Disease state prediction from resting state functional connectivity
نویسندگان
چکیده
منابع مشابه
Disease state prediction from resting state functional connectivity.
The application of multivoxel pattern analysis methods has attracted increasing attention, particularly for brain state prediction and real-time functional MRI applications. Support vector classification is the most popular of these techniques, owing to reports that it has better prediction accuracy and is less sensitive to noise. Support vector classification was applied to learn functional co...
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INTRODUCTION In combination with multivariate pattern analysis algorithms such as Support Vector Machines (SVM) [1] or Support Vector Regression (SVR) [2], functional connectivity offers a powerful tool for brain classification. With this approach, several groups have successfully identified and predicted brain characteristics using resting-state MRI data [35]. Specifically, Dosenbach et al. ha...
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In the cerebral cortex, the activity levels of neuronal populations are continuously fluctuating. When neuronal activity, as measured using functional MRI (fMRI), is temporally coherent across 2 populations, those populations are said to be functionally connected. Functional connectivity has previously been shown to correlate with structural (anatomical) connectivity patterns at an aggregate le...
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C. J. Honey, O. Sporns, L. Cammoun, X. Gigandet, J-P. Thiran, R. Meuli, and P. Hagmann 1 Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States, Signal Processing Laboratory 5, Ecole Polytechnique Fédérale de Lausanne, Lausanne, VD, Switzerland, Department of Radiology, University Hospital Center and University of Lausanne (CHUV), Lausanne, VD, Switze...
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ژورنال
عنوان ژورنال: Magnetic Resonance in Medicine
سال: 2009
ISSN: 0740-3194,1522-2594
DOI: 10.1002/mrm.22159