http://www.nitrc.org/forum/forum.php?forum_id=8756 <table border="0" width="100%"><tr><td align="left"/></tr></table> <p><b>Connectivity in fMRI: Blind Spots and Breakthroughs.</b></p> <p>IEEE Trans Med Imaging. 2018 Jul;37(7):1537-1550</p> <p>Authors: Solo V, Poline JB, Lindquist MA, Simpson SL, Bowman FD, Chung MK, Cassidy B</p> <p>Abstract<br/> In recent years, driven by scientific and clinical concerns, there has been an increased interest in the analysis of functional brain networks. The goal of these analyses is to better understand how brain regions interact, how this depends upon experimental conditions and behavioral measures and how anomalies (disease) can be recognized. In this paper, we provide, first, a brief review of some of the main existing methods of functional brain network analysis. But rather than compare them, as a traditional review would do, instead, we draw attention to their significant limitations and blind spots. Then, second, relevant experts, sketch a number of emerging methods, which can break through these limitations. In particular we discuss five such methods. The first two, stochastic block models and exponential random graph models, provide an inferential basis for network analysis lacking in the exploratory graph analysis methods. The other three addresses: network comparison via persistent homology, time-varying connectivity that distinguishes sample fluctuations from neural fluctuations, and network system identification that draws inferential strength from temporal autocorrelation.<br/> </p><p>PMID: 29969406 [PubMed - in process]</p> en-us Copyright 2000-2026 NITRC OSI Sun, 26 Apr 2026 15:19:57 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator