Teaching
Network Neuroscience & Brain Dynamic Analysis
This course explores the emerging interdisciplinary field of network neuroscience, integrating concepts from neuroscience, systems engineering, data science, and neural engineering. Students will engage with both classical and modern mathematical models of network sciences, as well as advanced approaches for analyzing brain dynamics. In addition, they will develop skills in peer reviewing journal articles and writing reviewer reports. Students will gain hands-on experience in neuro data analysis by applying these models and analyses to functional neuroimaging datasets such as EEG, LFP, and/or fMRI. The course emphasizes building intuition for the types of problems in brain science that can be addressed through network-based approaches, while also fostering analytical skills and promoting interdisciplinary research through team projects.
Tentative Course Schedule
Date | Course Section | Contents |
---|---|---|
Jan 27 | Introduction to Network Neuroscience | Session 1: Introduction to Network Neuroscience |
Session 2: Evolution of Network Neuroscience | ||
Feb 3 | Introduction to Network Neuroscience | Session 1: Key Works in Neuroscience |
Session 2: Key Works in Systems Engineering | ||
Feb 10 | Introduction to Network Neuroscience | Session 1: Key Works in Data Science |
Session 2: Emergence of Network Neuroscience Problems | ||
Feb 17 | Introduction to Mathematical Models | Session 1: Review and Discussion of Foundational Works |
Session 2: Introduction to Mathematical Models | ||
Feb 24 | Mathematical Models of Network Connectivity | Session 1: Classical Models: Dynamic Causal Modeling (DCM) |
Session 2: Classical Models: Granger Causality | ||
Mar 2 | Mathematical Models of Network Connectivity | Session 1: Classical Models: Multivariate Regression Models |
Session 2: New Models: Dynamic Graphical Modeling | ||
Mar 9 | Mathematical Models of Network Connectivity | Session 1: New Models: Deep Neural Networks (DNNs) |
Session 2: Application to Functional Neuroimaging Data I | ||
Mar 16 | Spring Break – No Class | |
Mar 23 | Mathematical Models of Network Connectivity | Session 1: Application to Functional Neuroimaging Data II |
Session 2: Model Comparison: Advantages | ||
Mar 30 | Brain Dynamics and Neuroimaging Data Analysis | Session 1: Model Comparison: Disadvantages |
Session 2: Neuroimaging Data Modalities: fMRI, EEG, LFP | ||
Apr 6 | Brain Dynamics and Neuroimaging Data Analysis | Session 1: Analytical Approaches: Time-Frequency Analysis |
Session 2: Analytical Approaches: Connectivity Analysis | ||
Apr 13 | Brain Dynamics and Neuroimaging Data Analysis | Session 1: Data Preprocessing Techniques & Feature Extraction |
Session 2: Team Project: Data Exploration & Initial Analysis | ||
Apr 20 | Interdisciplinary Team Projects | Session 1: Data Interpretation Skills |
Session 2: Team Project: Data Analysis & Troubleshooting | ||
Apr 27 | Interdisciplinary Team Projects | Session 1: Troubleshooting Data Analysis |
Session 2: Team Project: Refining Analysis & Preliminary Results | ||
May 4 | Interdisciplinary Team Projects | Session 1: Presentation Skills Workshop |
Session 2: Team Project: Presentation Preparation | ||
May 11 | Interdisciplinary Team Projects | Session 1: Final Project Presentations |
Session 2: Final Project Presentations (continued) |
Final Project
- Team Projects: Interdisciplinary teams will analyze real-world neuroimaging data (fMRI, EEG, LFP) to address a research question. The final project includes a written report and a presentation.