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Impact of the IWG/Dubois Criteria for Alzheimer’s Disease in Imaging Studies

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Abstract

In 2007, an International Working Group (IWG) provided a new conceptual framework according to which AD moves from a clinicopathological entity to a clinico-biological entity (Dubois et al. 2007). The new IWG/Dubois criteria stipulate that AD can be recognized in vivo on the presence of two associated features. The first is the evidence of an “amnestic syndrome of the hippocampal type” at least in the typical form of the disease. The importance of a specific memory pattern was highlighted because none of the other cognitive changes, which can be encountered in AD even at a prodromal stage, are specific of the disease. The second necessary feature is supportive evidence from biomarkers that were proposed for the first time for the diagnosis of AD. The biomarkers of AD were divided into two groups: (1) the pathophysiological markers (these markers identify AD pathology since they are strongly correlated with postmortem AD histopathological changes, and they are considered as markers of diagnosis and mainly consist in positive PET amyloid scan results or CSF changes) and (2) topographical markers (they reflect downstream damage and are rather markers of progression, more targeted at assessing change over time and predicting outcomes). They mainly consist in hippocampal atrophy on volumetric MRI or hypometabolism on fluorodeoxyglucose [FDG]-PET. As a consequence, CSF and MRI investigations are no longer proposed for excluding other etiologies of brain dysfunction but are primarily used for detecting AD-related changes. The added value of biomarkers and therefore the specificity of the IWG/Dubois criteria for the diagnosis of AD were further confirmed.

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References

  • American Psychiatric Association (2000) Diagnostic and statistical manual of mental disorders. American Psychiatric Association, Washington, DC

    Google Scholar 

  • Barkhof F et al (2007) The significance of medial temporal lobe atrophy: a postmortem MRI study in the very old. Neurology 69:1521–1527

    Article  CAS  PubMed  Google Scholar 

  • Bennett DA, Wilson RS, Boyle PA, Buchman AS, Schneider JA (2012) Relation of neuropathology to cognition in persons without cognitive impairment. Ann Neurol 72(4):599–609

    Article  PubMed Central  PubMed  Google Scholar 

  • Blennow K et al (2010) Cerebrospinal fluid and plasma biomarkers in Alzheimer disease. Nat Rev Neurol 6(3):131–144

    Article  CAS  PubMed  Google Scholar 

  • Bouwman FH et al (2010) New research criteria for the diagnosis of Alzheimer’s disease applied in a memory clinic population. Dement Geriatr Cogn Disord 30:1–7

    Article  CAS  PubMed  Google Scholar 

  • Braak H, Braak E (1991) Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol 82:239–259

    Article  CAS  PubMed  Google Scholar 

  • Buckner RL et al (2009) Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer’s disease. J Neurosci 29:1860–1873

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Buerger K et al (2006) CSF phosphorylated tau protein correlates with neocortical neurofibrillary pathology in Alzheimer’s disease. Brain 129:3035–3041

    Article  PubMed  Google Scholar 

  • Cairns NJ et al (2009) Absence of Pittsburgh compound B detection of cerebral amyloid beta in a patient with clinical, cognitive, and cerebrospinal fluid markers of Alzheimer disease: a case report. Arch Neurol 66:1557–1562

    PubMed Central  PubMed  Google Scholar 

  • Caramelli P et al (2011) The Pieta study: epidemiological investigation on successful brain aging in Caete (MG). Brazil. Methods and baseline cohort characteristics. Arq Neuropsiquiatr 69:579–584

    Article  PubMed  Google Scholar 

  • Chiu HF, Lam LC (2007) Relevance of outcome measures in different cultural groups–does one size fit all? Int Psychogeriatr 19:457–466

    Article  PubMed  Google Scholar 

  • Clark CM et al (2011) Use of florbetapir-PET for imaging beta-amyloid pathology. JAMA 305:275–283

    Article  CAS  PubMed  Google Scholar 

  • de Jager CA et al (2010) Retrospective evaluation of revised criteria for the diagnosis of Alzheimer’s disease using a cohort with post-mortem diagnosis. Int J Geriatr Psychiatry 25:988–997

    Article  PubMed  Google Scholar 

  • de Souza LC et al (2011) Cerebrospinal fluid biomarkers in the differential diagnosis of Alzheimer’s disease from other cortical dementias. J Neurol Neurosurg Psychiatry 82:240–246

    Article  PubMed  Google Scholar 

  • Delacourte A et al (1999) The biochemical pathway of neurofibrillary degeneration in aging and Alzheimer’s disease. Neurology 52:1158–1165

    Article  CAS  PubMed  Google Scholar 

  • den Heijer T et al (2010) A 10-year follow-up of hippocampal volume on magnetic resonance imaging in early dementia and cognitive decline. Brain 133:1163–1172

    Article  Google Scholar 

  • Devanand DP, Pelton GH, Zamora D, Liu X, Tabert MH, Goodkind M, Scarmeas N, Braun I, Stern Y, Mayeux R (2005) Predictive utility of apolipoprotein E genotype for Alzheimer disease in outpatients with mild cognitive impairment. Arch Neurol 62(6):975–980

    CAS  PubMed  Google Scholar 

  • Dickerson BC, Wolk DA, Alzheimer’s Disease Neuroimaging, I (2012) MRI cortical thickness biomarker predicts AD-like CSF and cognitive decline in normal adults. Neurology 78:84–90

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Dubois B, Albert ML (2004) Amnestic MCI or prodromal Alzheimer’s disease? Lancet Neurol 3:246–248

    Article  PubMed  Google Scholar 

  • Dubois B et al (2007) Research criteria for the diagnosis of Alzheimer’s disease: revising the NINCDS-ADRDA criteria. Lancet Neurol 6:734–746

    Article  PubMed  Google Scholar 

  • Dubois B et al (2010) Revising the definition of Alzheimer’s disease: a new lexicon. Lancet Neurol 9:1118–1127

    Article  PubMed  Google Scholar 

  • Engelborghs S et al (2008) Diagnostic performance of a CSF-biomarker panel in autopsy-confirmed dementia. Neurobiol Aging 29:1143–1159

    Article  PubMed  Google Scholar 

  • Fagan AM et al (2009) Decreased cerebrospinal fluid Abeta (42) correlates with brain atrophy in cognitively normal elderly. Ann Neurol 65:176–183

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Fagan AM et al (2011) Comparison of analytical platforms for cerebrospinal fluid measures of beta-amyloid 1–42, total tau, and p-tau181 for identifying Alzheimer disease amyloid plaque pathology. Arch Neurol 68:1137–1144

    Article  PubMed Central  PubMed  Google Scholar 

  • Fayed N et al (2008) Utility of different MR modalities in mild cognitive impairment and its use as a predictor of conversion to probable dementia. Acad Radiol 15:1089–1098

    Article  PubMed  Google Scholar 

  • Forsberg A et al (2010) High PIB retention in Alzheimer’s disease is an early event with complex relationship with CSF biomarkers and functional parameters. Curr Alzheimer Res 7:56–66

    Article  CAS  PubMed  Google Scholar 

  • Fotuhi M, Do D, Jack C (2012) Modifiable factors that alter the size of the hippocampus with ageing. Nat Rev Neurol 8:189–202

    CAS  PubMed  Google Scholar 

  • Galluzzi S et al (2010) The new Alzheimer’s criteria in a naturalistic series of patients with mild cognitive impairment. J Neurol 257:2004–2014

    Article  CAS  PubMed  Google Scholar 

  • Galton CJ et al (2001) Temporal lobe rating scale: application to Alzheimer’s disease and frontotemporal dementia. J Neurol Neurosurg Psychiatry 70:165–173

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Giedd JN et al (1995) Reliability of cerebral measures in repeated examinations with magnetic resonance imaging. Psychiatry Res 61:113–119

    Article  CAS  PubMed  Google Scholar 

  • Gomar JJ et al (2011) 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 68:961–969

    Article  PubMed  Google Scholar 

  • Hampel H et al (2008) Core candidate neurochemical and imaging biomarkers of Alzheimer’s disease. Alzheimers Dement 4:38–48

    Article  CAS  PubMed  Google Scholar 

  • Hansson O et al (2006) Association between CSF biomarkers and incipient Alzheimer’s disease in patients with mild cognitive impairment: a follow-up study. Lancet Neurol 5:228–234

    Article  CAS  PubMed  Google Scholar 

  • Harold D et al (2009) Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer’s disease. Nat Genet 41:1088–1093

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Ikonomovic MD et al (2008) Post-mortem correlates of in vivo PiB-PET amyloid imaging in a typical case of Alzheimer’s disease. Brain 131:1630–1645

    Article  PubMed Central  PubMed  Google Scholar 

  • Isaac M et al (2011) Qualification opinion of novel methodologies in the predementia stage of Alzheimer’s disease: cerebro-spinal-fluid related biomarkers for drugs affecting amyloid burden–regulatory considerations by European Medicines Agency focusing in improving benefit/risk in regulatory trials. Eur Neuropsychopharmacol 21:781–788

    Article  CAS  PubMed  Google Scholar 

  • Jack CR Jr et al (2010a) Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol 9:119–128

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Jack CR Jr et al (2010b) Brain beta-amyloid measures and magnetic resonance imaging atrophy both predict time-to-progression from mild cognitive impairment to Alzheimer’s disease. Brain 133:3336–3348

    Article  PubMed Central  PubMed  Google Scholar 

  • Jack CR Jr et al (2011) Introduction to the recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7:257–262

    Article  PubMed Central  PubMed  Google Scholar 

  • Jagust W (2009) Amyloid + activation = Alzheimer’s? Neuron 63:141–143

    Article  CAS  PubMed  Google Scholar 

  • Jagust WJ et al (2009) Relationships between biomarkers in aging and dementia. Neurology 73:1193–1199

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Jelic V, Wahlund LO (2007) Diagnostic imaging devices in Alzheimer’s disease. Expert Rev Med Devices 4:475–487

    Article  PubMed  Google Scholar 

  • Jicha GA et al (2006) Neuropathologic outcome of mild cognitive impairment following progression to clinical dementia. Arch Neurol 63:674–681

    Article  PubMed  Google Scholar 

  • Johnson KA et al (2012) Brain imaging in Alzheimer disease. Cold Spring Harb Perspect Med 2(4):a006213

    Article  PubMed Central  PubMed  Google Scholar 

  • Kantarci K (2007) 1H magnetic resonance spectroscopy in dementia. Br J Radiol 80(Spec No 2):S146–S152

    Article  CAS  PubMed  Google Scholar 

  • Koivunen J et al (2011) Amyloid PET imaging in patients with mild cognitive impairment: a 2-year follow-up study. Neurology 76:1085–1090

    Article  CAS  PubMed  Google Scholar 

  • Lambert JC et al (2009) Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer’s disease. Nat Genet 41:1094–1099

    Article  CAS  PubMed  Google Scholar 

  • Lo RY et al (2011) Longitudinal change of biomarkers in cognitive decline. Arch Neurol 68:1257–1266

    Article  PubMed  Google Scholar 

  • Magnin B et al (2009) Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI. Neuroradiology 51:73–83

    Article  PubMed  Google Scholar 

  • Mattsson N et al (2009) CSF biomarkers and incipient Alzheimer disease in patients with mild cognitive impairment. JAMA 302:385–393

    Article  CAS  PubMed  Google Scholar 

  • Mattsson N et al (2011) The Alzheimer’s Association external quality control program for cerebrospinal fluid biomarkers. Alzheimers Dement 7(386–395):e6

    PubMed  Google Scholar 

  • McKhann G et al (1984) Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology 34:939–944

    Article  CAS  PubMed  Google Scholar 

  • McKhann GM et al (2011) 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 7:263–269

    Article  PubMed Central  PubMed  Google Scholar 

  • Modrego PJ (2006) Predictors of conversion to dementia of probable Alzheimer type in patients with mild cognitive impairment. Curr Alzheimer Res 3(2):161–170

    Article  CAS  PubMed  Google Scholar 

  • Morris JC et al (2009) Pittsburgh compound B imaging and prediction of progression from cognitive normality to symptomatic Alzheimer disease. Arch Neurol 66:1469–1475

    PubMed Central  PubMed  Google Scholar 

  • Mosconi L (2005) 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 32:486–510

    Article  CAS  PubMed  Google Scholar 

  • O’Brien RJ, Resnick SM, Zonderman AB, Ferrucci L, Crain BJ, Pletnikova O, Rudow G, Iacono D, Riudavets MA, Driscoll I, Price DL, Martin LJ, Troncoso JC (2009) Neuropathologic studies of the Baltimore Longitudinal Study of Aging (BLSA). J Alzheimers Dis 18(3):665–675

    PubMed Central  PubMed  Google Scholar 

  • Okello A et al (2009) Conversion of amyloid positive and negative MCI to AD over 3 years: an 11C-PIB PET study. Neurology 73:754–760

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Ossenkoppele R et al (2012) Longitudinal imaging of Alzheimer pathology using [11C] PIB, [18 F] FDDNP and [18 F] FDG PET. Eur J Nucl Med Mol Imaging 39:990–1000

    Article  CAS  PubMed  Google Scholar 

  • Petersen RC et al (1999) Mild cognitive impairment: clinical characterization and outcome. Arch Neurol 56:303–308

    Article  CAS  PubMed  Google Scholar 

  • Qi Z et al (2010) Impairment and compensation coexist in amnestic MCI default mode network. Neuroimage 50:48–55

    Article  PubMed  Google Scholar 

  • Rami L et al (2012) Applying the new research diagnostic criteria: MRI findings and neuropsychological correlations of prodromal AD. Int J Geriatr Psychiatry 27:127–134

    Article  PubMed  Google Scholar 

  • Reiman EM et al (2004) Functional brain abnormalities in young adults at genetic risk for late-onset alzheimer’s dementia. Proc Natl Acad Sci U S A 101:284–289

    Google Scholar 

  • Resnick SM et al (2010) Longitudinal cognitive decline is associated with fibrillar amyloid-beta measured by [11C] PiB. Neurology 74:807–815

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Risacher SL et al (2009) Baseline MRI predictors of conversion from MCI to probable AD in the ADNI cohort. Curr Alzheimer Res 6:347–361

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Scheltens P et al (2012) Bapineuzumab IV phase 3 results. Abstracts of 5th conference “clinical trials in Alzheimer’s disease”. J Nutr Health Aging 16:797

    Google Scholar 

  • Schmand B et al (2011) Value of neuropsychological tests, neuroimaging, and biomarkers for diagnosing Alzheimer’s disease in younger and older age cohorts. J Am Geriatr Soc 59:1705–1710

    Article  PubMed  Google Scholar 

  • Schoonenboom NS et al (2008) CSF and MRI markers independently contribute to the diagnosis of Alzheimer’s disease. Neurobiol Aging 29:669–675

    Article  CAS  PubMed  Google Scholar 

  • Schoonenboom NS et al (2012) Cerebrospinal fluid markers for differential dementia diagnosis in a large memory clinic cohort. Neurology 78:47–54

    Article  CAS  PubMed  Google Scholar 

  • Seppala TT et al (2012) CSF biomarkers for Alzheimer disease correlate with cortical brain biopsy findings. Neurology 78:1568–1575

    Article  CAS  PubMed  Google Scholar 

  • Shaw LM et al (2009) Cerebrospinal fluid biomarker signature in Alzheimer’s disease neuroimaging initiative subjects. Ann Neurol 65:403–413

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Sperling RA et al (2011) 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 7:280–292

    Article  PubMed Central  PubMed  Google Scholar 

  • Stomrud E et al (2007) Cerebrospinal fluid biomarkers predict decline in subjective cognitive function over 3 years in healthy elderly. Dement Geriatr Cogn Disord 24:118–124

    Article  CAS  PubMed  Google Scholar 

  • Strozyk D et al (2003) CSF Abeta 42 levels correlate with amyloid-neuropathology in a population-based autopsy study. Neurology 60:652–656

    Article  CAS  PubMed  Google Scholar 

  • Tapiola T et al (2009) Cerebrospinal fluid {beta}-amyloid 42 and tau proteins as biomarkers of Alzheimer-type pathologic changes in the brain. Arch Neurol 66:382–389

    PubMed  Google Scholar 

  • Tolboom N et al (2009) Relationship of cerebrospinal fluid markers to 11C-PiB and 18F-FDDNP binding. J Nucl Med 50:1464–1470

    Article  CAS  PubMed  Google Scholar 

  • van de Pol LA et al (2006) Hippocampal atrophy on MRI in frontotemporal lobar degeneration and Alzheimer’s disease. J Neurol Neurosurg Psychiatry 77:439–442

    Article  PubMed Central  PubMed  Google Scholar 

  • Varma AR et al (1999) Evaluation of the NINCDS-ADRDA criteria in the differentiation of Alzheimer’s disease and frontotemporal dementia. J Neurol Neurosurg Psychiatry 66:184–188

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Verwey NA et al (2009) A worldwide multicentre comparison of assays for cerebrospinal fluid biomarkers in Alzheimer’s disease. Ann Clin Biochem 46:235–240

    Article  CAS  PubMed  Google Scholar 

  • Villemagne VL et al (2011) Longitudinal assessment of Aβ and cognition in aging and Alzheimer disease. Ann Neurol 69(1):181–92

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Wolk DA et al (2009) Amyloid imaging in mild cognitive impairment subtypes. Ann Neurol 65:557–568

    Article  PubMed Central  PubMed  Google Scholar 

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Correspondence to Bruno Dubois .

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Glossary

Alzheimer’s disease (AD) 

The whole clinical phase, no longer restricted to the dementia syndrome.

AD dementia

 When cognitive symptoms interfere with activity of daily living.

Alzheimer’s pathology

 Underlying neurobiological changes responsible for AD.

Asymptomatic at risk

 Cognitively normal individuals with positive pathophysiological biomarkers.

Atypical AD

 Less common but well-characterized clinical phenotypes that occur with Alzheimer’s pathology. The diagnosis of AD needs in vivo evidence of pathophysiological markers.

Mixed AD

 Patients who fulfill the criteria for AD and additionally present with clinical and biomarkers evidence of other comorbid disorders.

Mild cognitive impairment (MCI)

 Patients for whom there is no disease clearly identified.

Pathophysiological markers

 Biological changes that reflect the underlying AD pathology (CSF changes; PET amyloid). They are markers of diagnosis.

Presymptomatic AD

 Cognitively normal individuals with a proven AD autosomal dominant mutation.

Prodromal AD

 The early symptomatic, predementia phase of AD.

Topographical biomarkers

 Downstream markers of neurodegeneration that can be structural (MRI) or metabolic (FDG-PET). They are markers of progression.

Typical AD

 The most common clinical phenotype of AD, characterized by an amnestic syndrome of the hippocampal type.

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Dubois, B., Uspenskaya, O. (2014). Impact of the IWG/Dubois Criteria for Alzheimer’s Disease in Imaging Studies. In: Dierckx, R., Otte, A., de Vries, E., van Waarde, A., Leenders, K. (eds) PET and SPECT in Neurology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54307-4_13

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