Our goal is to characterize disease states using mathematical models.
These mathematical models infer pathophysiology from non-invasive data, including behavioural measures and neuronal activity.
By quantifying physiological and computational principles that underlie cognition, these models help us identify when patients’ learning and decision-making processes are abberant.
These models are also helping us work toward our long-term goal: to piece together a mechanistic understanding of at-risk mental states.
This could mean that (a) mental illness can be predicted before treatment is necessary, and (b) patients’ responses to treatments can be predicted before they begin, to avoid negative effects.
Recent Publications
Publication
9/1/2023
Derome, M. and Kozuharova, P. and Diaconescu, A.O. and Denève, S. and Jardri, R. and Allen, P.
Publication
8/1/2023
Hauke, D.J. and Charlton, C.E. and Schmit, A. and Griffiths, J. and Woods, S.W. and Ford, J.M. and Srihari, V.H. and Roth, V. and Diaconescu, A.O. and Mathalon, D.H.
Publication
6/29/2023
Hauke, D.J. and Charlton, C.E. and Schmit, A. and Griffiths, J. and Woods, S.W. and Ford, J.M. and Srihari, V.H. and Roth, V. and Diaconescu, A.O. and Mathalon, D.H.
© 2023 Cognitive Network Modelling