Our goal is to combine mathematical models with brain recordings from patients to understand the origin of neurological diseases and disorders and suggest treatments
We want to explain the mechanisms and heterogeneity of neurological diseases and disorders. In recent work, we combined computational models with brain imaging data obtained from patients to understand the pathology of schizophrenia. Together with colleagues from Harvard, University of Minnesota, UCL and King's College, we are studying abnormal neuromodulation in patients with neurological disorders during perceptual and working memory tasks. Our work combines computational modeling, EEG, MEG and PET imaging. Our aim is to understand the origin of these disorders and explain their heterogeneity by grouping subjects into different clusters (biotypes).
Á. Díez, S. Ranlund, D.A. Pinotsis, S. Calafato, M. Shaikh, M. Hall, M. Walshe, Á. Nevado; K.J. Friston, R. Adams and E. Bramon, Abnormal frontal intrinsic effective connectivity during the P300 evocation in patients with psychosis and their unaffected relatives, Human Brain Mapping, 38, 3262–3276, (2017)
R. Adams, M. Bauer, D.A. Pinotsis and K. Friston, Dynamic causal modelling of pursuit eye movements: a physiological validation with MEG, NeuroImage, 132, 175–189 (2016)
D.A. Pinotsis, G. Perry, V. Litvak, K. Singh, and K.J. Friston, Intersubject variability of induced gamma in the visual cortex: DCM with empirical Bayes and neural fields, Human Brain Mapping, 37, 4597–4614 (2016)
S. Ranlund, A. Diez, R. Adams, M. Shaikh, M. Walshe, A. Dutt, S. Petrella, A. Maeso, M. Constante, M. Hall, C. McDonald, R. Murray, K. Friston, D.A. Pinotsis, E. Bramon, Impaired prefrontal synaptic gain in people with psychosis and their relatives during the mismatch negativity, Human Brain Mapping, 37, 351-365 (2016)