ArticleNeuroImage. Clinical2018
Selecting the most relevant brain regions to discriminate Alzheimer's disease patients from healthy controls using multiple kernel learning: A comparison across functional and structural imaging modalities and atlases.
Article in NeuroImage. Clinical, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
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- Predicting the progression of MCI and Alzheimer's disease on structural brain integrity and other features with machine learning.GeroScience · 2026Article
- The phobic brain: Morphometric features correctly classify individuals with small animal phobia.Psychophysiology · 2025Article
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- Brain tissue segmentation in neurosurgery: a systematic analysis for quantitative tractography approaches.Acta neurologica Belgica · 2024Review
- Plasma neurofilament light chain associated with impaired regional cerebral blood flow in healthy individuals.Current journal of neurology · 2023Article
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- A Supervised Machine Learning Approach to Classify Brain Morphology of Professional Visual Artists versus Non-Artists.Sensors (Basel, Switzerland) · 2023Article
- Wavelet-Based Fractal Analysis of rs-fMRI for Classification of Alzheimer's Disease.Sensors (Basel, Switzerland) · 2022Article
- Individual prediction and classification of cognitive impairment in patients with white matter lesions based on gray matter volume.Annals of translational medicine · 2022Article
- Structural Features Related to Affective Instability Correctly Classify Patients With Borderline Personality Disorder. A Supervised Machine Learning Approach.Frontiers in psychiatry · 2022Article
- The Coupled Representation of Hierarchical Features for Mild Cognitive Impairment and Alzheimer's Disease Classification.Frontiers in neuroscience · 2022Article
- Abnormal Spontaneous Brain Activity and Cognitive Impairment in Obstructive Sleep Apnea.Nature and science of sleep · 2022Article
- Right-hemispheric language reorganization in patients with brain arteriovenous malformations: A functional magnetic resonance imaging study.Human brain mapping · 2021Article
- A robust brain signature region approach for episodic memory performance in older adults.Brain : a journal of neurology · 2021Article
- Artificial intelligence for molecular neuroimaging.Annals of translational medicine · 2021Review
- Artificial intelligence in single photon emission computed tomography (SPECT) imaging: a narrative review.Annals of translational medicine · 2021Review
- Machine Learning-Derived Multimodal Neuroimaging of Presurgical Target Area to Predict Individual's Seizure Outcomes After Epilepsy Surgery.Frontiers in cell and developmental biology · 2021Article
- Neuroimaging Research on Dementia in Brazil in the Last Decade: Scientometric Analysis, Challenges, and Peculiarities.Frontiers in neurology · 2021Review
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10 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundMachine learning techniques such as support vector machine (SVM) have been applied recently in order to accurately classify individuals with neuropsychiatric disorders such as Alzheimer's disease (AD) based on neuroimaging data. However, the multivariate nature of the SVM approach often precludes the identification of the brain regions that contribute most to classification accuracy. Multiple kernel learning (MKL) is a sparse machine learning method that allows the identification of the most relevant sources for the classification. By parcelating the brain into regions of interest (ROI) it is possible to use each ROI as a source to MKL (ROI-MKL).
methodsWe applied MKL to multimodal neuroimaging data in order to: 1) compare the diagnostic performance of ROI-MKL and whole-brain SVM in discriminating patients with AD from demographically matched healthy controls and 2) identify the most relevant brain regions to the classification. We used two atlases (AAL and Brodmann's) to parcelate the brain into ROIs and applied ROI-MKL to structural (T1) MRI,
resultsCompared to whole-brain SVM, the ROI-MKL approach resulted in better accuracies (with either atlas) for classification using
conclusionThe MKL-ROI approach highlights the high discriminative weight of a subset of brain regions of known relevance to AD, the selection of which contributes to increased classification accuracy when applied to
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