Anatomy and Neuroscience - Research Publications
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ItemBDNF VAL66MET polymorphism and memory decline across the spectrum of Alzheimer's disease(WILEY, 2021-06)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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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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ItemNo Preview AvailableLongitudinal Association of Intraindividual Variability With Cognitive Decline and Dementia: A Meta-Analysis(AMER PSYCHOLOGICAL ASSOC, 2021-10)OBJECTIVE: Intraindividual variability (IIV)-variance in an individual's cognitive performance-may be associated with subsequent cognitive decline and/or conversion to dementia in older adults. This novel measure of cognition encompasses two main operationalizations: inconsistency (IIV-I) and dispersion (IIV-D), referring to variance within or across tasks, respectively. Each operationalization can also be measured with or without covariates. This meta-analytic study explores the association between IIV and subsequent cognitive outcomes regardless of operational definition and measurement approach. METHOD: Longitudinal studies (N = 13) that have examined IIV in association with later cognitive decline and/or conversation to MCI/dementia were analyzed. The effect of IIV operationalization was explored. Additional subgroup analysis of measurement approaches could not be examined due to the limited number of appropriate studies available for inclusion. RESULTS: Meta-analytic estimates suggest IIV is associated with subsequent cognitive decline and/or conversion to MCI/dementia (r = .20, 95% CI [.09, .31]) with no significant difference between the two operationalisations observed (Q = 3.41, p = .065). CONCLUSION: Cognitive IIV, including both IIV-I and IIV-D operationalizations, appears to be associated with subsequent cognitive decline and/or dementia and may offer a novel indicator of incipient dementia in both clinical and research settings. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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ItemNo Preview AvailableAcute neuroimmune stimulation impairs verbal memory in adults: A PET brain imaging study(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2021-01)Psychiatric and neurologic disorders are often characterized by both neuroinflammation and cognitive dysfunction. To date, however, the relationship between neuroinflammation and cognitive dysfunction remains understudied in humans. Preclinical research indicates that experimental induction of neuroinflammation reliably impairs memory processes. In this paradigm development study, we translated those robust preclinical findings to humans using positron emission tomography (PET) imaging with [11C]PBR28, a marker of microglia, and lipopolysaccharide (LPS), a potent neuroimmune stimulus. In a sample of 18 healthy adults, we extended our previous findings that LPS administration increased whole-brain [11C]PBR28 availability by 31-50%, demonstrating a robust neuroimmune response (Cohen's ds > 1.6). We now show that LPS specifically impaired verbal learning and recall, hippocampal memory processes, by 11% and 22%, respectively (Cohen's ds > 0.9), but did not alter attention, motor, or executive processes. The LPS-induced increase in [11C]PBR28 binding was correlated with significantly greater decrements in verbal learning performance in the hippocampus (r = -0.52, p = .028), putamen (r = -0.50, p = .04), and thalamus (r = -0.55, p = .02). This experimental paradigm may be useful in investigating mechanistic relationships between neuroinflammatory signaling and cognitive dysfunction in psychiatric and neurologic disorders. It may also provide a direct approach to evaluate medications designed to rescue cognitive deficits associated with neuroinflammatory dysfunction.
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ItemA deep learning framework identifies dimensional representations of Alzheimer's Disease from brain structure(NATURE PORTFOLIO, 2021-12-03)Heterogeneity of brain diseases is a challenge for precision diagnosis/prognosis. We describe and validate Smile-GAN (SeMI-supervised cLustEring-Generative Adversarial Network), a semi-supervised deep-clustering method, which examines neuroanatomical heterogeneity contrasted against normal brain structure, to identify disease subtypes through neuroimaging signatures. When applied to regional volumes derived from T1-weighted MRI (two studies; 2,832 participants; 8,146 scans) including cognitively normal individuals and those with cognitive impairment and dementia, Smile-GAN identified four patterns or axes of neurodegeneration. Applying this framework to longitudinal data revealed two distinct progression pathways. Measures of expression of these patterns predicted the pathway and rate of future neurodegeneration. Pattern expression offered complementary performance to amyloid/tau in predicting clinical progression. These deep-learning derived biomarkers offer potential for precision diagnostics and targeted clinical trial recruitment.
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ItemPilot Evaluation of the Unsupervised, At-Home Cogstate Brief Battery in ADNI-2(IOS PRESS, 2021)BACKGROUND: There is a need for feasible, scalable assessments to detect cognitive impairment and decline. The Cogstate Brief Battery (CBB) is validated for Alzheimer's disease (AD) and in unsupervised and bring your own device contexts. The CBB has shown usability for self-completion in the home but has not been employed in this way in a multisite clinical trial in AD. OBJECTIVE: The objective of the pilot was to evaluate feasibility of at-home, self-completion of the CBB in the Alzheimer's Disease Neuroimaging Initiative (ADNI) over 24 months. METHODS: The CBB was included as a pilot for cognitively normal (CN) and mild cognitive impairment (MCI) participants in ADNI-2, invited to take the assessment in-clinic, then at at-home over a period of 24 months follow-up. Data were analyzed to explore acceptability/usability, concordance of in-clinic and at-home assessment, and validity. RESULTS: Data were collected for 104 participants (46 CN, 51 MCI, and 7 AD) who consented to provide CBB data. Subsequent analyses were performed for the CN and MCI groups only. Test completion rates were 100%for both the first in-clinic supervised and first at-home unsupervised assessments, with few repeat performances required. However, available follow-up data declined sharply over time. Good concordance was seen between in-clinic and at-home assessments, with non-significant and small effect size differences (Cohen's d between -0.04 and 0.28) and generally moderate correlations (r = 0.42 to 0.73). Known groups validity was also supported (11/16 comparisons with Cohen's d≥0.3). CONCLUSION: These data demonstrate the feasibility of use for the CBB for unsupervised at-home, testing, including MCI groups. Optimal approaches to the application of assessments to support compliance over time remain to be determined.
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ItemHigher Coffee Consumption Is Associated With Slower Cognitive Decline and Less Cerebral Aβ-Amyloid Accumulation Over 126 Months: Data From the Australian Imaging, Biomarkers, and Lifestyle Study(FRONTIERS MEDIA SA, 2021-11-19)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.
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ItemUsing imputation to provide harmonized longitudinal measures of cognition across AIBL and ADNI(NATURE PORTFOLIO, 2021-12-10)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 < 0.001) for clinical classification to measured data in the other dataset. Further, the increased power of the overall harmonised dataset was demonstrated by observing a significant association between CVLT-II test scores (imputed for ADNI) with PET Amyloid-β in MCI APOE-ε4 homozygotes in the imputed data (N = 65) but not for the original AIBL dataset (N = 11). These results suggest that MissForest can provide a practical solution for data harmonization using imputation across studies to improve power for more nuanced analyses.
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ItemMachine learning approaches to predicting amyloid status using data from an online research and recruitment registry: The Brain Health Registry(WILEY, 2021)INTRODUCTION: This study investigated the extent to which subjective and objective data from an online registry can be analyzed using machine learning methodologies to predict the current brain amyloid beta (Aβ) status of registry participants. METHODS: We developed and optimized machine learning models using data from up to 664 registry participants. Models were assessed on their ability to predict Aβ positivity using the results of positron emission tomography as ground truth. RESULTS: Study partner-assessed Everyday Cognition score was preferentially selected for inclusion in the models by a feature selection algorithm during optimization. DISCUSSION: Our results suggest that inclusion of study partner assessments would increase the ability of machine learning models to predict Aβ positivity.
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ItemFifteen Years of the Australian Imaging, Biomarkers and Lifestyle (AIBL) Study: Progress and Observations from 2,359 Older Adults Spanning the Spectrum from Cognitive Normality to Alzheimer's Disease(IOS PRESS, 2021)BACKGROUND: The Australian Imaging, Biomarkers and Lifestyle (AIBL) Study commenced in 2006 as a prospective study of 1,112 individuals (768 cognitively normal (CN), 133 with mild cognitive impairment (MCI), and 211 with Alzheimer's disease dementia (AD)) as an 'Inception cohort' who underwent detailed ssessments every 18 months. Over the past decade, an additional 1247 subjects have been added as an 'Enrichment cohort' (as of 10 April 2019). OBJECTIVE: Here we provide an overview of these Inception and Enrichment cohorts of more than 8,500 person-years of investigation. METHODS: Participants underwent reassessment every 18 months including comprehensive cognitive testing, neuroimaging (magnetic resonance imaging, MRI; positron emission tomography, PET), biofluid biomarkers and lifestyle evaluations. RESULTS: AIBL has made major contributions to the understanding of the natural history of AD, with cognitive and biological definitions of its three major stages: preclinical, prodromal and clinical. Early deployment of Aβ-amyloid and tau molecular PET imaging and the development of more sensitive and specific blood tests have facilitated the assessment of genetic and environmental factors which affect age at onset and rates of progression. CONCLUSION: This fifteen-year study provides a large database of highly characterized individuals with longitudinal cognitive, imaging and lifestyle data and biofluid collections, to aid in the development of interventions to delay onset, prevent or treat AD. Harmonization with similar large longitudinal cohort studies is underway to further these aims.