ArticleNeuroinformatics2015
Supervised Discriminative Group Sparse Representation for Mild Cognitive Impairment Diagnosis.
Article in Neuroinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic power of resting-state fMRI for detection of network connectivity in Alzheimer's disease and mild cognitive impairment: A systematic review.Human brain mapping · 2021Pooled it
- Abnormal functional connectivity density involvement in freezing of gait and its application for subtyping Parkinson's disease.Brain imaging and behavior · 2023Article
- Accurate module induced brain network construction for mild cognitive impairment identification with functional MRI.Frontiers in aging neuroscience · 2023Article
- A Fusion-Based Technique With Hybrid Swarm Algorithm and Deep Learning for Biosignal Classification.Frontiers in human neuroscience · 2022Article
- Identifying Internet Addiction and Evaluating the Efficacy of Treatment Based on Functional Connectivity Density: A Machine Learning Study.Frontiers in neuroscience · 2021Article
- Sparse Granger Causality Analysis Model Based on Sensors Correlation for Emotion Recognition Classification in Electroencephalography.Frontiers in computational neuroscience · 2021Article
- Identifying resting-state effective connectivity abnormalities in drug-naïve major depressive disorder diagnosis via graph convolutional networks.Human brain mapping · 2020Article
- A toolbox for brain network construction and classification (BrainNetClass).Human brain mapping · 2020Article
- Strength and Similarity Guided Group-level Brain Functional Network Construction for MCI Diagnosis.Pattern recognition · 2019Article
- Discriminative self-representation sparse regression for neuroimaging-based alzheimer's disease diagnosis.Brain imaging and behavior · 2019Article
- Multi-task fused sparse learning for mild cognitive impairment identification.Technology and health care : official journal of the European Society for Engineering and Medicine · 2018Article
- Improving Sparsity and Modularity of High-Order Functional Connectivity Networks for MCI and ASD Identification.Frontiers in neuroscience · 2018Article
- Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification.Human brain mapping · 2017Article
- Inter-subject Similarity Guided Brain Network Modeling for MCI Diagnosis.Machine learning in medical imaging. MLMI (Workshop) · 2017Article
- Hierarchical High-Order Functional Connectivity Networks and Selective Feature Fusion for MCI Classification.Neuroinformatics · 2017Article
- A novel relational regularization feature selection method for joint regression and classification in AD diagnosis.Medical image analysis · 2017Article
- Deep ensemble learning of sparse regression models for brain disease diagnosis.Medical image analysis · 2017Article
- Canonical feature selection for joint regression and multi-class identification in Alzheimer's disease diagnosis.Brain imaging and behavior · 2016Article
- Sparse temporally dynamic resting-state functional connectivity networks for early MCI identification.Brain imaging and behavior · 2016Article
- State-space model with deep learning for functional dynamics estimation in resting-state fMRI.NeuroImage · 2016Article
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4 authors.
Funding
Abstract
Research on an early detection of Mild Cognitive Impairment (MCI), a prodromal stage of Alzheimer's Disease (AD), with resting-state functional Magnetic Resonance Imaging (rs-fMRI) has been of great interest for the last decade. Witnessed by recent studies, functional connectivity is a useful concept in extracting brain network features and finding biomarkers for brain disease diagnosis. However, it still remains challenging for the estimation of functional connectivity from rs-fMRI due to the inevitable high dimensional problem. In order to tackle this problem, we utilize a group sparse representation along with a structural equation model. Unlike the conventional group sparse representation method that does not explicitly consider class-label information, which can help enhance the diagnostic performance, in this paper, we propose a novel supervised discriminative group sparse representation method by penalizing a large within-class variance and a small between-class variance of connectivity coefficients. Thanks to the newly devised penalization terms, we can learn connectivity coefficients that are similar within the same class and distinct between classes, thus helping enhance the diagnostic accuracy. The proposed method also allows the learned common network structure to preserve the network specific and label-related characteristics. In our experiments on the rs-fMRI data of 37 subjects (12 MCI; 25 healthy normal control) with a cross-validation technique, we demonstrated the validity and effectiveness of the proposed method, showing the diagnostic accuracy of 89.19 % and the sensitivity of 0.9167.
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Registered trials
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.