ArticleFrontiers in neuroscience2018
Non-linear ICA Analysis of Resting-State fMRI in Mild Cognitive Impairment.
Article in Frontiers in neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 3 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Functional magnetic resonance imaging, deep learning, and Alzheimer's disease: A systematic review.Journal of neuroimaging : official journal of the American Society of Neuroimaging · 2023Pooled it
- An Activation Likelihood Estimation Meta-Analysis of Specific Functional Alterations in Dorsal Attention Network in Mild Cognitive Impairment.Frontiers in neuroscience · 2022Pooled it
- Convergent Functional Changes of Default Mode Network in Mild Cognitive Impairment Using Activation Likelihood Estimation.Frontiers in aging neuroscience · 2021Pooled it
- Whole-Brain Static Functional Connectivity Disruptions Based on the Default Mode Network in Patients with Mild Cognitive Impairment.Brain topography · 2026Article
- Personalized functional network connectivity abnormalities in chronic insomnia disorder.Psychoradiology · 2026Article
- Subtypes of cocaine use disorder and their neurobehavioral profiles.Translational psychiatry · 2025Article
- EEG microstates, spectral analysis, and risk prediction in epilepsy comorbid with mild cognitive impairment: alteration in intrinsic brain activity.Journal of translational medicine · 2025Article
- Altered Default Mode Network Connectivity in Mild Cognitive Impairment: Insights from Resting-State fMRI Studies.Current Alzheimer research · 2025Review
- Subtypes in addiction and their neurobehavioral profiles across three functional domains.Translational psychiatry · 2023Article
- Independent Component Analysis with Functional Neuroscience Data Analysis.Journal of biomedical physics & engineering · 2023Article
- Differences in resting-state brain networks and gray matter between APOE ε2 and APOE ε4 carriers in non-dementia elderly.Frontiers in psychiatry · 2023Article
- The expanding horizons of network neuroscience: From description to prediction and control.NeuroImage · 2022Review
- Neural synchronization predicts marital satisfaction.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Healthy Aging Alters the Functional Connectivity of Creative Cognition in the Default Mode Network and Cerebellar Network.Frontiers in aging neuroscience · 2021Article
- Multiple Connection Pattern Combination From Single-Mode Data for Mild Cognitive Impairment Identification.Frontiers in cell and developmental biology · 2021Article
- Self-reference Network-Related Interactions During the Process of Cognitive Impairment in the Early Stages of Alzheimer's Disease.Frontiers in aging neuroscience · 2021Article
- Deep Learning of Static and Dynamic Brain Functional Networks for Early MCI Detection.IEEE transactions on medical imaging · 2020Article
- Group Similarity Constraint Functional Brain Network Estimation for Mild Cognitive Impairment Classification.Frontiers in neuroscience · 2020Article
- Article
- The Effect of Traditional Chinese Mind-Body Exercise (Baduanjin) and Brisk Walking on the Dorsal Attention Network in Older Adults With Mild Cognitive Impairment.Frontiers in psychology · 2019Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Compared to linear independent component analysis (ICA), non-linear ICA is more suitable for the decomposition of mixed components. Existing studies of functional magnetic resonance imaging (fMRI) data by using linear ICA assume that the brain's mixed signals, which are caused by the activity of brain, are formed through the linear combination of source signals. But the application of the non-linear combination of source signals is more suitable for the mixed signals of brain. For this reason, we investigated statistical differences in resting state networks (RSNs) on 32 healthy controls (HC) and 38 mild cognitive impairment (MCI) patients using post-nonlinear ICA. Post-nonlinear ICA is one of the non-linear ICA methods. Firstly, the fMRI data of all subjects was preprocessed. The second step was to extract independent components (ICs) of fMRI data of all subjects. In the third step, we calculated the correlation coefficient between ICs and RSN templates, and selected ICs of the largest spatial correlation coefficient. The ICs represent the corresponding RSNs. After finding out the eight RSNs of MCI group and HC group, one sample
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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.