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Then, a new associative classifier CLASS_Hiconst (CLassifier http://www.selleckchem.com/products/bmn-673.html based on ASSociation rules with High Confidence and Support) is modeled and used to diagnose the medical images. The performance of our approach is compared with two different classifiers Fuzzy-SVM and multilayer back propagation neural network (MLPNN) in terms of classifier efficiency with sensitivity, specificity, accuracy, positive predictive value, and negative predictive value. The experimental result shows 96% accuracy, 97% sensitivity, and 96% specificity and proves that association rule based classifier is a powerful tool in assisting the diagnosing process. ? 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 194�C203, 2013 ""Default-mode network (DMN) activity measured with functional magnetic resonance imaging (fMRI) represents dominant intrinsic neuronal activations of the human brain during rest as opposed to task periods. Previous studies have demonstrated the utility of DMNs in identifying characteristic traits such as hyperactivation and hypoactivation from group-level fMRI data. However, these group-level spatial patterns (SPs) were mostly based on random-effect (RFX) statistics determined using only the intersubject variability. To reduce the potentially significant level of variability in group-level SPs in RFX due to intrasubject variability, http://www.selleckchem.com/products/pexidartinib-plx3397.html we were motivated to adopt a mixed-effects (MFX) statistics that is using both intrasubject and intersubject variability. Publicly available group fMRI database during resting state was analyzed using a temporal concatenation-based group independent component (IC) analysis, and DMN-related ICs at the group-level were automatically selected. http://www.selleck.cn/products/nlg919.html The individual-level SPs of these DMN-related ICs were subsequently estimated using a dual-regression approach. Using these individual-level SPs, we evaluated the reproducibility and potential variability of the DMNs from the RFX and MFX statistics using performance measures including (1) neuronal activation levels, (2) percentages of overlap, (3) Pearson's spatial correlation coefficients, and (4) the distances between center-of-clusters. The resulting SPs from the MFX-based group inference showed a significantly greater level of reproducibility than those from the RFX-based group inference as tested in a bootstrapping framework Family-wise error (FWE)-corrected p