Medicine (Austin & Northern Health) - Research Publications

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    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.
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    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.
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    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.
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    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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    Empirically derived composite cognitive test scores to predict preclinical and clinical stages of Alzheimer’s disease
    Shishegar, R ; Chai, TY ; Cox, T ; Lamb, F ; Robertson, JS ; Laws, SM ; Porter, T ; Fripp, J ; Doecke, JD ; Tosun‐Turgut, D ; Maruff, PT ; Savage, G ; Rowe, CC ; Masters, CL ; Weiner, MW ; Villemagne, VLL ; Burnham, SC (Wiley, 2021-12)
    Abstract Background Alzheimer’s disease (AD) clinical trials require cognitive test scores that assess change in cognitive function accurately. Here, we propose new composite cognitive test scores to detect earlier stages of AD accurately by using the full neuropsychological testing battery (in ADNI) and a manifold learning dimension reduction technique namely UMAP. Method Data for this study included N=1585 ADNI participants ([492 cognitively normal (CN), 804 mild cognitively impaired (MCI), 289 AD; aged 73.8±7.1; 708 females]; Table 1). Subjects with 3 or more follow‐up sessions were included. Cognitive test scores with more than 60% missing data were excluded. Missing data within included test scores were imputed using the MissForest algorithm. A linear mixed model using all follow‐up data was applied to calculate the random slope (rate of change) and random intercept for each cognitive score and for each subject. The scores and demographic measurements: age, gender, years of education and APOE‐ɛ4 status were used to inform the UMAP. Levels for the output variable were defined as: 1) stable CN, 2) CN who progressed to MCI or probable dementia due to AD, 3) stable MCI, 4) MCI who progressed to dementia AD and 5) dementia due to AD. The model calculated two composite scores. These cognitive stages were predicted using Support Vector Machine (SVM) analysis of both the new composite scores and the traditional clinical rating measures of Clinical Dementia Rating (CDR) and Mini‐Mental State Examination (MMSE). Result Predicting cognitive stages using the proposed composite scores show a highly significant improvement with a 0.981 accuracy and 0.976 reliability (evaluated by Cohen's kappa coefficient), compared to using the combination of CDR and MMSE scores covaried for demographics, which had 0.660 accuracy and 0.567 reliability. Individuals’ clinical and preclinical stages with regards to UMAP two‐dimensional embedding and the clinical rating measures, CDR and MMSE, are presented in Figure 1. Table 2 reports the importance of the test measures on the UMAP components used in AD staging predictions. Conclusion The results here suggest that the proposed empirically derived composite cognitive test scores provides a practical solution to differentiate cognitive stages with a high accuracy and reliability.
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    Plasma p217+tau concordance with 18F-NAV4694 beta-amyloid and 18F-MK6240 tau PET in mild Alzheimer’s disease and cognitively unimpaired participants in the AIBL/ADNeT cohort
    Rowe, CC ; Doecke, JD ; Saad, ZS ; Triana‐Baltzer, G ; Slemmon, JR ; Krishnadas, N ; Fowler, CJ ; Rainey‐Smith, SR ; Ward, L ; Robertson, J ; Martins, RN ; Fripp, J ; Masters, CL ; Villemagne, VL ; Kolb, HC ; Dore, V (Wiley, 2021-12)
    Abstract Background Beta‐amyloid (Aß) PET assists diagnosis of Alzheimer’s disease (AD) and has an important role in selection for trials. Aß PET is costly. Reports suggest that plasma measures of phospho‐tau have high concordance with both Aß and tau PET. We compared a new assay, plasma p217+tau to Aß and tau PET. Method 181 MCI/mild AD and 222 cognitively unimpaired (CU) participants in AIBL and the Australian Dementia Network (ADNeT) trial screening program were studied. Aß PET threshold was set at 25 centiloids (CL) as the standard for defining a positive scan and also at 50 CL which best correlates with NIA/AA AD neuropathologic change criteria. In CU we also evaluated a 20 CL PET threshold as this may be used in preclinical trial selection. Tau PET threshold was set at the SUVR (normalised to cerebellar cortex) for a meta‐temporal ROI (entorhinal cortex, amygdala, parahippocampus, and fusiform, inferior and middle temporal gyri) at the 95th percentile of Aß PET negative CU. Plasma p217+tau was measured with SIMOA using a Janssen in‐house assay. ROC area under the curve (AUC) was generated with Youden Index defined sensitivity (sens) and specificity (spec). Result Rank‐order correlation between p217+tau and Aß CL was significant in MCI/AD: ρ=0.65, p<10‐22 and CU: ρ=0.45, p<10‐12 and with tau SUVR for MCI/AD:ρ=0.68, p<10‐25 and CU:ρ=0.32, p<10‐7. In MCI/AD the AUC for p217+tau to predict Aß >25CL was 0.91 (sens 83%, spec 85%) and at >50CL was 0.90 (sens 73%, spec 93%). For tau+ PET the AUC was 0.86 (sens 73%, spec 90%). In the CU, the AUC vs Aß >25CL was 0.84 (sens 81%, spec 81%) dropping to 0.79 (sens 72%, spec 82%) for the 20CL PET threshold. The AUC vs tau+ PET in CU was 0.87 (sens 90%, spec 76%). Conclusion Plasma p217+tau showed good concordance with PET defined Aß and tau positivity in both MCI/AD and CU indicating potential to support the diagnosis of AD and to screen for preclinical AD trials. Early triage with p217+tau may reduce the number of screening Aß PET needed to identify Aß+ PET CU for preclinical trials by 50%.
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    Unpacking cognitive composites: A longitudinal analysis
    Cox, T ; Shishegar, R ; Lim, YY ; Robertson, J ; Lamb, F ; Laws, SM ; Porter, T ; Fripp, J ; Doecke, JD ; Maruff, PT ; Savage, G ; Rowe, CC ; Masters, CL ; Villemagne, VL ; Burnham, SC (Wiley, 2021-12)
    Abstract Background The development of cognitive endpoints that can accurately assess changes in cognition over short time frames is crucial for clinical trials and research of Alzheimer’s disease (AD). Understanding the changing influence of contributing test scores on composites throughout the disease course provides the opportunity to optimise cognitive composite scores for different stages of AD. Method AIBL participants with declining cognitive performance were included in this study N=1275 [688 cognitively unimpaired (CU), 277 mild cognitively impaired (MCI), 310 AD; aged 73±9; 718 females]). Two cognitive composite scores (Episodic Memory (EM) and PACC) and their component test scores (California Verbal Learning Test‐II Delayed Recall (CVLT‐II DR), Logical Memory Delayed Recall (LMII), Rey Complex Figure Test 30 minute delayed recall (RCFT‐DR) and CVLT‐II DR, LMII, Digit Symbol Substitution Test (DS), MMSE, respectively) were evaluated. We first examined the relationship between each of component tests score for each composite. We then compared the extent to which longitudinal trajectories of each component test score and each cognitive composite score differed at each disease stage. Result CVLT‐II DR contributed the most to the EM composite followed by RCFT‐DR and LMII with the influence remaining unchanged across each disease stage. For PACC, CVLT‐II DR contributed the most to the initial decline, with MMSE and LMII contributing similar amounts and DS contributing the least. CVLT‐II DR contributed substantially to changes in PACC earlier in the disease course but MMSE drove the PACC change in later stages of disease. Initially, both composites follow similar longitudinal trajectories. However, the EM composite reaches a floor not observed for the PACC. Conclusion Understanding the temporal contribution of component tests scores on cognitive composites could provide improved cognitive endpoints tailored to use. For instance, MMSE is sensitive to change later in the disease trajectory and therefore should be included in a composite endpoint for trials in prodromal or clinical AD, however is unlikely to have value for preclinical AD trials.
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    Non-negative matrix factorisation improves Centiloid robustness in longitudinal studies
    Bourgeat, P ; Dore, V ; Doecke, J ; Ames, D ; Masters, CL ; Rowe, CC ; Fripp, J ; Villemagne, VL (ACADEMIC PRESS INC ELSEVIER SCIENCE, 2021-02-01)
    BACKGROUND: Centiloid was introduced to harmonise β-Amyloid (Aβ) PET quantification across different tracers, scanners and analysis techniques. Unfortunately, Centiloid still suffers from some quantification disparities in longitudinal analysis when normalising data from different tracers or scanners. In this work, we aim to reduce this variability using a different analysis technique applied to the existing calibration data. METHOD: All PET images from the Centiloid calibration dataset, along with 3762 PET images from the AIBL study were analysed using the recommended SPM pipeline. The PET images were SUVR normalised using the whole cerebellum. All SUVR normalised PiB images from the calibration dataset were decomposed using non-negative matrix factorisation (NMF). The NMF coefficients related to the first component were strongly correlated with global SUVR and were subsequently used as a surrogate for Aβ retention. For each tracer of the calibration dataset, the components of the NMF were computed in a way such that the coefficients of the first component would match those of the corresponding PiB. Given the strong correlations between the SUVR and the NMF coefficients on the calibration dataset, all PET images from AIBL were subsequently decomposed using the computed NMF, and their coefficients transformed into Centiloids. RESULTS: Using the AIBL data, the correlation between the standard Centiloid and the novel NMF-based Centiloid was high in each tracer. The NMF-based Centiloids showed a reduction of outliers, and improved longitudinal consistency. Furthermore, it removed the effects of switching tracers from the longitudinal variance of the Centiloid measure, when assessed using a linear mixed effects model. CONCLUSION: We here propose a novel image driven method to perform the Centiloid quantification. The methods is highly correlated with standard Centiloids while improving the longitudinal reliability when switching tracers. Implementation of this method across multiple studies may lend to more robust and comparable data for future research.
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    Increased cerebral blood flow with increased amyloid burden in the preclinical phase of alzheimer's disease
    Fazlollahi, A ; Calamante, F ; Liang, X ; Bourgeat, P ; Raniga, P ; Dore, V ; Fripp, J ; Ames, D ; Masters, CL ; Rowe, CC ; Connelly, A ; Villemagne, VL ; Salvado, O (WILEY, 2020-02)
    BACKGROUND: Arterial spin labeling (ASL) is an emerging MRI technique for noninvasive measurement of cerebral blood flow (CBF) that has been used to show hemodynamic changes in the brains of people with Alzheimer's disease (AD). CBF changes have been measured using positron emission tomography (PET) across the AD spectrum, but ASL showed limited success in measuring CBF variations in the preclinical phase of AD, where amyloid β (Aβ) plaques accumulate in the decades prior to symptom onset. PURPOSE: To investigate the relationship between CBF measured by multiphase-pseudocontinuous-ASL (MP-PCASL) and Aβ burden as measured by 11 C-PiB PET imaging in a study of cognitively normal (CN) subjects age over 65. STUDY TYPE: Cross-sectional. POPULATION: Forty-six CN subjects including 33 with low levels of Aβ burden and 13 with high levels of Aβ. FIELD STRENGTH/SEQUENCE: 3T/3D MP-PCASL. ASSESSMENT: The MP-PCASL method was chosen because it has a high signal-to-noise ratio. Furthermore, the data were analyzed using an efficient processing pipeline consisting of motion correction, ASL motion correction imprecision removal, temporal and spatial filtering, and partial volume effect correction. STATISTICAL TESTS: General Linear Model. RESULTS: In CN subjects positive for Aβ burden (n = 13), we observed a positive correlation between CBF and Aβ burden in the hippocampus, amygdala, caudate (P < 0.01), frontal, temporal, and insula (P < 0.05). DATA CONCLUSION: To the best of our knowledge, this is the first study using MP-PCASL in the study of AD, and the results suggest a potential compensatory hemodynamic mechanism that protects against pathology in the early stages of AD. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;51:505-513.
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    Fifteen Years of the Australian Imaging, Biomarkers and Lifestyle (AIBL) Study: Progress and Observations from 2,359 Older Adults Spanning the Spectrum from Cognitive Normality to Alzheimer's Disease
    Fowler, C ; Rainey-Smith, SR ; Bird, S ; Bomke, J ; Bourgeat, P ; Brown, BM ; Burnham, SC ; Bush, A ; Chadunow, C ; Collins, S ; Doecke, J ; Dore, V ; Ellis, KA ; Evered, L ; Fazlollahi, A ; Fripp, J ; Gardener, SL ; Gibson, S ; Grenfell, R ; Harrison, E ; Head, R ; Jin, L ; Kamer, A ; Lamb, F ; Lautenschlager, NT ; Laws, SM ; Li, Q-X ; Lim, L ; Lim, YY ; Louey, A ; Macaulay, SL ; Mackintosh, L ; Martins, RN ; Maruff, P ; Masters, CL ; McBride, S ; Milicic, L ; Peretti, M ; Pertile, K ; Porter, T ; Radler, M ; Rembach, A ; Robertson, J ; Rodrigues, M ; Rowe, CC ; Rumble, R ; Salvado, O ; Savage, G ; Silbert, B ; Soh, M ; Sohrabi, HR ; Taddei, K ; Taddei, T ; Thai, C ; Trounson, B ; Tyrrell, R ; Vacher, M ; Varghese, S ; Villemagne, VL ; Weinborn, M ; Woodward, M ; Xia, Y ; Ames, D (IOS PRESS, 2021)
    BACKGROUND: The Australian Imaging, Biomarkers and Lifestyle (AIBL) Study commenced in 2006 as a prospective study of 1,112 individuals (768 cognitively normal (CN), 133 with mild cognitive impairment (MCI), and 211 with Alzheimer's disease dementia (AD)) as an 'Inception cohort' who underwent detailed ssessments every 18 months. Over the past decade, an additional 1247 subjects have been added as an 'Enrichment cohort' (as of 10 April 2019). OBJECTIVE: Here we provide an overview of these Inception and Enrichment cohorts of more than 8,500 person-years of investigation. METHODS: Participants underwent reassessment every 18 months including comprehensive cognitive testing, neuroimaging (magnetic resonance imaging, MRI; positron emission tomography, PET), biofluid biomarkers and lifestyle evaluations. RESULTS: AIBL has made major contributions to the understanding of the natural history of AD, with cognitive and biological definitions of its three major stages: preclinical, prodromal and clinical. Early deployment of Aβ-amyloid and tau molecular PET imaging and the development of more sensitive and specific blood tests have facilitated the assessment of genetic and environmental factors which affect age at onset and rates of progression. CONCLUSION: This fifteen-year study provides a large database of highly characterized individuals with longitudinal cognitive, imaging and lifestyle data and biofluid collections, to aid in the development of interventions to delay onset, prevent or treat AD. Harmonization with similar large longitudinal cohort studies is underway to further these aims.