ArticleInterdisciplinary sciences, computational life sciences2021
Pathogenic Factors Identification of Brain Imaging and Gene in Late Mild Cognitive Impairment.
Article in Interdisciplinary sciences, computational life sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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10 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Functional changes of default mode network and structural alterations of gray matter in patients with irritable bowel syndrome: a meta-analysis of whole-brain studies.Frontiers in neuroscience · 2023Pooled it
- Asprosin-PTPRD endocrine resistance links brain dysfunction and systemic wasting in Alzheimer's disease.Research square · 2026Article
- Decoupling MCI-specific signatures from shared neurobiological substrates of cognitive aging via deep learning.NPJ digital medicine · 2026Article
- Linking oxysterols and different stages of mild cognitive impairment: insights from gut metabolites and N6-methyladenosine.Alzheimer's research & therapy · 2025Article
- Changes in Multiparametric Magnetic Resonance Imaging and Plasma Amyloid-Beta Protein in Subjective Cognitive Decline.Brain sciences · 2023Article
- Integrative Bayesian tensor regression for imaging genetics applications.Frontiers in neuroscience · 2023Article
- Article
- Research on Pathogenic Hippocampal Voxel Detection in Alzheimer's Disease Using Clustering Genetic Random Forest.Frontiers in psychiatry · 2022Article
- Hereditable variants of classical protein tyrosine phosphatase genes: Will they prove innocent or guilty?Frontiers in cell and developmental biology · 2022Review
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Authors and funding
4 authors at 1 institution in 1 country.
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Abstract
Mild cognitive impairment (MCI) is a dangerous signal of severe cognitive decline. It can be separated into two steps: early MCI (EMCI) and late MCI (LMCI). As the post-state of MCI and pre-state of Alzheimer's disease (AD), LMCI receives insufficient attention in the field of brain science, causing the internal mechanism of LMCI has not been well understood. To better explore the focus and pathological mechanism of LMCI, a method called genetic evolved random forest (GERF) is applied. Resting functional magnetic resonance imaging (rfMRI) and gene data are obtained from 62 subjects (36 LMCI and 26 normal controls), and Pearson correlation analysis is adopted to perform the multimodal fusion of two types of data to construct fusion features. We identified pathogenic brain regions and genes that are highly related to LMCI using GERF and achieves a good effect. Compared with the normal control (NC) group, the abnormal brain regions of LMCI are PUT.L, PreCG.L, IFGtriang.R, REC.R, DCG.R, PoCG.L, and HES.L, and the pathogenic genes are FHIT, RF00019, FRMD4A, PTPRD, and RBFOX1. More importantly, most of these risk genes and abnormal brain regions have been confirmed to be related to AD and MCI in previous studies. In this study, we mapped them to LMCI with higher accuracies, so as to provide a more robust understanding of the physiological mechanism of MCI.
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