There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Daniela Perani |
| Institution: | Vita-Salute San Raffaele University |
| Department: | Nuclear Medicine |
| Country: | |
| Proposed Analysis: | Title: 18F-FDG- and amyloid PET evaluation of gender effects in brain/cognitive reserve. Background The concept of reserve was originally introduced to explain the discrepancy between the amount of pathological findings and their clinical expression in Alzheimer’s Disease (AD) patients (Katzman et al., 1988). Since then, this notion has greatly developed (Barulli & Stern, 2013) and in vivo neuroimaging methods have provided several insights into its anatomo-functional correlates (Stern, 2012). A previous 18F-FDG PET study by our group found that higher education and occupation (as proxies of reserve) correlated with a more severe hypometabolism in temporo-parietal associative cortices and precuneus (adjusting for demographic and cognitive status) in AD and MCI subjects (Garibotto et al., 2008). Morbelli, Perneczky et al. (2013) found similar results comparing AD patients with high or low cognitive reserve (matched on demographic characteristics and clinical severity) (Morbelli, Perneczky, et al., 2013). In addition, a comparison between the two groups revealed an association between a higher cognitive reserve and a relative increased metabolism in the dorsolateral prefrontal cortex (Morbelli, Perneczky, et al., 2013), suggesting that an active model of reserve exists apart from the passive one (i.e. brain reserve). Noteworthy, our group (Garibotto et al., 2013) reported a correlation between higher cognitive reserve proxies and a higher/preserved AchE activity in structures part of the memory network. Some evidence also exists regarding the relationship between cognitive reserve and amyloid deposition in the brain. Kemppainen et al. (2008) compared mean uptake values of 11C-PiB in defined ROIs between high (N=12) vs. low (N=13) educated patients matched on clinical severity. They found a higher tracer retention in the ventrolateral prefrontal cortex among those with a higher education (Kemppainen et al., 2008). Another study applied a general linear model to test the potential influence of education, amyloid positivity (as measured by meas of 11C-PiB PET) and their interaction in predicting cognitive performances (Roe et al., 2009). They found that the scores on cognitive tests were more preserved for those amyloid positive subjects who had higher levels of education (Roe et al., 2009). Taken together, these findings suggest that a high cognitive reserve can at least partially preserve cognitive functioning in amyloid positive subjects. Main Aims According to several studies, women are affected by AD more frequently (Andersen et al., 1999) and more severely (Barnes et al., 2005; Sinforiani et al., 2010) than men. Barnes et al., (2005) showed that the link between neuropathology, as measured in post mortem specimen, and its clinical expression is stronger in women as compared to men (Barnes et al., 2005). Specifically, they found, by means of a logistic regression model, that for every unit increase in pathology (assessed on a 0 to 3 scale) women were significantly more likely to show clinical AD (Odds-ratio: 22,67) as compared to men (Odds-ratio: 2.82) (Barnes et al., 2005). To our knowledge, there is only a previous imaging study that tested the gender effect on brain reserve in AD using brain metabolism as measured by FDG PET (Perneczky, Drzezga, Diehl-Schmid, Li, & Kurz, 2007). Their analyses were limited to the passive model of reserve (i.e. brain reserve capacity). Concerning the relationship between cognitive reserve and amyloid deposition, there are no studies that directly evaluated the possible differences between men and women. Our aim is to investigate whether differences exist between males and females with AD dementia in early and prodromal phase in the relationship between levels of education and occupation as proxies and either brain metabolic activity (18F-FDG PET) or amyloid deposition (11C-PiB PET). Proposed Analyses We will process single-subject co-registered images according to our optimized SPM procedure (Della Rosa et al. 2014, Perani et al. 2014) that is here briefly summarized. All 18F-FDG PET images are normalized to a specific 18F-FDG PET template built on a large dataset of healthy controls and patients with dementia (Della Rosa et al., 2014). Normalized images are then smoothed by means of a Gaussian kernel (FWHM: 8-8-8 mm), before entering the voxel-level statistical comparison with a validated database for normality (Perani et al. 2014). This procedure generates SPM t-maps that identify areas of significant hypometabolism, allowing a rigorous correction for multiple comparisons (PFWE < 0,05). Age is entered as covariate in order to exclude its effect as nuisance variable. This optimized SPM protocol was validated in diagnostic and clinical practice (Perani et al., 2014; Cerami et al., 2014). We want to test the presence of both a passive and an active model of reserve in male and female probable AD patients. Education and occupation will be used as proxies of cognitive reserve (as in Garibotto et al., 2008, 2013) or combined in a global reserve index (RI). General linear models will be applied to test both positive and negative linear correlations between cognitive reserve and brain glucose metabolism (with demographic, genetics (APOE) and neuropsychological variables as covariates). This analysis will be performed in defined groups (e.g. Male vs. Female/ High RI vs. Low RI). In addition, correlation coefficients will be compared between these subgroups by means of difference of slopes analyses. Metabolic connectivity analyses (Morbelli, Arnaldi et al., 2013) will follow to test for differences in the expression of brain/cognitive reserve in male and female in a network perspective. The position of seed regions for whole-brain voxel-wise correlations will be defined according to the results of the previous correlation analyses. The role of cognitive reserve in modulating the relationship between amyloid load (assessed by amy-PET) and clinical expression according to the proxies will be assessed in both male and female. After a proper pre-processing, we will use the standardized uptake values ratio (SUVR) in predefined cortical ROIs. References Andersen, K., Launer, L. J., Dewey, M. E., Letenneur, L., Ott, A., Copeland, J. R., … Hofman, A. (1999). Gender differences in the incidence of AD and vascular dementia: The EURODEM Studies. EURODEM Incidence Research Group. Neurology, 53, 1992–1997. doi:10.1212/WNL.53.9.1992 Barnes, L., Wilson, R., Bienias, J. L., Schneider, J. A., Evans, D. A., & Bennett, D. A. (2005). Sex differences in the clinical manifestations of Alzheimer disease pathology. Archives of General Psychiatry, 62(June 2005), 685–691. Barulli, D., & Stern, Y. (2013). Efficiency, capacity, compensation, maintenance , plasticity : emerging concepts in cognitive reserve. Trends in Cognitive Sciences, 17(10), 502–509. doi:10.1016/j.tics.2013.08.012 Cerami, C., Della Rosa, P. A., Magnani, G., Santangelo, R., Marcone, A., Cappa, S. F., & Perani, D. (2014). Brain metabolic maps in Mild Cognitive Impairment predict heterogeneity of progression to dementia. NeuroImage: Clinical, 7, 187–194. doi:10.1016/j.nicl.2014.12.004 Della Rosa, P. A., Cerami, C., Gallivanone, F., Prestia, A., Caroli, A., Castiglioni, I., … Perani, D. (2014). A Standardized [(18)F]-FDG-PET Template for Spatial Normalization in Statistical Parametric Mapping of Dementia. Neuroinformatics, 12(4), 575–93. doi:10.1007/s12021-014-9235-4 Garibotto, V., Borroni, B., Kalbe, E., Herholz, K., Salmon, E., Holtoff, V., … Perani, D. (2008). Education and occupation as proxies for reserve in aMCI converters and AD. Neurology, 71(October), 1342–1349. Garibotto, V., Tettamanti, M., Marcone, A., Ioana, F., Panzacchi, A., Moresco, R., … Perani, D. (2013). Cholinergic activity correlates with reserve proxies in Alzheimer’s disease. Neurobiology of …, 34(11), 2694.e13–2694.e18. doi:10.1016/j.neurobiolaging.2013.05.020 Katzman, R., Terry, R., DeTeresa, R., Brown, T., Davies, P., Fuld, P., … Peck, A. (1988). Clinical, pathological, and neurochemical changes in dementia: a subgroup with preserved mental status and numerous neocortical plaques. Annals of Neurology, 23, 138–144. doi:10.1002/ana.410230206 Kemppainen, N. M., Aalto, S., Karrasch, M., Någren, K., Savisto, N., Oikonen, V., … Rinne, J. O. (2008). Cognitive reserve hypothesis: Pittsburgh Compound B and fluorodeoxyglucose positron emission tomography in relation to education in mild Alzheimer’s disease. Annals of Neurology, 63(1), 112–8. doi:10.1002/ana.21212 Morbelli, S., Arnaldi, D., Capitanio, S., Picco, A., Buschiazzo, A. & Nobili, F. (2013). Resting metabolic connectivity in Alzheimer ’s disease, 271–278. doi:10.1007/s40336-013-0027-x Morbelli, S., Perneczky, R., Drzezga, A., Frisoni, G. B., Caroli, A., van Berckel, B. N. M., … Nobili, F. (2013). Metabolic networks underlying cognitive reserve in prodromal Alzheimer disease: a European Alzheimer disease consortium project. Journal of Nuclear Medicine : Official Publication, Society of Nuclear Medicine, 54(6), 894–902. doi:10.2967/jnumed.112.113928 Perani, D., Anthony, P., Rosa, D., Cerami, C., Gallivanone, F., Fallanca, F., … Gianolli, L. (2014). Clinical Validation of an optimized SPM procedure for FDG-PET in dementia diagnosis in a clinical setting. NeuroImage: Clinical, 6, 445–454. doi:10.1016/j.nicl.2014.10.009 Perneczky, R., Drzezga, A., Diehl-Schmid, J., Li, Y., & Kurz, A. (2007). Gender differences in brain reserve: an (18)F-FDG PET study in Alzheimer’s disease. Journal of Neurology, 254(10), 1395–400. doi:10.1007/s00415-007-0558-z Roe, C. M., Mintun, M. A., Angelo, G. D., Xiong, C., Grant, E. A., & Morris, J. C. (2009). Alzheimer’s and cognitive reserve. Education effect varies with [11C]PIB Uptake. Archives of Neurology, 65(11), 1467–1471. doi:10.1001/archneur.65.11.1467.Alzheimer Sinforiani, E., Citterio, a, Zucchella, C., Bono, G., Corbetta, S., Merlo, P., & Mauri, M. (2010). Impact of gender differences on the outcome of Alzheimer’s disease. Dementia and Geriatric Cognitive Disorders, 30(2), 147–54. doi:10.1159/000318842 Stern, Y. (2012). Cognitive reserve in ageing and Alzheimer ’s disease. The Lancet Neurology, 11(11), 1006–1012. doi:10.1016/S1474-4422(12)70191-6 |
| Additional Investigators |
