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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. Published by Elsevier B.V.)

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.)

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: Depressed patients often suffer from sleep disturbance, which has been recognized to be responsible for glymphatic dysfunction. The purpose of this study was to investigate the coupling strength of global blood‑oxygen-level-dependent (gBOLD) signals and cerebrospinal fluid (CSF) inflow dynamics, which is a biomarker for glymphatic function, in depressed patients and to explore its potential relationship with sleep disturbance by using resting-state functional MRI., Methods: A total of 138 depressed patients (112 females, age: 34.70 ± 13.11 years) and 84 healthy controls (29 females, age: 36.6 ± 11.75 years) participated in this study. The gBOLD-CSF coupling strength was calculated to evaluate glymphatic function. Sleep disturbance was evaluated using the insomnia items (item 4 for insomnia-early, item 5 for insomnia-middle, and item 6 for insomnia-late) of The 17-item Hamilton Depression Rating Scale for depressed patients, which was correlated with the gBOLD-CSF coupling strength., Results: The depressed patients exhibited weaker gBOLD-CSF coupling relative to healthy controls (p = 0.022), possibly due to impairment of the glymphatic system. Moreover, the gBOLD-CSF coupling strength correlated with insomnia-middle (r = 0.097, p = 0.008) in depressed patients. Limitations This study is a cross-sectional study., Conclusion: Our findings shed light on the pathophysiology of depression, indicating that cerebral waste clearance system deficits are correlated with poor sleep quality in depressed patients., Competing Interests: Declaration of competing interest The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Distinguishing bipolar disorder (BD) and unipolar disorder (UD) remains challenging. To identify the common and diagnosis-specific neuropathological alterations and their potential molecular mechanisms in patients with UD and BD (with a current depressive episode)., Methods: Resting-state functional magnetic resonance imaging was obtained from 279 participants (95 BD patients, 107 UD patients and 77 health controls). Connectome gradients analysis was performed to explore the shared and diagnosis-specific gradient alterations in BD and UD. The Allen Human Brain Atlas data was used to explore the potential gene mechanisms of the gradient alterations., Results: BD and UD had shared hierarchical disorganisation, including downgrading and contraction from the unimodal sensory networks (vision and sensorimotor) to the transmodal cognitive networks (limbic, frontoparietal, dorsal attention, and default) (all P < 0.05, FDR corrected) in gradient 1 and gradient 2. The BD patients had specific connectome gradient dysfunction in the subcortical network. Moreover, the hierarchical disorganisation was closely correlated with profiles of gene expression specific to the neuroglial cells in the prefrontal cortex in BD and UD, while the most correlated gene ontology biological processes and function were concentrated in synaptic signalling, calcium ion binding, and transmembrane transporter activity., Conclusion: These findings reveal the shared and diagnosis-specific neurobiological mechanism underlying BD and UD patients, which advances our understanding of the neuromechanisms of these disorders., Competing Interests: Declaration of competing interest All co-authors have seen and agreed with the contents of the manuscript. We have no relevant conflicts of interest., (Copyright © 2023. Published by Elsevier B.V.)

Background: Previous studies suggested an association between functional alteration of the amygdala and typical major depressive disorder (MDD) symptoms. Examining whether and how the interaction between the amygdala and regions/functional networks is altered in patients with MDD is important for understanding its neural basis., Methods: Resting-state functional magnetic resonance imaging data were recorded from 67 patients with MDD and 74 age- and sex-matched healthy controls (HCs). A framework for large-scale network analysis based on seed mappings of amygdala sub-regions, using a multi-connectivity-indicator strategy (cross-correlation, total interdependencies (TI), Granger causality (GC), and machine learning), was employed. Multiple indicators were compared between the two groups. The altered indicators were ranked in a supporting-vector machine-based procedure and associated with the Hamilton Rating Scale for Depression scores., Results: The amygdala connectivity with the default mode network and ventral attention network regions was enhanced and that with the somatomotor network, dorsal frontoparietal network, and putamen regions in patients with MDD was reduced. The machine learning analysis highlighted altered indicators that were most conducive to the classification between the two groups., Limitations: Most patients with MDD received different pharmacological treatments. It is difficult to illustrate the medication state's effect on the alteration model because of its complex situation., Conclusion: The results indicate an unbalanced interaction model between the amygdala and functional networks and regions essential for various emotional and cognitive functions. The model can help explain potential aberrancy in the neural mechanisms that underlie the functional impairments observed across various domains in patients with MDD., Competing Interests: Conflict of interest The authors have declared no conflicts of interest for this article., (Copyright © 2023 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., Competing Interests: Conflict of interest The authors declare that there are no conflicts of interests., (Copyright © 2022. Published by Elsevier B.V.)