Functional connectivity of reflective and brooding rumination in depressed and healthy women.

Ruminative thinking is related to an increased risk for major depressive disorder (MDD) and perpetuates negative mood states. Rumination, uncontrollable negative thoughts about the self, may comprise both reflective and brooding components. However, only brooding rumination is consistently associated with increased negativity bias and negative coping styles, while reflective rumination has a less clear relationship with negative outcomes in healthy and depressed participants. The current study examined seed-to-voxel (S2.V) resting-state functional connectivity (FC) in a sample of healthy (HC) and depressed (MDD) adult women (HC: n=50, MDD: n=33). The S2V FC of six key brain regions, including the left and right amygdala, anterior and posterior cingulate cortex (ACC, PCC), and medial and dorsolateral prefrontal cortices (mPFC, dlPFC), was correlated with self-reported reflective and brooding rumination. Results indicate that HC and MDD participants had increased brooding rumination associated with decreased FC between the left amygdala and the right temporal pole. Moreover, reflective rumination was associated with distinct FC of the mPFC, PCC, and ACC with parietal, occipital, and cingulate regions. Depressed participants, compared with HC, exhibited decreased FC between the PCC and a region in the right middle frontal gyrus. The results of the current study add to the understanding of the neural underpinnings of different forms of self-related cognition-brooding and reflective rumination-in healthy and depressed women.

Multidimensional prediction of treatment response to antidepressants with cognitive control and functional MRI.

Predicting treatment response for major depressive disorder can provide a tremendous benefit for our overstretched health care system by reducing number of treatments and time to remission, thereby decreasing morbidity. The present study used neural and performance predictors during a cognitive control task to predict treatment response (% change in Hamilton Depression Rating Scale pre- to post-treatment). Forty-nine individuals diagnosed with major depressive disorder were enrolled with intent to treat in the open-label study; 36 completed treatment, had useable data, and were included in most data analyses. Participants included in the data analysis sample received treatment with escitalopram (n = 22) or duloxetine (n = 14) for 10 weeks. Functional MRI and performance during a Parametric Go/No-go test were used to predict per cent reduction in Hamilton Depression Rating Scale scores after treatment. Haemodynamic response function-based contrasts and task-related independent components analysis (subset of sample: n = 29) were predictors. Independent components analysis component beta weights and haemodynamic response function modelling activation during Commission errors in the rostral and dorsal anterior cingulate, mid-cingulate, dorsomedial prefrontal cortex, and lateral orbital frontal cortex predicted treatment response. In addition, more commission errors on the task predicted better treatment response. Together in a regression model, independent component analysis, haemodynamic response function-modelled, and performance measures predicted treatment response with 90% accuracy (compared to 74% accuracy with clinical features alone), with 84% accuracy in 5-fold, leave-one-out cross-validation. Convergence between performance markers and functional magnetic resonance imaging, including novel independent component analysis techniques, achieved high accuracy in prediction of treatment response for major depressive disorder. The strong link to a task paradigm provided by use of independent component analysis is a potential breakthrough that can inform ways in which prediction models can be integrated for use in clinical and experimental medicine studies.

Reliability, Convergent Validity and Time Invariance of Default Mode Network Deviations in Early Adult Major Depressive Disorder.

There is substantial variability across studies of default mode network (DMN) connectivity in major depressive disorder, and reliability and time-invariance are not reported. This study evaluates whether DMN dysconnectivity in remitted depression (rMDD) is reliable over time and symptom-independent, and explores convergent relationships with cognitive features of depression. A longitudinal study was conducted with 82 young adults free of psychotropic medications (47 rMDD, 35 healthy controls) who completed clinical structured interviews, neuropsychological assessments, and 2 resting-state fMRI scans across 2 study sites. Functional connectivity analyses from bilateral posterior cingulate and anterior hippocampal formation seeds in DMN were conducted at both time points within a repeated-measures analysis of variance to compare groups and evaluate reliability of group-level connectivity findings. Eleven hyper- (from posterior cingulate) and 6 hypo- (from hippocampal formation) connectivity clusters in rMDD were obtained with moderate to adequate reliability in all but one cluster (ICC's range = 0.50 to 0.76 for 16 of 17). The significant clusters were reduced with a principle component analysis (5 components obtained) to explore these connectivity components, and were then correlated with cognitive features (rumination, cognitive control, learning and memory, and explicit emotion identification). At the exploratory level, for convergent validity, components consisting of posterior cingulate with cognitive control network hyperconnectivity in rMDD were related to cognitive control (inverse) and rumination (positive). Components consisting of anterior hippocampal formation with social emotional network and DMN hypoconnectivity were related to memory (inverse) and happy emotion identification (positive). Thus, time-invariant DMN connectivity differences exist early in the lifespan course of depression and are reliable. The nuanced results suggest a ventral within-network hypoconnectivity associated with poor memory and a dorsal cross-network hyperconnectivity linked to poorer cognitive control and elevated rumination. Study of early course remitted depression with attention to reliability and symptom independence could lead to more readily translatable clinical assessment tools for biomarkers.

Multivariate pattern analysis strategies in detection of remitted major depressive disorder using resting state functional connectivity.

Understanding abnormal resting-state functional connectivity of distributed brain networks may aid in probing and targeting mechanisms involved in major depressive disorder (MDD). To date, few studies have used resting state functional magnetic resonance imaging (rs-fMRI) to attempt to discriminate individuals with MDD from individuals without MDD, and to our knowledge no investigations have examined a remitted (r) population. In this study, we examined the efficiency of support vector machine (SVM) classifier to successfully discriminate rMDD individuals from healthy controls (HCs) in a narrow early-adult age range. We empirically evaluated four feature selection methods including multivariate Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net feature selection algorithms. Our results showed that SVM classification with Elastic Net feature selection achieved the highest classification accuracy of 76.1% (sensitivity of 81.5% and specificity of 68.9%) by leave-one-out cross-validation across subjects from a dataset consisting of 38 rMDD individuals and 29 healthy controls. The highest discriminating functional connections were between the left amygdala, left posterior cingulate cortex, bilateral dorso-lateral prefrontal cortex, and right ventral striatum. These appear to be key nodes in the etiopathophysiology of MDD, within and between default mode, salience and cognitive control networks. This technique demonstrates early promise for using rs-fMRI connectivity as a putative neurobiological marker capable of distinguishing between individuals with and without rMDD. These methods may be extended to periods of risk prior to illness onset, thereby allowing for earlier diagnosis, prevention, and intervention.

Amygdala and dorsomedial hyperactivity to emotional faces in youth with remitted Major Depression.

We present neuroimaging markers of the remitted state of major depressive disorder (rMDD) during facial emotion perception in 84 individuals during fMRI. Participants comprised 47 individuals (aged 18-23) diagnosed with rMDD and 37 healthy controls (HCs). Participants classified emotional faces or animals (control condition) in the Facial Emotion Perception Test (FEPT) during fMRI. Behavioural performance on the FEPT did not differ significantly between groups. During fMRI, both groups demonstrated significant blood oxygen level-dependent (BOLD) activity in bilateral inferior frontal gyri for the faces minus animals (F-A) contrast. The rMDD group additionally showed BOLD activity during F-A in numerous regions, including the bilateral paracingulate gyri, middle temporal gyri and right amygdala. The rMDD group exhibited significantly greater activity than the HC group in regions including the bilateral middle temporal gyri and left superior frontal gyrus. Although the rMDD group did not manifest the behavioural performance deficits on facial emotion recognition tasks that have been observed in actively depressed individuals, the rMDD group nevertheless showed increased BOLD activity compared with never-depressed controls during F-A in multiple posterior brain regions, suggesting that persistent effects of illness or possible trait vulnerabilities may distinguish individuals with rMDD from never-depressed controls.

Biomarkers of intergenerational risk for depression: a review of mechanisms in longitudinal high-risk (LHR) studies.

BACKGROUND: Longitudinal research is critical for understanding the biological mechanisms underlying the development of depression. Researchers recruit high-risk cohorts to understand how risk is transmitted from one generation to the next. Biological measurements have been incorporated into these longitudinal high-risk (LHR) studies in order to illuminate mechanistic pathways. METHODS: To frame our review, we first present heritability estimates along the gene-by-environment continuum as a foundation. We then offer a Biomarkers of Intergenerational Risk for Depression (BIRD) model to describe the multiple hits individuals at risk receive and to allow for greater focus on the interactive effects of markers. BIRD allows for the known multifinality of pathways towards depression and considers the context (i.e., environment) in which these mechanisms emerge. Next, we review the extant LHR cohort studies that have assessed central nervous system (electroencephalography (EEG), neuroimaging), endocrine (hypothalamic-pituitary-adrenal axis (HPA)/cortisol), autonomic (startle, heart rate), genetic, sleep, and birth characteristics. RESULTS: Results to date, in conjunction with the proposed model, point towards several pathways of discovery in understanding mechanisms, providing clear direction for future research examining potential endophenotypes. LIMITATIONS: Our review is based on relatively narrow inclusion and exclusion criteria. As such, many interesting studies were excluded, but this weakness is offset by strengths such as the increased reliability of findings. CONCLUSIONS: Blanket prevention programs are inefficient and plagued by low effect sizes due to low rates of actual conversion to disorder. The inclusion of biomarkers of risk may lead to enhanced program efficiency by targeting those at greatest risk.