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IntroductionBlood-based diagnostics and prognostics in sporadic Alzheimer's disease (AD) are important for identifying at-risk individuals for therapeutic interventions.MethodsIn three stages, a total of 34 leukocyte antigens were examined by flow cytometry immunophenotyping. Data were analyzed by logistic regression and receiver operating characteristic (ROC) analyses.ResultsWe identified leukocyte markers differentially expressed in the patients with AD. Pathway analysis revealed a complex network involving upregulation of complement inhibition and downregulation of cargo receptor activity and Aβ clearance. A proposed panel including four leukocyte markers - CD11c, CD59, CD91, and CD163 - predicts patients' PET Aβ status with an area under the curve (AUC) of 0.93 (0.88 to 0.97). CD163 was the top performer in preclinical models. These findings have been validated in two independent cohorts.ConclusionOur finding of changes on peripheral leukocyte surface antigens in AD implicates the deficit in innate immunity. Leukocyte-based biomarkers prove to be both sensitive and practical for AD screening and diagnosis.

PurposeA Cognitive Task Force (CTF) was established for the MissionAD program with the aim of reducing the screen failure (SF) rate to ∼30% and thereby reduce unnecessary subject burden, site burden, and excess trial costs.Methods/subjectsThe MissionAD program consisted of 2 global phase 3 studies evaluating the BACE inhibitor elenbecestat in subjects with early Alzheimer disease. The CTF monitored and engaged with MissionAD clinical sites to provide support through collegial discussions to maximize the efficiency of the preconsent recruitment phase.ResultsThe CTF significantly improved cognitive screening efficiency in the MissionAD program, with a 24% decline in cognitive SF rate for the sites that the CTF contacted. The study-wide 11.5% reduction in cognitive SF rates were likely further driven by wider country-level initiatives in which CTF members held CTF-specific Investigator meetings with the recruitment staff, speaking to all sites on a country level regardless of their recruitment performance.ConclusionsThe establishment of a CTF to support efficient cognitive screening is highly recommended for future Alzheimer disease studies. Additional benefits included improved site relationships, increased engagement in MissionAD and access to a group of cognitive experts for consulting, with a focus on achieving more efficient trial recruitment.

This longitudinal study modeled children's complex executive function (EF) development using the Groton Maze Learning Task (GMLT). Using a cohort-sequential design, 147 children (61 males, 5.5-11 years) were recruited from six multicultural primary schools in Melbourne and Perth, Australia. Race/ethnicity data were not available. Children were assessed on the GMLT at 6-month intervals over 2-years between 2010 and 2012. Growth curve models describe age-related change from 5.5 to 12.5 years old. Results showed a quadratic growth trajectory on each measure of error--that is, those that reflect visuospatial memory, executive control (or the ability to apply rules for action), and complex EF. The ability to apply rules for action, while a rate-limiting factor in complex EF, develops rapidly over early-to-mid childhood.

Introduction: We investigated whether monthly assessments of a computerized cognitive composite (C3) could aid in the detection of differences in practice effects (PE) in clinically unimpaired (CU) older adults, and whether diminished PE were associated with Alzheimer's disease (AD) biomarkers and annual cognitive decline. Materials and Methods: N = 114 CU participants (age 77.6 ± 5.0, 61% female, MMSE 29 ± 1.2) from the Harvard Aging Brain Study completed the self-administered C3 monthly, at-home, on an iPad for one year. At baseline, participants underwent in-clinic Preclinical Alzheimer's Cognitive Composite-5 (PACC5) testing, and a subsample (n = 72, age = 77.8 ± 4.9, 59% female, MMSE 29 ± 1.3) had 1-year follow-up in-clinic PACC5 testing available. Participants had undergone PIB-PET imaging (0.99 ± 1.6 years before at-home baseline) and Flortaucipir PET imaging (n = 105, 0.62 ± 1.1 years before at-home baseline). Linear mixed models were used to investigate change over months on the C3 adjusting for age, sex, and years of education, and to extract individual covariate-adjusted slopes over the first 3 months. We investigated the association of 3-month C3 slopes with global amyloid burden and tau deposition in eight predefined regions of interest, and conducted Receiver Operating Characteristic analyses to examine how accurately 3-month C3 slopes could identify individuals that showed >0.10 SD annual decline on the PACC-5. Results: Overall, individuals improved on all C3 measures over 12 months (β = 0.23, 95% CI [0.21-0.25], p < 0.001), but improvement over the first 3 months was greatest (β = 0.68, 95% CI [0.59-0.77], p < 0.001), suggesting stronger PE over initial repeated exposures. However, lower PE over 3 months were associated with more global amyloid burden (r = -0.20, 95% CI [-0.38 - -0.01], p = 0.049) and tau deposition in the entorhinal cortex (r = -0.38, 95% CI [-0.54 - -0.19], p < 0.001) and inferior-temporal lobe (r = -0.23, 95% CI [-0.41 - -0.02], p = 0.03). 3-month C3 slopes exhibited good discriminative ability to identify PACC-5 decliners (AUC 0.91, 95% CI [0.84-0.98]), which was better than baseline C3 (p < 0.001) and baseline PACC-5 scores (p = 0.02). Conclusion: While PE are commonly observed among CU adults, diminished PE over monthly cognitive testing are associated with greater AD biomarker burden and cognitive decline. Our findings imply that unsupervised computerized testing using monthly retest paradigms can provide rapid detection of diminished PE indicative of future cognitive decline in preclinical AD.

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.

ASP4345, a novel dopamine D1 receptor positive allosteric modulator, is being evaluated for the treatment of cognitive impairment associated with schizophrenia (CIAS). This phase 1 multiple ascending-dose study (NCT02720263) assessed the safety, tolerability, and pharmacodynamics of ASP4345 in patients with schizophrenia/schizoaffective disorder. Pharmacodynamic assessments were Cogstate cognitive tests and electrophysiological biomarkers, including gamma-band power and phase synchronization in response to 40-Hz auditory steady-state stimulation, as well as mismatch negativity (MMN) and P3a event-related potentials. The sample size determination was based on standard practice in assessing safety and tolerability of a new chemical entity. Data were summarized by conversion of this data into effect sizes using descriptive and inferential statistics. A total of 36 randomized patients received ASP4345 (3, 15, 50, and 150 mg; n = 9 each dose) and 12 patients received placebo. Patients in the ASP4345 group experienced 73 treatment-emergent adverse events (TEAEs) and 34 TEAEs were reported for the placebo group. The most common TEAEs were headache and somnolence and nearly all TEAEs were mild in severity. No changes in mood or self-reports of suicidal ideation/behavior were observed. Improvements in performance on cognitive tests were noted, which suggests a potential improvement in psychomotor function and visual attention. Furthermore, positive changes in neurophysiological biomarkers (auditory steady-state response [ASSR] and MMN) suggest improvement in information processing. The findings need to be confirmed in studies with a larger patient population. Nonetheless, the trends in safety and pharmacodynamic data support further clinical development of ASP4345 for the treatment of CIAS.

BackgroundThere 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.ObjectiveThe 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.MethodsThe 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.ResultsData 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).ConclusionThese 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.

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

IntroductionRelationships 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).MethodsThree 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.ResultsWMHs 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.DiscussionA Brain Chart quantifying brain-aging trajectories was established, enabling the systematic evaluation of individuals' brain-aging patterns relative to this large consortium.

IntroductionThis 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.MethodsWe 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.ResultsStudy partner-assessed Everyday Cognition score was preferentially selected for inclusion in the models by a feature selection algorithm during optimization.DiscussionOur results suggest that inclusion of study partner assessments would increase the ability of machine learning models to predict Aβ positivity.

IntroductionAssessment of functional status is associated with risk of cognitive decline and diagnosis of dementia, and can be assessed by participants and study partners (SPs).MethodsIn 770 older adults enrolled in the Imaging Dementia-Evidence for Amyloid Scanning (IDEAS) study and the online Brain Health Registry (BHR), we estimated associations between online assessments and clinical variables related to Alzheimer's disease (AD) risk.ResultsWorse online learning scores and SP-reported functional decline were associated with higher probability of AD dementia diagnosis and poor in-clinic cognitive assessment, and with higher odds of amyloid beta (Aβ) positivity when combined with participants' report of less decline. SP report of functional decline conferred predictive value independent of online cognitive assessments. Participants underreported decline compared to SPs.DiscussionThe results support the validity of online assessments and their greater utilization in healthcare and research settings. Online SP-reported functional decline is an indicator of dementia and AD risk.

ObjectiveTo determine the time required for a preclinical Alzheimer disease population to decline in a meaningful way, use estimates of decline to update previous clinical trial design assumptions, and identify factors that modify β-amyloid (Aβ)-related decline.MethodsIn 1,120 cognitively unimpaired individuals from 3 international cohorts, we estimated the relationship between Aβ status and longitudinal changes across multiple cognitive domains and assessed interactions between Aβ and baseline factors. Power analyses were performed to explore sample size as a function of treatment effect.ResultsCognitively unimpaired Aβ+ participants approach mild cognitive impairment (MCI) levels of performance 6 years after baseline, on average. Achieving 80% power in a simulated 4-year treatment trial, assuming a 25% treatment effect, required 2,000 participants/group. Multiple factors interacted with Aβ to predict cognitive decline; however, these findings were all cohort-specific. Despite design differences across the cohorts, with large sample sizes and sufficient follow-up time, the Aβ+ groups declined consistently on cognitive composite measures.ConclusionsA preclinical AD population declines to the cognitive performance of an early MCI population in 6 years. Slowing this rate of decline by 40%-50% delays clinically relevant impairment by 3 years-a potentially meaningful treatment effect. However, assuming a 40%-50% drug effect highlights the difficulties in preclinical AD trial design, as a more commonly assumed treatment effect of 25% results in a required sample size of 2,000/group. Designers of preclinical AD treatment trials need to prepare for larger and longer trials than are currently being considered. Interactions with Aβ status were inconsistent and not readily generalizable.

Cognitive change affecting patients after anaesthesia and surgery has been recognised for more than 100 yr. Research into cognitive change after anaesthesia and surgery accelerated in the 1980s when multiple studies utilised detailed neuropsychological testing for assessment of cognitive change after cardiac surgery. This body of work consistently documented decline in cognitive function in elderly patients after anaesthesia and surgery, and cognitive changes have been identified up to 7.5 yr afterwards. Importantly, other studies have identified that the incidence of cognitive change is similar after non-cardiac surgery. Other than the inclusion of non-surgical control groups to calculate postoperative cognitive dysfunction, research into these cognitive changes in the perioperative period has been undertaken in isolation from cognitive studies in the general population. The aim of this work is to develop similar terminology to that used in cognitive classifications of the general population for use in investigations of cognitive changes after anaesthesia and surgery. A multispecialty working group followed a modified Delphi procedure with no prespecified number of rounds comprised of three face-to-face meetings followed by online editing of draft versions. Two major classification guidelines [Diagnostic and Statistical Manual for Mental Disorders, fifth edition (DSM-5) and National Institute for Aging and the Alzheimer Association (NIA-AA)] are used outside of anaesthesia and surgery, and may be useful for inclusion of biomarkers in research. For clinical purposes, it is recommended to use the DSM-5 nomenclature. The working group recommends that 'perioperative neurocognitive disorders' be used as an overarching term for cognitive impairment identified in the preoperative or postoperative period. This includes cognitive decline diagnosed before operation (described as neurocognitive disorder); any form of acute event (postoperative delirium) and cognitive decline diagnosed up to 30 days after the procedure (delayed neurocognitive recovery) and up to 12 months (postoperative neurocognitive disorder).

Background: Integrating scores from multiple cognitive tests into a single cognitive composite has been shown to improve sensitivity to detect AD-related cognitive impairment. However, existing composites have little sensitivity to amyloid-β status (Aβ +/–) in preclinical AD. Objective: Evaluate whether a data-driven approach for deriving cognitive composites can improve the sensitivity to detect Aβ status among cognitively unimpaired (CU) individuals compared to existing cognitive composites. Methods: Based on the data from the Anti-Amyloid Treatment in the Asymptomatic Alzheimer's Disease (A4) study, a novel composite, the Data-driven Preclinical Alzheimer's Cognitive Composite (D-PACC), was developed based on test scores and response durations selected using a machine learning algorithm from the Cogstate Brief Battery (CBB). The D-PACC was then compared with conventional composites in the follow-up A4 visits and in individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Result: The D-PACC showed a comparable or significantly higher ability to discriminate Aβ status [median Cohen's d = 0.172] than existing composites at the A4 baseline visit, with similar results at the second visit. The D-PACC demonstrated the most consistent sensitivity to Aβ status in both A4 and ADNI datasets. Conclusions: The D-PACC showed similar or improved sensitivity when screening for Aβ+ in CU populations compared to existing composites but with higher consistency across studies. [ABSTRACT FROM AUTHOR]

Impaired clearance of amyloid β (Aβ) in late-onset Alzheimer's disease (AD) affects disease progression. The role of peripheral monocytes in Aβ clearance from the central nervous system (CNS) is unclear. We use a flow cytometry assay to identify Aβ-binding monocytes in blood, validated by confocal microscopy, Western blotting, and mass spectrometry. Flow cytometry immunophenotyping and correlation with AD biomarkers are studied in 150 participants from the AIBL study. We also examine monocytes in human cerebrospinal fluid (CSF) and their migration in an APP/PS1 mouse model. The assay reveals macrophage-like Aβ-binding monocytes with high phagocytic potential in both the periphery and CNS. We find lower surface Aβ levels in mild cognitive impairment (MCI) and AD-dementia patients compared to cognitively unimpaired individuals. Monocyte infiltration from blood to CSF and migration from CNS to peripheral lymph nodes and blood are observed. Here we show that Aβ-binding monocytes may play a role in CNS Aβ clearance, suggesting their potential as a biomarker for AD diagnosis and monitoring. Impaired Aβ clearance in late-onset Alzheimer's disease (AD) affects progression. Here, the authors show that CD14 + CD16+ monocytes carry Aβ in peripheral circulation and CSF in AD, suggesting a role in Aβ clearance. [ABSTRACT FROM AUTHOR]

Objective Allelic variation in the brain-derived neurotrophic factor (BDNF) Val66Met polymorphism has been shown to moderate rates of cognitive decline in preclinical sporadic Alzheimer's disease (AD; i.e. Aβ + older adults), and pre-symptomatic autosomal dominant Alzheimer's disease (ADAD). In ADAD, Met66 was also associated with greater increases in CSF levels of total-tau (t-tau) and phosphorylated tau (p-tau181). This study sought to determine the extent to which BDNF Val66Met is associated with changes in episodic memory and CSF t-tau and p-tau181 in Aβ + older adults in early-stage sporadic AD. Method Aβ + Met66 carriers (n  = 94) and Val66 homozygotes (n  = 192) enrolled in the Alzheimer's Disease Neuroimaging Initiative who did not meet criteria for AD dementia, and with at least one follow-up neuropsychological and CSF assessment, were included. A series of linear mixed models were conducted to investigate changes in each outcome over an average of 2.8 years, covarying for CSF Aβ42, APOE ε4 status, sex, age, baseline diagnosis, and years of education. Results Aβ + Met66 carriers demonstrated significantly faster memory decline (d  = 0.33) and significantly greater increases in CSF t-tau (d  = 0.30) and p-tau181 (d  = 0.29) compared to Val66 homozygotes, despite showing equivalent changes in CSF Aβ42. Conclusions These findings suggest that reduced neurotrophic support, which is associated with Met66 carriage, may increase vulnerability to Aβ-related tau hyperphosphorylation, neuronal dysfunction, and cognitive decline even prior to the emergence of dementia. Additionally, these findings highlight the need for neuropsychological and clinicopathological models of AD to account for neurotrophic factors and the genes which moderate their expression. [ABSTRACT FROM AUTHOR]

Study objectivesTo investigate interactions between high and low amounts of sleep and other predictors of cognitive performance.MethodsWe used four cognitive tests to determine whether sleep time interacted with age, personal history of a memory problem, parental history of a memory problem, or personal concerns about memory and were associated with cognitive performance. Data were collected from an internet-based cohort study. We used an ordinary least squares regression with restricted cubic splines, controlling for demographic variables and comorbidities.ResultsWe found significant nonlinear interactions between (1) total sleep time and age and (2) total sleep time and personal history of a memory problem and cognitive performance. Short and long sleep durations and self-reported memory complaints were associated with poorer performance on a test of attention and this was true to a greater degree in younger and older adults. A repeat analysis excluding subjects reporting dementia was significant only for the test of attention.ConclusionsThese results extend existing data on sleep duration and cognition across the lifespan by combining in a single study the results from four specific cognitive tests, both younger and older adults, and four self-reported risk factors for cognitive impairment. Longitudinal studies with biomarkers should be undertaken to determine whether causal mechanisms, such as inflammation or amyloid buildup, account for these associations.

IntroductionThe purpose of this study is to compare online neuropsychological test performance of older adults across self-reported diagnoses of being cognitively normal, mild cognitive impairment, and dementia due to Alzheimer's disease and to determine the association of memory concerns and family history of dementia on cognitive performance.MethodsParticipants completed the Cogstate Brief Battery unsupervised at home.ResultsData from 6463 participants over the age of 55 years were analyzed. Adults with the diagnosis of mild cognitive impairment and Alzheimer's disease were associated with poorer performance on all cognitive tests than cognitively normal adults (P

IntroductionMethods for efficiently identifying cognitive decline and Alzheimer's disease (AD) are a critical unmet need. The goal of this work was to validate novel online study partner (SP)-reported outcomes to identify cognitive decline in older adults.MethodsIn older adults enrolled in the Brain Health Registry, we analyzed associations between SP-reported cognitive decline, measured by the Everyday Cognition Scale, and either (1) participant cognition, assessed by Cogstate Brief Battery or (2) participant-reported diagnosis of mild cognitive impairment or AD.ResultsWe found strong associations between SP-reported Everyday Cognition Scale and both Cogstate scores and participant diagnosis. The associations were cognitive domain specific, dependant on participant diagnosis, and were stronger in spouse dyads and those who knew each other longer.DiscussionCollecting SP-reported data online from a large cohort is feasible. Results support the construct validity of our approach, which has the potential to facilitate clinical AD and aging research.