Dr Camillo Porcaro is a computational neuroscientist with a core interest in the development of analytical methods for extracting information from non-invasive measures of brain activity. His research focuses on identifying functional brain sources from data obtained through neuroimaging techniques.
In 2008, he defended his PhD dissertation with honors in 'Functional Neuroimaging: from cells to systems' at the Institute for Advanced Biomedical Technologies (ITAB), University of Chieti, Italy. In the same year, Dr Porcaro became a Postdoctoral Fellow at the University of Birmingham, School of Psychology, using simultaneous EEG/fMRI. In 2011, he joined the Institute of Neuroscience, Newcastle University, after being awarded a highly competitive independent research position.
Since 2012, he has been an Independent Researcher at the Institute of Cognitive Sciences and Technologies (ISTC) - National Research Council (CNR), Rome, Italy. In 2014 and 2015, he was invited as Visiting Professor at Neural Control of Movement Lab, Department of Health Sciences and Technology ETH, Zurich, Switzerland. Since 2015, Dr Porcaro has been Adjunct Professor at Department of Information Engineering – Università Politecnica delle Marche, Ancona (professor of Bio-imaging and Brain Research at the department of Biomedical Engineering). Additionally, since 2016, he has been Visiting Professor at Department of Human Kinesiology, Movement Control & Neuroplasticity Research Group, KU Leuven, Belgium. His most successfully contribution has concerned source extraction with advanced methods that include Independent Component Analysis (ICA) and a modification of this algorithm called Functional Source Separation (FSS). He has developed various time and spectral constraints (the basis for the FSS algorithms) for the extraction and validation of primary cortical areas. Recently, he has started to apply FSS to EEG data recorded in the MRI environment, with the aim of improving EEG data quality at the single trial level. His current research is focusing on the development of functional constraints for the identifications of complex cortical network.