Alzheimer’s disease; Bayesian inference; Brain ageing; Brain morphology; Classification; Cognitive decline; Gaussian processes; Predictive modeling; Alzheimers disease; Brain aging; Brain morphologies; Clinical outcome; Covariates; Memory performance; Predictive models; Radiological and Ultrasound Technology; Radiology, Nuclear Medicine and Imaging; Computer Vision and Pattern Recognition; Health Informatics; Computer Graphics and Computer-Aided Design
Abstract :
[en] Neuroimaging markers based on Magnetic Resonance Imaging (MRI) combined with various other measures (such as genetic covariates, biomarkers, vascular risk factors, neuropsychological tests etc.) might provide useful predictions of clinical outcomes during the progression towards Alzheimer's disease (AD). The use of multiple features in predictive frameworks for clinical outcomes has become increasingly prevalent in AD research. However, many studies do not focus on systematically and accurately evaluating combinations of multiple input features. Hence, the aim of the present work is to explore and assess optimal combinations of various features for MR-based prediction of (1) cognitive status and (2) biomarker positivity with a multi-kernel learning Gaussian process framework. The explored features and parameters included (A) combinations of brain tissues, modulation, smoothing, and image resolution; (B) incorporating demographics & clinical covariates; (C) the impact of the size of the training data set; (D) the influence of dimensionality reduction and the choice of kernel types. The approach was tested in a large German cohort including 959 subjects from the multicentric longitudinal study of cognitive impairment and dementia (DELCODE). Our evaluation suggests the best prediction of memory performance was obtained for a combination of neuroimaging markers, demographics, genetic information (ApoE4) and CSF biomarkers explaining 57% of outcome variance in out-of-sample predictions. The highest performance for Aβ42/40 status classification was achieved for a combination of demographics, ApoE4, and a memory score while usage of structural MRI further improved the classification of individual patient's pTau status.
Disciplines :
Neurology
Author, co-author :
Nemali, A ; Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Germany, German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany. Electronic address: aditya.nemali@dzne.de
Vockert, N ; German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
Berron, D ; German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
Maas, A ; German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
Bernal, J ; German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
Yakupov, R ; German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
Peters, O; German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany, Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Department of Psychiatry, Hindenburgdamm 30, 12203, Berlin, Germany
Gref, D; Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Department of Psychiatry, Hindenburgdamm 30, 12203, Berlin, Germany
Cosma, N ; Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Department of Psychiatry, Hindenburgdamm 30, 12203, Berlin, Germany
Preis, L ; Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Department of Psychiatry, Hindenburgdamm 30, 12203, Berlin, Germany
Priller, J; German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany, Department of Psychiatry and Psychotherapy, Charité, Charitéplatz 1, 10117 Berlin, Germany, School of Medicine, Technical University of Munich, Department of Psychiatry and Psychotherapy, Munich, Germany, University of Edinburgh and UK DRI, Edinburgh, UK
Spruth, E; German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany, Department of Psychiatry and Psychotherapy, Charité, Charitéplatz 1, 10117 Berlin, Germany
Altenstein, S; German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany, Department of Psychiatry and Psychotherapy, Charité, Charitéplatz 1, 10117 Berlin, Germany
Lohse, A; Department of Psychiatry and Psychotherapy, Charité, Charitéplatz 1, 10117 Berlin, Germany
Fliessbach, K; German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany, University of Bonn Medical Center, Department of Neurodegenerative Disease and Geriatric Psychiatry/Psychiatry, Venusberg-Campus 1, 53127 Bonn, Germany
Kimmich, O; German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
Vogt, I; German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
Wiltfang, J ; German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany, Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Von-Siebold-Str. 5, 37075 Goettingen, Germany, Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
Hansen, N; Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Von-Siebold-Str. 5, 37075 Goettingen, Germany
Bartels, C ; Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Von-Siebold-Str. 5, 37075 Goettingen, Germany
Schott, B H; Leibniz Institute for Neurobiology, Magdeburg, Germany, German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany, Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Von-Siebold-Str. 5, 37075 Goettingen, Germany
Maier, F ; Department of Psychiatry, University of Cologne, Medical Faculty, Kerpener Strasse 62, 50924 Cologne, Germany
Meiberth, D; Department of Psychiatry, University of Cologne, Medical Faculty, Kerpener Strasse 62, 50924 Cologne, Germany
Glanz, W ; Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Germany
Incesoy, E ; Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Germany, German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
Butryn, M; German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
Buerger, K; German Center for Neurodegenerative Diseases (DZNE, Munich), Feodor-Lynen-Strasse 17, 81377 Munich, Germany, Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Feodor-Lynen-Strasse 17, 81377 Munich, Germany
Janowitz, D; Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Feodor-Lynen-Strasse 17, 81377 Munich, Germany
Pernecky, R; German Center for Neurodegenerative Diseases (DZNE, Munich), Feodor-Lynen-Strasse 17, 81377 Munich, Germany, Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany, Munich Cluster for Systems Neurology (SyNergy) Munich, Munich, Germany, Ageing Epidemiology Research Unit (AGE), School of Public Health, Imperial College London, London, UK
Rauchmann, B ; Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
Burow, L; Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
Teipel, S ; German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany, Department of Psychosomatic Medicine, Rostock University Medical Center, Gehlsheimer Str. 20, 18147 Rostock, Germany
Kilimann, I ; German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany, Department of Psychosomatic Medicine, Rostock University Medical Center, Gehlsheimer Str. 20, 18147 Rostock, Germany
Göerß, D ; Department of Psychosomatic Medicine, Rostock University Medical Center, Gehlsheimer Str. 20, 18147 Rostock, Germany
Dyrba, M ; German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
Laske, C; German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany, Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
Munk, M; German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany, Department of Psychiatry and Psychotherapy, University of Tuebingen, Tuebingen, Germany
Sanzenbacher, C; German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
Müller, S; Department of Psychiatry and Psychotherapy, University of Tuebingen, Tuebingen, Germany
Spottke, A; German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany, Department of Neurology, University of Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
Roy, N; German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
HENEKA, Michael ; German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany, Department of Psychiatry and Psychotherapy, University of Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
Brosseron, F ; German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
Roeske, S ; German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
Dobisch, L; German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
Ramirez, A ; German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany, Department of Neurology, University of Bonn, Venusberg-Campus 1, 53127 Bonn, Germany, Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Joseph-Stelzmann-Strasse 26, 50931 Köln, Germany, Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany, Department of Psychiatry & Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, San Antonio, TX, USA
Ewers, M; German Center for Neurodegenerative Diseases (DZNE, Munich), Feodor-Lynen-Strasse 17, 81377 Munich, Germany, Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Feodor-Lynen-Strasse 17, 81377 Munich, Germany
Dechent, P; MR-Research in Neurosciences, Department of Cognitive Neurology, Georg-August-University Goettingen, Germany
Scheffler, K ; Department for Biomedical Magnetic Resonance, University of Tübingen, 72076 Tübingen, Germany
Kleineidam, L; University of Bonn Medical Center, Department of Neurodegenerative Disease and Geriatric Psychiatry/Psychiatry, Venusberg-Campus 1, 53127 Bonn, Germany
Wolfsgruber, S; German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany, University of Bonn Medical Center, Department of Neurodegenerative Disease and Geriatric Psychiatry/Psychiatry, Venusberg-Campus 1, 53127 Bonn, Germany
Wagner, M ; German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany, University of Bonn Medical Center, Department of Neurodegenerative Disease and Geriatric Psychiatry/Psychiatry, Venusberg-Campus 1, 53127 Bonn, Germany
Jessen, F; German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany, Department of Psychiatry, University of Cologne, Medical Faculty, Kerpener Strasse 62, 50924 Cologne, Germany, Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Joseph-Stelzmann-Strasse 26, 50931 Köln, Germany
Duzel, E ✱; Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Germany, German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
Ziegler, G ✱; Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Germany, German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
Abdulkadir, A., Ronneberger, O., Tabrizi, S.J., Klöppel, S., Reduction of confounding effects with voxel-wise Gaussian process regression in structural MRI. 2014 International Workshop on Pattern Recognition in Neuroimaging, 2014, IEEE, 1–4.
Abi Nader, C., Ayache, N., Robert, P., Lorenzi, M., Initiative, A.D.N., et al. Monotonic Gaussian Process for spatio-temporal disease progression modeling in brain imaging data. Neuroimage, 205, 2020, 116266.
Abraham, A., Milham, M.P., Di Martino, A., Craddock, R.C., Samaras, D., Thirion, B., Varoquaux, G., Deriving reproducible biomarkers from multi-site resting-state data: An autism-based example. NeuroImage 147 (2017), 736–745.
Abrol, A., Fu, Z., Salman, M., Silva, R., Du, Y., Plis, S., Calhoun, V., Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning. Nat. Commun., 12(1), 2021, 353.
Alfaro-Almagro, F., McCarthy, P., Afyouni, S., Andersson, J.L., Bastiani, M., Miller, K.L., Nichols, T.E., Smith, S.M., Confound modelling in UK biobank brain imaging. NeuroImage, 224, 2021, 117002.
Amyot, F., Arciniegas, D.B., Brazaitis, M.P., Curley, K.C., Diaz-Arrastia, R., Gandjbakhche, A., Herscovitch, P., Hinds, S.R., Manley, G.T., Pacifico, A., et al. A review of the effectiveness of neuroimaging modalities for the detection of traumatic brain injury. J. Neurotrauma 32:22 (2015), 1693–1721.
Ansart, M., Epelbaum, S., Gagliardi, G., Colliot, O., Dormont, D., Dubois, B., Hampel, H., Durrleman, S., Alzheimer's Disease Neuroimaging Initiative*, G.T., the INSIGHT-preAD study, A., Reduction of recruitment costs in preclinical AD trials: validation of automatic pre-screening algorithm for brain amyloidosis. Stat. Methods Med. Res. 29:1 (2020), 151–164.
Arbabshirani, M.R., Plis, S., Sui, J., Calhoun, V.D., Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. Neuroimage 145 (2017), 137–165.
Ashburner, J., Friston, K.J., Computing average shaped tissue probability templates. Neuroimage 45:2 (2009), 333–341.
Ashburner, J., Friston, K.J., Diffeomorphic registration using geodesic shooting and Gauss–Newton optimisation. NeuroImage 55:3 (2011), 954–967.
Ba, M., Ng, K., Gao, X., Kong, M., Guan, L., Yu, L., Alzheimer's Disease Neuroimaging Initiative, H., The combination of apolipoprotein E4, age and Alzheimer's disease assessment scale–cognitive subscale improves the prediction of amyloid positron emission tomography status in clinically diagnosed mild cognitive impairment. Euro. J. Neurol. 26:5 (2019), 733–e53.
Bach, F.R., Lanckriet, G.R., Jordan, M.I., 2004. Multiple kernel learning, conic duality, and the SMO algorithm. In: Proceedings of the Twenty-First International Conference on Machine Learning. p. 6.
Barber, N., Educational and ecological correlates of IQ: A cross-national investigation. Intelligence 33:3 (2005), 273–284.
Bartrés-Faz, D., Arenaza-Urquijo, E.M., Structural and functional imaging correlates of cognitive and brain reserve hypotheses in healthy and pathological aging. Brain Topography 24:3 (2011), 340–357.
Beason-Held, L.L., Goh, J.O., An, Y., Kraut, M.A., O'Brien, R.J., Ferrucci, L., Resnick, S.M., Changes in brain function occur years before the onset of cognitive impairment. J. Neurosci. 33:46 (2013), 18008–18014.
Besson, F.L., La Joie, R., Doeuvre, L., Gaubert, M., Mézenge, F., Egret, S., Landeau, B., Barré, L., Abbas, A., Ibazizene, M., et al. Cognitive and brain profiles associated with current neuroimaging biomarkers of preclinical Alzheimer's disease. J. Neurosci. 35:29 (2015), 10402–10411.
Blennow, K., Zetterberg, H., Fagan, A.M., Fluid biomarkers in alzheimer disease. Cold Spring Harbor Perspect. Med., 2(9), 2012, a006221.
Bouwman, F., Schoonenboom, S., van Der Flier, W., Van Elk, E., Kok, A., Barkhof, F., Blankenstein, M., Scheltens, P., CSF biomarkers and medial temporal lobe atrophy predict dementia in mild cognitive impairment. Neurobiol. Aging 28:7 (2007), 1070–1074.
Bradley, R.H., Caldwell, B.M., The relation of home environment, cognitive competence, and IQ among males and females. Child Dev., 1980, 1140–1148.
Buckley, R.F., Sikkes, S., Villemagne, V.L., Mormino, E.C., Rabin, J.S., Burnham, S., Papp, K.V., Doré, V., Masters, C.L., Properzi, M.J., et al. Using subjective cognitive decline to identify high global amyloid in community-based samples: a cross-cohort study. Alzheimer's Dementia Diagnosis Assess. Dis. Monitoring 11:1 (2019), 670–678.
Canas, L.S., Sudre, C.H., De Vita, E., Nihat, A., Mok, T.H., Slattery, C.F., Paterson, R.W., Foulkes, A.J., Hyare, H., Cardoso, M.J., et al. Prion disease diagnosis using subject-specific imaging biomarkers within a multi-kernel Gaussian process. NeuroImage Clin., 24, 2019, 102051.
Challis, E., Hurley, P., Serra, L., Bozzali, M., Oliver, S., Cercignani, M., Gaussian process classification of Alzheimer's disease and mild cognitive impairment from resting-state fMRI. NeuroImage 112 (2015), 232–243.
Chételat, G., Eustache, F., Viader, F., Sayette, V.D.L., Pélerin, A., Mézenge, F., Hannequin, D., Dupuy, B., Baron, J.-C., Desgranges, B., FDG-PET measurement is more accurate than neuropsychological assessments to predict global cognitive deterioration in patients with mild cognitive impairment. Neurocase 11:1 (2005), 14–25.
Davatzikos, C., Genc, A., Xu, D., Resnick, S.M., Voxel-based morphometry using the RAVENS maps: methods and validation using simulated longitudinal atrophy. NeuroImage 14:6 (2001), 1361–1369.
Davatzikos, C., Sotiras, A., Fan, Y., Habes, M., Erus, G., Rathore, S., Bakas, S., Chitalia, R., Gastounioti, A., Kontos, D., Precision diagnostics based on machine learning-derived imaging signatures. Magnetic Resonance Imaging 64 (2019), 49–61.
Doraiswamy, P.M., Charles, H.C., Krishnan, K.R.R., Prediction of cognitive decline in early Alzheimer's disease. Lancet, 352(9141), 1998, 1678.
Dowling, N.M., Hermann, B., La Rue, A., Sager, M.A., Latent structure and factorial invariance of a neuropsychological test battery for the study of preclinical Alzheimer's disease. Neuropsychology, 24(6), 2010, 742.
Duan, L.L., Wang, X., Clancy, J.P., Szczesniak, R.D., Joint hierarchical Gaussian process model with application to personalized prediction in medical monitoring. Stat, 7(1), 2018, e178.
Dubois, J., Galdi, P., Paul, L.K., Adolphs, R., A distributed brain network predicts general intelligence from resting-state human neuroimaging data. Philos. Trans. R. Soc. B, 373(1756), 2018, 20170284.
Duchesne, S., Caroli, A., Geroldi, C., Collins, D.L., Frisoni, G.B., Relating one-year cognitive change in mild cognitive impairment to baseline MRI features. Neuroimage 47:4 (2009), 1363–1370.
Dyrba, M., Grothe, M., Kirste, T., Teipel, S.J., Multimodal analysis of functional and structural disconnection in Alzheimer's disease using multiple kernel SVM. Hum. Brain Map. 36:6 (2015), 2118–2131.
Dyrba, M., Hanzig, M., Altenstein, S., Bader, S., Ballarini, T., Brosseron, F., Buerger, K., Cantré, D., Dechent, P., Dobisch, L., et al. Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: evaluation in Alzheimer's disease. Alzheimer's Res. Therapy 13:1 (2021), 1–18.
Eustache, P., Nemmi, F., Saint-Aubert, L., Pariente, J., Péran, P., Multimodal magnetic resonance imaging in Alzheimer's disease patients at prodromal stage. J. Alzheimer's Dis. 50:4 (2016), 1035–1050.
Ezzati, A., Harvey, D.J., Habeck, C., Golzar, A., Qureshi, I.A., Zammit, A.R., Hyun, J., Truelove-Hill, M., Hall, C.B., Davatzikos, C., et al. Predicting amyloid-β levels in amnestic mild cognitive impairment using machine learning techniques. J. Alzheimer's Dis. 73:3 (2020), 1211–1219.
Fisher, C.K., Smith, A.M., Walsh, J.R., Machine learning for comprehensive forecasting of Alzheimer's disease progression. Sci. Rep. 9:1 (2019), 1–14.
Folstein, M.F., Folstein, S.E., McHugh, P.R., “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. J. Psychiatric Res. 12:3 (1975), 189–198.
Forsberg, A., Engler, H., Almkvist, O., Blomquist, G., Hagman, G., Wall, A., Ringheim, A., Långström, B., Nordberg, A., PET imaging of amyloid deposition in patients with mild cognitive impairment. Neurobiol. Aging 29:10 (2008), 1456–1465.
Franke, K., Ziegler, G., Klöppel, S., Gaser, C., the Alzheimer Disease Neuroimaging Initiative, G., Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: exploring the influence of various parameters. NeuroImage 50:3 (2010), 883–892.
Frisoni, G.B., Fox, N.C., Jack, C.R., Scheltens, P., Thompson, P.M., The clinical use of structural MRI in Alzheimer disease. Nat. Rev. Neurol. 6:2 (2010), 67–77.
Gönen, M., Alpaydın, E., Multiple kernel learning algorithms. J. Mach. Learn. Res. 12 (2011), 2211–2268.
Grober, E., Ocepek-Welikson, K., Teresi, J.A., The free and cued selective reminding test: evidence of psychometric adequacy. Psychol. Sci. Quart. 51:3 (2009), 266–282.
Grosenick, L., Klingenberg, B., Katovich, K., Knutson, B., Taylor, J.E., Interpretable whole-brain prediction analysis with GraphNet. NeuroImage 72 (2013), 304–321.
Gupta, Y., Lama, R.K., Kwon, G.-R., Weiner, M.W., Aisen, P., Weiner, M., Petersen, R., Jack, C.R. Jr., Jagust, W., Trojanowki, J.Q., et al. 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.
He, T., Kong, R., Holmes, A.J., Nguyen, M., Sabuncu, M.R., Eickhoff, S.B., Bzdok, D., Feng, J., Yeo, B.T., Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics. NeuroImage, 206, 2020, 116276.
Hojjati, S.H., Babajani-Feremi, A., Initiative, A.D.N., Prediction and modeling of neuropsychological scores in Alzheimer's disease using multimodal neuroimaging data and artificial neural networks. Front. Comput. Neurosci., 15, 2022, 769982.
Hu, Y., Hosseini, A., Kauwe, J.S., Gross, J., Cairns, N.J., Goate, A.M., Fagan, A.M., Townsend, R.R., Holtzman, D.M., Identification and validation of novel CSF biomarkers for early stages of Alzheimer's disease. Proteomics–Clin. Appl. 1:11 (2007), 1373–1384.
Humpel, C., Identifying and validating biomarkers for Alzheimer's disease. Trends Biotechnol. 29:1 (2011), 26–32.
Izquierdo, W., Martin, H., Cabrerizo, M., Barreto, A., Andrian, J., Rishe, N., Gonzalez-Arias, S., Loewenstein, D., Duara, R., Adjouadi, M., Robust prediction of cognitive test scores in Alzheimer's patients. 2017 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 2017, IEEE, 1–7.
Jack, C.R., Bennett, D.A., Blennow, K., Carrillo, M.C., Feldman, H.H., Frisoni, G.B., Hampel, H., Jagust, W.J., Johnson, K.A., Knopman, D.S., et al. A/T/N: an unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology 87:5 (2016), 539–547.
Jack, C.R. Jr., Knopman, D.S., Jagust, W.J., Petersen, R.C., Weiner, M.W., Aisen, P.S., Shaw, L.M., Vemuri, P., Wiste, H.J., Weigand, S.D., et al. Tracking pathophysiological processes in Alzheimer's disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol. 12:2 (2013), 207–216.
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., et al. Association of cerebral amyloid-β aggregation with cognitive functioning in persons without dementia. JAMA Psychiatry 75:1 (2018), 84–95.
Jessen, F., Amariglio, R., Boxtel, M., Breteler, M., Ceccaldi, M., Chételat, G., Dubois, B., Dufouil, C., Ellis, K., Flier, W., Glodzik, L., Harten, A.V., Leon, M., McHugh, P., Mielke, M., Molinuevo, J., Mosconi, L., Osorio, R., Perrotin, A., Petersen, R., Rabin, L., Rami, L., Reisberg, B., Rentz, D., Sachdev, P., Sayette, V., Saykin, A., Scheltens, P., Shulman, M.B., Slavin, M., Sperling, R., Stewart, R., Uspenskaya, O., Vellas, B., Visser, P., Wagner, M., A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer's disease. Alzheimer's Dementia 10 (2014), 844–852.
Jessen, F., Spottke, A., Boecker, H., Brosseron, F., Buerger, K., Catak, C., Fliessbach, K., Franke, C., Fuentes, M., Heneka, M.T., et al. Design and first baseline data of the DZNE multicenter observational study on predementia Alzheimer's disease (DELCODE). Alzheimer's Res. therapy 10:1 (2018), 1–10.
Jo, T., Nho, K., Saykin, A.J., Deep learning in Alzheimer's disease: diagnostic classification and prognostic prediction using neuroimaging data. Front. Aging Neurosci., 11, 2019, 220.
Jollans, L., Boyle, R., Artiges, E., Banaschewski, T., Desrivières, S., Grigis, A., Martinot, J.-L., Paus, T., Smolka, M.N., Walter, H., et al. Quantifying performance of machine learning methods for neuroimaging data. NeuroImage 199 (2019), 351–365.
Kandiah, N., Zhang, A., Cenina, A.R., Au, W.L., Nadkarni, N., Tan, L.C., Montreal cognitive assessment for the screening and prediction of cognitive decline in early Parkinson's disease. Parkinsonism Rel. Dis. 20:11 (2014), 1145–1148.
Knešaurek, K., Improving 18f-fluoro-d-glucose-positron emission tomography/computed tomography imaging in Alzheimer's disease studies. World J. Nucl. Med., 14(3), 2015, 171.
Ko, H., Ihm, J.-J., Kim, H.-G., Initiative, A.D.N., et al. Cognitive profiling related to cerebral amyloid beta burden using machine learning approaches. Front. Aging Neurosci., 11, 2019, 95.
Kohavi, R., et al. A study of cross-validation and bootstrap for accuracy estimation and model selection. Ijcai, vol. 14 no. 2, 1995, Montreal, Canada, 1137–1145.
Lee, J.H., Byun, M.S., Yi, D., Sohn, B.K., Jeon, S.Y., Lee, Y., Lee, J.-Y., Kim, Y.K., Lee, Y.-S., Lee, D.Y., Prediction of cerebral amyloid with common information obtained from memory clinic practice. Front. Aging Neurosci., 10, 2018, 309.
Lezak, M.D., Howieson, D.B., Loring, D.W., Fischer, J.S., et al. Neuropsychological assessment. 2004, Oxford University Press, USA.
Li, Z., Jiang, X., Wang, Y., Kim, Y., Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data. Emerg. topics Life Sci. 5:6 (2021), 765–777.
Lindsay, J., Laurin, D., Verreault, R., Hébert, R., Helliwell, B., Hill, G.B., McDowell, I., Risk factors for Alzheimer's disease: a prospective analysis from the Canadian study of health and aging. Am. J. Epidemiol. 156:5 (2002), 445–453.
Liu, J., Tian, X., Wang, J., Guo, R., Kuang, H., MTFIL-Net: automated Alzheimer's disease detection and MMSE score prediction based on feature interactive learning. 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2021, IEEE, 1002–1007.
Liu, M., Zhang, J., Adeli, E., Shen, D., Joint classification and regression via deep multi-task multi-channel learning for Alzheimer's disease diagnosis. IEEE Trans. Biomed. Eng. 66:5 (2018), 1195–1206.
Marquand, A.F., Brammer, M., Williams, S.C., Doyle, O.M., Bayesian multi-task learning for decoding multi-subject neuroimaging data. NeuroImage 92 (2014), 298–311.
Marquand, A., Howard, M., Brammer, M., Chu, C., Coen, S., Mourão-Miranda, J., Quantitative prediction of subjective pain intensity from whole-brain fMRI data using Gaussian processes. Neuroimage 49:3 (2010), 2178–2189.
Maserejian, N., Bian, S., Wang, W., Jaeger, J., Syrjanen, J.A., Aakre, J., Jack, C.R. Jr., Mielke, M.M., Gao, F., Initiative, A.D.N., et al. Practical algorithms for amyloid β probability in subjective or mild cognitive impairment. Alzheimer's Dementia Diagn. Assess. Dis. Monit. 11 (2019), 710–720.
Mateos-Pérez, J.M., Dadar, M., Lacalle-Aurioles, M., Iturria-Medina, Y., Zeighami, Y., Evans, A.C., Structural neuroimaging as clinical predictor: A review of machine learning applications. NeuroImage Clin. 20 (2018), 506–522.
McKhann, G.M., Knopman, D.S., Chertkow, H., Hyman, B.T., Jack, C.R. Jr., Kawas, C.H., Klunk, W.E., Koroshetz, W.J., Manly, J.J., Mayeux, R., et al. The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimer's Dementia 7:3 (2011), 263–269.
Mohs, R.C., Knopman, D., Petersen, R.C., Ferris, S.H., Ernesto, C., Grundman, M., Sano, M., Bieliauskas, L., Geldmacher, D., Clark, C., et al. Development of cognitive instruments for use in clinical trials of antidementia drugs: additions to the Alzheimer's disease assessment scale that broaden its scope. Alzheimer Dis. Assoc. Disorders, 1997.
Molinuevo, J.L., Rabin, L.A., Amariglio, R., Buckley, R., Dubois, B., Ellis, K.A., Ewers, M., Hampel, H., Klöppel, S., Rami, L., Reisberg, B., Saykin, A.J., Sikkes, S., Smart, C.M., Snitz, B.E., Sperling, R., van der Flier, W.M., Wagner, M., Jessen, F., Implementation of subjective cognitive decline criteria in research studies. Alzheimer's Dementia 1552-5260, 13(3), 2017, 296–311, 10.1016/j.jalz.2016.09.012 URL https://www.sciencedirect.com/science/article/pii/S1552526016330199.
Monté-Rubio, G.C., Falcón, C., Pomarol-Clotet, E., Ashburner, J., A comparison of various MRI feature types for characterizing whole brain anatomical differences using linear pattern recognition methods. NeuroImage 178 (2018), 753–768.
Mourao-Miranda, J., Bokde, A.L., Born, C., Hampel, H., Stetter, M., Classifying brain states and determining the discriminating activation patterns: support vector machine on functional MRI data. NeuroImage 28:4 (2005), 980–995.
Murphy, M.P., LeVine III, H., Alzheimer's disease and the amyloid-β peptide. J. Alzheimer's Dis. 19:1 (2010), 311–323.
Ossenkoppele, R., Schonhaut, D.R., Schöll, M., Lockhart, S.N., Ayakta, N., Baker, S.L., O'Neil, J.P., Janabi, M., Lazaris, A., Cantwell, A., et al. Tau PET patterns mirror clinical and neuroanatomical variability in Alzheimer's disease. Brain 139:5 (2016), 1551–1567.
Papp, K.V., Rentz, D.M., Orlovsky, I., Sperling, R.A., Mormino, E.C., Optimizing the preclinical Alzheimer's cognitive composite with semantic processing: the PACC5. Alzheimer's & Dementia Transl. Res. Clin. Interventions 3:4 (2017), 668–677.
Park, L.Q., Gross, A.L., McLaren, D.G., Pa, J., Johnson, J.K., Mitchell, M., Manly, J.J., Confirmatory factor analysis of the ADNI neuropsychological battery. Brain Imaging Behav. 6:4 (2012), 528–539.
Pereira, F., Mitchell, T., Botvinick, M., Machine learning classifiers and fMRI: a tutorial overview. Neuroimage 45:1 (2009), S199–S209.
Petermann, F., Lepach, A.C., Wechsler memory scale. 2012 Ed. in deutscher Übersetzung und Adaptation der WMS-IV von Davis Wechsler. Frankfurt a. M.: Pearson Assessment & Information GmbH.
Pettersson-Yeo, W., Benetti, S., Marquand, A.F., Joules, R., Catani, M., Williams, S.C., Allen, P., McGuire, P., Mechelli, A., An empirical comparison of different approaches for combining multimodal neuroimaging data with support vector machine. Front. Neurosci., 8, 2014, 189.
Polcher, A., Frommann, I., Koppara, A., Wolfsgruber, S., Jessen, F., Wagner, M., Face-name associative recognition deficits in subjective cognitive decline and mild cognitive impairment. J. Alzheimer's Dis. 56:3 (2017), 1185–1196.
Porsteinsson, A., Isaacson, R., Knox, S., Sabbagh, M., Rubino, I., Diagnosis of early Alzheimer's disease: clinical practice in 2021. J. Prevent. Alzheimer's Dis. 8 (2021), 371–386.
Prestia, A., Caroli, A., Wade, S.K., Van Der Flier, W.M., Ossenkoppele, R., Van Berckel, B., Barkhof, F., Teunissen, C.E., Wall, A., Carter, S.F., et al. Prediction of AD dementia by biomarkers following the NIA-AA and IWG diagnostic criteria in MCI patients from three European memory clinics. Alzheimer's Dementia 11:10 (2015), 1191–1201.
Rakotomamonjy, A., Bach, F., Canu, S., Grandvalet, Y., Simplemkl. J. Mach. Learn. Res. 9 (2008), 2491–2521.
Rao, A., Monteiro, J.M., Ashburner, J., Portugal, L., Fernandes, O., De Oliveira, L., Pereira, M., Mourao-Miranda, J., A comparison of strategies for incorporating nuisance variables into predictive neuroimaging models. 2015 International Workshop on Pattern Recognition in NeuroImaging, 2015, IEEE, 61–64.
Rao, A., Monteiro, J.M., Mourao-Miranda, J., Initiative, A.D., et al. Predictive modelling using neuroimaging data in the presence of confounds. NeuroImage 150 (2017), 23–49.
Rasmussen, C.E., Williams, C.K.I., Gaussian Processes for Machine Learning. 2006, MIT Press, Cambridge.
Rathore, S., Habes, M., Iftikhar, M.A., Shacklett, A., Davatzikos, C., A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages. NeuroImage 155 (2017), 530–548.
Reitan, R.M., Validity of the trail making test as an indicator of organic brain damage. Perceptual Motor Skills 8:3 (1958), 271–276.
Riedel, B.C., Thompson, P.M., Brinton, R.D., Age, APOE and sex: triad of risk of Alzheimer's disease. J. Steroid Biochem. Molecular Biol. 160 (2016), 134–147.
Rouleau, I., Salmon, D.P., Butters, N., Kennedy, C., McGuire, K., Quantitative and qualitative analyses of clock drawings in Alzheimer's and Huntington's disease. Brain Cognit. 18:1 (1992), 70–87.
Salvatore, C., Battista, P., Castiglioni, I., Frontiers for the early diagnosis of AD by means of MRI brain imaging and support vector machines. Curr. Alzheimer Res. 13:5 (2016), 509–533.
Sanderman, R., Coyne, J.C., Ranchor, A.V., Age: Nuisance variable to be eliminated with statistical control or important concern?. Patient Educ. Counsel. 61:3 (2006), 315–316.
Scheinost, D., Noble, S., Horien, C., Greene, A.S., Lake, E.M., Salehi, M., Gao, S., Shen, X., O'Connor, D., Barron, D.S., et al. Ten simple rules for predictive modeling of individual differences in neuroimaging. NeuroImage 193 (2019), 35–45.
Schulz, E., Speekenbrink, M., Krause, A., A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions. 2017, 10.1101/095190 bioRxiv arXiv:https://www.biorxiv.org/content/early/2017/10/10/095190.full.pdf.
Shawe-Taylor, J., Cristianini, N., et al. Kernel Methods for Pattern Analysis. 2004, Cambridge University Press.
Smith, A., Symbol digit modalities test (SDMT) manual (revised) western psychological services. Los Angeles, 1982.
Stern, Y., Arenaza-Urquijo, E.M., Bartrés-Faz, D., Belleville, S., Cantilon, M., Chetelat, G., Ewers, M., Franzmeier, N., Kempermann, G., Kremen, W.S., et al. Whitepaper: Defining and investigating cognitive reserve, brain reserve, and brain maintenance. Alzheimer's & Dementia 16:9 (2020), 1305–1311.
Stonnington, C.M., Chu, C., Klöppel, S., Jack, C.R. Jr., Ashburner, J., Frackowiak, R.S., Initiative, A.D.N., et al. Predicting clinical scores from magnetic resonance scans in Alzheimer's disease. Neuroimage 51:4 (2010), 1405–1413.
Su, W., Wang, X., Szczesniak, R.D., Flexible link functions in a joint hierarchical Gaussian process model. Biometrics 77:2 (2021), 754–764.
Sui, J., Adali, T., Yu, Q., Chen, J., Calhoun, V.D., A review of multivariate methods for multimodal fusion of brain imaging data. J. Neurosci. Methods 204:1 (2012), 68–81.
Thalmann, B., Monsch, A.U., Schneitter, M., Bernasconi, F., Aebi, C., Camachova-Davet, Z., Staehelin, H.B., The CERAD neuropsychological assessment battery (CERAD-NAB)—A minimal data set as a common tool for German-speaking Europe. Neurobiol. Aging(21), 2000, 30.
Tian, X., Liu, J., Kuang, H., Sheng, Y., Wang, J., The Alzheimer's Disease Neuroimaging Initiative, Z., MRI-based multi-task decoupling learning for alzheimer's disease detection and MMSE score prediction: A multi-site validation. 2022 arXiv preprint arXiv:2204.01708.
Tohka, J., Zijdenbos, A., Evans, A., Fast and robust parameter estimation for statistical partial volume models in brain MRI. NeuroImage 23:1 (2004), 84–97.
Tosun, D., Chen, Y.-F., Yu, P., Sundell, K.L., Suhy, J., Siemers, E., Schwarz, A.J., Weiner, M.W., Initiative, A.D.N., et al. Amyloid status imputed from a multimodal classifier including structural MRI distinguishes progressors from nonprogressors in a mild Alzheimer's disease clinical trial cohort. Alzheimer's & Dementia 12:9 (2016), 977–986.
Tosun, D., Joshi, S., Weiner, M.W., Alzheimer's Disease Neuroimaging Initiative, K.L., Neuroimaging predictors of brain amyloidosis in mild cognitive impairment. Annals Neurol. 74:2 (2013), 188–198.
Van Dam, N.T., Sano, M., Mitsis, E.M., Grossman, H.T., Gu, X., Park, Y., Hof, P.R., Fan, J., Functional neural correlates of attentional deficits in amnestic mild cognitive impairment. PLoS One, 8(1), 2013, e54035.
Varoquaux, G., Cross-validation failure: small sample sizes lead to large error bars. Neuroimage 180 (2018), 68–77.
Wang, Y., Fan, Y., Bhatt, P., Davatzikos, C., High-dimensional pattern regression using machine learning: from medical images to continuous clinical variables. Neuroimage 50:4 (2010), 1519–1535.
Wilson, A., Adams, R., Gaussian process kernels for pattern discovery and extrapolation. International Conference on Machine Learning, 2013, PMLR, 1067–1075.
Wolfsgruber, S., Kleineidam, L., Guski, J., Polcher, A., Frommann, I., Roeske, S., Spruth, E.J., Franke, C., Priller, J., Kilimann, I., Teipel, S., Buerger, K., Janowitz, D., Laske, C., Buchmann, M., Peters, O., Menne, F., Fuentes Casan, M., Wiltfang, J., Bartels, C., Düzel, E., Metzger, C., Glanz, W., Thelen, M., Spottke, A., Ramirez, A., Kofler, B., Fließ bach, K., Schneider, A., Heneka, M.T., Brosseron, F., Meiberth, D., Jessen, F., Wagner, M., on behalf of the DELCODE Study Group, P., Minor neuropsychological deficits in patients with subjective cognitive decline. Neurology 0028-3878, 95(9), 2020, e1134–e1143, 10.1212/WNL.0000000000010142 arXiv:https://n.neurology.org/content/95/9/e1134.full.pdf.
Wolfsgruber, S., Wagner, M., Schmidtke, K., Frölich, L., Kurz, A., Schulz, S., Hampel, H., Heuser, I., Peters, O., Reischies, F.M., et al. Memory concerns, memory performance and risk of dementia in patients with mild cognitive impairment. PLoS One, 9(7), 2014, e100812.
Woodard, J.L., Seidenberg, M., Nielson, K.A., Smith, J.C., Antuono, P., Durgerian, S., Guidotti, L., Zhang, Q., Butts, A., Hantke, N., et al. Prediction of cognitive decline in healthy older adults using fMRI. J. Alzheimer's Dis. 21:3 (2010), 871–885.
Yu, W., Xu, H., Co-attentive multi-task convolutional neural network for facial expression recognition. Pattern Recognit., 123, 2022, 108401.
Zhang, D., Shen, D., Initiative, A.D.N., et al. Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease. NeuroImage 59:2 (2012), 895–907.
Zhang, D., Wang, Y., Zhou, L., Yuan, H., Shen, D., Alzheimer's Disease Neuroimaging Initiative, S., et al. Multimodal classification of Alzheimer's disease and mild cognitive impairment. Neuroimage 55:3 (2011), 856–867.
Zhu, F., Panwar, B., Dodge, H.H., Li, H., Hampstead, B.M., Albin, R.L., Paulson, H.L., Guan, Y., COMPASS: A computational model to predict changes in MMSE scores 24-months after initial assessment of Alzheimer's disease. Sci. Rep. 6:1 (2016), 1–12.
Ziegler, G., Ridgway, G.R., Dahnke, R., Gaser, C., Alzheimer's Disease Neuroimaging Initiative, B.M., et al. Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjects. NeuroImage 97 (2014), 333–348.
Zu, C., Jie, B., Liu, M., Chen, S., Shen, D., Zhang, D., Label-aligned multi-task feature learning for multimodal classification of Alzheimer's disease and mild cognitive impairment. Brain Imaging Behav. 10:4 (2016), 1148–1159.