Advertisement

Episodic Memory–Related Imaging Features as Valuable Biomarkers for the Diagnosis of Alzheimer’s Disease: A Multicenter Study Based on Machine Learning

  • Author Footnotes
    1 YS and ZW contributed equally to this work.
    Yachen Shi
    Footnotes
    1 YS and ZW contributed equally to this work.
    Affiliations
    Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China
    Search for articles by this author
  • Author Footnotes
    1 YS and ZW contributed equally to this work.
    Zan Wang
    Footnotes
    1 YS and ZW contributed equally to this work.
    Affiliations
    Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China
    Search for articles by this author
  • Pindong Chen
    Affiliations
    Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    University of Chinese Academy of Sciences, Beijing, China
    Search for articles by this author
  • Piaoyue Cheng
    Affiliations
    Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China
    Search for articles by this author
  • Kun Zhao
    Affiliations
    Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    School of Biological Science and Medical Engineering, Beihang University, Beijing, China
    Search for articles by this author
  • Hongxing Zhang
    Affiliations
    Department of Psychology, Xinxiang Medical University, Xinxiang, China

    Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
    Search for articles by this author
  • Hao Shu
    Affiliations
    Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China
    Search for articles by this author
  • Lihua Gu
    Affiliations
    Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China
    Search for articles by this author
  • Lijuan Gao
    Affiliations
    Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China
    Search for articles by this author
  • Qing Wang
    Affiliations
    Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China
    Search for articles by this author
  • Haisan Zhang
    Affiliations
    Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
    Search for articles by this author
  • Chunming Xie
    Affiliations
    Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China
    Search for articles by this author
  • Yong Liu
    Correspondence
    Address correspondence to Zhijun Zhang, Ph.D
    Affiliations
    Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    University of Chinese Academy of Sciences, Beijing, China

    School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
    Search for articles by this author
  • Zhijun Zhang
    Correspondence
    Yong Liu, Ph.D.
    Affiliations
    Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China

    School of Life Science and Technology, The Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, China

    Department of Psychology, Xinxiang Medical University, Xinxiang, China

    Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
    Search for articles by this author
  • for theAlzheimer’s Disease Neuroimaging Initiative
  • Author Footnotes
    1 YS and ZW contributed equally to this work.
Published:December 28, 2020DOI:https://doi.org/10.1016/j.bpsc.2020.12.007

      Abstract

      Background

      Individualized and reliable biomarkers are crucial for diagnosing Alzheimer’s disease (AD). However, lack of accessibility and neurobiological correlation are the main obstacles to their clinical application. Machine learning algorithms can effectively identify personalized biomarkers based on the prominent symptoms of AD.

      Methods

      Episodic memory–related magnetic resonance imaging (MRI) features of 143 patients with amnesic mild cognitive impairment (MCI) were identified using a multivariate relevance vector regression algorithm. The support vector machine classification model was constructed using these MRI features and verified in 2 independent datasets (N = 994). The neurobiological basis was also investigated based on cognitive assessments, neuropathologic biomarkers of cerebrospinal fluid, and positron emission tomography images of amyloid-β plaques.

      Results

      The combination of gray matter volume and amplitude of low-frequency fluctuation MRI features accurately predicted episodic memory impairment in individual patients with amnesic MCI (r = .638) when measured using an episodic memory assessment panel. The MRI features that contributed to episodic memory prediction were primarily distributed across the default mode network and limbic network. The classification model based on these features distinguished patients with AD from normal control subjects with more than 86% accuracy. Furthermore, most identified episodic memory–related regions showed significantly different amyloid-β positron emission tomography measurements among the AD, MCI, and normal control groups. Moreover, the classification outputs significantly correlated with cognitive assessment scores and cerebrospinal fluid pathological biomarkers' levels in the MCI and AD groups.

      Conclusions

      Neuroimaging features can reflect individual episodic memory function and serve as potential diagnostic biomarkers of AD.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      References

        • Lane C.A.
        • Hardy J.
        • Schott J.M.
        Alzheimer’s disease.
        Eur J Neurol. 2018; 25: 59-70
        • Chehrehnegar N.
        • Nejati V.
        • Shati M.
        • Rashedi V.
        • Lotfi M.
        • Adelirad F.
        • et al.
        Early detection of cognitive disturbances in mild cognitive impairment: A systematic review of observational studies.
        Psychogeriatrics. 2020; 20: 212-228
        • Cummings J.
        The National Institute on Aging-Alzheimer’s Association Framework on Alzheimer’s disease: Application to clinical trials.
        Alzheimers Dement. 2019; 15: 172-178
        • Tulving E.
        Episodic memory: From mind to brain.
        Annu Rev Psychol. 2002; 53: 1-25
        • Chen J.
        • Zhang Z.
        • Li S.
        Can multi-modal neuroimaging evidence from hippocampus provide biomarkers for the progression of amnestic mild cognitive impairment?.
        Neurosci Bull. 2015; 31: 128-140
        • Gu L.
        • Zhang Z.
        Exploring structural and functional brain changes in mild cognitive impairment: A whole brain ALE meta-analysis for multimodal MRI.
        ACS Chem Neurosci. 2019; 10: 2823-2829
        • McDonough I.M.
        • Festini S.B.
        • Wood M.M.
        Risk for Alzheimer’s disease: A review of long-term episodic memory encoding and retrieval fMRI studies.
        Ageing Res Rev. 2020; 62: 101133
        • Bai F.
        • Zhang Z.
        • Watson D.R.
        • Yu H.
        • Shi Y.
        • Yuan Y.
        • et al.
        Abnormal functional connectivity of hippocampus during episodic memory retrieval processing network in amnestic mild cognitive impairment.
        Biol Psychiatry. 2009; 65: 951-958
        • Terry D.P.
        • Sabatinelli D.
        • Puente A.N.
        • Lazar N.A.
        • Miller L.S.
        A meta-analysis of fMRI activation differences during episodic memory in Alzheimer’s disease and mild cognitive impairment.
        J Neuroimaging. 2015; 25: 849-860
        • Schwindt G.C.
        • Black S.E.
        Functional imaging studies of episodic memory in Alzheimer’s disease: A quantitative meta-analysis.
        Neuroimage. 2009; 45: 181-190
        • Wang C.
        • Pan Y.
        • Liu Y.
        • Xu K.
        • Hao L.
        • Huang F.
        • et al.
        Aberrant default mode network in amnestic mild cognitive impairment: A meta-analysis of independent component analysis studies.
        Neurol Sci. 2018; 39: 919-931
        • Sartori J.M.
        • Reckziegel R.
        • Passos I.C.
        • Czepielewski L.S.
        • Fijtman A.
        • Sodre L.A.
        • et al.
        Volumetric brain magnetic resonance imaging predicts functioning in bipolar disorder: A machine learning approach.
        J Psychiatr Res. 2018; 103: 237-243
        • Siegel J.S.
        • Ramsey L.E.
        • Snyder A.Z.
        • Metcalf N.V.
        • Chacko R.V.
        • Weinberger K.
        • et al.
        Disruptions of network connectivity predict impairment in multiple behavioral domains after stroke.
        Proc Natl Acad Sci U S A. 2016; 113: E4367-E4376
        • Feng C.
        • Cui Z.
        • Cheng D.
        • Xu R.
        • Gu R.
        Individualized prediction of dispositional worry using white matter connectivity.
        Psychol Med. 2019; 49: 1999-2008
        • Mwangi B.
        • Wu M.J.
        • Cao B.
        • Passos I.C.
        • Lavagnino L.
        • Keser Z.
        • et al.
        Individualized prediction and clinical staging of bipolar disorders using neuroanatomical biomarkers.
        Biol Psychiatry Cogn Neurosci Neuroimaging. 2016; 1: 186-194
        • Tipping M.E.
        Sparse Bayesian learning and the relevance vector machine.
        J Mach Learn Res. 2001; 1: 211-244
        • Cui Z.
        • Gong G.
        The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features.
        Neuroimage. 2018; 178: 622-637
        • Wang Y.
        • Fan Y.
        • Bhatt P.
        • Davatzikos C.
        High-dimensional pattern regression using machine learning: From medical images to continuous clinical variables.
        Neuroimage. 2010; 50: 1519-1535
        • Viviano J.D.
        • Buchanan R.W.
        • Calarco N.
        • Gold J.M.
        • Foussias G.
        • Bhagwat N.
        • et al.
        Resting-state connectivity biomarkers of cognitive performance and social function in individuals with schizophrenia spectrum disorder and healthy control subjects.
        Biol Psychiatry. 2018; 84: 665-674
        • So J.H.
        • Madusanka N.
        • Choi H.K.
        • Choi B.K.
        • Park H.G.
        Deep learning for Alzheimer’s disease classification using texture features.
        Curr Med Imaging Rev. 2019; 15: 689-698
        • Lin W.
        • Gao Q.
        • Yuan J.
        • Chen Z.
        • Feng C.
        • Chen W.
        • et al.
        Predicting Alzheimer’s disease conversion from mild cognitive impairment using an extreme learning machine-based grading method with multimodal data.
        Front Aging Neurosci. 2020; 12: 77
        • Ezzati A.
        • Zammit A.R.
        • Harvey D.J.
        • Habeck C.
        • Hall C.B.
        • Lipton R.B.
        • et al.
        Optimizing machine learning methods to improve predictive models of Alzheimer’s disease.
        J Alzheimers Dis. 2019; 71: 1027-1036
        • Popuri K.
        • Ma D.
        • Wang L.
        • Beg M.F.
        Using machine learning to quantify structural MRI neurodegeneration patterns of Alzheimer’s disease into dementia score: Independent validation on 8,834 images from ADNI, AIBL, OASIS, and MIRIAD databases.
        Hum Brain Mapp. 2020; 41e25115
        • Wang L.
        • Liu Y.
        • Zeng X.
        • Cheng H.
        • Wang Z.
        • Wang Q.
        Region-of-interest based sparse feature learning method for Alzheimer’s disease identification.
        Comput Methods Programs Biomed. 2020; 187: 105290
        • Shahamat H.
        • Saniee Abadeh M.
        Brain MRI analysis using a deep learning based evolutionary approach.
        Neural Netw. 2020; 126: 218-234
        • Toshkhujaev S.
        • Lee K.H.
        • Choi K.Y.
        • Lee J.J.
        • Kwon G.R.
        • Gupta Y.
        • et al.
        Classification of Alzheimer’s disease and mild cognitive impairment based on cortical and subcortical features from MRI T1 brain images utilizing four different types of datasets.
        J Healthc Eng. 2020; 2020: 3743171
        • Zheng W.
        • Cui B.
        • Sun Z.
        • Li X.
        • Han X.
        • Yang Y.
        • et al.
        Application of generalized split linearized Bregman iteration algorithm for Alzheimer’s disease prediction.
        Aging (Albany NY). 2020; 12: 6206-6224
        • Basaia S.
        • Agosta F.
        • Wagner L.
        • Canu E.
        • Magnani G.
        • Santangelo R.
        • et al.
        Automated classification of Alzheimer’s disease and mild cognitive impairment using a single MRI and deep neural networks.
        Neuroimage Clin. 2019; 21: 101645
        • Wee C.Y.
        • Liu C.
        • Lee A.
        • Poh J.S.
        • Ji H.
        • Qiu A.
        • et al.
        Cortical graph neural network for AD and MCI diagnosis and transfer learning across populations.
        Neuroimage Clin. 2019; 23: 101929
        • Wang Y.
        • Xu C.
        • Park J.H.
        • Lee S.
        • Stern Y.
        • Yoo S.
        • et al.
        Diagnosis and prognosis of Alzheimer’s disease using brain morphometry and white matter connectomes.
        Neuroimage Clin. 2019; 23: 101859
        • Gupta Y.
        • Lee K.H.
        • Choi K.Y.
        • Lee J.J.
        • Kim B.C.
        • Kwon G.R.
        • et al.
        Early diagnosis of Alzheimer’s disease using combined features from voxel-based morphometry and cortical, subcortical, and hippocampus regions of MRI T1 brain images.
        PLoS One. 2019; 14e0222446
        • Frenzel S.
        • Wittfeld K.
        • Habes M.
        • Klinger-Konig J.
        • Bulow R.
        • Volzke H.
        • et al.
        A biomarker for Alzheimer’s disease based on patterns of regional brain atrophy.
        Front Psychiatry. 2019; 10: 953
        • Liu C.F.
        • Padhy S.
        • Ramachandran S.
        • Wang V.X.
        • Efimov A.
        • Bernal A.
        • et al.
        Using deep Siamese neural networks for detection of brain asymmetries associated with Alzheimer’s disease and mild cognitive impairment.
        Magn Reson Imaging. 2019; 64: 190-199
        • Duraisamy B.
        • Shanmugam J.V.
        • Annamalai J.
        Alzheimer disease detection from structural MR images using FCM based weighted probabilistic neural network.
        Brain Imaging Behav. 2019; 13: 87-110
        • Bhagwat N.
        • Viviano J.D.
        • Voineskos A.N.
        • Chakravarty M.M.
        • Alzheimer’s Disease Neuroimaging Initiative
        Modeling and prediction of clinical symptom trajectories in Alzheimer’s disease using longitudinal data.
        PLoS Comput Biol. 2018; 14e1006376
        • Samper-Gonzalez J.
        • Burgos N.
        • Bottani S.
        • Fontanella S.
        • Lu P.
        • Marcoux A.
        • et al.
        Reproducible evaluation of classification methods in Alzheimer’s disease: Framework and application to MRI and PET data.
        Neuroimage. 2018; 183: 504-521
        • Sorensen L.
        • Igel C.
        • Pai A.
        • Balas I.
        • Anker C.
        • Lillholm M.
        • et al.
        Differential diagnosis of mild cognitive impairment and Alzheimer’s disease using structural MRI cortical thickness, hippocampal shape, hippocampal texture, and volumetry.
        Neuroimage Clin. 2017; 13: 470-482
        • Previtali F.
        • Bertolazzi P.
        • Felici G.
        • Weitschek E.
        A novel method and software for automatically classifying Alzheimer’s disease patients by magnetic resonance imaging analysis.
        Comput Methods Programs Biomed. 2017; 143: 89-95
        • Sorensen L.
        • Igel C.
        • Liv Hansen N.
        • Osler M.
        • Lauritzen M.
        • Rostrup E.
        • et al.
        Early detection of Alzheimer’s disease using MRI hippocampal texture.
        Hum Brain Mapp. 2016; 37: 1148-1161
        • Jin D.
        • Zhou B.
        • Han Y.
        • Ren J.
        • Han T.
        • Liu B.
        • et al.
        Generalizable, reproducible, and neuroscientifically interpretable imaging biomarkers for Alzheimer’s disease.
        Adv Sci (Weinh). 2020; 7: 2000675
        • 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 Psychiatr Res. 1975; 12: 189-198
        • Savage R.M.
        • Gouvier W.D.
        Rey Auditory-Verbal Learning Test: The effects of age and gender, and norms for delayed recall and story recognition trials.
        Arch Clin Neuropsychol. 1992; 7: 407-414
        • Knopman D.S.
        • Lundt E.S.
        • Therneau T.M.
        • Vemuri P.
        • Lowe V.J.
        • Kantarci K.
        • et al.
        Entorhinal cortex tau, amyloid-beta, cortical thickness and memory performance in non-demented subjects.
        Brain. 2019; 142: 1148-1160
        • Shin M.S.
        • Park S.Y.
        • Park S.R.
        • Seol S.H.
        • Kwon J.S.
        Clinical and empirical applications of the Rey-Osterrieth Complex Figure Test.
        Nat Protoc. 2006; 1: 892-899
        • Shi Y.
        • Gu L.
        • Wang Q.
        • Gao L.
        • Zhu J.
        • Lu X.
        • et al.
        Platelet amyloid-beta protein precursor (AbetaPP) ratio and phosphorylated tau as promising indicators for early Alzheimer’s disease.
        J Gerontol A Biol Sci Med Sci. 2020; 75: 664-670
        • Shi Y.
        • Lu X.
        • Zhang L.
        • Shu H.
        • Gu L.
        • Wang Z.
        • et al.
        Potential value of plasma amyloid-beta, total tau, and neurofilament light for identification of early Alzheimer’s disease.
        ACS Chem Neurosci. 2019; 10: 3479-3485
        • Li J.C.
        • Jin D.
        • Li A.
        • Liu B.
        • Song C.Y.
        • Wang P.
        • et al.
        ASAF: Altered spontaneous activity fingerprinting in Alzheimer’s disease based on multisite fMRI.
        Science Bulletin. 2019; 64: 998-1010
        • Zhao K.
        • Ding Y.H.
        • Han Y.
        • Fan Y.
        • Alexander-Bloch A.F.
        • Han T.
        • et al.
        Independent and reproducible hippocampal radiomic biomarkers for multisite Alzheimer’s disease: Diagnosis, longitudinal progress and biological basis.
        Science Bulletin. 2020; 65: 1103-1113
        • Yan C.G.
        • Chen X.
        • Li L.
        • Castellanos F.X.
        • Bai T.J.
        • Bo Q.J.
        • et al.
        Reduced default mode network functional connectivity in patients with recurrent major depressive disorder.
        Proc Natl Acad Sci U S A. 2019; 116: 9078-9083
        • Hansson O.
        • Seibyl J.
        • Stomrud E.
        • Zetterberg H.
        • Trojanowski J.Q.
        • Bittner T.
        • et al.
        CSF biomarkers of Alzheimer’s disease concord with amyloid-beta PET and predict clinical progression: A study of fully automated immunoassays in BioFINDER and ADNI cohorts.
        Alzheimers Dement. 2018; 14: 1470-1481
        • Fan L.
        • Li H.
        • Zhuo J.
        • Zhang Y.
        • Wang J.
        • Chen L.
        • et al.
        The Human Brainnetome Atlas: A new brain atlas based on connectional architecture.
        Cereb Cortex. 2016; 26: 3508-3526
        • Jin D.
        • Wang P.
        • Zalesky A.
        • Liu B.
        • Song C.
        • Wang D.
        • et al.
        Grab-AD: Generalizability and reproducibility of altered brain activity and diagnostic classification in Alzheimer’s disease.
        Hum Brain Mapp. 2020; 41: 3379-3391
        • Liu Y.
        • Yu C.
        • Zhang X.
        • Liu J.
        • Duan Y.
        • Alexander-Bloch A.F.
        • et al.
        Impaired long distance functional connectivity and weighted network architecture in Alzheimer’s disease.
        Cereb Cortex. 2014; 24: 1422-1435
        • Gong Q.
        • Li L.
        • Du M.
        • Pettersson-Yeo W.
        • Crossley N.
        • Yang X.
        • et al.
        Quantitative prediction of individual psychopathology in trauma survivors using resting-state fMRI.
        Neuropsychopharmacology. 2014; 39: 681-687
        • Zhu J.
        • Zhu D.M.
        • Zhang C.
        • Wang Y.
        • Yang Y.
        • Yu Y.
        Quantitative prediction of individual cognitive flexibility using structural MRI.
        Brain Imaging Behav. 2019; 13: 781-788
        • Hsu C.W.
        • Lin C.J.
        A comparison of methods for multiclass support vector machines.
        IEEE Trans Neural Netw. 2002; 13: 415-425
        • Noble W.S.
        What is a support vector machine?.
        Nat Biotechnol. 2006; 24: 1565-1567
        • Xie Y.
        • Cui Z.
        • Zhang Z.
        • Sun Y.
        • Sheng C.
        • Li K.
        • et al.
        Identification of amnestic mild cognitive impairment using multi-modal brain features: A combined structural MRI and diffusion tensor imaging study.
        J Alzheimers Dis. 2015; 47: 509-522
        • Buckner R.L.
        • Andrews-Hanna J.R.
        • Schacter D.L.
        The brain’s default network: Anatomy, function, and relevance to disease.
        Ann N Y Acad Sci. 2008; 1124: 1-38
        • Buckner R.L.
        • DiNicola L.M.
        The brain’s default network: Updated anatomy, physiology and evolving insights.
        Nat Rev Neurosci. 2019; 20: 593-608
        • Qi H.
        • Liu H.
        • Hu H.
        • He H.
        • Zhao X.
        Primary disruption of the memory-related subsystems of the default mode network in Alzheimer’s disease: Resting-state functional connectivity MRI study.
        Front Aging Neurosci. 2018; 10: 344
        • Chhatwal J.P.
        • Schultz A.P.
        • Johnson K.
        • Benzinger T.L.
        • Jack Jr., C.
        • Ances B.M.
        • et al.
        Impaired default network functional connectivity in autosomal dominant Alzheimer disease.
        Neurology. 2013; 81: 736-744
        • Adriaanse S.M.
        • Sanz-Arigita E.J.
        • Binnewijzend M.A.
        • Ossenkoppele R.
        • Tolboom N.
        • van Assema D.M.
        • et al.
        Amyloid and its association with default network integrity in Alzheimer’s disease.
        Hum Brain Mapp. 2014; 35: 779-791
        • Sheline Y.I.
        • Raichle M.E.
        • Snyder A.Z.
        • Morris J.C.
        • Head D.
        • Wang S.
        • et al.
        Amyloid plaques disrupt resting state default mode network connectivity in cognitively normal elderly.
        Biol Psychiatry. 2010; 67: 584-587
        • Sperling R.A.
        • Laviolette P.S.
        • O’Keefe K.
        • O’Brien J.
        • Rentz D.M.
        • Pihlajamaki M.
        • et al.
        Amyloid deposition is associated with impaired default network function in older persons without dementia.
        Neuron. 2009; 63: 178-188
        • Mattsson N.
        • Insel P.S.
        • Donohue M.
        • Jogi J.
        • Ossenkoppele R.
        • Olsson T.
        • et al.
        Predicting diagnosis and cognition with (18)F-AV-1451 tau PET and structural MRI in Alzheimer’s disease.
        Alzheimers Dement. 2019; 15: 570-580
        • Pini L.
        • Geroldi C.
        • Galluzzi S.
        • Baruzzi R.
        • Bertocchi M.
        • Chito E.
        • et al.
        Age at onset reveals different functional connectivity abnormalities in prodromal Alzheimer’s disease.
        Brain Imaging Behav. 2020; 14: 2594-2605
        • Stonnington C.M.
        • Chu C.
        • Kloppel S.
        • Jack Jr., C.R.
        • Ashburner J.
        • Frackowiak R.S.
        • et al.
        Predicting clinical scores from magnetic resonance scans in Alzheimer’s disease.
        Neuroimage. 2010; 51: 1405-1413
        • Moradi E.
        • Hallikainen I.
        • Hanninen T.
        • Tohka J.
        • Alzheimer’s Disease Neuroimaging Initiative
        Rey’s Auditory Verbal Learning Test scores can be predicted from whole brain MRI in Alzheimer’s disease.
        Neuroimage Clin. 2017; 13: 415-427
        • Long Z.
        • Jing B.
        • Yan H.
        • Dong J.
        • Liu H.
        • Mo X.
        • et al.
        A support vector machine-based method to identify mild cognitive impairment with multi-level characteristics of magnetic resonance imaging.
        Neuroscience. 2016; 331: 169-176
        • Yang W.
        • Lui R.L.
        • Gao J.H.
        • Chan T.F.
        • Yau S.T.
        • Sperling R.A.
        • et al.
        Independent component analysis-based classification of Alzheimer’s disease MRI data.
        J Alzheimers Dis. 2011; 24: 775-783
        • Zheng Y.
        • Guo H.
        • Zhang L.
        • Wu J.
        • Li Q.
        • Lv F.
        Machine learning-based framework for differential diagnosis between vascular dementia and Alzheimer’s disease using structural MRI features.
        Front Neurol. 2019; 10: 1097
        • Teipel S.J.
        • Metzger C.D.
        • Brosseron F.
        • Buerger K.
        • Brueggen K.
        • Catak C.
        • et al.
        Multicenter resting state functional connectivity in prodromal and dementia stages of Alzheimer’s disease.
        J Alzheimers Dis. 2018; 64: 801-813