How the self is experienced is central to healthy emotional functioning as well as many disturbances in psychological functioning. This competing renewal uses structural, functional, and resting-state neuroimaging, coupled with passive smartphone sensing technology and ecological momentary assessments, to examine the affective components of self. Understanding the factors that contribute to changes in the affective aspects of self that result from environmental stressors has the potential to provide important insights into the development of mental disorders and help identify individuals who might be in greatest need of early intervention or treatment. Research findings during the prior two award periods (R01 MH059282) revealed several key brain regions involved in processing information related to self. Moreover, we discovered that structural and functional connectivity between these regions and other brain regions known to be involved in emotional processes are associated with measures of self-affect. The overarching goal of this research is to examine how brain connectivity and activity is related to change in subjective distress and associated functional impairment. An exciting aspect of the proposed work is that we will take advantage of the university setting to follow a large cohort of participants over their four years of college to assess how changes in self- affect are predicted by relevant brain networks as well as how those networks change over time. Tasks assessing self-affect will be performed during scanning. Given that approximately 30% of participants are likely to develop a significant subjective distress, one goal is to examine whether there are biomarkers that predict these outcomes. Additional scanning studies will induce interpersonal distress to examine the temporary inductions of affect on task performance. This project will use recently developed applications of network analysis to assess resting state connectivity in brain circuitry and its relation to self-affect and health- relevant outcomes. The guiding hypothesis of this research is that individual differences in the integrity of these networks can predict individual differences in vulnerability to stress and their relation to self-affect.
The specific aims of the study are: (1). Characterize neural networks that give rise to self-affect using diffusion tensor imaging, resting state functional connectivity, and task-related functional imaging. In addition, multivariate pattern analysis and representation similarity analysis will be used to classify participants as having high or low self-affect (e.g., self-esteem, depression, anxiety); (2). Examine how changes in self-affect that occur over time are reflected by changes within relevant brain networks and are predicted by baseline network connectivity; and (3). Examine how induced interpersonal distress impacts self-affect and related functional connectivity across networks. Understanding the factors that contribute to changes in self-affect that result from environmental stressors has the potential to provide important insights into the development of mental disorders and help identify individuals who might be in greatest need of early intervention or treatment.
How the self is experienced is central to healthy emotional functioning as well as many disturbances in psychological functioning. This project uses structural, functional, and resting-state neuroimaging, coupled with smartphone passive sensing technology and ecological momentary assessments, to examine the affective components of self. Ultimately, these measures may identify individual differences that give rise to significant subjective distress and impairments in daily living, thereby providing information about the development of mental disorders as well as their treatments.
|Chavez, Robert S; Heatherton, Todd F (2017) Structural integrity of frontostriatal connections predicts longitudinal changes in self-esteem. Soc Neurosci 12:280-286|
|Stoeckel, Luke E; Birch, Leann L; Heatherton, Todd et al. (2017) Psychological and neural contributions to appetite self-regulation. Obesity (Silver Spring) 25 Suppl 1:S17-S25|
|D'Andrea-Penna, Gina M; Frank, Sebastian M; Heatherton, Todd F et al. (2017) Distracting tracking: Interactions between negative emotion and attentional load in multiple-object tracking. Emotion 17:900-904|
|Chavez, Robert S; Heatherton, Todd F; Wagner, Dylan D (2016) Neural Population Decoding Reveals the Intrinsic Positivity of the Self. Cereb Cortex :|
|Wagner, Dylan D; Kelley, William M; Haxby, James V et al. (2016) The Dorsal Medial Prefrontal Cortex Responds Preferentially to Social Interactions during Natural Viewing. J Neurosci 36:6917-25|
|Gordon, Evan M; Laumann, Timothy O; Adeyemo, Babatunde et al. (2016) Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations. Cereb Cortex 26:288-303|
|Powers, Katherine E; Somerville, Leah H; Kelley, William M et al. (2016) Striatal Associative Learning Signals Are Tuned to In-groups. J Cogn Neurosci 28:1243-54|
|Powers, Katherine E; Chavez, Robert S; Heatherton, Todd F (2016) Individual differences in response of dorsomedial prefrontal cortex predict daily social behavior. Soc Cogn Affect Neurosci 11:121-6|
|Chen, Pin-Hao A; Whalen, Paul J; Freeman, Jonathan B et al. (2015) BRAIN REWARD ACTIVITY TO MASKED IN-GROUP SMILING FACES PREDICTS FRIENDSHIP DEVELOPMENT. Soc Psychol Personal Sci 8:415-421|
|Ma, Y (2015) Neuropsychological mechanism underlying antidepressant effect: a systematic meta-analysis. Mol Psychiatry 20:311-9|
Showing the most recent 10 out of 34 publications