TY - JOUR T1 - Characterising and Engaging a Computationally Defined Treatment Target for Depression JF - bioRxiv DO - 10.1101/114991 SP - 114991 AU - Erdem Pulcu AU - Michael Browning Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/03/08/114991.abstract N2 - Computational modelling of behaviour can identify neurocognitive processes which are not measurable using standard analyses and may thus be used to characterise novel psychiatric treatment targets. For example, computational work demonstrates that informative events, those which improve prediction of future outcomes, are preferentially processed. This suggests that the cognitive biases towards negative events which are causally associated with depression arise because patients overestimate how informative these events are. In this study we assess whether the estimated information content of positive relative to negative events is a viable treatment target, testing whether participants maintain separate valence specific estimates, whether altering the volatility of experienced outcomes modifies these estimates and their association with a physiological marker, central norepinepheric activity. 30 non-clinical participants completed a learning task in which choices led to both wins and losses. The information content of the outcomes was manipulated by varying their volatility. Computational modelling of participant choice was used to estimate learning rate and pupilometry data was collected as a measure of norepinepheric function. Participants independently altered the learning rates used for win and loss outcomes to reflect how informative the outcomes were. Pupil dilation was greater for informative than non-informative loss outcomes and was associated with participants’ loss learning rate. These results characterise a computationally defined potential treatment target for depression. The target was associated with norepinepheric function and was engaged by modifying the volatility of experienced events. By identifying novel treatment targets computational approaches may spur the development of a new generation of psychiatric treatments. ER -