FDG-PET for Prediction of AD Dementia in Mild Cognitive Impairment. A Review of the State of the Art with Particular Emphasis on the Comparison with Other Neuroimaging Modalities (MRI and Perfusion SPECT)

Author(s): Carlos A. Sanchez-Catasus, Gilles N. Stormezand, Peter Jan van Laar, Peter P. De Deyn, Mario Alvarez Sanchez, Rudi A.J.O. Dierckx

Journal Name: Current Alzheimer Research

Volume 14 , Issue 2 , 2017

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Abstract:

This review article aims at providing a state-of-the-art review of the role of fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging (FDG-PET) in the prediction of Alzheimer's dementia in subjects suffering mild cognitive impairment (MCI), with a particular focus on the predictive power of FDG-PET compared to structural magnetic resonance imaging (sMRI). We also address perfusion single photon emission computed tomography (SPECT) as a less costly and more accessible alternative to FDG-PET.

A search in PubMed was performed, taking into consideration relevant scientific articles published in English within the last five years and limited to human studies. This recent literature confirms the effectiveness of FDG-PET and sMRI for prediction of AD dementia in MCI. However, there are discordant results regarding which image modality is superior. This could be explained by the high variability of metrics used to evaluate both imaging modalities and/or by sampling/population issues such as age, disease severity and conversion time. FDG-PET seems to outperform sMRI in rapidly converting early-onset MCI individuals, whereas sMRI may outperform FDG-PET in late-onset MCI subjects, in which case FDG PET might only provide a complementary role. Although FDG-PET performs better than perfusion SPECT, current evidence confirms perfusion SPECT as a valid alternative when FDG- PET is not available. Finally, possible future directions in the field are discussed.

Keywords: Alzheimer’s disease, dementia, FDG-PET, perfusion SPECT, MRI, mild cognitive impairment.

[1]
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 9(1): 119-28.(2010);
[2]
Bateman RJ, Xiong C, Benzinger TL, Fagan AM, Goate A, Fox NC, et al. Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. N Engl J Med 367(9): 795-804.(2012);
[3]
Caroli A, Frisoni GB. The dynamics of Alzheimer’s disease biomarkers in the Alzheimer’s Disease Neuroimaging Initiative cohort. Neurobiol Aging 31(8): 1263-74.(2010);
[4]
Prestia A, Caroli A, van der Flier WM, Ossenkoppele R, Van BB, Barkhof F, et al. Prediction of dementia in MCI patients based on core diagnostic markers for Alzheimer disease. Neurology 80(11): 1048-56.(2013);
[5]
Galluzzi S, Geroldi C, Amicucci G, Bocchio-Chiavetto L, Bonetti M, Bonvicini C, et al. Supporting evidence for using biomarkers in the diagnosis of MCI due to AD. J Neurol 260(2): 640-50.(2013);
[6]
Jack CR Jr, Knopman DS, Jagust WJ, Petersen RC, Weiner MW, Aisen PS, et al. Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol 12(2): 207-16.(2013);
[7]
Jack CR Jr, Wiste HJ, Weigand SD, Knopman DS, Lowe V, Vemuri P, et al. Amyloid-first and neurodegeneration-first profiles characterize incident amyloid PET positivity. Neurology 81(20): 1732-40.(2013);
[8]
Perani D. FDG-PET and amyloid-PET imaging: the diverging paths. Curr Opin Neurol 27(4): 405-13.(2014);
[9]
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 7(3): 263-9.(2011);
[10]
Blennow K, Hampel H. CSF markers for incipient Alzheimer’s disease. Lancet Neurol 2(10): 605-13.(2003);
[11]
Shaw LM, Vanderstichele H, Knapik-Czajka M, Clark CM, Aisen PS, Petersen RC, et al. Cerebrospinal fluid biomarker signature in Alzheimer’s disease neuroimaging initiative subjects. Ann Neurol 65(4): 403-13.(2009);
[12]
Fagan AM, Mintun MA, Mach RH, Lee SY, Dence CS, Shah AR, et al. Inverse relation between in vivo amyloid imaging load and cerebrospinal fluid Abeta42 in humans. Ann Neurol 59(3): 512-9.(2006);
[13]
Jagust W. Positron emission tomography and magnetic resonance imaging in the diagnosis and prediction of dementia. Alzheimers Dement 2(1): 36-42.(2006);
[14]
Atiya M, Hyman BT, Albert MS, Killiany R. Structural magnetic resonance imaging in established and prodromal Alzheimer disease: a review. Alzheimer Dis Assoc Disord 17(3): 177-95.(2003);
[15]
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 7(3): 270-9.(2011);
[16]
Mosconi L, Sorbi S, Nacmias B, De Cristofaro MT, Fayyaz M, Bracco L, et al. Age and ApoE genotype interaction in Alzheimer’s disease: an FDG-PET study. Psychiatry Res 130(2): 141-51.(2004);
[17]
Drzezga A, Grimmer T, Riemenschneider M, Lautenschlager N, Siebner H, Alexopoulus P, et al. Prediction of individual clinical outcome in MCI by means of genetic assessment and (18)F-FDG PET. J Nucl Med 46(10): 1625-32.(2005);
[18]
Chetelat G, Eustache F, Viader F, De La Sayette V, Pelerin A, Mezenge F, et al. FDG-PET measurement is more accurate than neuropsychological assessments to predict global cognitive deterioration in patients with mild cognitive impairment. Neurocase 11(1): 14-25.(2005);
[19]
Nobili F, Salmaso D, Morbelli S, Girtler N, Piccardo A, Brugnolo A, et al. Principal component analysis of FDG PET in amnestic MCI. Eur J Nucl Med Mol Imaging 35(12): 2191-202.(2008);
[20]
Ishii H, Ishikawa H, Meguro K, Tashiro M, Yamaguchi S. Decreased cortical glucose metabolism in converters from CDR 0.5 to Alzheimer’s disease in a community: the Osaki-Tajiri Project. Int Psychogeriatr 21(1): 148-56.(2009);
[21]
Morbelli S, Piccardo A, Villavecchia G, Dessi B, Brugnolo A, Piccini A, et al. Mapping brain morphological and functional conversion patterns in amnestic MCI: a voxel-based MRI and FDG-PET study. Eur J Nucl Med Mol Imaging 37(1): 36-45.(2010);
[22]
Pagani M, Dessi B, Morbelli S, Brugnolo A, Salmaso D, Piccini A, et al. MCI patients declining and not-declining at mid-term follow-up: FDG-PET findings. Curr Alzheimer Res 7(4): 287-94.(2010);
[23]
Herholz K, Westwood S, Haense C, Dunn G. Evaluation of a calibrated (18)F-FDG PET score as a biomarker for progression in Alzheimer disease and mild cognitive impairment. J Nucl Med 52(8): 1218-26.(2011);
[24]
Ossenkoppele R, Tolboom N, Foster-Dingley JC, Adriaanse SF, Boellaard R, Yaqub M, et al. Longitudinal imaging of Alzheimer pathology using [11C]PIB, [18F]FDDNP and [18F]FDG PET. Eur J Nucl Med Mol Imaging 39(6): 990-1000.(2012);
[25]
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 45(2): 543-52.(2015);
[26]
Yuan Y, Gu ZX, Wei WS. Fluorodeoxyglucose-positron-emission tomography, single-photon emission tomography, and structural MR imaging for prediction of rapid conversion to Alzheimer disease in patients with mild cognitive impairment: a meta-analysis. AJNR Am J Neuroradiol 30(2): 404-10.(2009);
[27]
Herholz K. Perfusion SPECT and FDG-PET. Int Psychogeriatr 23(2): S25-31.(2011);
[28]
Bohnen NI, Djang DS, Herholz K, Anzai Y, Minoshima S. Effectiveness and safety of 18F-FDG PET in the evaluation of dementia: a review of the recent literature. J Nucl Med 53(1): 59-71.(2012);
[29]
Nasrallah IM, Wolk DA. Multimodality imaging of Alzheimer disease and other neurodegenerative dementias. J Nucl Med 55(12): 2003-11.(2014);
[30]
Perani D, Schillaci O, Padovani A, Nobili FM, Iaccarino L, Della Rosa PA, et al. A survey of FDG- and amyloid-PET imaging in dementia and GRADE analysis. BioMed Res Int 2014 785039(2014);
[31]
Shivamurthy VK, Tahari AK, Marcus C, Subramaniam RM. Brain FDG PET and the diagnosis of dementia. AJR Am J Roentgenol 204(1): W76-85.(2015);
[32]
Smailagic N, Vacante M, Hyde C, Martin S, Ukoumunne O, Sachpekidis C. 18F-FDG PET for the early diagnosis of Alzheimer’s disease dementia and other dementias in people with mild cognitive impairment (MCI). Cochrane Database Syst Rev 1.(2015);
[33]
Morbelli S, Garibotto V, Van De Giessen E, Arbizu J, Chételat G, Drezgza A, et al. A Cochrane review on brain [(18)F]FDG PET in dementia: limitations and future perspectives. Eur J Nucl Med Mol Imaging 42(10): 1487-91.(2015);
[34]
Landau SM, Harvey D, Madison CM, Reiman EM, Foster NL, Aisen PS, et al. Comparing predictors of conversion and decline in mild cognitive impairment. Neurology 75(3): 230-8.(2010);
[35]
Chen K, Ayutyanont N, Langbaum JB, Fleisher AS, Reschke C, Lee W, et al. Characterizing Alzheimer’s disease using a hypometabolic convergence index. Neuroimage 56(1): 52-60.(2011);
[36]
Prestia A, Caroli A, Herholz K, Reiman E, Chen K, Jagust WJ, et al. Diagnostic accuracy of markers for prodromal Alzheimer’s disease in independent clinical series. Alzheimers Dement 9(6): 677-86.(2013);
[37]
Prestia A, Caroli A, Wade SK, van der Flier WM, Ossenkoppele R, Van BB, et al. Prediction of AD dementia by biomarkers following the NIA-AA and IWG diagnostic criteria in MCI patients from three European memory clinics. Alzheimers Dement PII: S1552-.(2015);
[38]
Shaffer JL, Petrella JR, Sheldon FC, Choudhury KR, Calhoun VD, Coleman RE, et al. Predicting cognitive decline in subjects at risk for Alzheimer disease by using combined cerebrospinal fluid, MR imaging, and PET biomarkers. Radiology 266(2): 583-91.(2013);
[39]
Yu P, Dean RA, Hall SD, Qi Y, Sethuraman G, Willis BA, et al. Enriching amnestic mild cognitive impairment populations for clinical trials: optimal combination of biomarkers to predict conversion to dementia. J Alzheimers Dis 32(2): 373-85.(2012);
[40]
Trzepacz PT, Yu P, Sun J, Schuh K, Case M, Witte MM, et al. Comparison of neuroimaging modalities for the prediction of conversion from mild cognitive impairment to Alzheimer’s dementia. Neurobiol Aging 35(1): 143-51.(2014);
[41]
Schmand B, Eikelenboom P, van Gool WA. Value of diagnostic tests to predict conversion to Alzheimer’s disease in young and old patients with amnestic mild cognitive impairment. J Alzheimers Dis 29(3): 641-8.(2012);
[42]
Schmand B, Eikelenboom P, van Gool WA. Value of neuropsychological tests, neuroimaging, and biomarkers for diagnosing Alzheimer’s disease in younger and older age cohorts. J Am Geriatr Soc 59(9): 1705-10.(2011);
[43]
Dukart J, Mueller K, Villringer A, Kherif F, Draganski B, Frackowiak R, et al. Relationship between imaging biomarkers, age, progression and symptom severity in Alzheimer’s disease. Neuroimage Clin 3: 84-94.(2013);
[44]
La Joie R, Perrotin A, Barre L, Hommet C, Mezenge F, Ibazizene M, et al. Region-specific hierarchy between atrophy, hypometabolism, and beta-amyloid (Abeta) load in Alzheimer’s disease dementia. J Neurosci 32(46): 16265-73.(2012);
[45]
Frisoni GB, Bocchetta M, Chetelat G, Rabinovici GD, de Leon MJ, Kaye J, et al. Imaging markers for Alzheimer disease: which vs. how. Neurology 81(5): 487-500.(2013);
[46]
Walhovd KB, Fjell AM, Dale AM, McEvoy LK, Brewer J, Karow DS, et al. Multi-modal imaging predicts memory performance in normal aging and cognitive decline. Neurobiol Aging 31(7): 1107-21.(2010);
[47]
Karow DS, McEvoy LK, Fennema-Notestine C, Hagler DJ Jr, Jennings RG, Brewer JB, et al. Relative capability of MR imaging and FDG PET to depict changes associated with prodromal and early Alzheimer disease. Radiology 256(3): 932-42.(2010);
[48]
Walhovd KB, Fjell AM, Brewer J, McEvoy LK, Fennema-Notestine C, Hagler DJ Jr, et al. Combining MR imaging, positron-emission tomography, and CSF biomarkers in the diagnosis and prognosis of Alzheimer disease. AJNR Am J Neuroradiol 31(2): 347-54.(2010);
[49]
Liu X, Erikson C, Brun A. Cortical synaptic changes and gliosis in normal aging, Alzheimer’s disease and frontal lobe degeneration. Dementia 7(3): 128-34.(1996);
[50]
Mielke R, Schroder R, Fink GR, Kessler J, Herholz K, Heiss WD. Regional cerebral glucose metabolism and postmortem pathology in Alzheimer’s disease. Acta Neuropathol 91(2): 174-9.(1996);
[51]
Minoshima S, Foster NL, Kuhl DE. Posterior cingulate cortex in Alzheimer’s disease. Lancet 344(8926): 895.(1994);
[52]
Herholz K. Guidance for reading FDG PET scans in dementia patients. Q J Nucl Med Mol Imaging 58(4): 332-43.(2014);
[53]
Morbelli S, Brugnolo A, Bossert I, Buschiazzo A, Frisoni GB, Galluzzi S, et al. Visual versus semi- quantitative analysis of 18F-FDG-PET in amnestic MCI: an European Alzheimer’s Disease Consortium (EADC) project. J Alzheimers Dis 44(3): 815-26.(2015);
[54]
Lehman VT, Carter RE, Claassen DO, Murphy RC, Lowe V, Petersen RC, et al. Visual assessment versus quantitative three-dimensional stereotactic surface projection fluorodeoxyglucose positron emission tomography for detection of mild cognitive impairment and Alzheimer disease. Clin Nucl Med 37(8): 721-6.(2012);
[55]
Landau SM, Harvey D, Madison CM, Koeppe RA, Reiman EM, Foster NL, et al. Associations between cognitive, functional, and FDG-PET measures of decline in AD and MCI. Neurobiol Aging 32(7): 1207-18.(2011);
[56]
Herholz K, Salmon E, Perani D, Baron JC, Holthoff V, Frolich L, et al. Discrimination between Alzheimer dementia and controls by automated analysis of multicenter FDG PET. Neuroimage 17(1): 302-16.(2002);
[57]
Caroli A, Prestia A, Chen K, Ayutyanont N, Landau SM, Madison CM, et al. Summary metrics to assess Alzheimer disease-related hypometabolic pattern with 18F-FDG PET: head-to-head comparison. J Nucl Med 53(4): 592-600.(2012);
[58]
Cerami C, Della Rosa PA, Magnani G, Santangelo R, Marcone A, Cappa SF, et al. Brain metabolic maps in Mild Cognitive Impairment predict heterogeneity of progression to dementia. Neuroimage Clin 7: 187-94.(2015);
[59]
Perani D, Della Rosa PA, Cerami C, Gallivanone F, Fallanca F, Vanoli EG, et al. Validation of an optimized SPM procedure for FDG-PET in dementia diagnosis in a clinical setting. Neuroimage Clin 6: 445-54.(2014);
[60]
Teune LK, Strijkert F, Renken RJ, Izaks GJ, de Vries JJ, Segbers M, et al. The Alzheimer’s disease- related glucose metabolic brain pattern. Curr Alzheimer Res 11(8): 725-32.(2014);
[61]
Xia Y, Lu S, Wen L, Eberl S, Fulham M, Feng DD. Automated identification of dementia using FDG-PET imaging. BioMed Res Int 12(4): 575-93.(2014);
[62]
Arbizu J, Prieto E, Martinez-Lage P, Marti-Climent JM, Garcia-Granero M, Lamet I, et al. Automated analysis of FDG PET as a tool for single-subject probabilistic prediction and detection of Alzheimer’s disease dementia. Eur J Nucl Med Mol Imaging 40(9): 1394-405.(2013);
[63]
Della Rosa PA, Cerami C, Gallivanone F, Prestia A, Caroli A, Castiglioni I. et al. A standardized [18F]-FDG-PET template for spatial normalization in statistical parametric mapping of dementia. Neuroinformatics 12(4): 575-93.(2014);
[64]
Wenzel F, Young S, Wilke F, Apostolova I, Arlt S, Jahn H, et al. B-spline-based stereotactical normalization of brain FDG PET scans in suspected neurodegenerative disease: impact on voxel-based statistical single-subject analysis. Neuroimage 50(3): 994-1003.(2010);
[65]
Tromp D, Dufour A, Lithfous S, Pebayle T, Després O. Episodic memory in normal aging and Alzheimer disease: Insights from imaging and behavioral studies. Ageing Res Rev pii: S1568- 1637(15): 30019-2 .(2015);
[66]
Duara R, Loewenstein DA, Potter E, Appel J, Greig MT, Urs R, et al. Medial temporal lobe atrophy on MRI scans and the diagnosis of Alzheimer disease. Neurology 71(24): 1986-92.(2008);
[67]
Scahill RI, Schott JM, Stevens JM, Rossor MN, Fox NC. Mapping the evolution of regional atrophy in Alzheimer’s disease: unbiased analysis of fluid-registered serial MRI. Proc Natl Acad Sci USA 99(7): 4703-7.(2002);
[68]
Thompson PM, Hayashi KM, de Zubicaray G, Janke AL, Rose SE, Semple J, et al. Dynamics of gray matter loss in Alzheimer’s disease. J Neurosci 23(3): 994-1005.(2003);
[69]
Christensen GE, Joshi SC, Miller MI. Volumetric transformation of brain anatomy. IEEE Trans Med Imaging 16(6): 864-77.(1997);
[70]
Hsu YY, Schuff N, Du AT, Mark K, Zhu X, Hardin D, et al. Comparison of automated and manual MRI volumetry of hippocampus in normal aging and dementia. J Magn Reson Imaging 16(3): 305-10.(2002);
[71]
Holland D, Brewer JB, Hagler DJ, Fennema-Notestine C, Dale AM. Alzheimer’s Disease Neuroimaging Initiative. Subregional neuroanatomical change as a biomarker for Alzheimer’s disease. Proc Natl Acad Sci USA 106(49): 20954-9.(2009);
[72]
Good CD, Johnsrude IS, Ashburner J, Henson RN, Friston KJ, Frackowiak RS. A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage 14(1 Pt 1): 21-36.(2001);
[73]
Friese U, Meindl T, Herpertz SC, Reiser MF, Hampel H, Teipel SJ. Diagnostic utility of novel MRI- based biomarkers for Alzheimer’s disease: diffusion tensor imaging and deformation-based morphometry. J Alzheimers Dis 20(2): 477-90.(2010);
[74]
Ho AJ, Hua X, Lee S, Leow AD, Yanovsky I, Gutman B, et al. Comparing 3 T and 1.5 T MRI for tracking Alzheimer’s disease progression with tensor-based morphometry. Hum Brain Mapp 31(4): 499-514.(2010);
[75]
Du AT, Schuff N, Kramer JH, Rosen HJ, Gorno-Tempini ML, Rankin K, et al. Different regional patterns of cortical thinning in Alzheimer’s disease and frontotemporal dementia. Brain 130(Pt 4): 1159-66.(2007);
[76]
Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33(3): 341-55.(2002);
[77]
Mueller SG, Weiner MW, Thal LJ, Petersen RC, Jack C, Jagust W, et al. The Alzheimer’s disease neuroimaging initiative. Neuroimaging Clin N Am 15(4): 869-xii.(2005);
[78]
Galluzzi S, Geroldi C, Ghidoni R, Paghera B, Amicucci G, Bonetti M, et al. The new Alzheimer’s criteria in a naturalistic series of patients with mild cognitive impairment. J Neurol 257(12): 2004-14.(2010);
[79]
Dubois B, Feldman HH, Jacova C, Dekosky ST, Barberger-Gateau P, Cummings J, et al. Research criteria for the diagnosis of Alzheimer’s disease: revising the NINCDS-ADRDA criteria. Lancet Neurol 6(8): 734-46.(2007);
[80]
Dubois B, Feldman HH, Jacova C, Cummings JL, Dekosky ST, Barberger-Gateau P, et al. Revising the definition of Alzheimer’s disease: a new lexicon. Lancet Neurol 9(11): 1118-27.(2010);
[81]
Cui Y, Liu B, Luo S, Zhen X, Fan M, Liu T, et al. Identification of conversion from mild cognitive impairment to Alzheimer’s disease using multivariate predictors. PLoS One 6: 7.(2011);
[82]
Ewers M, Walsh C, Trojanowski JQ, Shaw LM, Petersen RC, Jack CR Jr, et al. Prediction of conversion from mild cognitive impairment to Alzheimer’s disease dementia based upon biomarkers and neuropsychological test performance. Neurobiol Aging 33(7): 1203-14.(2012);
[83]
Gomar JJ, Bobes-Bascaran MT, Conejero-Goldberg C, Davies P, Goldberg TE. Utility of combinations of biomarkers, cognitive markers, and risk factors to predict conversion from mild cognitive impairment to Alzheimer disease in patients in the Alzheimer’s disease neuroimaging initiative. Arch Gen Psychiatry 68(9): 961-9.(2011);
[84]
Gomar JJ, Conejero-Goldberg C, Davies P, Goldberg TE. Extension and refinement of the predictive value of different classes of markers in ADNI: four-year follow-up data. Alzheimers Dement 10(6): 704-12.(2014);
[85]
Hinrichs C, Singh V, Xu G, Johnson SC. Alzheimers Disease Neuroimaging Initiative. Predictive markers for AD in a multi-modality framework: an analysis of MCI progression in the ADNI population. Neuroimage 55(2): 574-89.(2011);
[86]
Zhang D, Shen D. Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers. PLoS One 7(3) e33182(2012);
[87]
Gray KR, Wolz R, Heckemann RA, Aljabar P, Hammers A, Rueckert D, et al. Alzheimer’s Disease Neuroimaging Initiative. Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer’s disease. Neuroimage 60(1): 221-9.(2012);
[88]
Dodge HH, Zhu J, Harvey D, Saito N, Silbert LC, Kaye JA, et al. Biomarker progressions explain higher variability in stage-specific cognitive decline than baseline values in Alzheimer disease. Alzheimers Dement 10(6): 690-703.(2014);
[89]
Cheng B, Liu M, Zhang D, Munsell B, Shen D. Domain Transfer Learning for MCI Conversion Prediction. IEEE Trans Biomed Eng (2015).
[90]
Jie B, Zhang D, Cheng B, Shen D. Alzheimer’s Disease Neuroimaging Initiative. Manifold regularized multitask feature learning for multimodality disease classification. Hum Brain Mapp 36(2): 489-507.(2014);
[91]
Young J, Modat M, Cardoso MJ, Mendelson A, Cash D, Ourselin S. Accurate multimodal probabilistic prediction of conversion to Alzheimer’s disease in patients with mild cognitive impairment. Neuroimage Clin 2: 735-45.(2013);
[92]
Kim D, Kim S, Risacher SL, Shen L, Ritchie MD, Weiner MW, et al. A Graph-Based Integration of Multimodal Brain Imaging Data for the Detection of Early Mild Cognitive Impairment (E-MCI). Multimodal Brain Image Anal 8159: 159-69.(2013);
[93]
Zhang D, Shen D. Alzheimer’s Disease Neuroimaging Initiative. Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease. Neuroimage 59(2): 895-907.(2012);
[94]
Zhang D, Wang Y, Zhou L, Yuan H, Shen D. Alzheimer’s Disease Neuroimaging Initiative. Multimodal classification of Alzheimer’s disease and mild cognitive impairment. Neuroimage 55(3): 856-67.(2011);
[95]
Kohannim O, Hua X, Hibar DP, Lee S, Chou YY, Toga AW, et al. Boosting power for clinical trials using classifiers based on multiple biomarkers. Neurobiol Aging 31(8): 1429-42.(2010);
[96]
Ewers M, Brendel M, Rizk-Jackson A, Rominger A, Bartenstein P, Schuff N, et al. Reduced FDG- PET brain metabolism and executive function predict clinical progression in elderly healthy subjects. Neuroimage Clin 4: 45-52.(2014);
[97]
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 7(3): 280-92.(2011);
[98]
Herholz K, Schopphoff H, Schmidt M, Mielke R, Eschner W, Scheidhauer K, et al. Direct comparison of spatially normalized PET and SPECT scans in Alzheimer’s disease. J Nucl Med 43(1): 21-6.(2002);
[99]
Jagust W, Thisted R, Devous MD, Sr., van HR, Mayberg H, Jobst K, et al. SPECT perfusion imaging in the diagnosis of Alzheimer’s disease: a clinical-pathologic study. Neurology 56(7): 950-6.(2001);
[100]
Huang C, Wahlund LO, Svensson L, Winblad B, Julin P. Cingulate cortex hypoperfusion predicts Alzheimer’s disease in mild cognitive impairment. BMC Neurol 2: 9.(2002);
[101]
Encinas M, De JR, Marcos A, Gil P, Barabash A, Fernandez C, et al. Regional cerebral blood flow assessed with 99mTc-ECD SPET as a marker of progression of mild cognitive impairment to Alzheimer’s disease. Eur J Nucl Med Mol Imaging 30(11): 1473-80.(2003);
[102]
Hirao K, Ohnishi T, Hirata Y, Yamashita F, Mori T, Moriguchi Y, et al. The prediction of rapid conversion to Alzheimer’s disease in mild cognitive impairment using regional cerebral blood flow SPECT. Neuroimage 28(4): 1014-21.(2005);
[103]
Borroni B, Anchisi D, Paghera B, Vicini B, Kerrouche N, Garibotto V, et al. Combined 99mTc-ECD SPECT and neuropsychological studies in MCI for the assessment of conversion to AD. Neurobiol Aging 27(1): 24-31.(2006);
[104]
Ishiwata A, Sakayori O, Minoshima S, Mizumura S, Kitamura S, Katayama Y. Preclinical evidence of Alzheimer changes in progressive mild cognitive impairment: a qualitative and quantitative SPECT study. Acta Neurol Scand 114(2): 91-6.(2006);
[105]
Nobili F, De CF, Frisoni GB, Portet F, Verhey F, Rodriguez G, et al. SPECT predictors of cognitive decline and Alzheimer’s disease in mild cognitive impairment. J Alzheimers Dis 17(4): 761-72.(2009);
[106]
Devanand DP, Van Heertum RL, Kegeles LS, Liu X, Jin ZH, Pradhaban G, et al. (99m)Tc hexamethyl-propylene-aminoxime single-photon emission computed tomography prediction of conversion from mild cognitive impairment to Alzheimer disease. Am J Geriatr Psychiatry 18(11): 959-72.(2010);
[107]
Wallin A, Gothlin M, Gustavsson M, Zetterberg H, Eckerstrom C, Blennow K, et al. Progression from mild to pronounced MCI is not associated with cerebrospinal fluid biomarker deviations. Dement Geriatr Cogn Disord 32(3): 193-7.(2011);
[108]
Kume K, Hanyu H, Sato T, Hirao K, Shimizu S, Kanetaka H, et al. Vascular risk factors are associated with faster decline of Alzheimer disease: a longitudinal SPECT study. J Neurol 258(7): 1295-303.(2011);
[109]
Boutoleau-Bretonniere C, Lebouvier T, Delaroche O, Lamy E, Evrard C, Charriau T, et al. Value of neuropsychological testing, imaging, and CSF biomarkers for the differential diagnosis and prognosis of clinically ambiguous dementia. J Alzheimers Dis 28(2): 323-36.(2012);
[110]
Alegret M, Cuberas-Borros G, Vinyes-Junque G, Espinosa A, Valero S, Hernandez I, et al. A two- year follow-up of cognitive deficits and brain perfusion in mild cognitive impairment and mild Alzheimer’s disease. J Alzheimers Dis 30(1): 109-20.(2012);
[111]
Park KW, Yoon HJ, Kang DY, Kim BC, Kim S, Kim JW. Regional cerebral blood flow differences in patients with mild cognitive impairment between those who did and did not develop Alzheimer’s disease. Psychiatry Res 203(2-3): 201-6.(2012);
[112]
Ito K, Mori E, Fukuyama H, Ishii K, Washimi Y, Asada T, et al. Prediction of outcomes in MCI with (123)I-IMP-CBF SPECT: a multicenter prospective cohort study. Ann Nucl Med 27(10): 898-906.(2013);
[113]
Sakai M, Hanyu H, Kume K, Sato T, Hirao K, Kanetaka H, et al. Rate of progression of Alzheimer’s disease in younger versus older patients: a longitudinal single photon emission computed tomography study. Geriatr Gerontol Int 13(3): 555-62.(2013);
[114]
Matsuda H. Role of neuroimaging in Alzheimer’s disease, with emphasis on brain perfusion SPECT. J Nucl Med 48(8): 1289-300.(2007);
[115]
Farid K, Caillat-Vigneron N, Sibon I. Is brain SPECT useful in degenerative dementia diagnosis? J Comput Assist Tomogr 35(1): 1-3.(2011);
[116]
Torosyan N, Silverman DH. Neuronuclear imaging in the evaluation of dementia and mild decline in cognition. Semin Nucl Med 42(6): 415-22.(2012);
[117]
Bloudek LM, Spackman DE, Blankenburg M, Sullivan SD. Review and meta-analysis of biomarkers and diagnostic imaging in Alzheimer’s disease. J Alzheimers Dis 26(4): 627-45.(2011);
[118]
Davison CM, O’Brien JT. A comparison of FDG-PET and blood flow SPECT in the diagnosis of neurodegenerative dementias: a systematic review. Int J Geriatr Psychiatry 29(6): 551-61.(2014);
[119]
Minoshima S, Giordani B, Berent S, Frey KA, Foster NL, Kuhl DE. Metabolic reduction in the posterior cingulate cortex in very early Alzheimer’s disease. Ann Neurol 42(1): 85-94.(1997);
[120]
Buckner RL, Snyder AZ, Shannon BJ, LaRossa G, Sachs R, Fotenos AF, et al. Molecular, structural, and functional characterization of Alzheimer’s disease: evidence for a relationship between default activity, amyloid, and memory. J Neurosci 25(34): 7709-17.(2005);
[121]
Hafkemeijer A, van der Grond J, Rombouts SA. Imaging the default mode network in aging and dementia. Biochim Biophys Acta 1822(3): 431-41.(2012);
[122]
Tijms BM, Wink AM, de Haan W, van der Flier WM, Stam CJ, Scheltens P, et al. Alzheimer’s disease: connecting findings from graph theoretical studies of brain networks. Neurobiol Aging 34(8): 2023-36.(2013);
[123]
Liu Z, Ke L, Liu H, Huang W, Hu Z. Changes in topological organization of functional PET brain network with normal aging. PLoS One 9(2) e88690(2014);
[124]
Seo EH, Lee DY, Lee JM, Park JS, Sohn BK, Lee DS, et al. Whole-brain functional networks in cognitively normal, mild cognitive impairment, and Alzheimer’s disease. PLoS One 8(1) e53922(2013);
[125]
Sanabria-Diaz G, Martinez-Montes E, Melie-Garcia L. Glucose metabolism during resting state reveals abnormal brain networks organization in the Alzheimer’s disease and mild cognitive impairment. PLoS One 8(7) e68860(2013);
[126]
Marchesi VT. Alzheimer’s dementia begins as a disease of small blood vessels, damaged by oxidative-induced inflammation and dysregulated amyloid metabolism: implications for early detection and therapy. FASEB J 25(1): 5-13.(2011);
[127]
Østergaard L, Aamand R, Gutiérrez-Jiménez E, Ho YC, Blicher JU, Madsen SM, et al. The capillary dysfunction hypothesis of Alzheimer’s disease. Neurobiol Aging 34(4): 1018-31.(2013);
[128]
Melie-García L, Sanabria-Diaz G, Sánchez-Catasús C. Studying the topological organization of the cerebral blood flow fluctuations in resting state. Neuroimage 64(1): 173-84.(2013);
[129]
Takahashi H, Ishii K, Hosokawa C, Hyodo T, Kashiwagi N, Matsuki M, et al. Clinical application of 3D arterial spin-labeled brain perfusion imaging for Alzheimer disease: comparison with brain perfusion SPECT. AJNR Am J Neuroradiol 35(5): 906-11.(2014);
[130]
Verfaillie SC, Adriaanse SM, Binnewijzend MA, Benedictus MR, Ossenkoppele R, Wattjes MP, et al. Cerebral perfusion and glucose metabolism in Alzheimer’s disease and frontotemporal dementia: two sides of the same coin? Eur Radiol 25(10): 3050-9.(2015);
[131]
Chen Y, Wolk DA, Reddin JS, Korczykowski M, Martinez PM, Musiek ES, et al. Voxel-level comparison of arterial spin-labeled perfusion MRI and FDG-PET in Alzheimer disease. Neurology 77(22): 1977-85.(2011);
[132]
Chao LL, Buckley ST, Kornak J, Schuff N, Madison C, Yaffe K, et al. ASL perfusion MRI predicts cognitive decline and conversion from MCI to dementia. Alzheimer Dis Assoc Disord 224(1): 19-27.(2010);
[133]
Wierenga CE, Hays CC, Zlatar ZZ. Cerebral blood flow measured by arterial spin labeling MRI as a preclinical marker of Alzheimer’s disease. J Alzheimers Dis 42(4): S411-9.(2014);
[134]
Xekardaki A, Rodriguez C, Montandon ML, Toma S, Tombeur E, Herrmann FR, et al. Arterial spin labeling may contribute to the prediction of cognitive deterioration in healthy elderly individuals. Radiology 274(2): 490-9.(2015);


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VOLUME: 14
ISSUE: 2
Year: 2017
Page: [127 - 142]
Pages: 16
DOI: 10.2174/1567205013666160629081956
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