ArticlePloS one2017
Structural and functional connectional fingerprints in mild cognitive impairment and Alzheimer's disease patients.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The application value of Rs-fMRI-based machine learning models for differentiating mild cognitive impairment from Alzheimer's disease: a systematic review and meta-analysis.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Pooled it
- Diagnostic power of resting-state fMRI for detection of network connectivity in Alzheimer's disease and mild cognitive impairment: A systematic review.Human brain mapping · 2021Pooled it
- Predicting the progression of MCI and Alzheimer's disease on structural brain integrity and other features with machine learning.GeroScience · 2026Article
- Integrating Biomarkers From Virtual Reality and Magnetic Resonance Imaging for the Early Detection of Mild Cognitive Impairment Using a Multimodal Learning Approach: Validation Study.Journal of medical Internet research · 2024Article
- Zoom-In Neural Network Deep-Learning Model for Alzheimer's Disease Assessments.Sensors (Basel, Switzerland) · 2022Article
- Enhancing cortical network-level participation coefficient as a potential mechanism for transfer in cognitive training in aMCI.NeuroImage · 2022Article
- Spatial-Temporal Dependency Modeling and Network Hub Detection for Functional MRI Analysis via Convolutional-Recurrent Network.IEEE transactions on bio-medical engineering · 2020Article
- Study on structural atrophy changes and functional connectivity measures in Alzheimer's disease.Journal of medical imaging (Bellingham, Wash.) · 2020Article
- Communicability Characterization of Structural DWI Subcortical Networks in Alzheimer's Disease.Entropy (Basel, Switzerland) · 2019Article
- How random is the random forest? Random forest algorithm on the service of structural imaging biomarkers for Alzheimer's disease: from Alzheimer's disease neuroimaging initiative (ADNI) database.Neural regeneration research · 2018Review
- What We Know About the Brain Structure-Function Relationship.Behavioral sciences (Basel, Switzerland) · 2018Review
- Cerebral Responses to Acupuncture at GV24 and Bilateral GB13 in Rat Models of Alzheimer's Disease.Behavioural neurology · 2018Article
- Random Forest Algorithm for the Classification of Neuroimaging Data in Alzheimer's Disease: A Systematic Review.Frontiers in aging neuroscience · 2017Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Regional volume atrophy and functional degeneration are key imaging hallmarks of Alzheimer's disease (AD) in structural and functional magnetic resonance imaging (MRI), respectively. We jointly explored regional volume atrophy and functional connectivity to better characterize neuroimaging data of AD and mild cognitive impairment (MCI). All data were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We compared regional volume atrophy and functional connectivity in 10 subcortical regions using structural MRI and resting-state functional MRI (rs-fMRI). Neuroimaging data of normal controls (NC) (n = 35), MCI (n = 40), and AD (n = 30) were compared. Significant differences of regional volumes and functional connectivity measures between groups were assessed using permutation tests in 10 regions. The regional volume atrophy and functional connectivity of identified regions were used as features for the random forest classifier to distinguish among three groups. The features of the identified regions were also regarded as connectional fingerprints that could distinctively separate a given group from the others. We identified a few regions with distinctive regional atrophy and functional connectivity patterns for NC, MCI, and AD groups. A three label classifier using the information of regional volume atrophy and functional connectivity of identified regions achieved classification accuracy of 53.33% to distinguish among NC, MCI, and AD. We identified distinctive regional atrophy and functional connectivity patterns that could be regarded as a connectional fingerprint.
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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.