The workshop aims to provide a hands-on learning opportunity through the use of parts of the software distributed via the ‘‘Methods in Brain Connectivity Inference through Multivariate Time Series Analysis’ book (2014) from CRC which we co-edited. In addition to examining in detail conceptual aspects associated to connectivity estimation from neuroelectrical and hemodynamic data we intend to shorten the learning curve of potential users of connectivity analysis software in regard to inferential aspects and the possible caveats and pitfalls the user may encounter. The full day of activity will consist of short morning overview talks, addressing the basic principles, methodological issues, caveat and pitfalls, and examples of application of the software to real data followed by an afternoon devoted to software use and one-to-one discussion with attendees who will have the opportunity to process workshop examples and their own data by bringing their own laptops. This will enable them to benefit from discussing their results and modelling diagnostics with the workshop experts. Attendee provided Matlab/Octave licenses are required for those wishing to take part in the practical activities.
List of Speakers (tentative)
Mingzhou Ding, University of Florida, United States, firstname.lastname@example.org
Presentation Title: Application of Granger causality to fMRI data: Methods and functional interpretation
Bio: Mingzhou Ding received his BS degree in astrophysics from Peking University in 1982 and his PhD degree in physics from University of Maryland in 1990. He is currently the J Crayton Pruitt Family Professor of Biomedical Engineering at University of Florida. Prof. Ding’s research focuses on multivariate signal processing, multimodal neuroimaging, cognitive neuroscience, and cognitive impairments in neurological and psychiatric disorders. He is known for developing causal functional connectivity measures and applying these measures to multimodal neural data. Prof. Ding is an elected fellow of AIMBE, a senior member of IEEE, and serves on the editorial board of Scientific Reports and Journal of Neuroscience. He has published 150 papers and these papers have received 12,000 Google Scholar Citations (H-index=60).
Koichi Sameshima, University of Sao Paulo, Brazil, email@example.com
Presentation Title: Methods in Brain Connectivity Inference through Multivariate Time Series Analysis
Bio: BS in EE, M.D. and Ph.D. from University of Sao Paulo, and postdoctoral training at University of California San Francisco on learning and plasticity with electrophysiological and behavioral protocols in animal models. His research interests are focused on neural information processing, plasticity and cognitive function in mammal brains. He has been working on frequency domain multivariate connectivity in the past decade. Right now he is involved in translational research in neurological diseases through the analyses of brain activity signals seeking to bring the benefits of connectivity analysis methods to the clinical domain.
Luiz A. Baccalá, University of São Paulo, Brazil, firstname.lastname@example.org
Presentation Title: Neural Connectivity Analysis Methods: Status and Challenges
Bio: After majoring in Electrical Engineering and Physics at the University of São Paulo (’84) Luiz Baccalà went on to obtain his M. Sc. from the same University (’91). He has since been involved in statistical signal processing and analysis and obtained his Ph. D. from the University of Pennsylvania (’95) by proposing new statistical methods of communication channel identification and equalisation. His current research interests focus at the investigation of multivariate time series methods for neural connectivity inference and for problems of inverse source determination using arrays of sensors that include fMRI imaging and multi electrode EEG processing.
Laura Astolfi, University of Rome Sapienza, Italy, email@example.com
Presentation Title: Connectivity Analysis based on high density EEG recordings: application to motor and cognitive functions in humans
Bio: Laura Astolfi (MS in Electronics Engineering, University of Rome Sapienza, PhD in Biomedical Engineering. University of Bologna) is currently an Assistant Professor at the University of Rome Sapienza and a Technical Manager at Fondazione Santa Lucia IRCCS. She is Chair of the IEEE EMBS Biomedical Signal Processing Technical Committee and Editor of the EMBC since 2010. Her research interests include brain functional connectivity estimation, high resolution EEG source reconstruction, multimodal integration of EEG and fMRI data, hyperscanning, consciousness, cognition and social Neuroscience. She authored 195 publications (h-index 29).
Organizer I: Luiz A. Baccalá-IEEE Member – EMBS TC BSP Member
Bio: Teaches applied statistical signal processing with research focusing multivariate time series methods for neural connectivity inference having co-edited ‘Methods in Brain Connectivity Inference through Multivariate Time Series Analysis’ (2014) from CRC with Koichi Sameshima.
Organizer II: Koichi Sameshima-IEEE Member – EMBS TC BSP Member
Bio: A BS in EE, M.D. and Ph.D. from University of Sao Paulo, and postdoctoral training at UCSF, USA. Research interests focus on neural information processing, plasticity and cognitive function in mammal brains; involvement in translational research seeking to bring benefits of brain connectivity methodology to the clinical domain.
Organizer III: Laura Astolfi – IEEE EMBS Member- EMBS TC BSP Chair
Bio: Laura Astolfi (PhD in Biomedical Eng. 2007) is currently an Assistant Professor at University of Rome, Sapienza. She is Vice-chair of the EMBS Technical Committee in Biomedical Signal Processing and an IEEE EMBC Associate Editor since 2010. She authored 195 papers published on peer-review international journals. Her h-index is 29.