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Cognition and Affect Regulation (CAR) Lab

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From Networks to the Real World: Integrating Neural and Autonomic Processes of Loss

1K23MH112769-01A1 – National Institute of Mental Health

Major depressive disorder (MDD) is the most common lifetime mental disorder and is associated with tremendous personal, economic and societal costs; thus, there is a need to better understand the processes underlying MDD to facilitate the development of mechanistically-driven interventions. The proposed research will link neural circuitry underlying cognitive control and affective processing to autonomic and behavioral measures of affect regulation in the laboratory and in daily life, among individuals with remitted MDD and healthy controls. An improved mechanistic understanding of cognitive-affective risk phenotypes for depression will inform the timely prevention, early detection, and advancement of novel treatments for affect dysregulation in depression.

Probing Autonomic and Network Mechanisms of Emotion Regulation in Major Depressive Disorder

Brain and Behavior Research Foundation (NARSAD Young Investigator Award)

Major depressive disorder (MDD) is a prevalent and debilitating disorder that places a heavy burden on society through healthcare service use, loss of productivity, and loss of life to suicide.  To develop more effective treatments and reduce risk for recurrence, there is an urgent need for a more precise mechanistic understanding of depression risk phenotypes. Despite having a greater need for effective emotion regulation, individuals with MDD experience less benefit from attempts to regulate than individuals without MDD, leading to prolonged negative emotion and suppressed parasympathetic nervous system activity. By identifying biomarkers of problematic responses to sadness, treatments can be developed that allow for regulatory intervention earlier than when relying exclusively on individuals’ self-reports of these processes. The proposed study aims to identify (a) brain networks important in disrupted emotion regulation that can be targeted to improve regulatory success, and (b) parasympathetic indicators of disrupted network functioning that are less invasive and more cost-effective to evaluate than fMRI. This study has important implications for the identification of neural and psychophysiological mechanisms of emotion regulation that have strong potential for translation to novel interventions. Parasympathetic activity (respiratory sinus arrhythmia [RSA], a plausible parasympathetic biomarker of depression risk) and regulatory effectiveness will be assessed during a laboratory-based sadness paradigm. This design will bridge key gaps in the field and build upon the candidate’s existing strengths in the experimental and longitudinal assessment of cognitive-affective vulnerabilities for depression in two new areas: neural network modeling and multimodal data integration. We hypothesize that maladaptive patterns of activation in neural networks supporting emotion processing (elevated) and cognitive control (diminished) will be associated with poorer regulation, indexed by poorer parasympathetic and emotional recovery from sadness. Given that RSA is a brief, inexpensive, and noninvasive measure that can be collected in any lab, we expect results to suggest that RSA can provide a remote window into brain networks underlying emotion regulation. This study has the potential to contribute to new tools (e.g., neuromodulation, biofeedback, behavioral techniques) to improve the detection and treatment of emotion dysregulation in depression.

Identifying Mechanisms of Proximal Suicide Risk Using Ambulatory Assessment

Portes Foundation and Institute of Medicine of Chicago

Suicide is the second leading cause of death among young adults in the United States and often occurs within the context of major depressive disorder.  Difficulties with regulating strong negative emotions is a candidate mechanism by which numerous risk factors may lead to suicidal behavior.  However, existing models of suicide risk have had only modest success, suggesting the need to go beyond traditional laboratory-based methods by measuring emotional processes in real-world contexts that are closer in time to the occurrence of suicidal ideation, and that have greater ecological validity.  The present study will use ambulatory assessment, involving wearable technology to measure physiological and behavioral components of emotion regulation in participants’ daily lives, among N=40 individuals with major depressive disorder who have experienced suicidal ideation within the past month (n=20 of whom have a past suicide attempt).  Participants will complete seven days of ambulatory assessment during which putative biological mechanisms of emotion regulation (sleep quality and parasympathetic activity), behavioral factors (emotions, regulation strategies), and environmental factors (activity, stressors, substance use) are assessed across the day.  By using an innovative, person-centered approach, this study will detect contexts when individuals are at particular risk for suicidal ideation, and will evaluate which individuals with ideation may be at risk for suicide attempts. These data have important implications for the development of personalized suicide prevention interventions to identify possible periods of suicide risk in real time, and intervene to improve affect regulation and related factors in ways that affect daily functioning, thereby reducing risk.