Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder.

Using MRI to diagnose mental disorders has been a long-term goal. Despite this, the vast majority of prior neuroimaging work has been descriptive rather than predictive. The current study applies support vector machine (SVM) learning to MRI measures of brain white matter to classify adults with Major Depressive Disorder (MDD) and healthy controls. In a precisely matched group of individuals with MDD (n =25) and healthy controls (n =25), SVM learning accurately (74%) classified patients and controls across a brain map of white matter fractional anisotropy values (FA). The study revealed three main findings: 1) SVM applied to DTI derived FA maps can accurately classify MDD vs. healthy controls; 2) prediction is strongest when only right hemisphere white matter is examined; and 3) removing FA values from a region identified by univariate contrast as significantly different between MDD and healthy controls does not change the SVM accuracy. These results indicate that SVM learning applied to neuroimaging data can classify the presence versus absence of MDD and that predictive information is distributed across brain networks rather than being highly localized. Finally, MDD group differences revealed through typical univariate contrasts do not necessarily reveal patterns that provide accurate predictive information.

The efficacy of Focused Acceptance and Commitment Therapy in VA primary care.

Focused Acceptance & Commitment Therapy (FACT) is a brief intervention based on traditional Acceptance and Commitment Therapy (ACT). Although there is a growing body of research on the efficacy of ACT for a variety of populations and disorders, there is little research to date on the use of FACT in group settings. This project is 1 of the first of its kind, as it examines data on psychological flexibility, health and mental health status, and symptom reduction from a 4-week FACT group. Participants in this study were 51 patients who attended this group as part of routine clinical care in a VA integrated primary care and mental health setting. They completed pre- and posttreatment measures of well-being, depression, anxiety, stress, psychological flexibility, and perceptions of physical and mental health functioning. Pre- to posttreatment analyses of variance demonstrated large effects for quality of life, F(1, 51) = 21.29, p < .001, η2 = 0.30, moderate effects for depressive symptoms, F(1, 51) = 11.47, p < .001, η2 = 0.08, and perceptions of mental health functioning (MCS scale), F(1, 51) = 9.67, p = .003, η2 = 0.11, and small effects for perceptions of perceived stress, F(1, 51) = 4.08, p = .04, η2 = 0.03, and physical health functioning (PCS scale), F(1, 51) = 6.60, p = .01, η2 = 0.08. There was a statistical trend for reductions in anxiety, F(1, 51) = 3.29, p = .07, η2 = 0.01, and a nonsignificant effect for psychological flexibility, F(1, 51) = 2.05, p = .16, η2 = 0.04. These data provide initial support for the implementation of a group-based FACT protocol within a VA primary care setting and help to lay a foundation for further, more controlled studies on Group FACT in future research. (PsycINFO Database Record

Mood-Reactive Self-Esteem and Depression Vulnerability: Person-Specific Symptom Dynamics via Smart Phone Assessment.

Cognitive theories of depression suggest that mood-reactive self-esteem, a pattern of cognitive reactivity where low self-esteem is temporally dependent on levels of sadness, represents vulnerability for depression. Few studies have directly tested this hypothesis, particularly using intensive data collection methods (i.e., experience sampling) required to capture the temporal dynamics of sadness and self-esteem as they unfold naturally, over time. In this study we used participants' smartphones to collect multiple daily ratings of sadness and self-esteem over three weeks, in the real world. We then applied dynamic factor modeling to explore theoretically driven hypotheses about the temporal dependency of self-esteem on sadness (i.e., mood-reactive self-esteem) and its relationship to indices of depression vulnerability both contemporaneously (e.g., rumination, sad mood persistence) and prospectively (e.g., future symptomatology). In sum, individuals who demonstrated mood-reactive self-esteem reported higher levels of rumination at baseline, more persistent sad mood over three weeks, and increased depression symptoms at the end of three weeks above and beyond a trait-like index of self-esteem. The integration of smartphone assessment and person-specific analytics employed in this study offers an exiting new avenue to advance the study and treatment of depression.

Attention bias modification for major depressive disorder: Effects on attention bias, resting state connectivity, and symptom change.

Cognitive theories of depression posit that selective attention for negative information contributes to the maintenance of depression. The current study experimentally tested this idea by randomly assigning adults with Major Depressive Disorder (MDD) to 4 weeks of computer-based attention bias modification designed to reduce negative attention bias or 4 weeks of placebo attention training. Findings indicate that compared to placebo training, attention bias modification reduced negative attention bias and increased resting-state connectivity within a neural circuit (i.e., middle frontal gyrus and dorsal anterior cingulate cortex) that supports control over emotional information. Further, pre- to post-training change in negative attention bias was significantly correlated with depression symptom change only in the active training condition. Exploratory analyses indicated that pre- to post-training changes in resting state connectivity within a circuit associated with sustained attention to visual information (i.e., precuenus and middle frontal gyrus) contributed to symptom improvement in the placebo condition. Importantly, depression symptoms did not change differentially between the training groups-overall, a 40% decrease in symptoms was observed across attention training conditions. Findings suggest that negative attention bias is associated with the maintenance of depression; however, deficits in general attentional control may also maintain depression symptoms, as evidenced by resting state connectivity and depression symptom improvement in the placebo training condition.

Cognitive control network connectivity in adolescent women with and without a parental history of depression.

BACKGROUND: Adolescent women with a parental history of depression are at high risk for the onset of major depressive disorder (MDD). Cognitive theories suggest this vulnerability involves deficits in cognitive control over emotional information. Among adolescent women with and without a parental history of depression, we examined differences in connectivity using resting state functional connectivity analysis within a network associated with cognitive control over emotional information. METHODS: Twenty-four depression-naïve adolescent women underwent resting state functional magnetic resonance imaging (fMRI). They were assigned to high-risk (n=11) and low-risk (n=13) groups based their parents' depression history. Seed based functional connectivity analysis was used to examine group differences in connectivity within a network associated with cognitive control. RESULTS: High-risk adolescents had lower levels of connectivity between a right inferior prefrontal region and other critical nodes of the attention control network, including right middle frontal gyrus and right supramarginal gyrus. Further, greater severity of the parents' worst episode of depression was associated with altered cognitive control network connectivity in their adolescent daughters. CONCLUSIONS: Depressed parents may transmit depression vulnerability to their adolescent daughters via alterations in functional connectivity within neural circuits that underlie cognitive control of emotional information.

Attentional biases and the persistence of sad mood in major depressive disorder.

This study examined whether attentional biases for emotional information are associated with impaired mood recovery following a sad mood induction among individuals with and without major depressive disorder (MDD). Attentional biases were assessed with an exogenous cuing task using emotional facial expressions as cues among adults with (n = 48) and without (n = 224) current MDD. Mood reactivity and recovery were measured following a sad mood induction. Mood reactivity strongly predicted mood recovery; however, this relationship was moderated by attentional biases for negative emotional stimuli. Biases for sad and fear stimuli were associated with diminished mood recovery following mood induction across the sample. However, biases for sad stimuli were associated with significantly greater impairments in mood recovery among individuals with MDD than healthy controls. Furthermore, within the MDD group, impaired mood recovery was positively associated with depression severity. These results suggest that attentional biases maintain depression, in part, by facilitating the persistence of sad mood.