Comparing Cognitive and Affective Predictors of Craving

Fri, 24 Apr 2020 00:00:00 +0000

Health-risking behaviors (HRBs), e.g., excessive consumption of alcohol, tobacco, drugs and energy-dense food, contribute to long-term health problems, particularly among individuals who experienced early life adversity (EA). Though traditional executive control tasks are commonly assumed to be relevant for predicting real-world HRBs, recent work has called into question the ecological and predictive validity of these tasks. This study explores the predictive validity of cognitive and affective neural measures derived from a more passive cue reactivity task in a community sample of adults with self-control problems and a history of early adversity. We extracted trial-level estimates of whole-brain expression of canonical “inhibitory control” and “craving” patterns while participants viewed images of personally relevant health-risking substances during the cue reactivity task. Statistical modeling showed that greater trial-level expression of the “craving” and “inhibitory control” patterns predicted higher and lower desire ratings, respectively, for cue reactivity stimuli. However, only “craving” pattern expression predicted measures of real-world craving in daily life. Taken together, these results suggest that, among individuals with self-control problems, the real-world predictive validity of passive neural measures of affective processes may be superior to that of neural measures of executive control.

For more details, see the pre-print and poster .

Devaluing energy-dense foods for cancer control

Fri, 24 Apr 2020 00:00:00 +0000

This project is a large-scale randomized-control trial that investigates the efficacy and mechanisms of a healthy eating invention. Specifically, the RCT compares a cognitive reappraisal training, in which participants change the way they think about unhealthy food, to a behavioral response training, in which participants modify their physical motor responses to food stimuli to train neural inhibitory control circuits. Currently, I am working on developing a precision medicine analysis pipeline around a series of additional measures that have been added to this parent R01 as part of the National Center for Biotechnology Information’s ADOPT Project (Accumulating Data to Optimally Predict Obesity Treatment). The ADOPT project aims to solve the precision medicine problem for obesity treatment by identifying core measures assessing a range of behavioral, biological, environmental, and psychosocial factors that contribute to obesity. My role in this project is to develop the analytical infrastructure to create and validate composites of low-cost, easily administered individual difference measures that moderate response to the healthy eating interventions in terms of both magnitude and timing.

neuroscience | Waseem Ashfaq

Comparing Cognitive and Affective Predictors of Craving

Devaluing energy-dense foods for cancer control