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Abstract Background Uncovering the functional relevance underlying verbal declarative memory (VDM) genome-wide association study (GWAS) results may facilitate the development of interventions to reduce age-related memory decline and dementia. Methods We performed multi-omics and pathway enrichment analyses of paragraph (PAR-dr) and word list (WL-dr) delayed recall GWAS from 29,076 older non-demented individuals of European descent. We assessed the relationship between single-variant associations and expression quantitative trait loci (eQTLs) in 44 tissues and methylation quantitative trait loci (meQTLs) in the hippocampus. We determined the relationship between gene associations and transcript levels in 53 tissues, annotation as immune genes, and regulation by transcription factors (TFs) and microRNAs. To identify significant pathways, gene set enrichment was tested in each cohort and meta-analyzed across cohorts. Analyses of differential expression in brain tissues were conducted for pathway component genes. Results The single-variant associations of VDM showed significant linkage disequilibrium (LD) with eQTLs across all tissues and meQTLs within the hippocampus. Stronger WL-dr gene associations correlated with reduced expression in four brain tissues, including the hippocampus. More robust PAR-dr and/or WL-dr gene associations were intricately linked with immunity and were influenced by 31 TFs and 2 microRNAs. Six pathways, including type I diabetes, exhibited significant associations with both PAR-dr and WL-dr. These pathways included fifteen MHC genes intricately linked to VDM performance, showing diverse expression patterns based on cognitive status in brain tissues. Conclusions VDM genetic associations influence expression regulation via eQTLs and meQTLs. The involvement of TFs, microRNAs, MHC genes, and immune-related pathways contributes to VDM performance in older individuals.

Background: The SKT is a short cognitive performance test designed to assess impairments in memory and cognitive abilities such as attention and speed of information processing. In 2019, new regression-based norms for the English version of the SKT were calculated. This study has two aims: to establish valid cut-offs for distinguishing between no cognitive impairment, mild cognitive impairment (MCI), and dementia (1) and to cross-validate the new norms for detecting MCI and dementia in a community sample of older adults with clinical diagnoses (2). Methods: The validation sample included 143 older adults (mean age = 87.7, SD = 3.55) from the Sydney Memory and Aging Study (MAS Study). Participants were classified as having normal cognition, MCI, or dementia solely based on a consensus diagnosis; in addition, three tests (SKT, Mini-Mental State Examination (MMSE), and Addenbrooke’s Cognitive Examination III (ACE-III)) to measure cognitive impairment were applied. Sensitivity and specificity for all three tests, as well as bivariate correlations, were calculated. Results: The sensitivity of the SKT for the differentiation of cognitive impairment (MCI or dementia) from normal cognition was 80.6%. The convergence between the SKT and the consensus diagnoses was 70.3% for all three diagnostic groups. All correlations between the three tests and the consensus diagnosis were significant (p < 0.01). Conclusions: In sum, it can be stated that the SKT is a valid tool for detecting early stages of cognitive impairment, performing very well in discriminating between no cognitive impairment and cognitive impairment (MCI or dementia).

Introduction: Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL) is a rare genetic condition with a broad phenotypic presentation. This study aims to establish the first Australian cohort of individuals affected by CADASIL (AusCADASIL) and examine its clinical features and longitudinal course, and to investigate neuroimaging and blood biomarkers to assist in early diagnosis and identify disease progression. Methods: Participants will be recruited from six study centres across Australia for an observational study of CADASIL. We aim to recruit 150 participants with diagnosed CADASIL, family history of CADASIL or suspected CADASIL symptoms, and 150 cognitively normal NOTCH3 negative individuals as controls. Participants will complete: 1) online questionnaires on medical and family history, mental health, and wellbeing; 2) neuropsychological evaluation; 3) neurological examination and brain MRI; 4) ocular examination and 5) blood sample donation. Participants will have annual follow-up for 4 years to assess their progression and will be asked to invite a study partner to corroborate their self-reported cognitive and functional abilities.Primary outcomes include cognitive function and neuroimaging abnormalities. Secondary outcomes include investigation of genetics and blood and ocular biomarkers. Data from the cohort will contribute to an international consortium, and cohort participants will be invited to access future treatment/health intervention trials. Discussion: AusCADASIL will be the first study of an Australian cohort of individuals with CADASIL. The study will identify common pathogenic variants in this cohort, and characterise the pattern of clinical presentation and longitudinal progression, including imaging features, blood and ocular biomarkers and cognitive profile.

Abstract Polygenic risk scores (PRSs) can boost risk prediction in late-onset Alzheimer’s disease (LOAD) beyond apolipoprotein E (APOE) but have not been leveraged to identify genetic resilience factors. Here, we sought to identify resilience-conferring common genetic variants in (1) unaffected individuals having high PRSs for LOAD, and (2) unaffected APOE-ε4 carriers also having high PRSs for LOAD. We used genome-wide association study (GWAS) to contrast “resilient” unaffected individuals at the highest genetic risk for LOAD with LOAD cases at comparable risk. From GWAS results, we constructed polygenic resilience scores to aggregate the addictive contributions of risk-orthogonal common variants that promote resilience to LOAD. Replication of resilience scores was undertaken in eight independent studies. We successfully replicated two polygenic resilience scores that reduce genetic risk penetrance for LOAD. We also showed that polygenic resilience scores positively correlate with polygenic risk scores in unaffected individuals, perhaps aiding in staving off disease. Our findings align with the hypothesis that a combination of risk-independent common variants mediates resilience to LOAD by moderating genetic disease risk.

Abstract Background Memory clinics (MCs) play a key role in accurate and timely diagnoses and treatment of dementia and mild cognitive impairment. However, within Australia, there are little data available on current practices in MCs, which hinder international comparisons for best practice, harmonisation efforts and national coordination. Here, we aimed to characterise current service profiles of Australian MCs. Methods The ‘Australian Dementia Network Survey of Expert Opinion on Best Practice and the Current Clinical Landscape’ was conducted between August-September 2020 as part of a larger-scale Delphi process deployed to develop national MC guidelines. In this study, we report on the subset of questions pertaining to current practice including wait-times and post-diagnostic care. Results Responses were received from 100 health professionals representing 60 separate clinics (45 public, 11 private, and 4 university/research clinics). The majority of participants were from clinics in metropolitan areas (79%) and in general were from high socioeconomic areas. While wait-times varied, only 28.3% of clinics were able to offer an appointment within 1-2 weeks for urgent referrals, with significantly more private clinics (58.3%) compared to public clinics (19.5%) being able to do so. Wait-times were less than 8 weeks for 34.5% of non-urgent referrals. Only 20.0 and 30.9% of clinics provided cognitive interventions or post-diagnostic support respectively, with 7.3% offering home-based reablement programs, and only 12.7% offering access to group-based education. Metropolitan clinics utilised neuropsychological assessments for a broader range of cases and were more likely to offer clinical trials and access to research opportunities. Conclusions In comparison to similar countries with comprehensive government-funded public healthcare systems (i.e., United Kingdom, Ireland and Canada), wait-times for Australian MCs are long, and post-diagnostic support or evidence-based strategies targeting cognition are not common practice. The timely and important results of this study highlight a need for Australian MCs to adopt a more holistic service of multidisciplinary assessment and post-diagnostic support, as well as the need for the number of Australian MCs to be increased to match the rising number of dementia cases.

Abstract Background Growing evidence suggests that there is an association between poor oral health and cognitive function in late adulthood. However, most studies to date have relied on cross-sectional research methods that do not permit inferences about the temporality of any association. Moreover, the few longitudinal studies that do exist have typically relied on small samples and quite limited cognitive or oral health assessments. The aim of the present study was therefore designed to provide the first direct evaluation of whether cognitive function is predictive of poor oral health in older adults. Methods This longitudinal research included data from 339 participants aged 70 years or older from The Sydney Memory and Ageing Study (MAS), a large cohort of healthy community-dwelling older adults. Cognitive function was assessed using a battery of tests at baseline (Wave 1) in 2005 and six years later (Wave 4) in 2011. In 2015 (Wave 6), participants were assessed for oral health using the Oral Health Assessment Tool (OHAT), number of functional occluding pairs of natural teeth and sublingual resting saliva pH (SRSpH). Ordinal least squares regression analysis was used to model the effect of cognitive function on total OHAT score, and binomial logistic regression used for SRSpH and occluding pairs of functional teeth. Results Two models were tested. In the partially adjusted model, age, gender and years of education were included. The fully adjusted model additionally included medical conditions, general health, depression, smoking, alcohol consumption, functionality, and dental care utilization. The key finding to emerge was that a six-year change in memory (from Wave 1 to Wave 4) was associated with lower sublingual resting saliva pH at Wave 6 in partially (Odds Ratio (OR) = 0.65) and fully adjusted model (OR = 0.63). Conclusions This longitudinal study provides further evidence that a relationship between cognitive function and oral health exists, and also points to this relationship potentially being bi-directional, as previous evidence suggests. The findings from the study also suggest that older adults who present with greater than normal memory decline at an earlier point in life were more likely to experience poor oral health when this was evaluated at a later time-point, four years later.

BackgroundIndividuals with subjective cognitive complaints (SCCs) are at an increased risk of dementia. Questions remain about participant-reported versus informant-reported SCCs as indicators of future dementia and about longitudinal changes in participant-and informant-reported SCCs and risk of incident dementia.MethodParticipants were 873 older adults (M = 78.65-years; 55% female) and 849 informants from the Sydney Memory and Ageing Study. Comprehensive assessments occurred biennially, and clinical diagnoses were made by expert consensus for 10-years. SCCs were participants’ and informants’ responses to a single binary question concerning their/the participant’s memory decline (Yes/No) over the first 6-years. Categorical latent growth curve analyses, using the logit transformation, were used to model SCC change over time. Associations of initial propensity to report SCCs at baseline, and change in propensity to report SCCs over time, with dementia risk were examined using Cox regression.Results70% of participants reported SCCs at baseline, with a proportional increase in the odds of reporting by 11% for each additional year in the study. In contrast, 22% of informants reported SCCs at baseline, with a proportional increase by 30% in the odds of reporting per year. Participants’ initial level of (p = 0.007), but not change in SCC reporting (p = 0.179), was associated with risk of dementia controlling for all covariates. Both informants’ initial level of (p

Abstract Background Subjective cognitive decline (SCD) is recognized as a risk stage for Alzheimer’s disease (AD) and other dementias, but its prevalence is not well known. We aimed to use uniform criteria to better estimate SCD prevalence across international cohorts. Methods We combined individual participant data for 16 cohorts from 15 countries (members of the COSMIC consortium) and used qualitative and quantitative (Item Response Theory/IRT) harmonization techniques to estimate SCD prevalence. Results The sample comprised 39,387 cognitively unimpaired individuals above age 60. The prevalence of SCD across studies was around one quarter with both qualitative harmonization/QH (23.8%, 95%CI = 23.3–24.4%) and IRT (25.6%, 95%CI = 25.1–26.1%); however, prevalence estimates varied largely between studies (QH 6.1%, 95%CI = 5.1–7.0%, to 52.7%, 95%CI = 47.4–58.0%; IRT: 7.8%, 95%CI = 6.8–8.9%, to 52.7%, 95%CI = 47.4–58.0%). Across studies, SCD prevalence was higher in men than women, in lower levels of education, in Asian and Black African people compared to White people, in lower- and middle-income countries compared to high-income countries, and in studies conducted in later decades. Conclusions SCD is frequent in old age. Having a quarter of older individuals with SCD warrants further investigation of its significance, as a risk stage for AD and other dementias, and of ways to help individuals with SCD who seek medical advice. Moreover, a standardized instrument to measure SCD is needed to overcome the measurement variability currently dominant in the field.

Objective Mild cognitive impairment (MCI) is considered an intermediate stage between normal cognitive function and dementia. Fall risk is increased in this group, but there is limited literature exploring specific fall risk factors that may be addressed in fall prevention strategies. The aim of this study was to examine risk factors for falls in older people with MCI, focusing on cognitive, psychological and physical factors. Methods Participants (n = 266, 45% women) were community-dwelling older people aged 70–90 years who met the criteria for MCI. Cognitive, psychological, sensorimotor and physical assessments, physical activity levels, medication use, general health and disability were ascertained at baseline. Falls were monitored prospectively for 12 months. Results During follow-up, 106 (40%) participants reported one or more falls. Poorer visual contrast sensitivity, increased postural sway, lower levels of weekly walking activity, higher levels of depressive symptoms and psychotropic medication use were significantly associated with faller status (≥1 falls) in univariable analyses. Of these factors, poor visual contrast sensitivity, increased postural sway and psychotropic medication use were found to be significant independent predictors of falls in multivariable analysis while controlling for age and sex. No measures of cognitive function were associated with falls. Conclusions Poor visual contrast sensitivity, impaired balance and psychotropic medication use predicted falls in community-dwelling people with MCI. These risk factors may be amenable to intervention, so these factors could be carefully considered in fall prevention programs for this population.

Abstract Data collected from clinical trials and cohort studies, such as dementia studies, are often high-dimensional, censored, heterogeneous and contain missing information, presenting challenges to traditional statistical analysis. There is an urgent need for methods that can overcome these challenges to model this complex data. At present there is no cure for dementia and no treatment that can successfully change the course of the disease. Machine learning models that can predict the time until a patient develops dementia are important tools in helping understand dementia risks and can give more accurate results than traditional statistical methods when modelling high-dimensional, heterogeneous, clinical data. This work compares the performance and stability of ten machine learning algorithms, combined with eight feature selection methods, capable of performing survival analysis of high-dimensional, heterogeneous, clinical data. We developed models that predict survival to dementia using baseline data from two different studies. The Sydney Memory and Ageing Study (MAS) is a longitudinal cohort study of 1037 participants, aged 70–90 years, that aims to determine the effects of ageing on cognition. The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a longitudinal study aimed at identifying biomarkers for the early detection and tracking of Alzheimer's disease. Using the concordance index as a measure of performance, our models achieve maximum performance values of 0.82 for MAS and 0.93 For ADNI.

In this research, we investigated the alterations in the directionality and strength of regional interactions within functionally changed brain networks and their relationship to cognitive decline during the aging process in normal elderly individuals. Thirty-seven cognitively normal elderly people received resting-state fMRI scans and cognitive assessments at baseline (age = 78.65 ± 3.56 years) and at 4-year follow-up. Functional connectivity analyses were used to identify networks containing brain regions whose functions changed with age as regions of interest. The spectral dynamic causal modeling (spDCM) method was used to estimate the causal interactions within networks in subjects at different time points and in subjects with different cognitive levels to explore the alterations with cognitive aging. The results showed that, at both time points, all the networks, except the frontal-parietal network (FPN) at baseline, had mutual interactions between each pair of nodes. Furthermore, when the subjects were divided with global cognition level, lost connections were only found in the subgroup with better performance. These indicated that elderly people appeared to need more interaction pathways between brain areas with cognitive decline. We also observed that the strength of the flow of information from the left angular gyrus to the precuneus, which is associated with activation of memory retrieval and the functional hub involved in various cognitive domains, was predictive of declines in executive function with the aging process, making it a potential predictor of such situation.

Christina M. Lill, who contributed to analysis of data, was inadvertently omitted from the author list in the originally published version of this article. This has now been corrected in both the PDF and HTML versions of the article.

This study investigated associations of adherence to the Australian Dietary Guidelines (ADG) with cognitive performance and cognitive decline over 6 years. We used longitudinal data from the Sydney Memory and Aging Study comprising 1037 community-dwelling non-demented participants aged 70–90 years. Dietary intake was assessed at baseline using the Dietary Questionnaire for Epidemiological Studies Version 2. Adherence to the ADG was scored using the Dietary Guideline Index 2013 (DGI-2013). Cognition was assessed using neuropsychological tests in six cognitive domains and global cognition at baseline and 2, 4 and 6 years later. Linear mixed models analysed the association between adherence to the ADG and cognitive function and cognitive decline over 6 years. Results indicated that overall adherence to the ADG was suboptimal (DGI-2013 mean score 43⋅8 with a standard deviation of 10⋅1; median score 44, range 12–73 with an interquartile range of 7). The percent of participants attaining recommended serves for the five food groups were 30⋅2 % for fruits, 11⋅2 % for vegetables, 54⋅6 % for cereals, 28⋅9 % for meat and alternatives and 2⋅1 % for dairy consumption. Adherence to the ADG was not associated with overall global cognition over 6 years (β = 0⋅000; 95 % CI: −0⋅007, 0⋅007; P = 0⋅95). Neither were DGI-2013 scores associated with change in global cognitive performance over 6 years (β = 0⋅002; 95 % CI: −0⋅002, 0⋅005; P = 0⋅41) nor in any individual cognitive domains. In conclusion, adherence to the ADG was not associated with cognitive health over time in this longitudinal analysis of older Australians. Future research is needed to provide evidence to support specific dietary guidelines for neurocognitive health among Australian older adults.

Centenarians without dementia can be considered as a model of successful ageing and resistance against age-related cognitive decline. Is there something special about their brain functional connectivity that helps them preserve cognitive function into the 11th decade of life? In a cohort of 57 dementia-free near-centenarians and centenarians (95–103 years old) and 66 cognitively unimpaired younger participants (76–79 years old), we aimed to investigate brain functional characteristics in the extreme age range using resting-state functional MRI. Using group-level independent component analysis and dual regression, results showed group differences in the functional connectivity of seven group-level independent component (IC) templates, after accounting for sex, education years, and grey matter volume, and correcting for multiple testing at family-wise error rate of 0.05. After Bonferroni correction for testing 30 IC templates, near-centenarians and centenarians showed stronger functional connectivity between right frontoparietal control network (FPCN) and left inferior frontal gyrus (Bonferroni-corrected p ​= ​0.024), a core region of the left FPCN. The investigation of between-IC functional connectivity confirmed the voxel-wise result by showing stronger functional connectivity between bilateral FPCNs in near-centenarians and centenarians compared to young-old controls. In addition, near-centenarians and centenarians had weaker functional connectivity between default mode network and fronto-temporo-parietal network compared to young-old controls. In near-centenarians and centenarians, stronger functional connectivity between bilateral FPCNs was associated with better cognitive performance in the visuospatial domain. The current study highlights the key role of bilateral FPCN connectivity in the reserve capacity against age-related cognitive decline.

Aging is associated with changes in brain functional patterns as well as cognition. The present research sought to investigate longitudinal changes in whole brain functional connectivity strength (FCS) and cognitive performance scores in very old cognitively unimpaired individuals. We studied 34 cognitively normal elderly individuals at both baseline and 4-year follow-up (baseline age = 78 ± 3.14 years) with resting-state functional magnetic resonance imaging (r-fMRI), structural MRI scans, and neuropsychological assessments conducted. Voxel-based whole brain FCS was calculated and we found that bilateral superior parietal and medial frontal regions showed decreased FCS, while the supplementary motor area (SMA) and insula showed increased FCS with age, along with a decrease in bilateral prefrontal cortical thickness. The changes of FCS in left precuneus were associated with an aging-related decline in global cognition. Taken together, our results suggest changes in FCS with aging with the precuneus as a hub and this may underlie changes in global cognition that accompany aging. These findings help better understand the normal aging mechanism.

Cognitive function is associated with health and important life outcomes. Here, the authors perform a genome-wide association study for general cognitive function in 300,486 individuals and identify genetic loci that implicate neural and cell developmental pathways in this trait.

Emerging evidence from lesion-symptom mapping (LSM) studies suggested that regional white matter lesions (WML) on strategic white matter (WM) fiber tracts are significantly associated with specific cognitive domains, independent of global WML burden. However, previous LSM investigations were mostly carried out in disease cohorts, with little evidence from community-based older individuals, making findings difficult to generalize. Moreover, most LSM studies applied a threshold to the probabilistic atlas, leading to the loss of information and threshold-dependent findings. Furthermore, it is still unclear whether associations between regional WML and cognition are independent of global grey matter (GM) and WM volumes, which have also been linked to cognition. In the current study, we undertook a region of interest (ROI) LSM study to examine the relationship between regional WML on strategic WM tracts and cognitive performance in a large community-based cohort of older individuals (N = 461; 70–90 years). WML were extracted using a publicly available pipeline, UBO Detector (https://cheba.unsw.edu.au/group/neuroimaging-pipeline). Mapping of WML to the Johns Hopkins University WM atlas was undertaken using an automated TOolbox for Probabilistic MApping of Lesions (TOPMAL), which we introduce here, and is implemented in UBO Detector. The results show that different patterns of brain structural volumes in the ageing brain were associated with different cognitive domains. Regional WML were associated with processing speed, executive function, and global cognition, independent of total GM, WM and WML volumes. Moreover, regional WML explained more variance in executive function, compared to total GM, WM and WML volumes. The current study highlights the importance of studying regional WML in age-related cognitive decline. Keywords: White matter lesions, Strategic white matter fiber tracts, Cognitive performance, Neuropsychological assessments, Community-dwelling older individuals

Abstract Introduction Informant‐reported subjective cognitive decline (iSCD) has been associated with a higher risk of conversion to dementia, but the findings of whole brain functional connectivity strength (FCS) changes in iSCD are limited. Methods The sample comprised 39 participants with iSCD and 39 age‐ and sex‐ matched healthy controls. The global absolute (aFCS) and relative functional connectivity strengths were estimated using weighted degree centrality and the z‐scores of the weighted degree centrality respectively. FreeSurfer was used for measuring cortical thickness. Results The aFCS was lower in iSCD primarily in left medial superior frontal, left precuneus, left parietal, right cuneus, and bilateral calcarine; while relative functional connectivity strength was higher in posterior cingulate cortex/precuneus compared with healthy controls. No significant differences in cortical thickness were observed. Discussion There are detectable changes of FCS in iSCD, with the precuneus possibly playing a compensatory role. FCS could therefore have a potential role to serve as one of the earliest neuroimaging markers of neurodegenerative disease.

Amnestic MCI (aMCI) and non-amnestic MCI (naMCI) are considered to differ in etiology and outcome. Accurately classifying MCI into meaningful subtypes would enable early intervention with targeted treatment. In this study, we employed structural magnetic resonance imaging (MRI) for MCI subtype classification. This was carried out in a sample of 184 community-dwelling individuals (aged 73–85 years). Cortical surface based measurements were computed from longitudinal and cross-sectional scans. By introducing a feature selection algorithm, we identified a set of discriminative features, and further investigated the temporal patterns of these features. A voting classifier was trained and evaluated via 10 iterations of cross-validation. The best classification accuracies achieved were: 77% (naMCI vs. aMCI), 81% (aMCI vs. cognitively normal (CN)) and 70% (naMCI vs. CN). The best results for differentiating aMCI from naMCI were achieved with baseline features. Hippocampus, amygdala and frontal pole were found to be most discriminative for classifying MCI subtypes. Additionally, we observed the dynamics of classification of several MRI biomarkers. Learning the dynamics of atrophy may aid in the development of better biomarkers, as it may track the progression of cognitive impairment.

Background: Amnestic mild cognitive impairment (aMCI) is considered to be a transitional stage between healthy aging and Alzheimer's disease (AD), and consists of two subtypes: single-domain aMCI (sd-aMCI) and multi-domain aMCI (md-aMCI). Individuals with md-aMCI are found to exhibit higher risk of conversion to AD. Accurate discrimination among aMCI subtypes (sd- or md-aMCI) and controls could assist in predicting future decline. Methods: We apply our novel thickness network (ThickNet) features to discriminate md-aMCI from healthy controls (NC). ThickNet features are extracted from the properties of a graph constructed from inter-regional co-variation of cortical thickness. We fuse these ThickNet features using multiple kernel learning to form a composite classifier. We apply the proposed ThickNet classifier to discriminate between md-aMCI and NC, sd-aMCI and NC and; and also between sd-aMCI and md-aMCI, using baseline T1 MR scans from the Sydney Memory and Ageing Study. Results: ThickNet classifier achieved an area under curve (AUC) of 0.74, with 70% sensitivity and 69% specificity in discriminating md-aMCI from healthy controls. The same classifier resulted in AUC = 0.67 and 0.67 for sd-aMCI/NC and sd-aMCI/md-aMCI classification experiments respectively. Conclusions: The proposed ThickNet classifier demonstrated potential for discriminating md-aMCI from controls, and in discriminating sd-aMCI from md-aMCI, using cortical features from baseline MRI scan alone. Use of the proposed novel ThickNet features demonstrates significant improvements over previous experiments using cortical thickness alone. This result may offer the possibility of early detection of Alzheimer's disease via improved discrimination of aMCI subtypes.