Top

BMC Geriatrics

Published in:

Open Access 01-12-2016 | Research article

Potential neuroimaging biomarkers of pathologic brain changes in Mild Cognitive Impairment and Alzheimer’s disease: a systematic review

Authors: Qingwei Ruan, Grazia D’Onofrio, Daniele Sancarlo, Zhijun Bao, Antonio Greco, Zhuowei Yu

Published in: BMC Geriatrics | Issue 1/2016

Abstract

Background

Neuroimaging-biomarkers of Mild Cognitive Impairment (MCI) allow an early diagnosis in preclinical stages of Alzheimer’s disease (AD). The goal in this paper was to review of biomarkers for Mild Cognitive Impairment (MCI) and Alzheimer’s disease (AD), with emphasis on neuroimaging biomarkers.

Methods

A systematic review was conducted from existing literature that draws on markers and evidence for new measurement techniques of neuroimaging in AD, MCI and non-demented subjects. Selection criteria included: 1) age ≥ 60 years; 2) diagnosis of AD according to NIAAA criteria, 3) diagnosis of MCI according to NIAAA criteria with a confirmed progression to AD assessed by clinical follow-up, and 4) acceptable clinical measures of cognitive impairment, disability, quality of life, and global clinical assessments.

Results

Seventy-two articles were included in the review. With the development of new radioligands of neuroimaging, today it is possible to measure different aspects of AD neuropathology, early diagnosis of MCI and AD become probable from preclinical stage of AD to AD dementia and non-AD dementia.

Conclusions

The panel of noninvasive neuroimaging-biomarkers reviewed provides a set methods to measure brain structural and functional pathophysiological changes in vivo, which are closely associated with preclinical AD, MCI and non-AD dementia. The dynamic measures of these imaging biomarkers are used to predict the disease progression in the early stages and improve the assessment of therapeutic efficacy in these diseases in future clinical trials.

Braak H, Del Tredici K. Where, when, and in what form does sporadic Alzheimer disease begin? Curr Opin Neurol. 2012;25(6):708–14.PubMed

McKhann GM, Knopman DS, Chertkow H, 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;7:263–9.PubMedPubMedCentral

Petersen RC, Smith GE, Waring SC, et al. Mild cognitive impairment: clinical characterization and outcome. Arch Neurol. 1999;56:303–8.PubMed

Albert MS, DeKosky ST, 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;7:270–9.PubMedPubMedCentral

Jansen WJ, Ossenkoppele MR, Knol DL, et al. Prevalence of cerebral amyloid pathology in persons without dementia A meta-analysis. JAMA. 2015;313(19):1924–38.PubMedPubMedCentral

Villemagne VL, Burnham S, Bourgeat P, et al. Amyloid β deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer’s disease: A prospective cohort study. Lancet Neurol. 2013;12(4):357–67.PubMed

Dixon-Woods M, Agarwal S, Jones D, et al. Synthesising qualitative and quantitative evidence: a review of possible methods. J Health Serv Res Policy. 2005;10:45–53.PubMed

Hannes K, Macaitis K. A move to more systematic and transparent approaches in qualitative evidence synthesis: update on a review of published papers. Qual Res. 2012;12:402–42.

Barnett-Page E, Thomas J. Methods for the synthesis of qualitative research: a critical review. BMC Med Res Methodol. 2009;9:59.PubMedPubMedCentral

10.

Grant MJ, Booth A. A typology of reviews: an analysis of 14 review types and associated methodologies. Health Info Libr J. 2009;26:91–108.PubMed

11.

Noel-Storr AH, McCleery JM, Richard E, et al. Reporting standards for studies of diagnostic test accuracy in dementia: The STARDdem Initiative. Neurology. 2014;83(4):364–73.PubMedPubMedCentral

12.

Jack Jr CR, Petersen RC, Xu YC, et al. Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment. Neurology. 1999;52(7):1397–403.PubMedPubMedCentral

13.

Risacher SL, Saykin AJ, West JD, et al. Baseline MRI predictors of conversion from MCI to probable AD in the ADNI cohort. Curr Alzheimer Res. 2009;6(4):347–61.PubMedPubMedCentral

14.

Whitwell JL, Przybelski SA, Weigand SD, et al. 3D maps from multiple MRI illustrate changing atrophy patterns as subjects progress from mild cognitive impairment to Alzheimer’s disease. Brain. 2007;130(Pt 7):1777–86.PubMedPubMedCentral

15.

Stoub TR, Bulgakova M, Leurgans S, et al. MRI predictors of risk of incident Alzheimer disease: a longitudinal study. Neurology. 2005;64(9):1520–4.PubMed

16.

Jack Jr CR. Alzheimer disease: new concepts on its neurobiology and the clinical role imaging will play. Radiology. 2012;263:344–61.PubMedPubMedCentral

17.

Wiest R, Burren Y, Hauf M, et al. Classification of mild cognitive impairment and Alzheimer disease using model-based MR and magnetization transfer imaging. Am J Neuroradiol. 2013;34(4):740–6.PubMed

18.

Nettiksimmons J, Harvey D, Brewer J, et al. Subtypes based on cerebrospinal fluid and magnetic resonance imaging markers in normal elderly predict cognitive decline. Neurobiol Aging. 2010;31(8):1419–28.PubMedPubMedCentral

19.

Kong D, Giovanello KS, Wang Y, et al. Predicting Alzheimer’s Disease Using Combined Imaging-Whole Genome SNP Data. J Alzheimers Dis. 2015;46(3):695–702.PubMedPubMedCentral

20.

Mangialasche F, Westman E, Kivipelto M, et al. Classification and prediction of clinical diagnosis of Alzheimer’s disease based on MRI and plasma measures of α-/γ-tocotrienols and γ-tocopherol. J Intern Med. 2013;273(6):602–21.PubMed

21.

Vemuri P, Wiste HJ, Weigand SD, et al. MRI and CSF biomarkers in normal, MCI, and AD subjects: diagnostic discrimination and cognitive correlations. Neurology. 2009;73(4):287–93.PubMedPubMedCentral

22.

Vemuri P, Wiste HJ, Weigand SD, et al. Serial MRI and CSF biomarkers in normal aging, MCI, and AD. Neurology. 2010;75(2):143–51.PubMedPubMedCentral

23.

Eckerström C, Olsson E, Bjerke M, et al. A combination of neuropsychological, neuroimaging, and cerebrospinal fluid markers predicts conversion from mild cognitive impairment to dementia. J Alzheimers Dis. 2013;36(3):421–31.PubMed

24.

Shaffer JL, Petrella JR, Sheldon FC, et al. Predicting cognitive decline in subjects at risk for Alzheimer disease by using combined cerebrospinal fluid, MR imaging, and PET biomarkers. Radiology. 2013;266(2):583–91.PubMedPubMedCentral

25.

Landau SM, Harvey D, Madison CM, et al. Comparing predictors of conversion and decline in mild cognitive impairment. Neurology. 2010;75(3):230–8.PubMedPubMedCentral

26.

van Rossum IA, Vos SJ, Burns L, et al. Injury markers predict time to dementia in subjects with MCI and amyloid pathology. Neurology. 2012;79(17):1809–16.PubMedPubMedCentral

27.

Karow DS, McEvoy LK, Fennema-Notestine C, et al. Relative capability of MR imaging and FDG PET to depict changes associated with prodromal and early Alzheimer disease. Radiology. 2010;256(3):932–42.PubMedPubMedCentral

28.

Kantarci K, Weigand SD, Przybelski SA, et al. Risk of dementia in MCI: combined effect of cerebrovascular disease, volumetric MRI, and 1H MRS. Neurology. 2009;72(17):1519–25.PubMedPubMedCentral

29.

Caselli RJ, Jack Jr CR, Petersen RC, et al. Asymmetric cortical degenerative syndromes: clinical and radiologic correlations. Neurology. 1992;42:1462–8.PubMed

30.

Varma AR, Adams W, Lloyd JJ, et al. Diagnostic patterns of regional atrophy on MRI and regional cerebral blood flow change on SPECT in young onset patients with Alzheimer’s disease, frontotemporal dementia and vascular dementia. Acta Neurol Scand. 2002;105(4):261–9.PubMed

31.

Niemantsverdriet E, Feyen BF, Le Bastard N, et al. Overdiagnosing vascular dementia using structural brain imaging for dementia work-up. J Alzheimers Dis. 2015;45(4):1039–43.PubMed

32.

Chu WZ, Gong L, Xu YQ, et al. Apolipoprotein E gene variants of Alzheimer’s disease and vascular dementia patients in a community population of Nanking. Med Chem. 2014;10(8):783–8.PubMed

33.

Smith CD, Johnson ES, Van Eldik LJ, et al. Peripheral (deep) but not periventricular MRI white matter hyperintensities are increased in clinical vascular dementia compared to Alzheimer’s disease. Brain Behav. 2016;16:e00438. doi:10.1002/brb3.438.

34.

Logue MW, Posner H, Green RC, et al. Magnetic resonance imaging-measured atrophy and its relationship to cognitive functioning in vascular dementia and Alzheimer’s disease patients. Alzheimers Dement. 2011;7(5):493–500.PubMedPubMedCentral

35.

Boutoleau-Bretonnière C, Lebouvier T, Delaroche O, et al. Value of neuropsychological testing, imaging, and CSF biomarkers for the differential diagnosis and prognosis of clinically ambiguous dementia. J Alzheimers Dis. 2012;28(2):323–36.PubMed

36.

Racine AM, Adluru N, Alexander AL, et al. Associations between white matter microstructure and amyloid burden in preclinical Alzheimer’s disease: A multimodal imaging investigation. Neuroimage Clin. 2014;4:604–14.PubMedPubMedCentral

37.

Dyrba M, Barkhof F, Fellgiebel A, et al. Predicting Prodromal Alzheimer’s disease in subjects with Mild Cognitive Impairment using machine learning classification of multimodal multicenter diffusion-tensor and magnetic resonance imaging data. J Neuroimaging. 2015;25(5):738–47.PubMed

38.

Balachandar R, John JP, Saini J, et al. A study of structural and functional connectivity in early Alzheimer’s disease using rest fMRI and diffusion tensor imaging. Int J Geriatr Psychiatry. 2015;30(5):497–4.PubMed

39.

Lee DY, Fletcher E, Martinez O, et al. Regional pattern of white matter microstructural changes in normal aging, MCI, and AD. Neurology. 2009;73(21):1722–8.PubMedPubMedCentral

40.

Zarei M, Damoiseaux JS, Morgese C, et al. Regional white matter integrity differentiates between vascular dementia and Alzheimer disease. Stroke. 2009;40(3):773–9.PubMed

41.

Stahl R, Dietrich O, Teipel SJ, et al. White matter damage in Alzheimer disease and mild cognitive impairment: assessment with diffusion-tensor MR imaging and parallel imaging techniques. Radiology. 2007;243(2):483–92.PubMed

42.

Meier IB, Manly JJ, Provenzano FA, et al. White matter predictors of cognitive functioning in older adults. J Int Neuropsychol Soc. 2012;18(3):414–27.PubMedPubMedCentral

43.

Smith EE, Egorova S, Blacker D, et al. Magnetic resonance imaging white matter hyperintensities and brain volume in the prediction of mild cognitive impairment and dementia. Arch Neurol. 2008;65(1):94–100.PubMed

44.

Yoshita M, Fletcher E, Harvey D, et al. Extent and distribution of white matter hyperintensities in normal aging, MCI, and AD. Neurology. 2006;67(12):2192–8.PubMedPubMedCentral

45.

Prescott JW, Guidon A, Doraiswamy PM, et al. The Alzheimer structural connectome: changes in cortical network topology with increased amyloid plaque burden. Radiology. 2014;273(1):175–84.PubMedPubMedCentral

46.

Teipel SJ, Grothe MJ, Filippi M, et al. Fractional anisotropy changes in Alzheimer’s disease depend on the underlying fiber tract architecture: a multiparametric DTI study using joint independent component analysis. J Alzheimers Dis. 2014;41(1):69–83.PubMed

47.

Zhang Y, Schuff N, Jahng GH, et al. Diffusion tensor imaging of cingulum fibers in mild cognitive impairment and Alzheimer disease. Neurology. 2007;68(1):13–9.PubMedPubMedCentral

48.

Kantarci K, Jack Jr CR, Xu YC, et al. Mild cognitive impairment and Alzheimer disease: regional diffusivity of water. Radiology. 2001;219(1):101–7.PubMedPubMedCentral

49.

Takahashi R, Ishii K, Senda M, et al. Equal sensitivity of early and late scans after injection of FDG for the detection of Alzheimer pattern: an analysis of 3D PET data from J-ADNI, a multi-center study. Ann Nucl Med. 2013;27(5):452–9.PubMed

50.

Mendez MF, Ottowitz W, Brown CV, et al. Dementia with leukoaraiosis: clinical differentiation by temporoparietal hypometabolism on (18)FDG-PETimaging. Dement Geriatr Cogn Disord. 1999;10(6):518–25.PubMed

51.

Mosconi L, De Santi S, Brys M, et al. Hypometabolism and altered cerebrospinal fluid markers in normal apolipoprotein E E4 carriers with subjective memory complaints. Biol Psychiatry. 2008;63(6):609–18.PubMed

52.

Mosconi L, Mistur R, Switalski R, et al. Declining brain glucose metabolism in normal individuals with a maternal history of Alzheimer disease. Neurology. 2009;72(6):513–20.PubMedPubMedCentral

53.

Chételat G, Desgranges B, de la Sayette V. Mild cognitive impairment: can FDG-PET predict who is to rapidly convert to Alzheimer’s disease? Neurology. 2003;60:1374–7.PubMed

54.

Klupp E, Förster S, Grimmer T, et al. In Alzheimer’s disease, hypometabolism in low-amyloid brain regions may be a functional consequence of pathologies in connected brain regions. Brain Connect. 2014;4(5):371–83.PubMed

55.

Kim MJ, Lee KM, Son YD, et al. Increased basal forebrain metabolism in mild cognitive impairment: an evidence for brain reserve in incipient dementia. J Alzheimers Dis. 2012;32(4):927–38.PubMed

56.

Landau SM, Harvey D, Madison CM, et al. Associations between cognitive, functional, and FDG-PET measures of decline in AD and MCI. Neurobiol Aging. 2011;32(7):1207–18.PubMed

57.

Shokouhi S, Claassen D, Kang H, et al. Longitudinal progression of cognitive decline correlates with changes in the spatial pattern of brain 18 F-FDG PET. J Nucl Med. 2013;54(9):1564–9.PubMedPubMedCentral

58.

Ossenkoppele R, Tolboom N, Foster-Dingley JC, et al. Longitudinal imaging of Alzheimer pathology using [11C]PIB, [18 F]FDDNP and [18 F]FDG PET. Eur J Nucl Med Mol Imaging. 2012;39(6):990–1000.PubMed

59.

Chen Y, Wolk DA, Reddin JS, et al. Voxel-level comparison of arterial spin-labeled perfusion MRI and FDG-PET in Alzheimer disease. Neurology. 2011;77(22):1977–85.PubMedPubMedCentral

60.

Albin RL, Koeppe RA, Burke JF, et al. Comparing fludeoxyglucose F18-PET assessment of regional cerebral glucose metabolism and [11C]dihydrotetrabenazine-PET in evaluation of early dementia and mild cognitive impairment. Arch Neurol. 2010;67(4):440–6.PubMedPubMedCentral

61.

Ji B, Maeda J, Sawada M, et al. Imaging of peripheral benzodiazepine receptor expression as biomarkers of detrimental versus beneficial glial responses in mouse models of Alzheimer’s and other CNS pathologies. J Neurosci. 2008;28(47):12255–67.PubMedPubMedCentral

62.

Zimmer ER, Leuzy A, Benedet AL, et al. Tracking neuroinflammation in Alzheimer’s disease: the role of positron emission tomography imaging. J Neuroinflammation. 2014;11:120.PubMedPubMedCentral

63.

Carter SF, Scholl M, Almkvist O, et al. Evidence for astrocytosis in prodromal Alzheimer disease provided by 11C-deuterium-L-deprenyl: a multitracer PET paradigm combining 11C-Pittsburgh compound B and 18 F-FDG. J Nucl Med. 2012;53:37–46.PubMed

64.

Balsinde J, Winstead MV, Dennis EA. Phospholipase A(2) regulation of arachidonic acid mobilization. FEBS Lett. 2002;531:2–6.PubMed

65.

Mizrahi R, Rusjan PM, Kennedy J, et al. Translocator protein (18 kDa) polymorphism (rs6971) explains in-vivo brain binding affinity of the PET radioligand [18 F]-FEPPA. J Cereb Blood Flow Metab. 2012;32:968–72.PubMedPubMedCentral

66.

Rosenberg PB, Wong DF, Edell SL, et al. Cognition and amyloid load in Alzheimer disease imaged with florbetapir F18(AV-45) positron emission tomography. Am J Geriatr Psychiatry. 2013;21(3):272–8.PubMedPubMedCentral

67.

Jagust WJ, Bandy D, Chen K, et al. The Alzheimer’s Disease Neuroimaging Initiative positron emission tomography core. Alzheimers Dement. 2010;6(3):221–9.PubMedPubMedCentral

68.

Klunk WE, Engler H, Nordberg A, et al. Imaging brain amyloid in Alzheimer’s disease with Pittsburgh Compound-B. Ann Neurol. 2004;55:306–19.PubMed

69.

Rowe CC, Ng S, Ackermann U, et al. Imaging beta-amyloid burden in aging and dementia. Neurology. 2007;68:1718–25.PubMed

70.

Doraiswamy PM, Sperling RA, Johnson K, et al. Florbetapir F 18 amyloid PET and 36-month cognitive decline: a prospective multicenter study. Mol Psychiatry. 2014;19(9):1044–51.PubMedPubMedCentral

71.

Doraiswamy PM, Sperling RA, Coleman RE, et al. Amyloid-β assessed by florbetapir F 18 PET and 18-month cognitive decline: a multicenter study. Neurology. 2012;79(16):1636–44.PubMed

72.

Okello A, Koivunen J, Edison P, et al. Conversion of amyloid positive and negative MCI to AD over 3 years: an 11C-PIB PET study. Neurology. 2009;73(10):754–60.PubMedPubMedCentral

73.

Johnson KA, Sperling RA, Gidicsin CM, et al. Florbetapir (F18-AV-45) PET to assess amyloid burden in Alzheimer’s disease dementia, mild cognitive impairment, and normal aging. Alzheimers Dement. 2013;9(5 Suppl):S72–83.PubMedPubMedCentral

74.

Camus V, Payoux P, Barré L, et al. Using PET with 18 F-AV-45 (florbetapir) to quantify brain amyloid load in a clinical environment. Eur J Nucl Med Mol Imaging. 2012;39(4):621–31.PubMedPubMedCentral

75.

Furst AJ, Rabinovici GD, Rostomian AH, et al. Cognition, glucose metabolism and amyloid burden in Alzheimer’s disease. Neurobiol Aging. 2012;33(2):215–25.PubMed

76.

Ong KT, Villemagne VL, Bahar-Fuchs A, et al. Aβ imaging with 18 F-florbetaben in prodromal Alzheimer’s disease: a prospective outcome study. J Neurol Neurosurg Psychiatry. 2015;86(4):431–6.PubMed

77.

Landau SM, Lu M, Joshi AD, et al. Comparing positron emission tomography imaging and cerebrospinal fluid measurements of β-amyloid. Ann Neurol. 2013;74(6):826–36.PubMedPubMedCentral

78.

Small GW, Siddarth P, Kepe V, et al. Prediction of cognitive decline by positron emission tomography of brain amyloid and tau. Arch Neurol. 2012;69(2):215–22.PubMedPubMedCentral

79.

Murray ME, Graff-Radford NR, Ross OA, et al. Neuropathologically defined subtypes of Alzheimer’s disease with distinct clinical characteristics: a retrospective study. Lancet Neurol. 2011;10(9):785–96.PubMedPubMedCentral

80.

Braak H, Braak E. Development of Alzheimer-related neurofibrillary changes in the neocortex inversely recapitulates cortical myelogenesis. Acta Neuropathol. 1996;92(2):197–201.PubMed

81.

Chien DT, Bahri S, Szardenings AK, et al. Early clinical PET imaging results with the novel PHF-tau radioligand [F-18]-T807. J Alzheimers Dis. 2013;34(2):457–68.PubMed

82.

Chien DT, Szardenings AK, Bahri S, et al. Early clinical PET imaging results with the novel PHF-tau radioligand [F18]-T808. J Alzheimers Dis. 2014;38(1):171–84.PubMed

83.

Maruyama M, Shimada H, Suhara T, et al. Imaging of tau pathology in a tauopathy mouse model and in Alzheimer patients compared to normal controls. Neuron. 2013;79(6):1094–108.PubMed

84.

Greicius MD, Srivastava G, Reiss AL, et al. Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. Proc Natl Acad Sci U S A. 2004;101(13):4637–42.PubMedPubMedCentral

85.

Pihlajamaki M, Jauhiainen AM, Soininen H. Structural and functional MRI in mild cognitive impairment. Curr Alzheimer Res. 2009;6(2):179–85.PubMed

86.

Jin M, Pelak VS, Cordes D. Aberrant default mode network in subjects with amnestic mild cognitive impairment using resting-state functional MRI. Magn Reson Imaging. 2012;30(1):48–61.PubMed

87.

Sperling RA, Dickerson BC, Pihlajamaki M, et al. Functional alterations in memory networks in early Alzheimer’s disease. Neuromolecular Med. 2010;12(1):27–43.PubMedPubMedCentral

88.

Gomez-Ramirez J, Wu J. Network-based biomarkers in Alzheimer’s disease: review and future directions. Front Aging Neurosci. 2014;6:12.PubMedPubMedCentral

89.

Wang Y, Risacher S, West J, et al. Altered default mode network connectivity in older adults with cognitive complaints and amnestic mild cognitive impairment. J Alzheimers Dis. 2013;35:751–60.PubMedPubMedCentral

90.

Zhu DC, Majumdar S, Korolev IO, et al. Alzheimer’s disease and amnestic mild cognitive impairment weaken connections within the default-mode network: a multi-modal imaging study. J Alzheimers Dis. 2013;34(4):969–84.PubMed

91.

Petrella JR, Sheldon FC, Prince SE, et al. Default mode network connectivity in stable vs progressive mild cognitive impairment. Neurology. 2011;76(6):511–7.PubMedPubMedCentral

92.

Flicker L. Modifiable lifestyle risk factors for Alzheimer’s disease. J Alzheimers Dis. 2010;20:803–11.PubMed

93.

Dickerson BC, Salat DH, Greve DN, et al. Increased hippocampal activation in mild cognitive impairment compared to normal aging and AD. Neurology. 2005;65(3):404–11.PubMedPubMedCentral

94.

O’Brien JL, O’Keefe KM, LaViolette PS, et al. Longitudinal fMRI in elderly reveals loss of hippocampal activation with clinical decline. Neurology. 2010;74(24):1969–76.PubMedPubMedCentral

95.

Mondadori CR, Buchmann A, Mustovic H, et al. Enhanced brain activity may precede the diagnosis of Alzheimer’s disease by 30 years. Brain. 2006;129(Pt 11):2908–22.PubMed

96.

Bero AW, Yan P, Roh JH, et al. Neuronal activity regulates the regional vulnerability to amyloid-β deposition. Nat Neurosci. 2011;14(6):750–6.PubMedPubMedCentral

97.

Kim J, Basak JM, Holtzman DM. The role of apolipoprotein E in Alzheimer’s disease. Neuron. 2009;63:287–303.PubMedPubMedCentral

98.

Lacalle-Aurioles M, Mateos-Pérez JM, Guzmán-De-Villoria JA, et al. Cerebral blood flow is an earlier indicator of perfusion abnormalities than cerebral blood volume in Alzheimer’s disease. J Cereb Blood Flow Metab. 2014;34(4):654–9.PubMedPubMedCentral

99.

Huang C, Wahlund LO, Svensson L, et al. Cingulate cortex hypoperfusion predicts Alzheimer’s disease in mild cognitive impairment. BMC Neurol. 2002;2:9.PubMedPubMedCentral

100.

Encinas M, De Juan R, Marcos A, 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. 2003;30(11):1473–80.PubMed

101.

Habert MO, Horn JF, Sarazin M, et al. Brain perfusion SPECT with an automated quantitative tool can identify prodromal Alzheimer’s disease among patients with mild cognitive impairment. Neurobiol Aging. 2011;32(1):15–23.PubMed

102.

Devanand DP, Van Heertum RL, Kegeles LS, et al. (99 m)Tc hexamethyl-propylene-aminoxime single-photon emission computed tomography prediction of conversion from mild cognitive impairment to Alzheimer disease. Am J Geriatr Psychiatry. 2010;18(11):959–72.PubMedPubMedCentral

103.

Chen YJ, Deutsch G, Satya R, et al. A semi-quantitative method for correlating brain disease groups with normal controls using SPECT: Alzheimer’s disease versus vascular dementia. Comput Med Imaging Graph. 2013;37(1):40–7.PubMed

104.

Borroni B, Anchisi D, Paghera B, et al. Combined 99mTc-ECD SPECT and neuropsychological studies in MCI for the assessment of conversion to AD. Neurobiol Aging. 2006;27(1):24–31.PubMed

105.

Gao YZ, Zhang JJ, Liu H, et al. Regional cerebral blood flow and cerebrovascular reactivity in Alzheimer’s disease and vascular dementia assessed by arterial spinlabeling magnetic resonance imaging. Curr Neurovasc Res. 2013;10(1):49–53.PubMed

106.

Hanyu H, Kume K, Sato T, et al. Regional differences in cortical benzodiazepine receptors of Alzheimer, vascular, and mixed dementiapatients. J Neurol Sci. 2012;323(1-2):71–6.PubMed

107.

Yoshikawa T, Murase K, Oku N, et al. Heterogeneity of cerebral blood flow in Alzheimer disease and vascular dementia. AJNR Am J Neuroradiol. 2003;24(7):1341–7.PubMed

108.

Sperling RA, Aisen PS, Beckett LA, 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.PubMedPubMedCentral

109.

Price JL, Morris JC. Tangles and plaques in nondementted aging and “preclinical” Alzheimer’s disease. Ann Neurol. 1999;45:358–68.PubMed

110.

Montagne A, Barnes SR, Sweeney MD, et al. Blood-brain barrier breakdown in the aging human hippocampus. Neuron. 2015;85(2):296–302.PubMedPubMedCentral

111.

Jack CRJ, Lowe VJ, Senjem ML, et al. 11C PiB and structural MRI provide complementary information in imaging of Alzheimer’s disease and amnestic mild cognitive impairment. Brain. 2008;131(Pt 3):665–80.PubMedPubMedCentral

Title: Potential neuroimaging biomarkers of pathologic brain changes in Mild Cognitive Impairment and Alzheimer’s disease: a systematic review
Authors: Qingwei Ruan
Grazia D’Onofrio
Daniele Sancarlo
Zhijun Bao
Antonio Greco
Zhuowei Yu
Publication date: 01-12-2016
Publisher: BioMed Central
Published in: BMC Geriatrics / Issue 1/2016
Electronic ISSN: 1471-2318
DOI: https://doi.org/10.1186/s12877-016-0281-7

ACC 2024 Congress Coverage

Heart Failure Video

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Potential neuroimaging biomarkers of pathologic brain changes in Mild Cognitive Impairment and Alzheimer’s disease: a systematic review

Abstract

Background

Methods

Results

Conclusions

ACC 2024 Congress Coverage

Year in Review: Heart failure and cardiomyopathies

Semaglutide benefits people with type 2 diabetes and obesity-related heart failure

Incentivised to exercise

New intervention for STEMI-related cardiogenic shock

» View more highlights on the ACC 2024 congress hub page

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

Please log in to get access to this content

Other articles of this Issue 1/2016

Strategies to reduce the risk of falling: Cohort study analysis with 1-year follow-up in community dwelling older adults

Clinical evaluation of an over-the-counter hearing aid (TEO First®) in elderly patients suffering of mild to moderate hearing loss

Effectiveness and cost-effectiveness of an in-home respite care program in supporting informal caregivers of people with dementia: design of a comparative study

Concurrent validity of the Swedish version of the life-space assessment questionnaire

Pain treatment for nursing home residents differs according to cognitive state – a cross-sectional study

Unravelling the potential mechanisms behind hospitalization-associated disability in older patients; the Hospital-Associated Disability and impact on daily Life (Hospital-ADL) cohort study protocol

ACC 2024 Congress Coverage

Year in Review: Heart failure and cardiomyopathies

Semaglutide benefits people with type 2 diabetes and obesity-related heart failure

Incentivised to exercise

New intervention for STEMI-related cardiogenic shock

» View more highlights on the ACC 2024 congress hub page