Evidence map›Paper›PMID 29710750›Full record

ArticleTechnology and health care : official journal of the European Society for Engineering and Medicine2018

Multi-task fused sparse learning for mild cognitive impairment identification.

Peng Yang, Dong Ni, Siping Chen, Tianfu Wang, Donghui Wu, Baiying Lei

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Article in Technology and health care : official journal of the European Society for Engineering and Medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Peng YangSchool of Biomedical Engineering, Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong, China.
Dong NiSchool of Biomedical Engineering, Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong, China.
Siping ChenSchool of Biomedical Engineering, Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong, China.
Tianfu WangSchool of Biomedical Engineering, Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong, China.
Donghui WuDepartment of Geriatric Psychiatry, Shenzhen Kangning Hospital, and Shenzhen Mental Health Center, Shenzhen, Guangdong, China.
Baiying LeiSchool of Biomedical Engineering, Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBrain functional connectivity network (BFCN) has been widely applied to identify biomarkers for the brain function understanding and brain diseases analysis.

objectiveBuilding a biologically meaningful brain network is a crucial work in these applications. For this task, sparse learning has been widely applied for the network construction. If multiple time-point data is added to the brain imaging application, the disease progression pattern in the longitudinal analysis can be better revealed.

methodsA novel longitudinal analysis for MCI classification is devised based on resting-state functional magnetic resonating imaging (rs-fMRI). Specifically, this paper proposes a novel multi-task learning method to integrate fused penalty by regularization. In addition, a novel objective function is developed for fused sparse learning via smoothness constraint.

resultsThe proposed method achieves the best classification performance with an accuracy of 95.74% for baseline and 93.64% for year 1 data.

conclusionsThe experimental results show that our proposed method achieves quite promising classification performance.

Indexed as

Support Vector MachineAlgorithmsBiomarkersBrainCognitive DysfunctionDatabases, FactualDisease ProgressionHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingBiomarkersbrain functional connectivity networklongitudinal analysisMild cognitive impairmentsmooth regularization

Identifiers

PMID29710750
PMCPMC6004967

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.