Publications by authors named "Jennifer R Gowins"

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.

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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.

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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.

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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.

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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.

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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.

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