Grant Report on PREDICT-ADFTD: Multimodal Imaging Prediction of AD/FTD and Differential Diagnosis-Journal of Psychiatry and Brain Science-CSCIED科技核心评价数据库-手机版

Grant Report on PREDICT-ADFTD: Multimodal Imaging Prediction of AD/FTD and Differential Diagnosis

Grant Report on PREDICT-ADFTD: Multimodal Imaging Prediction of AD/FTD and Differential Diagnosis

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DOI	10.20900/jpbs.20190017
刊名	Journal of Psychiatry and Brain Science JPBS
年，卷(期)	2019, 4(5)
作者	Lei Wang*,Ashley Heywood,Jane Stocks,Jinhyeong Bae,Da Ma,Karteek Popuri,Arthur W. Toga,Kejal Kantarci,Laurent Younes,Ian R. Mackenzie,Fengqing Zhang,Mirza Faisal Beg,Howard Rosen,Alzheimer’s Disease Neuroimaging Initiative,
作者单位	1 Northwestern University Feinberg School of Medicine, Chicago, 60611 IL, USA; 2 School of Engineering Science, Simon Fraser University, Burnaby, V6A1S6 BC, Canada; 3 Keck School of Medicine of University of Southern California, Los Angeles, 90033 CA, USA; 4 Departments of Neurology and Radiology, Mayo Clinic, Rochester, 55905 MN, USA; 5 Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, 21218 MD, USA; 6 Department of Pathology and Lab Medicine, University of British Columbia, Vancouver, B6T1Z4 BC, Canada; 7 Department of Psychology, Drexel University, Philadelphia, 19104 PA, USA; 8 Department of Neurology, University of California, San Francisco, 94143 CA, USA
Abstract	We report on the ongoing project “PREDICT-ADFTD: Multimodal Imaging Prediction of AD/FTD and Differential Diagnosis” describing completed and future work supported by this grant. This project is a multi-site, multi-study collaboration effort with research spanning seven sites across the US and Canada. The overall goal of the project is to study neurodegeneration within Alzheimer’s Disease, Frontotemporal Dementia, and related neurodegenerative disorders, using a variety of brain imaging and computational techniques to develop methods for the early and accurate prediction of disease and its course. The overarching goal of the project is to develop the earliest and most accurate biomarker that can differentiate clinical diagnoses to inform clinical trials and patient care. In its third year, this project has already completed several projects to achieve this goal, focusing on (1) structural MRI (2) machine learning and (3) FDG-PET and multimodal imaging. Studies utilizing structural MRI have identified key features of underlying pathology by studying hippocampal deformation that is unique to clinical diagnosis and also post-mortem confirmed neuropathology. Several machine learning experiments have shown high classification accuracy in the prediction of disease based on Convolutional Neural Networks utilizing MRI images as input. In addition, we have also achieved high accuracy in predicting conversion to DAT up to five years in the future. Further, we evaluated multimodal models that combine structural and FDG-PET imaging, in order to compare the predictive power of multimodal to unimodal models. Studies utilizing FDG-PET have shown significant predictive ability in the prediction and progression of disease.
KeyWord	dementia; Alzheimer’s Disease; brain imaging; FDG-PET; machine learning; Convolutional Neural Networks; morphometry; sex-differences; C9orf72
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1.Onyike CU, Diehl-Schmid J. The epidemiology of frontotemporal dementia. Int Rev Psychiatry. 2013;25(2):130-7. doi: 10.3109/09540261.2013.776523

2.Mercy L, Hodges JR, Dawson K, Barker RA, Brayne C. Incidence of early-onset dementias in Cambridgeshire, United Kingdom. Neurology. 2008;71(19):1496-9. doi: 10.1212/01.wnl.0000334277.16896.fa

3.Ratnavalli E, Brayne C, Dawson K, Hodges JR. The prevalence of frontotemporal dementia. Neurology. 2002;58(11):1615-21.

4.Avants BB, Cook PA, Ungar L, Gee JC, Grossman M. Dementia induces correlated reductions in white matter integrity and cortical thickness: A multivariate neuroimaging study with sparse canonical correlation analysis. Neuroimage. 2010;50(3):1004-16. doi: 10.1016/j.neuroimage.2010.01.041

5.Schroeter ML, Raczka K, Neumann J, von Cramon DY. Neural networks in frontotemporal dementia—a meta-analysis. Neurobiology Aging. 2008;29(3):418-26. doi: 10.1016/j.neurobiolaging.2006.10.023

6.Schroeter ML, Raczka K, Neumann J, Yves von Cramon D. Towards a nosology for frontotemporal lobar degenerations-a meta-analysis involving 267 subjects. Neuroimage. 2007;36(3):497-510. doi: 10.1016/j.neuroimage.2007.03.024

7.Whitwell JL, Przybelski SA, Weigand SD, Ivnik RJ, Vemuri P, Gunter JL, et al. Distinct anatomical subtypes of the behavioural variant of frontotemporal dementia: a cluster analysis study. Brain. 2009;132(Pt 11):2932-46. doi: 10.1093/brain/awp232

8.Borroni B, Brambati SM, Agosti C, Gipponi S, Bellelli G, Gasparotti R, et al. Evidence of white matter changes on diffusion tensor imaging in frontotemporal dementia. Arch Neurol. 2007;64(2):246-51. doi: 10.1001/archneur.64.2.246

9.Matsuo K, Mizuno T, Yamada K, Akazawa K, Kasai T, Kondo M, et al. Cerebral white matter damage in frontotemporal dementia assessed by diffusion tensor tractography. Neuroradiology. 2008;50(7):605-11. doi: 10.1007/s00234-008-0379-5

10.Whitwell JL, Avula R, Senjem ML, Kantarci K, Weigand SD, Samikoglu A, et al. Gray and white matter water diffusion in the syndromic variants of frontotemporal dementia. Neurology. 2010;74(16):1279-87. doi: 10.1212/WNL.0b013e3181d9edde

11.Zhang Y, Schuff N, Du AT, Rosen HJ, Kramer JH, Gorno-Tempini ML, et al. White matter damage in frontotemporal dementia and Alzheimer's disease measured by diffusion MRI. Brain. 2009;132(Pt 9):2579-92. doi: 10.1093/brain/awp071

12.Zhang Y, Tartaglia MC, Schuff N, Chiang GC, Ching C, Rosen HJ, et al. MRI signatures of brain macrostructural atrophy and microstructural degradation in frontotemporal lobar degeneration subtypes. J Alzheimers Dis. 2013;33(2):431-44. doi: 10.3233/JAD-2012-121156

13.Edwards-Lee T, Miller BL, Benson DF, Cummings JL, Russell GL, Boone K, et al. The temporal variant of frontotemporal dementia. Brain. 1997;120(Pt 6):1027-40

14.Frisoni GB, Beltramello A, Weiss C, Geroldi C, Bianchetti A, Trabucchi M. Usefulness of simple measures of temporal lobe atrophy in probable Alzheimer’s disease. Dementia. 1996;7(1):15-22

15.Jagust WJ, Reed BR, Seab JP, Kramer JH, Budinger TF. Clinical-physiologic correlates of Alzheimer’s disease and frontal lobe dementia. Am J Physiol Imaging. 1989;4(3):89-96

16.Miller BL, Cummings JL, Villanueva-Meyer J, Boone K, Mehringer CM, Lesser IM, et al. Frontal lobe degeneration: clinical, neuropsychological, and SPECT characteristics. Neurology. 1991;41(9):1374-82

17.Salmon E, Sadzot B, Maquet P, Degueldre C, Lemaire C, Rigo P, et al. Differential diagnosis of Alzheimer’s disease with PET. J Nucl Med. 1994;35(3):391-8. PubMed PMID: 8113882.

18.Rabinovici GD, Rosen HJ, Alkalay A, Kornak J, Furst AJ, Agarwal N, et al. Amyloid vs FDG-PET in the differential diagnosis of AD and FTLD. Neurology. 2011;77(23):2034-42. doi: 10.1212/WNL.0b013e31823b9c5e

19.Frisoni GB, Bocchetta M, Chetelat G, Rabinovici GD, de Leon MJ, Kaye J, et al. Imaging markers for Alzheimer disease: which vs how. Neurology. 2013;81(5):487-500. doi: 10.1212/WNL.0b013e31829d86e8

20.Harper L, Barkhof F, Scheltens P, Schott JM, Fox NC. An algorithmic approach to structural imaging in dementia. J Neurol Neurosurg Psychiatry. 2014;85(6):692-8. doi: 10.1136/jnnp-2013-306285

21.Karageorgiou E, Miller BL. Frontotemporal lobar degeneration: a clinical approach. Semin Neurol. 2014;34(2):189-201

22.Forman MS, Farmer J, Johnson JK, Clark CM, Arnold SE, Coslett HB, et al. Frontotemporal dementia: clinicopathological correlations. Ann Neurol. 2006;59(6):952-62. doi: 10.1002/ana.20873

23.Josephs KA, Whitwell JL, Weigand SD, Murray ME, Tosakulwong N, Liesinger AM, et al. TDP-43 is a key player in the clinical features associated with Alzheimer's disease. Acta Neuropathol. 2014;127(6):811-24. doi: 10.1007/s00401-014-1269-z

24.Shaw LM, Korecka M, Clark CM, Lee VM, Trojanowski JQ. Biomarkers of neurodegeneration for diagnosis and monitoring therapeutics. Nat Rev Drug Discov. 2007;6(4):295-303. doi: 10.1038/nrd2176

25.Raamana PR, Wen W, Kochan NA, Brodaty H, Sachdev PS, Wang L, et al. Novel ThickNet features for the discrimination of amnestic MCI subtypes. NeuroImage Clin. 2014;6:284-95. doi: 10.1016/j.nicl.2014.09.005

26.Raamana PR, Wen W, Kochan NA, Brodaty H, Sachdev PS, Wang L, et al. The Sub-Classification of Amnestic Mild Cognitive Impairment Using MRI-Based Cortical Thickness Measures. Front Neurol. 2014;5:76. doi: 10.3389/fneur.2014.00076

27.Raamana PR, Weiner MW, Wang L, Beg MF, Alzheimer’s Disease Neuroimaging Initiative. Thickness network features for prognostic applications in dementia. Neurobiol Aging. 2015;36(Suppl 1):S91-S102. doi: 10.1016/j.neurobiolaging.2014.05.040

28.Raamana PR, Rosen H, Miller B, Weiner MW, Wang L, Beg MF. Three-Class Differential Diagnosis among Alzheimer Disease, Frontotemporal Dementia, and Controls. Front Neurol. 2014;5:71. doi: 10.3389/fneur.2014.00071

29.Wang L, Beg F, Ratnanather T, Ceritoglu C, Younes L, Morris JC, et al. Large deformation diffeomorphism and momentum based hippocampal shape discrimination in dementia of the Alzheimer type. IEEE Trans Med Imaging. 2007;26(4):462-70. doi: 10.1109/TMI.2005.853923

30.Khan AR, Wang L, Beg MF. FreeSurfer-initiated fully-automated subcortical brain segmentation in MRI using Large Deformation Diffeomorphic Metric Mapping. Neuroimage. 2008;41(3):735-46. doi: 10.1016/j.neuroimage.2008.03.024

31.Wang L, Khan A, Csernansky JG, Fischl B, Miller MI, Morris JC, et al. Fully-automated, multi-stage hippocampus mapping in very mild Alzheimer disease. Hippocampus. 2009;19(6):541-8.

32.Ceyhan E, Beg MF, Ceritoglu C, Wang L, Morris JC, Csernansky JG, et al. Metric Distances between Hippocampal Shapes Indicate Different Rates of Change Over Time in Nondemented and Demented Subjects. Curr Alzheimer Res. 2012;9(8):972-81.

33.Ceyhan E, Beg MF, Ceritoglu C, Wang L, Morris JC, Csernansky JG, et al. Quantization and analysis of hippocampal morphometric changes due to dementia of Alzheimer type using metric distances based on large deformation diffeomorphic metric mapping. Comput Med Imaging Graph. 2011;35(4):275-93.

34.Wan J, Kim S, Inlow M, Nho K, Swaminathan S, Risacheri SL, et al. Hippocampal surface mapping of genetic risk factors in AD via sparse learning models. Med Image Comput Comput Assist Interv. 2011;14(Pt 2):376-83.

35.Wang L, Fagan AM, Shah AR, Beg MF, Csernansky JG, Morris JC, et al. Cerebrospinal Fluid Proteins Predict Longitudinal Hippocampal Degeneration in Early-stage Dementia of the Alzheimer Type. Alzheimer Dis Assoc Disord. 2011. doi: 10.1097/WAD.0b013e31823c0cf4

36.Beg MF, Raamana PR, Barbieri S, Wang L. Comparison of four shape features for detecting hippocampal shape changes in early Alzheimer’s. 2013;22(4):439-62. doi: 10.1177/0962280212448975

37.Cui Y, Wen W, Lipnicki DM, Beg MF, Jin JS, Luo S, et al. Automated detection of amnestic mild cognitive impairment in community-dwelling elderly adults: A combined spatial atrophy and white matter alteration approach. Neuroimage. 2012;59(2):1209-17. doi: S1053-8119(11)00900-1

38.Khan AR, Wang L, Beg MF. Multistructure large deformation diffeomorphic brain registration. IEEE Trans Biomed Eng. 2013;60(2):544-53. doi: 10.1109/TBME.2012.2230262

39.Lebed E, Jacova C, Wang L, Beg MF. Novel surface-smoothing based local gyrification index. IEEE Trans Med Imaging. 2013;32(4):660-9. doi: 10.1109/TMI.2012.2230640

40.Christensen A, Alpert K, Rogalski E, Cobia D, Rao J, Beg MF, et al. Hippocampal subfield surface deformity in non-semantic primary progressive aphasia. Alzheimers Dement (Amst). 2015;1(1):14-23. doi: 10.1016/j.dadm.2014.11.013

41.Khan AR, Wang L, Beg MF. Unified voxel- and tensor-based morphometry (UVTBM) using registration confidence. Neurobiol Aging. 2015;36(Suppl 1):S60-8. doi: 10.1016/j.neurobiolaging.2014.04.036

42.Ming J, Harms MP, Morris JC, Beg MF, Wang L. Integrated cortical structural marker for Alzheimer’s disease. Neurobiol Aging. 2015;36(Suppl 1):S53-9. doi: 10.1016/j.neurobiolaging.2014.03.042

43.Tang X, Holland D, Dale AM, Younes L, Miller MI. The diffeomorphometry of regional shape change rates and its relevance to cognitive deterioration in mild cognitive impairment and Alzheimer’s disease. Hum Brain Mapp. 2015;36(6):2093-117. Epub 2015/02/04. doi: 10.1002/hbm.22758

44.Younes L, Albert M, Miller MI, Biocard Research Team. Inferring changepoint times of medial temporal lobe morphometric change in preclinical Alzheimer’s disease. NeuroImage Clin. 2014;5:178-87. doi: 10.1016/j.nicl.2014.04.009

45.Qiu A, Younes L, Miller MI, Csernansky JG. Parallel transport in diffeomorphisms distinguishes the time-dependent pattern of hippocampal surface deformation due to healthy aging and the dementia of the Alzheimer’s type. Neuroimage. 2008;40(1):68-76. doi: 10.1016/j.neuroimage.2007.11.041

46.Soldan A, Pettigrew C, Lu Y, Wang MC, Selnes O, Albert M, et al. Relationship of medial temporal lobe atrophy, APOE genotype, and cognitive reserve in preclinical Alzheimer's disease. Hum Brain Mapp. 2015;36(7):2826-41. doi: 10.1002/hbm.22810

47.Tang X, Holland D, Dale AM, Younes L, Miller MI. Shape abnormalities of subcortical and ventricular structures in mild cognitive impairment and Alzheimer’s disease: detecting, quantifying, and predicting. Hum Brain Mapp. 2014;35(8):3701-25. doi: 10.1002/hbm.22431

48.Teipel SJ, Grothe M, Lista S, Toschi N, Garaci FG, Hampel H. Relevance of magnetic resonance imaging for early detection and diagnosis of Alzheimer disease. Med Clin North Am. 2013;97(3):399-424. doi: 10.1016/j.mcna.2012.12.013

49.Vemuri P, Weigand SD, Przybelski SA, Knopman DS, Smith GE, Trojanowski JQ, et al. Cognitive reserve and Alzheimer's disease biomarkers are independent determinants of cognition. Brain. 2011;134(Pt 5):1479-92. doi: 10.1093/brain/awr049

50.Atluri G, Padmanabhan K, Fang G, Steinbach M, Petrella JR, Lim K, et al. Complex biomarker discovery in neuroimaging data: Finding a needle in a haystack. Neuroimage Clin. 2013;3:123-31. doi: 10.1016/j.nicl.2013.07.004

51.Taylor JE, Worsley KJ. Detecting sparse signals in random fields, with an application to brain mapping. J Am Stat Assoc. 2007;102(479):913-28. doi: 10.1198/016214507000000815

52.Worsley KJ, Taylor JE, Carbonell F, Chung M, Duerden E, Bernhardt B, et al. SurfStat: A Matlab toolbox for the statistical analysis of univariate and multivariate surface and volumetric data using linear mixed effects models and random field theory. Available from: https://www.academia.edu/13699763/SurfStat_A_Matlab_toolbox_for_the_statistical_analysis_of_univariate_and_multivariate_surface_and_volumetric_data_using_linear_mixed_effects_models_and_random_field_theory. Accessed 2019 Oct 23.

53.Hanko V, Apple AC, Alpert KI, Warren KN, Schneider JA, Arfanakis K, et al. In vivo hippocampal subfield shape related to TDP-43, amyloid beta, and tau pathologies. Neurobiol Aging. 2019;74:171-81. doi: 10.1016/j.neurobiolaging.2018.10.013

54.Popuri K, Dowds E, Beg MF, Balachandar R, Bhalla M, Jacova C, et al. Gray matter changes in asymptomatic C9orf72 and GRN mutation carriers. Neuroimage Clin. 2018;18:591-8. doi: 10.1016/j.nicl.2018.02.017

55.Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med. 2015;372(9):793-5. doi: 10.1056/NEJMp1500523

56.Mazure CM, Swendsen J. Sex differences in Alzheimer’s disease and other dementias. Lancet Neurol. 2016;15(5):451-2. doi: 10.1016/S1474-4422(16)00067-3.

57.Sangha O, Stocks J, Popuri K, Wang L, Beg MF. Longitudinal Sex Differences in Gray Matter Atrophy for Alzheimer’s Disease. Presented at the Alzheimer’s Association International Conference; 14–18 July 2019; Los Angeles, CA, USA.

58.Sidey-Gibbons JAM, Sidey-Gibbons CJ. Machine learning in medicine: a practical introduction. BMC Med Res Methodol. 2019;19(1):64. doi: 10.1186/s12874-019-0681-4

59.Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2(4):230-43. Epub 2018/03/07. doi: 10.1136/svn-2017-000101

60.Li H, Liu Y, Gong P, Zhang C, Ye J, Alzheimers Disease Neuroimaging I. Hierarchical interactions model for predicting Mild Cognitive Impairment (MCI) to Alzheimer’s Disease (AD) conversion. PLoS One. 2014;9(1):e82450. doi: 10.1371/journal.pone.0082450

61.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. doi: 10.1016/j.nicl.2018.101645

62.Davatzikos C, Resnick SM, Wu X, Parmpi P, Clark CM. Individual patient diagnosis of AD and FTD via high-dimensional pattern classification of MRI. Neuroimage. 2008;41(4):1220-7. doi: 10.1016/j.neuroimage.2008.03.050

63.Ma D, Popuri K, Bhalla M, Sangha O, Lu D, Cao J, et al. Quantitative assessment of field strength, total intracranial volume, sex, and age effects on the goodness of harmonization for volumetric analysis on the ADNI database. Hum Brain Mapp. 2019;40(5):1507-27. doi: 10.1002/hbm.24463

64.Jack CR Jr, Bernstein MA, Fox NC, Thompson P, Alexander G, Harvey D, et al. The Alzheimer’s Disease Neuroimaging Initiative (ADNI): MRI methods. J Magn Reson Imaging. 2008;27(4):685-91. doi: 10.1002/jmri.21049

65.Ellis KA, Bush AI, Darby D, De Fazio D, Foster J, Hudson P, et al. The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer’s disease. Int Psychogeriatr. 2009;21(4):672-87.

66.Perry DC, Sturm VE, Seeley WW, Miller BL, Kramer JH, Rosen HJ. Anatomical correlates of reward-seeking behaviours in behavioural variant frontotemporal dementia. Brain. 2014;137(Pt 6):1621-6. doi: 10.1093/brain/awu075

67.Parkinson Progression Marker I. The Parkinson Progression Marker Initiative (PPMI). Prog Neurobiol. 2011;95(4):629-35. Epub 2011/09/21. doi: 10.1016/j.pneurobio.2011.09.005

68.Popuri K, Balachandar R, Alpert K, Lu D, Bhalla M, Mackenzie IR, et al. Development and validation of a novel dementia of Alzheimer's type (DAT) score based on metabolism FDG-PET imaging. Neuroimage Clin. 2018;18:802-13. doi: 10.1016/j.nicl.2018.03.007

69.Bae J, Heywood A, Stocks J, Jung Y, Popuri K, Beg M, et al. End-to-end 3D-Convolutional Neural Network for Predicting Conversion from Mild Cognitive Impairment (MCI) to Alzheimer’s Dementia (AD). Presented at the Alzheimer’s Association International Conference; 14–18 July 2019; Los Angeles, CA, USA.

70.Bae J, Heywood A, Stocks J, Jung Y, Popuri K, Beg M, et al. End-to-end 3D-Convolutional Neural Network, Presented at the Society for Neuroscience Conference; 19-23 October 2019; Chicago, IL, USA.

71.Dubois B, Feldman HH, Jacova C, Hampel H, Molinuevo JL, Blennow K, et al. Advancing research diagnostic criteria for Alzheimer’s disease: the IWG-2 criteria. Lancet Neurol. 2014;13(6):614-29. doi: 10.1016/S1474-4422(14)70090-0.

72.Sperling RA, Aisen PS, Beckett LA, Bennett DA, Craft S, Fagan AM, 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(3):280-92. doi: 10.1016/j.jalz.2011.03.003

73.Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, 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(3):270-9. doi: 10.1016/j.jalz.2011.03.008

74.McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR Jr, Kawas CH, 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. Alzheimers Dement. 2011;7(3):263-9. doi: 10.1016/j.jalz.2011.03.005

75.Scheinin NM, Aalto S, Kaprio J, Koskenvuo M, Raiha I, Rokka J, et al. Early detection of Alzheimer disease: (1)(1)C-PiB PET in twins discordant for cognitive impairment. Neurology. 2011;77(5):453-60. doi: 10.1212/WNL.0b013e318225118e

76.Ritter A, Cummings J. Fluid Biomarkers in Clinical Trials of Alzheimer’s Disease Therapeutics. Front Neurol. 2015;6:186. doi: 10.3389/fneur.2015.00186

77.Dennis EL, Thompson PM. Functional brain connectivity using fMRI in aging and Alzheimer’s disease. Neuropsychol Rev. 2014;24(1):49-62. doi: 10.1007/s11065-014-9249-6

78.Ito K, Fukuyama H, Senda M, Ishii K, Maeda K, Yamamoto Y, et al. Prediction of Outcomes in Mild Cognitive Impairment by Using 18F-FDG-PET: A Multicenter Study. J Alzheimers Dis. 2015;45(2):543-52. Epub 2015/01/16. doi: 10.3233/JAD-141338

79.Chetelat G, Desgranges B, de la Sayette V, Viader F, Eustache F, Baron JC. Mild cognitive impairment: Can FDG-PET predict who is to rapidly convert to Alzheimer’s disease? Neurology. 2003;60(8):1374-7. doi: 10.1212/01.wnl.0000055847.17752.e6

80.Mosconi L. Brain glucose metabolism in the early and specific diagnosis of Alzheimer’s disease. FDG-PET studies in MCI and AD. Eur J Nucl Med Mol Imaging. 2005;32(4):486-510. doi: 10.1007/s00259-005-1762-7

81.Scheltens NM, Galindo-Garre F, Pijnenburg YA, van der Vlies AE, Smits LL, Koene T, et al. The identification of cognitive subtypes in Alzheimer’s disease dementia using latent class analysis. J Neurol Neurosurg Psychiatry. 2016;87(3):235-43. doi: 10.1136/jnnp-2014-309582

82.Foster NL, Heidebrink JL, Clark CM, Jagust WJ, Arnold SE, Barbas NR, et al. FDG-PET improves accuracy in distinguishing frontotemporal dementia and Alzheimer’s disease. Brain. 2007;130(Pt 10):2616-35. doi: 10.1093/brain/awm177

83.Insel PS, Ossenkoppele R, Gessert D, Jagust W, Landau S, Hansson O, et al. Time to Amyloid Positivity and Preclinical Changes in Brain Metabolism, Atrophy, and Cognition: Evidence for Emerging Amyloid Pathology in Alzheimer’s Disease. Front Neurosci. 2017;11:281. doi: 10.3389/fnins.2017.00281

84.Jack CR Jr, Knopman DS, Jagust WJ, Shaw LM, Aisen PS, Weiner MW, et al. Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol. 2010;9(1):119-28. doi: 10.1016/S1474-4422(09)70299-6

85.Ishii K, Sasaki H, Kono AK, Miyamoto N, Fukuda T, Mori E. Comparison of gray matter and metabolic reduction in mild Alzheimer's disease using FDG-PET and voxel-based morphometric MR studies. Eur J Nucl Med Mol Imaging. 2005;32(8):959-63. doi: 10.1007/s00259-004-1740-5

86.Desikan RS, Segonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006;31(3):968-80. Epub 2006/03/15. doi: 10.1016/j.neuroimage.2006.01.021

87.Lu D, Popuri K, Ding GW, Balachandar R, Beg MF, Alzheimer’s Disease Neuroimaging I. Multiscale deep neural network based analysis of FDG-PET images for the early diagnosis of Alzheimer's disease. Med Image Anal. 2018;46:26-34. doi: 10.1016/j.media.2018.02.002

88.Libon DJ, Xie SX, Eppig J, Wicas G, Lamar M, Lippa C, et al. The heterogeneity of mild cognitive impairment: a neuropsychological analysis. J Int Neuropsychol Soc. 2010;16(1):84-93. doi: 10.1017/S1355617709990993

89.Zhang F, Niu X, Heywood A, Stocks J, Beg MF, Wang L. Using Multi-state Markov Transition Models and Multilevel Models to Identify Biomarkers of AD using ADNI FDG-PET data. Presented at the Alzheimer’s Association International Conference; 14–18 July 2019; Los Angeles, CA, USA.

90.Stocks J, Bae J., Sangha O, Popuri K, Beg MF, Wang L. The Relationship between Cortical Neurodegeneration and FDG-PET Hypometabolism as a Disease Marker Across Stages of Alzheimer’s Dementia. Presented at the Alzheimer’s Association International Conference; 14–18 July 2019; Los Angeles, CA, USA.

91.Cummings JL, Morstorf T, Zhong K. Alzheimer's disease drug-development pipeline: few candidates, frequent failures. Alzheimers Res Ther. 2014;6(4):37. doi: 10.1186/alzrt269

引用本文

Lei Wang*,Ashley Heywood,Jane Stocks,Jinhyeong Bae,Da Ma,Karteek Popuri,Arthur W. Toga,Kejal Kantarci,Laurent Younes,Ian R. Mackenzie,Fengqing Zhang,Mirza Faisal Beg,Howard Rosen,Alzheimer’s Disease Neuroimaging Initiative. Grant Report on PREDICT-ADFTD: Multimodal Imaging Prediction of AD/FTD and Differential Diagnosis, Journal of Psychiatry and Brain Science. 2019; 4; (5). https://doi.org/10.20900/jpbs.20190017.

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