A machine learning approach to screen for preclinical Alzheimer's disease

[en] Combining multimodal biomarkers could help in the early diagnosis of Alzheimer's disease (AD). We included 304 cognitively normal individuals from the INSIGHT-preAD cohort. Amyloid and neurodegeneration were assessed on 18F-florbetapir and 18F-fluorodeoxyglucose PET, respectively. We used a nested cross-validation approach with non-invasive features (electroencephalography [EEG], APOE4 genotype, demographic, neuropsychological and MRI data) to predict: 1/ amyloid status; 2/ neurodegeneration status; 3/ decline to prodromal AD at 5-year follow-up. Importantly, EEG was most strongly predictive of neurodegeneration, even when reducing the number of channels from 224 down to 4, as 4-channel EEG best predicted neurodegeneration (negative predictive value [NPV] = 82%, positive predictive value [PPV] = 38%, 77% specificity, 45% sensitivity). The combination of demographic, neuropsychological data, APOE4 and hippocampal volumetry most strongly predicted amyloid (80% NPV, 41% PPV, 70% specificity, 58% sensitivity) and most strongly predicted decline to prodromal AD at 5 years (97% NPV, 14% PPV, 83% specificity, 50% sensitivity). Thus, machine learning can help to screen patients at high risk of preclinical AD using non-invasive and affordable biomarkers. © 2021 Elsevier Inc.

Disciplines :

Neurology

Author, co-author :

Gaubert, S.; Institut du Cerveau, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, F75013, France, AP-HP, Hôpital Pitié-Salpêtrière, Institute of Memory and Alzheimer's Disease (IM2A), Centre of excellence of neurodegenerative disease (CoEN) and National Reference Centre for Rare or Early Dementias, Department of Neurology, Paris, F75013, France, Université de Paris, Lariboisière Fernand-Widal Hospital, Cognitive Neurology Center, Paris, France

Houot, M.; AP-HP, Hôpital Pitié-Salpêtrière, Institute of Memory and Alzheimer's Disease (IM2A), Centre of excellence of neurodegenerative disease (CoEN) and National Reference Centre for Rare or Early Dementias, Department of Neurology, Paris, F75013, France, Center for Clinical Investigation (CIC) Neurosciences, Institut du cerveau et de la Moelle épinière (ICM), Paris, F75013, France, Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l'Hôpital, Paris, F-75013, France

Raimondo, Federico ; Université de Liège - ULg

Ansart, M.; Institut du Cerveau, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, F75013, France, Inria, Aramis project-team, Paris, F-75013, France

Corsi, M.-C.; Institut du Cerveau, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, F75013, France, Inria, Aramis project-team, Paris, F-75013, France

Naccache, L.; Institut du Cerveau, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, F75013, France, AP-HP, Groupe hospitalier Pitié-Salpêtrière, Department of Neurophysiology, Paris, F-75013, France

Sitt, J. D.; Institut du Cerveau, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, F75013, France, Cognitive Neuroimaging Unit, Institut National de la Santé et de la Recherche Médicale, U992, Gif/Yvette, F-91191, France, NeuroSpin Centre, Institute of BioImaging Commissariat à l'Energie Atomique, Gif/Yvette, F-91191, France

Habert, M.-O.; Laboratoire d'Imagerie Biomédicale, Sorbonne Universités, UPMC Univ Paris 06, Inserm U1146, CNRS UMR 7371, Paris, France, AP-HP, Hôpital Pitié-Salpêtrière, Department of Nuclear Medicine, Paris, F-75013, France, Centre d'Acquisition et de Traitement des Images, CATI neuroimaging platform, France

Dubois, B.; Institut du Cerveau, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, F75013, France, AP-HP, Hôpital Pitié-Salpêtrière, Institute of Memory and Alzheimer's Disease (IM2A), Centre of excellence of neurodegenerative disease (CoEN) and National Reference Centre for Rare or Early Dementias, Department of Neurology, Paris, F75013, France

De Vico Fallani, F.; Institut du Cerveau, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, F75013, France, Inria, Aramis project-team, Paris, F-75013, France

Durrleman, S.; Institut du Cerveau, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, F75013, France, Inria, Aramis project-team, Paris, F-75013, France

Epelbaum, S.; Institut du Cerveau, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, F75013, France, AP-HP, Hôpital Pitié-Salpêtrière, Institute of Memory and Alzheimer's Disease (IM2A), Centre of excellence of neurodegenerative disease (CoEN) and National Reference Centre for Rare or Early Dementias, Department of Neurology, Paris, F75013, France, Inria, Aramis project-team, Paris, F-75013, France

Language :

English

Title :

A machine learning approach to screen for preclinical Alzheimer's disease

Publication date :

2021

Journal title :

Neurobiology of Aging

ISSN :

0197-4580

eISSN :

1558-1497

Publisher :

Elsevier, Netherlands

Volume :

105

Issue :

Septembre

Pages :

205-216

Peer reviewed :

Peer Reviewed verified by ORBi

Available on ORBi :

since 17 June 2021

Statistics

Number of views

51 (1 by ULiège)

Number of downloads

1 (1 by ULiège)

More statistics

Scopus citations^®

Scopus citations^®
without self-citations

OpenCitations

OpenAlex citations

Bibliography

Aghajani, H., Zahedi, E., Jalili, M., Keikhosravi, A., Vahdat, B.V., Diagnosis of early Alzheimer's disease based on EEG source localization and a standardized realistic head model. IEEE J. Biomed. Health Inform. 17 (2013), 1039–1045, 10.1109/JBHI.2013.2253326.
Al-Nuaimi, A.H.H., Jammeh, E., Sun, L., Ifeachor, E., Complexity measures for quantifying changes in electroencephalogram in Alzheimer's. Disease. Complexity 2018 (2018), 1–12, 10.1155/2018/8915079.
for the Alzheimer's Disease Neuroimaging Initiative* and the INSIGHT-preAD study Ansart, M., Epelbaum, S., Gagliardi, G., Colliot, O., Dormont, D., Dubois, B., Hampel, H., Durrleman, S., Reduction of recruitment costs in preclinical AD trials: validation of automatic pre-screening algorithm for brain amyloidosis. Stat. Methods Med. Res. 29 (2020), 151–164, 10.1177/0962280218823036.
Babiloni, C., Lizio, R., Marzano, N., Capotosto, P., Soricelli, A., Triggiani, A.I., Cordone, S., Gesualdo, L., Del Percio, C., Brain neural synchronization and functional coupling in Alzheimer's disease as revealed by resting state EEG rhythms. Int. J. Psychophysiol. 103 (2016), 88–102, 10.1016/j.ijpsycho.2015.02.008.
Babiloni, C., Percio, C.D., Lizio, R., Noce, G., Lopez, S., Soricelli, A., Ferri, R., Pascarelli, M.T., Catania, V., Nobili, F., Arnaldi, D., Famà, F., Orzi, F., Buttinelli, C., Giubilei, F., Bonanni, L., Franciotti, R., Onofrj, M., Stirpe, P., Fuhr, P., Gschwandtner, U., Ransmayr, G., Garn, H., Fraioli, L., Pievani, M., D'Antonio, F., Lena, C.D., Güntekin, B., Hanoğlu, L., Başar, E., Yener, G., Emek-Savaş, D.D., Triggiani, A.I., Taylor, J.P., Pandis, M.F.D., Vacca, L., Frisoni, G.B., Stocchi, F., Functional cortical source connectivity of resting state electroencephalographic alpha rhythms shows similar abnormalities in patients with mild cognitive impairment due to Alzheimer's and Parkinson's diseases. Clin. Neurophysiol. 129 (2018), 766–782, 10.1016/j.clinph.2018.01.009.
Babiloni, C., Visser, P.J., Frisoni, G., De Deyn, P.P., Bresciani, L., Jelic, V., Nagels, G., Rodriguez, G., Rossini, P.M., Vecchio, F., Colombo, D., Verhey, F., Wahlund, L.-O., Nobili, F., Cortical sources of resting EEG rhythms in mild cognitive impairment and subjective memory complaint. Neurobiol. Aging 31 (2010), 1787–1798, 10.1016/j.neurobiolaging.2008.09.020.
Bauer, C.M., Cabral, H.J., Killiany, R.J., Multimodal discrimination between normal aging, mild cognitive impairment and Alzheimer's disease and prediction of cognitive decline. Diagnostics (Basel), 8(1), 2018, 10.3390/diagnostics8010014.
Breiman, L., Random Forests. Mach. Learn. 45 (2001), 5–32.
Brueggen, K., Dyrba, M., Cardenas-Blanco, A., Schneider, A., Fliessbach, K., Buerger, K., Janowitz, D., Peters, O., Menne, F., Priller, J., Spruth, E., Wiltfang, J., Vukovich, R., Laske, C., Buchmann, M., Wagner, M., Röske, S., Spottke, A., Rudolph, J., Metzger, C.D., Kilimann, I., Dobisch, L., Düzel, E., Jessen, F., Teipel, S.J., Structural integrity in subjective cognitive decline, mild cognitive impairment and Alzheimer's disease based on multicenter diffusion tensor imaging. J. Neurol. 266 (2019), 2465–2474, 10.1007/s00415-019-09429-3.
Caminiti, S.P., Ballarini, T., Sala, A., Cerami, C., Presotto, L., Santangelo, R., Fallanca, F., Vanoli, E.G., Gianolli, L., Iannaccone, S., Magnani, G., Perani, D., BIOMARKAPD Project. FDG-PET and CSF biomarker accuracy in prediction of conversion to different dementias in a large multicentre MCI cohort. NeuroImage Clin 18 (2018), 167–177, 10.1016/j.nicl.2018.01.019.
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P., SMOTE: Synthetic minority over-sampling technique. J. Artif. Intell. Res. 16 (2002), 321–357, 10.1613/jair.953.
Cheng, B., Liu, M., Zhang, D., Munsell, B.C., Shen, D., Domain transfer learning for MCI conversion prediction. IEEE Trans. Biomed. Eng. 62 (2015), 1805–1817, 10.1109/TBME.2015.2404809.
Cummings, J., The national institute on aging—alzheimer's association framework on alzheimer's disease: application to clinical trials. Alzheimers Dement 15 (2019), 172–178, 10.1016/j.jalz.2018.05.006.
de Leon, M.J., Mosconi, L., Blennow, K., DeSanti, S., Zinkowski, R., Mehta, P.D., Pratico, D., Tsui, W., Saint Louis, L.A., Sobanska, L., Brys, M., Li, Y., Rich, K., Rinne, J., Rusinek, H., Imaging and CSF studies in the preclinical diagnosis of Alzheimer's disease. Ann. N. Y. Acad. Sci. 1097 (2007), 114–145, 10.1196/annals.1379.012.
Dickerson, B.C., Wolk, D.A., Alzheimer's Disease Neuroimaging Initiative. Biomarker-based prediction of progression in MCI: Comparison of AD signature and hippocampal volume with spinal fluid amyloid-β and tau. Front. Aging Neurosci., 5, 2013, 55, 10.3389/fnagi.2013.00055.
for the Alzheimer's Disease Neuroimaging Initiative Donohue, M.C., Sperling, R.A., Petersen, R., Sun, C.-K., Weiner, M.W., Aisen, P.S., Association between elevated brain amyloid and subsequent cognitive decline among cognitively normal persons. JAMA, 317, 2017, 2305, 10.1001/jama.2017.6669.
Dubois, B., Epelbaum, S., Nyasse, F., Bakardjian, H., Gagliardi, G., Uspenskaya, O., Houot, M., Lista, S., Cacciamani, F., Potier, M.-C., Bertrand, A., Lamari, F., Benali, H., Mangin, J.-F., Colliot, O., Genthon, R., Habert, M.-O., Hampel, H., Audrain, C., Auffret, A., Baldacci, F., Benakki, I., Bertin, H., Boukadida, L., Cavedo, E., Chiesa, P., Dauphinot, L., Dos Santos, A., Dubois, M., Durrleman, S., Fontaine, G., Genin, A., Glasman, P., Jungalee, N., Kas, A., Kilani, M., La Corte, V., Lehericy, S., Letondor, C., Levy, M., Lowrey, M., Ly, J., Makiese, O., Metzinger, C., Michon, A., Mochel, F., Poisson, C., Ratovohery, S., Revillon, M., Rojkova, K., Roy, P., Santos-Andrade, K., Schindler, R., Seux, L., Simon, V., Sole, M., Tandetnik, C., Teichmann, M., Thiebaut de Shotten, M., Younsi, N., Cognitive and neuroimaging features and brain β-amyloidosis in individuals at risk of Alzheimer's disease (INSIGHT-preAD): a longitudinal observational study. Lancet Neurol 17 (2018), 335–346, 10.1016/S1474-4422(18)30029-2.
Dubois, B., Feldman, H.H., Jacova, C., Hampel, H., Molinuevo, J.L., Blennow, K., DeKosky, S.T., Gauthier, S., Selkoe, D., Bateman, R., Cappa, S., Crutch, S., Engelborghs, S., Frisoni, G.B., Fox, N.C., Galasko, D., Habert, M.-O., Jicha, G.A., Nordberg, A., Pasquier, F., Rabinovici, G., Robert, P., Rowe, C., Salloway, S., Sarazin, M., Epelbaum, S., de Souza, L.C., Vellas, B., Visser, P.J., Schneider, L., Stern, Y., Scheltens, P., Cummings, J.L., Advancing research diagnostic criteria for Alzheimer's disease: the IWG-2 criteria. Lancet Neurol 13 (2014), 614–629, 10.1016/S1474-4422(14)70090-0.
Dubois, B., Hampel, H., Feldman, H.H., Scheltens, P., Aisen, P., Andrieu, S., Bakardjian, H., Benali, H., Bertram, L., Blennow, K., Broich, K., Cavedo, E., Crutch, S., Dartigues, J.-F., Duyckaerts, C., Epelbaum, S., Frisoni, G.B., Gauthier, S., Genthon, R., Gouw, A.A., Habert, M.-O., Holtzman, D.M., Kivipelto, M., Lista, S., Molinuevo, J.-L., O'Bryant, S.E., Rabinovici, G.D., Rowe, C., Salloway, S., Schneider, L.S., Sperling, R., Teichmann, M., Carrillo, M.C., Cummings, J., Jack, C.R., Preclinical Alzheimer's disease: Definition, natural history, and diagnostic criteria. Alzheimers Dement. J. Alzheimers Assoc. 12 (2016), 292–323, 10.1016/j.jalz.2016.02.002.
Dumurgier, J., Hanseeuw, B.J., Hatling, F.B., Judge, K.A., Schultz, A.P., Chhatwal, J.P., Blacker, D., Sperling, R.A., Johnson, K.A., Hyman, B.T., Gómez-Isla, T., Alzheimer's disease biomarkers and future decline in cognitive normal older adults. J. Alzheimers Dis. 60 (2017), 1451–1459, 10.3233/JAD-170511.
Engemann, D.A., Raimondo, F., King, J-R., Jas, M., Gramfort, A., Dehaene, S., Naccache, L., Sitt, J., Automated measurement and prediction of consciousness in vegetative and minimally conscious patients. ICML workshop on statistics, machine learning and neuroscience 2015, Lille, France, 2015, Lille, France 2015.
Engemann, D.A., Raimondo, F., King, J.-R., Rohaut, B., Louppe, G., Faugeras, F., Annen, J., Cassol, H., Gosseries, O., Fernandez-Slezak, D., Laureys, S., Naccache, L., Dehaene, S., Sitt, J.D., Robust EEG-based cross-site and cross-protocol classification of states of consciousness. Brain 141:11 (2018), 3179–3192, 10.1093/brain/awy251.
Epelbaum, S., Genthon, R., Cavedo, E., Habert, M.O., Lamari, F., Gagliardi, G., Lista, S., Teichmann, M., Bakardjian, H., Hampel, H., Dubois, B., Preclinical Alzheimer's disease: A systematic review of the cohorts underlying the concept. Alzheimers Dement. J. Alzheimers Assoc. 13 (2017), 454–467, 10.1016/j.jalz.2016.12.003.
Farrer, L.A., Cupples, L.A., Haines, J.L., Hyman, B., Kukull, W.A., Mayeux, R., Myers, R.H., Pericak-Vance, M.A., Risch, N., van Duijn, C.M., Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease. A meta-analysis. APOE and Alzheimer Disease Meta Analysis Consortium. JAMA 278 (1997), 1349–1356.
Frölich, L., Peters, O., Lewczuk, P., Gruber, O., Teipel, S.J., Gertz, H.J., Jahn, H., Jessen, F., Kurz, A., Luckhaus, C., Hüll, M., Pantel, J., Reischies, F.M., Schröder, J., Wagner, M., Rienhoff, O., Wolf, S., Bauer, C., Schuchhardt, J., Heuser, I., Rüther, E., Henn, F., Maier, W., Wiltfang, J., Kornhuber, J., Incremental value of biomarker combinations to predict progression of mild cognitive impairment to Alzheimer's dementia. Alzheimers Res. Ther., 9, 2017, 84, 10.1186/s13195-017-0301-7.
Gagliardi, G.P., Houot, M., Cacciamani, F., Habert, M.-O., Dubois, B., Epelbaum, S., The meta-memory ratio: A new cohort-independent way to measure cognitive awareness in asymptomatic individuals at risk for Alzheimer's disease. Alzheimers Res Ther, 12, 2020, 57, 10.1186/s13195-020-00626-1.
Gaubert, S., Raimondo, F., Houot, M., Corsi, M.-C., Naccache, L., Diego Sitt, J., Hermann, B., Oudiette, D., Gagliardi, G., Habert, M.-O., Dubois, B., De Vico Fallani, F., Bakardjian, H., Epelbaum, S., EEG evidence of compensatory mechanisms in preclinical Alzheimer's disease. Brain 142 (2019), 2096–2112, 10.1093/brain/awz150.
Gramfort, A., Luessi, M., Larson, E., Engemann, D.A., Strohmeier, D., Brodbeck, C., Parkkonen, L., Hämäläinen, M.S., MNE software for processing MEG and EEG data. NeuroImage 86 (2014), 446–460, 10.1016/j.neuroimage.2013.10.027.
Gupta, Y., Lama, R.K., Kwon, G.-R., Alzheimer's Disease Neuroimaging Initiative. Prediction and classification of Alzheimer's disease based on combined features from apolipoprotein-e genotype, cerebrospinal fluid, MR, and FDG-PET imaging biomarkers. Front. Comput. Neurosci, 13, 2019, 72, 10.3389/fncom.2019.00072.
Hays, C.C., Zlatar, Z.Z., Meloy, M.J., Bondi, M.W., Gilbert, P.E., Liu, T., Helm, J.L., Wierenga, C.E., Interaction of APOE, cerebral blood flow, and cortical thickness in the entorhinal cortex predicts memory decline. Brain Imaging Behav 14 (2020), 369–382, 10.1007/s11682-019-00245-x.
Hedden, T., Oh, H., Younger, A.P., Patel, T.A., Meta-analysis of amyloid-cognition relations in cognitively normal older adults. Neurology, 80, 2013, 1341, 10.1212/WNL.0b013e31828ab35d.
Hsu, P.J., Shou, H., Benzinger, T., Marcus, D., Durbin, T., Morris, J.C., Sheline, Y.I., Amyloid burden in cognitively normal elderly is associated with preferential hippocampal subfield volume loss. J. Alzheimers Dis. JAD 45 (2015), 27–33, 10.3233/JAD-141743.
Insel, P.S., Palmqvist, S., Mackin, R.S., Nosheny, R.L., Hansson, O., Weiner, M.W., Mattsson, N., Assessing risk for preclinical β-amyloid pathology with APOE, cognitive, and demographic information. Alzheimers Dement. Amst. Neth. 4 (2016), 76–84, 10.1016/j.dadm.2016.07.002.
Jack, C.R., Bennett, D.A., Blennow, K., Carrillo, M.C., Dunn, B., Haeberlein, S.B., Holtzman, D.M., Jagust, W., Jessen, F., Karlawish, J., Liu, E., Molinuevo, J.L., Montine, T., Phelps, C., Rankin, K.P., Rowe, C.C., Scheltens, P., Siemers, E., Snyder, H.M., Sperling, R., Elliott, C., Masliah, E., Ryan, L., Silverberg, N., NIA-AA Research Framework: Toward a biological definition of Alzheimer's disease. Alzheimer's & Dementia 14 (2018), 535–562, 10.1016/j.jalz.2018.02.018.
Jack, C.R., Knopman, D.S., Weigand, S.D., Wiste, H.J., Vemuri, P., Lowe, V., Kantarci, K., Gunter, J.L., Senjem, M.L., Ivnik, R.J., Roberts, R.O., Rocca, W.A., Boeve, B.F., Petersen, R.C., An operational approach to national institute on aging-Alzheimer's Association criteria for preclinical Alzheimer disease. Ann. Neurol. 71 (2012), 765–775, 10.1002/ana.22628.
Jansen, W.J., Ossenkoppele, R., Tijms, B.M., Fagan, A.M., Hansson, O., Klunk, W.E., van der Flier, W.M., Villemagne, V.L., Frisoni, G.B., Fleisher, A.S., Lleó, A., Mintun, M.A., Wallin, A., Engelborghs, S., Na, D.L., Chételat, G., Molinuevo, J.L., Landau, S.M., Mattsson, N., Kornhuber, J., Sabri, O., Rowe, C.C., Parnetti, L., Popp, J., Fladby, T., Jagust, W.J., Aalten, P., Lee, D.Y., Vandenberghe, R., Resende de Oliveira, C., Kapaki, E., Froelich, L., Ivanoiu, A., Gabryelewicz, T., Verbeek, M.M., Sanchez-Juan, P., Hildebrandt, H., Camus, V., Zboch, M., Brooks, D.J., Drzezga, A., Rinne, J.O., Newberg, A., de Mendonça, A., Sarazin, M., Rabinovici, G.D., Madsen, K., Kramberger, M.G., Nordberg, A., Mok, V., Mroczko, B., Wolk, D.A., Meyer, P.T., Tsolaki, M., Scheltens, P., Verhey, F.R.J., Visser, P.J., Aarsland, D., Alcolea, D., Alexander, M., Almdahl, I.S., Arnold, S.E., Baldeiras, I., Barthel, H., van Berckel, B.N.M., Blennow, K., van Buchem, M.A., Cavedo, E., Chen, K., Chipi, E., Cohen, A.D., Förster, S., Fortea, J., Frederiksen, K.S., Freund-Levi, Y., Gkatzima, O., Gordon, M.F., Grimmer, T., Hampel, H., Hausner, L., Hellwig, S., Herukka, S.-K., Johannsen, P., Klimkowicz-Mrowiec, A., Köhler, S., Koglin, N., van Laere, K., de Leon, M., Lisetti, V., Maier, W., Marcusson, J., Meulenbroek, O., Møllergård, H.M., Morris, J.C., Nordlund, A., Novak, G.P., Paraskevas, G.P., Perera, G., Peters, O., Ramakers, I.H.G.B., Rami, L., Rodríguez-Rodríguez, E., Roe, C.M., Rot, U., Rüther, E., Santana, I., Schröder, J., Seo, S.W., Soininen, H., Spiru, L., Stomrud, E., Struyfs, H., Teunissen, C.E., Vos, S.J.B., van Waalwijk van Doorn, L.J.C., Waldemar, G., Wallin, Å.K., Wiltfang, J., Zetterberg, H., Association of cerebral amyloid-β Aggregation with cognitive functioning in persons without dementia. JAMA Psychiatry 75 (2018), 84–95, 10.1001/jamapsychiatry.2017.3391.
Jeong, J., EEG dynamics in patients with Alzheimer's disease. Clin. Neurophysiol. 115 (2004), 1490–1505, 10.1016/j.clinph.2004.01.001.
Kim, K.W., Woo, S.Y., Kim, S., Jang, H., Kim, Y., Cho, S.H., Kim, S.E., Kim, S.J., Shin, B.-S., Kim, H.J., Na, D.L., Seo, S.W., Disease progression modeling of Alzheimer's disease according to education level. Sci. Rep., 10, 2020, 10.1038/s41598-020-73911-6.
King, J.-R., Sitt, J.D., Faugeras, F., Rohaut, B., El Karoui, I., Cohen, L., Naccache, L., Dehaene, S., Information sharing in the brain indexes consciousness in noncommunicative patients. Curr. Biol. 23 (2013), 1914–1919, 10.1016/j.cub.2013.07.075.
Lehmann, C., Koenig, T., Jelic, V., Prichep, L., John, R.E., Wahlund, L.-O., Dodge, Y., Dierks, T., Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG). J. Neurosci. Methods 161 (2007), 342–350, 10.1016/j.jneumeth.2006.10.023.
the Alzheimer's Disease Neuroimaging Initiative Leonenko, G., Shoai, M., Bellou, E., Sims, R., Williams, J., Hardy, J., Escott-Price, V., Genetic risk for alzheimer disease is distinct from genetic risk for amyloid deposition. Ann. Neurol. 86 (2019), 427–435, 10.1002/ana.25530.
Li, D., Mielke, M.M., An update on blood-based markers of Alzheimer's disease using the SiMoA platform. Neurol. Ther. 8 (2019), 73–82, 10.1007/s40120-019-00164-5.
Li, Q., Wu, X., Xu, L., Chen, K., Yao, L., Li, R., Multi-modal discriminative dictionary learning for Alzheimer's disease and mild cognitive impairment. Comput. Methods Programs Biomed. 150 (2017), 1–8, 10.1016/j.cmpb.2017.07.003.
Lin, W., Gao, Q., Yuan, J., Chen, Z., Feng, C., Chen, W., Du, M., Tong, T., Predicting Alzheimer's disease conversion from mild cognitive impairment using an extreme learning machine-based grading method with multimodal data. front. Aging Neurosci, 12, 2020, 77, 10.3389/fnagi.2020.00077.
López-Sanz, D., Bruña, R., Delgado-Losada, M.L., López-Higes, R., Marcos-Dolado, A., Maestú, F., Walter, S., Electrophysiological brain signatures for the classification of subjective cognitive decline: towards an individual detection in the preclinical stages of dementia. Alzheimers Res. Ther., 11, 2019, 49, 10.1186/s13195-019-0502-3.
McGraw, K.O., Wong, S.P., Forming inferences about some intraclass correlation coefficients. Psychol. Methods 1 (1996), 30–46, 10.1037/1082-989X.1.1.30.
Mielke, M.M., Hagen, C.E., Xu, J., Chai, X., Vemuri, P., Lowe, V.J., Airey, D.C., Knopman, D.S., Roberts, R.O., Machulda, M.M., Jack, C.R., Petersen, R.C., Dage, J.L., Plasma phospho-tau181 increases with Alzheimer's disease clinical severity and is associated with tau- and amyloid-positron emission tomography. Alzheimers Dement 14 (2018), 989–997, 10.1016/j.jalz.2018.02.013.
Mielke, M.M., Wiste, H.J., Weigand, S.D., Knopman, D.S., Lowe, V.J., Roberts, R.O., Geda, Y.E., Swenson-Dravis, D.M., Boeve, B.F., Senjem, M.L., Vemuri, P., Petersen, R.C., Jack, C.R., Indicators of amyloid burden in a population-based study of cognitively normal elderly. Neurology 79 (2012), 1570–1577, 10.1212/WNL.0b013e31826e2696.
Alzheimer's Disease Neuroimaging Initiative Moradi, E., Pepe, A., Gaser, C., Huttunen, H., Tohka, J., Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects. NeuroImage 104 (2015), 398–412, 10.1016/j.neuroimage.2014.10.002.
Mormino, E.C., Betensky, R.A., Hedden, T., Schultz, A.P., Amariglio, R.E., Rentz, D.M., Johnson, K.A., Sperling, R.A., Synergistic effect of β-amyloid and neurodegeneration on cognitive decline in clinically normal individuals. JAMA Neurol, 71, 2014, 1379, 10.1001/jamaneurol.2014.2031.
Alzheimer's Disease Neuroimaging Initiative Mormino, E.C., Kluth, J.T., Madison, C.M., Rabinovici, G.D., Baker, S.L., Miller, B.L., Koeppe, R.A., Mathis, C.A., Weiner, M.W., Jagust, W.J., Episodic memory loss is related to hippocampal-mediated beta-amyloid deposition in elderly subjects. Brain J. Neurol. 132 (2009), 1310–1323, 10.1093/brain/awn320.
Müller, K.R., Mika, S., Rätsch, G., Tsuda, K., Schölkopf, B., An introduction to kernel-based learning algorithms. IEEE Trans. Neural Netw. 12 (2001), 181–201, 10.1109/72.914517.
Nakamura, A., Cuesta, P., Fernández, A., Arahata, Y., Iwata, K., Kuratsubo, I., Bundo, M., Hattori, H., Sakurai, T., Fukuda, K., Washimi, Y., Endo, H., Takeda, A., Diers, K., Bajo, R., Maestú, F., Ito, K., Kato, T., Electromagnetic signatures of the preclinical and prodromal stages of Alzheimer's disease. Brain 141 (2018), 1470–1485, 10.1093/brain/awy044.
the Alzheimer's Disease Neuroimaging Initiative, the Swedish BioFINDER study Palmqvist, S., Insel, P.S., Zetterberg, H., Blennow, K., Brix, B., Stomrud, E., Mattsson, N., Hansson, O., Accurate risk estimation of β-amyloid positivity to identify prodromal Alzheimer's disease: Cross-validation study of practical algorithms. Alzheimers Dement 15 (2019), 194–204, 10.1016/j.jalz.2018.08.014.
Papp, K.V., Rentz, D.M., Mormino, E.C., Schultz, A.P., Amariglio, R.E., Quiroz, Y., Johnson, K.A., Sperling, R.A., Cued memory decline in biomarker-defined preclinical Alzheimer disease. Neurology 88 (2017), 1431–1438, 10.1212/WNL.0000000000003812.
Parnetti, L., Chipi, E., Salvadori, N., D'Andrea, K., Eusebi, P., Prevalence and risk of progression of preclinical Alzheimer's disease stages: a systematic review and meta-analysis. Alzheimers Res. Ther., 11, 2019, 7, 10.1186/s13195-018-0459-7.
Pettigrew, C., Soldan, A., Defining cognitive reserve and implications for cognitive aging. Curr. Neurol. Neurosci. Rep., 19, 2019, 1, 10.1007/s11910-019-0917-z.
Poil, S.-S., de Haan, W., van der Flier, W.M., Mansvelder, H.D., Scheltens, P., Linkenkaer-Hansen, K., Integrative EEG biomarkers predict progression to Alzheimer's disease at the MCI stage. Front. Aging Neurosci., 5, 2013, 10.3389/fnagi.2013.00058.
Poza, J., Gómez, C., García, M., Tola-Arribas, M.A., Carreres, A., Cano, M., Hornero, R., Spatio-temporal fluctuations of neural dynamics in mild cognitive impairment and Alzheimer's disease. Curr. Alzheimer Res., 14, 2017, 10.2174/1567205014666170309115656.
Reiman, E.M., Chen, K., Liu, X., Bandy, D., Yu, M., Lee, W., Ayutyanont, N., Keppler, J., Reeder, S.A., Langbaum, J.B.S., Alexander, G.E., Klunk, W.E., Mathis, C.A., Price, J.C., Aizenstein, H.J., DeKosky, S.T., Caselli, R.J., Fibrillar amyloid- burden in cognitively normal people at 3 levels of genetic risk for Alzheimer's disease. Proc. Natl. Acad. Sci. 106 (2009), 6820–6825, 10.1073/pnas.0900345106.
Ritter, K., Schumacher, J., Weygandt, M., Buchert, R., Allefeld, C., Haynes, J.-D., Multimodal prediction of conversion to Alzheimer's disease based on incomplete biomarkers. Alzheimers Dement. Amst. Neth. 1 (2015), 206–215, 10.1016/j.dadm.2015.01.006.
Roe, C.M., Fagan, A.M., Grant, E.A., Hassenstab, J., Moulder, K.L., Maue Dreyfus, D., Sutphen, C.L., Benzinger, T.L.S., Mintun, M.A., Holtzman, D.M., Morris, J.C., Amyloid imaging and CSF biomarkers in predicting cognitive impairment up to 7.5 years later. Neurology 80 (2013), 1784–1791, 10.1212/WNL.0b013e3182918ca6.
Scheef, L., Spottke, A., Daerr, M., Joe, A., Striepens, N., Kölsch, H., Popp, J., Daamen, M., Gorris, D., Heneka, M.T., Boecker, H., Biersack, H.J., Maier, W., Schild, H.H., Wagner, M., Jessen, F., Glucose metabolism, gray matter structure, and memory decline in subjective memory impairment. Neurology 79 (2012), 1332–1339, 10.1212/WNL.0b013e31826c1a8d.
Sitt, J.D., King, J.-R., El Karoui, I., Rohaut, B., Faugeras, F., Gramfort, A., Cohen, L., Sigman, M., Dehaene, S., Naccache, L., Large scale screening of neural signatures of consciousness in patients in a vegetative or minimally conscious state. Brain 137 (2014), 2258–2270, 10.1093/brain/awu141.
for the BIOCARD Research Team Soldan, A., Pettigrew, C., Cai, Q., Wang, M.-C., Moghekar, A.R., O'Brien, R.J., Selnes, O.A., Albert, M.S., Hypothetical preclinical alzheimer disease groups and longitudinal cognitive change. JAMA Neurol, 73, 2016, 698, 10.1001/jamaneurol.2016.0194.
ten Kate, M., Redolfi, A., Peira, E., Bos, I., Vos, S.J., Vandenberghe, R., Gabel, S., Schaeverbeke, J., Scheltens, P., Blin, O., Richardson, J.C., Bordet, R., Wallin, A., Eckerstrom, C., Molinuevo, J.L., Engelborghs, S., Van Broeckhoven, C., Martinez-Lage, P., Popp, J., Tsolaki, M., Verhey, F.R.J., Baird, A.L., Legido-Quigley, C., Bertram, L., Dobricic, V., Zetterberg, H., Lovestone, S., Streffer, J., Bianchetti, S., Novak, G.P., Revillard, J., Gordon, M.F., Xie, Z., Wottschel, V., Frisoni, G., Visser, P.J., Barkhof, F., MRI predictors of amyloid pathology: results from the EMIF-AD multimodal biomarker discovery study. Alzheimers Res. Ther., 10, 2018, 10.1186/s13195-018-0428-1.
Vogel, J.W., Vachon-Presseau, E., Pichet Binette, A., Tam, A., Orban, P., La Joie, R., Savard, M., Picard, C., Poirier, J., Bellec, P., Breitner, J.C.S., Villeneuve, S., Brain properties predict proximity to symptom onset in sporadic Alzheimer's disease. Brain 141 (2018), 1871–1883, 10.1093/brain/awy093.
Yan, T., Wang, Y., Weng, Z., Du, W., Liu, T., Chen, D., Li, X., Wu, J., Han, Y., Early-stage identification and pathological development of Alzheimer's disease using multimodal MRI. J. Alzheimers Dis. 68 (2019), 1013–1027, 10.3233/JAD-181049.
Yang, S., Bornot, J.M.sanchez, Wong-Lin, K., Prasad, G., M/EEG-based bio-markers to predict the mild cognitive impairment and alzheimer's disease: a review from the machine learning perspective. IEEE Trans. Biomed. Eng, 2019, 10.1109/TBME.2019.2898871 1–1.
Young, A.L., Oxtoby, N.P., Daga, P., Cash, D.M., Fox, N.C., Ourselin, S., Schott, J.M., Alexander, D.C., A data-driven model of biomarker changes in sporadic Alzheimer's disease. Brain 137 (2014), 2564–2577, 10.1093/brain/awu176.
Young, J., Modat, M., Cardoso, M.J., Mendelson, A., Cash, D., Ourselin, S., Accurate multimodal probabilistic prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment. NeuroImage Clin 2 (2013), 735–745, 10.1016/j.nicl.2013.05.004.
Yu, H., Lei, X., Song, Z., Wang, J., Wei, X., Yu, B., Functional brain connectivity in Alzheimer's disease: An EEG study based on permutation disalignment index. Phys. Stat. Mech. Its Appl. 506 (2018), 1093–1103, 10.1016/j.physa.2018.05.009.
Zhao, W., Luo, Y., Zhao, L., Mok, V., Su, L., Yin, C., Sun, Y., Lu, J., Shi, L., Han, Y., Automated brain MRI volumetry differentiates early stages of Alzheimer's disease from normal aging. J. Geriatr. Psychiatry Neurol. 32 (2019), 354–364, 10.1177/0891988719862637.