Executive Development Programme in Hands-On fNIRS Data Analysis

This course is tailored for senior neuroscientists, clinicians, and researchers looking to enhance their fNIRS (functional Near-Infrared Spectroscopy) data analysis skills. Participants will gain proficiency in practical fNIRS data processing, including signal preprocessing, artifact removal, and statistical analysis techniques.

Attendees will learn to use open-source software for fNIRS data analysis and interpret results to inform their research or clinical applications effectively. By the end, they will be able to design, execute, and analyze fNIRS studies with confidence.

1. 1. fNIRS Fundamentals: Learners will study the basics of functional near-infrared spectroscopy (fNIRS), including its principles, components, and advantages over other neuroimaging techniques. They will gain foundational knowledge necessary for understanding more complex fNIRS data analysis methods.
2. 2. Data Acquisition and Preprocessing: This module covers the process of acquiring fNIRS data and the steps involved in preprocessing the data for analysis. Learners will learn how to set up fNIRS experiments, collect data, and perform basic data cleaning and normalization techniques.
3. 3. Data Visualization and Exploration: Learners will explore various tools and techniques for visualizing fNIRS data to identify patterns and trends. They will gain skills in creating and interpreting time-series plots, spectrograms, and other visualization methods to understand brain activity patterns.
4. 4. Statistical Methods in fNIRS Analysis: This module introduces statistical methods essential for analyzing fNIRS data, including time-series analysis, event-related designs, and general linear models. Learners will learn how to apply statistical techniques to test hypotheses about brain function during specific tasks.
5. 5. Advanced Data Analysis Techniques: Building on foundational statistical methods, learners will delve into advanced analysis techniques such as independent component analysis (ICA), multivariate regression, and machine learning approaches for fNIRS data. They will gain expertise in applying these methods to complex experimental designs.
6. 6. Group-Level Analysis and Meta-Analysis: This module focuses on methods for analyzing fNIRS data across multiple subjects, including group-level statistical tests, meta-analysis techniques, and methods for identifying consistent neural signatures across studies. Learners will learn how to synthesize results from multiple fNIRS studies.
7. 7. Real-World Applications of fNIRS: In this module, learners will explore how fNIRS is applied in various fields such as education, sports science, and clinical settings. They will learn about the practical implications of fNIRS data analysis in these domains and how to tailor analysis methods to specific applications.
8. 8. Reporting and Presenting fNIRS Results: This module covers best practices for reporting and presenting fNIRS research findings. Learners will learn how to effectively communicate results using tables, figures, and written descriptions, as well as how to prepare presentations for both technical and non-technical audiences.
9. 9. Ethics and Regulatory Considerations in fNIRS Research: This module addresses ethical and regulatory considerations when conducting fNIRS research, including informed consent, data privacy, and compliance with institutional review board (IRB) regulations. Learners will gain a comprehensive understanding of the legal and ethical framework governing fNIRS research.
10. 10. Career Development in fNIRS Research: The final module focuses on career development in the field of fNIRS research. Learners will explore potential career paths, strategies for advancing in their field, and networking opportunities. They will also learn about funding sources, research grants, and collaboration opportunities in the fNIRS community.