Decomposed FDG PET-based phenotypic heterogeneity predicting clinical prognosis and decision-making in temporal lobe epilepsy patients.

OBJECTIVE: This study utilized a data-driven Bayesian model to automatically identify distinct latent disease factors represented by overlapping glucose metabolism patterns from 18F-Fluorodeoxyglucose PET (18F-FDG PET) to analyze heterogeneity among patients with TLE. METHODS: We employed unsupervised machine learning to estimate latent disease factors from 18F-FDG PET scans, representing whole-brain glucose metabolism patterns in seventy patients with TLE. We estimated the extent to which multiple distinct factors were expressed within each participant and analyzed their relevance to epilepsy burden, including seizure onset, duration, and frequency. Additionally, we established a predictive model for clinical prognosis and decision-making. RESULTS: We identified three latent disease factors: hypometabolism in the unilateral temporal lobe and hippocampus (factor 1), hypometabolism in bilateral prefrontal lobes (factor 2), and hypometabolism in bilateral temporal lobes (factor 3), variably co-expressed within each patient. Factor 3 demonstrated the strongest negative correlation with the age of onset and duration (r = - 0.33, - 0.38 respectively, P < 0.05). The supervised classifier, trained on latent disease factors for predicting patient-specific antiepileptic drug (AED) responses, achieved an area under the curve (AUC) of 0.655. For post-surgical seizure outcomes, the AUC was 0.857, and for clinical decision-making, it was 0.965. CONCLUSIONS: Decomposing 18F-FDG PET-based phenotypic heterogeneity facilitates individual-level predictions relevant to disease monitoring and personalized therapeutic strategies.

Aberrant temporal-spatial complexity of intrinsic fluctuations in major depression.

Accumulating evidence suggests that the brain is highly dynamic; thus, investigation of brain dynamics especially in brain connectivity would provide crucial information that stationary functional connectivity could miss. This study investigated temporal expressions of spatial modes within the default mode network (DMN), salience network (SN) and cognitive control network (CCN) using a reliable data-driven co-activation pattern (CAP) analysis in two independent data sets. We found enhanced CAP-to-CAP transitions of the SN in patients with MDD. Results suggested enhanced flexibility of this network in the patients. By contrast, we also found reduced spatial consistency and persistence of the DMN in the patients, indicating reduced variability and stability in individuals with MDD. In addition, the patients were characterized by prominent activation of mPFC. Moreover, further correlation analysis revealed that persistence and transitions of RCCN were associated with the severity of depression. Our findings suggest that functional connectivity in the patients may not be simply attenuated or potentiated, but just alternating faster or slower among more complex patterns. The aberrant temporal-spatial complexity of intrinsic fluctuations reflects functional diaschisis of resting-state networks as characteristic of patients with MDD.

Alterations in Plasma Lipidomic Profiles in Adult Patients with Schizophrenia and Major Depressive Disorder.

Background and Objectives: Lipidomics is a pivotal tool for investigating the pathogenesis of mental disorders. However, studies qualitatively and quantitatively analyzing peripheral lipids in adult patients with schizophrenia (SCZ) and major depressive disorder (MDD) are limited. Moreover, there are no studies comparing the lipid profiles in these patient populations. Materials and Method: Lipidomic data for plasma samples from sex- and age-matched patients with SCZ or MDD and healthy controls (HC) were obtained and analyzed by liquid chromatography-mass spectrometry (LC-MS). Results: We observed changes in lipid composition in patients with MDD and SCZ, with more significant alterations in those with SCZ. In addition, a potential diagnostic panel comprising 103 lipid species and another diagnostic panel comprising 111 lipid species could distinguish SCZ from HC (AUC = 0.953) or SCZ from MDD (AUC = 0.920) were identified, respectively. Conclusions: This study provides an increased understanding of dysfunctional lipid composition in the plasma of adult patients with SCZ or MDD, which may lay the foundation for identifying novel clinical diagnostic methods for these disorders.

Combined fractional anisotropy and subcortical volumetric abnormalities in healthy immigrants to high altitude: A longitudinal study.

The study of individuals at high-altitude (HA) exposure provides an important opportunity for unraveling physiological and psychological mechanism of brain underlying hypoxia condition. However, this has rarely been assessed longitudinally. We aim to explore the cognitive and cerebral microstructural alterations after chronic HA exposure. We recruited 49 college freshmen who immigrated to Tibet and followed up for 2 years. Control group consisted of 49 gender and age-matched subjects from sea level. Neuropsychological tests were also conducted to determine whether the subjects' cognitive function had changed in response to chronic HA exposure. Surface-based cortical and subcortical volumes were calculated from structural magnetic resonance imaging data, and tract-based spatial statistics (TBSS) analysis of white matter (WM) fractional anisotropy (FA) based on diffusion weighted images were performed. Compared to healthy controls, the high-altitude exposed individuals showed significantly lower accuracy and longer reaction times in memory tests. Significantly decreased gray matter volume in the caudate region and significant FA changes in multiple WM tracts were observed for HA immigrants. Furthermore, differences in subcortical volume and WM integration were found to be significantly correlated with the cognitive changes after 2 years' HA exposure. Cognitive functions such as working memory and psychomotor function were found to be impaired during chronic HA. Differences of brain subcortical volumes and WM integration between HA and sea-level participants indicated potential impairments in the brain structural modifications and microstructural integrity of WM tracts after HA exposure.

Quantitative Identification of Nonmuscle-Invasive and Muscle-Invasive Bladder Carcinomas: A Multiparametric MRI Radiomics Analysis.

BACKGROUND: Preoperative discrimination between nonmuscle-invasive bladder carcinomas (NMIBC) and the muscle-invasive ones (MIBC) is very crucial in the management of patients with bladder cancer (BC). PURPOSE: To evaluate the discriminative performance of multiparametric MRI radiomics features for precise differentiation of NMIBC from MIBC, preoperatively. STUDY TYPE: Retrospective, radiomics. POPULATION: Fifty-four patients with postoperative pathologically proven BC lesions (24 in NMIBC and 30 in MIBC groups) were included. FIELD STRENGTH/SEQUENCE: 3.0T MRI/T2 -weighted (T2 W) and multi-b-value diffusion-weighted (DW) sequences. ASSESSMENT: A total of 1104 radiomics features were extracted from carcinomatous regions of interest on T2 W and DW images, and the apparent diffusion coefficient maps. Support vector machine with recursive feature elimination (SVM-RFE) and synthetic minority oversampling technique (SMOTE) were used to construct an optimal discriminative model, and its performance was evaluated and compared with that of using visual diagnoses by experts. STATISTICAL TESTS: Chi-square test and Student's t-test were applied on clinical characteristics to analyze the significant differences between patient groups. RESULTS: Of the 1104 features, an optimal subset involving 19 features was selected from T2 W and DW sequences, which outperformed the other two subsets selected from T2 W or DW sequence in muscle invasion discrimination. The best performance for the differentiation task was achieved by the SVM-RFE+SMOTE classifier, with averaged sensitivity, specificity, accuracy, and area under the curve of receiver operating characteristic of 92.60%, 100%, 96.30%, and 0.9857, respectively, which outperformed the diagnostic accuracy by experts. DATA CONCLUSION: The proposed radiomics approach has potential for the accurate differentiation of muscle invasion in BC, preoperatively. The optimal feature subset selected from multiparametric MR images demonstrated better performance in identifying muscle invasiveness when compared with that from T2 W sequence or DW sequence only. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1489-1498.

Radiomics Strategy for Molecular Subtype Stratification of Lower-Grade Glioma: Detecting IDH and TP53 Mutations Based on Multimodal MRI.

BACKGROUND: Noninvasive detection of isocitrate dehydrogenase (IDH) and TP53 mutations are meaningful for molecular stratification of lower-grade gliomas (LrGG). PURPOSE: To explore potential MRI features reflecting IDH and TP53 mutations of LrGG, and propose a radiomics strategy for detecting them. STUDY TYPE: Retrospective, radiomics. POPULATION/SUBJECTS: A total of 103 LrGG patients were separated into development (n = 73) and validation (n = 30) cohorts. FIELD STRENGTH/SEQUENCE: T1 -weighted (before and after contrast-enhanced), T2 -weighted, and fluid-attenuation inversion recovery images from 1.5T (n = 37) or 3T (n = 66) scanners. ASSESSMENT: After data preprocessing, high-throughput features were derived from patients' volumes of interests of different sequences. The support vector machine-based recursive feature elimination (SVM-RFE) was adopted to find the optimal features for IDH and TP53 mutation detection. SVM models were trained and tested on development and validation cohort. The commonly used metric was used for assessing the efficiency. STATISTICAL TESTS: One-way analysis of variance (ANOVA), chi-square, or Fisher's exact test were applied on clinical characteristics to confirm whether significant differences exist between three molecular subtypes decided by IDH and TP53 status. Intraclass correlation coefficients were calculated to assess the robustness of the radiomics features. RESULTS: The constituent ratio of histopathologic subtypes was significantly different among three molecular subtypes (P = 0.017). SVM models for detecting IDH and TP53 mutation were established using 12 and 22 optimal features selected by SVM-RFE. The accuracies and area under the curves for IDH and TP53 mutations on the development cohort were 84.9%, 0.830, and 92.0%, 0.949, while on the validation cohort were 80.0%, 0.792, and 85.0%, 0.869, respectively. Furthermore, the stratified accuracies of three subtypes were 72.8%, 71.9%, and 70%, respectively. DATA CONCLUSION: Using a radiomics approach integrating SVM model and multimodal MRI features, molecular subtype stratification of LGG patients was implemented through detecting IDH and TP53 mutations. The results suggested that the proposed approach has promising detecting efficiency and T2 -weighted image features are more important than features from other images. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:916-926.

Association of early pregnancy body mass index and children's birth weight with risk of being overweight in childhood.

OBJECTIVES: To examine the potential effects of mothers' early pregnancy body mass index (BMI) and children's birth weight on childhood overweight/obesity at 1-6 years of age. METHODS: We performed a cohort study in Tianjin, China, using the health care records from the Tianjin maternal and child health care system with 36 719 mother-child pairs from early pregnancy to children at 6 years of age. Logistic regression analyses were used to assess the single and joint associations of maternal early pregnancy BMI and children's birth weight with the risks of children being overweight at 1-6 years of age in multivariable-adjusted models. RESULTS: Both maternal early pregnancy BMI and children's birth weight were positively associated with the risk of children being overweight at 1-6 years of age. Compared with children who were born to normal weight mothers and had low or normal birth weight (NBW; birth BMI < 85%), higher risks of being overweight at 1-6 years were found among children with low or NBW who were born to overweight mothers, and children with high NBW (BMI ≥ 85%) or macrosomia who were born to normal weight or overweight mothers. Lower risks of being overweight were found among those with low birth weight who were born to normal weight mothers. CONCLUSIONS: Maternal early pregnancy BMI may play a more important role than children's birth weight in the risk of being overweight from 4 to 6 years of age. Children with high or NBW need to be given more attention to prevent excessive weight gain.

Radiomics assessment of bladder cancer grade using texture features from diffusion-weighted imaging.

PURPOSE: To 1) describe textural features from diffusion-weighted images (DWI) and apparent diffusion coefficient (ADC) maps that can distinguish low-grade bladder cancer from high-grade, and 2) propose a radiomics-based strategy for cancer grading using texture features. MATERIALS AND METHODS: In all, 61 patients with bladder cancer (29 in high- and 32 in low-grade groups) were enrolled in this retrospective study. Histogram- and gray-level co-occurrence matrix (GLCM)-based radiomics features were extracted from cancerous volumes of interest (VOIs) on DWI and corresponding ADC maps of each patient acquired from 3.0T magnetic resonance imaging (MRI). A Mann-Whitney U-test was applied to select features with significant differences between low- and high-grade groups (P < 0.05). Then support vector machine with recursive feature elimination (SVM-RFE) and classification strategy was adopted to find an optimal feature subset and then to establish a classification model for grading. RESULTS: A total 102 features were derived from each VOI and among them, 47 candidate features were selected, which showed significant intergroup differences (P < 0.05). By the SVM-RFE method, an optimal feature subset including 22 features was further selected from candidate features. The SVM classifier using the optimal feature subset achieved the best performance in bladder cancer grading, with an area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity of 0.861, 82.9%, 78.4%, and 87.1%, respectively. CONCLUSION: Textural features from DWI and ADC maps can reflect the difference between low- and high-grade bladder cancer, especially those GLCM features from ADC maps. The proposed radiomics strategy using these features, combined with the SVM classifier, may better facilitate image-based bladder cancer grading preoperatively. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;46:1281-1288.

CYP11B2 gene polymorphism and essential hypertension among Tibetan, Dongxiang and Han populations from northwest of China.

Essential hypertension (EH) is a complex multifactorial condition influenced by both genetic and environmental factors; aldosterone synthase (CYP11B2) is a key enzyme which involves in the terminal steps of aldosterone synthesis. The result of relationship between C-344T of CYP11B2 polymorphism and EH was controversial. This study was undertaken to investigate the association of C-344T polymorphism with EH in the populations of Tibetan, Dongxiang and Han from northwest of China. A total of 2115 participants aged 18-70 years were enrolled in this study. In total, 1776 blood samples, including 545 Tibetan (305 hypertensive and 240 normotensive), 530 Dongxiang (254 hypertensive and 276 normotensive) and 701 Han (338 hypertensive and 363 normotensive), were analyzed successfully by using Snapshot minisequencing method, 30 samples were also performed by direct sequencing (5 hypertensive and 5 normotensive in each population, respectively). The frequencies of genotype and allele of CYP11B2 (C-344T) were not significantly different between EH group and control group in every ethnic population (p > 0.05). However, in female population of Tibetan, the frequencies of CC and CT genotype and C allele in EH group were higher than in control (p < 0.05) group. The frequencies of CC genotype and C allele in both the normotensive controls and EH patients in Tibetan population were higher than in Dongxiang and Han populations. Our study suggests that there is lack of association between C-344T polymorphism of CYP11B2 gene and EH in Dongxiang and Han populations, whereas the polymorphism was correlated with EH in female population of Tibetan.

[Epidemiological survey on hypertension among Tibetan monks in Gannan Tibetan Autonomous Prefecture of Gansu Province].

OBJECTIVE: To explore the prevalence, awareness, treatment and control rates of hypertension among Tibetan monks in Gannan Tibetan Autonomous Prefecture of Gansu Province, and to analyze risk factors of hypertension in this population. METHODS: A total of 984 (aged 18 and over) Tibetan monks in Gannan Tibetan Autonomous Prefecture of Gansu Province were included with cluster sampling method from March to June, 2014. General information was obtained by questionnaire and blood pressure was measured. Multivariate logistic regression analysis was applied to analyze the risk factors of blood pressure. RESULTS: Prevalence of hypertension in this population was 19.3% (190/984), and standardized prevalence rate was 21.7%. The prevalence of hypertension were 16.3% (82/502), 21.0% (60/286) and 24.5% (48/196), and standardized prevalence rate was 17.8%, 22.1% and 26.6% among Tibetan monks aged 18-39, 40-59 and ≥60 years, respectively. Rates of awareness, treatment and control of hypertension in Tibetan monks were 9.5% (18/984), 4.2% (8/984)and 1.6% (3/984), respectively. Multivariate logistic regression analysis showed that age, body mass index and family history of hypertension were independent risk factors of hypertension in this population (P<0.01 or 0.05). CONCLUSIONS: Prevalence, awareness, treatment and control rates of hypertension among Tibetan monks in Gannan Tibetan Autonomous Prefecture of Gansu Province are relatively low. Age, body mass index and family history of hypertension are risk factors of hypertension in this population.

Effective connectivity within human primary visual cortex predicts interindividual diversity in illusory perception.

Visual perception depends strongly on spatial context. A classic example is the tilt illusion where the perceived orientation of a central stimulus differs from its physical orientation when surrounded by tilted spatial contexts. Here we show that such contextual modulation of orientation perception exhibits trait-like interindividual diversity that correlates with interindividual differences in effective connectivity within human primary visual cortex. We found that the degree to which spatial contexts induced illusory orientation perception, namely, the magnitude of the tilt illusion, varied across healthy human adults in a trait-like fashion independent of stimulus size or contrast. Parallel to contextual modulation of orientation perception, the presence of spatial contexts affected effective connectivity within human primary visual cortex between peripheral and foveal representations that responded to spatial context and central stimulus, respectively. Importantly, this effective connectivity from peripheral to foveal primary visual cortex correlated with interindividual differences in the magnitude of the tilt illusion. Moreover, this correlation with illusion perception was observed for effective connectivity under tilted contextual stimulation but not for that under iso-oriented contextual stimulation, suggesting that it reflected the impact of orientation-dependent intra-areal connections. Our findings revealed an interindividual correlation between intra-areal connectivity within primary visual cortex and contextual influence on orientation perception. This neurophysiological-perceptual link provides empirical evidence for theoretical proposals that intra-areal connections in early visual cortices are involved in contextual modulation of visual perception.

BrainIB: Interpretable Brain Network-Based Psychiatric Diagnosis With Graph Information Bottleneck.

Developing new diagnostic models based on the underlying biological mechanisms rather than subjective symptoms for psychiatric disorders is an emerging consensus. Recently, machine learning (ML)-based classifiers using functional connectivity (FC) for psychiatric disorders and healthy controls (HCs) are developed to identify brain markers. However, existing ML-based diagnostic models are prone to overfitting (due to insufficient training samples) and perform poorly in new test environments. Furthermore, it is difficult to obtain explainable and reliable brain biomarkers elucidating the underlying diagnostic decisions. These issues hinder their possible clinical applications. In this work, we propose BrainIB, a new graph neural network (GNN) framework to analyze functional magnetic resonance images (fMRI), by leveraging the famed information bottleneck (IB) principle. BrainIB is able to identify the most informative edges in the brain (i.e., subgraph) and generalizes well to unseen data. We evaluate the performance of BrainIB against three baselines and seven state-of-the-art (SOTA) brain network classification methods on three psychiatric datasets and observe that our BrainIB always achieves the highest diagnosis accuracy. It also discovers the subgraph biomarkers that are consistent with clinical and neuroimaging findings. The source code and implementation details of BrainIB are freely available at the GitHub repository (https://github.com/SJYuCNEL/brain-and-Information-Bottleneck).

Large-scale effective connectivity analysis reveals the existence of two mutual inhibitory systems in patients with major depression.

It is posited that cognitive and affective dysfunction in patients with major depression disorder (MDD) may be caused by dysfunctional signal propagation in the brain. By leveraging dynamic causal modeling, we investigated large-scale directed signal propagation (effective connectivity) among distributed large-scale brain networks with 43 MDD patients and 56 healthy controls. The results revealed the existence of two mutual inhibitory systems: the anterior default mode network, auditory network, sensorimotor network, salience network and visual networks formed an "emotional" brain, while the posterior default mode network, central executive networks, cerebellum and dorsal attention network formed a "rational brain". These two networks exhibited excitatory intra-system connectivity and inhibitory inter-system connectivity. Patients were characterized by potentiated intra-system connections within the "emotional/sensory brain", as well as over-inhibition of the "rational brain" by the "emotional/sensory brain". The hierarchical architecture of the large-scale effective connectivity networks was then analyzed using a PageRank algorithm which revealed a shift of the controlling role of the "rational brain" to the "emotional/sensory brain" in the patients. These findings inform basic organization of distributed large-scale brain networks and furnish a better characterization of the neural mechanisms of depression, which may facilitate effective treatment.

Functional nanotransducer-mediated wireless neural modulation techniques.

Functional nanomaterials have emerged as versatile nanotransducers for wireless neural modulation because of their minimal invasion and high spatiotemporal resolution. The nanotransducers can convert external excitation sources (e.g. NIR light, x-rays, and magnetic fields) to visible light (or local heat) to activate optogenetic opsins and thermosensitive ion channels for neuromodulation. The present review provides insights into the fundamentals of the mostly used functional nanomaterials in wireless neuromodulation including upconversion nanoparticles, nanoscintillators, and magnetic nanoparticles. We further discussed the recent developments in design strategies of functional nanomaterials with enhanced energy conversion performance that have greatly expanded the field of neuromodulation. We summarized the applications of functional nanomaterials-mediated wireless neuromodulation techniques, including exciting/silencing neurons, modulating brain activity, controlling motor behaviors, and regulating peripheral organ function in mice. Finally, we discussed some key considerations in functional nanotransducer-mediated wireless neuromodulation along with the current challenges and future directions.

Targeting suicidal ideation in major depressive disorder with MRI-navigated Stanford accelerated intelligent neuromodulation therapy.

High suicide risk represents a serious problem in patients with major depressive disorder (MDD), yet treatment options that could safely and rapidly ameliorate suicidal ideation remain elusive. Here, we tested the feasibility and preliminary efficacy of the Stanford Accelerated Intelligent Neuromodulation Therapy (SAINT) in reducing suicidal ideation in patients with MDD. Thirty-two MDD patients with moderate to severe suicidal ideation participated in the current study. Suicidal ideation and depression symptoms were assessed before and after 5 days of open-label SAINT. The neural pathways supporting rapid-acting antidepressant and suicide prevention effects were identified with dynamic causal modelling based on resting-state functional magnetic resonance imaging. We found that 5 days of SAINT effectively alleviated suicidal ideation in patients with MDD with a high response rate of 65.63%. Moreover, the response rates achieved 78.13% and 90.63% with 2 weeks and 4 weeks after SAINT, respectively. In addition, we found that the suicide prevention effects of SAINT were associated with the effective connectivity involving the insula and hippocampus, while the antidepressant effects were related to connections of the subgenual anterior cingulate cortex (sgACC). These results show that SAINT is a rapid-acting and effective way to reduce suicidal ideation. Our findings further suggest that distinct neural mechanisms may contribute to the rapid-acting effects on the relief of suicidal ideation and depression, respectively.

Potentiation of the lateral habenula-ventral tegmental area pathway underlines the susceptibility to depression in mice with chronic pain.

Chronic pain often develops severe mood changes such as depression. However, how chronic pain leads to depression remains elusive and the mechanisms determining individuals' responses to depression are largely unexplored. Here we found that depression-like behaviors could only be observed in 67.9% of mice with chronic neuropathic pain, leaving 32.1% of mice with depression resilience. We determined that the spike discharges of the ventral tegmental area (VTA)-projecting lateral habenula (LHb) glutamatergic (Glu) neurons were sequentially increased in sham, resilient and susceptible mice, which consequently inhibited VTA dopaminergic (DA) neurons through a LHbGlu-VTAGABA-VTADA circuit. Furthermore, the LHbGlu-VTADA excitatory inputs were dampened via GABAB receptors in a pre-synaptic manner. Regulation of LHb-VTA pathway largely affected the development of depressive symptoms caused by chronic pain. Our study thus identifies a pivotal role of the LHb-VTA pathway in coupling chronic pain with depression and highlights the activity-dependent contribution of LHbGlu-to-VTADA inhibition in depressive behavioral regulation.

Transcriptomic decoding of regional cortical vulnerability to major depressive disorder.

Previous studies in small samples have identified inconsistent cortical abnormalities in major depressive disorder (MDD). Despite genetic influences on MDD and the brain, it is unclear how genetic risk for MDD is translated into spatially patterned cortical vulnerability. Here, we initially examined voxel-wise differences in cortical function and structure using the largest multi-modal MRI data from 1660 MDD patients and 1341 controls. Combined with the Allen Human Brain Atlas, we then adopted transcription-neuroimaging spatial correlation and the newly developed ensemble-based gene category enrichment analysis to identify gene categories with expression related to cortical changes in MDD. Results showed that patients had relatively circumscribed impairments in local functional properties and broadly distributed disruptions in global functional connectivity, consistently characterized by hyper-function in associative areas and hypo-function in primary regions. Moreover, the local functional alterations were correlated with genes enriched for biological functions related to MDD in general (e.g., endoplasmic reticulum stress, mitogen-activated protein kinase, histone acetylation, and DNA methylation); and the global functional connectivity changes were associated with not only MDD-general, but also brain-relevant genes (e.g., neuron, synapse, axon, glial cell, and neurotransmitters). Our findings may provide important insights into the transcriptomic signatures of regional cortical vulnerability to MDD.

Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis.

Recent resting-state functional connectivity magnetic resonance imaging studies have shown significant group differences in several regions and networks between patients with major depressive disorder and healthy controls. The objective of the present study was to investigate the whole-brain resting-state functional connectivity patterns of depressed patients, which can be used to test the feasibility of identifying major depressive individuals from healthy controls. Multivariate pattern analysis was employed to classify 24 depressed patients from 29 demographically matched healthy volunteers. Permutation tests were used to assess classifier performance. The experimental results demonstrate that 94.3% (P < 0.0001) of subjects were correctly classified by leave-one-out cross-validation, including 100% identification of all patients. The majority of the most discriminating functional connections were located within or across the default mode network, affective network, visual cortical areas and cerebellum, thereby indicating that the disease-related resting-state network alterations may give rise to a portion of the complex of emotional and cognitive disturbances in major depression. Moreover, the amygdala, anterior cingulate cortex, parahippocampal gyrus and hippocampus, which exhibit high discriminative power in classification, may play important roles in the pathophysiology of this disorder. The current study may shed new light on the pathological mechanism of major depression and suggests that whole-brain resting-state functional connectivity magnetic resonance imaging may provide potential effective biomarkers for its clinical diagnosis.

Increased thalamic gray matter volume induced by repetitive transcranial magnetic stimulation treatment in patients with major depressive disorder.

Purpose: Repetitive transcranial magnetic stimulation (rTMS) is an effective therapy in improving depressive symptoms in MDD patients, but the intrinsic mechanism is still unclear. In this study, we investigated the influence of rTMS on brain gray matter volume for alleviating depressive symptoms in MDD patients using structural magnetic resonance imaging (sMRI) data. Methods: Patients with first episode, unmedicated patients with MDD (n = 26), and healthy controls (n = 31) were selected for this study. Depressive symptoms were assessed before and after treatment by using the HAMD-17 score. High-frequency rTMS treatment was conducted in patients with MDD over 15 days. The rTMS treatment target is located at the F3 point of the left dorsolateral prefrontal cortex. Structural magnetic resonance imaging (sMRI) data were collected before and after treatment to compare the changes in brain gray matter volume. Results: Before treatment, patients with MDD had significantly reduced gray matter volumes in the right fusiform gyrus, left and right inferior frontal gyrus (triangular part), left inferior frontal gyrus (orbital part), left parahippocampal gyrus, left thalamus, right precuneus, right calcarine fissure, and right median cingulate gyrus compared with healthy controls (P < 0.05). After rTMS treatment, significant growth in gray matter volume of the bilateral thalamus was observed in depressed patients (P < 0.05). Conclusion: Bilateral thalamic gray matter volumes were enlarged in the thalamus of MDD patients after rTMS treatment and may be the underlying neural mechanism for the treatment of rTMS on depression.

Gut microbial signatures and differences in bipolar disorder and schizophrenia of emerging adulthood.

INTRODUCTION: Gut microbial disturbance has been established as potential pathogenesis of mental disorders. However, the signatures and differences regarding patients with schizophrenia (SCH) or bipolar disorder (BD) in emerging adulthood as well as their subtypes have been poorly addressed. METHODS: In the present study, stool samples obtained from 63 emerging adult patients with schizophrenia (SCH), 50 with bipolar disorder (BD), and 40 healthy controls (HC) were analyzed by 16 S rRNA gene sequencing; psychiatric symptoms and psychological, social, and professional functioning were also assessed. RESULTS: We found that gut microbiota composition was remarkably changed in the patients with SCH and BD. Moreover, the distinct gut microbiome signatures and their potential function in bipolar depression (BP-D) and SCH with predominantly negative symptoms (SCH-N) as well as bipolar mania (BP-M) and SCH with predominantly positive symptoms (SCH-P) were also observed. Furthermore, we identified diagnostic potential biomarkers that can distinguish BD from HC (38 genera, AUC = 0.961), SCH from HC (32 genera, AUC = 0.962), and BD from Scheme (13 genera, AUC = 0.823). Potential diagnostic biomarkers that can distinguish BD-D from SCH-N (16 genera, AUC = 0.969) and BD-M from SCH-P (31 genera, AUC = 0.938) were also identified. CONCLUSION: This study provides further understanding of abnormal gut microbiome in emerging adulthood patients with SCH and BD and lay the potential foundation for the development of microbe-based clinical diagnosis for BD and SCH.