Analyzing fMRI Data - The General Linear Model

Learn the fundamentals of analyzing functional magnetic resonance imaging (fMRI) data through a comprehensive lecture series focused on the General Linear Model (GLM). Explore the mathematical foundations and practical applications of GLM in neuroimaging research, understanding how this statistical framework enables researchers to identify brain activation patterns and make inferences about neural function. Master the theoretical concepts behind GLM implementation in fMRI analysis, including design matrix construction, parameter estimation, and statistical inference procedures. Gain insights into how the General Linear Model serves as the backbone for most fMRI data analysis pipelines, enabling the detection of task-related brain activity and the comparison of different experimental conditions. Develop a solid understanding of the statistical principles that underlie modern neuroimaging analysis techniques, preparing you for advanced work in cognitive neuroscience and brain imaging research.