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Background: Persistent neurocognitive deficits are often associated with poor outcomes of major depressive disorder (MDD). Executive dysfunction is the most common cognitive deficit in MDD. However, it remains unclear which subcomponent of executive dysfunction is state-independent with distinct neural substrates., Methods: A comprehensive neurocognitive test battery was used to assess four subcomponents of executive function (working memory, inhibition, shifting, and verbal fluency) in 95 MDD patients and 111 matched healthy controls (HCs). After 6 months of paroxetine treatment, 56 patients achieved clinical remission (rMDD) and completed the second-time neurocognitive test. Network-based statistics analysis was utilized to explore the changes in functional connectivity (FC)., Results: Compared with the HCs, all the four subcomponents of MDD patients were significantly impaired. After treatment, there was a significant improvement in working memory, inhibition, and verbal fluency in the rMDD group. And shifting and verbal fluency of the rMDD group remained impaired compared with the HCs. Fifteen functional connections were interrupted in the MDD group, and 11 connections remained in a disrupted state after treatment. Importantly, verbal fluency was negatively correlated with the disrupted FC between the right dorsal prefrontal cortex and the left inferior parietal lobule in patients with MDD and remitted MDD., Limitations: The correlation analysis of the association between cognitive impairment and connectivity alterations precluded us from making causal inferences., Conclusions: Verbal fluency is the potential state-independent cognitive deficit with distinct neural basis in patients with MDD., (Copyright © 2021. Published by Elsevier B.V.)

Background: there has been a recent increase in the use of connectome-based multivariate pattern analysis (MVPA) of resting-state functional magnetic resonance imaging (fMRI) data aimed at distinguishing patients with major depressive disorder (MDD) from healthy controls (HCs). However, the validity of this method needs to be confirmed in independent samples., Method: we used resting-state fMRI to explore whole-brain functional connectivity (FC) patterns characteristic of MDD and to confirm the effectiveness of MVPA in distinguishing MDD versus HC groups in two independent samples. The first sample set included 29 MDD patients and 33 HCs and second sample set included 46 MDD patients and 57 HCs., Results: for the first sample, we obtained a correct classification rate of 91.9% with a sensitivity of 89.6% and specificity of 93.9%. For the second sample, we observed a correct classification rate of 86.4% with a sensitivity of 84.8% and specificity of 87.7%. With both samples, we found that the majority of consensus FCs used for MDD identification were located in the salience network, default mode network, the cerebellum, visual cortical areas, and the affective network., Limitation: we did not analyze potential structural differences between the groups., Conclusion: results suggest that whole-brain FC patterns can be used to differentiate depressed patients from HCs and provide evidence for the potential use of connectome-based MVPA as a complementary tool in the clinical diagnosis of MDD., (Copyright © 2017 Elsevier B.V. All rights reserved.)

There is a high degree of overlap between the symptoms of major depressive disorder (MDD) and schizophrenia, but it remains unclear whether the similar symptoms are derived from convergent alterations in functional network connectivity. In this study, we performed a group independent component analysis on resting-state functional MRI data from 20 MDD patients, 24 schizophrenia patients, and 43 matched healthy controls. The functional network connectivity analysis revealed that, compared to healthy controls, the MDD and schizophrenia patients exhibited convergent decreased positive connectivity between the left and right fronto-parietal control network and decreased negative connectivity between the left control and medial visual networks. Furthermore, the MDD patients showed decreased negative connectivity between the left control and auditory networks, and the schizophrenia patients showed decreased positive connectivity between the bilateral control and language networks and decreased negative connectivity between the right control and dorsal attention networks. The convergent network connectivity alterations may underlie the common primary control and regulation disorders, and the divergent connectivity alterations may enable the distinction between the two disorders. All of the convergent and divergent network connectivity alterations were relevant to the control network, suggesting an important role of the network in the pathophysiology of MDD and schizophrenia., (Copyright © 2017. Published by Elsevier B.V.)

There has been increasing interest in multivariate pattern analysis (MVPA) as a means of distinguishing psychiatric patients from healthy controls using brain imaging. However, it remains unclear whether MVPA methods can accurately estimate the medication status of psychiatric patients. This study aims to develop an MVPA approach to accurately predict the antidepressant medication status of individuals with major depression on the basis of whole-brain resting-state functional connectivity MRI (rs-fcMRI). We investigated data from rs-fcMRI of 24 medication-naive depressed patients, 16 out of whom subsequently underwent antidepressant treatment and achieved clinical recovery, and 29 demographically similar controls. By training a linear support vector machine classifier and combining it with principal component analysis, the medication-naive patients were identified from the healthy controls with 100% accuracy. In addition, we found reliable correlations between MVPA prediction scores and clinical symptom severity. Moreover, the most discriminative functional connections were located within or across the cerebellum and default mode, affective, and sensorimotor networks, indicating that these networks may play important roles in major depression. Most importantly, only ∼30% of these discriminative connections were normalized in clinically recovered patients after antidepressant treatment. The current study may not only show the feasibility of estimating medication status by MVPA of whole-brain rs-fcMRI data in major depression but also shed new light on the pathological mechanism of this disorder., (Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.)

Behavioral evidence suggests functional remission or recovery in nonrefractory depressive disorder after successful antidepressant therapy, but the effect of treatment on brain structures remains unclear. It is possible that some specific structural changes are trait-related, in addition to some state-dependent anatomical regions that could be normalized by medications. In the current study, 32 treatment-naïve nonrefractory depressive disorder patients and 34 matched healthy controls underwent structural MRI scans and a subgroup including 16 patients underwent the second scans after clinical recovery with antidepressant treatment. The statistical analysis of gray matter images showed that the state-dependent regions mainly comprised the key regions within the lateral default, dorsal attention, and frontoparietal networks, whereas the trait-related regions included the medial prefrontal cortex, right anterior insula, thalamus, right parahippocampal gyrus, and right middle occipital gyrus. Moreover, the gray matter changes in the left orbitofrontal cortex, right middle frontal gyrus, left inferior parietal lobule, thalamus, left precentral gyrus, left precuneus, and right middle occipital gyrus were significantly positively correlated with the durations of the current depressive episode. The current findings may not only shed new light on the neuronal effect of antidepressant on depression but also provide potential evidence of the relapse risks for the clinically recovered patients after antidepressant treatment.

The current diagnosis of psychiatric disorders including major depressive disorder based largely on self-reported symptoms and clinical signs may be prone to patients' behaviors and psychiatrists' bias. This study aims at developing an unsupervised machine learning approach for the accurate identification of major depression based on single resting-state functional magnetic resonance imaging scans in the absence of clinical information. Twenty-four medication-naive patients with major depression and 29 demographically similar healthy individuals underwent resting-state functional magnetic resonance imaging. We first clustered the voxels within the perigenual cingulate cortex into two subregions, a subgenual region and a pregenual region, according to their distinct resting-state functional connectivity patterns and showed that a maximum margin clustering-based unsupervised machine learning approach extracted sufficient information from the subgenual cingulate functional connectivity map to differentiate depressed patients from healthy controls with a group-level clustering consistency of 92.5% and an individual-level classification consistency of 92.5%. It was also revealed that the subgenual cingulate functional connectivity network with the highest discriminative power primarily included the ventrolateral and ventromedial prefrontal cortex, superior temporal gyri and limbic areas, indicating that these connections may play critical roles in the pathophysiology of major depression. The current study suggests that subgenual cingulate functional connectivity network signatures may provide promising objective biomarkers for the diagnosis of major depression and that maximum margin clustering-based unsupervised machine learning approaches may have the potential to inform clinical practice and aid in research on psychiatric disorders., (Copyright © 2013 Wiley Periodicals, Inc.)

Major depression and schizophrenia are two of the most serious psychiatric disorders and share similar behavioral symptoms. Whether these similar behavioral symptoms underlie any convergent psychiatric pathological mechanisms is not yet clear. To address this issue, this study sought to investigate the whole-brain resting-state functional magnetic resonance imaging (MRI) of major depression and schizophrenia by using multivariate pattern analysis. Thirty-two schizophrenic patients, 19 major depressive disorder patients and 38 healthy controls underwent resting-state functional MRI scanning. A support vector machine in conjunction with intrinsic discriminant analysis was used to solve the multi-classification problem, resulting in a correct classification rate of 80.9% via leave-one-out cross-validation. The depression and schizophrenia groups both showed altered functional connections associated with the medial prefrontal cortex, anterior cingulate cortex, thalamus, hippocampus, and cerebellum. However, the prefrontal cortex, amygdala, and temporal poles were found to be affected differently by major depression and schizophrenia. Our preliminary study suggests that altered connections within or across the default mode network and the cerebellum may account for the common behavioral symptoms between major depression and schizophrenia. In addition, connections associated with the prefrontal cortex and the affective network showed promise as biomarkers for discriminating between the two disorders.

Background: Previous studies have suggested that the default mode network (DMN) plays a central role in the physiopathology of major depressive disorder (MDD). However, the effect of antidepressant treatment on functional connectivity within the DMN has yet to be established. Considering the very high rates of relapse in recovered subjects, we hypothesized that abnormalities in DMN functional connectivity would persist in recovered MDD subjects., Methods: Resting state functional magnetic resonance imaging images were collected from 24 MDD patients and 29 healthy control subjects. After 12 weeks of antidepressant treatment, 18 recovered MDD subjects were scanned again. Group independent component analysis was performed to decompose the resting state images into spatially independent components. Default mode subnetworks were identified using a template based on previous studies. Group differences in the ensuing subnetworks were tested using two-sample t tests., Results: Two spatially independent default mode subnetworks were detected in all subjects: the anterior subnetwork and the posterior subnetwork. Both subnetworks showed increased functional connectivity in pretreatment MDD subjects, relative to control subjects. Differences in the posterior subnetwork were normalized after antidepressant treatment, while abnormal functional connectivity persisted within the anterior subnetwork., Conclusions: Our findings suggest a dissociation of the DMN into subnetworks, where persistent abnormal functional connectivity within the anterior subnetwork in recovered MDD subjects may constitute a biomarker of asymptomatic depression and potential for relapse., (Copyright © 2013 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

In recent years, the cerebellum has been demonstrated to be involved in cognitive control and emotional processing and to play an important role in the pathology of major depressive disorder (MDD). The current study aims to explore the potential utility of selecting the altered cerebellar-cerebral functional connectivity as a classification feature to discriminate depressed patients from healthy controls. Twenty-four medication-free patients with major depression and 29 matched, healthy controls underwent resting-state functional magnetic resonance imaging. A promising classification accuracy of 90.6% was achieved using resting-state functional connectivity between predefined cerebellar seed regions and the voxels within the cerebrum as features. Moreover, the most discriminating functional connections were mainly located between the cerebellum and the anterior cingulate cortex, the ventromedial prefrontal cortex, the ventrolateral prefrontal cortex, the temporal lobe and the fusiform gyrus, which may contribute to the emotional and cognitive impairments observed in major depression. The current findings imply that the cerebellum might be considered as a node in the distributed disease-related brain network in major depression., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Recent resting-state functional connectivity magnetic resonance imaging studies have shown significant group differences in several regions and networks between patients with major depressive disorder and healthy controls. The objective of the present study was to investigate the whole-brain resting-state functional connectivity patterns of depressed patients, which can be used to test the feasibility of identifying major depressive individuals from healthy controls. Multivariate pattern analysis was employed to classify 24 depressed patients from 29 demographically matched healthy volunteers. Permutation tests were used to assess classifier performance. The experimental results demonstrate that 94.3% (P < 0.0001) of subjects were correctly classified by leave-one-out cross-validation, including 100% identification of all patients. The majority of the most discriminating functional connections were located within or across the default mode network, affective network, visual cortical areas and cerebellum, thereby indicating that the disease-related resting-state network alterations may give rise to a portion of the complex of emotional and cognitive disturbances in major depression. Moreover, the amygdala, anterior cingulate cortex, parahippocampal gyrus and hippocampus, which exhibit high discriminative power in classification, may play important roles in the pathophysiology of this disorder. The current study may shed new light on the pathological mechanism of major depression and suggests that whole-brain resting-state functional connectivity magnetic resonance imaging may provide potential effective biomarkers for its clinical diagnosis.

Magnetic resonance imaging studies have reported significant functional and structural differences between depressed patients and controls. Little attention has been given, however, to the abnormalities in anatomical connectivity in depressed patients. In the present study, we aim to investigate the alterations in connectivity of whole-brain anatomical networks in those suffering from major depression by using machine learning approaches. Brain anatomical networks were extracted from diffusion magnetic resonance images obtained from both 22 first-episode, treatment-naive adults with major depressive disorder and 26 matched healthy controls. Using machine learning approaches, we differentiated depressed patients from healthy controls based on their whole-brain anatomical connectivity patterns and identified the most discriminating features that represent between-group differences. Classification results showed that 91.7% (patients=86.4%, controls=96.2%; permutation test, p<0.0001) of subjects were correctly classified via leave-one-out cross-validation. Moreover, the strengths of all the most discriminating connections were increased in depressed patients relative to the controls, and these connections were primarily located within the cortical-limbic network, especially the frontal-limbic network. These results not only provide initial steps toward the development of neurobiological diagnostic markers for major depressive disorder, but also suggest that abnormal cortical-limbic anatomical networks may contribute to the anatomical basis of emotional dysregulation and cognitive impairments associated with this disease.

Background: Numerous studies have demonstrated the higher-order functions of the cerebellum, including emotion regulation and cognitive processing, and have indicated that the cerebellum should therefore be included in the pathophysiological models of major depressive disorder. The aim of this study was to compare the resting-state functional connectivity of the cerebellum in adults with major depression and healthy controls., Methods: Twenty adults with major depression and 20 gender-, age-, and education-matched controls were investigated using seed-based resting-state functional connectivity magnetic resonance imaging., Results: Compared with the controls, depressed patients showed significantly increased functional connectivity between the cerebellum and the temporal poles. However, significantly reduced cerebellar functional connectivity was observed in the patient group in relation to both the default-mode network, mainly including the ventromedial prefrontal cortex and the posterior cingulate cortex/precuneus, and the executive control network, mainly including the superior frontal cortex and orbitofrontal cortex. Moreover, the Hamilton Depression Rating Scale score was negatively correlated with the functional connectivity between the bilateral Lobule VIIb and the right superior frontal gyrus in depressed patients., Conclusions: This study demonstrated increased cerebellar coupling with the temporal poles and reduced coupling with the regions in the default-mode and executive control networks in adults with major depression. These differences between patients and controls could be associated with the emotional disturbances and cognitive control function deficits that accompany major depression. Aberrant cerebellar connectivity during major depression may also imply a substantial role for the cerebellum in the pathophysiological models of depression.