ArticleTherapeutic advances in neurological disorders2022
Functional MRI and
Article in Therapeutic advances in neurological disorders, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 10 citations in OpenAlex.
- Identifying key brain pathology in bipolar and unipolar depression using a region-specific brain aging trajectories approach: Insights from the Taiwan Aging and Mental Illness Cohort.Psychological medicine · 2025Article
- Gender-based Alzheimer's detection using ResNet-50 and binary dragonfly algorithm on neuroimaging.Frontiers in artificial intelligence · 2025Article
- The Association between the rs6656401 Locus of the CR1 Gene and Structural Alterations of Brain Effects in Han Chinese Patients with Alzheimer's Disease.Current Alzheimer research · 2025Article
- Predicting early Alzheimer's with blood biomarkers and clinical features.Scientific reports · 2024Article
- Gray matter reserve determines glymphatic system function in young-onset Alzheimer's disease: Evidenced by DTI-ALPS and compared with age-matched controls.Psychiatry and clinical neurosciences · 2023Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: In light of advancements in machine learning techniques, many studies have implemented machine learning approaches combined with data measures to predict and classify Alzheimer's disease. Studies that predicted cognitive status with longitudinal follow-up of amyloid-positive individuals remain scarce, however. Objective: We developed models based on voxel-wise functional connectivity (FC) density mapping and the presence of the Methods: We divided 122 participants into cognitive decline and stable cognition groups based on the participants' change rates in Mini-Mental State Examination scores. In addition, we included 68 participants from Alzheimer's Disease Neuroimaging Initiative (ADNI) database as an external validation data set. Subsequently, we developed two classification models: the first model included 99 voxels, and the second model included 99 voxels and the Results: The results revealed that both models demonstrated high accuracy in classifying the two groups in the hold-out test data set. The model for FC demonstrated good performance, with a mean Conclusion: Our findings suggest that the proposed models exhibited promising accuracy for predicting cognitive status after 1 year in amyloid-positive individuals. Notably, the combination of FC and the
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Registered trials
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.