Florey Department of Neuroscience and Mental Health - Research Publications

Permanent URI for this collection

Search Results

Now showing 1 - 10 of 143
  • Item
    No Preview Available
    Tackling Dementia Together via The Australian Dementia Network (ADNeT): A Summary of Initiatives, Progress and Plans.
    Naismith, SL ; Michaelian, JC ; Santos, C ; Mehrani, I ; Robertson, J ; Wallis, K ; Lin, X ; Ward, SA ; Martins, R ; Masters, CL ; Breakspear, M ; Ahern, S ; Fripp, J ; Schofield, PR ; Sachdev, PS ; Rowe, CC (IOS Press, 2023)
    In 2018, the Australian Dementia Network (ADNeT) was established to bring together Australia's leading dementia researchers, people with living experience and clinicians to transform research and clinical care in the field. To address dementia diagnosis, treatment, and care, ADNeT has established three core initiatives: the Clinical Quality Registry (CQR), Memory Clinics, and Screening for Trials. Collectively, the initiatives have developed an integrated clinical and research community, driving practice excellence in this field, leading to novel innovations in diagnostics, clinical care, professional development, quality and harmonization of healthcare, clinical trials, and translation of research into practice. Australia now has a national Registry for Mild Cognitive Impairment and dementia with 55 participating clinical sites, an extensive map of memory clinic services, national Memory and Cognition Clinic Guidelines and specialized screening for trials sites in five states. This paper provides an overview of ADNeT's achievements to date and future directions. With the increase in dementia cases expected over coming decades, and with recent advances in plasma biomarkers and amyloid lowering therapies, the nationally coordinated initiatives and partnerships ADNeT has established are critical for increased national prevention efforts, co-ordinated implementation of emerging treatments for Alzheimer's disease, innovation of early and accurate diagnosis, driving continuous improvements in clinical care and patient outcome and access to post-diagnostic support and clinical trials. For a heterogenous disorder such as dementia, which is now the second leading cause of death in Australia following cardiovascular disease, the case for adequate investment into research and development has grown even more compelling.
  • Item
  • Item
    No Preview Available
    Leukocyte Surface Biomarkers Implicate Deficits of Innate Immunity in Late‐onset Alzheimer’s Disease
    Li, Y ; Huang, X ; Fowler, C ; Doecke, JD ; Trounson, B ; Pertile, K ; Rumble, R ; Lim, YY ; Maruff, P ; Mintzer, JE ; Dore, V ; Rowe, C ; Fripp, J ; Wiley, JS ; Masters, CL ; Gu, BJ (Wiley, 2022-12)
    Background Alzheimer’s disease (AD) is characterized by amyloid‐β (Aβ) plaques, neurofibrillary tangles, reactive astrogliosis, and microgliosis. Aberrant Aβ accumulation starts 20–30 years before clinical onset, so biomarker test is essential to diagnose people living with early AD. PET imaging and CSF measurements allow the diagnosis of preclinical and prodromal AD in research and clinical trials, but their invasiveness and costliness might limit their application in hospital setting. Therefore, developing non‐invasive population screening tests is necessary for the early diagnosis of AD. Recent genetic findings strongly implicate the role of innate and adaptive immunity in AD and suggest that a systemic failure of cell‐mediated Aβ clearance contributes to AD onset and progression. Our research question was to develop an immune‐related blood‐based biomarker test to facilitate the diagnosis and prognosis of AD. It was hypothesized that the pattern of immune‐related receptors and molecules expressed on peripheral leukocytes could differentiate people living with AD from healthy population. Method We recruited 180 and 200 participants from AIBL in two discovery phases and validated our findings by an independent cohort of 112 participants from AIBL. A total of 34 innate and adaptive immunity‐related leukocyte antigens on peripheral lymphocytes, monocytes, and neutrophils were examined by flow cytometry immunophenotyping. Data was analysed by logistic regression and ROC analyses. Result We identified upregulated CD35, CD59, CD91, RAGE, and Scara‐1 expressions and downregulated CD11c, CD18, CD36, CD163, MerTK, and P2X7 expressions on leukocytes of MCI/AD patients. Significant correlation between them and Aβ burden, episodic memory, and PACC score was observed, such as CD59 and CD91. Pathway analysis revealed upregulation of complement inhibition and downregulation of cargo receptor activity and Aβ clearance in AD. We proposed a marker panel including CD11c, CD59, CD91 and CD163 and this panel predicted patients’ PET Aβ status with AUC of 0.93 (0.88 to 0.97), which was repeated in validation cohort. Regarding adaptive immunity, we did not see significant results. Conclusion Our study suggested deficits in innate immunity in AD, which is consistent with genomic studies. Our proposed leukocyte‐based biomarker panel might be sensitive and practical for AD screening and diagnosis.
  • Item
    No Preview Available
    Plasma glial fibrillary acidic protein is associated with reactive astrogliosis assessed via 18F-SMBT-1 PET
    Chatterjee, P ; Dore, V ; Pedrini, S ; Krishnadas, N ; Thota, RN ; Bourgeat, P ; Rainey‐Smith, S ; Burnham, SC ; Fowler, C ; Taddei, K ; Mulligan, RS ; Ames, D ; Masters, CL ; Fripp, J ; Rowe, C ; Martins, RN ; Villemagne, VL (Wiley, 2022-12)
    Background Reactive astrogliosis is an early event along the Alzheimer’s disease (AD) continuum. We have shown that plasma glial fibrillary acidic protein (GFAP), reflecting reactive astrogliosis, is elevated in cognitively unimpaired individuals with preclinical AD (Chatterjee et al., 2021). We reported similar findings using 18F‐SMBT‐1, a PET tracer for monoamine oxidase B (MAO‐B) (Villemagne et al., 2022). To provide further evidence of their relationship with reactive astrogliosis we investigated the association between GFAP and 18F‐SMBT‐1 in the same participants. Method Plasma GFAP, Aβ42 and Aβ40 levels were measured using the Single Molecule Array platform in 71 participants comprising 54 healthy controls (12 Aβ+ and 42 Aβ‐), 11 MCI(3 Aβ+ and 8 Aβ‐) and 6 probable AD(5 Aβ+ and 1 Aβ‐) patients from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing cohort. These participants also underwent 18F‐SMBT‐1 and Aβ PET imaging. Aβ imaging results were expressed in Centiloids (CL; ≥20 CL classified as Aβ+). 18F‐SMBT‐1 Standard Uptake Value Ratio (SUVR) were generated using the subcortical white matter as reference region. Linear regression analyses were carried out using plasma GFAP levels as the dependent variable and regional 18F‐SMBT‐1 SUVR as the independent variable, before and after adjusting for age, sex, soluble Aβ (plasma Aβ1‐42/Aβ1‐40 ratio) and insoluble Aβ (Aβ PET). Result Plasma GFAP was significantly associated with 18F‐SMBT‐1 SUVR in brain regions of early Aβ deposition, such as the supramarginal gyrus (SG, β=.361, p=.002), posterior cingulate (PC, β=.308, p=.009), lateral temporal (LT, β=.299, p=.011), lateral occipital (LO, β=.313, p=.008) before adjusting for any covariates. After adjusting for covariates age, sex and soluble Aβ, GFAP was significantly associated with 18F‐SMBT‐1 PET signal in the SG (β=.333, p<.001), PC (β=.278, p=.005), LT (β=.256, p=.009), LO (β=.296, p=.004) and superior parietal (SP, β=.243, p=.016). On adjusting for age, sex and insoluble Aβ, GFAP was significantly associated with SMBT‐1 PET in the SG (β=.211, p=.037) however only a trend towards significance was observed in the PC (β=.186, p=.052) and LT (β=.171, p=.067) (Figure 1). Conclusion There is an association between plasma GFAP and regional SMBT‐1 PET that is primarily driven by brain Aβ load.
  • Item
    No Preview Available
    Understanding the impact of PET amyloid cutpoints on prognostic modelling for cognitively normal individuals
    Goudey, B ; Fedyashov, V ; Fripp, J ; Rowe, C ; Maruff, P ; Masters, CL (Wiley, 2022-12)
    Background Amyloid beta (Aβ), measured using PET imaging, is a key biomarker for Alzheimer’s disease (AD) with the consequences of abnormally high Aβ levels (Aβ+) well‐established from prospective research cohorts. A critical question is whether the prognostic capabilities of Aβ can be improved further, for example by refinement of optimal criteria for abnormality. To date, existing studies have explored such issues using association analyses, which may not reflect performance in prognostic settings due to potential overfitting. Here, the impact of different Aβ cut‐points is determined in a cross‐validation framework, providing performance estimates on data from individuals that were not used for model construction, which better reflects realworld prognostic application. Using data for cognitively normal individuals (CN) from ADNI and AIBL, we estimate time to i) MCI or AD diagnosis and ii) cognitive deficit, defined as MMSE≤26. Method We analyse measurements from 344 and 748 CN from ADNI and AIBL respectively who have available PET Aβ scans. PET Aβ SUVRs were transformed to the centiloid scale (CL). For each task, the Aβ cut‐point is varied from ‐10 to 65CL and Cox models are constructed within 10 repeats of 10‐fold cross‐validation. From the resulting 100 models, performance is quantified as the median concordance index (i.e. Harrell’s C). Result Details of the two cohorts are shown in Table 1. Across both AIBL and ADNI, a PET only model shows robust performance for cut‐points within a wide range (5 and 50CL) for predicting either time to diagnosis cognitive deficit (Figure 1), with performance dropping rapidly outside this range. When additional covariates are included 2, we see maximal performance for lower cutpoints (5‐20CL) for diagnosis in ADNI and cognitive deficit in ADNI, while remaining tasks show improved performance with higher cut‐point ranges (20‐50CL). Trends in cut‐point are consistent regardless of covariates. Leaving Aβ as a continuous variable yields near‐optimal performance across all tasks. Conclusion Our results suggest that within a range (5 and 50CL), prognostic performance is robust to the choice of cut‐point for Aβ, suggesting further refinement of a single cut‐point within this range may not yield substantial improvements for prognostic tasks for CN individuals.
  • Item
    No Preview Available
    Alzheimer’s disease specific MRI brain regions are differentially associated with accelerated decline as defined using sigmoidal cognitive turning point methodology in amyloid‐positive AIBL participants
    Gillis, C ; Cespedes, MI ; Maserejian, NN ; Dore, V ; Maruff, P ; Fowler, C ; Rainey‐Smith, S ; Villemagne, VL ; Rowe, C ; Martins, RN ; Vacher, M ; Masters, CL ; Doecke, JD (Wiley, 2022-12)
    Background Variability in cognitive decline among adults with Alzheimer’s disease (AD) is seen across studies. While such variability is often modelled using linear models, in the Australian Imaging, Biomarkers and Lifestyle (AIBL) study, application of a sigmoidal methodology has shown excellent precision in modelling cognitive and biomarker changes. Here we expand these findings by examining associations of brain volumes in AD specific Regions of Interest (ROIs) with accelerated cognitive decline among amyloid‐beta positive (Ab+) AIBL participants. Method Longitudinal cognitive scores for the AIBL PACC, Language, Visuospatial functioning and CDR‐SB were mapped to sigmoidal trajectories, with a threshold defining the inflection point of accelerated cognitive decline. Participants to the left of the threshold were classified as having non‐accelerated decline (non‐accelerators), and participants beyond the threshold were classed as accelerators (Figure 1B). Using these classifications, we investigated differences in 16 ICV corrected ROI (left and right hemispheres pooled) for reductions in brain volume via generalised linear models adjusted for age, gender, and APOE‐e4 status. Three participant subgroups were tested: 1) Ab+/Tau unknown, 2) Ab+/Tau‐ and 3) Ab+/Tau+. Significant t‐values for the summed ROI volumes were mapped on a standard brain mesh for visualisation. Result Of regions tested, two stood out consistently amongst top markers in each of the participant subgroups and cognitive outcomes: 1) supramarginal volume and 2) middle temporal volume (Figure 1C). Largest volume differences between accelerators and non‐accelerators were seen in the Ab+/Tau+ group; whilst smallest p‐values were in the Ab+/Tau unknown group due to a larger sample size (Table 1). Brain mesh visualization showed most of the AD signature ROIs altered in accelerator groups as compared with non‐accelerator groups. Figure 1D shows the AD signature for each cognitive outcome amongst the Ab+/Tau participant group. Top ranked ROI for the left being middle temporal volume (T=7.10, PACC) and supramarginal volume (T=7.10, CDR‐SB). Conclusion Sigmoid analyses of MRI using binary cognitive scores show decreased ROI volumes in AIBL Ab+ participants with accelerated cognitive decline. This effect was mediated by known information on Tauopathy. Whilst effect sizes were high, smaller sample sizes in some groups affected p‐values and should therefore be replicated in larger samples.
  • Item
    No Preview Available
    Comparing the longitudinal progression of CSF biomarkers with PET Amyloid biomarkers for Alzheimer’s disease
    Cox, T ; Bourgeat, P ; Dore, V ; Doecke, JD ; Fripp, J ; Chatterjee, P ; Schindler, EE ; Benzinger, TLS ; Rowe, C ; Villemagne, VL ; Weiner, MW ; Morris, JC ; Masters, CL (Wiley, 2022-12)
    Background Cerebrospinal fluid (CSF) soluble biomarkers are useful at detecting pre‐clinical levels of Alzheimer’s disease (AD) biomarkers of b‐amyloid (Ab) and tau. Disease progression times for participants in longitudinal studies can be estimated for different biomarkers. Utilizing a new technique, this work compared the disease progression times between CSF and PET biomarkers. Methods Four hundred and ten participants from the Alzheimer’s Dementia Onset and Progression in International Cohorts (ADOPIC) including participants form ACS/OASIS, ADNI and AIBL with three or more data points of longitudinal CSF Ab42 and pTau181 (pTau) and Ab PET were selected. PET results were expressed in Centiloid (CL), (299 cognitively unimpaired, 107 mild cognitively impaired, 4 AD dementia; aged 69±9; 216 females (NAIBL=30, NADNI=252, NOASIS=128). Disease trajectory curves for individual biomarkers and the pTau/Ab42 ratio were created by: 1) Fitting a function to the rates of change of the variable of interest versus its mean value), 2) integrating the fit to obtain longitudinal trajectory curves as a function of disease progression time for each of the variables. The participants’ disease progression time along each curve were estimated. Threshold values for Ab PET and pTau/Ab42 ratios were calculated using a gaussian mixture model. Estimates of age of onset were calculated using the progression times. The participants’ disease progression times for each of the different variables were compared using rank correlations. Results Rank correlations for the progression times were: r(Ab42, Ab PET) = 0.75, r(pTau, Ab PET)=0.62, and r(pTau/Ab42, Ab PET)=0.83. The estimated ages at which participants’ reach Ab PET and the pTau/Ab42 ratio thresholds are compared in Fig 1, the average age at which were estimated to reach the threshold values were 55 yr for pTau/Ab42 (threshold of 0.021) and 61 yr for Ab PET (threshold of 22 CL). Conclusions The high correlation between pTau/Ab42 and Ab PET, indicates that pTau/Ab42 captures the progression of AD pathology better than the individual CSF biomarkers. On average participants’ reach abnormal levels of pTau/Ab42 earlier than Ab PET. Further work is required to understand individual variations in progression times.
  • Item
    No Preview Available
    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.
  • Item
    No Preview Available
    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.
  • Item
    No Preview Available
    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.