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• W2926762617 endingPage "748" @default.
• W2926762617 startingPage "736" @default.
• W2926762617 abstract "Objective: This study focused on the ADHD classification through functional connectivity (FC) analysis. Method: An ADHD classification method was proposed with subspace clustering and binary hypothesis testing, wherein partial information of test data was adopted for training. By hypothesizing the binary label (ADHD or control) for the test data, two feature sets of training FC data were generated during the feature selection procedure that employed both training and test data. Then, a multi-affinity subspace clustering approach was performed to obtain the corresponding subspace-projected feature sets. With the energy comparison of projected feature sets, we finally identified ADHD individuals for the test data. Results: Our method outperformed several state-of-the-art methods with the above 90% average identification accuracy. By the discriminative FC contribution analysis, it also proved the reliability of our method. Conclusion: Results demonstrate the remarkable classification performance of our method and reveal some useful brain circuits to identify ADHD." @default.
• W2926762617 created "2019-04-11" @default.
• W2926762617 creator A5023651959 @default.
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• W2926762617 date "2019-04-02" @default.
• W2926762617 modified "2023-10-17" @default.
• W2926762617 title "Identifying ADHD Individuals From Resting-State Functional Connectivity Using Subspace Clustering and Binary Hypothesis Testing" @default.
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• W2926762617 doi "https://doi.org/10.1177/1087054719837749" @default.
• W2926762617 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30938224" @default.
• W2926762617 hasPublicationYear "2019" @default.
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