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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: Functional connectivity has been shown to fluctuate over time. The present study aimed to identifying major depressive disorders (MDD) with dynamic functional connectivity (dFC) from resting-state fMRI data, which would be helpful to produce tools of early depression diagnosis and enhance our understanding of depressive etiology., Methods: The resting-state fMRI data of 178 subjects were collected, including 89 MDD and 89 healthy controls. We propose a spatio-temporal learning and explaining framework for dFC analysis. A yet effective spatio-temporal model is developed to classifying MDD from healthy controls with dFCs. The model is a stacking neural network model, which learns network structure information by a multi-layer perceptron based spatial encoder, and learns time-varying patterns by a Transformer based temporal encoder. We propose to explain the spatio-temporal model with a two-stage explanation method of importance feature extracting and disorder-relevant pattern exploring. The layer-wise relevance propagation (LRP) method is introduced to extract the most relevant input features in the model, and the attention mechanism with LRP is applied to extract the important time steps of dFCs. The disorder-relevant functional connections, brain regions, and brain states in the model are further explored and identified., Results: We achieved the best classification performance in identifying MDD from healthy controls with dFC data. The top important functional connectivity, brain regions, and dynamic states closely related to MDD have been identified., Limitations: The data preprocessing may affect the classification performance of the model, and this study needs further validation in a larger patient population., Conclusions: The experimental results demonstrate that the proposed spatio-temporal model could effectively classify MDD, and uncover structural and temporal patterns of dFCs in depression., Competing Interests: Declaration of competing interest None., (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.)

Background: Obsessive-compulsive disorder (OCD) is a classic disorder on the compulsivity spectrum, with diverse comorbidities. In the current study, we sought to understand OCD from a dimensional perspective by identifying multimodal neuroimaging patterns correlated with multiple phenotypic characteristics within the striatum-based circuits known to be affected by OCD., Methods: Neuroimaging measurements of local functional and structural features and clinical information were collected from 110 subjects, including 51 patients with OCD and 59 healthy control subjects. Linked independent component analysis (LICA) and correlation analysis were applied to identify associations between local neuroimaging patterns across modalities (including gray matter volume, white matter integrity, and spontaneous functional activity) and clinical factors., Results: LICA identified eight multimodal neuroimaging patterns related to phenotypic variations, including three related to symptoms and diagnosis. One imaging pattern (IC9) that included both the amplitude of low-frequency fluctuation measure of spontaneous functional activity and white matter integrity measures correlated negatively with OCD diagnosis and diagnostic scales. Two imaging patterns (IC10 and IC27) correlated with compulsion symptoms: IC10 included primarily anatomical measures and IC27 included primarily functional measures. In addition, we identified imaging patterns associated with age, gender, and emotional expression across subjects., Conclusions: We established that data fusion techniques can identify local multimodal neuroimaging patterns associated with OCD phenotypes. The results inform our understanding of the neurobiological underpinnings of compulsive behaviors and OCD diagnosis.

Objective: This study is aimed at exploring the regional changes in brain cortical morphology (thickness, volume, and surface area) in the early-blind adolescents (EBAs) by using the surface-based morphometric (SBM) method., Methods: High-resolution structural T1-weighted images (T1WI) of 23 early-blind adolescents (EBAs) and 21 age- and gender-matched normal-sighted controls (NSCs) were acquired. Structural indices, including cortical thickness (CT), cortical volume (CV), and surface area (SA), were analyzed by using FreeSurfer software, and the correlations between structural indices and the blindness duration were computed by Pearson correlation analysis., Results: Compared to controls, EBAs had significantly reduced CV and SA mainly in the primary visual cortex (V1) and decreased CV in the left vision-related cortices (r-MFC). There were no regions that EBAs had a significantly larger CV or SA than NSCs. EBAs had significantly increased CT in the V1 and strongly involved the visual cortex (right lateral occipital gyrus, LOG.R) and the left superior temporal gyrus (STG.L), while it had decreased CT in the left superior parietal lobule (SPL.L) and the right lingual gyrus (LING.R). Additionally, no correlation was found between cortical morphometric measures and clinical variables in the EBA group., Conclusions: SBM is a useful method for detecting human brain structural abnormalities in blindness. The results showed that these structural abnormalities in the visual cortex and visual-related areas outside the occipital cortex in the EBAs not only may be influenced by neurodevelopment, degeneration, plasticity, and so on but also involved the interaction of these factors after the early visual deprivation., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Fen Hou et al.)

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.)

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]