During seminar, we will first explore the role of positive-social emotion regulation and engaged functional brain networks. Then, we will see how to facilitate learning directional control over positive‑social emotion regulation networks using connectivity-based neurofeedback in healthy volunteers. We will continue with the evidence of progressive resting-state modulation of positive‑social emotion regulation networks of such neurofeedback training. Next, practical aspects of real-time fMRI neurofeedback will be discussed using open-source (py)OpenNFT software. The software performance will be showcased using real-time fMRI neurofeedback based on support-vector machine (SVM) classification and augmented reality feedback, as well as effective connectivity feedback for positive-social emotion regulation. We will discuss practical considerations of dual-blind clinical research trial for treatment of major depressive disorder (MDD) using connectivity-based neurofeedback and positive-social emotion regulation.
Dr. Yury Koush is a scientist at the Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University.
Dr. Koush conducted numerous studies investigating aspects of real-time data processing, neural underpinnings and clinical applications of real-time fMRI neurofeedback, studies investigating emotion regulation, visual-spatial perception, and attention in both neurotypical population and in clinical groups with depression, motor, and visual stroke. He is leading the open-source OpenNFT.org project for neurofeedback training and data analysis. His current work focuses on augmented reality, machine learning for fMRI, neurobiological characterization of brain function, aging and dysfunction using MRI and MRS, on combined MRI and MRS sequence development, on multimodal neuroimaging, and on applying neurofeedback to treatment-resistant psychiatric and neurological brain disorders.