ICA Applications in Brain Activation Studies: Algorithm Implementation and fMRI Analysis

In brain activation research, Independent Component Analysis (ICA) serves as a dimensionality reduction technique that filters out independent and neurologically significant activations. This demonstration presents two case studies: one featuring human fMRI scans provided by Denmark's Hvidovre University Hospital, and another with monkey fMRI data from the EU-sponsored MAPAWAMO project. The implementation allows comparative analysis of five algorithms - icaMS (Mean Square), icaML (Maximum Likelihood), icaMF (Multi-Frequency), icaMF with positive source constraints, and Principal Component Analysis (PCA). Detailed methodological comparisons are available in reference [2]. To deepen understanding, we explore ICA's neural applications for separating neurological signals from noise, discussing algorithm selection criteria based on signal sparsity and statistical independence. The code implementation typically involves preprocessing steps (detrending, normalization) before applying FastICA or Infomax algorithms through MATLAB's EEGLAB or SPM toolboxes. While ICA excels in blind source separation, users should consider its limitations regarding component interpretation and computational load. Ultimately, ICA proves powerful for decomposing multivariate brain signals, with this demonstration showcasing key implementations for neuroimaging research.