ArticleIEEE journal of biomedical and health informatics2020
Toward a Better Estimation of Functional Brain Network for Mild Cognitive Impairment Identification: A Transfer Learning View.
Article in IEEE journal of biomedical and health informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Identification of Major Depressive Disorder Using Multiple Functional Connection Patterns.CNS neuroscience & therapeutics · 2026Article
- Multiplex functional connectome graph transformer for cognitive vulnerability in dialysis-treated chronic kidney disease.Frontiers in psychiatry · 2026Article
- Assessing the accuracy of survival machine learning and traditional statistical models for Alzheimer's disease prediction over time: a study on the ADNI cohort.BMC medical research methodology · 2025Article
- Article
- Wavelet-enhanced boundary adaptation network for liver hemangioma segmentation in non-contrast CT.Frontiers in oncology · 2025Article
- Urine Albumin-to-Creatinine Ratio as an Indicator of Brain Activity Changes in Chronic Kidney Disease: A Resting-State fMRI Study.Brain and behavior · 2024Article
- Article
- Predicting Progression From Mild Cognitive Impairment to Alzheimer's Dementia With Adversarial Attacks.IEEE journal of biomedical and health informatics · 2024Article
- Comparison of brain gray matter volume changes in peritoneal dialysis and hemodialysis patients with chronic kidney disease: a VBM study.Frontiers in neuroscience · 2024Article
- Article
- Functional Connectivity Networks with Latent Distributions for Mild Cognitive Impairment Identification.Journal of digital imaging · 2023Article
- The reconfiguration pattern of individual brain metabolic connectome for Parkinson's disease identification.MedComm · 2023Article
- Fusing Multiview Functional Brain Networks by Joint Embedding for Brain Disease Identification.Journal of personalized medicine · 2023Article
- Brain disease research based on functional magnetic resonance imaging data and machine learning: a review.Frontiers in neuroscience · 2023Review
- Diagnosis of early mild cognitive impairment using a multiobjective optimization algorithm based on T1-MRI data.Scientific reports · 2022Article
- Machine learning based on the multimodal connectome can predict the preclinical stage of Alzheimer's disease: a preliminary study.European radiology · 2022Article
- The Altered Pattern of the Functional Connectome Related to Pathological Biomarkers in Individuals for Autism Spectrum Disorder Identification.Frontiers in neuroscience · 2022Article
- Diagnosis of Amnesic Mild Cognitive Impairment Using MGS-WBC and VGBN-LM Algorithms.Frontiers in aging neuroscience · 2022Article
- Individual Brain Morphological Connectome Indicator Based on Jensen-Shannon Divergence Similarity Estimation for Autism Spectrum Disorder Identification.Frontiers in neuroscience · 2022Article
- Correlation Between Serum High-Sensitivity C-Reactive Protein, Tumor Necrosis Factor-Alpha, Serum Interleukin-6 and White Matter Integrity Before and After the Treatment of Drug-Naïve Patients With Major Depressive Disorder.Frontiers in neuroscience · 2022Article
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4 authors.
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Abstract
Mild cognitive impairment (MCI) is an intermediate stage of brain cognitive decline, associated with increasing risk of developing Alzheimer's disease (AD). It is believed that early treatment of MCI could slow down the progression of AD, and functional brain network (FBN) could provide potential imaging biomarkers for MCI diagnosis and response to treatment. However, there are still some challenges to estimate a "good" FBN, particularly due to the poor quality and limited quantity of functional magnetic resonance imaging (fMRI) data from the target domain (i.e., MCI study). Inspired by the idea of transfer learning, we attempt to transfer information in high-quality data from source domain (e.g., human connectome project in this paper) into the target domain towards a better FBN estimation, and propose a novel method, namely NERTL (Network Estimation via Regularized Transfer Learning). Specifically, we first construct a high-quality network "template" based on the source data, and then use the template to guide or constrain the target of FBN estimation by a weighted l
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