We’re excited that our twin symposium on computational psychiatry @ SOBP 2016 was accepted.
Computational Psychiatry: A Bridge Across Levels of Analyses Towards High-Impact
Biological Psychiatry
Computational psychiatry consists of a collection of different approaches that
aim to more precisely delineate hypotheses and test predictions based on
explicit and quantitative models of presumed cognitive and affective functions
that are disturbed in psychiatric patients. The models are used to fit the data
from individual subjects, thereby “projecting” such data to a low-dimensional
space of model parameters that capture biological processes of interest; where
relevant, machine-learning techniques for prediction and classification can then
be applied in this new space.
The goal for this “double symposium” is to provide (a) tutorials on some of the
approaches in computational psychiatry and (b) examples of several programs of
research that focus on different mental disorders. The symposium participants
will be able to get a good overview of current techniques and recent findings,
which we hope will spawn future collaborations with computational experts.
Symposium 1
Symposium Chair: Deanna Barch
(1) Tutorial: Spiking Networks and Attractors – a Biophysical Approach to Computational Psychiatry: John Murray
(2) Dysfunction in Working Memory in Schizophrenia: John Krystal
(3) Prognostic Predictors using Ideal Bayesian Observer Approaches in Addiction: Martin Paulus
(4) Who is Afraid of Variability – Failure to Scale Expectations in Anxiety Disorder: Michael Brown
Symposium 2:
Symposium Chair: Daniel Pine
(1) Tutorial: Reinforcement Learning in Neuropsychiatric Disorders: Tiago Maia
(2) Computational Miscalibrations in Depression: Quentin Huys
(3) Computational Model for Emotion Regulation in Depression and PTSD: Amit Etkin
(4) Computationally relevant mechanisms of treatment response in CBT – changing parameters in depression and addiction: Pearl Chiu