Sparse Multiview Task-Centralized Ensemble Learning for ASD Diagnosis Based on Age- and Sex-Related Functional Connectivity Patterns
Year: 2018
Type: article
Abstract: Autism spectrum disorder (ASD) is an age- and sex-related neurodevelopmental disorder that alters the brain's functional connectivity (FC). The changes caused by ASD are associated with different age- and sex-related patterns in neuroimaging data. However, most contemporary computer-assisted ASD diagnosis methods ignore the aforementioned age-/sex-related patterns. In this paper, we propose a novel sparse multiview task-centralized (Sparse-MVTC) ensemble classification method for image-based ASD diagnosis. Specifically, with the age and sex information of each subject, we formulate the classification as a multitask learning problem, where each task corresponds to learning upon a specific age/sex group. We also extract multiview features per subject to better reveal the FC changes. Then, in Sparse-MVTC learning, we select a certain central task and treat the rest as auxiliary tasks. By considering both task-task and view-view relationships between the central task and each auxiliary task, we can learn better upon the entire dataset. Finally, by selecting the central task, in turn, we are able to derive multiple classifiers for each task/group. An ensemble strategy is further adopted, such that the final diagnosis can be integrated for each subject. Our comprehensive experiments on the ABIDE database demonstrate that our proposed Sparse-MVTC ensemble learning can significantly outperform the state-of-the-art classification methods for ASD diagnosis.
Source: IEEE Transactions on Cybernetics
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Funders: National Natural Science Foundation of China, Ministry of Science and Technology of the People's Republic of China, Government of Jiangsu Province, Science and Technology Commission of Shanghai Municipality, Natural Science Foundation of Jiangsu Province, National Institutes of Health, National Key Research and Development Program of China, National Institute on Aging, National Institute of Mental Health, National Institute of Biomedical Imaging and Bioengineering
Awards: G1600120695, G1666955300, Infant Brain Measurement and Super-Resolution Atlas Construction, G244047279, G2625284711, G2841821097, G3139536118, G3273398736, G3720663128, UNC/UMN Baby Connectome Project, G4725545720, G4739438220, G4825306545, G4864481528, G5016354363, Quantifying Brain Abnormality by Multimodality Neuroimage Analysis, G5103669862, G5810097220, G5960771336, G6645430908, G6908867551, Development of Robust Brain Measurement Tools Informed by Ultrahigh Field 7T MRI, G7737339183, G7816344677, G791296218
OpenAlex ID: W2809443510