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Multivariate classification of patients with Alzheimer’s and dementia with Lewy bodies using high-dimensional cortical thickness measurements: an MRI surface-based morphometric study

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Abstract

Context

Alzheimer’s disease (AD) and dementia with Lewy bodies (DLB) are the most common neurodegenerative dementia types. It is important to differentiate between them because of the differences in prognosis and treatment approaches.

Objective

Investigate if sparse partial least squares (SPLS) classification of cortical thickness measurements could differentiate between AD and DLB.

Methods

Two independent cohorts without MR-protocol alignment in Norway and Slovenia with 97 AD and DLB subjects were enrolled. Cortical thickness measurements acquired with Freesurfer were used in subsequent SPLS classification runs. The cohorts were analyzed separately and afterwards combined. The models were trained with leave-one-out cross validation and test datasets where used when available. To study the impact of MR-protocol alignment, the classifiers were additionally tested on sets drawn exclusively from the independent cohorts.

Results

The obtained sensitivity/specificity/AUC values were 94.4/88.89/0.978 and 88.2/94.1/0.969 in the Norwegian and Slovenian cohorts, respectively. Both cohorts showed AD-associated pattern of thinning in mid-anterior temporal, occipital and subgenual cingulate cortex, whereas the pattern supportive for DLB included thinning in dorsal cingulate, posterior temporal and lateral orbitofrontal regions. When combining the cohorts, sensitivity/specificity/AUC were 82.1/85.7/0.948 for the training and 77.8/75/0.731 for the testing datasets with the same pattern-of-difference. The models tested on datasets drawn exclusively from the independent cohorts did not produce adequate accuracy.

Conclusion

SPLS classification of cortical thickness is a good method for differentiating between AD and DLB, relatively stable even for mixed data, but not when tested on completely independent data drawn from different cohorts (without MR-protocol alignment).

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Fig. 1
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Acknowledgments

The study received financial support from Western Norway Regional Health Authority, the Norwegian Research Council and MedIm (Norwegian University of Science and Technology, Trondheim). In addition, MedViz (University of Bergen) supported us with computer power. The Slovenian study was financially supported by the Slovenian Research Agency. Also thanks to the Strategic Research Programme in Neuroscience at Karolinska Institutet (StratNeuro) and Swedish Brain Power. We are very thankful to the FreeSurfer Development Team and personally to Douglas N. Greve, who kindly suggested the solution for mapping the results into the brain space and, of course, to the R Development Team for their great project.

Conflicts of interest

The authors declare that they have no conflict of interest.

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Correspondence to Alexander V. Lebedev.

Appendix

Appendix

See Figs. 3 and 4.

Fig. 3
figure 3

Workflow. Image postprocessing and data analysis were performed in Freesurfer (blue box) and R programming language (brown box). First, the raw MRI images underwent steps for surface-based cortex reconstruction. Next, a cortical model of each individual was registered to a spherical atlas, providing matching across subjects, smoothed, and finally 327,684 measurements of cortical thickness for N subjects were concatenated into a N-by-327,684 matrix, which was used in the subsequent analysis. Then PCA-based outlier detection and GLM-based removal of age-related effects were performed by preparing the data to the further SPLS classification that utilizes variable selection within the context of PLS. Finally, the variable coefficients from the best model were mapped into the brain space in order to define the regions, which were the most relevant for the AD/DLB classification. The figure is partly based on the tutorial materials available on the official Freesurfer webpage (http://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial)

Fig. 4
figure 4

Model selection: two cohorts. The “hot-plot” illustrates the principle of the model selection, consisted of searching for optimal ‘K’ (latent components) and ‘eta’ (sparsity) parameters, based on model’s mean squared error of prediction (MSEP) validated using leave-one-out (LOO) strategy. The search starts from PLS modeling without variable selection (bottom row). “Colder” colors reflect lower MSEP, and, therefore, better models. Red frames indicates optimal ‘K’/'eta’ parameters and contain corresponding MSEP

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Lebedev, A.V., Westman, E., Beyer, M.K. et al. Multivariate classification of patients with Alzheimer’s and dementia with Lewy bodies using high-dimensional cortical thickness measurements: an MRI surface-based morphometric study. J Neurol 260, 1104–1115 (2013). https://doi.org/10.1007/s00415-012-6768-z

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