pinotsislab
pinotsislab

New Tools for Brain Scans

We develop computational tools to analyze data obtained using state-of-the-art brain recording techniques

We develop computational methods for extracting information hidden in observed brain responses.  We combine biophysical neural network models and observation (or forward) models that map from neural activity to sensor data (EEG, MEG, LFP etc). These combined models allow us to test detailed hypotheses about how the brain works. As recording techniques develop, new tools and computational methods are required. In recent work, we developed new methods for analyzing neural activity in different cortical depths (obtained with state-of-the-art laminar recordings) and locations within the same cortical layer (neural fields).

 

Selected papers 


D.A.Pinotsis, J.P. Geerts, L. Pinto, T.H.B. FitzGerald, V. Litvak,R. Auksztulewicz and K.J. Friston, Linking canonical microcircuits and neuronal activity: Dynamic Causal Modelling of Laminar Recordings, NeuroImage, 146, 355-366 (2017) 


D.A. Pinotsis, R. Loonis,  A. Bastos,  E.K. Miller and K.J. Friston, Bayesian modelling of induced responses and neuronal rhythms, Brain Topography – Special Issue: Controversies in EEG Source Analysis, doi:10.1007/s10548-016-0526-y (2016) 

              Top 5 Most Downloaded Brain Topography Article for 2016


K. J. Friston, A. M. Bastos, D. A.Pinotsis and V. Litvak, LFP and oscillations – what do they tell us?, Current Opinion in Neurobiology: Brain rhythms and dynamic coordination, 31:1-6 (2015)

 Most Downloaded Current Opinion in Neurobiology Article for 2015


D.A. Pinotsis and K.J. Friston, Extracting novel information from neuroimaging data using neural fields, EPJ Nonlinear Biomedical Physics, 2:5 (2014)


R. Moran, D.A. Pinotsis and K.J. Friston, Neural Masses and Fields in Dynamic Causal Modelling, Frontiers in Computational Neuroscience,  7:57. doi: 10.3389/fncom.2013.00057 (2013)