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.

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.

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.

Enduring effect of abuse: Childhood maltreatment links to altered theory of mind network among adults.

Childhood maltreatment (CM) confers a great risk of maladaptive development outcomes later in life, however, the neurobiological mechanism underlying this vulnerability is still unclear. The present study aimed to investigate the long-term consequences of CM on neural connectivity while controlling for psychiatric conditions, medication, and, substance abuse. A sample including adults with (n = 40) and without CM (n = 50) completed Childhood Trauma Questionnaire (CTQ), personality questionnaires, and resting-state functional magnetic resonance imaging scan were recruited for the current study. The whole-brain functional connectivity (FC) was evaluated using an unbiased, data-driven, multivariate pattern analysis method. Relative to controls, adults with CM suffered a higher level of temperament and impulsivity and showed decreased FC between the insula and superior temporal gyrus (STG) and between inferior parietal lobule (IPL) and middle frontal gyrus, STG, and dorsal anterior cingulate cortex (dACC), while increased FC between IPL and cuneus and superior frontal gyrus (SFG) regions. The FCs of IPL with dACC and SFG were correlated with the anxious and cyclothymic temperament and attentional impulsivity. Moreover, these FCs partially mediated the relationship between CM and attentional impulsivity. Our results suggest that CM has a significant effect on the modulation of FC within theory of mind (ToM) network even decades later in adulthood, and inform a new framework to account for how CM results in the development of impulsivity. The novel findings reveal the neurobiological consequences of CM and provide new clues to the prevention and intervention strategy to reduce the risk of the development of psychopathology.

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.

Use of the RBANS to Evaluate Cognition in Patients with Schizophrenia and Metabolic Syndrome: a Meta-Analysis of Case-Control Studies.

Schizophrenia is associated with an increased risk of metabolic syndrome (MetS), which is an important risk factor for developing cognitive impairment in the general population. A few case-control studies have explored the relationship between MetS and cognitive deficits in individuals with schizophrenia but with inconsistent findings. This meta-analysis of case-control studies was carried out to explore the association between MetS and cognitive performance in patients with schizophrenia. Only case-control studies assessing the association of cognitive function and MetS in patients with schizophrenia were identified. Cognitive function was assessed using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) scale. Six case-control studies (n = 992) comparing cognition between patients with schizophrenia with MetS (n = 426) and those without MetS (n = 566) using the RBANS were identified. Compared to patients with schizophrenia without MetS, patients with schizophrenia and MetS had significantly more impairments in RBANS total scores [standardized mean difference (SMD) = -0.26, 95% confidence interval (CI): -0.51 to -0.02; I2 = 72%; p = 0.03], immediate memory (SMD = -0.32, 95% CI: -0.54 to -0.10; I2 = 66%; p = 0.005), attention (SMD = -0.29, 95% CI: -0.56 to -0.02; I2 = 77%; p = 0.03), and delayed memory (SMD = -0.24, 95% CI: -0.46 to -0.03; I2 = 64%; p = 0.03). No group difference was found regarding visuospatial skills and language (p > 0.05). This meta-analysis found that schizophrenia patients with MetS had worse performance on certain cognitive tasks than non-MetS patients.

Covariation between spontaneous neural activity in the insula and affective temperaments is related to sleep disturbance in individuals with major depressive disorder.

BACKGROUND: Affective temperaments have been considered antecedents of major depressive disorder (MDD). However, little is known about how the covariation between alterations in brain activity and distinct affective temperaments work collaboratively to contribute to MDD. Here, we focus on the insular cortex, a critical hub for the integration of subjective feelings, emotions, and motivations, to examine the neural correlates of affective temperaments and their relationship to depressive symptom dimensions. METHODS: Twenty-nine medication-free patients with MDD and 58 healthy controls underwent magnetic resonance imaging scanning and completed the Temperament Evaluation of Memphis, Pisa, Paris and San Diego (TEMPS). Patients also received assessments of the Hamilton Depression Rating Scale (HDRS). We used multivariate analyses of partial least squares regression and partial correlation analyses to explore the associations among the insular activity, affective temperaments, and depressive symptom dimensions. RESULTS: A profile (linear combination) of increased fractional amplitude of low-frequency fluctuations (fALFF) of the anterior insular subregions (left dorsal agranular-dysgranular insula and right ventral agranuar insula) was positively associated with an affective-temperament (depressive, irritable, anxious, and less hyperthymic) profile. The covariation between the insula-fALFF profile and the affective-temperament profile was significantly correlated with the sleep disturbance dimension (especially the middle and late insomnia scores) in the medication-free MDD patients. CONCLUSIONS: The resting-state spontaneous activity of the anterior insula and affective temperaments collaboratively contribute to sleep disturbances in medication-free MDD patients. The approach used in this study provides a practical way to explore the relationship of multivariate measures in investigating the etiology of mental disorders.

Polygenic effects of schizophrenia on hippocampal grey matter volume and hippocampus-medial prefrontal cortex functional connectivity.

BACKGROUND: Schizophrenia is a complex mental disorder with high heritability and polygenic inheritance. Multimodal neuroimaging studies have also indicated that abnormalities of brain structure and function are a plausible neurobiological characterisation of schizophrenia. However, the polygenic effects of schizophrenia on these imaging endophenotypes have not yet been fully elucidated. AIMS: To investigate the effects of polygenic risk for schizophrenia on the brain grey matter volume and functional connectivity, which are disrupted in schizophrenia. METHOD: Genomic and neuroimaging data from a large sample of Han Chinese patients with schizophrenia (N = 509) and healthy controls (N = 502) were included in this study. We examined grey matter volume and functional connectivity via structural and functional magnetic resonance imaging, respectively. Using the data from a recent meta-analysis of a genome-wide association study that comprised a large number of Chinese people, we calculated a polygenic risk score (PGRS) for each participant. RESULTS: The imaging genetic analysis revealed that the individual PGRS showed a significantly negative correlation with the hippocampal grey matter volume and hippocampus-medial prefrontal cortex functional connectivity, both of which were lower in the people with schizophrenia than in the controls. We also found that the observed neuroimaging measures showed weak but similar changes in unaffected first-degree relatives of patients with schizophrenia. CONCLUSIONS: These findings suggested that genetically influenced brain grey matter volume and functional connectivity may provide important clues for understanding the pathological mechanisms of schizophrenia and for the early diagnosis of schizophrenia.

Effect of selective serotonin reuptake inhibitor on prefrontal-striatal connectivity is dependent on the level of TNF-α in patients with major depressive disorder.

BACKGROUND: We hypothesize that the tumor necrosis factor-α (TNF-α) may play a role in disturbing the effect of selective serotonin reuptake inhibitor (SSRI) on the striatal connectivity in patients with major depressive disorder (MDD). METHODS: We performed a longitudinal observation by combining resting-state functional magnetic resonance imaging (rs-fMRI) and biochemical analyses to identify the abnormal striatal connectivity in MDD patients, and to evaluate the effect of TNF-α level on these abnormal connectivities during SSRI treatment. Eighty-five rs-fMRI scans were collected from 25 MDD patients and 35 healthy controls, and the scans were repeated for all the patients before and after a 6-week SSRI treatment. Whole-brain voxel-wise functional connectivity (FC) was calculated by correlating the rs-fMRI time courses between each voxel and the striatal seeds (i.e. spherical regions placed at the striatums). The level of TNF-α in serum was evaluated by Milliplex assay. Factorial analysis was performed to assess the interaction effects of 'TNF-α × treatment' in the regions with between-group FC difference. RESULTS: Compared with controls, MDD patients showed significantly higher striatal FC in the medial prefrontal cortex (MPFC) and bilateral middle/superior temporal cortices before SSRI treatment (p < 0.001, uncorrected). Moreover, a significant interaction effect of 'TNF-α × treatment' was found in MPFC-striatum FC in MDD patients (p = 0.002), and the significance remained after adjusted for age, gender, head motion, and episode of disease. CONCLUSION: These findings provide evidence that treatment-related brain connectivity change is dependent on the TNF-α level in MDD patients, and the MPFC-striatum connectivities possibly serve as an important target in the brain.

Investigation into local white matter abnormality in emotional processing and sensorimotor areas using an automatically annotated fiber clustering in major depressive disorder.

This work presents an automatically annotated fiber cluster (AAFC) method to enable identification of anatomically meaningful white matter structures from the whole brain tractography. The proposed method consists of 1) a study-specific whole brain white matter parcellation using a well-established data-driven groupwise fiber clustering pipeline to segment tractography into multiple fiber clusters, and 2) a novel cluster annotation method to automatically assign an anatomical tract annotation to each fiber cluster by employing cortical parcellation information across multiple subjects. The novelty of the AAFC method is that it leverages group-wise information about the fiber clusters, including their fiber geometry and cortical terminations, to compute a tract anatomical label for each cluster in an automated fashion. We demonstrate the proposed AAFC method in an application of investigating white matter abnormality in emotional processing and sensorimotor areas in major depressive disorder (MDD). Seven tracts of interest related to emotional processing and sensorimotor functions are automatically identified using the proposed AAFC method as well as a comparable method that uses a cortical parcellation alone. Experimental results indicate that our proposed method is more consistent in identifying the tracts across subjects and across hemispheres in terms of the number of fibers. In addition, we perform a between-group statistical analysis in 31 MDD patients and 62 healthy subjects on the identified tracts using our AAFC method. We find statistical differences in diffusion measures in local regions within a fiber tract (e.g. 4 fiber clusters within the identified left hemisphere cingulum bundle (consisting of 14 clusters) are significantly different between the two groups), suggesting the ability of our method in identifying potential abnormality specific to subdivisions of a white matter structure.

Three-Dimensional Eye Shape, Myopic Maculopathy, and Visual Acuity: The Zhongshan Ophthalmic Center-Brien Holden Vision Institute High Myopia Cohort Study.

PURPOSE: To investigate the relationship among eye shape, myopic maculopathy, and visual acuity in highly myopic eyes using 3-dimensional (3D) magnetic resonance imaging (MRI). DESIGN: Observational case series. PARTICIPANTS: The study included 190 eyes of 95 participants with bilateral high myopia from the Zhongshan Ophthalmic Center-Brien Holden Vision Institute High Myopia Cohort Study, with spherical power ≤-6.00 diopters (D) in both eyes. METHODS: The participants underwent best-corrected visual acuity (BCVA), fundus photography, cycloplegic refraction, and ocular biometry. Posterior staphyloma was identified with 3D MRI, and ocular shapes were categorized into spheroidal, ellipsoidal, conical, nasally distorted, temporally distorted, and barrel shapes according to the inferior view from T2-weighted 3D MRI (Achieva 3.0T; Philips Medical Systems, Best, the Netherlands). Myopic maculopathy was graded as C0 to C4 according to the International Photographic Classification and Grading System using fundus photography. The statistical significance of the differences in distribution of myopic maculopathy and BCVA in eyes with different ocular shapes was determined using Pearson's chi-square tests. MAIN OUTCOME MEASURES: Distributions of myopic maculopathy and BCVA in relation to different eye shapes. RESULTS: The mean spherical equivalent and axial length were -11.74±4.10 D and 28.18±1.73 mm in the right eyes, respectively. The same ocular shapes were observed in both eyes in 69 participants (72.6%). The predominant shape was spheroidal (53.7%), followed by nasally distorted and conical types (both 14.7%). C2 and above myopic maculopathy was observed in all barrel-shaped eyes, in 75% temporally distorted eyes, and in 71.4% nasally distorted and conical eyes. In eyes with posterior staphyloma (n = 22), 18 (81.8%) had C2 and above myopic maculopathy compared with 40 eyes (54.8%) without posterior staphyloma (n = 73). Eyes with temporal and nasal distortion, and eyes with staphyloma were more likely to have BCVA <20/40. CONCLUSIONS: Not all highly myopic eyes are deformed. Spheroid was the predominant ocular shape in this series of young patients with high myopia bilaterally. Barrel-shaped and temporally distorted eyes present significant myopic maculopathy, whereas eyes with posterior staphyloma display more severe chorioretinal atrophy. Eyes of more deformed shapes tend to have more severe myopic maculopathy and worse BCVA.

Genetic Effects on Fine-Grained Human Cortical Regionalization.

Various brain structural and functional features such as cytoarchitecture, topographic mapping, gyral/sulcal anatomy, and anatomical and functional connectivity have been used in human brain parcellation. However, the fine-grained intrinsic genetic architecture of the cortex remains unknown. In the present study, we parcellated specific regions of the cortex into subregions based on genetic correlations (i.e., shared genetic influences) between the surface area of each pair of cortical locations within the seed region. The genetic correlations were estimated by comparing the correlations of the surface area between monozygotic and dizygotic twins using bivariate twin models. Our genetic subdivisions of diverse brain regions were reproducible across 2 independent datasets and corresponded closely to fine-grained functional specializations. Furthermore, subregional genetic correlation profiles were generally consistent with functional connectivity patterns. Our findings indicate that the magnitude of the genetic covariance in brain anatomy could be used to delineate the boundaries of functional subregions of the brain and may be of value in the next generation human brain atlas.

Abnormal Anterior Cingulate N-Acetylaspartate and Executive Functioning in Treatment-Resistant Depression After rTMS Therapy.

BACKGROUND: Cognitive impairment is a key feature of treatment-resistant depression (TRD) and can be related to the anterior cingulate cortex (ACC) function. Repetitive transcranial magnetic stimulation (rTMS) as an antidepressant intervention has increasingly been investigated in the last two decades. However, no studies to date have investigated the association between neurobiochemical changes within the anterior cingulate and executive dysfunction measured in TRD being treated with rTMS. METHODS: Thirty-two young depressed patients with treatment-resistant unipolar depression were enrolled in a double-blind, randomized study [active (n=18) vs. sham (n=14)]. ACC metabolism was investigated before and after high-frequency (15 Hz) rTMS using 3-tesla proton magnetic resonance spectroscopy (1H-MRS). The results were compared with 28 age- and gender-matched healthy controls. Executive functioning was measured with the Wisconsin Card Sorting Test (WCST) among 34 subjects with TRD and 28 healthy subjects. RESULTS: Significant reductions in N-acetylaspartate (NAA) and choline-containing Compound levels in the left ACC were found in subjects with TRD pre-rTMS when compared with healthy controls. After successful treatment, NAA levels increased significantly in the left ACC of subjects and were not different from those of age-matched controls. In the WCST, more perseverative errors and fewer correct numbers were observed in TRD subjects at baseline. Improvements in both perseverative errors and correct numbers occurred after active rTMS. In addition, improvement of perseverative errors was positively correlated with enhancement of NAA levels in the left ACC in the active rTMS group. CONCLUSIONS: Our results suggest that the NAA concentration in the left ACC is associated with an improvement in cognitive functioning among subjects with TRD response to active rTMS.

Hypothalamic-pituitary-adrenal axis functioning and dysfunctional attitude in depressed patients with and without childhood neglect.

BACKGROUND: To date, the relationships between childhood neglect, hypothalamic-pituitary-adrenal (HPA) axis functioning and dysfunctional attitude in depressed patients are still obscure. METHODS: The Childhood Trauma Questionnaire (CTQ) was used to assess childhood emotional neglect and physical neglect. Twenty-eight depressed patients with childhood neglect and 30 depressed patients without childhood neglect from Guangzhou Psychiatric Hospital were compared with 29 age- and gender-matched control subjects without childhood neglect and 22 control subjects with childhood neglect. Cortisol awakening response, the difference between the cortisol concentrations at awakening and 30 minutes later, provided a measure of HPA axis functioning. The Dysfunctional Attitude Scale measured cognitive schema. RESULTS: HPA axis functioning was significantly increased in depressed patients with childhood neglect compared with depressed patients without childhood neglect (p < 0.001). HPA axis activity in the control group with childhood neglect was significantly higher than in the depressed group without childhood neglect (p < 0.001). Total scores of childhood neglect were positively correlated with HPA axis functioning and dysfunctional attitude scores, but not with severity of depression. We did not find correlations with HPA axis functioning and dysfunctional attitude or with the Hamilton Rating Scale for Depression scores. CONCLUSIONS: Childhood neglect may cause hyperactivity of the HPA axis functioning and dysfunctional attitude, but does not affect depression severity.

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.

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.

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.

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.

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.

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.