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Objectives: To explore the suicide risk-specific disruption of cortical hierarchy in major depressive disorder (MDD) patients with diverse suicide risks. Methods: Ninety-two MDD patients with diverse suicide risks and 38 matched controls underwent resting-state functional MRI. Connectome gradient analysis and stepwise functional connectivity (SFC) analysis were used to characterize the suicide risk-specific alterations of cortical hierarchy in MDD patients. Results: Relative to controls, patients with suicide attempts (SA) had a prominent compression from the sensorimotor system; patients with suicide ideations (SI) had a prominent compression from the higher-level systems; non-suicide patients had a compression from both the sensorimotor system and higher-level systems, although it was less prominent relative to SA and SI patients. SFC analysis further validated this depolarization phenomenon. Conclusion: This study revealed MDD patients had suicide risk-specific disruptions of cortical hierarchy, which advance our understanding of the neuromechanisms of suicidality in MDD patients. [ABSTRACT FROM AUTHOR]

Background: Suboptimal medication adherence is a major reason for failure in the management of major depressive disorder (MDD), childhood trauma might be an essential risk factor of suboptimal medication adherence. This study aimed to comprehensively explore the associations between different types of childhood trauma and medication adherence among patients with MDD, and to test whether resilience has moderating effects on the foregoing associations. Methods: Participants were from the Depression Cohort in China (ChiCTR registry number 1900022145), 282 MDD patients with completed both baseline and 12-weeks follow-up investigations were included in this study. The diagnosis of MDD was assessed by trained psychiatrists using the Mini-International Neuropsychiatric Interview (M.I.N.I.). Childhood trauma was evaluated using the Childhood Trauma Questionnaire-28 item Short Form (CTQ-SF), and resilience was evaluated using the Connor-Davidson Resilience Scale (CD-RISC). Demographic characteristics, depression symptoms, anxiety symptoms, suicidal ideation, suicidal attempt, insomnia symptoms, and painful somatic symptoms were also investigated. Participants were divided into groups of optimal and suboptimal adherence based on their Medication Adherence Rating Scale scores. Logistic regression and stratified analyses were performed. Results: A total of 234 participants (83%) reported suboptimal medication adherence. After adjusting for covariates, CTQ total scores (AOR = 1.03, 95%CI = 1.01–1.06), CTQ measures of sexual abuse (AOR = 1.17, 95%CI = 1.01–1.37), and CTQ measures of physical neglect (AOR = 1.12, 95%CI = 1.02–1.23) were all associated with an increased likelihood of suboptimal adherence. There were significant moderating effects of resilience on the associations of childhood trauma (P = 0.039) and physical neglect (P = 0.034) with medication adherence. The stratification analyses showed that CTQ total scores and CTQ measures of physical neglect were independently associated with an increased risk of suboptimal adherence among patients with MDD with low-resilience or moderate-resilience, while not significantly associated with suboptimal adherence in those with high-resilience. Conclusion: Childhood trauma was a significant risk factor of suboptimal adherence among patients with MDD, and resilience moderated the foregoing association. Obtaining a history of childhood trauma and assessing resilience may help identify patients with suboptimal adherence when providing MDD pharmacotherapy. Psychiatrists may consider enhancing resilience to cope with the adverse effects of childhood trauma on medication adherence. [ABSTRACT FROM AUTHOR]

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]

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. 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. 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. p FDR = 0.045) and predicted the gap between patients' chronological age and brain age (r = 0.756, p < 0.001). Evaluation of brain dynamics was constrained by MRI temporal resolution. 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. • Spatial and temporal brain aberrations linked to Major Depressive Disorder (MDD). • SPT integrates spatial and temporal features as a potential MDD biomarker. • SPT effectively discriminates between MDD and healthy populations via machine learning. • SPT correlates with depression severity and predicts differences in brain-age predictions. [ABSTRACT FROM AUTHOR]

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. 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. 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. 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. • All participants had a strong gBOLD-CSF coupling, which correlated with their age. • Depressed patients had significantly weaker gBOLD-CSF coupling than healthy controls. • Strength of gBOLD-CSF coupling correlated with insomnia-middle in depressed patients. [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]

• qihMT can be used to investigate the alterations of myelin content in rMDD. • Convergent and divergent WM alterations could be found from qihMT and qMT. • Disruption of myelin integrity in fornix could be gradually cumulative. 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). 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. 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). 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. [ABSTRACT FROM AUTHOR]