ArticleFrontiers in cell and developmental biology2021
Multiple Connection Pattern Combination From Single-Mode Data for Mild Cognitive Impairment Identification.
Article in Frontiers in cell and developmental biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Classification of neurodegenerative diseases using brain effective connectivity and machine learning techniques: a systematic review.Frontiers in neurology · 2025Pooled it
- Investigating Brain Functional Connectivity and Its Correlation With Cognitive Dysfunction in Chronic Kidney Disease Patients via Resting-State fMRI.Brain and behavior · 2025Article
- Wavelet-enhanced boundary adaptation network for liver hemangioma segmentation in non-contrast CT.Frontiers in oncology · 2025Article
- Comparison of brain gray matter volume changes in peritoneal dialysis and hemodialysis patients with chronic kidney disease: a VBM study.Frontiers in neuroscience · 2024Article
- Incorporating multi-stage diagnosis status to mine associations between genetic risk variants and the multi-modality phenotype network in major depressive disorder.Frontiers in psychiatry · 2023Article
- Elevated serum platelet count inhibits the effects of brain functional changes on cognitive function in patients with mild cognitive impairment: A resting-state functional magnetic resonance imaging study.Frontiers in aging neuroscience · 2023Article
- A Deep Spatiotemporal Attention Network for Mild Cognitive Impairment Identification.Frontiers in aging neuroscience · 2022Article
- Abnormal regional homogeneity and amplitude of low frequency fluctuation in chronic kidney patients with and without dialysis.Frontiers in neuroscience · 2022Article
- Diffusion tensor imaging of the brain white matter microstructure in patients with chronic kidney disease and its correlation with cognition.Frontiers in neurology · 2022Article
- Rich-Club Organization Disturbances of the Individual Morphological Network in Subjective Cognitive Decline.Frontiers in aging neuroscience · 2022Article
- Morphologic brain network predicts levodopa responsiveness in Parkinson disease.Frontiers in aging neuroscience · 2022Article
- Adaptive noise depression for functional brain network estimation.Frontiers in psychiatry · 2022Article
- Multi-band network fusion for Alzheimer's disease identification with functional MRI.Frontiers in psychiatry · 2022Article
- Altered Intra- and Inter-Network Functional Connectivity in Patients With Crohn's Disease: An Independent Component Analysis-Based Resting-State Functional Magnetic Resonance Imaging Study.Frontiers in neuroscience · 2022Article
- Dysfunction in the Interaction of Information Between and Within the Bilateral Primary Sensory Cortex.Frontiers in aging neuroscience · 2022Article
- The Altered Pattern of the Functional Connectome Related to Pathological Biomarkers in Individuals for Autism Spectrum Disorder Identification.Frontiers in neuroscience · 2022Article
- Research on the Mechanism of Cognitive Decline in Patients With Acoustic Neuroma.Frontiers in neuroscience · 2022Article
- Alternation of the Rich-Club Organization of Individual Brain Metabolic Networks in Parkinson's Disease.Frontiers in aging neuroscience · 2022Article
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Mild cognitive impairment (MCI) is generally considered to be a key indicator for predicting the early progression of Alzheimer's disease (AD). Currently, the brain connection (BC) estimated by fMRI data has been validated to be an effective diagnostic biomarker for MCI. Existing studies mainly focused on the single connection pattern for the neuro-disease diagnosis. Thus, such approaches are commonly insufficient to reveal the underlying changes between groups of MCI patients and normal controls (NCs), thereby limiting their performance. In this context, the information associated with multiple patterns (e.g., functional connectivity or effective connectivity) from single-mode data are considered for the MCI diagnosis. In this paper, we provide a novel multiple connection pattern combination (MCPC) approach to combine different patterns based on the kernel combination trick to identify MCI from NCs. In particular, sixty-three MCI cases and sixty-four NC cases from the ADNI dataset are conducted for the validation of the proposed MCPC method. The proposed method achieves 87.40% classification accuracy and significantly outperforms methods that use a single pattern.
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