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Individual classification of ADHD patients by integrating multiscale neuroimaging markers and advanced pattern recognition techniques
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Cheng, Wei, Ji, Xiaoxi, Zhang, Jie and Feng, Jianfeng (2012) Individual classification of ADHD patients by integrating multiscale neuroimaging markers and advanced pattern recognition techniques. Frontiers in Systems Neuroscience, Vol.6 (No.58). doi:10.3389/fnsys.2012.00058 ISSN 1662-5137.
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Official URL: http://dx.doi.org/10.3389/fnsys.2012.00058
Abstract
Accurate classification or prediction of the brain state across individual subject, i.e., healthy, or with brain disorders, is generally a more difficult task than merely finding group differences. The former must be approached with highly informative and sensitive biomarkers as well as effective pattern classification/feature selection approaches. In this paper, we propose a systematic methodology to discriminate attention deficit hyperactivity disorder (ADHD) patients from healthy controls on the individual level. Multiple neuroimaging markers that are proved to be sensitive features are identified, which include multiscale characteristics extracted from blood oxygenation level dependent (BOLD) signals, such as regional homogeneity (ReHo) and amplitude of low-frequency fluctuations. Functional connectivity derived from Pearson, partial, and spatial correlation is also utilized to reflect the abnormal patterns of functional integration, or, dysconnectivity syndromes in the brain. These neuroimaging markers are calculated on either voxel or regional level. Advanced feature selection approach is then designed, including a brain-wise association study (BWAS). Using identified features and proper feature integration, a support vector machine (SVM) classifier can achieve a cross-validated classification accuracy of 76.15% across individuals from a large dataset consisting of 141 healthy controls and 98 ADHD patients, with the sensitivity being 63.27% and the specificity being 85.11%. Our results show that the most discriminative features for classification are primarily associated with the frontal and cerebellar regions. The proposed methodology is expected to improve clinical diagnosis and evaluation of treatment for ADHD patient, and to have wider applications in diagnosis of general neuropsychiatric disorders.
Item Type: | Journal Article | ||||
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Subjects: | Q Science > QP Physiology R Medicine > RJ Pediatrics |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||
Library of Congress Subject Headings (LCSH): | Attention-deficit hyperactivity disorder -- Diagnosis, Brain -- Abnormalities | ||||
Journal or Publication Title: | Frontiers in Systems Neuroscience | ||||
Publisher: | Frontiers Media S.A. | ||||
ISSN: | 1662-5137 | ||||
Official Date: | 2012 | ||||
Dates: |
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Volume: | Vol.6 | ||||
Number: | No.58 | ||||
DOI: | 10.3389/fnsys.2012.00058 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Open Access (Creative Commons) | ||||
Date of first compliant deposit: | 22 December 2015 | ||||
Date of first compliant Open Access: | 22 December 2015 | ||||
Funder: | Royal Society (Great Britain), Guo jia zi ran ke xue ji jin wei yuan hui (China) [National Natural Science Foundation of China] (NSFC) | ||||
Grant number: | 61104143 (NSFC), 61004104 (NSFC) |
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