http://www.nitrc.org/forum/forum.php?forum_id=3525 <p class="abstract"><div class="Abstract" lang="en"><a name="Abs1"></a><span class="AbstractHeading">Abstract&nbsp;&nbsp;</span><div class="normal">Diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) have been widely used to study structural and functional brain connectivity in recent years. A common assumption used in many previous functional brain connectivity studies is the temporal stationarity. However, accumulating literature evidence has suggested that functional brain connectivity is under temporal dynamic changes in different time scales. In this paper, a novel and intuitive approach is proposed to model and detect dynamic changes of functional brain states based on multimodal fMRI/DTI data. The basic idea is that functional connectivity patterns of all fiber-connected cortical voxels are concatenated into a descriptive functional feature vector to represent the brain’s state, and the temporal change points of brain states are decided by detecting the abrupt changes of the functional vector patterns via the sliding window approach. Our extensive experimental results have shown that meaningful brain state change points can be detected in task-based fMRI/DTI, resting state fMRI/DTI, and natural stimulus fMRI/DTI data sets. Particularly, the detected change points of functional brain states in task-based fMRI corresponded well to the external stimulus paradigm administered to the participating subjects, thus partially validating the proposed brain state change detection approach. The work in this paper provides novel perspective on the dynamic behaviors of functional brain connectivity and offers a starting point for future elucidation of the complex patterns of functional brain interactions and dynamics. </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/s12021-012-9157-y</li><li><span class="labelName">Authors</span><ul> <li>Xiang Li, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Boyd GSRC 420, Athens, GA 30602, USA</li><li>Chulwoo Lim, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Boyd GSRC 420, Athens, GA 30602, USA</li><li>Kaiming Li, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Boyd GSRC 420, Athens, GA 30602, USA</li><li>Lei Guo, School of Automation, Northwestern Polytechnic University, Xi’an, China</li><li>Tianming Liu, Department of Computer Science and 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 4:51:08 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator