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Prefrontal networks dynamically related to recovery from major depressive disorder: a longitudinal pharmacological fMRI study.

ABSTRACT: Due to lacking predictors of depression recovery, successful treatment of major depressive disorder (MDD) is frequently only achieved after therapeutic optimization leading to a prolonged suffering of patients. This study aimed to determine neural prognostic predictors identifying non-remitters prior or early after treatment initiation. Moreover, it intended to detect time-sensitive neural mediators indicating depression recovery. This longitudinal, interventional, single-arm, open-label, phase IV, pharmacological functional magnetic resonance imaging (fMRI) study comprised four scans at important stages prior (day 0) and after escitalopram treatment initiation (day 1, 28, and 56). Totally, 22 treatment-free MDD patients (age mean?±?SD: 31.5?±?7.7; females: 50%) suffering from a concurrent major depressive episode without any comorbid DSM-IV axis I diagnosis completed the study protocol. Primary outcome were neural prognostic predictors of depression recovery. Enhanced de-activation of anterior medial prefrontal cortex (amPFC, single neural mediator) indicated depression recovery correlating with MADRS score and working memory improvements. Strong dorsolateral PFC (dlPFC) activation and weak dlPFC-amPFC, dlPFC-posterior cingulate cortex (PCC), dlPFC-parietal lobe (PL) coupling (three prognostic predictors) hinted at depression recovery at day 0 and 1. Preresponse prediction of continuous (dlPFC-PL: R2day1?=?55.9%, 95% CI: 22.6-79%, P?

SUBMITTER: Meyer BM

PROVIDER: S-EPMC6362173 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Prefrontal networks dynamically related to recovery from major depressive disorder: a longitudinal pharmacological fMRI study.

Meyer Bernhard M BM Rabl Ulrich U Huemer Julia J Bartova Lucie L Kalcher Klaudius K Provenzano Julian J Brandner Christoph C Sezen Patrick P Kasper Siegfried S Schatzberg Alan F AF Moser Ewald E Chen Gang G Pezawas Lukas L

Translational psychiatry 20190204 1

Due to lacking predictors of depression recovery, successful treatment of major depressive disorder (MDD) is frequently only achieved after therapeutic optimization leading to a prolonged suffering of patients. This study aimed to determine neural prognostic predictors identifying non-remitters prior or early after treatment initiation. Moreover, it intended to detect time-sensitive neural mediators indicating depression recovery. This longitudinal, interventional, single-arm, open-label, phase ...[more]

PMID: 30718459

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Project description:Background: Major Depressive Disorder (MDD) is a moderately heritable disorder with a high lifetime prevalence. At present, laboratory blood tests to support MDD diagnosis are not available. Methods: We used a classifier approach on blood gene expression profiles of a unique set of non-medicated subjects (MDD patients and controls) to select genes of which expression is predictive for disease status. To reveal blood gene expression changes related to MDD disease, we applied a powerful ex vivo stimulus to the blood, i.e. incubation with lipopolysaccharide (LPS; 10 ng/ml blood). Results: Based on LPS-stimulated blood gene expression using whole-genome microarrays in 42 subjects (primary cohort; 21 MDD patients (mean age 42.3 years), 21 healthy controls (mean age 41.9 years)), we identified a set of genes (CAPRIN1, CLEC4A, KRT23, MLC1, PLSCR1, PROK2, ZBTB16) that serves as a molecular signature of MDD. These findings were validated for the primary cohort using an independent quantitative PCR method (P = 0.007). The difference between depressive patients and controls was confirmed (P = 0.019) in a replication cohort of 13 patients with MDD (mean age 42.8 years) and 14 controls (mean age 45.6 years). The MDD-signature score comprised of expression levels of 7 genes could discriminate depressive patients from controls with sensitivity of 76.9% and specificity of 71.8%. Conclusions: We show for the first time that molecular analysis of stimulated blood cells can be used as an endophenotype for MDD diagnosis, which is a milestone in establishing biomarkers for neuropsychiatric disorders with moderate heritability in general. Our results may provide a new entry point for following and predicting treatment outcome, as well as prediction of severity and recurrence of MDD. In total, 33 MDD patients and 34 healthy controls were analyzed using basal gene expression in whole blood, and gene expression from whole blood that was stimulated with LPS for 5-6 h, using microarrays. Patients were arbitrarily selected from all patients to serve as primary cohort (nMDD = 21 (MDD01-MDD21); nControls = 21 (Con01-Con21)), or replication cohort (nMDD = 12 (MDD22-MDD35); nControls = 13 (Con22-Con37)) using microarrays. This submission does not include Samples CON21_LPS or CON30_LPS.

Project description:ImportanceHeroin addiction and related mortality impose a devastating toll on society, with little known about the neurobiology of this disease or its treatment. Poor inhibitory control is a common manifestation of prefrontal cortex (PFC) impairments in addiction, and its potential recovery following treatment is largely unknown in heroin (or any drug) addiction.ObjectiveTo study inhibitory control brain activity in iHUD and HC, before and after 15 weeks of inpatient treatment in the former.DesignA longitudinal cohort study (11/2020-03/2022) where iHUD and HC underwent baseline and follow-up fMRI scans. Average follow-up duration: 15 weeks.SettingThe iHUD and HC were recruited from treatment facilities and surrounding neighborhoods, respectively.ParticipantsTwenty-six iHUD [40.6±10.1 years; 7 (29.2%) women] and 24 age-/sex-matched HC [41.1±9.9 years; 9 (37.5%) women].InterventionFollowing the baseline scan, inpatient iHUD continued to participate in a medically-assisted program for an average of 15 weeks (abstinence increased from an initial 183±236 days by 65±82 days). The HC were scanned at similar time intervals.Main outcomes and measuresBehavioral performance as measured by the stop-signal response time (SSRT), target detection sensitivity (d', proportion of hits in go vs. false-alarms in stop trials), and brain activity (blood-oxygen level dependent signal differences) during successful vs. failed stops in the stop signal task.ResultsAs we previously reported, at time 1 and as compared to HC, iHUD exhibited similar SSRT but impaired d' [t(38.7)=2.37, p=.023], and lower anterior and dorsolateral PFC (aPFC, dlPFC) activity (p<.001). Importantly, at time 2, there were significant gains in aPFC and dlPFC activity in the iHUD (group*session interaction, p=.002); the former significantly correlated with increases in d' specifically in iHUD (p=.012).Conclusions and relevanceCompared to HC, the aPFC and dlPFC impairments in the iHUD at time 1 were normalized at time 2, which was associated with individual differences in improvements in target detection sensitivity. For the first time in any drug addiction, these results indicate a treatment-mediated inhibitory control brain activity recovery. These neurobehavioral results highlight the aPFC and dlPFC as targets for intervention with a potential to enhance self-control recovery in heroin addiction.

Project description:ObjectivesThis study aims to summarise the evidence on more than 140 pharmacological and non-pharmacological treatment options for major depressive disorder (MDD) and to evaluate the confidence that patients and clinicians can have in the underlying science about their effects.DesignThis is a review of systematic reviews.Data sourcesThis study used MEDLINE, Embase, Cochrane Library, PsycINFO and Epistemonikos from 2011 up to February 2017 for systematic reviews of randomised controlled trials in adult patients with acute-phase MDD.MethodsWe dually reviewed abstracts and full-text articles, rated the risk of bias of eligible systematic reviews and graded the strength of evidence.ResultsNineteen systematic reviews provided data on 28 comparisons of interest. For general efficacy, only second-generation antidepressants were supported with high strength evidence, presenting small beneficial treatment effects (standardised mean difference: -0.35; 95% CI -0.31 to -0.38), and a statistically significantly higher rate of discontinuation because of adverse events than patients on placebo (relative risk (RR) 1.88; 95%?CI 1.0 to 3.28).Only cognitive behavioural therapy is supported by reliable evidence (moderate strength of evidence) to produce responses to treatment similar to those of second-generation antidepressants (45.5% vs 44.2%; RR 1.10; 95%?CI 0.93 to 1.30). All remaining comparisons of non-pharmacological treatments with second-generation antidepressants either led to inconclusive results or had substantial methodological shortcomings (low or insufficient strength of evidence).ConclusionsIn contrast to pharmacological treatments, the majority of non-pharmacological interventions for treating patients with MDD are not evidence based. For patients with strong preferences against pharmacological treatments, clinicians should focus on therapies that have been compared directly with antidepressants.Trial registration numberInternational Prospective Register of Systematic Reviews (PROSPERO) registration number: 42016035580.

Project description:BackgroundDifferences in within-person emotion dynamics may be an important source of heterogeneity in depression. To investigate these dynamics, researchers have previously combined multilevel regression analyses with network representations. However, sparse network methods, specifically developed for longitudinal network analyses, have not been applied. Therefore, this study used this approach to investigate population-level and individual-level emotion dynamics in healthy and depressed persons and compared this method with the multilevel approach.MethodsTime-series data were collected in pair-matched healthy persons and major depressive disorder (MDD) patients (n = 54). Seven positive affect (PA) and seven negative affect (NA) items were administered electronically at 90 times (30 days; thrice per day). The population-level (healthy vs. MDD) and individual-level time series were analyzed using a sparse longitudinal network model based on vector autoregression. The population-level model was also estimated with a multilevel approach. Effects of different preprocessing steps were evaluated as well. The characteristics of the longitudinal networks were investigated to gain insight into the emotion dynamics.ResultsIn the population-level networks, longitudinal network connectivity was strongest in the healthy group, with nodes showing more and stronger longitudinal associations with each other. Individually estimated networks varied strongly across individuals. Individual variations in network connectivity were unrelated to baseline characteristics (depression status, neuroticism, severity). A multilevel approach applied to the same data showed higher connectivity in the MDD group, which seemed partly related to the preprocessing approach.ConclusionsThe sparse network approach can be useful for the estimation of networks with multiple nodes, where overparameterization is an issue, and for individual-level networks. However, its current inability to model random effects makes it less useful as a population-level approach in case of large heterogeneity. Different preprocessing strategies appeared to strongly influence the results, complicating inferences about network density.

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Prefrontal networks dynamically related to recovery from major depressive disorder: a longitudinal pharmacological fMRI study.

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Prefrontal networks dynamically related to recovery from major depressive disorder: a longitudinal pharmacological fMRI study.

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