We have been witnessing heightened interest in the study of dynamics of neural systems.
Technological advances in instrumentation allow for the continuous monitoring of biological signals at ever increasing spatial and temporal resolution. For instance, novel approaches using multiple electrode techniques are now commonly used for the study of function of brain areas in animal study. However the use of quantitative analysis of electroencephalography still has limited application in clinical practice, and we still rely mostly on EEG trace detection capacity of clinical neurophysiologists. The data analysis practitioners know that results based on any quantitative methods should be interpreted and valued with great care, since any mathematical analysis methods of real-world data hold strength and limitations. Partial directed coherence was developed in this scenario and here we will examine the asymptotic behavior of its newly defined general form, gPDC. Both its confidence interval and null hypothesis testing results will be presented and illustrated.
Native of Kagoshima-ken, Japan, he is graduated in Electrical Engineering (79) and Medicine (85) from the University of São Paulo, got a Ph. D. degree in Neurophysiology at the same university (92), and did post-doctoral training in Neuroscience at the University of California at San Francisco (94). He is a co-founding member of Discipline of Medical Informatics (86), and holds an Associate Professorship at the Department of Radiology and Oncology at the Faculty of Medicine of the University of São Paulo. His main research interests are the study of neural plasticity, cognitive function and information processing aspects of mammalian brain through behavioral, electrophysiological and computational neuroscience approaches.
To functionally characterize multichannel neural/brain activity and correlate to animal or human behavior, he is pursuing and developing robust and clinically useful methods and measures for brain dynamics staging and neural connectivity inferences, for which co-introduced the notion of partial directed coherence. At the graduate level, he teaches courses in neural plasticity and learning, electrophysiological methodology in the study of cognitive functions, and quantitative analysis of biomedical data.
【Luiz Antonio Baccalá】
A graduate in both Electrical Engineering and Physics from the University of São Paulo (83), with an M. Sc. in EE (91) from the same university, followed by a Ph. D. Degree at the University of Pennsylvania(95). Holding an Associate Professorship at the Telecommunications and Control Engineering at the Polytechnic School of the University of São Paulo, his research interests center around statistical signal processing and analysis with applications in both telecommunications and biology, in which he has actively engaged in developing methods for neural connectivity inference with the introduction of the notion of Partial Directed Coherence. At the graduate level, he teaches advanced courses in spectral estimation, time series analysis and time-frequency signal representations.