ArticleFrontiers in neuroinformatics2018
Analysis of Alzheimer's Disease Based on the Random Neural Network Cluster in fMRI.
Article in Frontiers in neuroinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- Article
- Altered functional connectivity of primary olfactory cortex-hippocampus-frontal cortex in subjective cognitive decline during odor stimulation.Human brain mapping · 2024Article
- Deep Learning-Based Ensembling Technique to Classify Alzheimer's Disease Stages Using Functional MRI.Journal of healthcare engineering · 2023Article
- A Novel Key Features Screening Method Based on Extreme Learning Machine for Alzheimer's Disease Study.Frontiers in aging neuroscience · 2022Article
- Project, toolkit, and database of neuroinformatics ecosystem: A summary of previous studies on "Frontiers in Neuroinformatics".Frontiers in neuroinformatics · 2022Review
- Hippocampal Subregion and Gene Detection in Alzheimer's Disease Based on Genetic Clustering Random Forest.Genes · 2021Article
- Static and dynamic functional connectivity supports the configuration of brain networks associated with creative cognition.Scientific reports · 2021Article
- Detecting genetic associations with brain imaging phenotypes in Alzheimer's disease via a novel structured SCCA approach.Medical image analysis · 2020Article
- Classification and Graphical Analysis of Alzheimer's Disease and Its Prodromal Stage Using Multimodal Features From Structural, Diffusion, and Functional Neuroimaging Data and the APOE Genotype.Frontiers in aging neuroscience · 2020Article
- Brain Structural Correlates of Odor Identification in Mild Cognitive Impairment and Alzheimer's Disease Revealed by Magnetic Resonance Imaging and a Chinese Olfactory Identification Test.Frontiers in neuroscience · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As Alzheimer's disease (AD) is featured with degeneration and irreversibility, the diagnosis of AD at early stage is important. In recent years, some researchers have tried to apply neural network (NN) to classify AD patients from healthy controls (HC) based on functional MRI (fMRI) data. But most study focus on a single NN and the classification accuracy was not high. Therefore, this paper used the random neural network cluster which was composed of multiple NNs to improve classification performance. Sixty one subjects (25 AD and 36 HC) were acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. This method not only could be used in the classification, but also could be used for feature selection. Firstly, we chose Elman NN from five types of NNs as the optimal base classifier of random neural network cluster based on the results of feature selection, and the accuracies of the random Elman neural network cluster could reach to 92.31% which was the highest and stable. Then we used the random Elman neural network cluster to select significant features and these features could be used to find out the abnormal regions. Finally, we found out 23 abnormal regions such as the precentral gyrus, the frontal gyrus and supplementary motor area. These results fully show that the random neural network cluster is worthwhile and meaningful for the diagnosis of AD.
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