Prince M, Bryce R, Albanese E, Wimo A, Ribeiro W, Ferri CP. The global prevalence of dementia: a systematic review and meta-analysis. Alzheimers Dement. 2013;9(1):63–75. e2.
Article
PubMed
Google Scholar
Alzheimer A. About a peculiar disease of the cortex. Gen J Psychiatry Psychiatr Leg Med. 1907;64(1-2):146–8.
Serrano-Pozo A, Frosch MP, Masliah E, Hyman BT. Neuropathological alterations in Alzheimer disease. Cold Spring Harb Perspect Med. 2011;1(1):a006189.
Article
PubMed
PubMed Central
Google Scholar
Ayton S, Faux NG, Bush AI. Alzheimer’s Disease Neuroimaging I. Ferritin levels in the cerebrospinal fluid predict Alzheimer’s disease outcomes and are regulated by APOE. Nat Commun. 2015;6:6760.
Article
CAS
PubMed
PubMed Central
Google Scholar
Bartzokis G, Sultzer D, Mintz J, et al. In vivo evaluation of brain iron in Alzheimer’s disease and normal subjects using MRI. Biol Psychiatry. 1994;35(7):480–7.
Article
CAS
PubMed
Google Scholar
Liu B, Moloney A, Meehan S, et al. Iron promotes the toxicity of amyloid beta peptide by impeding its ordered aggregation. J Biol Chem. 2011;286(6):4248–56.
Article
CAS
PubMed
Google Scholar
Meadowcroft MD, Connor JR, Smith MB, Yang QX. MRI and histological analysis of beta-amyloid plaques in both human Alzheimer’s disease and APP/PS1 transgenic mice. J Magn Reson Imaging. 2009;29(5):997–1007.
Article
PubMed
PubMed Central
Google Scholar
van Bergen JM, Li X, Hua J, et al. Colocalization of cerebral iron with amyloid beta in mild cognitive impairment. Sci Rep. 2016;6:35514.
Article
PubMed
PubMed Central
Google Scholar
Wood H. Alzheimer disease: iron—the missing link between ApoE and Alzheimer disease? Nat Rev Neurol. 2015;11(7):369.
Article
CAS
PubMed
Google Scholar
Albert MS. Changes in cognition. Neurobiol Aging. 2011;32 Suppl 1:S58–63.
Article
PubMed
PubMed Central
Google Scholar
Elias MF, Beiser A, Wolf PA, Au R, White RF, D’Agostino RB. The preclinical phase of Alzheimer disease: a 22-year prospective study of the Framingham Cohort. Arch Neurol. 2000;57(6):808–13.
Article
CAS
PubMed
Google Scholar
Fabrigoule C, Rouch I, Taberly A, et al. Cognitive process in preclinical phase of dementia. Brain. 1998;121(Pt 1):135–41.
Article
PubMed
Google Scholar
Small BJ, Herlitz A, Fratiglioni L, Almkvist O, Backman L. Cognitive predictors of incident Alzheimer’s disease: a prospective longitudinal study. Neuropsychology. 1997;11(3):413–20.
Article
CAS
PubMed
Google Scholar
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.
Article
PubMed
PubMed Central
Google Scholar
Dubois B, Hampel H, Feldman HH, et al. Preclinical Alzheimer’s disease: definition, natural history, and diagnostic criteria. Alzheimers Dement. 2016;12(3):292–323.
Article
PubMed
Google Scholar
Bertram L, Lill CM, Tanzi RE. The genetics of Alzheimer disease: back to the future. Neuron. 2010;68(2):270–81.
Article
CAS
PubMed
Google Scholar
Corder EH, Saunders AM, Strittmatter WJ, et al. Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families. Science. 1993;261(5123):921–3.
Article
CAS
PubMed
Google Scholar
Huang XT, Qian ZM, He X, et al. Reducing iron in the brain: a novel pharmacologic mechanism of huperzine A in the treatment of Alzheimer’s disease. Neurobiol Aging. 2014;35(5):1045–54.
Article
CAS
PubMed
Google Scholar
Liu CC, Kanekiyo T, Xu H, Bu G. Apolipoprotein E and Alzheimer disease: risk, mechanisms and therapy. Nat Rev Neurol. 2013;9(2):106–18.
Article
CAS
PubMed
PubMed Central
Google Scholar
Ward A, Crean S, Mercaldi CJ, et al. Prevalence of apolipoprotein E4 genotype and homozygotes (APOE e4/4) among patients diagnosed with Alzheimer’s disease: a systematic review and meta-analysis. Neuroepidemiology. 2012;38(1):1–17.
Article
PubMed
Google Scholar
Klunk WE, Engler H, Nordberg A, et al. Imaging brain amyloid in Alzheimer’s disease with Pittsburgh Compound-B. Ann Neurol. 2004;55(3):306–19.
Article
CAS
PubMed
Google Scholar
Mathis CA, Wang Y, Holt DP, Huang GF, Debnath ML, Klunk WE. Synthesis and evaluation of 11C-labeled 6-substituted 2-arylbenzothiazoles as amyloid imaging agents. J Med Chem. 2003;46(13):2740–54.
Article
CAS
PubMed
Google Scholar
Jagust WJ, Landau SM, Shaw LM, et al. Relationships between biomarkers in aging and dementia. Neurology. 2009;73(15):1193–9.
Article
CAS
PubMed
PubMed Central
Google Scholar
Ossenkoppele R, Jansen WJ, Rabinovici GD, et al. Prevalence of amyloid PET positivity in dementia syndromes: a meta-analysis. JAMA. 2015;313(19):1939–49.
Article
PubMed
PubMed Central
Google Scholar
Lorenzi M, Donohue M, Paternico D, et al. Enrichment through biomarkers in clinical trials of Alzheimer’s drugs in patients with mild cognitive impairment. Neurobiol Aging. 2010;31(8):1443–51. 51 e1.
Article
CAS
PubMed
Google Scholar
Deistung A, Schafer A, Schweser F, Biedermann U, Turner R, Reichenbach JR. Toward in vivo histology: a comparison of quantitative susceptibility mapping (QSM) with magnitude-, phase-, and R2*-imaging at ultra-high magnetic field strength. Neuroimage. 2013;65:299–314.
Article
PubMed
Google Scholar
Langkammer C, Schweser F, Krebs N, et al. Quantitative susceptibility mapping (QSM) as a means to measure brain iron? A post mortem validation study. Neuroimage. 2012;62(3):1593–9.
Article
PubMed
PubMed Central
Google Scholar
Li X, Vikram DS, Lim IA, Jones CK, Farrell JA, van Zijl PC. Mapping magnetic susceptibility anisotropies of white matter in vivo in the human brain at 7 T. Neuroimage. 2012;62(1):314–30.
Article
PubMed
PubMed Central
Google Scholar
van Bergen JMG, Hua J, Unschuld PG, et al. Quantitative susceptibility mapping suggests altered brain iron in premanifest Huntington’s disease. Am J Neuroradiol. 2015. In press. doi:10.3174/ajnr.A4617.
Ogawa S, Lee TM, Kay AR, Tank DW. Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci U S A. 1990;87(24):9868–72.
Article
CAS
PubMed
PubMed Central
Google Scholar
Worsley KJ, Poline JB, Vandal AC, Friston KJ. Tests for distributed, nonfocal brain activations. Neuroimage. 1995;2(3):183–94.
Article
CAS
PubMed
Google Scholar
Donahue MJ, Hoogduin H, Smith SM, et al. Spontaneous blood oxygenation level-dependent fMRI signal is modulated by behavioral state and correlates with evoked response in sensorimotor cortex: a 7.0-T fMRI study. Hum Brain Mapp. 2011;33:511–22.
Donahue MJ, Hoogduin H, van Zijl P, Jezzard P, Luijten PR, Hendrikse J. Blood oxygenation level-dependent (BOLD) total and extravascular signal changes and dR2* in human visual cortex at 1.5, 3.0 and 7.0 T. NMR Biomed. 2010;23:1–11.
Article
Google Scholar
Lenglet C, Abosch A, Yacoub E, De Martino F, Sapiro G, Harel N. Comprehensive in vivo mapping of the human basal ganglia and thalamic connectome in individuals using 7T MRI. PLoS One. 2012;7(1):e29153.
Article
CAS
PubMed
PubMed Central
Google Scholar
Theysohn N, Qin S, Maderwald S, et al. Memory-related hippocampal activity can be measured robustly using FMRI at 7 tesla. J Neuroimaging. 2013;23(4):445–51.
Article
PubMed
Google Scholar
Hua J, Qin Q, van Zijl PC, Pekar JJ, Jones CK. Whole-brain three-dimensional T2-weighted BOLD functional magnetic resonance imaging at 7 Tesla. Magn Reson Med. 2014;72(6):1530–40.
Article
PubMed
Google Scholar
Friston KJ, Frith CD, Liddle PF, Frackowiak RS. Functional connectivity: the principal-component analysis of large (PET) data sets. J Cereb Blood Flow Metab. 1993;13(1):5–14.
Article
CAS
PubMed
Google Scholar
Buckner RL, Snyder AZ, Shannon BJ, et al. Molecular, structural, and functional characterization of Alzheimer’s disease: evidence for a relationship between default activity, amyloid, and memory. J Neurosci. 2005;25(34):7709–17.
Article
CAS
PubMed
Google Scholar
Greicius MD, Srivastava G, Reiss AL, Menon V. 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.
Article
CAS
PubMed
PubMed Central
Google Scholar
Toussaint PJ, Maiz S, Coynel D, et al. Characteristics of the default mode functional connectivity in normal ageing and Alzheimer’s disease using resting state fMRI with a combined approach of entropy-based and graph theoretical measurements. Neuroimage. 2014;101:778–86.
Article
PubMed
Google Scholar
Schreiner SJ, Liu X, Gietl AF, et al. Regional fluid-attenuated inversion recovery (FLAIR) at 7 Tesla correlates with amyloid beta in hippocampus and brainstem of cognitively normal elderly subjects. Front Aging Neurosci. 2014;6:240.
Article
PubMed
PubMed Central
Google Scholar
Allen EA, Damaraju E, Plis SM, Erhardt EB, Eichele T, Calhoun VD. Tracking whole-brain connectivity dynamics in the resting state. Cereb Cortex. 2014;24(3):663–76.
Article
PubMed
Google Scholar
Chang C, Glover GH. Time-frequency dynamics of resting-state brain connectivity measured with fMRI. Neuroimage. 2010;50(1):81–98.
Article
PubMed
Google Scholar
Handwerker DA, Roopchansingh V, Gonzalez-Castillo J, Bandettini PA. Periodic changes in fMRI connectivity. Neuroimage. 2012;63(3):1712–9.
Article
PubMed
PubMed Central
Google Scholar
Leonardi N, Richiardi J, Gschwind M, et al. Principal components of functional connectivity: a new approach to study dynamic brain connectivity during rest. Neuroimage. 2013;83:937–50.
Article
PubMed
Google Scholar
Seeley WW, Crawford RK, Zhou J, Miller BL, Greicius MD. Neurodegenerative diseases target large-scale human brain networks. Neuron. 2009;62(1):42–52.
Article
CAS
PubMed
PubMed Central
Google Scholar
Sheline YI, Raichle ME, Snyder AZ, et al. Amyloid plaques disrupt resting state default mode network connectivity in cognitively normal elderly. Biol Psychiatry. 2010;67(6):584–7.
Article
CAS
PubMed
Google Scholar
Sperling RA, Laviolette PS, O’Keefe K, et al. Amyloid deposition is associated with impaired default network function in older persons without dementia. Neuron. 2009;63(2):178–88.
Article
CAS
PubMed
PubMed Central
Google Scholar
Buckner RL, Sepulcre J, Talukdar T, et al. Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer’s disease. J Neurosci. 2009;29(6):1860–73.
Article
CAS
PubMed
PubMed Central
Google Scholar
Elman JA, Madison CM, Baker SL, et al. Effects of beta-amyloid on resting state functional connectivity within and between networks reflect known patterns of regional vulnerability. Cereb Cortex. 2016;26(2):695–707.
PubMed
Google Scholar
Gietl AF, Warnock G, Riese F, et al. Regional cerebral blood flow estimated by early PiB uptake is reduced in mild cognitive impairment and associated with age in an amyloid-dependent manner. Neurobiol Aging. 2015;36:1619–28
World_Medical_Association. Declaration of Helsinki. Law Med Health Care. 1991;19(3–4):264–5.
Google Scholar
Steininger SC, Liu X, Gietl A, et al. Cortical amyloid beta in cognitively normal elderly adults is associated with decreased network efficiency within the cerebro-cerebellar system. Front Aging Neurosci. 2014;6:52.
Article
PubMed
PubMed Central
Google Scholar
Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12(3):189–98.
Article
CAS
PubMed
Google Scholar
Härting C, Markowitsch HJ, Neufeld H, Calabrese P, Diesinger K, Kessler J. Wechsler Memory Test—Revised version (WMS-R). Bern: Huber; 2000.
Helmstaedter C, Durwen HF. The Verbal Learning and Retention Test. A useful and differentiated tool in evaluating verbal memory performance. Schweiz Arch Neurol Psychiatr. 1990;141(1):21–30.
CAS
PubMed
Google Scholar
Nicholas LE, Brookshire RH, MacLennan DL, Schumacher JG, Porrazzo SA. The Boston Naming Test: revised administration and scoring procedures and normative information for non-brain-damaged adults. Clinical Aphasiology. 1988;18:103–15.
Google Scholar
Thalmann B, Monsch AU, Bernasconi F, et al. CERAD—Consortium to Establish a Registry for Alzheimer’s Disease Assessment Battery—deutsche Fassung. Basel: Geriatrische Universitätsklinik; 1997.
Google Scholar
Tombaugh TN. Trail Making Test A and B: normative data stratified by age and education. Arch Clin Neuropsychol. 2004;19(2):203–14.
Article
PubMed
Google Scholar
Marques JP, Kober T, Krueger G, van der Zwaag W, Van de Moortele PF, Gruetter R. MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field. Neuroimage. 2010;49(2):1271–81.
Article
PubMed
Google Scholar
Richiardi J, Gschwind M, Simioni S, Annoni JM, Greco B, Hagmann P, Schluep M, Vuilleumier P, Van De Ville D. Classifying minimally disabled multiple sclerosis patients from resting state functional connectivity. NeuroImage. 2012;62:2021–33.
Article
PubMed
Google Scholar
Chao-Gan Y, Yu-Feng Z. DPARSF: a MATLAB toolbox for “pipeline” data analysis of resting-state fMRI. Front Syst Neurosci. 2010;4:13.
PubMed
PubMed Central
Google Scholar
Tzourio-Mazoyer N, Landeau B, Papathanassiou D, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. 2002;15(1):273–89.
Article
CAS
PubMed
Google Scholar
Leonardi N, Van De Ville D. On spurious and real fluctuations of dynamic functional connectivity during rest. Neuroimage. 2015;104:430–6.
Article
PubMed
Google Scholar
Krishnan A, Williams LJ, McIntosh AR, Abdi H. Partial least squares (PLS) methods for neuroimaging: a tutorial and review. Neuroimage. 2011;56(2):455–75.
Article
PubMed
Google Scholar
Schonemann PH. On two-sided orthogonal Procrustes problems. Psychometrika. 1968;33(1):19–33.
Article
CAS
PubMed
Google Scholar
Sporns O. Networks of the brain. Cambridge: MIT Press; 2011. p. 9–17.
Google Scholar
Holm S. A simple sequentially rejective Bonferroni test procedure. Scand J Stat. 1979;6:65–70.
Google Scholar
Storey JD. A direct approach to false discovery rates. J R Statist Soc B. 2002;64(3):479–98.
Article
Google Scholar
Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. Mahwah: Lawrence Erlbaum Associates; 1988.
Google Scholar
Mintun MA, Larossa GN, Sheline YI, et al. [11C]PIB in a nondemented population: potential antecedent marker of Alzheimer disease. Neurology. 2006;67(3):446–52.
Article
CAS
PubMed
Google Scholar
Jack Jr CR, 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.
Article
PubMed
PubMed Central
Google Scholar
Frisoni GB, Bocchetta M, Chetelat G, et al. Imaging markers for Alzheimer disease: which vs how. Neurology. 2013;81(5):487–500.
Article
CAS
PubMed
PubMed Central
Google Scholar
Sorg C, Riedl V, Muhlau M, et al. Selective changes of resting-state networks in individuals at risk for Alzheimer’s disease. Proc Natl Acad Sci U S A. 2007;104(47):18760–5.
Article
CAS
PubMed
PubMed Central
Google Scholar
Nyberg L, Lovden M, Riklund K, Lindenberger U, Backman L. Memory aging and brain maintenance. Trends Cogn Sci. 2012;16(5):292–305.
Article
PubMed
Google Scholar
Schaie KW. When does age-related cognitive decline begin? Salthouse again reifies the cross-sectional fallacy. Neurobiol Aging. 2009;30(4):528–9. discussion 530–3.
Article
PubMed
PubMed Central
Google Scholar
Machulda MM, Pankratz VS, Christianson TJ, et al. Practice effects and longitudinal cognitive change in normal aging vs. incident mild cognitive impairment and dementia in the Mayo Clinic Study of Aging. Clin Neuropsychol. 2013;27(8):1247–64.
Article
PubMed
Google Scholar
Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U S A. 2005;102(27):9673–8.
Article
CAS
PubMed
PubMed Central
Google Scholar
Damoiseaux JS, Rombouts SA, Barkhof F, et al. Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci U S A. 2006;103(37):13848–53.
Article
CAS
PubMed
PubMed Central
Google Scholar
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(3):270–9.
Article
PubMed
PubMed Central
Google Scholar
Altamura S, Muckenthaler MU. Iron toxicity in diseases of aging: Alzheimer’s disease, Parkinson’s disease and atherosclerosis. J Alzheimers Dis. 2009;16(4):879–95.
PubMed
Google Scholar
De Reuck JL, Deramecourt V, Auger F, et al. Iron deposits in post-mortem brains of patients with neurodegenerative and cerebrovascular diseases: a semi-quantitative 7.0 T magnetic resonance imaging study. Eur J Neurol. 2014;21(7):1026–31.
Article
PubMed
Google Scholar
Jellinger KA. The relevance of metals in the pathophysiology of neurodegeneration, pathological considerations. Int Rev Neurobiol. 2013;110:1–47.
Article
CAS
PubMed
Google Scholar
Kruer MC. The neuropathology of neurodegeneration with brain iron accumulation. Int Rev Neurobiol. 2013;110:165–94.
Article
CAS
PubMed
PubMed Central
Google Scholar
Nunez MT, Urrutia P, Mena N, Aguirre P, Tapia V, Salazar J. Iron toxicity in neurodegeneration. Biometals. 2012;25(4):761–76.
Article
CAS
PubMed
Google Scholar
Chuang JY, Lee CW, Shih YH, Yang T, Yu L, Kuo YM. Interactions between amyloid-beta and hemoglobin: implications for amyloid plaque formation in Alzheimer’s disease. PLoS One. 2012;7(3):e33120.
Article
CAS
PubMed
PubMed Central
Google Scholar
Grundke-Iqbal I, Fleming J, Tung YC, Lassmann H, Iqbal K, Joshi JG. Ferritin is a component of the neuritic (senile) plaque in Alzheimer dementia. Acta Neuropathol. 1990;81(2):105–10.
Article
CAS
PubMed
Google Scholar
Ill-Raga G, Ramos-Fernandez E, Guix FX, et al. Amyloid-beta peptide fibrils induce nitro-oxidative stress in neuronal cells. J Alzheimers Dis. 2010;22(2):641–52.
CAS
PubMed
Google Scholar
Jagust WJ, Mormino EC. Lifespan brain activity, beta-amyloid, and Alzheimer’s disease. Trends Cogn Sci. 2011;15(11):520–6.
Article
PubMed
PubMed Central
Google Scholar
Becerril-Ortega J, Bordji K, Freret T, Rush T, Buisson A. Iron overload accelerates neuronal amyloid-beta production and cognitive impairment in transgenic mice model of Alzheimer’s disease. Neurobiol Aging. 2014;35(10):2288–301.
Article
CAS
PubMed
Google Scholar
Bush AI. The metal theory of Alzheimer’s disease. J Alzheimers Dis. 2013;33 Suppl 1:S277–81.
PubMed
Google Scholar
Villemagne VL, Pike KE, Chetelat G, et al. Longitudinal assessment of Abeta and cognition in aging and Alzheimer disease. Ann Neurol. 2011;69(1):181–92.
Article
CAS
PubMed
PubMed Central
Google Scholar
Mormino EC, Betensky RA, Hedden T, et al. Synergistic effect of beta-amyloid and neurodegeneration on cognitive decline in clinically normal individuals. JAMA Neurol. 2014;71(11):1379–85.
Article
PubMed
PubMed Central
Google Scholar