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Regional Magnetic Resonance Imaging Measures for Multivariate Analysis in Alzheimer’s Disease and Mild Cognitive Impairment
Open accessAbstract
Automated structural magnetic resonance imaging (MRI) processing pipelines are gaining popularity for Alzheimer's disease (AD) research. They generate regional volumes, cortical thickness measures and other measures, which can be used as input for multivariate analysis. It is not clear which combination of measures and normalization approach are most useful for AD classification and to predict mild cognitive impairment (MCI) conversion. The current study includes MRI scans from 699 subjects [AD, MCI and controls (CTL)] from the Alzheimer's disease Neuroimaging Initiative (ADNI). The Freesurfer pipeline was used to generate regional volume, cortical thickness, gray matter volume, surface area, mean curvature, gaussian curvature, folding index and curvature index measures. 259 variables were used for orthogonal partial least square to latent structures (OPLS) multivariate analysis. Normalisation approaches were explored and the optimal combination of measures determined. Results indicate that cortical thickness measures should not be normalized, while volumes should probably be normalized by intracranial volume (ICV). Combining regional cortical thickness measures (not normalized) with cortical and subcortical volumes (normalized with ICV) using OPLS gave a prediction accuracy of 91.5 % when distinguishing AD versus CTL. This model prospectively predicted future decline from MCI to AD with 75.9 % of converters correctly classified. Normalization strategy did not have a significant effect on the accuracies of multivariate models containing multiple MRI measures for this large dataset. The appropriate choice of input for multivariate analysis in AD and MCI is of great importance. The results support the use of un-normalised cortical thickness measures and volumes normalised by ICV.
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Machine learning in mental health: a scoping review of methods and applications
Shatte, A. B. R., Hutchinson, D. M., & Teague, S. J. (2019) · Psychological Medicine · doi:10.1017/s0033291719000151 · Cited by 1,047
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Predicting alzheimer’s disease progression using multi-modal deep learning approach
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Alzheimer's disease biomarker discovery using SOMAscan multiplexed protein technology
Sattlecker, M., Kiddle, S. J., Newhouse, S., Proitsi, P., Nelson, S., Williams, S., Johnston, C., Killick, R., Simmons, A., Westman, E., et al. (2014) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2013.09.016 · Cited by 223
Self-citation
Metadata
- Authors
- Westman, E., Aguilar, C., Muehlboeck, J.-S., & Simmons, A.
- Venue
- Brain Topography
- Published
- 2012-08-14
- Volume
- 26
- Issue
- 1
- Pages
- 9-23
- ISSN
- 0896-0267 (ISSN-L)
- 1573-6792
- OpenAlex
- W1994694505
- Cited by
- 207
- References
- 10
- FWCI
- 12.30
- Type
- journal article
- Language
- en
- Open access
- hybrid
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