Using path signatures to predict a diagnosis of Alzheimer’s disease
Autoři:
P. J. Moore aff001; T. J. Lyons aff001; J. Gallacher aff002;
Působiště autorů:
Mathematical Institute, University of Oxford, Oxford, United Kingdom
aff001; Department of Psychiatry, University of Oxford, Oxford, United Kingdom
aff002
Vyšlo v časopise:
PLoS ONE 14(9)
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pone.0222212
Souhrn
The path signature is a means of feature generation that can encode nonlinear interactions in data in addition to the usual linear terms. It provides interpretable features and its output is a fixed length vector irrespective of the number of input points or their sample times. In this paper we use the path signature to provide features for identifying people whose diagnosis subsequently converts to Alzheimer’s disease. In two separate classification tasks we distinguish converters from 1) healthy individuals, and 2) individuals with mild cognitive impairment. The data used are time-ordered measurements of the whole brain, ventricles and hippocampus from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We find two nonlinear interactions which are predictive in both cases. The first interaction is change of hippocampal volume with time, and the second is a change of hippocampal volume relative to the volume of the whole brain. While hippocampal and brain volume changes are well known in Alzheimer’s disease, we demonstrate the power of the path signature in their identification and analysis without manual feature selection. Sequential data is becoming increasingly available as monitoring technology is applied, and the path signature method is shown to be a useful tool in the processing of this data.
Klíčová slova:
Biology and life sciences – Research and analysis methods – Neuroscience – Cognitive science – Learning and memory – Computer and information sciences – Anatomy – Medicine and health sciences – Diagnostic medicine – Neurology – Cognitive neurology – Cognitive impairment – Cognitive neuroscience – Imaging techniques – Mental health and psychiatry – Dementia – Alzheimer's disease – Neurodegenerative diseases – Brain – Cognition – Memory – Neuroimaging – Artificial intelligence – Machine learning – Brain diseases – Hippocampus – Alzheimer's disease diagnosis and management
Zdroje
1. Nestor PJ, Scheltens P, Hodges JR. Advances in the early detection of Alzheimer’s disease. Nature medicine. 2004;10(7):S34. doi: 10.1038/nrn1433
2. Gauthier S, Reisberg B, Zaudig M, Petersen RC, Ritchie K, Broich K, et al. Mild cognitive impairment. The Lancet. 2006;367(9518):1262–1270. doi: 10.1016/S0140-6736(06)68542-5
3. Peterson R, Stevens J, Ganguli M, Tangalos E, Cummings J, DeKosky S. Practice parameter: early detection of dementia: mild cognitive impairment (an evidence based review). Report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology. 2001;56(9):1133–1142. doi: 10.1212/WNL.56.9.1133
4. Marinescu RV, Oxtoby NP, Young AL, Bron EE, Toga AW, Weiner MW, et al. TADPOLE Challenge: Prediction of Longitudinal Evolution in Alzheimer’s Disease. ArXiv e-prints. 2018;.
5. Hastie T, Tibshirani R, Friedman J, Hastie T, Friedman J, Tibshirani R. The elements of statistical learning. vol. 2. Springer; 2009.
6. Arribas IP, Goodwin GM, Geddes JR, Lyons T, Saunders KE. A signature-based machine learning model for distinguishing bipolar disorder and borderline personality disorder. Translational Psychiatry. 2018;8(1):274. doi: 10.1038/s41398-018-0334-0
7. Xie Z, Sun Z, Jin L, Ni H, Lyons T. Learning spatial-semantic context with fully convolutional recurrent network for online handwritten chinese text recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2017;.
8. Chevyrev I, Kormilitzin A. A primer on the signature method in machine learning. arXiv preprint arXiv:160303788. 2016;.
9. Bron EE, Smits M, Van Der Flier WM, Vrenken H, Barkhof F, Scheltens P, et al. Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge. NeuroImage. 2015;111:562–579. doi: 10.1016/j.neuroimage.2015.01.048 25652394
10. Sarica A, Cerasa A, Quattrone A, Calhoun V. Editorial on Special Issue: Machine learning on MCI; 2018.
11. Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Green RC, et al. Recent publications from the Alzheimer’s Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials. Alzheimer’s & Dementia. 2017;.
12. Ellis KA, Bush AI, Darby D, De Fazio D, Foster J, Hudson P, et al. The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer’s disease. International Psychogeriatrics. 2009;21(4):672–687. doi: 10.1017/S1041610209009405
13. Lovestone S, Francis P, Kloszewska I, Mecocci P, Simmons A, Soininen H, et al. AddNeuroMed—the European collaboration for the discovery of novel biomarkers for Alzheimer’s disease. Annals of the New York Academy of Sciences. 2009;1180(1):36–46. doi: 10.1111/j.1749-6632.2009.05064.x
14. Ganz M, Greve DN, Fischl B, Konukoglu E, the ADNI Initiative, et al. Relevant feature set estimation with a knock-out strategy and random forests. NeuroImage. 2015;122:131–148. doi: 10.1016/j.neuroimage.2015.08.006 26272728
15. Lebedev A, Westman E, Van Westen G, Kramberger M, Lundervold A, Aarsland D, et al. Random Forest ensembles for detection and prediction of Alzheimer’s disease with a good between-cohort robustness. NeuroImage: Clinical. 2014;6:115–125. doi: 10.1016/j.nicl.2014.08.023
16. Li H, Liu Y, Gong P, Zhang C, Ye J, the ADNI Initiative, et al. Hierarchical interactions model for predicting Mild Cognitive Impairment (MCI) to Alzheimer’s Disease (AD) conversion. PloS one. 2014;9(1):e82450. doi: 10.1371/journal.pone.0082450
17. Moore P, Lyons TJ, Gallacher J, the ADNI Initiative, et al. Random forest prediction of Alzheimer’s disease using pairwise selection from time series data. PlOS ONE. 2019;14(2):e0211558. doi: 10.1371/journal.pone.0211558
18. Schmitter D, Roche A, Maréchal B, Ribes D, Abdulkadir A, Bach-Cuadra M, et al. An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer’s disease. NeuroImage: Clinical. 2015;7:7–17. doi: 10.1016/j.nicl.2014.11.001
19. Sørensen L, Igel C, Pai A, Balas I, Anker C, Lillholm M, et al. Differential diagnosis of mild cognitive impairment and Alzheimer’s disease using structural MRI cortical thickness, hippocampal shape, hippocampal texture, and volumetry. NeuroImage: Clinical. 2017;13:470–482. doi: 10.1016/j.nicl.2016.11.025
20. Westman E, Simmons A, Muehlboeck JS, Mecocci P, Vellas B, Tsolaki M, et al. AddNeuroMed and ADNI: similar patterns of Alzheimer’s atrophy and automated MRI classification accuracy in Europe and North America. Neuroimage. 2011;58(3):818–828. doi: 10.1016/j.neuroimage.2011.06.065
21. Tibshirani R. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological). 1996;58(1):267–288.
22. Chen KT. Integration of paths–A faithful representation of paths by noncommutative formal power series. Transactions of the American Mathematical Society. 1958;89(2):395–407. doi: 10.2307/1993193
23. Boedihardjo H, Geng X, Lyons T, Yang D. The signature of a rough path: uniqueness. Advances in Mathematics. 2016;293:720–737. doi: 10.1016/j.aim.2016.02.011
24. Hambly B, Lyons T. Uniqueness for the signature of a path of bounded variation and the reduced path group. Annals of Mathematics. 2010; p. 109–167. doi: 10.4007/annals.2010.171.109
25. Lyons T. Rough paths, Signatures and the modelling of functions on streams. arXiv preprint arXiv:14054537. 2014;.
26. Lyons T, Qian Z. System control and rough paths, (2002);.
27. Christ M, Braun N, Neuffer J, Kempa-Liehr AW. Time Series FeatuRe Extraction on basis of scalable hypothesis tests (tsfresh–A Python package). Neurocomputing. 2018;307:72–77. doi: 10.1016/j.neucom.2018.03.067
28. Chincarini A, Sensi F, Rei L, Gemme G, Squarcia S, Longo R, et al. Integrating longitudinal information in hippocampal volume measurements for the early detection of Alzheimer’s disease. NeuroImage. 2016;125:834–847. doi: 10.1016/j.neuroimage.2015.10.065
Článok vyšiel v časopise
PLOS One
2019 Číslo 9
- Metamizol jako analgetikum první volby: kdy, pro koho, jak a proč?
- Nejasný stín na plicích – kazuistika
- Masturbační chování žen v ČR − dotazníková studie
- Těžké menstruační krvácení může značit poruchu krevní srážlivosti. Jaký management vyšetření a léčby je v takovém případě vhodný?
- Fixní kombinace paracetamol/kodein nabízí synergické analgetické účinky
Najčítanejšie v tomto čísle
- Graviola (Annona muricata) attenuates behavioural alterations and testicular oxidative stress induced by streptozotocin in diabetic rats
- CH(II), a cerebroprotein hydrolysate, exhibits potential neuro-protective effect on Alzheimer’s disease
- Comparison between Aptima Assays (Hologic) and the Allplex STI Essential Assay (Seegene) for the diagnosis of Sexually transmitted infections
- Assessment of glucose-6-phosphate dehydrogenase activity using CareStart G6PD rapid diagnostic test and associated genetic variants in Plasmodium vivax malaria endemic setting in Mauritania