Accounting for Dynamic Fluctuations across Time when Examining fMRI Test-Retest Reliability: Analysis of a Reward Paradigm in the EMBARC Study

Chase, Henry W.; Fournier, Jay C.; Greenberg, Tsafrir; Almeida, Jorge R.; Stiffler, Richelle; Zevallos, Carlos R.; Aslam, Haris; Cooper, Crystal; Deckersbach, Thilo; Weyandt, Sarah; Adams, Phillip; Toups, Marisa; Carmody, Tom; Oquendo, Maria A.; Peltier, Scott; Fava, Maurizio; McGrath, Patrick J.; Weissman, Myrna M.; Parsey, Ramin; McInnis, Melvin G.; Kurian, Benji; Trivedi, Madhukar H.; Phillips, Mary L.

Longitudinal investigation of the neural correlates of reward processing in depression may represent an important step in defining effective biomarkers for antidepressant treatment outcome prediction, but the reliability of reward-related activation is not well understood. Thirty-seven healthy control participants were scanned using fMRI while performing a reward-related guessing task on two occasions, approximately one week apart. Two main contrasts were examined: right ventral striatum (VS) activation fMRI BOLD signal related to signed prediction errors (PE) and reward expectancy (RE). We also examined bilateral visual cortex activation coupled to outcome anticipation. Significant VS PE-related activity was observed at the first testing session, but at the second testing session, VS PE-related activation was significantly reduced. Conversely, significant VS RE-related activity was observed at time 2 but not time 1. Increases in VS RE-related activity from time 1 to time 2 were significantly associated with decreases in VS PE-related activity from time 1 to time 2 across participants. Intraclass correlations (ICCs) in VS were very low. By contrast, visual cortex activation had much larger ICCs, particularly in individuals with high quality data. Dynamic changes in brain activation are widely predicted, and failure to account for these changes could lead to inaccurate evaluations of the reliability of functional MRI signals. Conventional measures of reliability cannot distinguish between changes specified by algorithmic models of neural function and noisy signal. Here, we provide evidence for the former possibility: reward-related VS activations follow the pattern predicted by temporal difference models of reward learning but have low ICCs.


Also Published In

More About This Work

Academic Units
Published Here
February 22, 2016