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Cited by 78Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatmentsCancer-related cognitive impairment studies

MRI Characterizes the Progressive Course of AD and Predicts Conversion to Alzheimer’s Dementia 24 Months Before Probable Diagnosis

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Salvatore, Christian, Cerasa, Antonio, Castiglioni, Isabella·2018·Frontiers in Aging Neuroscience
DOI 10.3389/fnagi.2018.00135PMC PMC5977985
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Abstract

There is no disease-modifying treatment currently available for AD, one of the more impacting neurodegenerative diseases affecting more than 47.5 million people worldwide. The definition of new approaches for the design of proper clinical trials is highly demanded in order to achieve non-confounding results and assess more effective treatment. In this study, a cohort of 200 subjects was obtained from the Alzheimer's Disease Neuroimaging Initiative. Subjects were followed-up for 24 months, and classified as AD (50), progressive-MCI to AD (50), stable-MCI (50), and cognitively normal (50). Structural T1-weighted MRI brain studies and neuropsychological measures of these subjects were used to train and optimize an artificial-intelligence classifier to distinguish mild-AD patients who need treatment (AD + pMCI) from subjects who do not need treatment (sMCI + CN). The classifier was able to distinguish between the two groups 24 months before AD definite diagnosis using a combination of MRI brain studies and specific neuropsychological measures, with 85% accuracy, 83% sensitivity, and 87% specificity. The combined-approach model outperformed the classification using MRI data alone (72% classification accuracy, 69% sensitivity, and 75% specificity). The patterns of morphological abnormalities localized in the temporal pole and medial-temporal cortex might be considered as biomarkers of clinical progression and evolution. These regions can be already observed 24 months before AD definite diagnosis. The best neuropsychological predictors mainly included measures of functional abilities, memory and learning, working memory, language, visuoconstructional reasoning, and complex attention, with a particular focus on some of the sub-scores of the FAQ and AVLT tests.

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  • Using machine learning to quantify neurodegeneration patterns in structural MRI, studies have predicted MCI conversion to AD with modest accuracy ranging from 63 to 85% depending on the cohort, imaging modality and models used.

    “Using machine learning to quantify neurodegeneration patterns in structural MRI, studies have managed to predict MCI conversion to AD with modest accuracy ranging from 63 to 85% depending on the cohort, imaging modality and models used [25–27].”

    From The use of neuroimaging techniques in the early and differential diagnosis of dementia (2023) · cites this paper as [27]

    The Discussion reports that machine-learning studies using structural MRI to distinguish progressive from stable MCI had varying accuracies, supporting the general point. But its reported MRI-only range is 51–75%, not 63–85%. The paper’s 85% result (Abstract; Discussion) used MRI plus neuropsychological measures and classified a broader group than MCI conversion alone. (LLM, conf=0.88)

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Research works printed but not linked: 41

  1. Battista P. Salvatore C. Castiglioni I. (2017). Optimizing neuropsychological assessments for cognitive, behavioral, and functional impairment classification: a machine learning study. 2017: 1850909. 10.1155/2017/1850909 28255200 PMC5307249 · DOI
  2. Bishop C. M. (2006). Pattern Recognition and Machine Learning. New York, NY: Springer, 98– 108.
  3. Casanova R. Hsu F.-C. Sink K. M. Rapp S. R. Williamson J. D. Resnick S. M. (2013). Alzheimer’s disease risk assessment using large-scale machine learning methods. 8: e77949. 10.1371/journal.pone.0077949 24250789 PMC3826736 · DOI
  4. Cerasa A. Castiglioni I. Salvatore C. Funaro A. Martino I. Alfano S. (2015). Biomarkers of eating disorders using support vector machine analysis of structural neuroimaging data: preliminary results. 2015: 924814. 10.1155/2015/924814 26648660 PMC4663371 · DOI
  5. Cortes C. Vapnik V. (1995). Support-vector networks. 20 273– 297. 10.1023/A:1022627411411 · DOI
  6. Cui Y. Liu B. Luo S. Zhen X. Fan M. Liu T. (2011). Identification of conversion from mild cognitive impairment to Alzheimer’s disease using multivariate predictors. 6: e21896. 10.1371/journal.pone.0021896 21814561 PMC3140993 · DOI
  7. Cuingnet R. Gerardin E. Tessieras J. Auzias G. Lehéricy S. Habert M. O. (2011). Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database. 56 766– 781. 10.1016/j.neuroimage.2010.06.013 20542124 · DOI
  8. Diciotti S. Ginestroni A. Bessi V. Giannelli M. Tessa C. Bracco L. (2012). Identification of mild Alzheimer’s disease through automated classification of structural MRI features. 2012 428– 431. 10.1109/EMBC.2012.6345959 23365920 · DOI
  9. Dubois B. Feldman H. H. Jacova C. DeKosky S. T. Barberger-Gateau P. Cummings J. (2007). Research criteria for the diagnosis of Alzheimer’s disease: revising the NINCDS-ADRDA criteria. 6 734– 746. 10.1016/S1474-4422(07)70178-3 17616482 · DOI · Paper page
  10. Dukart J. Sambataro F. Bertolino A. (2015). Accurate prediction of conversion to Alzheimer’s disease using imaging, genetic, and neuropsychological biomarkers. 49 1143– 1159. 10.3233/JAD-150570 26599054 · DOI
  11. Eskildsen S. F. Coupe P. Fonov V. S. Pruessner J. C. Collins D. L. (2015). Structural imaging biomarkers of Alzheimer’s disease: predicting disease progression. 36(Suppl. 1), S23– S31. 10.1016/j.neurobiolaging.2014.04.034 25260851 · DOI
  12. Folstein M. F. Folstein S. E. McHugh P. R. (1975). Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. 12 189– 198. 10.1016/0022-3956(75)90026-6 1202204 · DOI · Paper page
  13. Gainotti G. Quaranta D. Vita M. G. Marra C. (2014). Neuropsychological predictors of conversion from mild cognitive impairment to Alzheimer’s disease. 38 481– 495. 24002185
  14. Grabner G. Janke A. L. Budge M. M. Smith D. Pruessner J. Collins D. L. (2006). Symmetric atlasing and model based segmentation: an application to the hippocampus in older adults. 9 58– 66. 17354756
  15. Haufe S. Meinecke F. Görgen K. Dähne S. Haynes J. D. Blankertz B. (2014). On the interpretation of weight vectors of linear models in multivariate neuroimaging. 87 96– 110. 10.1016/j.neuroimage.2013.10.067 24239590 · DOI
  16. Huys Q. J. Maia T. V. Frank M. J. (2016). Computational psychiatry as a bridge from neuroscience to clinical applications. 19 404– 413. 10.1038/nn.4238 26906507 PMC5443409 · DOI · Paper page
  17. Jovicich J. Czanner S. Greve D. Haley E. Van Der Kouwe A. Gollub R. (2006). Reliability in multi-site structural MRI studies: Effects of gradient non-linearity correction on phantom and human data. 30 436– 443. 10.1016/j.neuroimage.2005.09.046 16300968 · DOI
  18. Khan A. Corbett A. Ballard C. (2017). Emerging treatments for Alzheimer’s disease for non-amyloid and non-tau targets. 17 683– 695. 10.1080/14737175.2017.1326818 28490260 · DOI
  19. Khedher L. Ramírez J. Górriz J. M. Brahim A. Segovia F. The Alzheimer’s Disease Neuroimaging Initiative (2015). Early diagnosis of Alzheimer’s disease based on partial least squares, principal component analysis and support vector machine using segmented MRI images. 151 139– 150. 10.1016/j.neucom.2014.09.072 · DOI · Paper page
  20. Koikkalainen J. Pölönen H. Mattila J. van Gils M. Soininen H. Lötjönen J. (2012). Improved classification of Alzheimer’s disease data via removal of nuisance variability. 7: e31112. 10.1371/journal.pone.0031112 22348041 PMC3278425 · DOI
  21. Kriegeskorte N. Simmons W. K. Bellgowan P. S. Baker C. I. (2009). Circular analysis in systems neuroscience – the dangers of double dipping. 12 535– 540. 10.1038/nn.2303 19396166 PMC2841687 · DOI · Paper page
  22. López M. Ramírez J. Górriz J. M. Álvarez I. Salas-Gonzalez D. Segovia F. (2011). Principal component analysis-based techniques and supervised classification schemes for the early detection of Alzheimer’s disease. 74 1260– 1271. 10.1016/j.neucom.2010.06.025 · DOI
  23. McKhann G. Drachman D. Folstein M. Katzman R. Price D. Stadlan E. M. (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. 34 939– 939. 10.1212/WNL.34.7.939 6610841 · DOI · Paper page
  24. Moradi E. Pepe A. Gaser C. Huttunen H. Tohka J. (2015). Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects. 104 398– 412. 10.1016/j.neuroimage.2014.10.002 25312773 PMC5957071 · DOI
  25. Morris J. C. (1993). The clinical dementia rating (CDR): current version and scoring rules. 43 2412– 2412. 10.1212/WNL.43.11.2412-a 8232972 · DOI · Paper page
  26. Nanni L. Salvatore C. Cerasa A. Castiglioni I. (2016). Combining multiple approaches for the early diagnosis of Alzheimer’s disease. 84 259– 266. 10.1016/j.patrec.2016.10.010 · DOI
  27. Narayana P. A. Brey W. W. Kulkarni M. V. Sievenpiper C. L. (1988). Compensation for surface coil sensitivity variation in magnetic resonance imaging. 6 271– 274. 10.1016/0730-725X(88)90401-8 3398733 · DOI
  28. O’Hanlon E. Newell F. N. Mitchell K. J. (2013). Combined structural and functional imaging reveals cortical deactivations in grapheme-color synaesthesia. 4: 755. 10.3389/fpsyg.2013.00755 24198794 PMC3812565 · DOI
  29. Peters F. Villeneuve S. Belleville S. (2014). Predicting progression to dementia in elderly subjects with mild cognitive impairment using both cognitive and neuroimaging predictors. 38 307– 318. 10.3233/JAD-130842 23963293 · DOI
  30. Ramírez J. Górriz J. M. Segovia F. Chaves R. Salas-Gonzalez D. López M. (2010). Computer aided diagnosis system for the Alzheimer’s disease based on partial least squares and random forest SPECT image classification. 472 99– 103. 10.1016/j.neulet.2010.01.056 20117177 · DOI
  31. Ritter K. Schumacher J. Weygandt M. Buchert R. Allefeld C. Haynes J.-D. (2015). Multimodal prediction of conversion to Alzheimer’s disease based on incomplete biomarkers. 1 206– 215. 10.1016/j.dadm.2015.01.006 27239505 PMC4877756 · DOI
  32. Runtti H. Mattila J. Van Gils M. Koikkalainen J. Soininen H. Lötjönen J. (2014). Quantitative evaluation of disease progression in a longitudinal mild cognitive impairment cohort. 39 49– 61. 10.3233/JAD-130359 24121959 · DOI
  33. Salvatore C. Battista P. Castiglioni I. (2015a). Frontiers for the early diagnosis of AD by means of MRI brain imaging and support vector machines. 13 509– 533. 26567735
  34. Salvatore C. Cerasa A. Battista P. Gilardi M. C. Quattrone A. Castiglioni I. (2015b). Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer’s disease: a machine learning approach. 9: 307 10.3389/fnins.2015.00307 PMC4555016 26388719 · DOI
  35. Sperling R. Mormino E. Johnson K. (2014). The evolution of preclinical Alzheimer’s disease: implications for prevention trials. 84 608– 622. 10.1016/j.neuron.2014.10.038 25442939 PMC4285623 · DOI · Paper page
  36. Sperling R. A. Jack C. R. Jr. Black S. E. Frosch M. P. Greenberg S. M. Hyman B. T. (2011). Amyloid-related imaging abnormalities in amyloid-modifying therapeutic trials: recommendations from the Alzheimer’s association research roundtable workgroup. 7 367– 385. 10.1016/j.jalz.2011.05.2351 21784348 PMC3693547 · DOI · Paper page
  37. Tosun D. Chen Y. F. Yu P. Sundell K. Suhy J. Siemens E. (2016). Amyloid status imputed from a multimodal classifier including structural MRI distinguishes progressors from nonprogressors in a mild Alzheimer’s disease clinical trial cohort. 12 977– 986. 10.1016/j.jalz.2016.03.009 27109039 · DOI
  38. Weiner M. W. Veitch D. P. Aisen P. S. Beckett L. A. Cairns N. J. Green R. C. (2017). Recent publications from the Alzheimer’s disease neuroimaging initiative: reviewing progress toward improved AD clinical trials. 13 e1– e85. 10.1016/j.jalz.2016.11.007 28342697 PMC6818723 · DOI
  39. Wernick M. N. Yang Y. Brankov J. G. Yourganov G. Strother S. C. (2010). Machine learning in medical imaging. 27 25– 38. 10.1109/MSP.2010.936730 25382956 PMC4220564 · DOI
  40. Wold S. Ruhe H. Wold H. Dunn W. J. (1984). The collinearity problem in linear regression: the partial least squares approach to generalized inverse. 5 735– 743. 10.1137/0905052 · DOI · Paper page
  41. Ye J. Farnum M. Yang E. Verbeeck R. Lobanov V. Raghavan N. (2012). Sparse learning and stability selection for predicting MCI to AD conversion using baseline ADNI data. 12: 46. 10.1186/1471-2377-1246 22731740 PMC3477025 · DOI

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Sorted by citations

Authors
Salvatore, C., Cerasa, A., & Castiglioni, I.
Affiliations
Institute of Molecular Bioimaging and Physiology (IT)
Corresponding author
Castiglioni, Isabella
ORCID
Venue
Frontiers in Aging Neuroscience
Publisher
Frontiers Media SA
Published
2018-05-24
Volume
10
Pages
135-135
ISSN
1663-4365 (ISSN-L)
DOI
10.3389/fnagi.2018.00135
PMC
PMC5977985
OpenAlex
W2802777797
Cited by
78
References
5
Citations by year
2026: 4 · 2025: 9 · 2024: 12 · 2023: 10 · 2022: 8 · 2021: 15 · 2020: 11 · 2019: 7 · 2018: 2
FWCI
6.89
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Aging Neuroscience (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • BOA (University of Milano-Bicocca) (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • U.S. Department of Defense (U01 AG024904, W81XWH, W81XWH-12-2-, W81XWH1220012, AG024904)
  • Alzheimer's Association (U01 AG024904, W81XWH-12-2-0012)
  • Bristol-Myers Squibb (U01 AG024904, AG024904, W81XWH-12-2-0012)
  • Eli Lilly and Company (U01 AG024904, AG024904, W81XWH-12-2-0012)
  • Pfizer (AG024904, W81XWH-12-2-0012, U01 AG024904)
  • Biogen (W81XWH-12-2-0012, U01 AG024904, AG024904)
  • F. Hoffmann-La Roche (W81XWH-12-2-0012, AG024904, U01 AG024904)
  • Meso Scale Diagnostics (U01 AG024904, AG024904, W81XWH-12-2-0012)
  • Alzheimer's Disease Neuroimaging Initiative (W81XWH-12-2–0012, U01AG024904, AG024904, W81XWH-12-2-)
  • BioClinica (U01 AG024904, W81XWH-12-2-0012, AG024904)
  • Novartis Pharmaceuticals Corporation (W81XWH-12-2-0012, U01 AG024904, AG024904)
  • Eisai (W81XWH-12-2-0012, U01 AG024904, AG024904)
  • Servier (AG024904, W81XWH-12-2-0012, U01 AG024904)
  • H. Lundbeck A/S (U01 AG024904, AG024904, W81XWH-12-2-0012)
  • IXICO (AG024904, W81XWH-12-2-0012, U01 AG024904)
  • National Institutes of Health (3u01ag024904-10s2, W81XWH, W81XWH-12-2-0012, U01-AG024904, AG024904)
  • Genentech (W81XWH-12-2-0012, AG024904, U01 AG024904)
  • Canadian Institutes of Health Research (U01 AG024904, W81XWH-12-2-0012)
  • National Institute on Aging (U01-AG-024904)
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Cancer-related cognitive impairment studies
Keywords
Alzheimer's disease, mild cognitive impairment, conversion to dementia, structural MRI, T1-weighted imaging, artificial intelligence classifiers, neuropsychological measures, temporal pole, medial temporal cortex, Alzheimer's Disease Neuroimaging Initiative, functional capacity, disease progression biomarkers

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