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Cited by 249Dementia and Cognitive Impairment ResearchArtificial Intelligence in HealthcareAlzheimer's disease research and treatments

The DOI matches its Crossref record; 62 of the 64 references it cites were found in a registry or an index.

Accurate multimodal probabilistic prediction of conversion to Alzheimer’s disease in patients with mild cognitive impairment

Open access
Young, Jonathan, Modat, Marc, Cardoso, Manuel J., Mendelson, Alex, Cash, Dave, Ourselin, Sebastien·2013·NeuroImage Clinical
DOI 10.1016/j.nicl.2013.05.004PMC PMC3777690
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Abstract

Accurately identifying the patients that have mild cognitive impairment (MCI) who will go on to develop Alzheimer's disease (AD) will become essential as new treatments will require identification of AD patients at earlier stages in the disease process. Most previous work in this area has centred around the same automated techniques used to diagnose AD patients from healthy controls, by coupling high dimensional brain image data or other relevant biomarker data to modern machine learning techniques. Such studies can now distinguish between AD patients and controls as accurately as an experienced clinician. Models trained on patients with AD and control subjects can also distinguish between MCI patients that will convert to AD within a given timeframe (MCI-c) and those that remain stable (MCI-s), although differences between these groups are smaller and thus, the corresponding accuracy is lower. The most common type of classifier used in these studies is the support vector machine, which gives categorical class decisions. In this paper, we introduce Gaussian process (GP) classification to the problem. This fully Bayesian method produces naturally probabilistic predictions, which we show correlate well with the actual chances of converting to AD within 3 years in a population of 96 MCI-s and 47 MCI-c subjects. Furthermore, we show that GPs can integrate multimodal data (in this study volumetric MRI, FDG-PET, cerebrospinal fluid, and APOE genotype with the classification process through the use of a mixed kernel). The GP approach aids combination of different data sources by learning parameters automatically from training data via type-II maximum likelihood, which we compare to a more conventional method based on cross validation and an SVM classifier. When the resulting probabilities from the GP are dichotomised to produce a binary classification, the results for predicting MCI conversion based on the combination of all three types of data show a balanced accuracy of 74%. This is a substantially higher accuracy than could be obtained using any individual modality or using a multikernel SVM, and is competitive with the highest accuracy yet achieved for predicting conversion within three years on the widely used ADNI dataset.

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Crossref · 10.1016/j.nicl.2013.05.004

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Figures

Fig. 1 Pipeline by which kernels are constructed from features extracted from each type of data, before being summed to produce a combined kernel.
Fig. 1 Pipeline by which kernels are constructed from features extracted from each type of data, before being summed to produce a combined kernel.CC BY-NC-ND · PubMed Central
Fig. 2 Relation between AD and MCI classification.
Fig. 2 Relation between AD and MCI classification.CC BY-NC-ND · PubMed Central
Fig. 3 Empirical risk vs. corrected predicted risk for the PET group.
Fig. 3 Empirical risk vs. corrected predicted risk for the PET group.CC BY-NC-ND · PubMed Central
Fig. 4 Empirical risk vs. corrected predicted risk for the PET-CSF group.
Fig. 4 Empirical risk vs. corrected predicted risk for the PET-CSF group.CC BY-NC-ND · PubMed Central

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Metadata

Authors
Young, J., Modat, M., Cardoso, M. J., Mendelson, A., Cash, D., & Ourselin, S.
Affiliations
University College London (GB)
Corresponding author
Young, Jonathan
ORCID
Venue
NeuroImage Clinical
Publisher
Elsevier BV
Published
2013
Volume
2
Pages
735-745
ISSN
2213-1582 (ISSN-L)
DOI
10.1016/j.nicl.2013.05.004
PMC
PMC3777690
OpenAlex
W1978763244
Cited by
249
References
64
Citations by year
2026: 12 · 2025: 10 · 2024: 11 · 2023: 11 · 2022: 19 · 2021: 24 · 2020: 25 · 2019: 28 · 2018: 32 · 2017: 22 · 2016: 15 · 2015: 31 · 2014: 7 · 2013: 1
FWCI
12.52
Type
journal article
Language
en
Open access
Licence
Locations
  • NeuroImage Clinical (publishedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • UCL Discovery (University College London) (submittedVersion)
Funders
Engineering and Physical Sciences Research Council (EP/H046410/1)
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
Keywords
Alzheimer's disease, mild cognitive impairment, conversion prediction, Gaussian process classification, probabilistic forecasting, multimodal data integration, mixed kernel, volumetric MRI, FDG-PET, cerebrospinal fluid biomarkers, APOE genotype, support vector machine comparison, ADNI

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