Medicine (Austin & Northern Health) - Research Publications

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18F-MK6240 longitudinal tau PET in ageing and Alzheimer’s disease

Krishnadas, N ; Dore, V ; Mulligan, RS ; Tyrrell, R ; Bozinovski, S ; Huang, K ; Lamb, F ; Burnham, SC ; Villemagne, VL ; Rowe, CC (Wiley, 2021-12)

Background Longitudinal tau PET may prove useful for clinical trials, through its ability to detect patterns and rates of in vivo tau accumulation in ageing and Alzheimer’s disease (AD). Clinical trials are increasingly targeting the preclinical phase of AD. Flortaucipir studies estimate a 3% annual increase in global cortical tau SUVR in amyloid‐β positive (Aβ+ve) cognitively impaired (CI) cohorts, whereas either no change, or low rates of increase (0.5%), have been demonstrated in Aβ+ve cognitively unimpaired (CU) cohorts. F‐18 MK6240 is a novel tau tracer, with high target to background binding. We aimed to evaluate regional rates of 18F‐MK6240 accumulation in ageing and the AD continuum. Method We performed PET acquisition 90‐100 minutes post‐injection of 185MBq (±10%) 18F‐MK6240 at baseline and 12 months for 67 Aβ‐ve CU, 20 Aβ+ve CU and 19 Aβ+ve CI participants. SUVR (standardized uptake value ratio) for the entorhinal cortex, amygdala, hippocampus, parahippocampus and composite regions of interest (ROI) (Me, mesial temporal; Te, temporoparietal cortices) were generated using the cerebellar cortex as the reference region. Result Age did not significantly differ between the groups (mean age 74 ± 4.4 Aβ‐ve CU, 76.2 ± 5.7 Aβ+ve CU, 72.5 ± 6.4 Aβ+ve CI). Aβ+ve participants (CU and CI) had higher baseline tau SUVR and higher annual percentage increases in tau SUVR compared to Aβ‐ve participants in all regions examined (Table 1) (Figure 1). CU Aβ+ve participants had larger increases in Me vs Te (1.6% vs 0.7%), while CI Aβ+ve participants had larger increases in Te vs Me (4.3% vs 1.9%). Compared to Aβ‐ve CU participants, Aβ+ve CU participants had higher increases in the amygdala (2.9% vs 1.8%) and entorhinal cortex (1.9% vs 0.7%). Conclusion Longitudinal tau imaging using 18F‐MK6240 discriminates between ageing and stages of AD. Rate of accumulation in preclinical AD (Aβ+ve CU) was highest in mesial temporal regions, while in CI individuals, rates were highest in the temporoparietal cortex. The amygdala and entorhinal cortex may be early regions to discriminate tau accumulation between Aβ‐ve CU and Aβ+ve groups. However, as the variance is large, the precision of these estimates may be refined with a larger sample size. Recruitment is ongoing.
Examining the structural correlates of amyloid‐beta in people with DLB

Gajamange, S ; Yassi, N ; Chin, KS ; Desmond, PM ; Villemagne, VL ; Rowe, CC ; Watson, R (Wiley, 2021-12)

Background Dementia with Lewy bodies (DLB) is a neurodegenerative disorder characterized pathologically by the deposition of alpha synuclein. Many patients with DLB also have brain compatible with Alzheimer’s disease (namely Amyloid‐β and tau), which can lead to challenges with clinical diagnosis and management. In this study we aim to understand the influence of Aβ on brain atrophy in DLB patients. Method 19 participants with probable DLB underwent 3T MRI T1‐weighted (voxel size=0.8x0.8x0.8mm3, TR=2400ms, TE=2.31ms) and β‐amyloid (Aβ) PET (radiotracer 18F‐NAV4694) imaging. Participants were grouped into Aβ negative (n=10; age=71.6±5.8 years) and Aβ positive (n=9; age=75.1±4.3 years) with a threshold of 50 centiloid units to identify neuropathological change (Amadoru et al. 2020). Brain volume measures (regional subcortical grey matter and global white and grey matter) were segmented from T1‐weighted images with FreeSurfer (Fischl et al. 2002, Fischl 2012). Given previous literature suggesting prominence of thalamic structural changes in DLB, we also specifically analysed changes in the thalamus by segmenting the thalamus into 25 nuclei, which were then grouped into six regions (anterior, lateral, ventral, intralaminar, medial and posterior) (Watson et al. 2017, Iglesias et al. 2018). All brain volumes were expressed as fractions of intracranial volume to account for differences in head size. Group comparison analyses were not controlled for age and sex as both these covariates did not statistically differ between groups. Result Brain volume differed significantly between Aβ‐ and Aβ+ DLB patients in the left thalamus (Aβ‐:4.39±0.37x103, Aβ+:4.07±0.19x103, p=0.03) and right thalamus (Aβ‐:4.17±0.34x103, Aβ+:3.84±0.22 x103, p=0.03). Specifically, the ventral (LEFT; Aβ‐:1.78±0.15, Aβ+:1.63±0.14, p=0.03. RIGHT; Aβ‐:1.83±0.15, Aβ+:1.65±0.12, p=0.01) and posterior (LEFT; Aβ‐:1.30±0.12, Aβ+:1.17±0.10, p=0.04. RIGHT; Aβ‐:1.42±0.14, Aβ+:1.21±0.12, p=0.003) regions were significantly reduced in Aβ+ compared to Aβ‐ DLB patients. Conclusion We demonstrated significant thalamic atrophy in Aβ+ patients compared to Aβ‐ DLB patients. We did not observe significant differences in grey matter and hippocampal volume between patient groups. This study showed that AD‐related processes in DLB patients are associated with thalamic atrophy, specifically in the ventral and posterior regions. Future studies would benefit a larger DLB cohort to further understand the association between AD‐related pathology and the regional thalamic correlates of clinical function.
Lipidomic signatures for APOE genotypes provides new insights about mechanisms of resilience in Alzheimer’s disease

Wang, T ; Huynh, K ; Giles, C ; Lim, WLF ; Duong, T ; Mellett, NA ; Smith, A ; Olshansky, G ; Drew, BG ; Cadby, G ; Melton, PE ; Hung, J ; Beilby, J ; Watts, GF ; Chatterjee, P ; Martins, I ; Laws, SM ; Bush, AI ; Rowe, CC ; Villemagne, VL ; Ames, D ; Masters, CL ; Arnold, M ; Kastenmüller, G ; Nho, K ; Saykin, AJ ; Baillie, R ; Han, X ; Martins, RN ; Moses, E ; Kaddurah‐Daouk, RF ; Meikle, PJ (Wiley, 2021-12)

Background The apolipoprotein E gene (APOE) genotype is the first and strongest genetic risk factor for late‐onset Alzheimer’s disease and has emerged as a novel therapeutic target for AD. The encoded protein (Apolipoprotein E, APOE) is well‐known to be involved in lipoprotein transport and metabolism, but its effect on lipid metabolic pathways and the potential mediating effect of these on disease risk have not been fully defined. Method We performed lipidomic analysis on three independent cohorts (AIBL, n = 693; ADNI, n=207; BHS, n=4,384) and defined the association between APOE polymorphisms (ε4 and ε2) and plasma lipid species. To identify associations independent of lipoprotein metabolism, the analyses was performed with adjustment for clinical lipids (total cholesterol, HDL‐C and triglycerides). Causal mediation analysis was performed to estimate the proportion of risk in the outcome model explained by a direct effect of APOE genotype on prevalent AD — the average direct effect (ADE) — and the proportion that was mediated by lipid species or lipidomic risk models — the average causal mediation effect (ACME). Result We identified multiple associations of species from lipid classes such as ceramide, hexosylceramide, sphingomyelin, plasmalogens, alkyldiacylglycerol and cholesteryl esters with APOE polymorphisms (ε4 and ε2) that were independent of clinical lipoprotein measurements. There were 104 and 237 lipid species associated with APOE ε4 and ε2 respectively which were largely discordant. Of these 116 were also associated with Alzheimer’s disease. Individual lipid species (notably the alkyldiacylglycerol subspecies) or lipidomic risk models of APOE genotypes mediated up to 10% and 30% of APOE ε4 and ε2 treatment effect on AD risks respectively. Conclusion We demonstrate a strong relationship between APOE polymorphisms and peripheral lipid species. Lipids species mediate a proportion of the effects of APOE genotypes in risk of AD, particularly resilience with e2. Our results highlight the involvement of lipids in how APOE e2 mediates its resilience to AD and solidify their involvement with the disease pathway.
Identification of neurodevelopmental gene variants implicated in age‐related brain morphological changes and cortical atrophy

Vacher, M ; Porter, T ; Milicic, L ; Dore, V ; Bourgeat, P ; Villemagne, VLL ; Doecke, JD ; Laws, SM ; Heng, JI (Wiley, 2021-12)

Background Neurodevelopmental genes and their associated protein products are involved in a number of biological processes essential for brain assembly. Despite their relative importance, the impact of genetic variation in neurodevelopmental genes on the neurodegenerative and neurocognitive changes that occur in Alzheimer’s disease, is poorly characterised. Here, we investigated the associations between Single Nucleotide Polymorphisms (SNPs) in neurodevelopmental genes and brain volumetrics. Method From a curated list of 40 genes related to neurodevelopmental processes, we identified a set of 233 independent SNPs. The genotype data was generated from 715 unrelated individuals (Amyloid Beta (Ab) ‐ N=328, Ab+ N=387), enrolled in the Australian Imaging, Biomarker & Lifestyle (AIBL) study. We focused this research on the cortical grey, subcortical white matter, ventricular, and hippocampal volumes and used linear mixed models to assess whether specific genotypes were associated to regional volume changes over time. The associations with the traits of interest were assessed cross‐sectionally at baseline and longitudinally, over a 12‐year time span. Result At baseline, cross‐sectional analyses revealed one significant association between the variant, rs2923137, located in DRC7 (<20KB KATNB1) and ventricular volume (p = 2.13e‐4, β = 5.8). In the longitudinal analyses, we found that rs1142749, a marker located near TUBB4B, was consistently associated with accelerated rate of change in grey matter (p = 9.2e‐4, β = 0.034), hippocampal (p = 2.9e‐3, β = 0.025) and ventricular volume (p = 2.1e‐3, β = ‐0.028). Further, we observed that the strength and effects of these associations were exacerbated in Ab+ individuals but were absent in Ab negative sub‐population. Another noticeable link was identified between rs2555172 (DCHS1) and hippocampal volume change (p = 1.4e‐3, β = ‐0.024). The identified associations were independent of variation due to the APOE e4 allele and remained significant after correction for multiple comparisons. Conclusion The results support the hypothesis that genes associated with neurodevelopmental processes and signalling mechanisms are relevant to Alzheimer’s Disease. The identified associations suggest that mutations in key neurodevelopment genes could be linked to accelerated atrophy in specific areas of the brain.
Feasibility of short imaging protocols for [18F]PI-2620 tau‐PET in progressive supranuclear palsy

Song, M ; Scheifele, M ; Barthel, H ; van Eimeren, T ; Beyer, L ; Marek, K ; Eckenweber, F ; Palleis, C ; Finze, A ; Kaiser, L ; Kern, M ; Nitschmann, A ; Biechele, G ; Katzdobler, S ; Bischof, GN ; Hammes, J ; Jessen, F ; Saur, D ; Schroeter, ML ; Rumpf, J ; Rullmann, M ; Schildan, A ; Patt, M ; Neumaier, B ; Stephens, AW ; Rauchmann, B ; Perneczky, R ; Levin, J ; Classen, J ; Höglinger, G ; Bartenstein, P ; Boening, G ; Ziegler, S ; Villemagne, VLL ; Drzezga, A ; Seibyl, JP ; Sabri, O ; Brendel, M (Wiley, 2021-12)

Background Dynamic 60‐minute positron‐emission‐tomography (PET) imaging with the novel tau radiotracer [18F]PI‐2620 facilitated accurate discrimination between patients with progressive supranuclear palsy (PSP) and healthy controls (HCs). We now aimed to investigate if shorter acquisition and static time windows of [18F]PI‐2620 tau‐PET can be used for imaging of patients with PSP. Method We evaluated 37 patients at five different centers with probable or possible PSP Richardson syndrome (PSP‐RS) together with ten HCs. [18F]PI‐2620 PET was performed by a dynamic 60 minute scan. Distribution volume ratios (DVRs, multilinear reference tissue model 2, cerebellar reference) were calculated using full and truncated scan durations (0‐60, 0‐50, 0‐40, 0‐30, and 0‐20 minutes p.i.). Standardized uptake value ratios (SUVrs, cerebellar reference) were obtained from static imaging windows with 20 minutes duration (20‐40, 30‐50, and 40‐60 minutes p.i.). All DVR and SUVr data were compared with regard to their potential to discriminate patients with PSP‐RS from HCs in predefined subcortical and cortical target regions (effect size, receiver operating area under the curve (AUC), multi‐region classifier). Finally, we tested if shorter [18F]PI‐2620 PET imaging can also be applied to patients with Alzheimer’s disease (n=11). Result The effect size of 0‐50 and 0‐40 DVR was equivalent to 0‐60 DVR (averaged Cohen’s d: 1.22 and 1.16 vs. 1.26), whereas the performance dropped for 0‐30 or 0‐20 DVR. The 20‐40 SUVr indicated the best performance of all short static acquisition windows (averaged Cohen’s d: 0.99). The globus pallidus internus discriminated patients with PSP and healthy controls at a similarly high level for 0‐60 DVR (AUC: 0.96), 0‐40 DVR (AUC: 0.96), and 20‐40 SUVr (AUC: 0.94). The multi‐region classifier sensitivity of these time windows was consistently 86%. 0‐40 DVR showed similar performance in Alzheimer’s disease when compared to 0‐60 DVR. Conclusion Short dynamic acquisition and static imaging windows can be used for [18F]PI‐2620 PET imaging of PSP. 0‐40 minute dynamic scanning offers the best balance between accuracy and economic scanning and is may also be suitable for [18F]PI‐2620 PET imaging of Alzheimer’s disease.
Towards a universal cortical tau sampling mask

Dore, V ; Bohorquez, SS ; Leuzy, A ; Shimada, H ; Bullich, S ; Bourgeat, P ; Burnham, SC ; Huang, K ; Krishnadas, N ; Fripp, J ; Takado, Y ; Stephens, AW ; Weimer, R ; Rowe, CC ; Higuchi, M ; Hansson, O ; Villemagne, VL (Wiley, 2021-12)

Background The introduction of the AT(N) framework raised several issues in regards to the definition of T+. What brain regions should be sampled? Based on one or on multiple tracers? In this work, we developed a “universal” cortical tau mask for the AD continuum derived from all the major tau ligands. This “universal” cortical mask will serve as the common tau area for all tracers over which several different regional sampling VOI or composites can be then applied. Guaranteeing sampling of the same common regions is the first step to develop a common scale for all tau tracers: the CenTauR. Method 464 participants underwent tau scans with either 18F‐AV1451 (CN=54/AD=24), 18F‐MK6240 (CN=157/AD=22), 18F‐PI2620 (CN=10/AD=21), 18F‐PM‐PBB3 (CN=30/AD=28), 18F‐GTP1 (CN=15/AD=38) or 18F‐RO948 (CN=35/AD=30). All CN were Aß‐ and all AD were Aß+. The tau scans were spatially normalized using CapAIBL and the cerebellar cortex was used as reference region. For each tracer, a difference image between the means of the Aß‐ CN and Aß+ AD patients was generated. Difference images were subsequently thresholded at 1/3 of the difference between Aß‐ CN and Aß+ AD in the inferior temporal lobe. A single tau specific mask was then constructed from the intersection of all the specific tau tracer masks. A MRI‐derived grey matter mask at PET resolution was applied to the composite mask only sampling grey matter regions. Finally, the mask was mirrored and fused to remove the hemispherical asymmetry of tau pathology. Agreement between masks was assessed by dice‐scores. Result Visually, all the tracer‐specific masks appeared very similar. None of the known off‐target binding regions were discernible in the resulting masks (Figure 1). There was good agreement between all masks, with dice‐scores of 0.60 and 0.66 for cortical regions. Conclusion We constructed an “universal” tau mask for the AD continuum based on all the commonly used tau tracers aiming at standardizing tau sampling and quantification across tracers and across centres. The “universal” tau mask demarcates a tau specific space that can then be sub‐segmented into smaller regions to focus on specific areas or composite regions that might better capture early tau deposition and spreading.
Relationship between amyloid and tau levels and its impact on tau spreading

Dore, V ; Krishnadas, N ; Bourgeat, P ; Huang, K ; Li, S ; Burnham, SC ; Masters, CL ; Fripp, J ; Villemagne, VL ; Rowe, CC (Wiley, 2021-12)

Background Previous studies have shown that Aß‐amyloid (Aß) likely promotes tau to spread beyond the medial temporal lobe. However, the Aß levels necessary for tau to spread in the neocortex is still unclear. Method 466 participants underwent tau imaging with [18F]MK6420 and Aß imaging with [18F]NAV4694 (Fig. 1). Aß scans were quantified on the Centiloid (CL) scale with a cut‐off of 25CL for abnormal levels of Aß (A+). Tau scans were quantified in three regions of interest (ROI) (mesial temporal (Me); temporoparietal neocortex (Te); and rest of neocortex (R)) and four mesial temporal region (entorhinal cortex, amygdala, hippocampus and parahippocampus) using the cerebellar cortex as reference region. Regional tau thresholds were established as the 95%ile of the cognitively unimpaired A‐ subjects. The prevalence of abnormal tau levels (T+) along the Centiloid continuum was determined. Result The plots of prevalence of T+ (Fig. 2) show earlier and greater increase along the Centiloid continuum in the medial temporal area compared to neocortex. Prevalence of T+ was low but associated with Aß level between 10‐40 CL reaching 23% in Me, 15% in Te and 11% in R. Between 40‐70 CL, the prevalence of T+ subjects per CL increased four‐fold faster and at 70 CL was 64% in Me, 51% in Te and 37% in R. In cognitively unimpaired (Fig. 3), there were no T+ in R below 50 CL. The highest prevalence of T+ was found in the entorhinal cortex, reaching 40% at 40 CL and 80% at 60 CL. Conclusion Outside the entorhinal cortex, abnormal levels of cortical tau on PET are rarely found with Aß levels below 40 CL. Above 40 CL prevalence of T+ accelerates in all areas. Moderate Aß levels are required before neocortical tau becomes detectable.
Investigating the impact of scatter correction on Centiloid

Li, S ; O'Keefe, G ; Gillman, A ; Burnham, SC ; Masters, CL ; Williams, R ; Rowe, CC ; Fripp, J ; Bourgeat, P ; Villemagne, VL ; Dore, V (Wiley, 2021-12)

Abstract Background Centiloid (CL) is a semiquantitative measure of amyloid‐β burden based on the ratio between neo‐cortical target region and the whole cerebellum. Photon scatter is one of the major sources of noise in PET data. Scatter correction methods are very different across PET cameras. In this study, we investigate how scatter correction affects the CL for inter/intra‐cameras by comparing PET images with/without scatter correction. Method 203 subjects (Siemens mCT Biography (N=109), Philips Gemini TF 64 (N=94)) from the AIBL study were scanned with 18F‐NAV6240. All PET images were reconstructed off‐line with scatter correction enabled and then disabled. All images were processed by CapAIBL to generate CL values. The ΔCL, which was calculated as the difference between the scatter corrected CL and non‐scatter corrected CL, was investigated to quantify the impact of scatter correction. For the intra‐camera comparison, all subjects were categorised in three groups based on the head tilt along anteroposterior axis compared to the MNI‐152 template. For inter‐camera comparison, the head tilt angle was included as covariate into an ANOVA. Hierarchical linear regressions that included head tilt and camera models were used as covariates to investigate their effect on CL. Result Figure 1 shows that scatter correction has a larger impact on the cerebellum cortex than in neocortex. Figure 2 shows that scatter correction depends on the head position, and it has more impact on subjects with higher CL for both cameras. Figure 3 presents that scatter corrections from two different cameras have different impact on CL values. Both head tilt and camera model had an impact on the scatter correction with (F=19.5, p=1.67e‐5) and (F=109.4, p=1.1e‐20), respectively. The b coefficients were 0.38CL/degree for head tilt and 8.2CL for the camera model, where more scatter is corrected on the Siemens CT than on the Philips Gemini TF. Conclusion Our results show that difference in scatter correction from different cameras have a significant impact on CL, it is yet to be known whether it is caused by hardware difference or software difference. Additional investigations using test‐retest data is needed to further characterize the real impact of scatter correction on Centiloid.
Higher coffee consumption is associated with slower cognitive decline and Aβ‐amyloid accumulation over 126 months: Data from the AIBL study

Gardener, SL ; Rainey‐Smith, SR ; Villemagne, VLL ; Fripp, J ; Dore, V ; Bourgeat, P ; Taddei, K ; Masters, CL ; Maruff, PT ; Rowe, CC ; Ames, D ; Martins, RN (Wiley, 2021-12)

Background Worldwide, coffee is one of the most popular beverages consumed. Several studies have suggested a protective role of coffee, including reduced risk of Alzheimer’s disease (AD). However, there is limited longitudinal data available in cohorts of older adults reporting associations of coffee intake with cognitive decline, in distinct domains, and investigating the neuropathological mechanisms underpinning these associations. Method The aim of the current study was to investigate the relationship between self‐reported baseline coffee intake (mean = 280 ± 323 g/day) and cognitive decline assessed using a comprehensive neuropsychological battery, over 126 months, in 227 cognitively normal individuals from the Australian Imaging, Biomarkers, and Lifestyle (AIBL) study. We also sought to investigate the relationship between coffee intake and cerebral Aβ‐amyloid accumulation and brain volumes in a subset of individuals (n=60; and n=51, respectively) over 126 months. Result Higher baseline coffee consumption was associated with slower cognitive decline in executive function, attention, and the AIBL Preclinical AD Cognitive Composite (PACC; shown to reliably measure the first signs of cognitive decline in at‐risk cognitively normal populations) over 126 months. Higher baseline coffee consumption was also associated with slower Aβ‐amyloid accumulation over 126 months, and lower risk of transitioning from ‘negative’ Aβ‐amyloid status to ‘moderate’, and ‘very high’ Aβ‐amyloid burden over the same time period. There were no associations between coffee intake and atrophy in total grey matter, white matter, or hippocampal volume. Conclusion Our results further support the hypothesis that coffee intake may be a protective factor against AD, with increased coffee consumption reducing cognitive decline potentially by slowing cerebral Aβ‐amyloid accumulation, and thus attenuating the associated neurotoxicity from Aβ‐amyloid‐mediated oxidative stress and inflammatory processes. Further investigation is required to evaluate how coffee intake could be incorporated as one modifiable lifestyle factor aimed at delaying AD onset.
How lifestyle shapes the brain: Associations between physical activity, sleep, beta‐amyloid and cognitive function in older adults

Sewell, KR ; Rainey‐Smith, SR ; Villemagne, VLL ; Peiffer, JJ ; Sohrabi, HR ; Taddei, K ; Ames, D ; Maruff, PT ; Laws, SM ; Masters, CL ; Rowe, CC ; Martins, RN ; Erickson, KI ; Brown, BM (Wiley, 2021-12)

Abstract Background Lifestyle factors such as sleep and physical activity influence risk of cognitive decline and dementia. Higher habitual physical activity and optimal sleep are associated with better cognitive function and lower levels of Alzheimer’s disease biomarkers, including beta‐amyloid (Aß). There is currently a poor understanding of how physical activity may influence the relationship between sleep and cognition, and whether exercise and sleep interact to influence cognition and Aß. Developing this understanding is crucial for creating effective lifestyle interventions for dementia prevention. Method Data from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study were utilised to determine whether self‐reported physical activity moderates the cross‐sectional relationship between self‐reported sleep parameters (duration, efficiency, latency, disturbance, quality), cognitive function (episodic memory, attention and processing speed, executive function), and brain Aß (quantified by amyloid positron emission tomography, using the Centiloid scale). Analyses were adjusted for age, sex, APOE ε4 carriage, mood, premorbid intelligence, and collection point. Participants were 404 community‐dwelling cognitively normal older adults aged 60 and above (75.3 5.7 years). Data from a subset of participants (n = 220, aged 75.2 5.6 years) were used for analyses with AB as the outcome. Result Physical activity moderated the relationship between sleep duration and episodic memory (ß = ‐.09, SE = .03, p = .005), and sleep efficiency and episodic memory (ß = ‐.08, SE = .03, p = .016). Physical activity moderated the relationship between sleep duration and A® (ß = ‐.12, SE = .06, p = .036), and sleep quality and Aß (ß = .12, SE = .06, p = .029). Conclusion Physical activity may play an important role in the relationship between sleep and cognitive function, and sleep and brain Aß. Future longitudinal and intervention studies in this area are crucial for informing interventions for dementia prevention.

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