The general aim of her research is to understand how brain changes in neurological and psychiatric disorders. Therefore she use nonlinear measures taken from Nonlinear Dynamical Systems Theory to analyze electrophysiological signals. Also she combines findings from engineering, biophysics and medical physics, as well as results of other researchers working on different approaches of treating those disorders. With the fast development of different methods of analysis and understanding of underlying physiology she strongly believes that early prediction of several progressive diseases is possible. Also a proper use of those nonlinear biomarkers can make clinical diagnostic process easier. Her current research focuses on early detection of biomarkers of depressive disorders as well as elucidating remission from exacerbation, and in addition, utilization of machine learning methods for further classification. In addition she also works on mechanisms of short term audio-verbal memory formation and its nonlinear characterization, as well as forecasting the responders to varied therapies used in treatments of depression.

Milena Čukić Radenković
Department for General Physiology and Biophysics, University of Belgrade, Belgrade, Serbia and Amsterdam Health and Technology Institute, HealthInc, Amsterdam, the Netherlands
The general aim of her research is to understand how brain changes in neurological and psychiatric disorders. Therefore she use nonlinear measures taken from Nonlinear Dynamical Systems Theory to analyze electrophysiological signals. Also she combines findings from engineering, biophysics and medical physics, as well as results of other researchers working on different approaches of treating those disorders. With the fast development of different methods of analysis and understanding of underlying physiology she strongly believes that early prediction of several progressive diseases is possible. Also a proper use of those nonlinear biomarkers can make clinical diagnostic process easier. Her current research focuses on early detection of biomarkers of depressive disorders as well as elucidating remission from exacerbation, and in addition, utilization of machine learning methods for further classification. In addition she also works on mechanisms of short term audio-verbal memory formation and its nonlinear characterization, as well as forecasting the responders to varied therapies used in treatments of depression.

view as:

View: 24 48 ALL