Computational fingerprinting: a new approach to motivation deficit in neuropsychiatric diseases

Motivation can be defined as a function that activates and directs the behavior toward goals. In neuropsychiatric conditions, motivation deficits such as apathy are frequent and interfere with treatment observance and clinical outcome. The current standard approach is to assess motivation deficits with subjective questionnaires that depend on patients' insight and provide no information about underlying pathophysiological mechanisms. Here, I present a novel approach that consists in fitting computational models to the behavior observed in objective tests, so as to infer parameters that specify the patient's motivational state. These computational parameters provide (1) an explanation at the cognitive level (e.g., reduced sensitivity to reward), (2) a dysfunction at the neural level (e.g., altered dopamine release) and (3) a prediction of treatment outcome (e.g., improvement with psychostimulants). Computational fingerprinting may therefore pave the way toward a more personalized medicine.