Use of multimodality imaging and artificial intelligence for diagnosis and prognosis of early stages of Alzheimer's disease

Published:January 10, 2018DOI:
      Alzheimer's disease (AD) is a major neurodegenerative disease and the most common cause of dementia. Currently, no treatment exists to slow down or stop the progression of AD. There is converging belief that disease-modifying treatments should focus on early stages of the disease, that is, the mild cognitive impairment (MCI) and preclinical stages. Making a diagnosis of AD and offering a prognosis (likelihood of converting to AD) at these early stages are challenging tasks but possible with the help of multimodality imaging, such as magnetic resonance imaging (MRI), fluorodeoxyglucose (FDG)-positron emission topography (PET), amyloid-PET, and recently introduced tau-PET, which provides different but complementary information. This article is a focused review of existing research in the recent decade that used statistical machine learning and artificial intelligence methods to perform quantitative analysis of multimodality image data for diagnosis and prognosis of AD at the MCI or preclinical stages. We review the existing work in 3 subareas: diagnosis, prognosis, and methods for handling modality-wise missing data—a commonly encountered problem when using multimodality imaging for prediction or classification. Factors contributing to missing data include lack of imaging equipment, cost, difficulty of obtaining patient consent, and patient drop-off (in longitudinal studies). Finally, we summarize our major findings and provide some recommendations for potential future research directions.


      AD (Alzheimer's disease), MCI (mild cognitive impairment), MRI (magnetic resonance imaging), FDG (fluorodeoxyglucose), PET (positron emission topography), FDA (Food and Drug Administration), NIA (National Institute of Aging), AA (Alzheimer's Association), IWG (International Working Group), ML (machine learning), AI (artificial intelligence), NC (normal control), CSF (cerebrospinal fluid), APOE (apolipoprotein E), SNAP (suspected non-Alzheimer pathology), SVDs (singular-vector decompositions), SUVR (standard uptake value ratio), SVM (support vector machine), ICA (independent component analysis), MKL (multiple kernel learning), ROI (region of interest), ADNI (Alzheimer's Disease Neuroimaging Initiative), MMSE (Mini-Mental State Examination), ADAS-Cog (Alzheimer's Disease Assessment Scale-Cognitive subscale), GP (Gaussian process), NPSEs (Neuropsychological Status Exam Score), VBM (voxel-based morphometry), TBM (tensor-based morphometry), AUC (area under the curve), PLS (partial least square), CDR (clinical dementia rating), CNN (convolutional neural network), DL (deep learning)
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        • Alzheimer's Association
        2016 Alzheimer's disease facts and figures.
        Alzheimers Dement. 2016; 12 (Available at): 459-509
        • Alzheimer's Association
        FDA-approved treatments for Alzheimer's.
        (Available at)
        • Sperling R.A.
        • Aisen P.S.
        • Beckett L.A.
        • et al.
        Toward defining the preclinical stages of Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease.
        Alzheimers Dement. 2011; 7 (Available at): 280-292
        • Albert M.S.
        • DeKosky S.T.
        • Dickson D.
        • et al.
        The diagnosis of mild cognitive impairment due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease.
        Alzheimers Dement. 2011; 7 (Available at): 270-279
        • Carrillo M.C.
        • Dean R.A.
        • Nicolas F.
        • et al.
        Revisiting the framework of the National Institute on Aging-Alzheimer's Association diagnostic criteria.
        Alzheimers Dement. 2013; 9: 594-601
        • Visser P.J.
        • Vos S.
        • Van Rossum I.
        • Scheltens P.
        Comparison of International Working Group criteria and National Institute on Aging-Alzheimer's Association criteria for Alzheimer's disease.
        Alzheimers Dement. 2012; 8 (Available at): 560-563
        • Knopman D.S.
        • Jack C.R.
        • Wiste H.J.
        • et al.
        Selective worsening of brain injury biomarker abnormalities in cognitively normal elderly persons with β-Amyloidosis.
        JAMA Neurol. 2013; 70 (Available at): 1030
        • Lowe V.J.
        • Kemp B.J.
        • Jack C.R.
        • et al.
        Comparison of 18F-FDG and PiB PET in cognitive impairment.
        J Nucl Med. 2009; 50 (Available at): 878-886
        • Yi D.
        • Lee D.Y.
        • Sohn B.K.
        • et al.
        Beta-Amyloid associated differential effects of APOE ε4 on brain metabolism in cognitively normal elderly.
        Am J Geriatr Psychiatry. 2014; 22 (Available at): 961-970
        • Knopman D.S.
        • Jack C.R.
        • Wiste H.J.
        • et al.
        Short-term clinical outcomes for stages of NIA-AA preclinical Alzheimer disease.
        Neurology. 2012; 78 (Available at): 1576-1582
        Date accessed: September 9, 2017
        • Wirth M.
        • Villeneuve S.
        • Haase C.M.
        • et al.
        Associations between Alzheimer disease biomarkers, neurodegeneration, and cognition in cognitively normal older people.
        JAMA Neurol. 2013; 77 (Available at): 1619-1628
        • Jack C.R.
        • Knopman D.S.
        • Weigand S.D.
        • et al.
        An operational approach to National Institute on Aging-Alzheimer's Association criteria for preclinical Alzheimer disease.
        Ann Neurol. 2012; 71 (Available at): 765-775
        Date accessed: September 9, 2017
        • Knopman D.S.
        • Jack C.R.
        • Wiste H.J.
        • et al.
        Brain injury biomarkers are not dependent on β-amyloid in normal elderly.
        Ann Neurol. 2013; 73 (Available at): 472-480
        Date accessed: September 9, 2017
        • Brier M.R.
        • Gordon B.A.
        • Friedrichsen K.
        • et al.
        Tau and Aβ imaging, CSF measures, and cognition in Alzheimer's disease.
        Sci Transl Med. 2016; 8 (Available at): 338ra66
        • Jack C.R.
        • Wiste H.J.
        • Vemuri P.
        • et al.
        Brain beta-amyloid measures and magnetic resonance imaging atrophy both predict time-to-progression from mild cognitive impairment to Alzheimer's disease.
        Brain. 2010; 133: 3336-3348
        • Ritter K.
        • Schumacher J.
        • Weygandt M.
        • Buchert R.
        • Allefeld C.
        • Haynes J.D.
        Alzheimer's Disease Neuroimaging Initiative. Multimodal prediction of conversion to Alzheimer's disease based on incomplete biomarkers.
        Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring. 2015; 1: 206-215
        • Shaffer J.L.
        • Petrella J.R.
        • Sheldon F.C.
        • et al.
        Predicting cognitive decline in subjects at risk for Alzheimer disease by using combined.
        Radiology. 2013; 266: 583-591
        • Zhang D.
        • Wang Y.
        • Zhou L.
        • Yuan H.
        • Shen D.
        Multimodal classification of Alzheimer's disease and mild cognitive impairment.
        Neuroimage. 2011; 55 (Available at): 856-867
        • Liu F.
        • Wee C.Y.
        • Chen H.
        • Shen D.
        Inter-modality relationship constrained multimodality multitask feature selection for Alzheimer's disease and mild cognitive impairment identification.
        Neuroimage. 2014; 84 (Available at): 466-475
        • Zhang D.
        • Shen D.
        Multimodal multitask learning for joint prediction of multiple regression and classification variables in Alzheimer's disease.
        Neuroimage. 2012; 59 (Available at): 895-907
        • Cheng B.
        • Liu M.
        • Zhang D.
        • Munsell B.C.
        • Shen D.
        Domain transfer learning for MCI conversion prediction.
        IEEE Trans Biomed Eng. 2015; 62: 1805-1817
        • 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. 2013; 2 (Available at): 735-745
        • Hinrichs C.
        • Singh V.
        • Xu G.
        • Johnson S.C.
        Predictive markers for AD in a multimodality framework: an analysis of MCI progression in the ADNI population.
        Neuroimage. 2011; 55 (Available at): 574-589
        • Zhang D.
        • Shen D.
        Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers.
        PLoS ONE. 2012; 7 (e33182)
        • Wang P.
        • Chen K.
        • Yao L.
        • et al.
        Multimodal classification of mild cognitive impairment based on partial least squares.
        J Alzheimers Dis. 2016; 54: 359-371
        • Yuan L.
        • Wang Y.
        • Thompson P.M.
        • Narayan V.A.
        • Ye J.
        Multisource feature learning for joint analysis of incomplete multiple heterogeneous neuroimaging data.
        Neuroimage. 2012; 61 (Available at): 622-632
        • Xiang S.
        • Yuan L.
        • Fan W.
        • Wang Y.
        • Thompson P.M.
        • Ye J.
        Bi-level multisource learning for heterogeneous block-wise missing data.
        Neuroimage. 2014; 102 (Available at): 192-206
        • Thung K.H.
        • Wee C.Y.
        • Yap P.T.
        • Shen D.
        Neurodegenerative disease diagnosis using incomplete multimodality data via matrix shrinkage and completion.
        Neuroimage. 2014; 91 (Available at): 386-400
        • Liu M.
        • Zhang J.
        • Yap P.T.
        • Shen D.
        View-aligned hypergraph learning for Alzheimer's disease diagnosis with incomplete multimodality data.
        Med Image Anal. 2017; 36 (Available at): 123-134
        • Li R.
        • Zhang W.
        • Suk H.
        • et al.
        Deep learning based imaging data completion for improved brain disease diagnosis.
        Med Image Comput Comput Assist Interv. 2014; 17: 305-312