http://www.nitrc.org/forum/forum.php?forum_id=2531 <p class="abstract"><div class="Abstract" lang="en"><a name="Abs1"></a><span class="AbstractHeading">Abstract&nbsp;&nbsp;</span><div class="normal">Studying connectivities among functional brain regions and the functional dynamics on brain networks has drawn increasing interest. A fundamental issue that affects functional connectivity and dynamics studies is how to determine the best possible functional brain regions or ROIs (regions of interest) for a group of individuals, since the connectivity measurements are heavily dependent on ROI locations. Essentially, identification of accurate, reliable and consistent corresponding ROIs is challenging due to the unclear boundaries between brain regions, variability across individuals, and nonlinearity of the ROIs. In response to these challenges, this paper presents a novel methodology to computationally optimize ROIs locations derived from task-based fMRI data for individuals so that the optimized ROIs are more consistent, reproducible and predictable across brains. Our computational strategy is to formulate the individual ROI location optimization as a group variance minimization problem, in which group-wise consistencies in functional/structural connectivity patterns and anatomic profiles are defined as optimization constraints. Our experimental results from multimodal fMRI and DTI data show that the optimized ROIs have significantly improved consistency in structural and functional profiles across individuals. These improved functional ROIs with better consistency could contribute to further study of functional interaction and dynamics in the human brain. </div> </div></p><ul> <li><span class="labelName">Content Type </span><span class="labelValue">Journal Article</span></li><li>Category Original Article</li><li>Pages 1-18</li><li>DOI 10.1007/s12021-012-9142-5</li><li><span class="labelName">Authors</span><ul> <li>Kaiming Li, School of Automation, Northwestern Polytechnical University, Xi’an, China</li><li>Lei Guo, School of Automation, Northwestern Polytechnical University, Xi’an, China</li><li>Dajiang Zhu, Department of Computer Science & Bioimaging Research Center, The University of Georgia, Boyd GSRC 420, Athens, GA 30602, USA</li><li>Xintao Hu, School of Automation, Northwestern Polytechnical University, Xi’an, China</li><li>Junwei Han, School of Automation, Northwestern Polytechnical University, Xi’an, China</li><li>Tianming Liu, Department of Computer Science & Bioimaging Research Center, The University of Georgia, Boyd GSRC 420, Athens, GA 30602, USA</li> </ul></li> </ul><ul class="parents"> <ul class="details"> <li><span class="header labelName">Journal </span><span class="labelValue"><a href="http://www.springerlink.com/content/120559/">Neuroinformatics</a></span></li><li><span class="labelName">Online ISSN </span><span class="labelValue">1559-0089</span></li><li><span class="labelName">Print ISSN </span><span class="labelValue">1539-2791</span></li> </ul> </ul> en-us Copyright 2000-2026 NITRC OSI Mon, 20 Apr 2026 2:04:29 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator