Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/101765
Title: Clustered subsampling for clinically informed diagnostic brain mapping
Authors: Bjornsdotter, Malin
Sona, Diego
Rosenthal, Sonny
Dauwels, Justin
Keywords: DRNTU::Social sciences::Psychology
Issue Date: 2012
Source: Bjornsdotter, M., Sona, D., Rosenthal, S., & Dauwels, J. (2012). Clustered subsampling for clinically informed diagnostic brain mapping. 2012 15th International Conference on Information Fusion (FUSION), 593-599.
Conference: International Conference on Information Fusion (FUSION) (15th : 2012)
Abstract: Brain based diagnostic systems have recently received attention as a tool in the characterization and diagnosis of a variety of neurodevelopmental and psychiatric disorders. Nonetheless, a majority of disorders are still diagnosed entirely based on symptom assessments and behavioral correlates. We therefore propose a method for fusing brain responses with clinical measures for improved diagnosis. To this end, we utilized the flexibility of clustered random subspace brain mapping to detect regions where brain responses in conjunction with a clinical measure could reliably differentiate patients from control subjects. We demonstrate the approach on realistically simulated functional magnetic resonance imaging (fMRI) brain activity and a clinical parameter. We show that the method efficiently identifies brain regions where fused analysis of brain responses and clinical parameters improves diagnosis compared to either measure alone. The proposed method is easy to implement and highly flexible, offering an appealing basis for multimodal brain mapping.
URI: https://hdl.handle.net/10356/101765
http://hdl.handle.net/10220/19664
URL: http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6289856&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6289856
Schools: School of Electrical and Electronic Engineering 
Wee Kim Wee School of Communication and Information 
Rights: © 2012 International Society of Information Fusion. This paper was published in 2012 15th International Conference on Information Fusion (FUSION) and is made available as an electronic reprint (preprint) with permission of International Society of Information Fusion. The paper can be found at the following official URL:http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6289856&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6289856. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers
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