Courses
At this moment, we offer two courses for the neuroscience program (M.Sc.). Students from other programs and departments are welcome to join.
Introduction to network control theory for psychiatry and neurosciences
The application of network control in neurosciences and psychiatry is growing fast. This course aims to familiarize the students with the conceptual framework of network control theory and to enable them to use this framework in practice. The employment of network control theory has multiple prerequisites. The most important ones are a map of the connections between the elements of the system (i.e., a network) and a quantitative law that captures the temporal relation between the elements. In this course, we discuss a selected number of concepts from dynamical systems theory, how to build a network model of the systems, and follow to discuss the concepts of controllability and observability. Throughout the course, we will use hands-on examples of neuroimaging and symptom data to better understand the concepts and to enable the students to use the contents of the course in practice. We will implement all the models in MATLAB.
The course will be presented as a “Methodenkenntnisse” which will take four weeks of Praktikum at the department of psychiatry and psychiatry (start flexible). Beyond teaching hours, we will have 8 hours of exercises each week. The practices will have two parts of coding in MATLAB and written questions. At the end of the course, each student needs to A) hand in a report of max 10 pages (80%) and B) write and explain the mathematical formula related to the basic concepts of controllability and observability in an oral exam. Both concepts will be discussed, and the formula will be presented during the course.
Photo by Alina Grubnyak
Computational Psychiatry · Current Topics and Trends
Computational Psychiatry encompasses the mathematical approach to understanding mental disorders that aims to combine data at multiple levels (neural data, symptoms, behavioral data, etc.) to improve the prediction and treatment of mental illness. Depending on the problem and requirements, a variety of tools are used, including machine learning, reinforcement learning, and dynamical systems theory. In this course, we will systematically (but briefly) introduce the main concepts behind these tools and discuss their respective strengths and weaknesses. We will then explore their use cases in the study of mental disorders, with a focus on major depression using different data types (neuroimaging, symptoms, stimuli, behavioral data, etc.). We also discuss other topics that are relevant to the course such as memory engrams and mind wandering, predictive coding, reproducibility, and some other varying topics. Beginning in the third week of the course, we will present and discuss in depth several selected recent publications in the course.
The course will be presented as a “Profilmodul” which will take a full semester (14 weeks) at the department of psychiatry and psychiatry. Beyond teaching hours, we will have 8 hours of exercises each week. The practices will have two parts of coding in MATLAB and written questions. At the end of the course, each student needs to hand in a report of max 10 pages and take an oral exam.
Prof. Dr. Hamidreza Jamalabadi
Philipps-Universität Marburg
Klinik für Psychiatrie und Psychotherapie
Rudolf-Bultmann-Straße 8
35039 Marburg
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