Florey Department of Neuroscience and Mental Health - Research Publications

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Head-to-Head comparison between Philips Gemini TF64 and Siemens Biograph Vision 600 for brain amyloid Centiloid quantitation

Li, S ; Bourgeat, P ; Bozinovski, S ; Huang, K ; Guzman, R ; Williams, R ; Fripp, J ; Villemagne, VL ; Rowe, C ; Dore, V (Wiley, 2022-12-01)

Abstract Background The Centiloid (CL) scale calibrates the beta‐amyloid (Aβ) deposition from different PET tracers to a standardised 0‐100 CL unit scale. As imaging sites update their PET cameras, most are switching to digital detector systems with superior resolution and sensitivity that may affect quantitation. This has significant implications for dementia clinical trials. In this study, we examine the impact on CL quantification between Philips Gemini TF64 and Siemens Biograph Vision 600. Method Seven subjects (76.4±2.2 yo) were imaged with 18F‐NAV4694 on both Gemini TF64 and Biograph Vision consecutively with an average scan interval of 25.1±11.2 weeks. The injected doses were 200MBq and 100MBq, respectively. On the Gemini TF64, the PET images were reconstructed by LOR‐RAMLA algorithm with smoothing parameter setup as ‘SHARP’. On Biograph Vision, the PET images were reconstructed by OSEM‐3D (8 iterations and 5 subsets, TOF enabled) with 3mm post Gaussian smoothing. A T1 MRI image was acquired for each subject. As per the standard Centiloid method the whole cerebellum was used as the reference in SUVR images, and all images were processed using CapAIBL to calculate the CL using both MR‐based and MR‐Less spatial normalisation. Result Figure 1 shows the CL images of a subject scanned on Gemini TF64 and Biograph Vision within sixteen weeks. The Biograph Vision images have higher contrast and higher spatial resolution despite using half of the dose. Figure 2 shows the linear regression plot of the scanner comparison. Biograph Vision CL are progressively higher than those obtained from the Gemini TF64 as the CL value rises (Table 1). There were no significant differences between the MR‐based and MR‐less results. Conclusion Biograph Vision yields higher SUVR and therefore CL values compared to Gemini TF64 in a head‐to‐head comparison. These results show that the selection of PET camera has a significant impact on CL quantification, which needs to be considered when merging cohorts from different studies or changing cameras during longitudinal studies or trials. These initial results indicate that the CL difference could be corrected by a linear transform.
Development of harmonized and co‐calibrated scores for memory, executive functioning, language, and visuospatial in the AIBL Study, ADNI, and NACC datasets

Crane, PK ; Trittschuh, EH ; Mez, JB ; Saykin, AJ ; Sanders, RE ; Gibbons, LE ; Lee, ML ; Scollard, P ; Choi, S ; Rainey‐Smith, S ; Chooi, CK ; Gavett, BE ; Maruff, P ; Ames, D ; Culhane, JE ; Gauthreaux, K ; Chan, KCG ; Biber, S ; Stephens, K ; Kukull, WA ; Dumitrescu, L ; Hohman, TJ ; Mukherjee, S (Wiley, 2022-12)

Background The Australian Imaging, Biomarkers and Lifestyle (AIBL) Study is a prospective study collecting extensive cognitive, clinical, fluid, and imaging biomarkers data from older adults living in Australia. Integration of outcomes between large prospective studies of AD will provide greater precision in models of AD brain‐behavior relationships, so it is important to align composite scores for cognitive domains between such studies. Methods Detailed methods for AIBL, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the National Alzheimer’s Coordinating Center (NACC) have been published. Briefly, AIBL participants had cognition assessed with an extensive neuropsychological test battery alongside health and biomarker assessments at entry and each 18‐months thereafter. Granular‐level cognitive data were obtained and an expert panel of two neuropsychologists and a behavioral neurologist categorized each element as assessing memory, executive functioning, language, visuospatial, or none of these, exactly as we have done previously. We also identified elements we had previously calibrated from other studies; after careful quality control and confirmation these served as anchors enabling co‐calibration. We used confirmatory factor analysis bi‐factor models to calibrate the AIBL battery with other studies. We used those calibrations to obtain co‐calibrated scores for all AIBL participants at every study visit. Here we show descriptive statistics for baseline visits, separately by diagnosis (normal cognition, mild cognitive impairment (MCI), dementia) for two enrollment waves for AIBL as well as for each phase of ADNI and across the Uniform Data Set (UDS) 1 & 2 (UDS1/2) and UDS3 time periods for NACC. Results Box plots for memory, executive functioning, language, and visuospatial for people with normal cognition are in Figure 1, MCI in Figure 2, and dementia in Figure 3. These figures show there is substantial cognitive variation across waves within these disease stage groups and across studies. Conclusion Co‐calibrated neuropsychological domain scores provide a common metric for integrating cognitive data across studies. Co‐calibrated scores aggregated across large prospective AD studies such as AIBL, ADNI, and NACC provide a foundation for large‐scale models of the development of AD and can serve as phenotypes for genetics studies. Co‐calibrated scores are available from AIBL, ADNI, and from NACC.
CenTauRz: A standardized quantification of tau PET scans

Dore, V ; Bullich, S ; Bohorquez, SS ; Leuzy, A ; Shimada, H ; Rowe, C ; Bourgeat, P ; Lopresti, BJ ; Huang, K ; Krishnadas, N ; Fripp, J ; Takado, Y ; Stephens, AW ; Weimer, R ; Higuchi, M ; Hansson, O ; Villemagne, VL (Wiley, 2022-12-01)

Background: Over the past decade, several PET tracers were developed to visualise and quantify tau pathology in vivo. However, all these tracers have distinct off-target binding, different dynamic ranges and likely different levels of non-specific binding resulting in large variability in semiquantification. We propose to standardise the sampling and the quantification across all available tau tracers. Method: 549 participants underwent tau scans with either 18F-FTP (Cognitively Unimpaired (CU)=54/AD=14), 18F-MK6240 (CU=186/AD=89), 18F-PI2620 (CU=17/AD=21), 18F-PM-PBB3 (CU=30/AD=28), 18F-GTP1 (CU=7/AD=38) or 18F-RO948 (CU=35/AD=30). All CU individuals were Aβ- and all AD were Aβ+. The tau scans were spatially normalized using CapAIBL and the cerebellar cortex was used as reference region. We constructed a “universal” tau mask from the intersection of all the specific tau tracer masks, after subtracting AD from CU. All tau PET studies were sampled with a Mesial Temporal (MTL) and a Meta Temporal (MetaT) composites constrained by the universal mask. For each tracer and in composite, the mean and standard deviation of the Aβ- CU SUVR for each tau tracer were used to generate z-scores (CenTauRz). Result: Using a threshold of 2 CenTauRz in the MetaT regions, all tracers highly discriminated Aβ+ AD from Aβ- CU (ACC=[0.94-1], sens=[0.84-1], spec=[0.96-1]) with mean CenTauRz for the different AD cohorts ranging from 8 to 14. Lower accuracy was observed in the MTL (ACC=[0.78-1]) due to lower sensitivity in some cohorts [0.65-1] however, the specificity was similar to that in the MetaT composite (spec=[0.94,1]). Conclusion: All tracers exhibited comparably high discriminative power to separate Aβ+ AD from Aβ- CU, where AD Aβ+ displayed a consistent range of CenTauRz across tracers. However, there were some differences between cohorts. For example, different PET scanners, with different sensitivities were used. For some cohorts, scans were selected as extreme representative cases, while for others the scans were more representative of clinical settings, with AD patients at early stages (with low or negative tau scans) or with suspected hippocampal sparing subtype that likely explains the lower accuracy in the MTL for some cohorts. Further studies with larger cohorts to validate the universal mask and CenTauRz scale are ongoing.
Prevalence and Associations of Frailty in Preclinical Alzheimer’s Disease

Lee, K ; Huynh, A ; Moe, T ; Amadoru, S ; Zisis, G ; Raman, R ; Aisen, P ; Ernstrom, K ; Sperling, RA ; Masters, CL ; Yates, PA (Wiley, 2022-12)

Background The prevalence of frailty in asymptomatic Alzheimer’s disease is not clear. In this cohort screened for a preclinical Alzheimer’s disease clinical trial, we aimed to compare the prevalence of frailty between participants with and without elevated amyloid as determined by PET and determine if frailty influences the relationship between amyloid and cognition. Method Analysis of pre‐randomization data from the Anti‐Amyloid Treatment in Asymptomatic Alzheimer’s (A4) Study, a clinical prevention trial of an anti‐amyloid monoclonal antibody in individuals who were cognitively normal with elevated amyloid burden, to determine a cumulative‐deficits Frailty Index (FI). Logistic regression was used to investigate the difference in the prevalence of frailty, defined as a FI greater than 0.25, according to amyloid status (Aβ+/‐), adjusted for age, gender and education. ANCOVA was used to examine the influence of frailty on the relationship between amyloid status and cognition (Preclinical Alzheimer Cognitive Composite [PACC] score), including an interaction term of frailty and amyloid, adjusted for age, gender and education. Result 4,486 participants were included (mean age 71.3±4.7 years, 1323 participants Aβ+(29.5%), 59.4% female). Adjusting for age, sex and education, Aβ+ participants were 1.48 times more likely to be frail compared to Aβ− (p<0.001). Frail participants had a lower PACC score compared to non‐frail participants (p<0.001). Both frailty and amyloid status were associated with poorer cognition after adjusting for age, sex and education (p<0.001), but frailty did not influence the relationship between cognition and amyloid status. Conclusion There is strong evidence that elevated amyloid burden was associated with an increased risk of frailty and that frailty reduced cognitive performance compared to non‐frail participants in this cohort screened for a preclinical AD trial. These relationships, the potential underlying mechanisms, and whether this may be applicable to longitudinal outcomes, warrant further study.
Contribution of modifiable dementia risk factors to cognitive performance and subjective cognition in middle‐aged adults

Bransby, L ; Rosenich, E ; Buckley, RF ; Yassi, N ; Maruff, P ; Pase, MP ; Lim, YY (Wiley, 2022-12)

Background Characterization of the contribution of modifiable risk factors (MRF) to dementia generally consider MRFs individually, despite strong evidence that MRFs co‐occur. In a large group of middle‐aged adults, the prevalence and co‐occurrence of MRFs for dementia was determined, spanning five broad domains (mood, lifestyle behaviours (e.g., physical inactivity), cardiovascular health, cognitive/social engagement, and sleep). We then investigated relationships between MRFs, both individually and combined, to cognitive performance and subjective cognition. Method Middle‐aged adults (n = 1610), most (70%) with a family history of dementia, enrolled in the Healthy Brain Project, completed an extensive set of questionnaires about their physical and psychological health and lifestyle. Participants also completed the Cogstate Brief Battery (CBB), and the Cognitive Function Instrument (CFI), a measure of subjective cognition. Participants were classified according to the number of domains (mood, lifestyle behaviours, cardiovascular health, cognitive/social engagement, and sleep, ranging from 0‐5) in which they reported at least one MRF. Age, sex, education and ethnicity were adjusted for in analyses. Result Most participants (65%) reported at least one MRF in two or more domains (Fig 1A). Compared to participants reporting no MRFs, participants who reported at least one MRF in 3‐5 domains showed worse memory performance and reported greater subjective cognitive concerns, with magnitudes of differences moderate to large (d = 0.30‐0.93; Fig 1B). Participants who reported at least one MRF in five domains also showed worse attention than those reporting no MRFs (d = 0.58). When individual MRFs were considered simultaneously, MRFs in the mood (e.g., anxiety symptomatology) and cognitive/social engagement domains (e.g., leisure activities) were associated with worse attention and memory performance. Individual MRFs reflecting mood and sleep symptomatology were associated with greater subjective cognitive concerns. Conclusion In middle‐aged community dwelling adults, multidomain MRFs for dementia are highly prevalent and co‐occur, and are associated with poorer cognitive outcomes. This suggests that the presence of multiple MRFs as early as midlife may have negative neurological outcomes, however, this will need to be explored in future neuroimaging studies. These findings indicate that multidomain lifestyle prevention trials in middle‐aged adults may be useful to delay or prevent future cognitive impairment or dementia.
Cerebrospinal fluid neurofilament light chain differentiates behavioural variant frontotemporal dementia progressors from ‘phenocopy’ non‐progressors

Keem, MH ; Eratne, D ; Lewis, C ; Kang, M ; Walterfang, M ; Loi, SM ; Kelso, W ; Cadwallader, C ; Berkovic, SF ; Li, Q ; Masters, CL ; Collins, S ; Santillo, A ; Velakoulis, D (Wiley, 2022-12)

Background Distinguishing behavioural variant frontotemporal dementia (bvFTD) from non‐neurodegenerative ‘non‐progressor’, ‘phenocopy’ mimics of frontal lobe dysfunction, can be one of the most challenging clinical dilemmas. A biomarker of neuronal injury, neurofilament light chain (NfL), could reduce misdiagnosis and delay. Method Cerebrospinal fluid (CSF) NfL, amyloid beta 1‐42 (AB42), total and phosphorylated tau (T‐tau, P‐tau) levels were examined in patients with an initial diagnosis of bvFTD. Based on follow up information, patients were categorised as Progressors. Non‐Progressors were subtyped in to Phenocopy Non‐Progressors (non‐neurological/neurodegenerative final diagnosis), and Static Non‐Progressors (static deficits, not fully explained by non‐neurological/neurodegenerative causes). Result Forty‐three patients were included: 20 Progressors, 23 Non‐Progressors (15 Phenocopy, 8 Static), 20 controls. NfL concentrations were lower in Non‐Progressors (Non‐Progressors Mean, M=554pg/mL, 95%CI:[461, 675], Phenocopy Non‐Progressors M=459pg/mL, 95%CI:[385, 539], Static Non‐Progressors M=730pg/mL, 95%CI:[516, 940]), compared to bvFTD Progressors (M=2397pg/mL, 95%CI:[1607, 3332]). NfL distinguished Progressors from Non‐Progressors with the highest accuracy (area under the curve 0.92, 90%/87% sensitivity/specificity, 86%/91% positive/negative predictive value, 88% accuracy). Static Non‐Progressors tended to have higher T‐tau and P‐tau levels compared to Phenocopy Non‐Progressors. Conclusion This study demonstrated strong diagnostic utility of CSF NfL in distinguishing bvFTD from phenocopy non‐progressor variants, at baseline, with high accuracy, in a real‐world clinical setting. This has important clinical implications to improve outcomes for patients and clinicians facing this challenging clinical dilemma, as well as for healthcare services, and clinical trials. Further research is required to investigate heterogeneity within the non‐progressor group and potential diagnostic algorithms, and prospective studies are underway assessing plasma NfL.
Modifiable dementia risk factors associated with higher CSF tau and poorer cognition in middle‐aged adults

Bransby, L ; Rosenich, E ; Buckley, RF ; Maruff, P ; Yassi, N ; Lim, YY (Wiley, 2022-12)

Background Modifiable factors in domains such as mood, lifestyle behaviours, cardiovascular health, cognitive/social engagement, and sleep are associated with increased risk for dementia. These modifiable risk factors could promote Alzheimer’s disease (AD) related biological processes, such as beta‐amyloid (Aβ) or tau accumulation, but these associations remain poorly understood. This study aimed to determine associations between modifiable dementia risk factors with Aβ, tau, and cognition in cognitively unimpaired middle‐aged adults. Method Middle‐aged adults (n = 82) (age range 40‐70) enrolled in a biomarker sub‐study of the Healthy Brain Project completed self‐report questionnaires about their physical and psychological health and lifestyle. Cerebrospinal fluid (CSF) levels of Aβ42, total tau (t‐tau), and phosphorylated tau (p‐tau) (Roche Elecsys) were obtained. A comprehensive neuropsychological battery was administered to measure cognition, and composite scores were derived for memory, executive function, and the Preclinical Alzheimer’s Cognitive Composite (PACC). Participants were classified according to reporting normal modifiable risk (NMR) (risk in ≤1 domains; n = 34) or high modifiable risk (HMR) (risk in ≥2 domains; n = 48) across five domains (mood, lifestyle behaviours, cardiovascular health, cognitive and social engagement, and sleep). Result Compared to those with NMR, those with HMR had increased t‐tau (d = 0.54, p = .021) and p‐tau (d = 0.47, p = .044) levels but did not differ on Aβ42 (d = 0.23, p = .319) after adjusting for age and sex. With age, sex, and education adjusted for, HMR participants showed worse performance on PACC (d = 0.69, p = .003), executive function (d = 0.55, p = .016), and episodic memory (d = 0.40, p = 0.076) compared to NMR participants. Differences in cognitive performance remained when levels of Aβ42, t‐tau and p‐tau levels were controlled in the models [PACC (d = 0.59, p = .011), memory (d = 0.64, p = .005) and executive function (d = 0.45, p = .049)]. Conclusion Modifiable dementia risk factors across multiple domains are related to higher total and phosphorylated tau levels and with subtle cognitive deficits, but not with Aβ, in middle‐aged adults. Together with the observation that differences in cognition across modifiable risk groups remained when Aβ and tau levels were controlled statistically, this suggests that modifiable dementia risk factors may increase risk through neurodegenerative processes non‐specific to AD, such as increased cerebrovascular burden, although this needs to be confirmed in future neuroimaging studies.
Plasma pTau181/Aβ42 identifies cognitive change earlier than CSF pTau181/Ab42

Fowler, C ; Stoops, E ; Rainey‐Smith, S ; Vanmechelen, E ; Vanbrabant, J ; Dewit, N ; Mauroo, K ; Rowe, C ; Fripp, J ; Li, Q ; Bourgeat, P ; Collins, S ; Martins, RN ; Masters, CL ; Maruff, P ; Doecke, JD (Wiley Open Access, 2022-12)

Background Plasma biomarkers now show an accuracy in detecting Amyloid Beta (Aβ) similar to AD biomarkers derived from cerebral spinal fluid (CSF). However, the ability of plasma AD biomarkers, alone or in combination, to predict cognitive decline has not yet been compared to that of CSF AD biomarkers. Method Plasma biomarker data from 233 participants’ first visit in the Australian Imaging, Biomarkers and Lifestyle study (AIBL) was submitted to linear mixed effects models (LME) to quantify the relationship with change in cognition (measured using the AIBL PACC) and in clinical disease stage (CDR SoB) in both PET Aβ‐ (Centiloid value <20CL) and Aβ+ (Centiloid value ≥20CL) participant subgroups. Separate models were used to assess CSF (Elecsys) and plasma (ADx NeuroSciences) data for Aβ42, pTau181 and the pTau181/Aβ42 ratio. Biomarker values were classified into low vs high levels based on ROC‐derived thresholds optimizing separation of PET Aβ status (low vs high at 20 CL). Changes in cognitive and clinical symptoms were then compared between the low/high plasma biomarker groups. Result In Aβ‐ participants, no significant interactions between binary biomarker classification and time were observed for AIBL PACC or CDR SoB, for either CSF or plasma biomarkers. In the Aβ+ participants, interactions between the binary plasma biomarker classification and change in cognition were greater in magnitude that those detected for CSF biomarker classification. For plasma, abnormally high values of both pTau181 and the pTau181/Aβ42 ratio predicted a significant increase over time in CDR SoB (Figure 1H & 1L) and a significant decrease over time in the AIBL PACC score (Figure 1F & 1J), compared the group with low values on the same biomarkers. In cognitively unimpaired Aβ‐ participants, the AIBL PACC score declined in those with abnormally high values of the pTau181 and the pTau181/Aβ42 ratio (Figure 1F & 1J). Conclusion Assays to measure pTau181 and Aβ42 in the plasma possess an accuracy equivalent to those derived from CSF. In particular, abnormally high levels of plasma pTau181 or the ratio of pTau181 to Aβ42 ratio provide a strong prediction of early cognitive changes, even in those with normal PET Aβ status.
Cross‐sectional and longitudinal comparison of 18F‐MK6240 and 18F‐Flortaucipir in populations matched for centiloid, age and MMSE

Bourgeat, P ; Krishnadas, N ; Dore, V ; Mulligan, RS ; Tyrrell, R ; Bozinovski, S ; Huang, K ; Lamb, F ; Fripp, J ; Villemagne, VL ; Rowe, C (Wiley Open Access, 2022-12)

Background Longitudinal tau quantification may provide a useful outcome measure in disease‐specific therapeutic trials. Different tau PET tracers may have different sensitivity to longitudinal changes, but without a head‐to‐head comparison, equating results from different cohorts using different tracers can be biased. In this study, we aim to minimise this bias by matching participants in two cohorts imaged using 18F‐MK6240 and 18F‐Flortaucipir (FTP). Method A subset of 93 participants from AIBL and 93 from ADNI, imaged at baseline and 1 year later using 18F‐MK6240 and 18F‐FTP, respectively, were matched based on baseline clinical diagnosis, MMSE, age, and Centiloid value (CL). PET images were analysed with CapAIBL. Amyloid positivity (+/‐) was defined based on a threshold of 25CL. Subjects were grouped as 34 cognitively unimpaired amyloid negative (CU‐) and 24 positive (CU+), 18 mild cognitive impairment positive (MCI+) and 17 Alzheimer’s disease positive (AD+). Tracer retention was measured in the mesial temporal (Me), meta‐temporal (MT), temporoparietal (Te) and rest of the cortex (R). T‐tests were employed to assess group separation at baseline using SUVR and longitudinally using SUVR/Yr. Result As per selection criteria, there were no significant differences in age, MMSE or Centiloid between the cohorts using 18F‐MK6240 or 18F‐FTP in each subgroups. Baseline SUVR were significantly different between CU‐/CU+, CU+/MCI+ and CU+/AD+ in all regions for both tracers, except for CU‐/CU+ in R for 18F‐MK6240 (Figure 1). Using 18F‐MK6240, rate of change in CU+ was significantly higher than CU‐ in MT and Te, and both MCI+ and AD+ were higher than CU+ in R (Figure 2.Left). Using 18F‐FTP, rate of change in MCI+ was significantly higher than CU+ in Te, and AD+ higher than CU+ in MT, Te and R (Figure 2.Right). Conclusion In our matched cohorts using 18F‐MK6240 or 18F‐FTP, we found that, at baseline, both tracers can detect significant differences between clinical groups. However, 18F‐MK6240 was able to detect higher rates of accumulation at preclinical stages (CU+). These results in well‐matched cohorts indicate that 18F‐MK6240 might be a more sensitive tracer to detect early accumulation. Longitudinal head‐to‐head comparison will be required to confirm these results.
Objectively measured physical activity and cognition in cognitively normal older adults: A longitudinal analysis of the Australian Imaging Biomarkers and Lifestyle (AIBL) study

Sewell, KR ; Rainey‐Smith, S ; Villemagne, VL ; Peiffer, JJ ; Sohrabi, HR ; Taddei, K ; Ames, D ; Maruff, P ; Laws, SM ; Masters, CL ; Rowe, C ; Martins, RN ; Erickson, KI ; Brown, BM (Wiley Open Access, 2022-12)

Background Physical inactivity is one of the greatest modifiable risk factors for dementia and research shows physical activity can delay cognitive decline in older adults. However, much of this research has used subjective physical activity data and a single follow‐up cognitive assessment. Further studies using objectively measured physical activity and comprehensive cognitive data measured at multiple timepoints are required. Methods Participants were 199 community‐dwelling cognitively normal older adults (68.7 5.9 years) from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study. Actigraphy was used to measure physical activity at baseline, yielding measures of intensity (peak counts), total activity (total counts) and energy expenditure (kilocalories; k/cal). Cognitive function was assessed using a cognitive battery administered every 18‐months from baseline (3‐11 years follow‐up), yielding composite scores for episodic memory, executive function, attention and processing speed, and global cognition. Results Higher baseline energy expenditure predicted improvements in episodic memory and maintained global cognition over time (β = 0.011, SE = 0.005, p = 0.031; β = 0.009, SE = 0.004, p = 0.047, respectively). Both physical activity intensity and total activity predicted global cognition, such that those with higher peak and total counts had better cognition over time (β = 0.012, SE = 0.004, p = 0.005; β = 0.012, SE = 0.004, p = 0.005, respectively). Finally, higher total activity predicted improved episodic memory over time (β = 0.011, SE = 0.005, p = .022). Conclusion These results suggest that physical activity is associated with preserved cognitive function over time, and that activity intensity may play an important role. This research further highlights the importance of early intervention to prevent cognitive decline and may aid in informing lifestyle interventions for dementia prevention.

Florey Department of Neuroscience and Mental Health - Research Publications

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