Jerzy Pniewski and Leopold Infeld COLLOQUIUM 13 June
On Monday, 13 JUNE, at 4.30 p.m., Jerzy Pniewski and Leopold Infeld COLLOQUIUM of the Faculty of Physics of the University of Warsaw will take place. It will be the last Colloquium of this academic year.
The meeting will be held in the traditional form in room 0.03 and will also be webcast.
Our guest will be:
prof. Katarzyna Cieslak-Blinowska,
University of Warsaw and Institute of Biocybernetics and Biomedical Engineering, PAN
who will give a lecture:
"Information Transfer between Brain Structures in Norm and Pathology".
Prof. Katarzyna Cieślak-Blinowska, whose doctoral renewal ceremony took place on 20 April 2022, deals with the functioning of the human brain in her research work. Among other things, she created the Directed Transfer Function method, which is used to determine the directional transmission of brain electrical activity.
This method has become a fundamental and widely used tool in brain research based on EEG signals. During the lecture, we will be able to learn first-hand about the operation and application of the Directed Transfer Function in the study of interactions between structures in the human brain.
Before the lecture, from 4 p.m., we invite you as in the past (!) for informal discussions over coffee and cakes in the lobby in front of room 0.03.
The evaluation of interactions between brain structures plays a central role in understanding normal and pathological brain function. At present, a large bulk of evidence concerning the localization of active areas in the brain is available due to neuroimaging methods. However, much less is known about interactions between them. The connectivity patterns in the brain may be determined by means of electroencephalographic (EEG) activity analysis, providing that adequate signal processing methods are applied. The most appropriate methods involve multivariate autoregressive measures and, among them, Directed Transfer Function (DTF). Based on the Granger causality principle, this measure exposes directed relations between brain structures as a function of frequency, is robust in respect to noise, mitigates volume conduction, can identify reciprocal interactions, and is free from the common drive effect. Contrary to popular bivariate methods (e.g., correlation or coherence), which are biased by spurious connections, it yields sparse, clear-cut functional connectivity patterns. In its time-varying form, DTF reveals dynamical interactions between brain structures. The application of DTF to motor and cognitive tasks and to the assessment of pathological brain states will be presented. Moreover, the suitability of DTF for the analysis of signals of different origins will be shown