ADNI data is made available to researchers around the world. As such, there are many active research projects accessing and applying the shared ADNI data. To further encourage Alzheimer’s disease research collaboration, and to help prevent duplicate efforts, the list below shows the specific research focus of the active ADNI investigations. This information is requested annually as a requirement for data access.
|Principal Investigator's Name:||Karl Herholz|
|Institution:||University of Manchester|
|Department:||Wolfson Molecular Imaging Centre|
|Country:||United Kingdom (Great Britain)|
|Proposed Analysis:||Background Alzheimer’s disease (AD) is one of the most common neuropsychiatric disorders of the late life that is characterized by deficits of cognitive and behavioural functions, personality changes and impaired activities of daily living. Currently 24.3 million persons worldwide are suffering from this irreversible, progressive neurodegenerative disorder. Against the background of a considerable ageing population structure worldwide a growing number of affected people towards 42.3 million in 2020 and 81.1 million in 2040 are assumed . AD is characterized by regional impairment of cerebral glucose metabolism in neocortical association areas (posterior cingulated, temporoparietal and frontal multimodal association cortex) that can be measured by positron emission tomography (PET) with [18F] fluorodeoxyglucose (FDG). These changes are also already present in those patients with mild cognitive impairment (MCI) who will progress to dementia within 1-2 years [2, 6]. Cross-sectional and longitudinal studies demonstrated that regional metabolic impairment is closely related to dementia severity and cognitive impairment . FDG PET therefore is a suitable candidate as an imaging biomarker to diagnose AD at the clinical stage of MCI, and to evaluate the efficacy of drugs that aim at modifying the progression of AD . Previous work There is a need for standardised performance and analysis of FDG PET data for their use in clinical trials. Such standardisation was developed and evaluated within “Network for Efficiency and Standardisation of Dementia Diagnosis” (NEST-DD, funded by the EC within FP5) that collected retrospective and prospective data from 10 PET centers. It comprised a retrospective study of FDG PET scans comprising 110 normal controls and 395 patients with probable Alzheimer’s disease to develop and validate an automated voxel-based procedure for diagnosis of AD and discrimination between AD and healthy controls . All FDG PET scans were acquired at a resting state with eyes closed and ears unplugged. The PET data were processed by Gaussian filtering within a cortical mask which successfully minimised the influence of scanner resolution. Standard SPM techniques were used for spatial normalisation. An adjustment for the influence of age on FDG uptake was made for each voxel by regression analysis. For developing a diagnostic test a normal range of FDG uptake in individual voxels was established in a subsample of 49 control subjects. In this reference group the variance of predicted values was calculated for each voxel and abnormal voxels were defined in individual images as those voxels whose values were lower than 95% age-adjusted prediction limits. The sum of t values over all voxels with FDG uptake below the 95% age-adjusted prediction limit was selected as a global indicator of scan abnormality (t sum). To focus the search for abnormal voxels to those areas that are typical for AD, a mask was defined by all voxels that had shown a close correlation with MMSE in patients with probable AD. The sum over all t values of voxels with FDG uptake below the 95% age-adjusted prediction limit within this AD mask (AD t sum) was calculated for each individual. The indicators of scan abnormality, t sum and AD t sum, were tested for their ability to discriminate between patients with probable AD and age-matched controls which had not been used for definition of abnormal FDG uptake. They yielded a sensitivity and a specificity of 93% for distinction of mild to moderate probable AD from normal controls and even in very mild dementia (MMSE ≥ 24) sensitivity was still 84% and specificity was 93%. This automated method for discrimination between AD and normal controls was implemented as the Alzheimer’s discrimination tool (PALZ tool) in the software PMOD (PMOD Technologies, Switzerland). Given the FDG PET data of patients with suspected AD the program performs a fully automatic discrimination analysis. The outcome is documented in a report showing the brain areas with significant uptake reduction (p<0.05) and stating a criterion of scan abnormality together with its error probability. In conjunction with clinical symptoms, an abnormal finding supports the diagnosis of AD. Proposal The intention of our present project is further validation of the automated procedure described above in an independent sample of controls and AD patients. In addition we wish to test its power for prediction of progression in patients with MCI and to establish its suitability for monitoring of disease progression. Therefore we ask for access to ADNI FDG PET and associated clinical data of normal controls, patients with mild cognitive impairment (MCI) and AD patients. As a first step, the normal range of local FDG uptake including age-adjustement will be established in a subset of the ADNI normal subject sample. It is not possible to use NEST-DD reference values because they were obtained with eyes closed compared to an eyes open procedure of the ADNI PET data. We then will proceed with our established procedure as described above by testing normal controls and AD patients to establish sensitivity and specificity of the discriminiation and to compare it with the results in the prospective NEST-DD sample. The predictive power of the method will be tested in ADNI MCI patients, using time to conversion to clinical dementia as the relevant outcome parameter to be predicted. Longitudinal FDG PET data in normals, MCI and AD patients will be used to determine signal change and the associated variance to preform power calculations for the use of this procedure in intervention trials. References 1. Alexander GE, Chen K, Pietrini P, Rapoport SI, Reiman EM (2002) Longitudinal PET Evaluation of Cerebral Metabolic Decline in Dementia: A Potential Outcome Measure in Alzheimer's Disease Treatment Studies. AmJ Psychiatry 159:738-745 2. Anchisi D, Borroni B, Franceschi M, Kerrouche N, Kalbe E, Beuthien-Beumann B, Cappa S, Lenz O, Ludecke S, Marcone A, Mielke R, Ortelli P, Padovani A, Pelati O, Pupi A, Scarpini E, Weisenbach S, Herholz K, Salmon E, Holthoff V, Sorbi S, Fazio F, Perani D (2005) Heterogeneity of brain glucose metabolism in mild cognitive impairment and clinical progression to Alzheimer disease. Arch Neurol 62:1728-1733 3. Ferri CP, Prince M, Brayne C, Brodaty H, Fratiglioni L, Ganguli M, Hall K, Hasegawa K, Hendrie H, Huang Y, Jorm A, Mathers C, Menezes PR, Rimmer E, Scazufca M (2005) Global prevalence of dementia: a Delphi consensus study. Lancet 366:2112-2117 4. Herholz K (2003) PET studies in dementia. Annals of Nuclear Medicine 17:79-89 5. Herholz K, Salmon E, Perani D, Baron JC, Holthoff V, Frolich L, Schonknecht P, Ito K, Mielke R, Kalbe E, Zundorf G, Delbeuck X, Pelati O, Anchisi D, Fazio F, Kerrouche N, Desgranges B, Eustache F, Beuthien-Baumann B, Menzel C, Schroder J, Kato T, Arahata Y, Henze M, Heiss WD (2002) Discrimination between Alzheimer dementia and controls by automated analysis of multicenter FDG PET. Neuroimage 17:302-316 6. Minoshima S, Giordani B, Berent S, Frey KA, Foster NL, Kuhl DE (1997) Metabolic reduction in the posterior cingulate cortex in very early Alzheimer's disease. AnnNeurol 42:85-94|