Major depressive disorder (MDD) is a prevalent and debilitating disorder that is characterized by high levels of negative affect (NA). One mechanism that may serve as a phenotype for depression risk is impaired cognitive control of NA following loss, which is also linked to atypical parasympathetic responses to loss. The Candidate will extend his background in cognitive and affective risk factors for MDD to examine the neural networks supporting cognitive control and affect regulation integrating an RDoC loss construct across multiple modalities. This will develop his mechanistic understanding and expertise in interactions between cognitive and affective systems that underlie dysfunctional self-regulation in depression. The Candidate will learn to: 1) evaluate task-based activity and interactions between intrinsic connectivity networks supporting cognitive control and emotion processing (Training Aim 1 (TA1)); 2) integrate multi-modal, multi-level data and learn the advanced statistical modeling necessary to dimensionally link fMRI to parasympathetic and affective responses (TA2). In addition, in the latter years of the award, the candidate will learn to use the methodology of EMA and ambulatory parasympathetic assessment to link neural networks that support the cognitive control of emotion to lab and real-world affective/physiological regulation (TA3). In line with these training aims, the Candidate's short-term career goals are to understand the neural networks underlying the cognitive control of emotion, and to test the ecological validity of lab-based assessments of neural and parasympathetic responses to loss for affect regulation. This Career Development Award will allow the Candidate to advance the cognitive neuroscience of depression with the long-term career goal of identifying cognitive and affective phenotypic risk markers for the development and progression of mood disorders. The University of Illinois at Chicago (UIC) is the ideal setting for the candidate's research, with ongoing development of local resources such as an independent research-dedicated 3T scanner and one of only 22 nationwide National Network of Depression Centers. Mentor Scott Langenecker is an expert in the cognitive and affective neuroscience of mood disorders across the adult lifespan, a leader in RDoC research techniques, and has an established expertise in mechanistic approaches for studying depression. The Specific Research Aims afford an excellent opportunity for the Candidate to learn and demonstrate expertise in the necessary skills to propel him to independence. Contextually-appropriate task-based activation in networks supporting cognitive control and emotion processing (Specific Aim 1) will be evaluated among thirty-five young adults (ages 18-27) with a history of MDD who are currently remitted (rMDD) and thirty-five matched healthy controls (HCs). Parasympathetic activity and affect regulation will be assessed during a laboratory-based loss paradigm and linked dimensionally to task-based network activation (Specific Aim 2). Pilot data also are collected later in the award for a seven-day prospective period via ambulatory assessment with EMA.
Specific Aim 1 addresses TA1 by affording the Candidate the opportunity to assess disrupted network functioning in rMDD.
Specific Aim 2 supports TA2 by allowing the Candidate to learn novel procedural and statistical methods to assess dimensional relationships to link disrupted network functioning to parasympathetic and affective responses. Along with methodology integration consultant Erika Forbes and statistical consultant Donald Hedeker, the mentoring team will provide the Candidate guidance and supervision on the integration of multi-modal data and preparation toward the development of an independent laboratory. TA3 is met through the Exploratory Aims in the latter part of the award, via the application of ambulatory parasympathetic assessment and EMA (with Co- Mentor Robin Mermelstein and Consultant Tim Trull) to link real-world affect regulation with affective responses to loss in the lab, and with neural networks that support the cognitive control of emotion. This will eventually enable the Candidate to move his research out of the lab and into real world contexts. The data collection provided by this study will provide critical preliminary data for planned R01 submissions examining cognitive-affective mechanisms underlying affect regulation and that influence risk for the onset and progression of depression. Executing the complimentary and integrated training and research aims will promote the long-term career goals of the Candidate and establish his independent expertise in identifying neural, psychophysiological, and behavioral targets that influence the course of mood disorders.
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.
| O'Donnell, Lisa A; Ellis, Alissa J; Van de Loo, Margaret M et al. (2018) Mood instability as a predictor of clinical and functional outcomes in adolescents with bipolar I and bipolar II disorder. J Affect Disord 236:199-206 |
| Stange, Jonathan P; Jenkins, Lisanne M; Hamlat, Elissa J et al. (2018) Disrupted engagement of networks supporting hot and cold cognition in remitted major depressive disorder. J Affect Disord 227:183-191 |
| Stange, Jonathan P; Zulueta, John; Langenecker, Scott A et al. (2018) Let your fingers do the talking: Passive typing instability predicts future mood outcomes. Bipolar Disord 20:285-288 |
