Dataset Information

Common Brain Networks Between Major Depressive-Disorder Diagnosis and Symptoms of Depression That Are Validated for Independent Cohorts.

ABSTRACT: Large-scale neuroimaging data acquired and shared by multiple institutions are essential to advance neuroscientific understanding of pathophysiological mechanisms in psychiatric disorders, such as major depressive disorder (MDD). About 75% of studies that have applied machine learning technique to neuroimaging have been based on diagnoses by clinicians. However, an increasing number of studies have highlighted the difficulty in finding a clear association between existing clinical diagnostic categories and neurobiological abnormalities. Here, using resting-state functional magnetic resonance imaging, we determined and validated resting-state functional connectivity related to depression symptoms that were thought to be directly related to neurobiological abnormalities. We then compared the resting-state functional connectivity related to depression symptoms with that related to depression diagnosis that we recently identified. In particular, for the discovery dataset with 477 participants from 4 imaging sites, we removed site differences using our recently developed harmonization method and developed a brain network prediction model of depression symptoms (Beck Depression Inventory-II [BDI] score). The prediction model significantly predicted BDI score for an independent validation dataset with 439 participants from 4 different imaging sites. Finally, we found 3 common functional connections between those related to depression symptoms and those related to MDD diagnosis. These findings contribute to a deeper understanding of the neural circuitry of depressive symptoms in MDD, a hetero-symptomatic population, revealing the neural basis of MDD.

SUBMITTER: Yamashita A

PROVIDER: S-EPMC8224760 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Common Brain Networks Between Major Depressive-Disorder Diagnosis and Symptoms of Depression That Are Validated for Independent Cohorts.

Yamashita Ayumu A Sakai Yuki Y Yamada Takashi T Yahata Noriaki N Kunimatsu Akira A Okada Naohiro N Itahashi Takashi T Hashimoto Ryuichiro R Mizuta Hiroto H Ichikawa Naho N Takamura Masahiro M Okada Go G Yamagata Hirotaka H Harada Kenichiro K Matsuo Koji K Tanaka Saori C SC Kawato Mitsuo M Kasai Kiyoto K Kato Nobumasa N Takahashi Hidehiko H Okamoto Yasumasa Y Yamashita Okito O Imamizu Hiroshi H

Frontiers in psychiatry 20210610

Large-scale neuroimaging data acquired and shared by multiple institutions are essential to advance neuroscientific understanding of pathophysiological mechanisms in psychiatric disorders, such as major depressive disorder (MDD). About 75% of studies that have applied machine learning technique to neuroimaging have been based on diagnoses by clinicians. However, an increasing number of studies have highlighted the difficulty in finding a clear association between existing clinical diagnostic cat ...[more]

PMID: 34177657

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Project description:BackgroundWe investigated the efficacy of electroconvulsive therapy in patients with major depressive disorder and concomitant anxiety symptoms and explored the relationships between depression symptoms and anxiety symptoms during acute electroconvulsive therapy.MethodsMajor depressive disorder inpatients (N = 130) requiring electroconvulsive therapy were recruited for a maximum of 12 treatments each. Depression symptoms, using the core factor subscale derived from the 17-item Hamilton Depression Rating Scale, and anxiety symptoms, using the anxiety/somatization subscale from the Hamilton Depression Rating Scale-17, were assessed before electroconvulsive therapy, after every 3 electroconvulsive therapy treatments, and after the final electroconvulsive therapy. Both core factor subscale and anxiety/somatization subscale scores were converted to T-score units to compare the degrees of changes between depression symptoms and anxiety symptoms after electroconvulsive therapy. The relationships between core factor subscale and anxiety/somatization subscale were analyzed using the cross-lagged longitudinal model during acute electroconvulsive therapy.ResultsA total 116 patients who completed at least the first 3 electroconvulsive therapy treatments were included in the analysis. Reduction of core factor scale T-scores was significantly greater than that of anxiety/somatization subscale T-scores. The model satisfied all indices of goodness-of-fit (chi-square = 30.204, df = 24, P = 0.178, Tucker-Lewis Index = 0.976, Comparative Fit Index = 0.989, Root Mean Square Error of Approximation = 0.047). Core factor subscale changes did not definitely predict subsequent anxiety/somatization subscale changes.ConclusionsElectroconvulsive therapy is effective in the acute treatment of major depressive disorder patients associated with anxiety symptoms. Anxiety symptoms improved less than depression symptoms during acute electroconvulsive therapy. However, earlier reduction in depression symptoms does not definitely drive subsequent relief in anxiety symptoms.

Project description:BackgroundAs one of the most common diseases, major depressive disorder (MDD) has a significant adverse impact on the li of patients. As a mild form of depression, subclinical depression (SD) serves as an indicator of progression to MDD. This study analyzed the degree centrality (DC) for MDD, SD, and healthy control (HC) groups and identified the brain regions with DC alterations.MethodsThe experimental data were composed of resting-state functional magnetic resonance imaging (rs-fMRI) from 40 HCs, 40 MDD subjects, and 34 SD subjects. After conducting a one-way analysis of variance, two-sample t-tests were used for further analysis to explore the brain regions with changed DC. Receiver operating characteristic (ROC) curve analysis of single index and composite index features was performed to analyze the distinguishable ability of important brain regions.ResultsFor the comparison of MDD vs. HC, increased DC was found in the right superior temporal gyrus (STG) and right inferior parietal lobule (IPL) in the MDD group. For SD vs. HC, the SD group showed a higher DC in the right STG and the right middle temporal gyrus (MTG), and a smaller DC in the left IPL. For MDD vs. SD, increased DC in the right middle frontal gyrus (MFG), right IPL, and left IPL, and decreased DC in the right STG and right MTG was found in the MDD group. With an area under the ROC (AUC) of 0.779, the right STG could differentiate MDD patients from HCs and, with an AUC of 0.704, the right MTG could differentiate MDD patients from SD patients. The three composite indexes had good discriminative ability in each pairwise comparison, with AUCs of 0.803, 0.751, and 0.814 for MDD vs. HC, SD vs. HC, and MDD vs. SD, respectively.ConclusionAltered DC in the STG, MTG, IPL, and MFG were identified in depression groups. The DC values of these altered regions and their combinations presented good discriminative ability between HC, SD, and MDD. These findings could help to find effective biomarkers and reveal the potential mechanisms of depression.

Project description:BackgroundMaternal depressive symptoms are a common phenomenon during pregnancy and are related to negative outcomes for child development and health. Modifications in child DNA methylation are discussed as an underlying mechanism for the association between prenatal depressive symptoms and alterations in child outcomes. However, formerly reported genome-wide associations have yet to be replicated.MethodsIn an epigenome-wide association study (EWAS), alterations of DNA methylation related to maternal prenatal depressive symptoms were investigated in buccal cell samples from 174 children (n = 52 exposed to prenatal depressive symptoms; 6-9 years old) of the German longitudinal study FRAMES-FRANCES. Whole blood samples from the independent, age-comparable ARIES subsample of the ARIES/ALSPAC study (n = 641; n = 159 exposed to prenatal depressive symptoms; 7-8 years old) were examined as a confirmation sample. Depressive symptoms were assessed with the Edinburgh Postnatal Depression Scale. DNA methylation was analyzed with the Infinium Human Methylation 450k BeadChip. Modifications in single CpGs, regions, and biological pathways were investigated. Results were adjusted for age and birth outcomes as well as postnatal and current maternal depressive symptoms. Analyses were performed for the whole sample as well as separated for sex.ResultsThe EWAS yielded no differentially methylated CpG or region as well as no accordance between samples withstanding correction for multiple testing. In pathway analyses, no overlapping functional domain was found to be enriched for either sample. A comparison of current and former findings suggests some overlapping methylation modifications from infancy to childhood. Results suggest that there might be sex-specific differential methylation, which should be further investigated in additional studies.ConclusionsThe current, mainly nonsignificant, results challenge the assumption of consistent modifications of DNA methylation in children exposed to prenatal depressive symptoms. Despite the relatively small sample size used in this study, this lack of significant results may reflect diverse issues of environmental epigenetic studies, which need to be addressed in future research.

Dataset Information

Common Brain Networks Between Major Depressive-Disorder Diagnosis and Symptoms of Depression That Are Validated for Independent Cohorts.

Publications

Common Brain Networks Between Major Depressive-Disorder Diagnosis and Symptoms of Depression That Are Validated for Independent Cohorts.

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