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Pretreatment brain connectivity during positive emotion upregulation predicts decreased anhedonia following behavioral activation therapy for depression.

ABSTRACT: BACKGROUND:Neurobiological predictors of antidepressant response may help guide treatment selection and improve response rates to available treatments for major depressive disorder (MDD). Behavioral activation therapy for depression (BATD) is an evidence-based intervention designed to ameliorate core symptoms of MDD by promoting sustained engagement with value-guided, positively-reinforcing activities. The present study examined pre-treatment task-based functional brain connectivity as a predictor of antidepressant response to BATD. METHODS:Thirty-three outpatients with MDD and 20 nondepressed controls completed a positive emotion regulation task during fMRI after which participants with MDD received up to 15 sessions of BATD. We used generalized psychophysiological interaction analyses to examine group differences in pre-treatment functional brain connectivity during intentional upregulation of positive emotion to positive images. Hierarchical linear models were used to examine whether group differences in functional connectivity predicted changes in depression and anhedonia over the course of BATD. RESULTS:Compared to controls, participants with MDD exhibited decreased connectivity between the left middle frontal gyrus and right temporoparietal regions during upregulation of positive emotion. Within the MDD group, decreased connectivity of these regions predicted greater declines in anhedonia symptoms over treatment. LIMITATIONS:Future studies should include comparison treatments and longitudinal follow-up to clarify the unique effects of BATD on neural function and antidepressant response. CONCLUSIONS:Results are consistent with previous work showing BATD may be particularly effective for individuals with greater disturbances in brain reward network function, but extend these findings to highlight the importance of frontotemporoparietal connectivity in targeting symptoms of low motivation and engagement.

SUBMITTER: Walsh EC

PROVIDER: S-EPMC6411035 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Pretreatment brain connectivity during positive emotion upregulation predicts decreased anhedonia following behavioral activation therapy for depression.

Walsh Erin C EC Eisenlohr-Moul Tory A TA Minkel Jared J Bizzell Joshua J Petty Chris C Crowther Andrew A Carl Hannah H Smoski Moria J MJ Dichter Gabriel S GS

Journal of affective disorders 20180917

<h4>Background</h4>Neurobiological predictors of antidepressant response may help guide treatment selection and improve response rates to available treatments for major depressive disorder (MDD). Behavioral activation therapy for depression (BATD) is an evidence-based intervention designed to ameliorate core symptoms of MDD by promoting sustained engagement with value-guided, positively-reinforcing activities. The present study examined pre-treatment task-based functional brain connectivity as a ...[more]

PMID: 30245249

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Project description:Hypothalamus communication with the rest of the brain and peripheral target tissues is critically important for many physiological and psychological functions. These functions include maintaining neuroendocrine circadian rhythms and managing affective processes. The hypothalamus maintains both direct neural connections within the brain and it also controls a variety of neuroendocrine processes that can influence target tissues throughout the body. Dysregulation of the hypothalamic pituitary adrenal axis and hyperactivity of the subgenual cortex are both frequently observed in depression. However, many details of how the hypothalamus, the hypothalamic pituitary adrenal (HPA) axis, and the subgenual cingulate interact with each other are unknown. We hypothesized that resting-state functional connectivity between the hypothalamus and the subgenual cortex would be associated with altered circadian rhythm in patients with depression and depressive symptoms. We also hypothesized that this would be most apparent in patients that have major depression with psychotic symptoms, who typically have the most robust HPA-axis dysregulation. Resting-state functional magnetic resonance imaging (fMRI) scans were collected to observe low-frequency resting-state functional connectivity patterns of the hypothalamus in 39 healthy participants, 39 patients with major depression, and 22 patients with major depression with psychotic symptoms. Hourly overnight measures of cortisol secretion and multiple measures of psychiatric symptom severity were also collected on all. Strong hypothalamic functional connectivity with the subgenual cortex was observed in healthy participants. This connectivity was significantly reduced in patients with psychotic major depression. Increased cortisol secretion during the circadian nadir and reduced connectivity were both associated with symptom severity. Reduced connectivity and high cortisol secretion during the circadian nadir are both useful for explaining a significant amount of variance in symptom severity that occurs between healthy participants and depressed patients. However, only cortisol secretion was useful for explaining the severity of symptoms within the depressed groups. This study suggests that the communication between the hypothalamus and the subgenual cortex is disrupted in patients with major depression with psychotic features. It also suggests that these disruptions are associated with increased symptom severity and may be a cause or a consequence of cortisol dysregulation.

Project description:Major depressive disorder (MDD) is a debilitating condition that affects over 14 million Americans. Remission occurs only in a minority of individuals after first-line antidepressant treatment (∼35%); predictors of treatment outcome are therefore needed. Using PET imaging with a radiotracer specific for the serotonin transporter (5-HTT), 11C-McN5652, we found that patients with MDD who did not achieve remission after 12 mo of naturalistic treatment had lower pretreatment midbrain and amygdala binding than healthy volunteers. Here, using a superior 5-HTT tracer, 11C-DASB, we repeated this study with a prospective design with 8 wk of standardized treatment with escitalopram. As this same cohort also underwent 11C-WAY100635 scans (serotonin-1A receptor [5-HT1A]), we examined whether using both pretreatment 5-HTT and 5-HT1A binding could improve prediction of posttreatment remission status. Methods: Thirty-one healthy controls (Hamilton Depression Rating Scale-24 item [HDRS-24] = 1.7) and 26 medication-free patients with MDD (HDRS-24 = 24.8) underwent PET scanning using 11C-DASB. MDD subjects then received 8 wk of standardized pharmacotherapy with escitalopram. The relationship between pretreatment binding and posttreatment clinical status was examined. Arterial blood samples were collected to calculate the metabolite-corrected arterial input function. The outcome measure was VT/fP (VT is volume of distribution in region of interest, fP is free fraction in plasma). Remission was defined as a posttreatment depression score of less than 10 as well as 50% or more reduction in the score from baseline, resulting in 14 nonremitters (HDRS-24 = 17.6) and 12 remitters (HDRS-24 = 5.3). Results: A linear mixed-effects model comparing group differences in the a priori regions of interest (amygdala and midbrain) revealed a significant difference in amygdala binding between controls and remitters (P = 0.03, unadjusted), where remitters had an 11% lower amygdala binding than controls. Differences in amygdala binding between remitters and nonremitters approached significance (P = 0.06). No additional differences were found between any groups (all P > 0.05). Additionally, we found no relationship between pretreatment amygdala binding and posttreatment depression score, and were unable to predict posttreatment depression severity using both pretreatment 5-HTT (in the amygdala) and 5-HT1A binding (in the raphe). Conclusion: These results suggest 5-HTT amygdala binding should be examined further, in conjunction with other measures, as a potential biomarker for remission after standardized escitalopram treatment.

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.

Project description:BackgroundFunctional magnetic resonance imaging research suggests that major depressive disorder (MDD) in both adults and adolescents is marked by aberrant connectivity of the default mode network (DMN) during resting state. However, emotional dysregulation is also a key feature of MDD. No studies to date have examined emotion-related DMN pathology in adolescent depression. Comprehensively understanding the dynamics of DMN connectivity across brain states in individuals with depression with short disease histories could provide insight into the etiology of MDD.MethodsWe collected functional magnetic resonance imaging data during an emotion identification task and during resting state from 26 medication-free adolescents (13-17 years old) with MDD and 37 well-matched healthy control subjects. We examined between-group differences in blood oxygenation level-dependent task responses and emotion-dependent and resting-state functional connectivity of the two primary nodes of the DMN: medial prefrontal cortex and posterior cingulate cortex (PCC). Additionally, we examined between-group differences in DMN functional connectivity and its relationship to depression severity and onset.ResultsRelative to healthy control subjects, unmedicated adolescents with MDD demonstrated reduced medial prefrontal cortex and PCC emotion-related deactivation and greater medial prefrontal cortex and PCC emotion-dependent functional connectivity with precuneus, cingulate gyrus, and striatum/subcallosal cingulate gyrus. The PCC-subcallosal cingulate connectivity remained inflexibly elevated in the subjects with MDD versus healthy control subjects during resting state. Stronger PCC emotion-dependent functional connectivity was associated with greater depression severity and an earlier age of depression onset.ConclusionsAdolescent depression is associated with inflexibly elevated DMN connections. Given more recent evidence of DMN maturation throughout adolescence, our findings suggest that early-onset depression adversely affects normal development of functional brain networks.

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Pretreatment brain connectivity during positive emotion upregulation predicts decreased anhedonia following behavioral activation therapy for depression.

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Pretreatment brain connectivity during positive emotion upregulation predicts decreased anhedonia following behavioral activation therapy for depression.

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