Posters

Read research from Cogstate scientists as presented at industry conferences.

A biomarker to aid Alzheimer’s disease staging: sTREM2 is decreased in Amyloid positive/Tau negative, yet increased once Tau aggregates leading to increased cognitive decline

Presented at CTAD 2023

The development of high accuracy biofluid assays now allows the use of fluid-based biomarkers into Alzheimer’s disease (AD) clinical pathological models. Integration of amyloid and tau biomarkers into AD models has confirmed the centrality of amyloid and tau biology in AD-related neurodegeneration, and to the expression of AD symptoms, such as cognitive decline, and clinical disease progression. AD disease models are now seeking to exploit and use validated fluid biomarkers of other neurodegenerative processes, such as neuroinflammation, to increase understanding of AD beyond amyloid and tau. The soluble triggering receptor expressed on myeloid cells 2 (sTREM2) can be measured in the CSF, providing an opportunity to determine the extent to which measurement of neuroinflammation can add information to amyloid, tau and neurodegeneration based (ATN) models of AD related cognitive decline.

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The effect of APOE ε4 status on subregional basal forebrain atrophy in Alzheimer’s disease

Presented at CTAD 2023

Dysfunction of the cholinergic BF system and deposition of amyloid-β (Aβ) are early pathological features in Alzheimer’s disease (AD). The Apolipoprotein E (APOE) ε4 allele is the strongest genetic risk factor for late-onset AD and exacerbates Aβ accumulation and cognitive decline. Objective: To investigate the effects of APOE ε4 carriage on rates of volume loss in basal forebrain (BF) subregions among older individuals.

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Do Alzheimer’s Risk Genes also Predict Cognitive Decline?

Presented at CTAD 2023

It is the priority of candidate Alzheimer’s disease (AD) therapeutics to slow progression and preserve quality of life for those on the disease trajectory. To effectively demonstrate and measure this, randomised controlled trials would ideally control for factors that alter rates of cognitive decline independent of the intervention under study. Genome wide association studies (GWAS) have now implicated >40 genes that appear to be associated with elevated disease risk1-3. The pathways involved have shed light on possible underlying disease mechanisms. Could these (or other) genes help to predict rates of cognitive decline?

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A comparison of machine learning based-composite cognitive test scores to track cognitive decline in early stages of Alzheimer’s disease dementia: ADOPIC study

Presented at AAIC 2023

Clinical trials for early dementia require cognitive measures that capture disease progression across multiple domains. Precise cognitive assessment is crucial for tracking early Alzheimer’s disease (AD) dementia. Current cognitive endpoints, like the Preclinical Alzheimer Cognitive Composite (PACC), average standardized change from baseline scores. The use of a machine learning (ML)-based cognitive composite score, computed through principal component analysis (PCA), enhanced the ability to track cognitive decline in individuals with mild cognitive impairment (MCI) progressors compared to PACC, while remaining comparable in cognitively unimpaired (CU) progressors

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Adaptation and Modification of Digital Cognitive Assessments for Smartphone-based and Unsupervised Conduct

Presented at AAIC 2023

Remote and decentralized methods offer advantages in clinical trials, such as reducing burden, improving recruitment, and enabling high-frequency assessment. Smartphone-based cognitive assessment, particularly in BYOD trials, allows for novel designs. However, understanding potential errors from delivery platforms (e.g., smartphones vs. computers) is crucial. Cogstate Brief Battery (CBB) data from young adults in a crossover study (n=60) and a large study (n=35,000) of unsupervised smartphone-based assessments were utilized to gather insights into digital assessments in remote settings.

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Amyloid-β and monocytes in Alzheimer’s disease

Presented at AAIC 2023

This study explores whether human monocyte-derived macrophages can eliminate brain Aβ and migrate to the periphery for antigen presentation, addressing this puzzle.

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Cognitive function in older adults with hearing loss; outcomes for treated versus untreated groups at 3-year follow-up.

Presented at AAIC 2023

Cognition remained stable for hearing aid users but declined for non-users at a 3-year follow-up. Hearing aid treatment may delay cognitive decline. Referral for hearing screening and rehabilitation could help minimize cognitive decline in older adults. Hearing loss, affecting 70% of adults aged ≥70, is independently associated with cognitive decline and considered a modifiable risk factor for dementia. Limited evidence exists on the impact of hearing aid use on cognition in older adults beyond 6-12 months, highlighting the need for further objective studies. This longitudinal cohort study compared outcomes of new hearing aid users with those without over a 3-year period

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

Presented at AAIC 2023

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

Presented at AAIC 2023

Alzheimer’s disease (AD) consists of pre-clinical, prodromal, and clinical phases. CSF biomarkers (A/T/N) aid in characterizing the AD biological state. Assessing eight CSF markers, including sTREM2, α-synuclein, and YKL-40, can predict changes in cognition. These biomarkers may help discern cognitive decline rates in amyloid-positive individuals. Validation of findings is underway in a separate population.

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Discovering Alzheimer’s disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering

Presented at AAIC 2023

Alzheimer’s disease and co-pathology exhibit diverse brain atrophy patterns. Genetic variants are associated with heterogeneous imaging patterns in brain diseases. Machine learning methods can analyze neuroanatomical heterogeneity and identify genetically-driven disease subtypes with distinct brain phenotypes. Statistical tests adjusted for APOE e4 were used to identify significant SNPs, providing insights into potential genetic influences on disease characteristics.

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