Abnormal resting-state functional connectivity of the right anterior cingulate cortex in chronic ketamine users and its correlation with cognitive impairments.

BACKGROUND: Chronic ketamine use leads to cognitive impairments, however, the neural mechanisms underpinning these impairments are still unclear. AIMS: Many studies showed Anterior cingulate cortex (ACC)is strongly involved in cognition and drug addiction, as supported by our previous studies. The objective of this study was to assess the variations in resting-state functional connectivity (FC) changes in the right anterior cingulate cortex (ACC) of chronic ketamine users (CKUs) and their relationship with cognitive performance. METHODS: The study enrolled 28 chronic ketamine users (CKUs) and 30 healthy controls (HCs). Resting-state functional magnetic resonance imaging (fMRI) data were gathered from both groups. Cognitive functions were evaluated using the MATRICS Consensus Cognitive Battery (MCCB). RESULTS: CKUs demonstrated significantly poorer cognitive performance than HCs in various cognitive domains, including Visual Learning, Speed of Processing, Working Memory, and the composite score of MCCB. Group-level comparisons revealed that CKUs exhibited enhanced functional connectivity between the right ACC and the right postcentral gyrus (PCG) compared to HCs. There was a positive relationship between the connectivity of right ACC-PCG and reasoning and problem-solving score, but there was no significant association with the characteristics of ketamine use. CONCLUSION: CKUs showed enhanced connectivity between the right ACC and the right PCG. This enhanced functional connectivity may indicate functional compensation for cognitive deficits in CKUs, especially for reasoning and problem-solving impairments in CKUs.

Spontaneous neural activity underlying neutral and happy speech recognition in noise and its association with psychiatric symptoms in patients with schizophrenia.

BACKGROUND: Deficits in speech and emotion perception are intertwined with psychiatric symptoms. How the happy prosody embedded in speech affects target speech-in-noise recognition (TSR) and relates to psychiatric symptoms in patients with schizophrenia (SCHs) remains unclear. This study examined spontaneous brain activity underlying happy TSR and its association with psychiatric symptom dimensions in SCHs. METHODS: Fifty-four SCHs and 59 healthy control participants (HCs) underwent the TSR task, Positive and Negative Syndrome Scale (PANSS) assessment, and magnetic resonance imaging scanning. Multivariate analyses of partial least squares (PLS) regression were used to explore the associations between whole-brain fractional amplitude of low-frequency fluctuations (fALFF), happy-neutral TSR (target pseudo-sentences were uttered in happy and neutral prosodies), and five PANSS factor scores (excitement/hostility, depression/anxiety, cognition, positive, and negative). RESULTS: The happy prosody did not alter TSR or TSR changing rates in either SCHs or HCs. SCHs exhibited lower happy and neutral TSR than HCs. A fALFF PLS component (including precentral/postcentral gyrus, Subcallosal Cortex, several temporal regions, and cerebellum) was associated with happy and neutral TSR. SCHs demonstrated higher PLS fALFF scores and PLS TSR scores than HCs. In SCHs, PLS fALFF scores were correlated with the PANSS positive factor score, and PLS TSR scores were correlated with the PANSS cognition factor score. CONCLUSIONS: The positive-psychiatric-symptoms-related spontaneous activity profile was associated with happy and neutral TSR, contributing to the cognition psychiatric symptoms dimension. The findings suggest the potential to improve positive and cognitive symptoms by enhancing happy and neutral TSR in schizophrenia based on neuroplasticity.

Exploring Schizophrenia Classification Through Multimodal MRI and Deep Graph Neural Networks: Unveiling Brain Region-Specific Weight Discrepancies and Their Association With Cell-Type Specific Transcriptomic Features.

BACKGROUND AND HYPOTHESIS: Schizophrenia (SZ) is a prevalent mental disorder that imposes significant health burdens. Diagnostic accuracy remains challenging due to clinical subjectivity. To address this issue, we explore magnetic resonance imaging (MRI) as a tool to enhance SZ diagnosis and provide objective references and biomarkers. Using deep learning with graph convolution, we represent MRI data as graphs, aligning with brain structure, and improving feature extraction, and classification. Integration of multiple modalities is expected to enhance classification. STUDY DESIGN: Our study enrolled 683 SZ patients and 606 healthy controls from 7 hospitals, collecting structural MRI and functional MRI data. Both data types were represented as graphs, processed by 2 graph attention networks, and fused for classification. Grad-CAM with graph convolution ensured interpretability, and partial least squares analyzed gene expression in brain regions. STUDY RESULTS: Our method excelled in the classification task, achieving 83.32% accuracy, 83.41% sensitivity, and 83.20% specificity in 10-fold cross-validation, surpassing traditional methods. And our multimodal approach outperformed unimodal methods. Grad-CAM identified potential brain biomarkers consistent with gene analysis and prior research. CONCLUSIONS: Our study demonstrates the effectiveness of deep learning with graph attention networks, surpassing previous SZ diagnostic methods. Multimodal MRI's superiority over unimodal MRI confirms our initial hypothesis. Identifying potential brain biomarkers alongside gene biomarkers holds promise for advancing objective SZ diagnosis and research in SZ.

Neural substrates of affective temperaments: An intersubject representational similarity analysis to resting-state functional magnetic resonance imaging in nonclinical subjects.

Previous research has suggested that certain types of the affective temperament, including depressive, cyclothymic, hyperthymic, irritable, and anxious, are subclinical manifestations and precursors of mental disorders. However, the neural mechanisms that underlie these temperaments are not fully understood. The aim of this study was to identify the brain regions associated with different affective temperaments. We collected the resting-state functional magnetic resonance imaging (fMRI) data from 211 healthy adults and evaluated their affective temperaments using the Temperament Evaluation of Memphis, Pisa, Paris and San Diego Autoquestionnaire. We used intersubject representational similarity analysis to identify brain regions associated with each affective temperament. Brain regions associated with each affective temperament were detected. These regions included the prefrontal cortex, anterior cingulate cortex (ACC), precuneus, amygdala, thalami, hippocampus, and visual areas. The ACC, lingual gyri, and precuneus showed similar activity across several affective temperaments. The similarity in related brain regions was high among the cyclothymic, irritable, and anxious temperaments, and low between hyperthymic and the other affective temperaments. These findings may advance our understanding of the neural mechanisms underlying affective temperaments and their potential relationship to mental disorders and may have potential implications for personalized treatment strategies for mood disorders.

Effects of childhood neglect on regional brain activity and corresponding functional connectivity in major depressive disorder and healthy people: Risk factor or resilience?

BACKGROUND: Childhood neglect is a high risk factor for major depressive disorder (MDD). However, the effects of childhood neglect on regional brain activity and corresponding functional connectivity in MDD patients and healthy populations remains unclear. METHODS: Regional homogeneity, amplitude of low-frequency fluctuations (ALFF), fractional ALFF, degree centrality, and voxel-mirrored homotopic connectivity were extensively calculated to explore intraregional brain activity in MDD patients with childhood neglect and in healthy populations with childhood neglect. Functional connectivity analysis was then performed using regions showing abnormal brain activity in regional homogeneity/ALFF/fractional ALFF/degree centrality/voxel-mirrored homotopic connectivity analysis as seed. Partial correlation analysis and moderating effect analysis were used to explore the relationship between childhood neglect, abnormal brain activity, and MDD severity. RESULTS: We found decreased brain function in the inferior parietal lobe and cuneus in MDD patients with childhood neglect. In addition, we detected that childhood neglect was significant associated with abnormal cuneus brain activity in MDD patients and that abnormal cuneus brain activity moderated the relationship between childhood neglect and MDD severity. In contrast, higher brain function was observed in the inferior parietal lobe and cuneus in healthy populations with childhood neglect. CONCLUSIONS: Our results provide new evidence for the identification of neural biomarkers in MDD patients with childhood neglect. More importantly, we identify brain activity characteristics of resilience in healthy populations with childhood neglect, providing more clues to identify neurobiological markers of resilience to depression after suffering childhood neglect.

Multi-modal MRI measures reveal sensory abnormalities in major depressive disorder patients: A surface-based study.

BACKGROUND: Multi-modal magnetic resonance imaging (MRI) measures are supposed to be able to capture different brain neurobiological aspects of major depressive disorder (MDD). A fusion analysis of structural and functional modalities may better reveal the disease biomarker specific to the MDD disease. METHODS: We recruited 30 MDD patients and 30 matched healthy controls (HC). For each subject, we acquired high-resolution brain structural images and resting-state fMRI (rs-fMRI) data using a 3 T MRI scanner. We first extracted the brain morphometric measures, including the cortical volume (CV), cortical thickness (CT), and surface area (SA), for each subject from the structural images, and then detected the structural clusters showing significant between-group differences in each measure using the surface-based morphology (SBM) analysis. By taking the identified structural clusters as seeds, we performed seed-based functional connectivity (FC) analyses to determine the regions with abnormal FC in the patients. Based on a logistic regression model, we performed a classification analysis by selecting these structural and functional cluster-wise measures as features to distinguish the MDD patients from the HC. RESULTS: The MDD patients showed significantly lower CV in a cluster involving the right superior temporal gyrus (STG) and middle temporal gyrus (MTG), and lower SA in three clusters involving the bilateral STG, temporal pole gyrus, and entorhinal cortex, and the left inferior temporal gyrus, and fusiform gyrus, than the controls. No significant difference in CT was detected between the two groups. By taking the above-detected clusters as seeds to perform the seed-based FC analysis, we found that the MDD patients showed significantly lower FC between STG/MTG (CV's cluster) and two clusters located in the bilateral visual cortices than the controls. The logistic regression model based on the structural and functional features reached a classification accuracy of 86.7% (p < 0.001) between MDD and controls. CONCLUSION: The present study showed sensory abnormalities in MDD patients using the multi-modal MRI analysis. This finding may act as a disease biomarker distinguishing MDD patients from healthy individuals.

Altered cortical myelin in the salience and default mode networks in major depressive disorder patients: A surface-based analysis.

INTRODUCTION: Evidence from previous genetic and post-mortem studies suggested that the myelination abnormality contributed to the pathogenesis of major depressive disorder (MDD). However, image-level alterations in cortical myelin content associated with MDD are still unclear. METHODS: The high-resolution T1-weighted (T1w) and T2-weighted (T2w) brain 3D structural images were obtained from 52 MDD patients and 52 healthy controls (HC). We calculated the vertex-based T1w/T2w ratio using the HCP structural pipelines to characterize individual cortical myelin maps at the fs_LR 32 k surface. We attempted to detect the clusters with significant differences in cortical myelin content between MDD and HC groups. We correlated the cluster-wise averaged myelin value and the clinical performances in MDD patients. RESULTS: The MDD patients showed significantly lower cortical myelin content in the cluster involving the left insula, orbitofrontal cortex, superior temporal cortex, transverse temporal gyrus, inferior frontal cortex, superior frontal gyrus, anterior cingulate cortex, precentral cortex, and postcentral cortex. The correlation analysis showed a significantly positive correlation between the cluster-wise cortical myelin content and the onset age of MDD patients. CONCLUSION: The MDD patients showed lower cortical myelin content in regions of the default mode network regions and salience network than healthy controls.

Negative-emotion-induced reduction in speech-in-noise recognition is associated with source-monitoring deficits and psychiatric symptoms in mandarin-speaking patients with schizophrenia.

BACKGROUND: Patients with schizophrenia (SCH) have deficits in source monitoring (SM), speech-in-noise recognition (SR), and auditory prosody recognition. This study aimed to test the covariation between SM and SR alteration induced by negative prosodies and their association with psychiatric symptoms in SCH. METHODS: Fifty-four SCH patients and 59 healthy controls (HCs) underwent a speech SM task, an SR task, and the assessment of positive and negative syndrome scale (PANSS). We used the multivariate analyses of partial least squares (PLS) regression to explore the associations among SM (external/internal/new attribution error [AE] and response bias [RB]), SR alteration/release induced by four negative-emotion (sad, angry, fear, and disgust) prosodies of target speech, and psychiatric symptoms. RESULTS: In SCH, but not HCs, a profile (linear combination) of SM (especially the external-source RB) was positively associated with a profile of SR reductions (induced especially by the angry prosody). Moreover, two SR reduction profiles (especially in the anger and sadness conditions) were related to two profiles of psychiatric symptoms (negative symptoms, lack of insight, and emotional disturbances). The two PLS components explained 50.4% of the total variances of the release-symptom association. CONCLUSION: Compared to HCs, SCH is more likely to perceive the external-source speech as internal/new source speech. The SM-related SR reduction induced by the angry prosody was mainly associated with negative symptoms. These findings help understand the psychopathology of SCH and may provide a potential direction to improve negative symptoms via minimizing emotional SR reduction in schizophrenia.

Cortico-basal ganglia networks dysfunction associated with disease severity in patients with idiopathic blepharospasm.

Background: Structural changes occur in brain regions involved in cortico-basal ganglia networks in idiopathic blepharospasm (iBSP); whether these changes influence the function connectivity patterns of cortico-basal ganglia networks remains largely unknown. Therefore, we aimed to investigate the global integrative state and organization of functional connections of cortico-basal ganglia networks in patients with iBSP. Methods: Resting-state functional magnetic resonance imaging data and clinical measurements were acquired from 62 patients with iBSP, 62 patients with hemifacial spasm (HFS), and 62 healthy controls (HCs). Topological parameters and functional connections of cortico-basal ganglia networks were evaluated and compared among the three groups. Correlation analyses were performed to explore the relationship between topological parameters and clinical measurements in patients with iBSP. Results: We found significantly increased global efficiency and decreased shortest path length and clustering coefficient of cortico-basal ganglia networks in patients with iBSP compared with HCs, however, such differences were not observed between patients with HFS and HCs. Further correlation analyses revealed that these parameters were significantly correlated with the severity of iBSP. At the regional level, the functional connectivity between the left orbitofrontal area and left primary somatosensory cortex and between the right anterior part of pallidum and right anterior part of dorsal anterior cingulate cortex was significantly decreased in patients with iBSP and HFS compared with HCs. Conclusion: Dysfunction of the cortico-basal ganglia networks occurs in patients with iBSP. The altered network metrics of cortico-basal ganglia networks might be served as quantitative markers for evaluation of the severity of iBSP.

Cortical thickness and curvature abnormalities in patients with major depressive disorder with childhood maltreatment: Neural markers of vulnerability?

BACKGROUND: Childhood maltreatment has been related to various disadvantageous lifetime outcomes. However, the brain structural alterations that occur in major depressive disorder (MDD) patients with childhood maltreatment are incompletely investigated. METHODS: We extensively explored the cortical abnormalities including cortical volume, surface area, thickness, sulcal depth, and curvature in maltreated MDD patients. Twoway ANOVA was performed to distinguish the effects of childhood maltreatment and depression on structural abnormalities. Partial correlation analysis was performed to explore the relationship between childhood maltreatment and cortical abnormalities. Moreover, we plotted the receiver operating characteristic curve to examine whether the observed cortical abnormalities could be used as neuro biomarkers to identify maltreated MDD patients. RESULTS: We reach the following findings: (i) relative to MDD without childhood maltreatment, MDD patients with childhood maltreatment existed increased cortical curvature in inferior frontal gyrus; (ii) compared to HC without childhood maltreatment, decreased cortical thickness was observed in anterior cingulate cortex and medial prefrontal cortex in MDD patients with childhood maltreatment; (iii) we confirmed the inseparable relationship between cortical curvature alterations in inferior frontal gyrus as well as childhood maltreatment; (iv) cortical curvature abnormality in inferior frontal gyrus could be applied as neural biomarker for clinical identification of MDD patients with childhood maltreatment. CONCLUSIONS: Childhood maltreatment have a significant effects on cortical thickness and curvature abnormalities involved in inferior frontal gyrus, anterior cingulate cortex and medial prefrontal cortex, constituting the vulnerability to depression.

Transcranial alternating current stimulation for schizophrenia: a systematic review of randomized controlled studies.

Background: In randomized clinical trials (RCTs) investigating the application of transcranial alternating current stimulation (tACS) in schizophrenia, inconsistent results have been reported. The purpose of this exploratory systematic review of RCTs was to evaluate tACS as an adjunct treatment for patients with schizophrenia based on its therapeutic effects, tolerability, and safety. Methods: Our analysis included RCTs that evaluated adjunctive tACS' effectiveness, tolerability, and safety in schizophrenia patients. Three independent authors extracted data and synthesized it using RevMan 5.3 software. Results: Three RCTs involving 76 patients with schizophrenia were encompassed in the analysis, with 40 participants receiving active tACS and 36 receiving sham tACS. Our study revealed a significant superiority of active tACS over sham tACS in improving total psychopathology (standardized mean difference [SMD] = -0.61, 95% confidence interval [CI]: -1.12, -0.10; I2 = 16%, p = 0.02) and negative psychopathology (SMD = -0.65, 95% CI: -1.11, -0.18; I2 = 0%, p = 0.007) in schizophrenia. The two groups, however, showed no significant differences in positive psychopathology, general psychopathology, or auditory hallucinations (all p > 0.05). Two RCTs examined the neurocognitive effects of tACS, yielding varied findings. Both groups demonstrated similar rates of discontinuation due to any reason and adverse events (all p > 0.05). Conclusion: Adjunctive tACS is promising as a viable approach for mitigating total and negative psychopathology in individuals diagnosed with schizophrenia. However, to gain a more comprehensive understanding of tACS's therapeutic effects in schizophrenia, it is imperative to conduct extensive, meticulously planned, and well-documented RCTs.

Aberrant static and dynamic functional connectivity of amygdala subregions in patients with major depressive disorder and childhood maltreatment.

Major depressive disorder (MDD) with childhood maltreatment is a heterogeneous clinical phenotype of depression with prominent features of brain disconnectivity in areas linked to maltreatment-related emotion processing (e.g., the amygdala). However, static and dynamic alterations of functional connectivity in amygdala subregions have not been investigated in MDD with childhood maltreatment. Here, we explored whether amygdala subregions (i.e., medial amygdala [MeA] and lateral amygdala [LA]) exhibited static functional connectivity (sFC) and dynamic functional connectivity (dFC) disruption, and whether these disruptions were related to childhood maltreatment. We compared sFC and dFC patterns in MDD with childhood maltreatment (n = 48), MDD without childhood maltreatment (n = 30), healthy controls with childhood maltreatment (n = 57), and healthy controls without childhood maltreatment (n = 46). The bilateral MeA and LA were selected as the seeds in the FC analysis. The results revealed a functional connectivity disruption pattern in maltreated MDD patients, characterized by sFC and dFC abnormalities involving the MeA, LA, and theory of mind-related brain areas including the middle occipital area, middle frontal gyrus, superior medial frontal gyrus, angular gyrus, supplementary motor areas, middle temporal gyrus, middle cingulate gyrus, and calcarine gyrus. Significant correlations were detected between impaired dFC patterns and childhood maltreatment. Furthermore, the dFC disruption pattern served as a moderator in the relationship between sexual abuse and depression severity. Our findings revealed neurobiological features of childhood maltreatment, providing new evidence regarding vulnerability to psychiatric disorders.

Abnormal fractional Amplitude of Low-Frequency Fluctuation in chronic ketamine users.

BACKGROUND: Ketamine has become a major substance of abuse worldwide. Nevertheless, The long-term effects of ketamine use on intrinsic spontaneous neural activity remain unknown. OBJECTIVES: In the present study, rs-fMRI was used to explore whether chronic ketamine use changes the intrinsic spontaneous neural activity, and whether the intrinsic spontaneous neural activity changes in chronic ketamine users(CKUs) are associated with cognitive impairments observed in chronic ketamine users. METHODS: 28 CKUs and 30 healthy controls(HC) were enrolled. The fractional amplitude of low-frequency fluctuations (fALFF) was measured to evaluate the intrinsic spontaneous neural activity in multiple brain regions. Cognitive alterations were assessed using MATRICS Consensus Cognitive Battery (MCCB). RESULTS: CKUs showed higher fALFF in the right parahippocampal gyrus(PHG), right anterior cingulate cortex(ACC), left cerebellar vermis, left posterior cingulate cortex(PCC), bilateral caudate, and lower fALFF in the right middle occipital gyrus(MOG), left cuneus, right precuneus. The fALFF in the right PHG, left cerebellar vermis, bilateral caudate, right ACC of CKUs presented a negative correlation with the average quantity of ketamine use/day(g) and estimated total ketamine consumption. The fALFF in left PCC had a negative correlation with the average quantity of ketamine use/day(g). Speed of processing on MCCB presented a negative correlation with the fALFF in the right MOG. CONCLUSION: Our study found abnormal fALFF in multiple brain areas in CKUs, which indicated the changes of intrinsic spontaneous neural activity in multiple brain areas. The changes of fALFF were associated with the severity of ketamine use and cognitive impairment in CKUs.

Prediction of Facial Emotion Recognition Ability in Patients With First-Episode Schizophrenia Using Amplitude of Low-Frequency Fluctuation-Based Support Vector Regression Model.

Objective: There were few studies that had attempted to predict facial emotion recognition (FER) ability at the individual level in schizophrenia patients. In this study, we developed a model for the prediction of FER ability in Chinese Han patients with the first-episode schizophrenia (FSZ). Materials and Methods: A total of 28 patients with FSZ and 33 healthy controls (HCs) were recruited. All subjects underwent resting-state fMRI (rs-fMRI). The amplitude of low-frequency fluctuation (ALFF) method was selected to analyze voxel-level spontaneous neuronal activity. The visual search experiments were selected to evaluate the FER, while the support vector regression (SVR) model was selected to develop a model based on individual rs-fMRI brain scan. Results: Group difference in FER ability showed statistical significance (P < 0.05). In FSZ patients, increased mALFF value were observed in the limbic lobe and frontal lobe, while decreased mALFF value were observed in the frontal lobe, parietal lobe, and occipital lobe (P < 0.05, AlphaSim correction). SVR analysis showed that abnormal spontaneous activity in multiple brain regions, especially in the right posterior cingulate, right precuneus, and left calcarine could effectively predict fearful FER accuracy (r = 0.64, P = 0.011) in patients. Conclusion: Our study provides an evidence that abnormal spontaneous activity in specific brain regions may serve as a predictive biomarker for fearful FER ability in schizophrenia.

Intrinsic brain abnormalities in female major depressive disorder patients with childhood trauma: A resting-state functional magnetic resonance imaging study.

Objective: Childhood trauma is a strong predictor of major depressive disorder (MDD). Women are more likely to develop MDD than men. However, the neural basis of female MDD patients with childhood trauma remains unclear. We aimed to identify the specific brain regions that are associated with female MDD patients with childhood trauma. Methods: We recruited 16 female MDD patients with childhood trauma, 16 female MDD patients without childhood trauma, and 20 age- and education level-matched healthy controls. All participants underwent resting-state functional magnetic resonance imaging (MRI). Regional brain activity was evaluated as the amplitude of low-frequency fluctuation (ALFF). Furthermore, functional connectivity (FC) analyses were performed on areas with altered ALFF to explore alterations in FC patterns. Results: There was increased ALFF in the left middle frontal gyrus (MFG) and the right postcentral gyrus (PoCG) in MDD with childhood trauma compared with MDD without childhood trauma. The areas with significant ALFF discrepancies were selected as seeds for the FC analyses. There was increased FC between the left MFG and the bilateral putamen gyrus. Moreover, ALFF values were correlated with childhood trauma severity. Conclusion: Our findings revealed abnormal intrinsic brain activity and FC patterns in female MDD patients with childhood trauma, which provides new possibilities for exploring the pathophysiology of this disorder in women.

Aberrant brain connectivity is associated with childhood maltreatment in individuals with major depressive disorder.

Although childhood maltreatment confers a high risk for the development of major depressive disorder, the neurobiological mechanisms underlying this connection remain unknown. The present study sought to identify the specific resting-state networks associated with childhood maltreatment. We recruited major depressive disorder patients with and without a history of childhood maltreatment (n = 31 and n = 30, respectively) and healthy subjects (n = 80). We used independent component analysis to compute inter- and intra- network connectivity. We found that individuals with major depressive disorder and childhood maltreatment could be characterized by the following network disconnectivity model relative to healthy subjects: (i) decreased intra-network connectivity in the left frontoparietal network and increased intra-network connectivity in the right frontoparietal network, (ii) decreased inter-network connectivity in the posterior default mode network-auditory network, posterior default mode network-limbic system, posterior default mode network-anterior default mode network, auditory network-medial visual network, lateral visual network - medial visual network, medial visual network-sensorimotor network, medial visual network - anterior default mode network, occipital pole visual network-dorsal attention network, and posterior default mode network-anterior default mode network, and (iii) increased inter-network connectivity in the sensorimotor network-ventral attention network, and dorsal attention network-ventral attention network. Moreover, we found significant correlations between the severity of childhood maltreatment and the intra-network connectivity of the frontoparietal network. Our study demonstrated that childhood maltreatment is integrally associated with aberrant network architecture in patients with major depressive disorder.

Prediction of childhood maltreatment and subtypes with personalized functional connectome of large-scale brain networks.

Childhood maltreatment (CM) has a long impact on physical and mental health of children. However, the neural underpinnings of CM are still unclear. In this study, we aimed to establish the associations between functional connectome of large-scale brain networks and influences of CM evaluated through Childhood Trauma Questionnaire (CTQ) at the individual level based on resting-state functional magnetic resonance imaging data of 215 adults. A novel individual functional mapping approach was employed to identify subject-specific functional networks and functional network connectivities (FNCs). A connectome-based predictive modeling (CPM) was used to estimate CM total and subscale scores using individual FNCs. The CPM established with FNCs can well predict CM total scores and subscale scores including emotion abuse, emotion neglect, physical abuse, physical neglect, and sexual abuse. These FNCs primarily involve default mode network, fronto-parietal network, visual network, limbic network, motor network, dorsal and ventral attention networks, and different networks have distinct contributions to predicting CM and subtypes. Moreover, we found that CM showed age and sex effects on individual functional connections. Taken together, the present findings revealed that different types of CM are associated with different atypical neural networks which provide new clues to understand the neurobiological consequences of childhood adversity.

Altered Variability and Concordance of Dynamic Resting-State Functional Magnetic Resonance Imaging Indices in Patients With Major Depressive Disorder and Childhood Trauma.

Childhood trauma is a non-specific risk factor for major depressive disorder (MDD). resting-state functional magnetic resonance imaging (R-fMRI) studies have demonstrated changes in regional brain activity in patients with MDD who experienced childhood trauma. However, previous studies have mainly focused on static characteristics of regional brain activity. This study aimed to determine the specific brain regions associated with MDD with childhood trauma by performing temporal dynamic analysis of R-fMRI data in three groups of patients: patients with childhood trauma-associated MDD (n = 48), patients without childhood trauma-associated MDD (n = 30), and healthy controls (n = 103). Dynamics and concordance of R-fMRI indices were calculated and analyzed. In patients with childhood trauma-associated MDD, a lower dynamic amplitude of low-frequency fluctuations was found in the left lingual gyrus, whereas a lower dynamic degree of centrality was observed in the right lingual gyrus and right calcarine cortex. Patients with childhood trauma-associated MDD showed a lower voxel-wise concordance in the left middle temporal and bilateral calcarine cortices. Moreover, group differences (depressed or not) significantly moderated the relationship between voxel-wise concordance in the right calcarine cortex and childhood trauma history. Overall, patients with childhood trauma-associated MDD demonstrated aberrant variability and concordance in intrinsic brain activity. These aberrances may be an underlying neurobiological mechanism that explains MDD from the perspective of temporal dynamics.

Early improvement as a predictor of final remission in patients with treatment-resistant depression receiving electroconvulsive therapy with ketofol anesthesia.

OBJECTIVE: To examine whether early symptom improvement can predict eventual remission following electroconvulsive therapy (ECT) with ketamine plus propofol (ketofol) anesthesia in patients with treatment-resistant depression (TRD). METHODS: Thirty Han Chinese subjects suffering from TRD were administered ketofol anesthesia during ECT. Remission was defined as a score of ≤7 on the 17-item Hamilton Depression Rating Scale (HAMD-17). Receiver operating characteristic (ROC) curves were applied to identify the number of ECT sessions (i.e., 1, 2, 3, or 4 ECT sessions) that had the best discriminative capacity for eventual remission. The best definition of early improvement to predict final remission was determined by using the Youden index. RESULTS: Of the 30 patients with TRD, 16 (53.3%) and 30 (100%) were classified as remitters and responders, respectively. A 45% reduction in the HAMD-17 score after 3 ECT sessions was the optimum definition of early improvement in the prediction of eventual remission, with relatively good sensitivity (88%) and specificity (93%). Patients with than without early improvement had a greater possibility of achieving favorable ECT outcomes. CONCLUSION: Final remission of TRD following ECT with ketofol anesthesia appeared to be predicted by early improvement, as indicated by a 45% reduction in HAMD-17 score after 3 ECT sessions.

Consistent brain structural abnormalities and multisite individualised classification of schizophrenia using deep neural networks.

BACKGROUND: Previous analyses of grey and white matter volumes have reported that schizophrenia is associated with structural changes. Deep learning is a data-driven approach that can capture highly compact hierarchical non-linear relationships among high-dimensional features, and therefore can facilitate the development of clinical tools for making a more accurate and earlier diagnosis of schizophrenia. AIMS: To identify consistent grey matter abnormalities in patients with schizophrenia, 662 people with schizophrenia and 613 healthy controls were recruited from eight centres across China, and the data from these independent sites were used to validate deep-learning classifiers. METHOD: We used a prospective image-based meta-analysis of whole-brain voxel-based morphometry. We also automatically differentiated patients with schizophrenia from healthy controls using combined grey matter, white matter and cerebrospinal fluid volumetric features, incorporated a deep neural network approach on an individual basis, and tested the generalisability of the classification models using independent validation sites. RESULTS: We found that statistically reliable schizophrenia-related grey matter abnormalities primarily occurred in regions that included the superior temporal gyrus extending to the temporal pole, insular cortex, orbital and middle frontal cortices, middle cingulum and thalamus. Evaluated using leave-one-site-out cross-validation, the performance of the classification of schizophrenia achieved by our findings from eight independent research sites were: accuracy, 77.19-85.74%; sensitivity, 75.31-89.29% and area under the receiver operating characteristic curve, 0.797-0.909. CONCLUSIONS: These results suggest that, by using deep-learning techniques, multidimensional neuroanatomical changes in schizophrenia are capable of robustly discriminating patients with schizophrenia from healthy controls, findings which could facilitate clinical diagnosis and treatment in schizophrenia.