INSPIRE Lab Imaging- and Neuro-computations for Precision Informatics Research

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