Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Divergent topological architecture of the default mode network as a pretreatment predictor of early antidepressant response in major depressive disorder.

ABSTRACT: Identifying a robust pretreatment neuroimaging marker would be helpful for the selection of an optimal therapy for major depressive disorder (MDD). We recruited 82 MDD patients [n?=?42 treatment-responsive depression (RD) and n?=?40 non-responding depression (NRD)] and 50 healthy controls (HC) for this study. Based on the thresholded partial correlation matrices of 58 specific brain regions, a graph theory approach was applied to analyse the topological properties. When compared to HC, both RD and NRD patients exhibited a lower nodal degree (D_nodal) in the left anterior cingulate gyrus; as for RD, the D_nodal of the left superior medial orbitofrontal gyrus was significantly reduced, but the right inferior orbitofrontal gyrus was increased (all P?

SUBMITTER: Hou Z

PROVIDER: S-EPMC5155246 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Divergent topological architecture of the default mode network as a pretreatment predictor of early antidepressant response in major depressive disorder.

Hou Zhenghua Z Wang Zan Z Jiang Wenhao W Yin Yingying Y Yue Yingying Y Zhang Yuqun Y Song Xiaopeng X Yuan Yonggui Y

Scientific reports 20161214

Identifying a robust pretreatment neuroimaging marker would be helpful for the selection of an optimal therapy for major depressive disorder (MDD). We recruited 82 MDD patients [n = 42 treatment-responsive depression (RD) and n = 40 non-responding depression (NRD)] and 50 healthy controls (HC) for this study. Based on the thresholded partial correlation matrices of 58 specific brain regions, a graph theory approach was applied to analyse the topological properties. When compared to HC, both RD a ...[more]

PMID: 27966645

Similar Datasets

Transcutaneous Vagus Nerve Stimulation Modulates Default Mode Network in Major Depressive Disorder.

Project description:Depression is the most common form of mental disorder in community and health care settings and current treatments are far from satisfactory. Vagus nerve stimulation (VNS) is a Food and Drug Administration approved somatic treatment for treatment-resistant depression. However, the involvement of surgery has limited VNS only to patients who have failed to respond to multiple treatment options. Transcutaneous VNS (tVNS) is a relatively new, noninvasive VNS method based on the rationale that there is afferent/efferent vagus nerve distribution on the surface of the ear. The safe and low-cost characteristics of tVNS have the potential to significantly expand the clinical application of VNS.In this study, we investigated how tVNS can modulate the default mode network (DMN) functional connectivity (FC) in mild or moderate major depressive disorder (MDD) patients. Forty-nine MDD patients were recruited and received tVNS or sham tVNS (stVNS) treatments.Thirty-four patients completed the study and were included in data analysis. After 1 month of tVNS treatment, the 24-item Hamilton Depression Rating Scale score reduced significantly in the tVNS group as compared with the stVNS group. The FC between the DMN and anterior insula and parahippocampus decreased; the FC between the DMN and precuneus and orbital prefrontal cortex increased compared with stVNS. All these FC increases are also associated with 24-item Hamilton Depression Rating Scale reduction.tVNS can significantly modulate the DMN FC of MDD patients; our results provide insights to elucidate the brain mechanism of tVNS treatment for MDD patients.

| S-EPMC4838995 | biostudies-literature

Reduced default mode network functional connectivity in patients with recurrent major depressive disorder.

Project description:Major depressive disorder (MDD) is common and disabling, but its neuropathophysiology remains unclear. Most studies of functional brain networks in MDD have had limited statistical power and data analysis approaches have varied widely. The REST-meta-MDD Project of resting-state fMRI (R-fMRI) addresses these issues. Twenty-five research groups in China established the REST-meta-MDD Consortium by contributing R-fMRI data from 1,300 patients with MDD and 1,128 normal controls (NCs). Data were preprocessed locally with a standardized protocol before aggregated group analyses. We focused on functional connectivity (FC) within the default mode network (DMN), frequently reported to be increased in MDD. Instead, we found decreased DMN FC when we compared 848 patients with MDD to 794 NCs from 17 sites after data exclusion. We found FC reduction only in recurrent MDD, not in first-episode drug-naïve MDD. Decreased DMN FC was associated with medication usage but not with MDD duration. DMN FC was also positively related to symptom severity but only in recurrent MDD. Exploratory analyses also revealed alterations in FC of visual, sensory-motor, and dorsal attention networks in MDD. We confirmed the key role of DMN in MDD but found reduced rather than increased FC within the DMN. Future studies should test whether decreased DMN FC mediates response to treatment. All R-fMRI indices of data contributed by the REST-meta-MDD consortium are being shared publicly via the R-fMRI Maps Project.

| S-EPMC6500168 | biostudies-literature

Biotypes of major depressive disorder: Neuroimaging evidence from resting-state default mode network patterns.

Project description:BackgroundMajor depressive disorder (MDD) is heterogeneous disorder associated with aberrant functional connectivity within the default mode network (DMN). This study focused on data-driven identification and validation of potential DMN-pattern-based MDD subtypes to parse heterogeneity of the disorder.MethodsThe sample comprised 1397 participants including 690 patients with MDD and 707 healthy controls (HC) registered from multiple sites based on the REST-meta-MDD Project in China. Baseline resting-state functional magnetic resonance imaging (rs-fMRI) data was recorded for each participant. Discriminative features were selected from DMN between patients and HC. Patient subgroups were defined by K-means and principle component analysis in the multi-site datasets and validated in an independent single-site dataset. Statistical significance of resultant clustering were confirmed. Demographic and clinical variables were compared between identified patient subgroups.ResultsTwo MDD subgroups with differing functional connectivity profiles of DMN were identified in the multi-site datasets, and relatively stable in different validation samples. The predominant dysfunctional connectivity profiles were detected among superior frontal cortex, ventral medial prefrontal cortex, posterior cingulate cortex and precuneus, whereas one subgroup exhibited increases of connectivity (hyperDMN MDD) and another subgroup showed decreases of connectivity (hypoDMN MDD). The hyperDMN subgroup in the discovery dataset had age-related severity of depressive symptoms. Patient subgroups had comparable demographic and clinical symptom variables.ConclusionsFindings suggest the existence of two neural subtypes of MDD associated with different dysfunctional DMN connectivity patterns, which may provide useful evidence for parsing heterogeneity of depression and be valuable to inform the search for personalized treatment strategies.

| S-EPMC7724374 | biostudies-literature

Regional default mode network connectivity in major depressive disorder: modulation by acute intravenous citalopram.

Project description:The relationship between altered default mode network (DMN) connectivity and abnormal serotonin function in major depressive disorder (MDD) has not been investigated. Using intravenous citalopram and resting-state fMRI, we investigated DMN intra-network connectivity and serotonin function in 77 healthy controls and patients with MDD. There were no significant main effects of MDD or citalopram on DMN intra-network connectivity; however, significant interactions indicated that group differences under saline were modified by citalopram. In MDD patients during saline infusion, in contrast with controls, the DMN (i) did not include the precuneus that was instead part of an anti-correlated network but (ii) did include amygdala that was part of the anti-correlated network in controls. Citalopram infusion in MDD patients restored the pattern seen in controls under saline. In healthy controls, citalopram infusion disengaged the precuneus from the DMN and engaged the amygdala, partially reproducing the abnormalities seen under saline in MDD. In exploratory analyses within the MDD group, greater rumination self-ratings were associated with greater intra-network connectivity of the anterior cingulate cortex with the DMN. We hypothesise that, in MDD, disengagement of the precuneus from the DMN relates to overgeneral memory bias in rumination. The opposite effect, with greater engagement of the amygdala in the DMN, reflects the negative valence of rumination. Reversal of these abnormalities by citalopram suggests that they may be related to impaired serotonin function. That citalopram engaged the amygdala in the DMN in controls may relate to the paradoxical effects on aversive processing seen with acute SSRIs in healthy subjects.

| S-EPMC6420575 | biostudies-literature

Default Mode Connectivity in Major Depressive Disorder Measured Up to 10 Days After Ketamine Administration.

Project description:BACKGROUND:The symptoms of major depressive disorder (MDD) are rapidly alleviated by administration of a single dose of the glutamatergic modulator ketamine. However, few studies have investigated the potential sustained neural effects of this agent beyond immediate infusion. This study used functional magnetic resonance imaging to examine the effect of a single ketamine infusion on the resting state default mode network (DMN) at 2 and 10 days after a single ketamine infusion in unmedicated subjects with MDD as well as healthy control subjects (HCs). METHODS:Data were drawn from a double-blind, placebo-controlled crossover study of 58 participants (33 with MDD and 25 HCs) who received an intravenous infusion of either ketamine hydrochloride (0.5 mg/kg) or placebo on 2 separate test days spaced 2 weeks apart. Eight minutes of functional magnetic resonance imaging resting state data was acquired at baseline and at about 2 and 10 days after both infusions. The DMN was defined using seed-based correlation and was compared across groups and scans. RESULTS:In subjects with MDD, connectivity between the insula and the DMN was normalized compared with HCs 2 days postketamine infusion. This change was reversed after 10 days and did not appear in either of the placebo scans. Group-specific connectivity differences in drug response were observed, most notably in the insula in subjects with MDD and in the thalamus in HCs. CONCLUSIONS:Connectivity changes in the insula in subjects with MDD suggest that ketamine may normalize the interaction between the DMN and salience networks, supporting the triple network dysfunction model of MDD.

| S-EPMC6093808 | biostudies-literature

Project description:Major depressive disorder (MDD) is a common psychiatric disorder which is associated with an accelerated biological aging. However, little is known whether such process would be reflected by a more rapid aging of the brain function. In this study, we tested the hypothesis that MDD would be characterized by accelerated aging of the brain's default-mode network (DMN) functions. Resting-state functional magnetic resonance imaging data of 971 MDD patients and 902 healthy controls (HCs) was analyzed, which was drawn from a publicly accessible, multicenter dataset in China. Strength of functional connectivity (FC) and temporal variability of dynamic functional connectivity (dFC) within the DMN were calculated. Age-related effects on FC/dFC were estimated by linear regression models with age, diagnosis, and diagnosis-by-age interaction as variables of interest, controlling for sex, education, site, and head motion effects. The regression models revealed (1) a significant main effect of age in the predictions of both FC strength and dFC variability; and (2) a significant main effect of diagnosis and a significant diagnosis-by-age interaction in the prediction of FC strength, which was driven by stronger negative correlation between age and FC strength in MDD patients. Our results suggest that (1) both healthy participants and MDD patients experience decrease in DMN FC strength and increase in DMN dFC variability along age; and (2) age-related decrease in DMN FC strength may occur at a faster rate in MDD patients than in HCs. However, further longitudinal studies are still needed to understand the causation between MDD and accelerated aging of brain.

| S-EPMC8785895 | biostudies-literature

Default-mode and task-positive network activity in major depressive disorder: implications for adaptive and maladaptive rumination.

Project description:BackgroundMajor depressive disorder (MDD) has been associated reliably with ruminative responding; this kind of responding is composed of both maladaptive and adaptive components. Levels of activity in the default-mode network (DMN) relative to the task-positive network (TPN), as well as activity in structures that influence DMN and TPN functioning, may represent important neural substrates of maladaptive and adaptive rumination in MDD.MethodsWe used a unique metric to estimate DMN dominance over TPN from blood oxygenation level-dependent data collected during eyes-closed rest in 17 currently depressed and 17 never-disordered adults. We calculated correlations between this metric of DMN dominance over TPN and the depressive, brooding, and reflective subscales of the Ruminative Responses Scale, correcting for associations between these measures both with one another and with severity of depression. Finally, we estimated and compared across groups right fronto-insular cortex (RFIC) response during initiations of ascent in DMN and in TPN activity.ResultsIn the MDD participants, increasing levels of DMN dominance were associated with higher levels of maladaptive, depressive rumination and lower levels of adaptive, reflective rumination. Moreover, our RFIC state-change analysis showed increased RFIC activation in the MDD participants at the onset of increases in TPN activity; conversely, healthy control participants exhibited increased RFIC response at the onset of increases in DMN activity.ConclusionsThese findings support a formulation in which the DMN undergirds representation of negative, self-referential information in depression, and the RFIC, when prompted by increased levels of DMN activity, initiates an adaptive engagement of the TPN.

| S-EPMC3144981 | biostudies-literature

All roads lead to the default-mode network-global source of DMN abnormalities in major depressive disorder.

Project description:Major depressive disorder (MDD) is a psychiatric disorder characterized by abnormal resting state functional connectivity (rsFC) in various neural networks and especially in default-mode network (DMN). However, inconsistent findings, i.e., increased and decreased DMN rsFC, have been reported, which raise the question for the source of DMN changes in MDD. Testing whether the DMN abnormalities in MDD can be traced to either a local, i.e., intra-network, or a global, i.e., inter-network, source, we conducted a novel sequence of rsFC analyses, i.e., global FC, intra-network FC, and inter-network FC. Moreover, all analyses were conducted without global signal regression (non-GSR) and with GSR in order to identify the impact of specifically the global component of functional connectivity on within-network functional connectivity within specifically the DMN. In MDD our findings demonstrate (i) increased representation of global signal correlation (GSCORR) in DMN regions, as confirmed independently by degree of centrality (DC) and by an independent DMN template, (ii) increased within-network DMN rsFC, (iii) highly increased inter-network rsFC of both lower- and higher order non-DMN networks with DMN, (iv) high accuracy in classifying MDD vs. healthy subjects by using GSCORR as predictor. Further supporting the global, i.e., non-DMN source of within-network rsFC of the DMN, all results were obtained only when including the global signal, i.e., non-GSR, but not when conducting GSR. Together, we show for the first time increased global signal representation within rsFC of DMN as stemming from inter-network sources as distinguished from local sources, i.e., within- or intra-DMN.

| S-EPMC7547732 | biostudies-literature

A bayesian approach to examining default mode network functional connectivity and cognitive performance in major depressive disorder.

Project description:To reconcile the inconsistency of the association between the resting-state functional connectivity (RSFC) and cognitive performance in healthy and depressed groups due to high variance of both measures, we proposed a Bayesian spatio-temporal model to precisely and accurately estimate the RSFC in depressed and nondepressed participants. This model was employed to estimate spatially-adjusted functional connectivity (saFC) in the extended default mode network (DMN) that was hypothesized to correlate with cognitive performance in both depressed and nondepressed. Multiple linear regression models were used to study the relationship between DMN saFC and cognitive performance scores measured in the following four cognitive domains while adjusting for age, sex, and education. In ROI pairs including the posterior cingulate (PCC) and anterior cingulate (ACC) cortex regions, the relationship between connectivity and cognition was found only with the Bayesian approach. Moreover, only the Bayesian approach was able to detect a significant diagnostic difference in the association in ROI pairs, including both PCC and ACC regions, due to smaller variance for the saFC estimator. The results confirm that a reliable and precise saFC estimator, based on the Bayesian model, can foster scientific discovery that may not be feasible with the conventional ROI-based FC estimator (denoted as 'AVG-FC').

| S-EPMC7369149 | biostudies-literature

Spontaneous transient states of fronto-temporal and default-mode networks altered by suicide attempt in major depressive disorder.

Project description:Major depressive disorder (MDD) is associated with increased suicidality, and it's still challenging to identify suicide in clinical practice. Although suicide attempt (SA) is the most relevant precursor with multiple functional abnormalities reported from neuroimaging studies, little is known about how the spontaneous transient activated patterns organize and coordinate brain networks underlying SA. Thus, we obtained resting-state magnetoencephalography data for two MDD subgroups of 44 non-suicide patients and 34 suicide-attempted patients, together with 49 matched health-controls. For the source-space signals, Hidden Markov Model (HMM) helped to capture the sub-second dynamic activity via a hidden sequence of finite number of states. Temporal parameters and spectral activation were acquired for each state and then compared between groups. Here, HMM states characterized the spatiotemporal signatures of eight networks. The activity of suicide attempters switches more frequently into the fronto-temporal network, as the time spent occupancy of fronto-temporal state is increased and interval time is decreased compared with the non-suicide patients. Moreover, these changes are significantly correlated with Nurses' Global Assessment of Suicide Risk scores. Suicide attempters also exhibit increased state-wise activations in the theta band (4-8 Hz) in the posterior default mode network centered on posterior cingulate cortex, which can't be detected in the static spectral analysis. These alternations may disturb the time allocations of cognitive control regulations and cause inflexible decision making to SA. As the better sensitivity of dynamic study in reflecting SA diathesis than the static is validated, dynamic stability could serve as a potential neuronal marker for SA.

| S-EPMC8794625 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data