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Abstract Introduction The current study examined the contributions of comprehensive neuropsychological assessment and volumetric assessment of selected mesial temporal subregions on structural magnetic resonance imaging (MRI) to identify patients with amnestic mild cognitive impairment (aMCI) and mild probable Alzheimer's disease (AD) dementia in a memory clinic cohort. Methods Comprehensive neuropsychological assessment and automated entorhinal, transentorhinal, and hippocampal volume measurements were conducted in 40 healthy controls, 38 patients with subjective memory symptoms, 16 patients with aMCI, 16 patients with mild probable AD dementia. Multinomial logistic regression was used to compare the neuropsychological and MRI measures. Results Combining the neuropsychological and MRI measures improved group membership prediction over the MRI measures alone but did not improve group membership prediction over the neuropsychological measures alone. Conclusion Comprehensive neuropsychological assessment was an important tool to evaluate cognitive impairment. The mesial temporal volumetric MRI measures contributed no diagnostic value over and above the determinations made through neuropsychological assessment.

Purpose: Positron emission tomography (PET) provides in vivo quantification of amyloid-β (Aβ) pathology. Established methods for assessing Aβ burden can be affected by physiological and technical factors. Novel, data-driven metrics have been developed to account for these sources of variability. We aimed to evaluate the performance of four of these amyloid PET metrics against conventional techniques, using a common set of criteria. Methods: Three cohorts were used for evaluation: Insight 46 (N=464, [18F]florbetapir), AIBL (N=277, [18F]flutemetamol), and an independent test-retest data (N=10, [18F]flutemetamol). Established metrics of amyloid tracer uptake included the Centiloid (CL) and where dynamic data was available, the non-displaceable binding potential (BPND). The four data-driven metrics computed were the amyloid load (Aβ load), the Aβ-PET pathology accumulation index (Aβ index), the Centiloid derived from non-negative matrix factorisation (CLNMF), and the amyloid pattern similarity score (AMPSS). These metrics were evaluated using reliability and repeatability in test-retest data, associations with BPND and CL, variability of the rate of change and sample size estimates to detect a 25% slowing in Aβ accumulation. Results: All metrics showed good reliability. Aβ load, Aβ index and CLNMF were strong associated with the BPND. The associations with CL suggest that cross-sectional measures of CLNMF, Aβ index and Aβ load are robust across studies. Sample size estimates for secondary prevention trial scenarios were the lowest for CLNMF and Aβ load compared to the CL. Conclusion: Among the novel data-driven metrics evaluated, the Aβ load, the Aβ index and the CLNMF can provide comparable performance to more established quantification methods of Aβ PET tracer uptake. The CLNMF and Aβ load could offer a more precise alternative to CL, although further studies in larger cohorts should be conducted.

The recent biological redefinition of Alzheimer’s Disease (AD) has spurred the development of statistical models that relate changes in biomarkers with neurodegeneration and worsening condition linked to AD. The ability to measure such changes may facilitate earlier diagnoses for affected individuals and help in monitoring the evolution of their condition. Amongst such statistical tools, disease progression models (DPMs) are quantitative, data-driven methods that specifically attempt to describe the temporal dynamics of biomarkers relevant to AD. Due to the heterogeneous nature of this disease, with patients of similar age experiencing different AD-related changes, a challenge facing longitudinal mixed-effects-based DPMs is the estimation of patient-realigning time-shifts. These time-shifts are indispensable for meaningful biomarker modelling, but may impact fitting time or vary with missing data in jointly estimated models. In this work, we estimate an individual’s progression through Alzheimer’s disease by combining multiple biomarkers into a single value using a probabilistic formulation of principal components analysis. Our results show that this variable, which summarises AD through observable biomarkers, is remarkably similar to jointly estimated time-shifts when we compute our scores for the baseline visit, on cross-sectional data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Reproducing the expected properties of clinical datasets, we confirm that estimated scores are robust to missing data or unavailable biomarkers. In addition to cross-sectional insights, we can model the latent variable as an individual progression score by repeating estimations at follow-up examinations and refining long-term estimates as more data is gathered, which would be ideal in a clinical setting. Finally, we verify that our score can be used as a pseudo-temporal scale instead of age to ignore some patient heterogeneity in cohort data and highlight the general trend in expected biomarker evolution in affected individuals.

Abstract INTRODUCTION The Centiloid (CL) project was developed to harmonize the quantification of amyloid beta (Aβ) positron emission tomography (PET) scans to a unified scale. The CL neocortical mask was defined using 11C Pittsburgh compound B (PiB), overlooking potential differences in regional distribution among Aβ tracers. We created a universal mask using an independent dataset of five Aβ tracers, and investigated its impact on inter‐tracer agreement, tracer variability, and group separation. METHODS Using data from the Alzheimer's Dementia Onset and Progression in International Cohorts (ADOPIC) study (Australian Imaging Biomarkers and Lifestyle + Alzheimer's Disease Neuroimaging Initiative + Open Access Series of Imaging Studies), age‐matched pairs of mild Alzheimer's disease (AD) and healthy controls (HC) were selected: 18F‐florbetapir (N = 147 pairs), 18F‐florbetaben (N = 22), 18F‐flutemetamol (N = 10), 18F‐NAV (N = 42), 11C‐PiB (N = 63). The images were spatially and standardized uptake value ratio normalized. For each tracer, the mean AD–HC difference image was thresholded to maximize the overlap with the standard neocortical mask. The universal mask was defined as the intersection of all five masks. It was evaluated on the Global Alzheimer's Association Interactive Network (GAAIN) head‐to‐head datasets in terms of inter‐tracer agreement and variance in the young controls (YC) and on the ADOPIC dataset comparing separation between HC/AD and HC/mild cognitive impairment (MCI). RESULTS In the GAAIN dataset, the universal mask led to a small reduction in the variance of the YC, and a small increase in the inter‐tracer agreement. In the ADOPIC dataset, it led to a better separation between HC/AD and HC/MCI at baseline. DISCUSSION The universal CL mask led to an increase in inter‐tracer agreement and group separation. Those increases were, however, very small, and do not provide sufficient benefits to support departing from the existing standard CL mask, which is suitable for the quantification of all Aβ tracers. HIGHLIGHTS This study built an amyloid universal mask using a matched cohort for the five most commonly used amyloid positron emission tomography tracers. There was a high overlap between each tracer‐specific mask. Differences in quantification and group separation between the standard and universal mask were small. The existing standard Centiloid mask is suitable for the quantification of all amyloid beta tracers.

Abstract INTRODUCTION Recently, an increasing number of tau tracers have become available. There is a need to standardize quantitative tau measures across tracers, supporting a universal scale. We developed several cortical tau masks and applied them to generate a tau imaging universal scale. METHOD One thousand forty‐five participants underwent tau scans with either 18F‐flortaucipir, 18F‐MK6240, 18F‐PI2620, 18F‐PM‐PBB3, 18F‐GTP1, or 18F‐RO948. The universal mask was generated from cognitively unimpaired amyloid beta (Aβ)− subjects and Alzheimer's disease (AD) patients with Aβ+. Four additional regional cortical masks were defined within the constraints of the universal mask. A universal scale, the CenTauRz, was constructed. RESULTS None of the regions known to display off‐target signal were included in the masks. The CenTauRz allows robust discrimination between low and high levels of tau deposits. DISCUSSION We constructed several tau‐specific cortical masks for the AD continuum and a universal standard scale designed to capture the location and degree of abnormality that can be applied across tracers and across centers. The masks are freely available at https://www.gaain.org/centaur‐project.

Abstract Background With a growing number of loci associated with late-onset (sporadic) Alzheimer’s disease (AD), the polygenic contribution to AD is now well established. The development of polygenic risk score approaches have shown promising results for identifying individuals at higher risk of developing AD, thereby facilitating the development of preventative and therapeutic strategies. A polygenic hazard score (PHS) has been proposed to quantify age-specific genetic risk for AD. In this study, we assessed the predictive power and transferability of this PHS in an independent cohort, to support its clinical utility. Results Using genotype and imaging data from 780 individuals enrolled in the Australian Imaging, Biomarkers and Lifestyle (AIBL) study, we investigated associations between the PHS and several AD-related traits, including 1) cross-sectional Aβ-amyloid (Aβ) deposition, 2) longitudinal brain atrophy, 3) longitudinal cognitive decline, 4) age of onset. Except in the cognitive domain, we obtained results that were consistent with previously published findings. The PHS was associated with increased Aβ burden, faster regional brain atrophy and an earlier age of onset. Conclusion Overall, the results support the predictive power of a PHS, however, with only marginal improvement compared to apolipoprotein E alone.

Abstract Background Tau deposition in the mesial temporal lobe (MTL) in the absence of amyloid-β (Aβ−) occurs with aging. The tau PET tracer 18F-MK6240 has low non-specific background binding so is well suited to exploration of early-stage tau deposition. The aim of this study was to investigate the associations between MTL tau, age, hippocampal volume (HV), cognition, and neocortical tau in Aβ− cognitively unimpaired (CU) individuals. Methods One hundred and ninety-nine Aβ− participants (Centiloid < 25) who were CU underwent 18F-MK6240 PET at age 75 ± 5.2 years. Tau standardized uptake value ratio (SUVR) was estimated in mesial temporal (Me), temporoparietal (Te), and rest of the neocortex (R) regions and four Me sub-regions. Tau SUVR were analyzed as continuous variables and compared between high and low MTL SUVR groups. Results In this cohort with a stable clinical classification of CU for a mean of 5.3 years prior to and at the time of tau PET, MTL tau was visually observed in 9% of the participants and was limited to Braak stages I–II. MTL tau was correlated with age (r = 0.24, p < 0.001). Age contributed to the variance in cognitive scores but MTL tau did not. MTL tau was not greater with subjective memory complaint, nor was there a correlation between MTL tau and Aβ Centiloid value, but high tau was associated with smaller HV. Participants with MTL tau had higher tau SUVR in the neocortex but this was driven by the cerebellar reference region and was not present when using white matter normalization. Conclusions In an Aβ− CU cohort, tau tracer binding in the mesial temporal lobe was age-related and associated with smaller hippocampi, but not with subjective or objective cognitive impairment.

Abstract Background Multicentre clinical trials evaluating the role of 18F-Fluoroethyl-l-tyrosine (18F-FET) PET as a diagnostic biomarker in glioma management have highlighted a need for standardised methods of data analysis. 18F-FET uptake normalised against background in the contralateral brain is a standard imaging technique to delineate the biological tumour volume (BTV). Quantitative analysis of 18F-FET PET images requires a consistent and robust background activity. Currently, defining background activity involves the manual selection of an arbitrary region of interest, a process that is subject to large variability. This study aims to eliminate methodological errors in background activity definition through the introduction of a semiautomated method for region of interest selection. A new method for background activity definition, involving the semiautomated generation of mirror-image (MI) reference regions, was compared with the current state-of-the-art method, involving manually drawing crescent-shape (gCS) reference regions. The MI and gCS methods were tested by measuring values of background activity and resulting BTV of 18F-FET PET scans of ten patients with recurrent glioblastoma multiforme generated from inputs provided by seven readers. To assess intra-reader variability, each scan was evaluated six times by each reader. Intra- and inter-reader variability in background activity and BTV definition was assessed by means of coefficient of variation. Results Compared to the gCS method, the MI method showed significantly lower intra- and inter-reader variability both in background activity and in BTV definition. Conclusions The proposed semiautomated MI method minimises intra- and inter-reader variability, providing a valuable approach for standardisation of 18F-FET PET quantitative parameters. Trial registration ANZCTR, ACTRN12618001346268. Registered 9 August 2018, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=374253

Summary: Background: Tau positron emission tomography (PET) imaging enables longitudinal observation of tau accumulation in Alzheimer's disease (AD). 18F-MK6240 is a high affinity tracer for the paired helical filaments of tau in AD, widely used in clinical trials, despite sparse longitudinal natural history data. We aimed to evaluate the natural history of tau accumulation, and the impact of disease stage and reference region on the magnitude and effect size of regional change. Methods: One hundred and eighty-four participants: 89 cognitively unimpaired (CU) beta-amyloid negative (Aβ−), 44 CU Aβ+, 51 cognitively impaired Aβ+ (26 with mild cognitive impairment [MCI] and 25 with dementia) had follow-up 18F-MK6240 PET for one to four years (median 1.48). Regional standardised uptake value ratios (SUVR) were generated. Two reference regions were examined: cerebellar cortex and eroded subcortical white matter. Findings: CU Aβ− participants had very low rates of tau accumulation in the mesial temporal lobe (MTL). In CU Aβ+, significantly higher rate of accumulation was seen in the MTL (particularly the amygdala), extending into the inferior temporal lobes. In MCI Aβ+, the rate of accumulation was greatest in the lateral temporal, parietal and lateral occipital cortex, and plateaued in the MTL. Accumulation was global in AD Aβ+, except for a plateau in the MTL. The eroded subcortical white matter reference region showed no significant advantage over the cerebellar cortex and appeared prone to spill-over in AD participants. Data fitting suggested approximately 15–20 years to accumulate tau to typical AD levels. Interpretation: Tau accumulation occurs slowly. Rates vary according to brain region, disease stage and tend to plateau at high levels. Rates of tau accumulation are best measured in the MTL and inferior temporal cortex in preclinical AD and in large neocortical areas, in MCI and AD. Funding: NHMRC; Cerveau Technologies.

Abstract To improve understanding of Alzheimer’s disease, large observational studies are needed to increase power for more nuanced analyses. Combining data across existing observational studies represents one solution. However, the disparity of such datasets makes this a non-trivial task. Here, a machine learning approach was applied to impute longitudinal neuropsychological test scores across two observational studies, namely the Australian Imaging, Biomarkers and Lifestyle Study (AIBL) and the Alzheimer's Disease Neuroimaging Initiative (ADNI) providing an overall harmonised dataset. MissForest, a machine learning algorithm, capitalises on the underlying structure and relationships of data to impute test scores not measured in one study aligning it to the other study. Results demonstrated that simulated missing values from one dataset could be accurately imputed, and that imputation of actual missing data in one dataset showed comparable discrimination (p

Introduction: The Centiloid scale was developed to harmonise the quantification of β-amyloid (Aβ) PET images across tracers, scanners, and processing pipelines. However, several groups have reported differences across tracers and scanners even after centiloid conversion. In this study, we aim to evaluate the impact of different pre and post-processing harmonisation steps on the robustness of longitudinal Centiloid data across three large international cohort studies. Methods: All Aβ PET data in AIBL (N = 3315), ADNI (N = 3442) and OASIS3 (N = 1398) were quantified using the MRI-based Centiloid standard SPM pipeline and the PET-only pipeline CapAIBL. SUVR were converted into Centiloids using each tracer's respective transform. Global Aβ burden from pre-defined target cortical regions in Centiloid units were quantified for both raw PET scans and PET scans smoothed to a uniform 8 mm full width half maximum (FWHM) effective smoothness. For Florbetapir, we assessed the performance of using both the standard Whole Cerebellum (WCb) and a composite white matter (WM)+WCb reference region. Additionally, our recently proposed quantification based on Non-negative Matrix Factorisation (NMF) was applied to all spatially and SUVR normalised images. Correlation with clinical severity measured by the Mini-Mental State Examination (MMSE) and effect size, as well as tracer agreement in 11C-PiB-18F-Florbetapir pairs and longitudinal consistency were evaluated. Results: The smoothing to a uniform resolution partially reduced longitudinal variability, but did not improve inter-tracer agreement, effect size or correlation with MMSE. Using a Composite reference region for 18F-Florbetapir improved inter-tracer agreement, effect size, correlation with MMSE, and longitudinal consistency. The best results were however obtained when using the NMF method which outperformed all other quantification approaches in all metrics used. Conclusions: FWHM smoothing has limited impact on longitudinal consistency or outliers. A Composite reference region including subcortical WM should be used for computing both cross-sectional and longitudinal Florbetapir Centiloid. NMF improves Centiloid quantification on all metrics examined.

Increasing evidence suggests that kynurenine pathway (KP) dyshomeostasis may promote disease progression in dementia. Studies in Alzheimer's disease (AD) patients confirm KP dyshomeostasis in plasma and cerebrospinal fluid (CSF) which correlates with amyloid-β and tau pathology. Herein, we performed the first comprehensive study assessing baseline levels of KP metabolites in participants enrolling in the Australian Imaging Biomarkers Flagship Study of Aging. Our purpose was to test the hypothesis that changes in KP metabolites may be biomarkers of dementia processes that are largely silent. We used a cross-sectional analytical approach to assess non-progressors (N = 73); cognitively normal (CN) or mild cognitive impairment (MCI) participants at baseline and throughout the study, and progressors (N = 166); CN or MCI at baseline but progressing to either MCI or AD during the study. Significant KP changes in progressors included increased 3-hydroxyanthranilic acid (3-HAA) and 3-hydroxyanthranilic acid/anthranilic acid (3-HAA/AA) ratio, the latter having the largest effect on the odds of an individual being a progressor (OR 35.3; 95% CI between 14 and 104). 3-HAA levels were hence surprisingly bi-phasic, high in progressors but low in non-progressors or participants who had already transitioned to MCI or dementia. This is a new, unexpected and interesting result, as most studies of the KP in neurodegenerative disease show reduced 3-HAA/AA ratio after diagnosis. The neuroprotective metabolite picolinic acid was also significantly decreased while the neurotoxic metabolite 3-hydroxykynurenine increased in progressors. These results were significant even after adjustment for confounders. Considering the magnitude of the OR to predict change in cognition, it is important that these findings are replicated in other populations. Independent validation of our findings may confirm the utility of 3-HAA/AA ratio to predict change in cognition leading to dementia in clinical settings.

BackgroundThe residual approach to measuring cognitive reserve (using the residual reserve index) aims to capture cognitive resilience conferred by cognitive reserve, but may be confounded by factors representing brain resilience. We sought to distinguish between brain and cognitive resilience by comparing interactions between the residual reserve index and amyloid, tau, and neurodegeneration [“AT(N)”] biomarkers when predicting executive function. We hypothesized that the residual reserve index would moderate at least one path from an AT(N) biomarker to executive function (consistent with cognitive resilience), as opposed to moderating a path between two AT(N) biomarkers (suggestive of brain resilience).MethodsParticipants (N = 332) were from the Alzheimer’s Disease Neuroimaging Initiative. The residual reserve index represented the difference between observed and predicted memory performance (a positive residual reserve index suggests higher cognitive reserve). AT(N) biomarkers were: CSF β-amyloid1–42/β-amyloid1–40 (A), plasma phosphorylated tau-181 (T), and FDG metabolism in AD-specific regions ([N]). AT(N) biomarkers (measured at consecutive time points) were entered in a sequential mediation model testing the indirect effects from baseline amyloid to executive function intercept (third annual follow-up) and slope (baseline to seventh follow-up), via tau and/or FDG metabolism. The baseline residual reserve index was entered as a moderator of paths between AT(N) biomarkers (e.g., amyloid-tau), and paths between AT(N) biomarkers and executive function.ResultsThe residual reserve index interacted with amyloid pathology when predicting FDG metabolism: the indirect effect of amyloid → FDG metabolism → executive function intercept and slope varied as a function of the residual reserve index. With lower amyloid pathology, executive function performance was comparable at different levels of the residual reserve index, but a higher residual reserve index was associated with lower FDG metabolism. With higher amyloid pathology, a higher residual reserve index predicted better executive function via higher FDG metabolism.ConclusionThe effect of the residual reserve index on executive function performance via FDG metabolism was consistent with cognitive resilience. This suggests the residual reserve index captures variation in cognitive reserve; specifically, neural efficiency, and neural capacity to upregulate metabolism to enhance cognitive resilience in the face of greater amyloid pathology. Implications for future research include the potential bidirectionality between neural efficiency and amyloid accumulation.

Abstract Background CSF biomarkers are well-established for routine clinical use, yet a paucity of comparative assessment exists regarding CSF extraction methods during lumbar puncture. Here, we compare in detail biomarker profiles in CSF extracted using either gravity drip or aspiration. Methods Biomarkers for β-amyloidopathy (Aβ1–42, Aβ1–40), tauopathy (total tau), or synapse pathology (BACE1, Neurogranin Trunc-p75, α-synuclein) were assessed between gravity or aspiration extraction methods in a sub-population of the Australian Imaging, Biomarkers and Lifestyle (AIBL) study (cognitively normal, N = 36; mild cognitive impairment, N = 8; Alzheimer’s disease, N = 6). Results High biomarker concordance between extraction methods was seen (concordance correlation > 0.85). Passing Bablock regression defined low beta coefficients indicating high scalability. Conclusions Levels of these commonly assessed CSF biomarkers are not influenced by extraction method. Results of this study should be incorporated into new consensus guidelines for CSF collection, storage, and analysis of biomarkers.

Dysfunction of the cholinergic basal forebrain (BF) neurotransmitter system, including cholinergic axon denervation of the cortex, plays an important role in cognitive decline and dementia. A validated method to directly quantify cortical cholinergic terminal integrity enables exploration of the involvement of this system in diverse cognitive profiles associated with dementia, particularly at a prodromal stage. In this study, we used the radiotracer [18F]-fluoroethoxybenzovesamicol (FEOBV) as a direct measure of cholinergic terminal integrity and investigated its value for the assessment of cholinergic denervation in the cortex and associated cognitive deficits. Eighteen participants (8 with mild cognitive impairment (MCI) and 10 cognitively unimpaired controls) underwent neuropsychological assessment and brain imaging using FEOBV and [18F]-florbetaben for amyloid-β imaging. The MCI group showed a significant global reduction of FEOBV retention in the cortex and in the parietal and occipital cortices specifically compared to the control group. The global cortical FEOBV retention of all participants positively correlated with the BF, hippocampus and grey matter volumes, but no association was found between the global FEOBV retention and amyloid-β status. Topographic profiles from voxel-wise analysis of FEOBV images revealed significant positive correlations with the cognitive domains associated with the underlying cortical areas. Overlapping profiles of decreased FEOBV were identified in correlation with impairment in executive function, attention and language, which covered the anterior cingulate gyrus, olfactory cortex, calcarine cortex, middle temporal gyrus and caudate nucleus. However, the absence of cortical atrophy in these areas suggested that reduced cholinergic terminal integrity in the cortex is an important factor underlying the observed cognitive decline in early dementia. Our results provide support for the utility and validity of FEOBV PET for quantitative assessment of region-specific cholinergic terminal integrity that could potentially be used for early detection of cholinergic dysfunction in dementia following further validation in larger cohorts.

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 from cohorts of older adults reporting associations of coffee intake with cognitive decline, in distinct domains, and investigating the neuropathological mechanisms underpinning any such associations.Methods: The aim of the current study was to investigate the relationship between self-reported habitual coffee intake, and cognitive decline assessed using a comprehensive neuropsychological battery in 227 cognitively normal older adults from the Australian Imaging, Biomarkers, and Lifestyle (AIBL) study, over 126 months. In a subset of individuals, we also investigated the relationship between habitual coffee intake and cerebral Aβ-amyloid accumulation (n = 60) and brain volumes (n = 51) over 126 months.Results: Higher baseline coffee consumption was associated with slower cognitive decline in executive function, attention, and the AIBL Preclinical AD Cognitive Composite (PACC; shown reliably to measure the first signs of cognitive decline in at-risk cognitively normal populations), and lower likelihood of transitioning to mild cognitive impairment or AD status, over 126 months. Higher baseline coffee consumption was also associated with slower Aβ-amyloid accumulation over 126 months, and lower risk of progressing to “moderate,” “high,” or “very high” Aβ-amyloid burden status over the same time-period. There were no associations between coffee intake and atrophy in total gray matter, white matter, or hippocampal volume.Discussion: Our results further support the hypothesis that coffee intake may be a protective factor against AD, with increased coffee consumption potentially reducing cognitive decline 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 whether coffee intake could be incorporated as a modifiable lifestyle factor aimed at delaying AD onset.

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.

Background: Biomarkers that are indicative of early biochemical aberrations are needed to predict the risk of dementia onset and progression in Alzheimer’s disease (AD). We assessed the utility of cerebrospinal fluid (CSF) neurofilament light (NfL) chain for screening preclinical AD, predicting dementia onset among cognitively healthy (CH) individuals, and the rate of cognitive decline amongst individuals with mild cognitive impairment (MCI). Methods: Neurofilament light levels were measured in CSF samples of participants (CH, n = 154 and MCI, n = 32) from the Australian Imaging, Biomarkers and Lifestyle study of ageing (AIBL). Cases of preclinical AD were identified using biomarker-guided classification (CH, amyloid-β [Aβ]+, phosphorylated-tau [P-tau]+ and total-tau [T-tau]±; A+T+/N±). The prediction of dementia onset (questionable dementia) among CH participants was assessed as the risk of conversion from Clinical Dementia Rating [CDR = 0] to CDR ≥ 0.5 over 6 years. Mixed linear models were used to assess the utility of baseline CSF NfL levels for predicting the rate of cognitive decline among participants with MCI over 4.5 years. Results: Neurofilament light levels were significantly higher in preclinical AD participants (CH, A+T+/N±) as compared to A-T-N- (p < 0.001). Baseline levels of CSF NfL were higher in CH participants who converted to CDR ≥ 0.5 over 6 years (p = 0.045) and the risk of conversion to CDR ≥ 0.5 was predicted (hazard ratio [HR] 1.60, CI 1.03–2.48, p = 0.038). CH participants with CSF NfL > cut-off were at a higher risk of developing dementia (HR 4.77, CI 1.31–17.29, p = 0.018). Participants with MCI and with higher baseline levels of CSF NfL (>median) had a higher rate of decline in cognition over 4.5 years. Conclusion: An assessment of CSF NfL levels can help to predict dementia onset among CH vulnerable individuals and cognitive decline among those with MCI.

Background and aims: Preterm birth imposes a high risk for developing neuromotor delay. Earlier prediction of adverse outcome in preterm infants is crucial for referral to earlier intervention. This study aimed to predict abnormal motor outcome at 2 years from early brain diffusion magnetic resonance imaging (MRI) acquired between 29 and 35 weeks postmenstrual age (PMA) using a deep learning convolutional neural network (CNN) model. Methods: Seventy-seven very preterm infants (born

Abstract Introduction This study assessed the utility of cerebrospinal fluid (CSF) neurofilament light (NfL) in Alzheimer's disease (AD) diagnosis, its association with amyloid and tau pathology, as well as its potential to predict brain atrophy, cognition, and amyloid accumulation. Methods CSF NfL concentration was measured in 221 participants from the Australian Imaging, Biomarkers & Lifestyle Flagship Study of Ageing (AIBL). Results CSF NfL levels as well as NfL/amyloid β (Aβ42) were significantly elevated in AD compared to healthy controls (HC; P < .001), and in mild cognitive impairment (MCI) compared to HC (P = .008 NfL; P < .001 NfL/Aβ42). CSF NfL and NfL/Aβ42 differentiated AD from HC with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.84 and 0.90, respectively. CSF NfL and NfL/Aβ42 predicted cortical amyloid load, brain atrophy, and cognition. Discussion CSF NfL is a biomarker of neurodegeneration, correlating with cognitive impairment and brain neuropathology.

Abstract Introduction Patients with amnestic mild cognitive impairment (aMCI) are heterogeneous as regard to their amyloid status. The present study aimed at highlighting the neuropsychological, brain atrophy, and hypometabolism profiles of amyloid‐positive (Aβpos) versus amyloid‐negative (Aβneg) aMCI patients. Methods Forty‐four aMCI patients and 24 Aβneg healthy controls underwent neuropsychological, structural magnetic resonance imaging and 18F‐fluorodeoxyglucose positron emission tomography scans. Data were compared between groups in specific regions of interest and voxelwise with statistical parametric mapping. Results When directly comparing Aβpos to Aβneg aMCI, the former had lower performances in episodic memory tests (P = .02 to P

Backgroundβ-amyloid (Aβ) plaques in brain's grey matter (GM) are one of the pathological hallmarks of Alzheimer's disease (AD), and can be imaged in vivo using Positron Emission Tomography (PET) with (11)C or (18)F radiotracers. Estimating Aβ burden in cortical GM has been shown to improve diagnosis and monitoring of AD. However, lacking structural information in PET images requires such assessments to be performed with anatomical MRI scans, which may not be available at different clinical settings or being contraindicated for particular reasons. This study aimed to develop an MR-less Aβ imaging quantification method that requires only PET images for reliable Aβ burden estimations.Materials and methodsThe proposed method has been developed using a multi-atlas based approach on (11)C-PiB scans from 143 subjects (75 PiB+ and 68 PiB- subjects) in AIBL study. A subset of 20 subjects (PET and MRI) were used as atlases: 1) MRI images were co-registered with tissue segmentation; 2) 3D surface at the GM-WM interfacing was extracted and registered to a canonical space; 3) Mean PiB retention within GM was estimated and mapped to the surface. For other participants, each atlas PET image (and surface) was registered to the subject's PET image for PiB estimation within GM. The results are combined by subject-specific atlas selection and Bayesian fusion to generate estimated surface values.ResultsAll PiB+ subjects (N = 75) were highly correlated between the MR-dependent and the PET-only methods with Intraclass Correlation (ICC) of 0.94, and an average relative difference error of 13% (or 0.23 SUVR) per surface vertex. All PiB- subjects (N = 68) revealed visually akin patterns with a relative difference error of 16% (or 0.19 SUVR) per surface vertex.ConclusionThe demonstrated accuracy suggests that the proposed method could be an effective clinical inspection tool for Aβ imaging scans when MRI images are unavailable.

Cross-sectional genetic association studies have reported equivocal results on the relationship between the brain-derived neurotrophic factor (BDNF) Val66Met and risk of Alzheimer's disease (AD). As AD is a neurodegenerative disease, genetic influences may become clearer from prospective study. We aimed to determine whether BDNF Val66Met polymorphism influences changes in memory performance, hippocampal volume, and Aβ accumulation in adults with amnestic mild cognitive impairment (aMCI) and high Aβ.Thirty-four adults with aMCI were recruited from the Australian, Imaging, Biomarkers and Lifestyle (AIBL) Study. Participants underwent PiB-PET and structural MRI neuroimaging, neuropsychological assessments and BDNF genotyping at baseline, 18 month, and 36 month assessments.In individuals with aMCI and high Aβ, Met carriers showed significant and large decline in episodic memory (d = 0.90, p = .020) and hippocampal volume (d = 0.98, p = .035). BDNF Val66Met was unrelated to the rate of Aβ accumulation (d = -0.35, p = .401).Although preliminary due to the small sample size, results of this study suggest that high Aβ levels and Met carriage may be useful prognostic markers of accelerated decline in episodic memory, and reductions in hippocampal volume in individuals in the prodromal or MCI stage of AD.