Florey Department of Neuroscience and Mental Health - Research Publications
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ItemNo Preview AvailableCerebrospinal fluid neurofilament light predicts longitudinal diagnostic change in patients with psychiatric and neurodegenerative disorders(CAMBRIDGE UNIV PRESS, 2023-04-28)OBJECTIVE: People with neuropsychiatric symptoms often experience delay in accurate diagnosis. Although cerebrospinal fluid neurofilament light (CSF NfL) shows promise in distinguishing neurodegenerative disorders (ND) from psychiatric disorders (PSY), its accuracy in a diagnostically challenging cohort longitudinally is unknown. METHODS: We collected longitudinal diagnostic information (mean = 36 months) from patients assessed at a neuropsychiatry service, categorising diagnoses as ND/mild cognitive impairment/other neurological disorders (ND/MCI/other) and PSY. We pre-specified NfL > 582 pg/mL as indicative of ND/MCI/other. RESULTS: Diagnostic category changed from initial to final diagnosis for 23% (49/212) of patients. NfL predicted the final diagnostic category for 92% (22/24) of these and predicted final diagnostic category overall (ND/MCI/other vs. PSY) in 88% (187/212), compared to 77% (163/212) with clinical assessment alone. CONCLUSIONS: CSF NfL improved diagnostic accuracy, with potential to have led to earlier, accurate diagnosis in a real-world setting using a pre-specified cut-off, adding weight to translation of NfL into clinical practice.
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ItemNo Preview AvailableDisease progression modelling of Alzheimer's disease using probabilistic principal components analysis(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2023-09)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.
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ItemNo Preview AvailableExploring the significance of lipids in Alzheimer's disease and the potential of extracellular vesicles(WILEY, 2023-08-31)Lipids play a significant role in maintaining central nervous system (CNS) structure and function, and the dysregulation of lipid metabolism is known to occur in many neurological disorders, including Alzheimer's disease. Here we review what is currently known about lipid dyshomeostasis in Alzheimer's disease. We propose that small extracellular vesicle (sEV) lipids may provide insight into the pathophysiology and progression of Alzheimer's disease. This stems from the recognition that sEV likely contributes to disease pathogenesis, but also an understanding that sEV can serve as a source of potential biomarkers. While the protein and RNA content of sEV in the CNS diseases have been studied extensively, our understanding of the lipidome of sEV in the CNS is still in its infancy.
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ItemPrion disease in Indigenous Australians(WILEY, 2021-07-01)BACKGROUND: Indigenous Australians are at increased risk of developing dementia - Alzheimer disease and mixed dementia diagnoses are the most common. While prion diseases have been reported in Indigenous peoples of Papua New Guinea and the United States, the occurrence and phenotype of prion disease in Indigenous Australians is hitherto unreported. AIM: To report the incidence rate and clinical phenotype of Creutzfeldt-Jakob disease (CJD) in Indigenous Australians. METHOD: Crude sporadic CJD (sCJD) incidence rates and indirect age standardisation of all CJD were assessed to calculate the standardised mortality ratio (SMR) of the Indigenous Australian population in comparison to the all-resident Australian population, along with analysis of clinical phenotypes. RESULTS: We report an illustrative case of an Indigenous Australian from regionally remote Western Australia dying from typical 'probable' sCJD 2 months after disease onset, with Australian National CJD Registry (ANCJDR) surveillance overall demonstrating eight Indigenous Australians dying from sCJD (five post-mortem confirmed, three classified as 'probable') with a clinical phenotype similar to non-indigenous people, including median age at death of 61 years (interquartile range IQR = 16 years) and median duration of illness of 3 months (IQR = 1.6 months). Indigenous Australians with sCJD were geographically dispersed throughout Australia. The calculated overall crude annual rate of sCJD in Indigenous Australians compared to the remainder of the Australian population was not significantly different (0-3.87/million for Indigenous Australians; 0.94-1.83/million for non-indigenous). The overall indirect age-standardised CJD mortality ratio for the indigenous population for the years 2006-2018 was 1.49 (95% CI, 0.75-2.98), also not significantly different to the all-resident Australian population. CONCLUSION: CJD occurs in Indigenous Australians with clinical phenotype and occurrence rates similar to non-Indigenous Australians. These findings contrast with a previous report where the incidence rate of CJD in a non-Australian indigenous population was reported to be decreased.
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ItemBDNF VAL66MET polymorphism and memory decline across the spectrum of Alzheimer's disease(WILEY, 2021-01-06)The brain-derived neurotrophic factor (BDNF) Val66Met (rs6265) polymorphism has been shown to moderate the extent to which memory decline manifests in preclinical Alzheimer's disease (AD). To date, no study has examined the relationship between BDNF and memory in individuals across biologically confirmed AD clinical stages (i.e., Aβ+). We aimed to understand the effect of BDNF on episodic memory decline and clinical disease progression over 126 months in individuals with preclinical, prodromal and clinical AD. Participants enrolled in the Australian Imaging, Biomarkers and Lifestyle (AIBL) study who were Aβ + (according to positron emission tomography), and cognitively normal (CN; n = 238), classified as having mild cognitive impairment (MCI; n = 80), or AD (n = 66) were included in this study. Cognition was evaluated at 18 month intervals using an established episodic memory composite score over 126 months. We observed that in Aβ + CNs, Met66 was associated with greater memory decline with increasing age and were 1.5 times more likely to progress to MCI/AD over 126 months. In Aβ + MCIs, there was no effect of Met66 on memory decline or on disease progression to AD over 126 months. In Aβ + AD, Val66 homozygotes showed greater memory decline, while Met66 carriers performed at a constant and very impaired level. Our current results illustrate the importance of time and disease severity to clinicopathological models of the role of BDNF Val66Met in memory decline and AD clinical progression. Specifically, the effect of BDNF on memory decline is greatest in preclinical AD and reduces as AD clinical disease severity increases.
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ItemModeling autosomal dominant Alzheimer's disease with machine learning(WILEY, 2021-06-01)INTRODUCTION: Machine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease. METHODS: Longitudinal structural magnetic resonance imaging, amyloid positron emission tomography (PET), and fluorodeoxyglucose PET were acquired in 131 mutation carriers and 74 non-carriers from the Dominantly Inherited Alzheimer Network; the groups were matched for age, education, sex, and apolipoprotein ε4 (APOE ε4). A deep neural network was trained to predict disease progression for each modality. Relief algorithms identified the strongest predictors of mutation status. RESULTS: The Relief algorithm identified the caudate, cingulate, and precuneus as the strongest predictors among all modalities. The model yielded accurate results for predicting future Pittsburgh compound B (R2 = 0.95), fluorodeoxyglucose (R2 = 0.93), and atrophy (R2 = 0.95) in mutation carriers compared to non-carriers. DISCUSSION: Results suggest a sigmoidal trajectory for amyloid, a biphasic response for metabolism, and a gradual decrease in volume, with disease progression primarily in subcortical, middle frontal, and posterior parietal regions.
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ItemThe Brain Chart of Aging: Machine-learning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans(WILEY, 2021-01)INTRODUCTION: Relationships between brain atrophy patterns of typical aging and Alzheimer's disease (AD), white matter disease, cognition, and AD neuropathology were investigated via machine learning in a large harmonized magnetic resonance imaging database (11 studies; 10,216 subjects). METHODS: Three brain signatures were calculated: Brain-age, AD-like neurodegeneration, and white matter hyperintensities (WMHs). Brain Charts measured and displayed the relationships of these signatures to cognition and molecular biomarkers of AD. RESULTS: WMHs were associated with advanced brain aging, AD-like atrophy, poorer cognition, and AD neuropathology in mild cognitive impairment (MCI)/AD and cognitively normal (CN) subjects. High WMH volume was associated with brain aging and cognitive decline occurring in an ≈10-year period in CN subjects. WMHs were associated with doubling the likelihood of amyloid beta (Aβ) positivity after age 65. Brain aging, AD-like atrophy, and WMHs were better predictors of cognition than chronological age in MCI/AD. DISCUSSION: A Brain Chart quantifying brain-aging trajectories was established, enabling the systematic evaluation of individuals' brain-aging patterns relative to this large consortium.
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ItemCerebrospinal fluid neurofilament light and cerebral atrophy in younger-onset dementia and primary psychiatric disorders(WILEY, 2023-09)BACKGROUND AND AIMS: Neurodegeneration underpins the pathological processes of younger-onset dementia (YOD) and has been implicated in primary psychiatric disorders (PSYs). Cerebrospinal fluid (CSF) neurofilament light (NfL) has been used to investigate neurodegeneration severity through correlation with structural brain changes in various conditions, but has seldom been evaluated in YOD and PSYs. METHODS: This retrospective study included patients with YOD or PSYs with magnetic resonance imaging (MRI) of the brain and CSF NfL analysis. Findings from brain MRI were analysed using automated volumetry (volBrain) to measure white matter (WM), grey matter (GM) and whole brain (WB) volumes expressed as percentages of total intracranial volume. Correlations between NfL and brain volume measurements were computed whilst adjusting for age. RESULTS: Seventy patients (47 with YOD and 23 with PSY) were identified. YOD types included Alzheimer disease and behavioural variant frontotemporal dementia. PSY included schizophrenia and major depressive disorder. MRI brain sequences were either fast spoiler gradient-echo (FSPGR) or magnetization-prepared rapid acquisition gradient-echo (MPRAGE). In the total cohort, higher NfL was associated with reduced WB in the FSPGR and MPRAGE sequences (r = -0.402 [95% confidence interval (CI), -0.593 to -0.147], P = 0.008 and r = -0.625 [95% CI, -0.828 to -0.395], P < 0.001, respectively). Higher NfL was related to reduced GM in FSPGR (r = 0.385 [95% CI, -0.649 to -0.014], P = 0.017) and reduced WM in MPRAGE (r = -0.650 [95% CI, -0.777 to -0.307], P < 0.001). Similar relationships were seen in YOD, but not in PSY. CONCLUSION: Higher CSF NfL is related to brain atrophy in YOD, further supporting its use as a nonspecific marker of neurodegeneration severity.
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ItemBiomarker clustering in autosomal dominant Alzheimer's disease(WILEY, 2022-04-01)INTRODUCTION: As the number of biomarkers used to study Alzheimer's disease (AD) continues to increase, it is important to understand the utility of any given biomarker, as well as what additional information a biomarker provides when compared to others. METHODS: We used hierarchical clustering to group 19 cross-sectional biomarkers in autosomal dominant AD. Feature selection identified biomarkers that were the strongest predictors of mutation status and estimated years from symptom onset (EYO). Biomarkers identified included clinical assessments, neuroimaging, cerebrospinal fluid amyloid, and tau, and emerging biomarkers of neuronal integrity and inflammation. RESULTS: Three primary clusters were identified: neurodegeneration, amyloid/tau, and emerging biomarkers. Feature selection identified amyloid and tau measures as the primary predictors of mutation status and EYO. Emerging biomarkers of neuronal integrity and inflammation were relatively weak predictors. DISCUSSION: These results provide novel insight into our understanding of the relationships among biomarkers and the staging of biomarkers based on disease progression.
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ItemDiagnostic and prognostic plasma biomarkers for preclinical Alzheimer's disease(WILEY, 2022-06)INTRODUCTION: This study involved a parallel comparison of the diagnostic and longitudinal monitoring potential of plasma glial fibrillary acidic protein (GFAP), total tau (t-tau), phosphorylated tau (p-tau181 and p-tau231), and neurofilament light (NFL) in preclinical Alzheimer's disease (AD). METHODS: Plasma proteins were measured using Simoa assays in cognitively unimpaired older adults (CU), with either absence (Aβ-) or presence (Aβ+) of brain amyloidosis. RESULTS: Plasma GFAP, t-tau, p-tau181, and p-tau231 concentrations were higher in Aβ+ CU compared with Aβ- CU cross-sectionally. GFAP had the highest effect size and area under the curve (AUC) in differentiating between Aβ+ and Aβ- CU; however, no statistically significant differences were observed between the AUCs of GFAP, p-tau181, and p-tau231, but all were significantly higher than the AUC of NFL, and the AUC of GFAP was higher than the AUC of t-tau. The combination of a base model (BM), comprising the AD risk factors, age, sex, and apolipoprotein E gene (APOE) ε4 status with GFAP was observed to have a higher AUC (>90%) compared with the combination of BM with any of the other proteins investigated in the current study. Longitudinal analyses showed increased GFAP and p-tau181 in Aβ+ CU and increased NFL in Aβ- CU, over a 12-month duration. GFAP, p-tau181, p-tau231, and NFL showed significant correlations with cognition, whereas no significant correlations were observed with hippocampal volume. DISCUSSION: These findings highlight the diagnostic and longitudinal monitoring potential of GFAP and p-tau for preclinical AD.