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Translational Neurodegeneration
Published in: Translational Neurodegeneration 1/2018

Open Access 01-12-2018 | Review

Cerebral glucose metabolic prediction from amnestic mild cognitive impairment to Alzheimer’s dementia: a meta-analysis

Authors: Hai Rong Ma, Li Qin Sheng, Ping Lei Pan, Gen Di Wang, Rong Luo, Hai Cun Shi, Zhen Yu Dai, Jian Guo Zhong

Published in: Translational Neurodegeneration | Issue 1/2018

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Abstract

Brain 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) has been utilized to monitor disease conversion from amnestic mild cognitive impairment (aMCI) to Alzheimer’s dementia (AD). However, the conversion patterns of FDG-PET metabolism across studies are not conclusive. We conducted a voxel-wise meta-analysis using Seed-based d Mapping that included 10 baseline voxel-wise FDG-PET comparisons between 93 aMCI converters and 129 aMCI non-converters from nine longitudinal studies. The most robust and reliable metabolic alterations that predicted conversion from aMCI to AD were localized in the left posterior cingulate cortex (PCC)/precuneus. Furthermore, meta-regression analyses indicated that baseline mean age and severity of cognitive impairment, and follow-up duration were significant moderators for metabolic alterations in aMCI converters. Our study revealed hypometabolism in the left PCC/precuneus as an early feature in the development of AD. This finding has important implications in understanding the neural substrates for AD conversion and could serve as a potential imaging biomarker for early detection of AD as well as for tracking disease progression at the predementia stage.
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Literature
1.
Chan KY, Wang W, Wu JJ, Liu L, Theodoratou E, Car J, et al. Epidemiology of Alzheimer's disease and other forms of dementia in China, 1990-2010: a systematic review and analysis. Lancet. 2013;381:2016–23.CrossRefPubMed
2.
Goodman RA, Lochner KA, Thambisetty M, Wingo TS, Posner SF, Ling SM. Prevalence of dementia subtypes in U.S. Medicare fee-for-service beneficiaries, 2011-2013. Alzheimers Dement. 2017;13:28–37.CrossRefPubMed
3.
Scheltens P, Blennow K, Breteler MM, de Strooper B, Frisoni GB, Salloway S, et al. Alzheimer’s disease. Lancet. 2016;388:505–17.CrossRefPubMed
4.
5.
Pandya SY, Clem MA, Silva LM, Woon FL. Does mild cognitive impairment always lead to dementia? A review. J Neurol Sci. 2016;369:57–62.CrossRefPubMed
6.
Hoilund-Carlsen PF, Barrio JR, Gjedde A, Werner TJ, Alavi A. Circular inference in dementia diagnostics. J Alzheimers Dis. 2018;
7.
Kato T, Inui Y, Nakamura A, Ito K. Brain fluorodeoxyglucose (FDG) PET in dementia. Ageing Res Rev. 2016;30:73–84.CrossRefPubMed
8.
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;32:486–510.CrossRefPubMed
9.
Mosconi L, Tsui WH, Herholz K, Pupi A, Drzezga A, Lucignani G, et al. Multicenter standardized 18F-FDG PET diagnosis of mild cognitive impairment, Alzheimer’s disease, and other dementias. J Nucl Med. 2008;49:390–8.CrossRefPubMedPubMedCentral
10.
He W, Liu D, Radua J, Li G, Han B, Sun Z. Meta-analytic comparison between PIB-PET and FDG-PET results in Alzheimer’s disease and MCI. Cell Biochem Biophys. 2015;71:17–26.CrossRefPubMed
11.
Langbaum JB, Chen K, Lee W, Reschke C, Bandy D, Fleisher AS, et al. Categorical and correlational analyses of baseline fluorodeoxyglucose positron emission tomography images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). NeuroImage. 2009;45:1107–16.CrossRefPubMedPubMedCentral
12.
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. Am J Neuroradiol. 2009;30:404–10.CrossRefPubMed
13.
Smailagic N, Vacante M, Hyde C, Martin S, Ukoumunne O, Sachpekidis C. (1)(8)F-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. 2015;1:Cd010632.PubMed
14.
Chételat 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;60:1374–7.CrossRefPubMed
15.
Mosconi L, Perani D, Sorbi S, Herholz K, Nacmias B, Holthoff V, et al. MCI conversion to dementia and the APOE genotype: a prediction study with FDG-PET. Neurology. 2004;63:2332–40.CrossRefPubMed
16.
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. 2010;7:287–94.CrossRefPubMed
17.
Yi D, Choe YM, Byun MS, Sohn BK, Seo EH, Han J, et al. Differences in functional brain connectivity alterations associated with cerebral amyloid deposition in amnestic mild cognitive impairment. Front Aging Neurosci. 2015;7:15.CrossRefPubMedPubMedCentral
18.
Teipel SJ, Kurth J, Krause B, Grothe MJ. The relative importance of imaging markers for the prediction of Alzheimer’s disease dementia in mild cognitive impairment - beyond classical regression. Neuroimage Clin. 2015;8:583–93.CrossRefPubMedPubMedCentral
19.
Salmon E, Lekeu F, Garraux G, Guillaume B, Magis D, Luxen A, et al. Metabolic correlates of clinical heterogeneity in questionable Alzheimer's disease. Neurobiol Aging. 2008;29:1823–9.CrossRefPubMed
20.
Kim SH, Seo SW, Yoon DS, Chin J, Lee BH, Cheong HK, et al. Comparison of neuropsychological and FDG-PET findings between early- versus late-onset mild cognitive impairment: a five-year longitudinal study. Dement Geriatr Cogn Disord. 2010;29:213–23.CrossRefPubMed
21.
Drzezga A, Lautenschlager N, Siebner H, Riemenschneider M, Willoch F, Minoshima S, et al. Cerebral metabolic changes accompanying conversion of mild cognitive impairment into Alzheimer's disease: a PET follow-up study. Eur J Nucl Med Mol Imaging. 2003;30:1104–13.CrossRefPubMed
22.
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. 2010;37:36–45.CrossRefPubMed
23.
Fouquet M, Desgranges B, Landeau B, Duchesnay E, Mezenge F, de la Sayette V, et al. Longitudinal brain metabolic changes from amnestic mild cognitive impairment to Alzheimer’s disease. Brain. 2009;132:2058–67.CrossRefPubMedPubMedCentral
24.
Schroeter ML, Stein T, Maslowski N, Neumann J. Neural correlates of Alzheimer’s disease and mild cognitive impairment: a systematic and quantitative meta-analysis involving 1351 patients. NeuroImage. 2009;47:1196–206.CrossRefPubMedPubMedCentral
25.
Eickhoff SB, Nichols TE, Laird AR, Hoffstaedter F, Amunts K, Fox PT, et al. Behavior, sensitivity, and power of activation likelihood estimation characterized by massive empirical simulation. NeuroImage. 2016;137:70–85.CrossRefPubMedPubMedCentral
26.
Lim L, Radua J, Rubia K. Gray matter abnormalities in childhood maltreatment: a voxel-wise meta-analysis. Am J Psychiatry. 2014;171:854–63.CrossRefPubMed
27.
Norman LJ, Carlisi C, Lukito S, Hart H, Mataix-Cols D, Radua J, et al. Structural and functional brain abnormalities in attention-deficit/hyperactivity disorder and obsessive-compulsive disorder: a comparative meta-analysis. JAMA Psychiatry. 2016;73(8):815–25.CrossRefPubMed
28.
Radua J, Rubia K, Canales-Rodriguez EJ, Pomarol-Clotet E, Fusar-Poli P, Mataix-Cols D. Anisotropic kernels for coordinate-based meta-analyses of neuroimaging studies. Front Psychiatry. 2014;5:13.CrossRefPubMedPubMedCentral
29.
Radua J, Mataix-Cols D. Voxel-wise meta-analysis of grey matter changes in obsessive-compulsive disorder. Br J Psychiatry. 2009;195:393–402.CrossRefPubMed
30.
Radua J, Mataix-Cols D, Phillips ML, El-Hage W, Kronhaus DM, Cardoner N, et al. A new meta-analytic method for neuroimaging studies that combines reported peak coordinates and statistical parametric maps. European Psychiatry. 2012;27:605–11.CrossRefPubMed
31.
Sheng L, Ma H, Zhong J, Shang H, Shi H, Pan P. Motor and extra-motor gray matter atrophy in amyotrophic lateral sclerosis: quantitative meta-analyses of voxel-based morphometry studies. Neurobiol Aging. 2015;36:3288–99.CrossRefPubMed
32.
Shen B, Gao Y, Zhang W, Lu L, Zhu J, Pan Y, et al. Resting state fMRI reveals increased subthalamic nucleus and sensorimotor cortex connectivity in patients with Parkinson's disease under medication. Front Aging Neurosci. 2017;9:74.PubMedPubMedCentral
33.
Wang WY, Yu JT, Liu Y, Yin RH, Wang HF, Wang J, et al. Voxel-based meta-analysis of grey matter changes in Alzheimer’s disease. Transl Neurodegener. 2015;4:6.CrossRefPubMedPubMedCentral
34.
Li HJ, Hou XH, Liu HH, Yue CL, He Y, Zuo XN. Toward systems neuroscience in mild cognitive impairment and Alzheimer’s disease: a meta-analysis of 75 fMRI studies. Hum Brain Mapp. 2015;36:1217–32.CrossRefPubMed
35.
Alegria AA, Radua J, Rubia K. Meta-analysis of fMRI studies of disruptive behavior disorders. Am J Psychiatry. 2016;173:1119–30.CrossRefPubMed
36.
Pan P, Zhu L, Yu T, Shi H, Zhang B, Qin R, et al. Aberrant spontaneous low-frequency brain activity in amnestic mild cognitive impairment: a meta-analysis of resting-state fMRI studies. Ageing Res Rev. 2017;35:12–21.CrossRefPubMed
37.
Petersen RC, Doody R, Kurz A, Mohs RC, Morris JC, Rabins PV, et al. Current concepts in mild cognitive impairment. Arch Neurol. 2001;58:1985–92.CrossRefPubMed
38.
Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen E. Mild cognitive impairment: clinical characterization and outcome. Arch Neurol. 1999;56:303–8.CrossRefPubMed
39.
Winblad B, Palmer K, Kivipelto M, Jelic V, Fratiglioni L, Wahlund LO, et al. Mild cognitive impairment--beyond controversies, towards a consensus: report of the international working group on mild cognitive impairment. J Intern Med. 2004;256:240–6.CrossRefPubMed
40.
41.
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;7:270–9.CrossRefPubMedPubMedCentral
42.
Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson GD, Rennie D, et al. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis of observational studies in epidemiology (MOOSE) group. JAMA. 2000;283:2008–12.CrossRefPubMed
43.
Li W, Chen Z, Wu M, Zhu H, Gu L, Zhao Y, et al. Characterization of brain blood flow and the amplitude of low-frequency fluctuations in major depressive disorder: a multimodal meta-analysis. J Affect Disord. 2016;210:303–11.CrossRefPubMed
44.
Radua J, Grau M, van den Heuvel OA, Thiebaut de Schotten M, Stein DJ, Canales-Rodriguez EJ, et al. Multimodal voxel-based meta-analysis of white matter abnormalities in obsessive-compulsive disorder. Neuropsychopharmacology. 2014;39:1547–57.CrossRefPubMedPubMedCentral
45.
Sohn BK, Yi D, Seo EH, Choe YM, Kim JW, Kim SG, et al. Comparison of regional gray matter atrophy, white matter alteration, and glucose metabolism as a predictor of the conversion to Alzheimer’s disease in mild cognitive impairment. J Korean Med Sci. 2015;30:779–87.CrossRefPubMedPubMedCentral
46.
Jacobs HI, Van Boxtel MP, Jolles J, Verhey FR, Uylings HB. Parietal cortex matters in Alzheimer's disease: an overview of structural, functional and metabolic findings. Neurosci Biobehav Rev. 2012;36:297–309.CrossRefPubMed
47.
Cavanna AE, Trimble MR. The precuneus: a review of its functional anatomy and behavioural correlates. Brain. 2006;129:564–83.CrossRefPubMed
48.
Leech R, Sharp DJ. The role of the posterior cingulate cortex in cognition and disease. Brain. 2014;137:12–32.CrossRefPubMed
49.
Mak LE, Minuzzi L, MacQueen G, Hall G, Kennedy SH, Milev R. The default mode network in healthy individuals: a systematic review and meta-analysis. Brain Connectivity. 2017;7:25–33.CrossRefPubMed
50.
Fransson P, Marrelec G. The precuneus/posterior cingulate cortex plays a pivotal role in the default mode network: evidence from a partial correlation network analysis. NeuroImage. 2008;42:1178–84.CrossRefPubMed
51.
Schacter DL, Addis DR, Hassabis D, Martin VC, Spreng RN, Szpunar KK. The future of memory: remembering, imagining, and the brain. Neuron. 2012;76:677–94.CrossRefPubMed
52.
Garces P, Angel Pineda-Pardo J, Canuet L, Aurtenetxe S, Lopez ME, Marcos A, et al. The default mode network is functionally and structurally disrupted in amnestic mild cognitive impairment - a bimodal MEG-DTI study. NeuroImage Clinical. 2014;6:214–21.CrossRefPubMedPubMedCentral
53.
Cha J, Jo HJ, Kim HJ, Seo SW, Kim HS, Yoon U, et al. Functional alteration patterns of default mode networks: comparisons of normal aging, amnestic mild cognitive impairment and Alzheimer’s disease. Eur J Neurosci. 2013;37:1916–24.CrossRefPubMedPubMedCentral
54.
Pievani M, Pini L, Ferrari C, Pizzini FB, Boscolo Galazzo I, Cobelli C, et al. Coordinate-based meta-analysis of the default mode and salience network for target identification in non-invasive brain stimulation of Alzheimer’s disease and behavior variant frontotemporal dementia networks. J Alzheimers Dis. 2017; in press
55.
Addis DR, Wong AT, Schacter DL. Remembering the past and imagining the future: common and distinct neural substrates during event construction and elaboration. Neuropsychologia. 2007;45:1363–77.CrossRefPubMed
56.
Johannsen P, Jakobsen J, Gjedde A. Statistical maps of cerebral blood flow deficits in Alzheimer's disease. Eur J Neurol. 2000;7:385–92.CrossRefPubMed
57.
58.
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;9:119–28.CrossRefPubMedPubMedCentral
59.
Medina D, DeToledo-Morrell L, Urresta F, Gabrieli JD, Moseley M, Fleischman D, et al. White matter changes in mild cognitive impairment and AD: a diffusion tensor imaging study. Neurobiol Aging. 2006;27:663–72.CrossRefPubMed
60.
61.
Loewenstein DA, Barker WW, Chang JY, Apicella A, Yoshii F, Kothari P, et al. Predominant left hemisphere metabolic dysfunction in dementia. Arch Neurol. 1989;46:146–52.CrossRefPubMed
62.
Thompson PM, Mega MS, Woods RP, Zoumalan CI, Lindshield CJ, Blanton RE, et al. Cortical change in Alzheimer's disease detected with a disease-specific population-based brain atlas. Cereb Cortex. 2001;11:1–16.CrossRefPubMed
63.
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. 2003;23:994–1005.CrossRefPubMed
64.
Singh V, Chertkow H, Lerch JP, Evans AC, Dorr AE, Kabani NJ. Spatial patterns of cortical thinning in mild cognitive impairment and Alzheimer’s disease. Brain. 2006;129:2885–93.CrossRefPubMed
65.
Daianu M, Jahanshad N, Nir TM, Toga AW, Jack CR Jr, Weiner MW, et al. Breakdown of brain connectivity between normal aging and Alzheimer’s disease: a structural k-core network analysis. Brain Connect. 2013;3:407–22.CrossRefPubMedPubMedCentral
66.
Ferreira LK, Diniz BS, Forlenza OV, Busatto GF, Zanetti MV. Neurostructural predictors of Alzheimer’s disease: a meta-analysis of VBM studies. Neurobiol Aging. 2011;32:1733–41.CrossRefPubMed
67.
Kim JH, Lee JW, Kim GH, Roh JH, Kim MJ, Seo SW, et al. Cortical asymmetries in normal, mild cognitive impairment, and Alzheimer’s disease. Neurobiol Aging. 2012;33:1959–66.CrossRefPubMed
68.
Long X, Zhang L, Liao W, Jiang C, Qiu B. Distinct laterality alterations distinguish mild cognitive impairment and Alzheimer’s disease from healthy aging: statistical parametric mapping with high resolution MRI. Hum Brain Mapp. 2013;34:3400–10.CrossRefPubMed
69.
Barnes J, Scahill RI, Schott JM, Frost C, Rossor MN, Fox NC. Does Alzheimer’s disease affect hippocampal asymmetry? Evidence from a cross-sectional and longitudinal volumetric MRI study. Dement Geriatr Cogn Disord. 2005;19:338–44.CrossRefPubMed
70.
Eickhoff SB, Laird AR, Grefkes C, Wang LE, Zilles K, Fox PT. Coordinate-based activation likelihood estimation meta-analysis of neuroimaging data: a random-effects approach based on empirical estimates of spatial uncertainty. Hum Brain Mapp. 2009;30:2907–26.CrossRefPubMedPubMedCentral
71.
Turkeltaub PE, Eickhoff SB, Laird AR, Fox M, Wiener M, Fox P. Minimizing within-experiment and within-group effects in activation likelihood estimation meta-analyses. Hum Brain Mapp. 2012;33:1–13.CrossRefPubMed
72.
73.
Welton T, Kent D, Constantinescu CS, Auer DP, Dineen RA. Functionally relevant white matter degradation in multiple sclerosis: a tract-based spatial meta-analysis. Radiology. 2015;275:89–96.CrossRefPubMed
74.
75.
Bastin C, Kerrouche N, Lekeu F, Adam S, Guillaume B, Lemaire C, et al. Controlled memory processes in questionable Alzheimer’s disease: a view from neuroimaging research. J Alzheimers Dis. 2010;20:547–60.CrossRefPubMed
Metadata
Title
Cerebral glucose metabolic prediction from amnestic mild cognitive impairment to Alzheimer’s dementia: a meta-analysis
Authors
Hai Rong Ma
Li Qin Sheng
Ping Lei Pan
Gen Di Wang
Rong Luo
Hai Cun Shi
Zhen Yu Dai
Jian Guo Zhong
Publication date
01-12-2018
Publisher
BioMed Central
Published in
Translational Neurodegeneration / Issue 1/2018
Electronic ISSN: 2047-9158
DOI
https://doi.org/10.1186/s40035-018-0114-z

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