Applications of ICA: understanding individual differences in brain function

This talk will provide an overview of the practical applications of ICA to investigate individual differences in functional organization in health and disease. The talk will begin with a description of different outputs from the ICA-dual regression pipeline: subject-specific mode spatial maps, timeseries, and amplitudes. I will summarize the rich literature investigating the relationships of these ICA outputs to behavior and symptomatology in large-scale datasets including the Human Connectome Project and UK Biobank. Specific examples related to arousal, mental health, genetics, and the general positive-negative behavioral axis will be discussed.

The software run-through will provide the audience with intuition for the role of dimensionality in ICA decomposition by demonstrating how relatively larger networks get split into multiple separate subcomponents as dimensionality increases. In the strengths/limitations section, ICA will be contrasted with other statistical network decomposition approaches, such as seed-based analysis and probabilistic functional modes. Seed-based analysis involves selecting a seed region of interest and computing the whole-brain connectivity map with the seed region. Benefits of seed-based analysis include simplicity and interpretability, while disadvantages include seed sensitivity and lack of multivariate network modelling. Probabilistic functional modes offer a Bayesian alternative to ICA with benefits from improved sensitivity to individual differences and ability to model network overlap. By comparison, ICA allows multivariate network modelling while being limited by the lack of sensitivity to network overlap due to the independence constraint. The talk will close with a brief discussion of the challenges posed by analytical flexibility in brain representations. For example, it can be difficult to take two papers on the same topic using different mode-based analyses and determine whether the results are consistent or not. As such, the field needs new tools to facilitate cross-study comparison and interpretation.