Independent component analysis decomposition of neuroimaging data

Independent component analysis (ICA) is one of the most widely used approaches for estimating intrinsic networks from fMRI data and has been in use for over 25 years now. Over that time, numerous extensions and variations of ICA, a method that moves beyond second order methods and focuses on higher order statistics under the broader umbrella of (joint) blind and semi-blind source separation, have been developed including group ICA, spatial and temporal ICA, spatially constrained ICA, hierarchical ICA, nonlinear ICA, dynamic ICA, multimodal ICA, and much more. ICA has contributed in no small part to a transformation of our study of functional neuroimaging data. In this talk I will attempt to summarize the methods developed over the past 25 years, as well as provide an overview of how ICA has been used to decompose neuroimaging data, estimate intrinsic networks, and visualize transitions and relationships within and between networks. I will attempt to provide comprehensive classification of the many ICA approaches into categories, highlighting methodological aspects and their relationship to one another and other methods such as seed-based approaches, gradient approaches, and others. I will also provide examples of the scientific contributions these approaches have made to our understanding of the healthy and disordered human brain. In addition to this, my talk will summarize the various tools and approaches that are available to the community for implementing ICA on one’s own data. A few hands-on examples using the GIFT/FIT tools will be provided, including interoperable versions that work with the brain imaging data structure (BIDS) and which provides easy to use, containerized tools for use on any computer environment. The talk will be designed to be didactic and interactive, allowing for a robust engagement with the attendees with a goal of facilitating not just facts but also an intuitive understanding of the approaches and providing a pathway for individuals to use the tools for themselves. I will also place ICA within the larger context of brain modes and gradients, which can help those that use these approaches understand the correspondence, as well as strengths and limitations of the many published studies to date in addition to setting the stage for future innovative developments and finding going forward. In sum, the goal of this talk is to showcase the exciting ways ICA has been used with a eye towards enabling attendees to leverage existing approaches to generate new findings and extensions going forward.