ArticleFrontiers in neuroscience2020
Group Similarity Constraint Functional Brain Network Estimation for Mild Cognitive Impairment Classification.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- The reconfiguration pattern of individual brain metabolic connectome for Parkinson's disease identification.MedComm · 2023Article
- Rich-Club Organization Disturbances of the Individual Morphological Network in Subjective Cognitive Decline.Frontiers in aging neuroscience · 2022Article
- Temporal-spatial dynamic functional connectivity analysis in schizophrenia classification.Frontiers in neuroscience · 2022Article
- The Altered Pattern of the Functional Connectome Related to Pathological Biomarkers in Individuals for Autism Spectrum Disorder Identification.Frontiers in neuroscience · 2022Article
- Multiple Connection Pattern Combination From Single-Mode Data for Mild Cognitive Impairment Identification.Frontiers in cell and developmental biology · 2021Article
- Article
- Group Similarity Constraint Functional Brain Network Estimation for Mild Cognitive Impairment Classification.Frontiers in neuroscience · 2020Article
- Constructing Dynamic Functional Networks via Weighted Regularization and Tensor Low-Rank Approximation for Early Mild Cognitive Impairment Classification.Frontiers in cell and developmental biology · 2020Article
- Effective and Accurate Diagnosis of Subjective Cognitive Decline Based on Functional Connection and Graph Theory View.Frontiers in neuroscience · 2020Article
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6 authors.
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Abstract
Functional brain network (FBN) provides an effective biomarker for understanding brain activation patterns and a diagnostic criterion for neurodegenerative diseases detections. Unfortunately, it remains challenges to estimate the biologically meaningful or discriminative FBNs accurately, because of the poor quality of functional magnetic resonance imaging data or our limited understanding of human brain. In this study, a novel FBN estimation model based on group similarity prior was proposed. In particular, we extended the FBN estimation model to tensor form and incorporated the tensor trace-norm regularizer to formulate the group similarity constraint. To verify the proposed method, we conducted experiments on identifying mild cognitive impairments (MCIs) from normal controls (NCs) based on the estimated FBNs. Experimental results illustrated that our method is effective in modeling FBNs. Consequently, we achieved 91.97% classification accuracy, outperforming the state-of-the-art methods. The
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