Hramov Alexander

Abstract: The lecture will focus on the diagnosis of neural diseases using functional brain networks reconstructed from fMRI and EEG data. To analyze functional networks, mathematical approaches based on the calculation of various metrics of network topology organization, as well as machine learning methods - LDA, graph neural networks and contrastive learning - are considered. The results of classification accuracy of patients with MDD and children with ASD using the above approaches are presented.

Speaker:Alexander E. Hramov was born on September 20, 1974, in Saratov, Russia. He received the specialist degree in Electronic Engineering from Saratov State University, Russia in 1996, and the Ph.D. degree in Electronic Engineering from Saratov State University, Russia in 1999. In 2005, he defended his doctoral dissertation in Mathematics and Physics. From 1999 to 2014, he held positions as a Researcher, Associate Professor, and Full Professor at Saratov State University, Russia. From 2014 to 2018, he served as a Leading Researcher in the Science and Educational Center of Artificial Intelligence Systems and Neurotechnology and as the Head of the Department of Automation, Control, and Mechatronics at Saratov State Technical University, Russia. From 2019 to 2021, he was a Professor and Head of the Laboratory of Neuroscience and Cognitive Technology at Innopolis University, Kazan, Russia. Currently, he holds the position of Head of the Baltic Center for Neurotechnology and Artificial Intelligence at Immanuel Kant Baltic Federal University, Kaliningrad, Russia. His research interests include complex network theory, methods of brain diagnostics, development of AI methods for neuroimaging data processing, and applied research in digital medicine, neurotechnologies and education.