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Background: Major depressive disorder (MDD) is a widespread mental health issue, impacting spatial and temporal aspects of brain activity. The neural mechanisms behind MDD remain unclear. To address this gap, we introduce a novel measure, spatiotemporal topology (SPT), capturing both the hierarchy and dynamic attributes of brain activity in depressive disorder patients., Methods: We analyzed fMRI data from 285 MDD inpatients and 141 healthy controls (HC). SPT was assessed by coupling brain gradient measurement and time delay estimation. A nested machine learning process distinguished between MDD and HC using SPT. Person's correlation tested the link between SPT's and symptom severity, and another machine learning method predicted the gap between patients' chronological and brain age., Results: SPT demonstrated significant differences between patients and healthy controls (F = 2.944, p < 0.001). Machine learning approaches revealed SPT's ability to discriminate between patients and healthy controls (Accuracy = 0.65, Sensitivity = 0.67, Specificity = 0.64). Moreover, SPT correlated with the severity of depression symptom (r = 0.32. pFDR = 0.045) and predicted the gap between patients' chronological age and brain age (r = 0.756, p < 0.001)., Limitations: Evaluation of brain dynamics was constrained by MRI temporal resolution., Conclusions: Our study introduces SPT as a promising metric to characterize the spatiotemporal signature of brain function, providing insights into deviant brain activity associated with depressive disorders and advancing our understanding of their psychopathological mechanisms., Competing Interests: Declaration of competing interest The authors report no competing interests., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Previous neuroimaging and pathological studies have found myelin-related abnormalities in bipolar disorder (BD), which prompted the use of magnetic resonance (MR) imaging technology sensitive to neuropathological changes to explore its neuropathological basis. We holistically investigated alterations in myelin within BD patients by inhomogeneous magnetization transfer (ihMT), which is sensitive and specific to myelin content., Methods: Thirty-one BD and 42 healthy controls (HC) were involved. Four MR metrics, i.e., ihMT ratio (ihMTR), pseudo-quantitative ihMT (qihMT), magnetization transfer ratio and pseudo-quantitative magnetization transfer (qMT), were compared between groups using analysis methods based on whole-brain voxel-level and white matter regions of interest (ROI), respectively., Results: The voxel-wise analysis showed significantly inter-group differences of ihMTR and qihMT in the corpus callosum. The ROI-wise analysis showed that ihMTR, qihMT, and qMT values in BD group were significantly lower than that in HC group in the genu and body of corpus callosum, left anterior limb of the internal capsule, left anterior corona radiate, and bilateral cingulum (p < 0.001). And the qihMT in genu of corpus callosum and right cingulum were negatively correlated with depressive symptoms in BD group., Limitations: This study is based on cross-sectional data and the sample size is limited., Conclusion: These findings suggest the reduced myelin content of anterior midline structure in the bipolar patients, which might be a critical pathophysiological feature of BD., Competing Interests: Declaration of competing interest The authors report no declarations of interest., (Copyright © 2024. Published by Elsevier B.V.)

Gray matter (GM) atrophy is well documented in patients with major depressive disorder (MDD), but its underlying mechanism remains unknown. This study aimed to examine the GM atrophy in MDD patients with diverse suicidal ideations (SIs) and to explore whether those alterations were driven by connections. GM volume was estimated in 163 patients with recurrent MDD (comprising 122 with SI [MDDSI] and 41 without SI [MDDNSI]) and 134 health controls (HCs). A two-sample t-test was used to identify GM volume abnormalities in MDD patients and their subgroups. Functional connectivity was computed between pairs of aberrant GM in both patients and HCs, which were further compared with the connectivity of random brain regions. A permutation test was performed to assess its significance. Propensity score matching (PSM) was further performed to validate the main results. Compared with HCs, the MDDNSI group exhibited GM atrophy in 24 regions, with the largest effect sizes found in the frontal and parietal lobes, while the MDDSI group exhibited more widespread GM atrophy involving 49 regions, with the largest effect sizes in the frontal lobe, parietal lobe, temporal lobe, and the limbic system. Furthermore, patients and HCs exhibited significantly increased functional connectivity between regions with GM atrophy compared with randomly selected regions (p < 0.05). PSM analysis presented similar results to the main analysis. MDD patients had diverse GM atrophy features according to their SI tendency. Moreover, connectome architecture modulates the GM atrophy in MDD patients, implying the possibility that connections drive these pathological changes., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purposes: The recently developed myelin imaging method, inhomogeneous magnetization transfer (ihMT), is a surrogate measure of myelin content. The goal of the current study was to investigate alterations in myelin integrity in patients with recurrent major depressive disorder (rMDD)., Methods: Fifty-two patients with rMDD (36 female and 16 male) and 42 healthy controls (HC, 29 female and 13 male) were included. Two ihMT indices, quantitative ihMT (qihMT) and quantitative MT (qMT), were estimated from the ihMT data. A 50 white matter atlas was used to extract the regional quantitative values for each subject. The differences in qihMT and qMT values between the rMDD and HC groups were compared by a general linear model. Pearson correlation analyses were conducted to investigate associations between the significantly altered ihMT indices and clinical measures (Hamilton Depression Rating Scale scores and disease duration) in rMDD group., Results: The rMDD group showed significantly lower qihMT values in the fornix, left anterior limb of internal capsule, and left sagittal stratum, and lower qMT values in the fornix and left anterior limb of internal capsule than those of the HC group (p < 0.05, false discovery rate corrected). Both the qihMT and qMT values in the fornix of patients with rMDD were negatively correlated with disease duration (qihMT: r = -0.478, p < 0.001, Bonferroni corrected; qMT: r = -0.433, p = 0.001, Bonferroni corrected)., Conclusion: Our findings suggested that rMDD is associated with myelin impairment in the fornix, left anterior limb of internal capsule, and left sagittal stratum. In addition, this disruption of myelin integrity in the fornix could be cumulative as the disease progresses., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Image segmentation is the key step for quantitative analysis of brain tissues (white matter, gray matter and cerebrospinal fluid). Based on genetic algorithm and fuzzy C-means (FCM) approach, a fast and fully automatic segmentation method of brain tissues named genetic fuzzy clustering algorithm is introduced in this paper. The method operates slice by slice based on three main steps: The non-brain tissues are removed from the original head MR images at first using an auto-threshold method; then the initial cluster centers of FCM are determined by genetic algorithm; and finally brain tissues are segmented into white matter, grey matter and cerebrospinal fluid by FCM via only one iteration computation. The experiment results have shown that the segmentation method proposed by this paper has faster speed and higher accuracy compared with fast fuzzy c-means algorithm which is commonly used in segmentation of brain tissues.

Treatment-resistant depression (TRD) is a chronic and severe psychiatric illness associated with limited therapeutic options. Deep brain stimulation (DBS) is a promising therapy for TRD patients. However, its safety and efficacy are still unclear. Here we reported the safety and efficacy of lateral habenula (LHb) DBS for a TRD patient who had failed medical, psychological, electroconvulsive, and ketamine therapy. The DBS system is compatible with 3T magnetic resonance imaging along with local field potential (LFP) streaming. Two DBS electrodes were implanted at the bilateral LHb without any complication. The patient showed acute stimulation effects and achieved long-term improvements in his depression, anxiety, and sleep with left LHb 160 Hz frequency stimulation, accompanying the change of LFPs. These results provided clinical evidence toward the safety and efficacy and electrophysiological basis of LHb DBS for TRD. [ABSTRACT FROM AUTHOR]

Background: Suicide risk stratification and individual-level prediction among major depressive disorder (MDD) is important but unrecognized. Here, we construct models to detect suicidality in MDD using machine learning (ML) and whole-brain functional connectivity (FC).Methods: A cross-sectional assessment was conducted on 200 subjects, including 126 MDD with high suicide risk (HSR; 73 patients with suicidal ideation [SI], 53 patients with suicidal attempts [SA]), 36 patients with low suicide risk (LSR) and 38 healthy controls (HCs). Whole-brain FC features were calculated, the least absolute shrinkage and selection operator (LASSO) method was used for feature selection. A support vector machine (SVM) was performed to build models to distinguish MDD from HCs, and for suicide risk stratification among MDD. Leave-one-out cross-validation (LOOCV) was performed for validation.Results: The models constructed using SVM on whole-brain FC had powerful classification efficiency in screening MDD from HCs (accuracy = 88.50 %), and in suicide risk stratification among MDD patients (with accuracy = 84.56 % and 74.60 % in classifying patients with HSR or LSR, and SA or SI, respectively). Subsequent analysis demonstrated that intra-network dysconnectivity in the sensorimotor network and inter-network dysconnectivity between the default and dorsal attention network could characterize HSR and SA in MDD, separately.Limitations: This study was a single center cohort study without external validation.Conclusion: These findings indicate ML approaches are useful in suicide risk stratification among MDD based on whole-brain FC, which may help to identify individuals with different suicide risks in MDD and provide an individual-level prediction. [ABSTRACT FROM AUTHOR]

Background: The present study aimed to explore the difference in the brain function and structure between patients with major depressive disorder (MDD) and healthy controls (HCs) using two-center and multi-modal MRI data, which would be helpful to investigate the pathogenesis of MDD.Methods: The subjects were collected from two hospitals. One including 140 patients with MDD and 138 HCs was used as primary cohort. Another one including 29 patients with MDD and 52 HCs was used as validation cohort. Functional and structural magnetic resonance images (MRI) were acquired to extract four types of features: functional connectivity (FC), amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and gray matter volume (GMV). Then classifiers using different combinations among the four types of selected features were respectively built to discriminate patients from HCs. Different templates were applied and the results under different templates were compared.Results: The classifier built with the combination of FC, ALFF, and GMV under the AAL template discriminated patients from HCs with the best performance (AUC=0.916, ACC=84.8%). The regions selected in all the different templates were mainly located in the default mode network, affective network, prefrontal cortex.Limitations: First, the sample size of the validation cohort was limited. Second, diffusion tensor imaging data were not collected.Conclusion: The performance of classifier was improved by using multi-modal MRI imaging. Different templates would be suitable for different types of analysis. The regions selected in all the different templates are possibly the core regions to investigate the pathophysiology of MDD. [ABSTRACT FROM AUTHOR]