI3K in rat with type 2 diabetes.

Exercise can increase skeletal muscle sensitivity to insulin, improve insulin resistance and regulate glucose homeostasis in rat models of type 2 diabetes. However, the potential mechanism remains poorly understood. In this study, we established a male Sprague-Dawley rat model of type 2 diabetes, with insulin resistance and ß cell dysfunction, which was induced by a high-fat diet and low-dose streptozotocin to replicate the pathogenesis and metabolic characteristics of type 2 diabetes in humans. We also investigated the possible mechanism by which chronic and acute exercise improves metabolism, and the phosphorylation and expression of components of AMP-activated protein kinase (AMPK) and downstream components of phosphatidylinositol 3-kinase (PI3K) signaling pathways in the soleus. As a result, blood glucose, triglyceride, total cholesterol, and free fatty acid were significantly increased, whereas insulin level progressively declined in diabetic rats. Interestingly, chronic and acute exercise reduced blood glucose, increased phosphorylation and expression of AMPKα1/2 and the isoforms AMPKα1 and AMPKα2, and decreased phosphorylation and expression of AMPK substrate, acetyl CoA carboxylase (ACC). Chronic exercise upregulated phosphorylation and expression of AMPK upstream kinase, LKB1. But acute exercise only increased LKB1 expression. In particular, exercise reversed the changes in protein kinase C (PKC)ζ/λ phosphorylation, and PKCζ phosphorylation and expression. Additionally, exercise also increased protein kinase B (PKB)/Akt1, Akt2 and GLUT4 expression, but AS160 protein expression was unchanged. Chronic exercise elevated Akt (Thr(308)) and (Ser(473)) and AS160 phosphorylation. Finally, we found that exercise increased peroxisome proliferator-activated receptor-γ coactivator 1 (PGC1) mRNA expression in the soleus of diabetic rats. These results indicate that both chronic and acute exercise influence the phosphorylation and expression of components of the AMPK and downstream to PIK3 (aPKC, Akt), and improve GLUT4 trafficking in skeletal muscle. These data help explain the mechanism how exercise regulates glucose homeostasis in diabetic rats.