DocumentCode :
3311619
Title :
Discriminative analysis of resting-state brain functional connectivity patterns of Attention-Deficit Hyperactivity Disorder using Kernel Principal Component Analysis
Author :
Xunheng Wang ; Yun Jiao ; Zuhong Lu
Author_Institution :
Key Lab. of Child Dev. & Learning Sci., Southeast Univ., Nanjing, China
Volume :
3
fYear :
2011
fDate :
26-28 July 2011
Firstpage :
1938
Lastpage :
1941
Abstract :
Resting state fMRI is an emerging research area that can reveal disorders and dysfunction of human brain. Resting state functional connectivity patterns have been reported for diagnosis of different diseases. In this study, Kernel Principal Component Analysis (KPCA) method based on connectivity matrix of each functional meaningful brain regions was applied to discriminate adult with Attention-Deficit Hyperactivity Disorder (ADHD) from normal controls. Firstly, functional connectivity matrix was obtained as classify patterns. Secondly, Kendall tau rank correlation coefficient was applied to select features with high discriminative power, while KPCA was applied to find the abnormal pattern of ADHD in a much lower mainfold. Finally, the two groups of ADHD and normal participants were classified by a Support Vector Machine (SVM) classifier. Experimental results showed that SVM based on KPCA can produce a correct classification rate of 81% using a leave-one-out cross validation, which indicate that KPCA is an effective method that can greatly improve the final discriminative performance. Moreover, we examined the brain regions with statistically significant difference, like the frontal cortex, insula, cingulate cortex, postcentral gyrus, thalamus, middle temporal cortex, well confirmed with previous findings on ADHD. From the classification performance, we conclude that KPCA based on functional connectivity matrix can provide useful information for diagnosis of ADHD and even other diseases.
Keywords :
biomedical MRI; brain; feature extraction; matrix algebra; medical disorders; medical image processing; pattern classification; principal component analysis; support vector machines; Kendall tau rank correlation coefficient; attention-deficit hyperactivity disorder; connectivity matrix; discriminative analysis; kernel principal component analysis; pattern classification; resting state fMRI; resting-state brain functional connectivity patterns; support vector machine; Brain; Diseases; Kernel; Magnetic resonance imaging; Principal component analysis; Support vector machines; ADHD; KPCA; discriminative analysis; resting state fMRI;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-61284-180-9
Type :
conf
DOI :
10.1109/FSKD.2011.6019911
Filename :
6019911
Link To Document :
بازگشت