High‐accuracy machine learning techniques for functional connectome fingerprinting and cognitive state decoding
- LINK https://onlinelibrary.wiley.com/doi/abs/10.1002/hbm.26423
- AUTHORS Andrew Hannum, Mario A Lopez, Saúl A Blanco, Richard F Betzel
The human brain is a complex network comprised of functionally and anatomically interconnected brain regions. A growing number of studies have suggested that empirical estimates of brain networks may be useful for discovery of biomarkers of disease and cognitive state. A prerequisite for realizing this aim, however, is that brain networks also serve as reliable markers of an individual. Here, using Human Connectome Project data, we build upon recent studies examining brain‐based fingerprints of individual subjects and cognitive states based on cognitively demanding tasks that assess, for example, working memory, theory of mind, and motor function. Our approach achieves accuracy of up to 99% for both identification of the subject of an fMRI scan, and for classification of the cognitive state of a previously unseen subject in a scan.