ArticleHuman brain mapping2015
Multimodal analysis of functional and structural disconnection in Alzheimer's disease using multiple kernel SVM.
Article in Human brain mapping, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers, 3 of them syntheses that pooled it.
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Who cites it
79 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled 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
- Reproducible Evaluation of Diffusion MRI Features for Automatic Classification of Patients with Alzheimer's Disease.Neuroinformatics · 2021Pooled it
- Interpretable multimodal MRI fusion in Alzheimer's disease classification using extended parallel multilink joint ICA and 3D ResNet.iScience · 2026Article
- Structuro-Functional Differentiation and Coupling of Gyri and Sulci in the Neonatal Cortex.Human brain mapping · 2026Article
- Structural Neuroimaging Markers of Dementia: Insights from ROC Curve Analysis.Research square · 2025Article
- AI-powered integration of multimodal imaging in precision medicine for neuropsychiatric disorders.Cell reports. Medicine · 2025Review
- Pilot functional magnetic resonance imaging study of clemastine-aided functional recovery on a mouse model of demyelination.Neuroprotection (Chichester, England) · 2025Article
- TGNet: tensor-based graph convolutional networks for multimodal brain network analysis.BioData mining · 2024Article
- Prediction of cognitive performance differences in older age from multimodal neuroimaging data.GeroScience · 2024Article
- fMRI-based Alzheimer's disease detection via functional connectivity analysis: a systematic review.PeerJ. Computer science · 2024Article
- End-stage renal disease accompanied by mild cognitive impairment: A study and analysis of trimodal brain network fusion.PloS one · 2024Article
- Reproducible Abnormalities and Diagnostic Generalizability of White Matter in Alzheimer's Disease.Neuroscience bulletin · 2023Article
- BrainGB: A Benchmark for Brain Network Analysis With Graph Neural Networks.IEEE transactions on medical imaging · 2023Article
- Signed graph representation learning for functional-to-structural brain network mapping.Medical image analysis · 2023Article
- Identifying Alzheimer's disease and mild cognitive impairment with atlas-based multi-modal metrics.Frontiers in aging neuroscience · 2023Article
- Classification and prediction of cognitive performance differences in older age based on brain network patterns using a machine learning approach.Network neuroscience (Cambridge, Mass.) · 2023Article
- Microstructural mapping of dentate gyrus pathology in Alzheimer's disease: A 16.4 Tesla MRI study.NeuroImage. Clinical · 2023Article
- Neurodegenerative diseases-Caps: a capsule network based early screening system for the classification of neurodegenerative diseases.Cognitive neurodynamics · 2022Article
- Modern views of machine learning for precision psychiatry.Patterns (New York, N.Y.) · 2022Review
19 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease (AD) patients exhibit alterations in the functional connectivity between spatially segregated brain regions which may be related to both local gray matter (GM) atrophy as well as a decline in the fiber integrity of the underlying white matter tracts. Machine learning algorithms are able to automatically detect the patterns of the disease in image data, and therefore, constitute a suitable basis for automated image diagnostic systems. The question of which magnetic resonance imaging (MRI) modalities are most useful in a clinical context is as yet unresolved. We examined multimodal MRI data acquired from 28 subjects with clinically probable AD and 25 healthy controls. Specifically, we used fiber tract integrity as measured by diffusion tensor imaging (DTI), GM volume derived from structural MRI, and the graph-theoretical measures 'local clustering coefficient' and 'shortest path length' derived from resting-state functional MRI (rs-fMRI) to evaluate the utility of the three imaging methods in automated multimodal image diagnostics, to assess their individual performance, and the level of concordance between them. We ran the support vector machine (SVM) algorithm and validated the results using leave-one-out cross-validation. For the single imaging modalities, we obtained an area under the curve (AUC) of 80% for rs-fMRI, 87% for DTI, and 86% for GM volume. When it came to the multimodal SVM, we obtained an AUC of 82% using all three modalities, and 89% using only DTI measures and GM volume. Combined multimodal imaging data did not significantly improve classification accuracy compared to the best single measures alone.
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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 OpenQuestion graph.