ArticleBMC psychiatry2019
Brain network alterations in individuals with and without mild cognitive impairment: parallel independent component analysis of AV1451 and AV45 positron emission tomography.
Article in BMC psychiatry, 2019. 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, 17 citations in OpenAlex.
- The Amyloid-β Pathway in Alzheimer's Disease.Molecular psychiatry · 2021Pooled it
- Article
- Abnormal Association Between Neural Activity and Genetic Expressions of Impulsivity in Attention-Deficit/Hyperactivity Disorder: An Adolescent Brain Cognitive Development Study.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2025Article
- Advance and Prospect of Positron Emission Tomography in Alzheimer's disease research.Molecular psychiatry · 2025Review
- A Review of Machine Learning Approaches for Brain Positron Emission Tomography Data Analysis.Nuclear medicine and molecular imaging · 2024Review
- Alterations of Structural Network Efficiency in Early-Onset and Late-Onset Alzheimer's Disease.Journal of clinical neurology (Seoul, Korea) · 2024Article
- A review of the flortaucipir literature for positron emission tomography imaging of tau neurofibrillary tangles.Brain communications · 2024Review
- Affective symptoms and regional cerebral tau burden in early-stage Alzheimer's disease.International journal of geriatric psychiatry · 2021Article
- Financial Capacity and Regional Cerebral Tau in Cognitively Normal Older Adults, Mild Cognitive Impairment, and Alzheimer's Disease Dementia.Journal of Alzheimer's disease : JAD · 2021Article
- Resting-state functional connectivity and amyloid burden influence longitudinal cortical thinning in the default mode network in preclinical Alzheimer's disease.NeuroImage. Clinical · 2020Article
- PET Imaging of Neutrophils Infiltration in Alzheimer's Disease Transgenic Mice.Frontiers in neurology · 2020Article
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Authors and funding
7 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundAmyloid β (Aβ) and tau proteins are considered as critical factors that affect Alzheimer's disease (AD) and mild cognitive impairment (MCI). Although many studies have conducted on these two proteins, little study has investigated the relationship between their spatial distributions. This study aims to explore the associations of spatial patterns between Aβ deposition and tau deposition in patients with MCI and normal control (NC).
methodsWe used multimodality positron emission tomography (PET) data from a clinically heterogeneous population of patients with MCI and NC. All data were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database containing information of 65 patients with MCI and 75 NC who both had undergone AV45 (Aβ) and AV1451 (tau) PET. To assess the spatial distribution of Aβ and tau deposition, we employed parallel independent component analysis (pICA), which enabled the joint analysis of multimodal imaging data. pICA was conducted to identify the significant difference and correlation relationship of brain networks between Aβ PET and tau PET in MCI and NC groups.
resultsOur results revealed the strongly correlated network between Aβ PET and tau PET were colocalized with the default-mode network (DMN). Simultaneously, in comparison of the spatial distribution between Aβ PET and tau PET, it was found that the significant differences between MCI and NC were mainly distributed in DMN, cognitive control network and visual networks. The altered brain networks obtained from pICA analysis are consistent with the abnormalities of brain network in MCI patients.
conclusionsFindings suggested the abnormal spatial distribution regions of tau PET were correlated with the abnormal spatial distribution regions of Aβ PET, and both of which were located in DMN network. This study revealed that combining pICA with multimodal imaging data is an effective approach for distinguishing MCI patients from NC group.
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