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ObjectivesTo explore the suicide risk-specific disruption of cortical hierarchy in major depressive disorder (MDD) patients with diverse suicide risks.MethodsNinety-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.ResultsRelative 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.ConclusionThis study revealed MDD patients had suicide risk-specific disruptions of cortical hierarchy, which advance our understanding of the neuromechanisms of suicidality in MDD patients.

ObjectiveInter-hemispheric network dysconnectivity has been well-documented in patients with recurrent major depressive disorder (MDD). However, it has remained unclear how structural networks between bilateral hemispheres relate to inter-hemispheric functional dysconnectivity and depression severity in MDD. Our study attempted to investigate the alterations in corpus callosum macrostructural and microstructural as well as inter-hemispheric homotopic functional connectivity (FC) in patients with recurrent MDD and to determine how these alterations are related with depressive severity.Materials and MethodsResting-state functional MRI (fMRI), T1WI anatomical images and diffusion tensor MRI of the whole brain were performed in 140 MDD patients and 44 normal controls matched for age, sex, years of education. We analyzed the macrostructural and microstructural integrity as well as voxel-mirrored homotopic functional connectivity (VMHC) of corpus callosum (CC) and its five subregion. Two-sample t-test was used to investigate the differences between the two groups. Significant subregional metrics were correlated with depression severity by spearman's correlation analysis, respectively.ResultsCompared with control subjects, MDD patients had significantly attenuated inter-hemispheric homotopic FC in the bilateral medial prefrontal cortex, and impaired anterior CC microstructural integrity (each comparison had a corrected P < 0.05), whereas CC macrostructural measurements remained stable. In addition, disruption of anterior CC microstructural integrity correlated with a reduction in FC in the bilateral medial prefrontal cortex, which correlated with depression severity in MDD patients. Furthermore, disruption of anterior CC integrity exerted an indirect influence on depression severity in MDD patients through an impairment of inter-hemispheric homotopic FC.ConclusionThese findings may help to advance our understanding of the neurobiological basis of depression by identifying region-specific interhemispheric dysconnectivity.

The present study aims to identify suicide risks in major depressive disorders (MDD) patients from structural MRI (sMRI) data using deep learning. In this paper, we collected the sMRI data of 288 MDD patients, including 110 patients with suicide ideation (SI), 93 patients with suicide attempts (SA), and 85 patients without suicidal ideation or attempts (NS). And we developed interpretable deep neural network models to classify patients in three tasks including SA-versus-SI, SA-versus-NS, and SI-versus-NS, respectively. Furthermore, we interpreted the models by extracting the important features that contributed most to the classification, and further discussed these features or ROI/brain regions. • Suicidal behaviors among MDD patients could be identified by sMRI data. • Deep learning could be used to classify suicide attempts, ideation, and non-ideation in MDD patients. • The interpretable deep learning methods are helpful to improve the capability of suicide prediction among MDD patients. [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]

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. 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. 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. The data preprocessing may affect the classification performance of the model, and this study needs further validation in a larger patient population. The experimental results demonstrate that the proposed spatio-temporal model could effectively classify MDD, and uncover structural and temporal patterns of dFCs in depression. • A spatio-temporal learning model for identifying MDD with dFC • An explanation method for uncovering structure and time-varying pattern • The brain regions and brain states from altered functionality in MDD [ABSTRACT FROM AUTHOR]

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. 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. 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. 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. 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. • Communication between amygdala and essential networks is unbalanced in MDD. • Amygdala-DMN/VAN interactions are aberrantly enhanced in MDD. • Amygdala-SMN/FPN/putamen interactions are aberrantly attenuated in MDD. • Multi-connectivity-indicator analysis combined with SVM was adopted for modeling. [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]