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A clinically-translatable machine learning algorithm for the prediction of Alzheimer’s disease conversion: further evidence of its accuracy via a transfer learning approach

Published online by Cambridge University Press:  14 November 2018

Massimiliano Grassi Open the ORCID record for Massimiliano Grassi [Opens in a new window] ,
David A. Loewenstein ,
Daniela Caldirola ,
Koen Schruers ,
Ranjan Duara  and
Giampaolo Perna
Massimiliano Grassi*
Affiliation:
Department of Clinical Neurosciences, Hermanas Hospitalarias, Villa San Benedetto Menni Hospital, FoRiPsi, Albese con Cassano, Como, Italy
David A. Loewenstein
Affiliation:
Department of Psychiatry and Behavioral Sciences, Miller School of Medicine, University of Miami, Miami, Florida, USA Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, Florida, USA Center on Aging, Miller School of Medicine, University of Miami, Miami, Florida, USA
Daniela Caldirola
Affiliation:
Department of Clinical Neurosciences, Hermanas Hospitalarias, Villa San Benedetto Menni Hospital, FoRiPsi, Albese con Cassano, Como, Italy
Koen Schruers
Affiliation:
Research Institute of Mental Health and Neuroscience and Department of Psychiatry and Neuropsychology, Faculty of Health, Medicine and Life Sciences, University of Maastricht, Maastricht, the Netherlands
Ranjan Duara
Affiliation:
Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, Florida, USA Courtesy Professor of Neurology, Department of Neurology, University of Florida College of Medicine, Gainesville, Florida, USA Herbert Wertheim College of Medicine, Florida International University, Miami, Florida, USA
Giampaolo Perna
Affiliation:
Department of Clinical Neurosciences, Hermanas Hospitalarias, Villa San Benedetto Menni Hospital, FoRiPsi, Albese con Cassano, Como, Italy Department of Psychiatry and Behavioral Sciences, Miller School of Medicine, University of Miami, Miami, Florida, USA Research Institute of Mental Health and Neuroscience and Department of Psychiatry and Neuropsychology, Faculty of Health, Medicine and Life Sciences, University of Maastricht, Maastricht, the Netherlands Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy Mantovani Foundation, Arconate, Italy
*
Correspondence should be addressed to: Massimiliano Grassi, Department of Clinical Neurosciences, Villa San Benedetto Menni, Hermanas Hospitalarias, FoRiPsi, via Roma 16, 22032 Albese con Cassano (Como), Italy. Phone: +39 031 4291511; Fax: +39 031 427246. Email: [email protected].
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Abstract

Background:

In a previous study, we developed a highly performant and clinically-translatable machine learning algorithm for a prediction of three-year conversion to Alzheimer’s disease (AD) in subjects with Mild Cognitive Impairment (MCI) and Pre-mild Cognitive Impairment. Further tests are necessary to demonstrate its accuracy when applied to subjects not used in the original training process. In this study, we aimed to provide preliminary evidence of this via a transfer learning approach.

Methods:

We initially employed the same baseline information (i.e. clinical and neuropsychological test scores, cardiovascular risk indexes, and a visual rating scale for brain atrophy) and the same machine learning technique (support vector machine with radial-basis function kernel) used in our previous study to retrain the algorithm to discriminate between participants with AD (n = 75) and normal cognition (n = 197). Then, the algorithm was applied to perform the original task of predicting the three-year conversion to AD in the sample of 61 MCI subjects that we used in the previous study.

Results:

Even after the retraining, the algorithm demonstrated a significant predictive performance in the MCI sample (AUC = 0.821, 95% CI bootstrap = 0.705–0.912, best balanced accuracy = 0.779, sensitivity = 0.852, specificity = 0.706).

Conclusions:

These results provide a first indirect evidence that our original algorithm can also perform relevant generalized predictions when applied to new MCI individuals. This motivates future efforts to bring the algorithm to sufficient levels of optimization and trustworthiness that will allow its application in both clinical and research settings.

Keywords

Alzheimer’s diseaseclinical prediction rulemachine learningmild cognitive impairmentpersonalized medicineprecision medicinetransfer learning
Type
Original Research Article
Information
International Psychogeriatrics , Volume 31 , Issue 7: Issue Theme: Diverse Approaches to Stemming Cognitive Decline , July 2019 , pp. 937 - 945
Copyright
© International Psychogeriatric Association 2018 

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References

Agarwal, S., Ghanty, P. and Pal, N. R. (2015). Identification of a small set of plasma signalling proteins using neural network for prediction of Alzheimer’s disease. Bioinformatics, 31, 2505–2513. doi: 10.1093/bioinformatics/btv173.CrossRefGoogle ScholarPubMed
American Psychiatric Association. (2000). Diagnostic and Statistical Manual of Mental Disorders DSM-IV-TR (Text Revision), 4 edn. Washington, DC: American Psychiatric Association.Google Scholar
Apostolova, L. G., et al. (2014). ApoE4 effects on automated diagnostic classifiers for mild cognitive impairment and Alzheimer’s disease. Neuroimage Clinical, 4, 461–472. doi: 10.1016/j.nicl.2013.12.012.CrossRefGoogle ScholarPubMed
Benedict, R. H. and Zgaljardic, D. J. (1998). Practice effects during repeated administrations of memory tests with and without alternate forms. Journal of Clinical and Experimental Neuropsychology, 20, 339–352. doi: 10.1076/jcen.20.3.339.822.CrossRefGoogle ScholarPubMed
Brooks, L. G. and Loewenstein, D. A. (2010). Assessing the progression of mild cognitive impairment to Alzheimer’s disease: current trends and future directions. Alzheimers Research & Therapy, 2, 28. doi: 10.1186/alzrt52.Google ScholarPubMed
Cheng, B., Zhang, D., Chen, S., Kaufer, D. I., Shen, D. and Alzheimer’s Disease Neuroimaging Initiative. (2013). Semi-supervised multimodal relevance vector regression improves cognitive performance estimation from imaging and biological biomarkers. Neuroinformatics, 11, 339–353. doi: 10.1007/s12021-013-9180-7.CrossRefGoogle ScholarPubMed
Clark, D. G., et al. (2014). Latent information in fluency lists predicts functional decline in persons at risk for Alzheimer disease. Cortex, 55, 202–218. doi: 10.1016/j.cortex.2013.12.013.CrossRefGoogle ScholarPubMed
Collij, L. E., et al. (2016). Application of machine learning to arterial spin labeling in mild cognitive impairment and Alzheimer disease. Radiology, 281, 865–875. doi: 10.1148/radiol.2016152703.CrossRefGoogle ScholarPubMed
Cui, Y., et al. (2011). Identification of conversion from mild cognitive impairment to Alzheimer’s disease using multivariate predictors. PLoS One, 6, e21896. doi: 10.1371/journal.pone.0021896.CrossRefGoogle ScholarPubMed
Duara, R., et al. (2008). Medial temporal lobe atrophy on MRI scans and the diagnosis of Alzheimer disease. Neurology, 71, 1986–1992. doi: 10.1212/01.wnl.0000336925.79704.9f.CrossRefGoogle Scholar
Duara, R., et al. (2010). Diagnosis and staging of mild cognitive impairment, using a modification of the clinical dementia rating scale: the mCDR. International Journal of Geriatric Psychiatry, 25, 282–289. doi: 10.1002/gps.2334.CrossRefGoogle ScholarPubMed
Dukart, J., Sambataro, F. and Bertolino, A. (2016). Accurate prediction of conversion to Alzheimer’s disease using imaging, genetic, and neuropsychological biomarkers. Journal of Alzheimer’s Disease, 49, 1143–1159. doi: 10.3233/JAD-150570.CrossRefGoogle ScholarPubMed
Efron, B. (1987). Better bootstrap confidence intervals. Journal of the American Statistical Association, 82, 171–185. doi: 10.1080/01621459.1987.10478410.CrossRefGoogle Scholar
Grassi, M., et al. (2018). A clinically-translatable machine learning algorithm for the prediction of Alzheimer’s disease conversion in individuals with mild and premild cognitive impairment. Journal of Alzheimer’s Disease, 61, 1555–1573. doi: 10.3233/jad-170547.CrossRefGoogle ScholarPubMed
Hinrichs, C., Singh, V., Xu, G., Johnson, S. C. and Alzheimers Disease Neuroimaging Initiative. (2011). Predictive markers for AD in a multi-modality framework: an analysis of MCI progression in the ADNI population. Neuroimage, 55, 574–589. doi: 10.1016/j.neuroimage.2010.10.081.CrossRefGoogle Scholar
Hojjati, S. H., Ebrahimzadeh, A., Khazaee, A., Babajani-Feremi, A. and Alzheimer’s Disease Neuroimaging Initiative. (2017). Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM. Journal of Neuroscience Methods, 282, 69–80. doi: 10.1016/j.jneumeth.2017.03.006.CrossRefGoogle ScholarPubMed
Loewenstein, D. A., Curiel, R. E., Duara, R. and Buschke, H. (2017). Novel cognitive paradigms for the detection of memory impairment in preclinical Alzheimer’s disease. Assessment, 25, 348–359. doi: 10.1177/1073191117691608.CrossRefGoogle ScholarPubMed
Loewenstein, D. A., et al. (2004). Semantic interference deficits and the detection of mild Alzheimer’s disease and mild cognitive impairment without dementia. Journal of the International Neuropsychological Society, 10, 91–100. doi: 10.1017/s1355617704101112.CrossRefGoogle ScholarPubMed
Long, X., Chen, L., Jiang, C., Zhang, L. and Alzheimer’s Disease Neuroimaging Initiative. (2017). Prediction and classification of Alzheimer disease based on quantification of MRI deformation. PLoS One, 12, e0173372. doi: 10.1371/journal.pone.0173372.CrossRefGoogle ScholarPubMed
Mathotaarachchi, S., et al. (2017). Identifying incipient dementia individuals using machine learning and amyloid imaging. Neurobiology of Aging, 59, 80–90. doi: 10.1016/j.neurobiolaging.2017.06.027.CrossRefGoogle ScholarPubMed
McKhann, G., et al. (1984). Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services task force on Alzheimer’s disease. Neurology, 34, 939. doi: 10.1212/WNL.34.7.939.CrossRefGoogle ScholarPubMed
Minhas, S., Khanum, A., Riaz, F., Alvi, A. and Khan, S. A. (2016). A non parametric approach for mild cognitive impairment to AD conversion prediction: results on longitudinal data. IEEE Journal of Biomedical and Health Informatics, 21, 1403–1410. doi: 10.1109/JBHI.2016.2608998.CrossRefGoogle Scholar
Moradi, E., Pepe, A., Gaser, C., Huttunen, H., Tohka, J. and Alzheimer’s Disease Neuroimaging Initiative. (2015). Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects. Neuroimage, 104, 398–412. doi: 10.1016/j.neuroimage.2014.10.002.CrossRefGoogle ScholarPubMed
Morris, J. C. (1993). The clinical dementia rating (CDR): current version and scoring rules. Neurology, 43, 2412–2414. doi: 10.1212/WNL.43.11.2412-a.CrossRefGoogle ScholarPubMed
Nho, K., et al. (2010). Automatic prediction of conversion from mild cognitive impairment to probable Alzheimer’s disease using structural magnetic resonance imaging. AMIA Annual Symposium Proceedings, 2010, 542–546.Google ScholarPubMed
Plant, C., et al. (2010). Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer’s disease. Neuroimage, 50, 162–174. doi: 10.1016/j.neuroimage.2009.11.046.CrossRefGoogle ScholarPubMed
Retico, A., et al. (2015). Predictive models based on support vector machines: whole-brain versus regional analysis of structural MRI in the Alzheimer’s disease. Journal of Neuroimaging, 25, 552–563. doi: 10.1111/jon.12163.CrossRefGoogle ScholarPubMed
Scheltens, P., et al. (1992). Atrophy of medial temporal lobes on MRI in “probable” Alzheimer’s disease and normal ageing: diagnostic value and neuropsychological correlates. Journal of Neurology, Neurosurgery, and Psychiatry, 55, 967–972. doi: 10.1136/jnnp.55.10.967.CrossRefGoogle ScholarPubMed
Urs, R. et al. (2009). Visual rating system for assessing magnetic resonance images: a tool in the diagnosis of mild cognitive impairment and Alzheimer disease. Journal of Computer Assisted Tomography, 33, 73–78. doi: 10.1097/RCT.0b013e31816373d8.CrossRefGoogle Scholar
Wechsler, D. (1997). WMS-III: Wechsler Memory Scale Administration and Scoring Manual. San Antonio: Psychological Corporation.Google Scholar
Weiss, K., Khoshgoftaar, T. M. and Wang, D. (2016). A survey of transfer learning. Journal of Big Data, 3, 9. doi: 10.1186/s40537-016-0043-6.CrossRefGoogle Scholar
Westman, E., Aguilar, C., Muehlboeck, J. S. and Simmons, A. (2013). Regional magnetic resonance imaging measures for multivariate analysis in Alzheimer’s disease and mild cognitive impairment. Brain Topography, 26, 9–23. doi: 10.1007/s10548-012-0246-x.CrossRefGoogle ScholarPubMed
Young, J., et al. (2013). Accurate multimodal probabilistic prediction of conversion to Alzheimer’s disease in patients with mild cognitive impairment. Neuroimage Clinical, 2, 735–745. doi: 10.1016/j.nicl.2013.05.004.CrossRefGoogle ScholarPubMed