Australian Imaging , Biomarkers And Lifestyle Research Articles

Distance-based novelty detection model for identifying individuals at risk of developing Alzheimer's disease.

Novelty detection (ND, also known as one-class classification) is a machine learning technique used to identify patterns that are typical of the majority class and can discriminate deviations as novelties. In the context of Alzheimer's disease (AD), ND could be employed to detect abnormal or atypical behavior that may indicate early signs of cognitive decline or the presence of the disease. To date, few research studies have used ND to discriminate the risk of developing AD and mild cognitive impairment (MCI) from healthy controls (HC). In this work, two distinct cohorts with highly heterogeneous data, derived from the Australian Imaging Biomarkers and Lifestyle (AIBL) Flagship Study of Ageing project and the Fujian Medical University Union Hospital (FMUUH) China, were employed. An innovative framework with built-in easily interpretable ND models constructed solely on HC data was introduced along with proposing a strategy of distance to boundary (DtB) to detect MCI and AD. Subsequently, a web-based graphical user interface (GUI) that incorporates the proposed framework was developed for non-technical stakeholders. Our experimental results indicate that the best overall performance of detecting AD individuals in AIBL and FMUUH datasets was obtained by using the Mixture of Gaussian-based ND algorithm applied to single modality, with an AUC of 0.8757 and 0.9443, a sensitivity of 96.79% and 89.09%, and a specificity of 89.63% and 90.92%, respectively. The GUI offers an interactive platform to aid stakeholders in making diagnoses of MCI and AD, enabling streamlined decision-making processes. More importantly, the proposed DtB strategy could visually and quantitatively identify individuals at risk of developing AD.

Exploring the association between cancer and cognitive impairment in the Australian Imaging Biomarkers and Lifestyle (AIBL) study

An inverse association between cancer and Alzheimer’s disease (AD) has been demonstrated; however, the association between cancer and mild cognitive impairment (MCI), and the association between cancer and cognitive decline are yet to be clarified. The AIBL dataset was used to address these knowledge gaps. The crude and adjusted odds ratios for MCI/AD and cognitive decline were compared between participants with/without cancer (referred to as C+ and C− participants). A 37% reduction in odds for AD was observed in C+ participants compared to C− participants after adjusting for all confounders. The overall risk for MCI and AD in C+ participants was reduced by 27% and 31%, respectively. The odds of cognitive decline from MCI to AD was reduced by 59% in C+ participants after adjusting for all confounders. The risk of cognitive decline from MCI to AD was halved in C+ participants. The estimated mean change in Clinical Dementia Rating-Sum of boxes (CDR-SOB) score per year was 0.23 units/year higher in C− participants than in C+ participants. Overall, an inverse association between cancer and MCI/AD was observed in AIBL, which is in line with previous reports. Importantly, an inverse association between cancer and cognitive decline has also been identified.

Association Between β-Amyloid Accumulation and Incident Dementia in Individuals 80 Years or Older Without Dementia.

While the highest prevalence of dementia occurs in individuals older than 80 years, most imaging studies focused on younger populations. The rates of β-amyloid (Aβ) accumulation and the effect of Alzheimer disease (AD) pathology on progression to dementia in this age group remain unexplored. In this study, we examined the relationship between changes in Aβ deposition over time and incident dementia in nondemented individuals followed during a period of 11 years. We examined 94 participants (age 85.9 + 2.8 years) who had up to 5 measurements of Pittsburgh compound-B (PiB)-PET and clinical evaluations from 2009 to 2020. All 94 participants had 2 PiB-PET scans, 76 participants had 3 PiB-PET scans, 18 participants had 4 PiB-PET scans, and 10 participants had 5 PiB-PET scans. The rates of Aβ deposition were compared with 120 nondemented individuals younger than 80 years (69.3 ± 5.4 years) from the Australian Imaging, Biomarker, and Lifestyle (AIBL) study who had 3 or more annual PiB-PET assessments. By 2020, 49% of the participants developed dementia and 63% were deceased. There was a gradual increase in Aβ deposition in all participants whether they were considered Aβ positive or negative at baseline. In a Cox model controlled for age, sex, education level, APOE-4 allele, baseline Mini-Mental State Examination, and mortality, short-term change in Aβ deposition was not significantly associated with incident dementia (HR 2.19 (0.41-11.73). However, baseline Aβ burden, cortical thickness, and white matter lesions volume were the predictors of incident dementia. Aβ accumulation was faster (p = 0.01) in the older cohort (5.6%/year) when compared with AIBL (4.1%/year). In addition, baseline Aβ deposition was a predictor of short-term change (mean time 1.88 years). There was an accelerated Aβ accumulation in cognitively normal individuals older than 80 years. Baseline Aβ deposition was a determinant of incident dementia and short-term change in Aβ deposition suggesting that an active Aβ pathologic process was present when these participants were cognitively normal. Consequently, age may not be a limiting factor for the use of the emergent anti-Aβ therapies.

Identification of objective cognitive decline in preclinical Alzheimer’ disease

AbstractBackgroundIn Alzheimer’s disease (AD), a long preclinical and prodromal period precedes dementia onset in which cognition declines slowly, but at variable rates for different domains. Positive AD biomarkers, (e.g., amyloid, Tau, or APOE ε4 carriage), increase rates of cognitive decline and transition to dementia. However, the extent to which cognitive decline, identified in some older adults, reflects neurodegenerative disease or normal aging in the absence of knowledge about biomarkers or genes is not well understood. We applied a novel approach to classify cognitive decline in individuals using six cognitive composite scores (AIBL Pre‐clinical Alzheimer’s Cognitive Composite (AIBL‐PACC), episodic recall, attention, executive function, language and recognition). We examine group‐wise differences across cognitive composite scores and assessed frequencies for AD biomarker positivity amongst the novel groups.MethodProspective data from 1,389 Cognitively unimpaired (CU) participants from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of ageing were analysed using an unsupervised multivariate mixture modelling framework, accounting for age, gender and APOE ε4 allele status. Participants cognitive status was classified for their final visit. The cognitive profile and biomarker characteristics of adults with abnormal cognitive decline was compared.ResultDifferences in composite score values between cognitive decline and no decline groups were strongest for episodic recall (Cohen’s d = 1.49) and the AIBL‐PACC score (Cohen’s d = 1.45, Table 1, Figure 1A). By comparison, differences in composite scores between Aβ‐PET groups, or APOE ε4 allele carriers/non‐carriers were of less magnitude (Table 1, Figure 1). Participants with cognitive decline were more likely to be amyloid positive, (decline: 42% vs no decline: 28%, p<0.0001), but not ε4 allele carriers (decline: 28% VS no decline: 27%, p>0.05).ConclusionA measure of objective cognitive decline, created using flexible mixture models with six cognitive composite scores from AIBL, has utility for discerning participants with early changes in cognition. Based upon using the cognitive data alone, this method was able to detect amyloid positivity in those CU participants with cognitive decline.

Cross‐sectional investigation of synaptic markers Neurogranin and BACE1 in CSF from the AIBL study

AbstractBackgroundSynaptic disfunction is a feature of Alzheimer’s disease. Recent studies show both pre‐ and post‐synaptic termini proteins change in concentration in the CSF across the A/T/N framework. Here we investigate BACE1, neurogranin (Ng) and alpha‐synuclein, along with total‐tau (t‐tau) and amyloid‐β 1‐42 (Aβ42), in relation to PET‐amyloid status.MethodParticipants (n = 105) with CSF and matching PET‐amyloid scans from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study were analysed for CSF synaptic markers BACE1, Ng and alpha‐synuclein using commercially available antibody assays (EUROIMMUN). BACE1 used ADx401 (5G7) and ADx402 (10B8) antibodies, Neurogranin trunc p75 used monoclonal ADx403 (ADxNGCI2) and ADx451 (ADxNGCT1) antibodies, and alpha‐synuclein used ADx301 and ADx302 antibodies. Total‐tau and Aβ42 were measured using the Roche Elecsys®. PET‐amyloid status was defined using a Centiloid of <25 as negative, and 25 or greater positive.ResultCross‐sectional changes in biomarker values between the PET‐amyloid negative and positive groups were seen for Ng (p<0.05), but not for BACE1 or alpha‐syn. The ratio of Ng/BACE1 improved separation between groups (p<0.001). Total‐tau displayed the greatest differentiation of a single biomarker (p = 2.7×10−10) and this separation was improved using a ratio of t‐tau/BACE1 (p = 3.2×10−12). For predicting PET‐amyloid status (adjusting for age, APOE e4 and gender), t‐tau (AUC: 0.87 [95%CI: 0.80‐0.95]) was slightly improved using the ratio of t‐tau/BACE1 (AUC: 0.90 [95%CI: 0.82‐0.97]) to the same degree as using t‐tau/Aβ42 (AUC: 0.90 [95%CI: 0.82‐0.97]), although this was not significant (p>0.05).ConclusionThis study saw within a PET‐amyloid positive subpopulation of AIBL an increase in neurogranin, a post‐synaptic marker. The presynaptic marker BACE1 alone was not changed in concentration, but when BACE1 was used in a ratio with t‐tau, a marker of neuronal degradation, the differentiation between amyloid negative and positive groups increased. The predictive ability between t‐tau/BACE1 and t‐tau/Aβ42 to estimate PET‐amyloid load is comparable and measurements incorporating BACE1 may provide an alternative to Aβ42‐based assays and the inherent variability associated with Aβ42 measurement between testing sites.

Towards a clinical timeline of Alzheimer’s disease; a multivariate cognitive perspective

AbstractBackgroundAlzheimer’s disease (AD) is a multifactorial disease characterised by a long period of neuronal loss which manifests clinically as cognitive decline and ultimately dementia. Understanding the nature and magnitude of cognitive decline prior to dementia is critical to understanding the disease course. Here we quantify change in six cognitive composite scores to compare their temporal sequence and rates of change early in the AD disease course.MethodAmyloid‐beta positive (Aβ+) cognitively unimpaired (CU), MCI and AD participants from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of ageing with ≥three study visits (N = 857), were used to derive composite scores for executive function, language, attention and processing, recognition, visuospatial, and a general composite used in clinical trials (AIBL‐PACC). A multivariate method for combining cognitive change trajectory curves was used to place each composite score trajectory in a common numerical space. Using the mean AIBL‐PACC scores for the prodromal group (MCI‐Aβ+) as a reference, change over a three‐year period for each composite was estimated. Given the AIBL requirement for a minimum 1.5 SD change in two or more cognitive domains to justify progression to MCI/AD, disease time was set with the zero point at a ‐1.5 AIBL‐PACC score, representing a 1.5 SD decline in AIBL‐PACC from the mean CU Ab‐ population at baseline.ResultThe AIBL‐PACC, language and visuospatial functioning scores showed the steepest trajectories indicating AD related decline in these scores was fastest (Figure 1). AIBL‐PACC reached the ‐1.5 point on the y‐axis first followed closely by memory recognition, with steeper trajectory of decline for the AIBL‐PACC across the age range. Focusing on AIBL‐PACC, it took 2.15 years to transition from ‐1.5 to the mean of the prodromal group (‐1.87). At five years past the start of the prodromal stage, the data suggests a drop in the AIBL‐PACC to ‐2.54, an approximate decline of ∼0.22 points per year.ConclusionThis is the first presentation of a novel multivariate disease trajectory method examining expected change across the AD continuum among Aβ+ persons. A 3‐year change in the PACC composite demonstrated here will be relevant to measure disease progression during clinical trials.

CSF markers YKL40, sTREM2, and a‐synuclein enhance the Alzheimer’s disease A/T/N criteria to detect early changes in cognition

AbstractBackgroundAlzheimer’s disease (AD) is a multifactorial disease including pre‐clinical, prodromal and clinical phases. CSF “A/T/N” (Ab42, pTau181, tTau) biomarkers help characterize the biological state of AD. The primary aim was to assess contributions of eight CSF markers for predicting changes in cognition.MethodCSF from 237 participants (CN: 176[74.3%], MCI: 33[13.9%], AD: 28[11.8%]) of the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of ageing was used to measure Ab42 (GenII assay), pTau181, and tTau (Elecsys®) along with neurogranin, neurofilament light (NFL), α‐synuclein, glial fibrillary acidic protein (GFAP), chitinase‐3‐like protein 1 (YKL‐40), soluble triggering receptor expressed on myeloid cells‐2 (sTREM2), s100 calcium‐binding protein B (s100B), and interleukin‐6 (IL‐6) from the NeuroToolKit (NTK) panel of exploratory prototype assays (all Roche Diagnostics International Ltd). NTK biomarker concentrations were measured amongst each of four ATN groups at baseline; 1) A‐/T‐/N‐ (reference group), 2) A+/T±/N± (amyloid+), 3) A‐/T±/N± (amyloid‐), and 4) A+/T+/N+. Change in cognition was assessed using the pre‐clinical Alzheimer’s cognitive composite (PACC) and the clinical dementia rating sum of boxes (CDR‐SB) scores, in all four ATN groups, stratified by low/high NTK biomarker over a minimum of 36 months. The primary outcome was defined as change in cognition in amyloid+ participants modulated by the addition of NTK markers to ATN groups.ResultA+/T+/N+ participants had faster cognitive decline on both PACC and CDR‐SB over time compared to all other groups (Figure 1A, PACC; Figure 1B, CDR‐SB). Within the amyloid+ group, participants with high sTREM2 (K+) had a faster rate of decline in PACC compared to those with low sTREM2 (K‐, p = 0.04; Figure 1C), whilst participants with high α‐synuclein had a faster increase in CDR‐SB compared with those with low α‐synuclein (p = 0.005; Figure 1D). Amongst participants who were A+/T+/N+, those with high YKL40 had a significantly faster rate of decline in PACC (p = 0.017; Figure 1E), and a faster increase in CDR‐SB, (p = 0.017; Figure 1F) compared with those who with low YKL40.ConclusionBiomarkers of microglial activation (sTREM2), synaptic function and synuclein metabolism (α‐synuclein), and astrocytic activation (YKL‐40) may be appropriate to discern rates of cognitive change within amyloid+ participants. Findings are being validated in a separate population.

Combined effect of persistent cytomegalovirus infection and Tumor Necrosis Factor‐α Polymorphism on age‐related cognitive decline

AbstractBackgroundHuman cytomegalovirus (HCMV) is a ubiquitous infectious agent, widely distributed in human populations. Its infection has been correlated with a number of age‐related disorders but associations with neurocognitive decline are not seen consistently. The Tumor Necrosis Factor‐α (TNF) is a cytokinine playing a key role in inflammation and TNF polymorphisms have been associated with various autoimmune diseases. In this study, we investigated the impact of TNF genotype and HCMV on age‐related cognitive decline.MethodThe study cohort consisted of 200 Caucasian Australians (aged 59‐89 years) drawn from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study. We used linear regression models to assess the effect of TNF genotype, HCMV and their interaction on a series of domain‐specific composite scores (Preclinical Alzheimer Cognitive Composite; PACC, Recognition, Attention Processing, Language). The models accounted for the potential effect of age, education level and Apolipoprotein E genotype.ResultThe interaction between HCMV and TNF genotype significantly predicted PACC (b = 5.3; SE = 2.56; p = 0.04) and Recognition scores (b = 1.9; SE = 0.78; p = 0.01). Interactions were not significant in the other domains investigated (attention processing & language).ConclusionThe results suggest that the presence of TNF polymorphism, combined with HCMV infection have a protective effect against cognitive loss. Further research is needed to understand the underpinning mechanisms.

High risk of obstructive sleep apnoea is associated with higher baseline plasma glial fibrillary acidic protein and predicts decline in levels over time in cognitively unimpaired older adults

AbstractBackgroundFindings from animal studies report links between obstructive sleep apnoea (OSA) models and elevated glial fibrillary acidic protein (GFAP). In humans, elevated plasma GFAP, reflecting reactive astrogliosis, is considered an early event in the Alzheimer’s disease (AD) continuum. The current study investigated whether OSA risk predicts the slope of plasma GFAP in cognitively unimpaired older adults.MethodLinear mixed effect model analyses were conducted on 70 cognitively unimpaired older adult (M = 73.1, SD = 5.2, 57.1% female) participants of the Australian Imaging, Biomarkers and Lifestyle (AIBL) Study of Ageing. Individuals completed the well‐validated STOP‐Bang questionnaire to assess OSA risk (Low; Intermediate; High), and underwent positron emission tomography (PET) to quantify brain Aβ burden. Plasma GFAP was measured using the Simoa® platform over three timepoints (range 30.06 ‐ 99.45 months). The model included, a random intercept, a three‐way interaction between OSA risk, baseline brain Aβ status and time, adjusting for Apolipoprotein E (APOE) ε4 status, sex, baseline age, and baseline body mass index.ResultHigh risk of OSA and high brain Aβ (>25 Centiloids) were independently associated with higher baseline plasma GFAP and forecast a negative longitudinal plasma GFAP trajectory.ConclusionThese novel findings suggest that OSA mechanisms, independent of prior brain Aβ accumulation, may contribute to early reactive astrogliosis in cognitively unimpaired older adults. Additionally, a decline in plasma GFAP was observed during the preclinical period, both in those at high risk of OSA and those at greater risk of AD due to high brain Aβ.

The effect of basal forebrain atrophy on the rate of cognitive decline in older adults without dementia

AbstractBackgroundIn early Alzheimer’s disease, degeneration of the cholinergic basal forebrain (BF) system correlates with amyloid‐β (Aβ) burden and contributes to cognitive decline, particularly in the domains of memory and attention processing. However, the nature of these interrelationships remains unclear. This study investigated the effects of Aβ and BF atrophy on cognitive decline of memory and attention in older individuals without dementia.MethodThe BF volumes were quantified from MRIs of 577 participants (73.3 ± 6.3 years old, 55.1% female) from the Australian Imaging, Biomarker and Lifestyle (AIBL) study, including 495 cognitive unimpaired (CU) individuals and 82 with mild cognitive impairment. Aβ‐PET assessment was completed at baseline, and Aβ+ was determined using Centiloid > 20. The cognitive decline in memory and attention was assessed using the relevant AIBL composite scores, over 6.0 ± 3.1 years (up to 13 years). BF z‐scores were calculated using the mean and standard deviation of BF volumes in the CU Aβ‐ group, and z‐scores < ‐1 was classified as BF atrophy (BF+). Participants were then grouped according to the Aβ status and presence of BF atrophy (Table 1). Linear mixed‐effects models assessed differences in rates of cognitive decline in memory and attention across different classification groups. Magnitudes of differences in slopes of decline were expressed using Cohen’s d. ResultIn Aβ‐ individuals, BF atrophy was associated with faster rates of cognitive decline in both memory and attention, with medium effect sizes (Table 2 and Figure 1). In re‐analyses restricted to Aβ+ subjects, BF atrophy remained related to memory decline (p = 0.035, d = 0.33) but not to decline in attention. Compared to Aβ‐ subjects without BF atrophy, both Aβ+ groups had faster decline in both domains, with large effect sizes (all d > 0.8).ConclusionThese findings indicate that the effects of Aβ and BF atrophy are additive in terms of their influence on the rate of decline in memory. However, BF atrophy influences the rate of decline in attention only in the absence of abnormal Aβ burden.

Physical activity and brain amyloid beta: A longitudinal analysis of cognitively unimpaired older adults.

The current study evaluated the relationship between habitual physical activity (PA) levels and brain amyloid beta (Aβ) over 15 years in a cohort of cognitively unimpaired older adults. PA and Aβ measures were collected over multiple timepoints from 731 cognitively unimpaired older adults participating in the Australian Imaging, Biomarkers and Lifestyle (AIBL) Study of Aging. Regression modeling examined cross-sectional and longitudinal relationships between PA and brain Aβ. Moderation analyses examined apolipoprotein E (APOE) ε4 carriage impact on the PA-Aβ relationship. PA was not associated with brain Aβ at baseline (β=-0.001, p=0.72) or over time (β=-0.26, p=0.24). APOE ε4 status did not moderate the PA-Aβ relationship over time (β=0.12, p=0.73). Brain Aβ levels did not predict PA trajectory (β=-54.26, p=0.59). Our study did not identify a relationship between habitual PA and brain Aβ levels. Physical activity levels did not predict brain amyloid beta (Aβ) levels over time in cognitively unimpaired older adults (≥60 years of age). Apolipoprotein E (APOE) ε4 carrier status did not moderate the physical activity-brain Aβ relationship over time. Physical activity trajectories were not impacted by brain Aβ levels.

Deep joint learning of pathological region localization and Alzheimer’s disease diagnosis

The identification of Alzheimer’s disease (AD) using structural magnetic resonance imaging (sMRI) has been studied based on the subtle morphological changes in the brain. One of the typical approaches is a deep learning-based patch-level feature representation. For this approach, however, the predetermined patches before learning the diagnostic model can limit classification performance. To mitigate this problem, we propose the BrainBagNet with a position-based gate (PG), which applies position information of brain images represented through the 3D coordinates. Our proposed method represents the patch-level class evidence based on both MR scan and position information for image-level prediction. To validate the effectiveness of our proposed framework, we conducted comprehensive experiments comparing it with state-of-the-art methods, utilizing two publicly available datasets: the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Australian Imaging, Biomarkers and Lifestyle (AIBL) dataset. Furthermore, our experimental results demonstrate that our proposed method outperforms the existing competing methods in terms of classification performance for both AD diagnosis and mild cognitive impairment conversion prediction tasks. In addition, we performed various analyses of the results from diverse perspectives to obtain further insights into the underlying mechanisms and strengths of our proposed framework. Based on the results of our experiments, we demonstrate that our proposed framework has the potential to advance deep-learning-based patch-level feature representation studies for AD diagnosis and MCI conversion prediction. In addition, our method provides valuable insights, such as interpretability, and the ability to capture subtle changes, into the underlying pathological processes of AD and MCI, benefiting both researchers and clinicians.

Longitudinal basal forebrain atrophy in association with amyloid‐β positivity and dementia severity

AbstractBackgroundDysfunction of the cholinergic basal forebrain (BF) system and deposition of amyloid‐β (Aβ) are early pathological features in Alzheimer’s disease (AD). Degeneration of the BF in association with Aβ burden and cognitive decline has not been well studied, particularly in AD prior to dementia. We assessed the longitudinal trajectory patterns of the global BF volume, and the relationship to Aβ levels and clinical disease severity in older adults. The hippocampal volume (HV) trajectories were also assessed for reference.MethodVolumetric measures of the BF and HV were quantified from the MRIs of 521 participants (age = 72.6±5.9, 52.8% female, CDR range 0‐1) from the Australian Imaging, Biomarker and Lifestyle (AIBL) study, who had Aβ‐PET imaging and longitudinal structural MRI (follow‐up over 4.3±2.6 years). Aβ load was quantified using CapAIBL and Aβ+ was defined for centiloid values >20. Participants were classified at baseline based on their being cognitively unimpaired (CU, CDR = 0) or symptomatic (CDR = 0.5‐1) and Aβ status, which were CU Aβ‐, CU Aβ+ (preclinical AD), symptomatic Aβ‐ and symptomatic Aβ+ (prodromal AD and AD) (Table 1). BF and HV volumes were standardized using the mean and standard deviation of the relevant measures in the CU Aβ‐ group at baseline. Linear mixed effect models were used to compare the changes over time (slope) of BF and HV in different clinical groups. The magnitude of the difference in slopes between groups was assessed using Cohen’s d (summarised in Table 2). ResultIn CU Aβ‐, age‐related atrophy was greater in the BF compared to the HV (Cohen’s d = 0.811). Compared to the CU Aβ‐ group, the rate of atrophy was significantly faster for BF and HV in the preclinical AD group, and this decline was even greater in the prodromal AD and AD group (Figure 1 and Table 2). The symptomatic Aβ‐ group showed greater rate of BF atrophy than the CU Aβ‐ group, while there was no difference in the rate of HV atrophy.ConclusionThese findings provided prospective evidence confirming early vulnerability of the BF in aging as well as differentiated BF degeneration that was associated with baseline Aβ positivity and dementia severity.

The relationship between objective physical activity and change in cognitive function.

The current study investigated the association between objectively measured physical activity and cognition in older adults over approximately 8 years. We utilized data from 199 cognitively unimpaired individuals from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study, aged ≥60. Actigraphy was used to measure physical activity (intensity, total activity, and energy expenditure) at baseline. Cognition was assessed using a comprehensive cognitive battery every 18-months. Higher baseline energy expenditure predicted better episodic recall memory and global cognition over the follow-up period (p=0.031; p=0.047, respectively). Those with higher physical activity intensity and greater total activity also had better global cognition over time (both p=0.005). Finally, higher total physical activity predicted improved episodic recall memory over time (p=0.022). These results suggest that physical activity can preserve cognition and that activity intensity may play an important role in this association. Greater total physical activity predicts preserved episodic memory and global cognition. Moderate intensity physical activity (>3.7 metabolic equivalents of task [MET]) predicts preserved global cognition. Expending>373 kilocalories per day may benefit episodic memory and global cognition.

Alzheimer’s disease specific MRI brain regions are differentially associated with accelerated decline as defined using sigmoidal cognitive turning point methodology in amyloid‐positive AIBL participants

AbstractBackgroundVariability in cognitive decline among adults with Alzheimer’s disease (AD) is seen across studies. While such variability is often modelled using linear models, in the Australian Imaging, Biomarkers and Lifestyle (AIBL) study, application of a sigmoidal methodology has shown excellent precision in modelling cognitive and biomarker changes. Here we expand these findings by examining associations of brain volumes in AD specific Regions of Interest (ROIs) with accelerated cognitive decline among amyloid‐beta positive (Ab+) AIBL participants.MethodLongitudinal cognitive scores for the AIBL PACC, Language, Visuospatial functioning and CDR‐SB were mapped to sigmoidal trajectories, with a threshold defining the inflection point of accelerated cognitive decline. Participants to the left of the threshold were classified as having non‐accelerated decline (non‐accelerators), and participants beyond the threshold were classed as accelerators (Figure 1B). Using these classifications, we investigated differences in 16 ICV corrected ROI (left and right hemispheres pooled) for reductions in brain volume via generalised linear models adjusted for age, gender, and APOE‐e4 status. Three participant subgroups were tested: 1) Ab+/Tau unknown, 2) Ab+/Tau‐ and 3) Ab+/Tau+. Significant t‐values for the summed ROI volumes were mapped on a standard brain mesh for visualisation.ResultOf regions tested, two stood out consistently amongst top markers in each of the participant subgroups and cognitive outcomes: 1) supramarginal volume and 2) middle temporal volume (Figure 1C). Largest volume differences between accelerators and non‐accelerators were seen in the Ab+/Tau+ group; whilst smallest p‐values were in the Ab+/Tau unknown group due to a larger sample size (Table 1). Brain mesh visualization showed most of the AD signature ROIs altered in accelerator groups as compared with non‐accelerator groups. Figure 1D shows the AD signature for each cognitive outcome amongst the Ab+/Tau participant group. Top ranked ROI for the left being middle temporal volume (T=7.10, PACC) and supramarginal volume (T=7.10, CDR‐SB).ConclusionSigmoid analyses of MRI using binary cognitive scores show decreased ROI volumes in AIBL Ab+ participants with accelerated cognitive decline. This effect was mediated by known information on Tauopathy. Whilst effect sizes were high, smaller sample sizes in some groups affected p‐values and should therefore be replicated in larger samples.

Objectively measured physical activity and cognition in cognitively normal older adults: A longitudinal analysis of the Australian Imaging Biomarkers and Lifestyle (AIBL) study

AbstractBackgroundPhysical inactivity is one of the greatest modifiable risk factors for dementia and research shows physical activity can delay cognitive decline in older adults. However, much of this research has used subjective physical activity data and a single follow‐up cognitive assessment. Further studies using objectively measured physical activity and comprehensive cognitive data measured at multiple timepoints are required.MethodsParticipants were 199 community‐dwelling cognitively normal older adults (68.7 5.9 years) from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study. Actigraphy was used to measure physical activity at baseline, yielding measures of intensity (peak counts), total activity (total counts) and energy expenditure (kilocalories; k/cal). Cognitive function was assessed using a cognitive battery administered every 18‐months from baseline (3‐11 years follow‐up), yielding composite scores for episodic memory, executive function, attention and processing speed, and global cognition.ResultsHigher baseline energy expenditure predicted improvements in episodic memory and maintained global cognition over time (β = 0.011, SE = 0.005, p = 0.031; β = 0.009, SE = 0.004, p = 0.047, respectively). Both physical activity intensity and total activity predicted global cognition, such that those with higher peak and total counts had better cognition over time (β = 0.012, SE = 0.004, p = 0.005; β = 0.012, SE = 0.004, p = 0.005, respectively). Finally, higher total activity predicted improved episodic memory over time (β = 0.011, SE = 0.005, p = .022).ConclusionThese results suggest that physical activity is associated with preserved cognitive function over time, and that activity intensity may play an important role. This research further highlights the importance of early intervention to prevent cognitive decline and may aid in informing lifestyle interventions for dementia prevention.

Sex differences in the protective effects of cognitive reserve

AbstractBackgroundCognitive reserve is the term used to describe the apparent ability of one’s cognitive processes or neural networks to adapt in response to neuropathology and brain injury. This protective ability is proposed to account for individual differences in age‐associated cognitive decline and Alzheimer’s disease (AD) risk, where higher levels of cognitive reserve are associated with more preserved cognitive functioning and delayed onset of AD dementia. Women have a higher prevalence of AD than men, and account for two‐thirds of AD diagnoses. Sex differences in the protective ability of cognitive reserve may account for differences in Alzheimer’s disease prevalence between men and women, but few studies have explored this possibility. This study evaluates sex as a moderator of the relationship between cognitive reserve and brain volume effects on executive function.MethodThe study used archival data from 997 older adult participants in the Australian Imaging, Biomarkers, and Lifestyle(AIBL) Flagship Study of Ageing (see Table 1 for descriptives). Structural equation modelling was used to estimate a proxy for cognitive reserve and to run the analytical model. Cognitive reserve was defined as the variance in episodic memory not explained by demographics (e.g., education) and brain integrity (i.e., the residual reserve index; Reed et al., 2010). Executive functioning intercept and slope – derived from up to 7 study visits (18‐month follow‐up intervals) – were regressed onto the main and interaction effects of the residual reserve index, brain integrity (i.e., the combination of grey matter, hippocampal, and white matter hyperintensity volumes), and sex, plus covariates (e.g., number of apolipoprotein alleles; age).ResultA significant three‐way interaction was observed between cognitive reserve, brain integrity, and sex; Women benefitted more than men from the protective effects of cognitive reserve at comparably low levels of brain integrity. Further analyses also showed that women, on average, have higher levels of cognitive reserve as reflected by the residual reserve index.ConclusionSex differences in cognitive reserve highlight a potential sex‐related mechanism by which cognitive reserve confers protection against cognitive decline in the face of low brain integrity. These findings further our understanding of potential factors that maximise the protective effect of cognitive reserve.

AlzoSure® Predict, a simple minimally‐invasive blood test to predict the early onset of Alzheimer’s disease before the manifestation of clinical symptoms

AbstractBackgroundOngoing research seeks to identify blood‐based biomarkers able to predict the onset and progression of Alzheimer’s disease (AD). AlzoSure® Predict is a rapid, minimally‐invasive blood test that predicts the early onset of AD up to, at least, 6 years in advance of clinical symptoms.MethodsThe AlzoSure® Predict test, an IP‐LC‐MS/MS based method, was performed to quantify the AZ284® peptide of an unfolded variant of the p53 protein (U‐p53AZ). The data were compared with the neuropsychological diagnosis in a longitudinal cohort of 479 subjects between 60 and 85 years old from the Australian Imaging, Biomarkers, and Lifestyle (AIBL) study. The cohort consists of 237 cognitive normal, including both subjects with no and subjective memory complaints (NMC/SMC) and 98 mild cognitive impairment (MCI), 141 AD, and 3 other dementia patients that were followed‐up every 18 months.ResultsThe data confirms the strong diagnostic value of U‐p53AZ with a sensitivity of 95% for AD, discriminating it from NMC and SMC subjects with an AUC of 99.8% (95% CI: 99.6‐100.0%) and a specificity of 99.6%, and from MCI patients at the early symptomatic stage with an AUC of 95.7% (95% CI: 92.8‐98.5%) and a specificity of 91.8%.In the prognostic setting, U‐p53AZ showed a significantly better performance (P<0.0001) with an AUC of 99.0% (95%CI: 98.2‐99.8%) compared to amyloid b‐PET (Ab‐PET) with an AUC of 83.5% (95% CI: 79.5‐87.5%). Due to unavailability of data, Ab‐PET performance was determined on a subset of 422 subjects. In a multifactorial Cox proportional hazards model, U‐p53AZ proved to be the major contributor and and the best performing, independent predictor for cognitive decline to neuropsychological AD over time. In this model with normalized continuous risk factors, U‐p53AZ was the most significant contributor (P<.0001) with a hazards ratio of 2.92, compared to Aβ‐PET (P<.0155), APOE ε4 (P<.2323), and age (P<.0106).ConclusionThe present study confirms the diagnostic performance of U‐p53AZ for AD and expands its potential into the prognostic field. Additional studies are going to be performed based on both ADRC and ADNI samples to further prove the prognostic power and the utility of the AlzoSure® Predict test.

Suboptimal sleep efficiency and duration predicts rate of accumulation of Aβ‐ Amyloid in cognitively normal older adults

AbstractBackgroundThere is increasing evidence of a bidirectional relationship between suboptimal sleep and brain Aβ‐amyloid (Aβ) accumulation. Suboptimal sleep is suggested to both result from and contribute to the accumulation of brain Aβ. This study tested whether aspects of self‐reported sleep quality predict the longitudinal accumulation of brain Aβ.MethodLinear mixed effect model analyses were conducted on 192 cognitively normal older adults (M = 73.8 years, SD = 5.8, 51.6% female). These individuals, drawn from the Australian Imaging, Biomarkers and Lifestyle (AIBL) Study of Ageing, had self‐reported sleep characteristics assessed at baseline and positron emission tomography‐determined brain Aβ burden measured over a minimum of three AIBL timepoints (range 33.3‐73.9 months). Analyses included random slopes and intercepts, interaction for apolipoprotein E (APOE) ε4 allele status and time, adjusting for sex and baseline age.ResultSleep duration of <5 hours and sleep efficiency of <65% both significantly forecast the longitudinal trajectory of Aβ accumulation in the whole cohort (sleep duration <5 hrs β = 4.70 ± 1.58, p = 0.003; sleep efficiency <65% β = 2.94 ± 0.91, p = 0.001) and in APOE ε4 non‐carriers (sleep duration <5 hrs β = 4.70 ± 1.53, p = 0.003; sleep efficiency <65% β = 2.90 ± 0.87, p = 0.001) but not in APOE ε4 carriers.ConclusionThese findings indicate a role for self‐reported suboptimal sleep efficiency and duration in the accumulation of Alzheimer's disease neuropathology in cognitively normal individuals. Individuals with suboptimal sleep may benefit from an intervention to improve these sleep parameters to lessen future Alzheimer's risk. Our findings also suggest the potential utility of self‐report screening for sleep efficiency and duration as a predictor of risk of Aβ accumulation.

Asymmetry-enhanced attention network for Alzheimer’s diagnosis with structural Magnetic Resonance Imaging

Background and objective:With the aging of the global population becoming severe, Alzheimer’s disease (AD) has become one of the world’s most common senile diseases. Many studies have suggested that the brain’s left–right asymmetry is one of the possible diagnostic landmarks for AD. However, most published approaches to classification problems may not adequately explore the asymmetry between the left and right hemispheres. At the same time, the relationship between asymmetry traits and other classifier features remains understudied. Methods:In this paper, we proposed an asymmetry enhanced attention network (ASEAN) for AD diagnosis that effectively combines the anatomical asymmetry characteristics of the brain to enhance the accuracy and stability of classification tasks. First, we proposed a multi-scale asymmetry feature extraction module (MSAF) that can extract the asymmetry features of the brain from various scales. Second, we proposed an asymmetry refinement module (ARM) that considers the dependency between feature maps to suppress the irrelevant regions of the asymmetric feature maps. In addition, a parameter-free attention module was introduced to infer 4D attention weights and improve the network’s representation capability. Results:The proposed method achieved performance improvements on two databases: Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Australian Imaging, Biomarkers and Lifestyle (AIBL). For the classification tasks on ADNI, the proposed method achieves 92.1% accuracy, 96.2% sensitivity, and 91.3% specificity on the AD vs. CN (Cognitively Normal) task. Compared with state-of-the-art methods, the proposed method could achieve comparable results. Conclusion:The proposed model can extract long-range left–right brain similarity as complementary information and improve the model’s diagnostic performance. A large number of experiments also support the model’s validity. At the same time, this work provides a valuable reference for other neurological diseases, particularly those that exhibit left–right brain asymmetry during development.