Detecting abnormalities in resting-state dynamics: An unsupervised learning approach

Much of the research in this direction has aimed at identifying connectivity based biomarkers, restricting the analysis to so-called "static" functional connectivity measures that quantify the average degree of synchrony between brain regions. For e.g., machine learning based strategies have been used with static connectivity measures to parcellate the brain into functional networks, and extract individual-level predictions about cognitive state or clinical condition [2]. In recent years, there has been a surge in the study of the temporal dynamics of rsfMRI data, offering a complementary perspective on the functional connectome and how it is altered in disease, development, and aging [14]. However, to our knowledge, there has been a dearth of machine learning applications to dynamic rsfMRI analysis. Thanks to large-scale datasets, modern machine learning methods have fueled significant progress in computer vision. Compared to natural vision applications, however, medical imaging poses a unique set of challenges. Data, particularly labeled data, are often scarce in medical imaging applications. This makes data-hungry methods such as supervised CNNs possibly less useful. One potential approach to tackle the limited sample size issue is to exploit unsupervised arXiv:1908.06168v1