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Dimensional depression severity in women with major depression and post-traumatic stress disorder correlates with fronto-amygdalar hypoconnectivty.

ABSTRACT: Depressive symptoms are common in multiple psychiatric disorders and are frequent sequelae of trauma. A dimensional conceptualization of depression suggests that symptoms should be associated with a continuum of deficits in specific neural circuits. However, most prior investigations of abnormalities in functional connectivity have typically focused on a single diagnostic category using hypothesis-driven seed-based analyses. Here, using a sample of 105 adult female participants from three diagnostic groups (healthy controls, n=17; major depression, n=38; and post-traumatic stress disorder, n=50), we examine the dimensional relationship between resting-state functional dysconnectivity and severity of depressive symptoms across diagnostic categories using a data-driven analysis (multivariate distance-based matrix regression). This connectome-wide analysis identified foci of dysconnectivity associated with depression severity in the bilateral amygdala. Follow-up seed analyses using subject-specific amygdala segmentations revealed that depression severity was associated with amygdalo-frontal hypo-connectivity in a network of regions including bilateral dorsolateral prefrontal cortex, anterior cingulate and anterior insula. In contrast, anxiety was associated with elevated connectivity between the amygdala and the ventromedial prefrontal cortex. Taken together, these results emphasize the centrality of the amygdala in the pathophysiology of depressive symptoms, and suggest that dissociable patterns of amygdalo-frontal dysconnectivity are a critical neurobiological feature across clinical diagnostic categories.

SUBMITTER: Satterthwaite TD

PROVIDER: S-EPMC4840084 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Dimensional depression severity in women with major depression and post-traumatic stress disorder correlates with fronto-amygdalar hypoconnectivty.

Satterthwaite T D TD Cook P A PA Bruce S E SE Conway C C Mikkelsen E E Satchell E E Vandekar S N SN Durbin T T Shinohara R T RT Sheline Y I YI

Molecular psychiatry 20150929 7

Depressive symptoms are common in multiple psychiatric disorders and are frequent sequelae of trauma. A dimensional conceptualization of depression suggests that symptoms should be associated with a continuum of deficits in specific neural circuits. However, most prior investigations of abnormalities in functional connectivity have typically focused on a single diagnostic category using hypothesis-driven seed-based analyses. Here, using a sample of 105 adult female participants from three diagno ...[more]

PMID: 26416545

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Project description:BackgroundSelf-stigma refers to the internalisation of negative societal views and stereotypes. Self-stigma has been well-characterised in the context of mental disorders such as schizophrenia but has received little attention in relation to post-traumatic stress disorder (PTSD).ObjectiveThis work aimed to determine the prevalence of self-stigma in a sample of adults with PTSD and to establish factors associated with the internalisation of stigma in this population.MethodParticipants were 194 adults (mean age 46.07 (SD = 12.39); 64.4% female; 96.6% white Caucasian; residing in the UK), who self-reported a diagnosis of PTSD and currently screened positive for the disorder according to the PTSD Checklist for DSM-5 (PCL-5). Structured interviews and validated self-report questionnaires were used to ascertain clinical and sociodemographic information for analysis.ResultsThe prevalence of self-stigma measured by the Internalized Stigma of Mental Illness Scale (ISMIS) was 41.2% (95% CI 34.24-48.22). There was no evidence of an association between self-stigma and gender (β = -2.975 (95% CI -7.046-1.097) p = .151), age (β = 0.007 (95% CI -0.152-0.165) p = .953), sexual trauma (β = 0.904 (95% CI -3.668-5.476) p = .697), military trauma (β = -0.571 (95% CI -4.027-7.287) p = .571). Self-stigma was associated with lower income and higher levels of anxiety (β = 5.722 (95% CI 2.922-8.522) p = <.001), depression (β = 6.937 (95% CI 4.287-9.588) p = <.000), and traumatic stress symptoms (β = 3.880 (95% CI 1.401-6.359) p = .002).ConclusionsThe results indicate that self-stigma may be a significant issue among people with a diagnosis of PTSD. Further work is needed to understand the long-term impact and to develop interventions to address the internalisation of stigma in this population.HighlightsThe prevalence of self-stigma among a sample of participants with PTSD was 41.2%.There was no evidence of an association between self-stigma and gender, age or sexual / military trauma.Self-stigma was associated with lower income and higher levels of anxiety, depression, and traumatic stress symptoms.

Project description:Background:Electroconvulsive therapy (ECT) is arguably the most effective available treatment for severe depression. Recent studies have used MRI data to predict clinical outcome to ECT and other antidepressant therapies. One challenge facing such studies is selecting from among the many available metrics, which characterize complementary and sometimes non-overlapping aspects of brain function and connectomics. Here, we assessed the ability of aggregated, functional MRI metrics of basal brain activity and connectivity to predict antidepressant response to ECT using machine learning. Methods:A radial support vector machine was trained using arterial spin labeling (ASL) and blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) metrics from n?=?46 (26 female, mean age 42) depressed patients prior to ECT (majority right-unilateral stimulation). Image preprocessing was applied using standard procedures, and metrics included cerebral blood flow in ASL, and regional homogeneity, fractional amplitude of low-frequency modulations, and graph theory metrics (strength, local efficiency, and clustering) in BOLD data. A 5-repeated 5-fold cross-validation procedure with nested feature-selection validated model performance. Linear regressions were applied post hoc to aid interpretation of discriminative features. Results:The range of balanced accuracy in models performing statistically above chance was 58-68%. Here, prediction of non-responders was slightly higher than for responders (maximum performance 74 and 64%, respectively). Several features were consistently selected across cross-validation folds, mostly within frontal and temporal regions. Among these were connectivity strength among: a fronto-parietal network [including left dorsolateral prefrontal cortex (DLPFC)], motor and temporal networks (near ECT electrodes), and/or subgenual anterior cingulate cortex (sgACC). Conclusion:Our data indicate that pattern classification of multimodal fMRI metrics can successfully predict ECT outcome, particularly for individuals who will not respond to treatment. Notably, connectivity with networks highly relevant to ECT and depression were consistently selected as important predictive features. These included the left DLPFC and the sgACC, which are both targets of other neurostimulation therapies for depression, as well as connectivity between motor and right temporal cortices near electrode sites. Future studies that probe additional functional and structural MRI metrics and other patient characteristics may further improve the predictive power of these and similar models.

Dataset Information

Dimensional depression severity in women with major depression and post-traumatic stress disorder correlates with fronto-amygdalar hypoconnectivty.

Publications

Dimensional depression severity in women with major depression and post-traumatic stress disorder correlates with fronto-amygdalar hypoconnectivty.

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