ArticleFrontiers in psychiatry2022
A Triple-Network Dynamic Connection Study in Alzheimer's Disease.
Article in Frontiers in psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Acupuncture on mild cognitive impairment: A systematic review of neuroimaging studies.Frontiers in aging neuroscience · 2023Pooled it
- Article
- The interplay between insomnia symptoms and Alzheimer's disease across three main brain networks.Sleep · 2024Article
- Aberrant Brain Triple-Network Effective Connectivity Patterns in Type 2 Diabetes Mellitus.Diabetes therapy : research, treatment and education of diabetes and related disorders · 2024Article
- Resting-State Functional Connectivity Difference in Alzheimer's Disease and Mild Cognitive Impairment Using Threshold-Free Cluster Enhancement.Diagnostics (Basel, Switzerland) · 2023Article
- Temporal Irreversibility of Large-Scale Brain Dynamics in Alzheimer's Disease.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
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Authors and funding
7 authors.
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Abstract
Alzheimer's disease (AD) was associated with abnormal organization and function of large-scale brain networks. We applied group independent component analysis (Group ICA) to construct the triple-network consisting of the saliency network (SN), the central executive network (CEN), and the default mode network (DMN) in 25 AD, 60 mild cognitive impairment (MCI) and 60 cognitively normal (CN) subjects. To explore the dynamic functional network connectivity (dFNC), we investigated dynamic time-varying triple-network interactions in subjects using Group ICA analysis based on k-means clustering (GDA-k-means). The mean of brain state-specific network interaction indices (meanNII) in the three groups (AD, MCI, CN) showed significant differences by ANOVA analysis. To verify the robustness of the findings, a support vector machine (SVM) was taken meanNII, gender and age as features to classify. This method obtained accuracy values of 95, 94, and 77% when classifying AD vs. CN, AD vs. MCI, and MCI vs. CN, respectively. In our work, the findings demonstrated that the dynamic characteristics of functional interactions of the triple-networks contributed to studying the underlying pathophysiology of AD. It provided strong evidence for dysregulation of brain dynamics of AD.
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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 Socratic graph.