Reward vs. Punishment: An fMRI Analysis Approach to Identifying the Neural Substrates of Motivation and Cognitive Control Ya’el C. Courtney, Debbie Yee, Marie Krug, Jo Etzel & Todd S. Braver Methods: Liquid Incentives Background Results: fMRI Why liquid incentives? It has been suggested that primary incentives offer distinct conceptual and methodological advantages over secondary incentives for several reasons. Primarily, a liquid can be immediately perceived as “good” or “bad”, and is more effective in this way than gaining or “losing” money in an experimental setup. Liquids used for each participant were selected from among several options based upon Session 1 ratings on Liking and Intensity (1-7 Likert scale) Reward: 4 different fruit juices Punishment: saltwater, quinine Neutral: water, various dilutions of “saliva” (KCl/NaCO3) solution Motivation increases cognitive control and task performance (Botvinick and Braver 2015) Incentives used as rewards and punishments both improve performance (Wachter et al 2009) Impairments in cognitive control (and particularly an abnormal response to motivation) underlie disorders such as schizophrenia, anxiety, depression, eating disorders, and addictions. Research has made great progress in discovering the behavioral and neural mechanisms that underlie motivation and cognitive control. However, a significant question that remains to be addressed is: Do rewards and punishments utilize the same or different neural substrates to yield motivational effects? HYPOTHESIS: Reward and punishment will result in comparable task performance, but utilize distinct neural substrates. Task Performance - Incentive Success Rate: As hypothesized, reward and punishment incentives resulted in comparable behavior task performance. Reward (reward obtained): M = 59.1% Punishment (punishment avoided): M = 57.9% ns (T = .446, p = .659) Liking: *Reward Liquid liked more than Punishment Liquid (T = 36.045, p < .001) and Neutral Liquid (T = 14.872, p < .001) *Punishment Liquid liked less than Neutral Liquid (T = 14.684, p < .001) Intensity: *Reward Liquid (T = 20.119, p < .001) and Punishment Liquid (T = 19.304, p < .001) higher in intensity than Neutral Liquid Processing Pipeline Methods: Participants and Design Data pre-processed using Analysis of Functional Neuroimaging (AFNI) software ver. AFNI_16.2.06 Participants: N=33, healthy adults from WashU and the Saint Louis community (19-39 years; mean = 25.9; 16 male) Experiment Overview: Session 1: Behavioral Testing *Practice Face-Word Task Switching *Taste and rate reward, punishment, and neutral liquids *Individual differences questionnaires Session 2: fMRI 1, Reward Session 3: fMRI 2, Punishment *For fMRI sessions, subjects practiced the Face-Word task outside the scanner, then performed 3 baseline runs and 6 incentive runs of the Face-Word task inside the scanner. *Reward/Punishment order of fMRI testing was counterbalanced between subjects. Results of significance testing on contrasts between Punish-Baseline and Reward-Baseline conditions yield interesting regions of activation unique to each condition. Conclusions & Future Directions First study to isolate primary reward vs. punishment incentives in a challenging cognitive environment Reward and punishment incentives result in comparable behavioral task performance. Preliminary evidence that regions associated with reward processing show activation where hypothesized (insula for punishment condition, medial PFC). Data are now pre-processed, filtered to the highest quality, and these positive contrast results give sufficient cause to move forward with Multi-voxel pattern analysis. Quality Control: Motion, Standard Deviations, Means Data for this experiment were collected in 2011, so a major part of the analysis incorporated thorough quality control measures to ensure accurate results of GLM contrasts and later MVPA work. Motion: At right: motion in six directions (x, y, z, roll, pitch, and yaw) for a good (S033) and a bad subject (S001). Each graph displays two days of scans, with three baseline and six incentive trials per day. A vertical black line along the bottom of a graph represents a frame that was discarded due to excess motion. Motion censoring yielded only 12 high quality subjects out of the original 33. NOTE: Excessive motion may be due in part to the nature of liquid incentive delivery in the scanner. Trial Structure + Cue CTI Target TFI Feedback Flicker Attend Face ! HEALTH Multi-Voxel Pattern Analysis: GLM analysis focuses on the relationship between cognitive brain variable and individual voxel activation MVPA is able to analyze multi-voxel patterns of activity, which may be important if reward vs. punishment pathways utilize similar magnitudes of activation, but different patterns. 4300 ms Up to 2500 ms RT - 2500 + 500/3000ms 300 ms 400 ms Face-Word Task-Switching paradigm: Cue: Indicates which task to perform and incentive value of trial Attend Face: respond whether face is male or female in gender Attend Word: respond whether word is 2 syllables or not (1 or 3 syllables) Baseline runs: cue color meaningless Incentive runs: cue color indicated non-incentive (NI) (no liquid rewards or punishments possible) or incentive (I) trials Feedback: Indicates trial outcome For baseline and NI trials- no liquids received For I trials: If fast (< than 40th percentile baseline RT) & accurate: Reward Condition: rewarding liquid (juice) Punishment Condition: neutral liquid If too slow or inaccurate: Reward Condition: neutral liquid Punishment Condition: punishment liquid (saltwater or quinine) Acknowledgements Voxel-wise mean and standard deviation: For each run for each subject, voxel-wise mean and standard deviation images were generated. These standard deviation images are an additional quality control: brighter areas indicate either CSF and blood (present in every subject), or excessive motion (clearly brighter brains). Mean images allow visual checks of orientation and displacement. At left: images from S033 (good) and S001 (unusable) demonstrate the utility of this quality control measure. This work was funded by the BP-ENDURE Neuroscience Pipeline Program, supported by NINDS grant R25NS090985 through Dr. Erik Herzog. Much gratitude to program co-director Dr. Diana Jose-Edwards as well.