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Brain aging process is influenced by various lifestyle, environmental and genetic factors, as well as by age-related and often coexisting pathologies. Magnetic resonance imaging and artificial intelligence methods have been instrumental in understanding neuroanatomical changes that occur during aging. Large, diverse population studies enable identifying comprehensive and representative brain change patterns resulting from distinct but overlapping pathological and biological factors, revealing intersections and heterogeneity in affected brain regions and clinical phenotypes. Herein, we leverage a state-of-the-art deep-representation learning method, Surreal-GAN, and present methodological advances and extensive experimental results elucidating brain aging heterogeneity in a cohort of 49,482 individuals from 11 studies. Five dominant patterns of brain atrophy were identified and quantified for each individual by respective measures, R-indices. Their associations with biomedical, lifestyle and genetic factors provide insights into the etiology of observed variances, suggesting their potential as brain endophenotypes for genetic and lifestyle risks. Furthermore, baseline R-indices predict disease progression and mortality, capturing early changes as supplementary prognostic markers. These R-indices establish a dimensional approach to measuring aging trajectories and related brain changes. They hold promise for precise diagnostics, especially at preclinical stages, facilitating personalized patient management and targeted clinical trial recruitment based on specific brain endophenotypic expression and prognosis.

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.

BackgroundThis study aims to understand whether and how participant characteristics (age, gender, education, ethnocultural identity) are related to their feedback about taking a remote, unsupervised, online cognitive assessment.MethodsThe Brain Health Registry is a public online registry which includes cognitive assessments. Multivariable ordinal regressions assessed associations between participant characteristics and feedback responses of older (55+) participants (N=11,553) regarding their Cogstate Brief Battery assessment experience.ResultsHigher age, secondary education or less, Latino identity, and female gender were associated with a poorer assessment experience; higher age and a non-White identity were associated with experiencing the assessment instructions as less clear; and higher age, non-White identity, and secondary education or less were associated with rating additional human support with the assessment as more useful.DiscussionOur findings highlight the importance of improving the design and instructions of unsupervised, remote, online cognitive assessments to better suit the needs of diverse communities.

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.

IntroductionThe feasibility and validity of unsupervised, longitudinal brief computerized cognitive batteries is unknown.MethodsParticipants aged 56-90 (N = 19476) from the Brain Health Registry (BHR) completed the CogState Brief Battery (CBB) at 6-month intervals over a period of 5 years. We used linear mixed-effects models to assess whether cross-sectional and longitudinal performance on CBB within BHR was associated with demographic and cognitive characteristics. We also defined a group of CBB decliners based on subject-specific slopes and estimated associations between decliner status and participant characteristics.ResultsWe found weak associations between longitudinal change in CBB and participant characteristics. Cross-sectional CBB scores were significantly associated with participant characteristics such as age, gender, ethnicity, self-reported disease status, and memory concern. CBB decliners were more likely to self-report mild cognitive impairment (MCI) and memory concerns.DiscussionCross-sectional, remote CBB shows evidence of construct validity, but our results suggest that longitudinal assessment may not provide additional value for identifying those at risk for and with cognitive impairment.

This correlational survey study examined professional development, participatory management, and teachers' job performance in public secondary schools in Ogun State, Nigeria. The sample size comprised 504 participants selected through a multi-stage sampling technique from 12,745 teachers in 217 public secondary schools. The study was guided by two null hypotheses, tested at 0.05 level of significance. Two researcher-designed instruments namely. Participatory Management and Professional Development Questionnaire (PM/PDQ) and Teachers' Job Performance Scale (TJPS), were used to collect data. The Pearson Product-Moment Correlation statistical tool was used for data analysis. Results showed that teachers' job performance was significantly and positively related to both professional development and participatory management respectively. The study concluded that teachers' job performance could be enhanced through these two management intervention strategies. It was therefore, recommended among other things, that concerned authorities should improve on existing teachers' professional development programmes and encourage participatory management among the teachers.

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.

Job concern is a common element in any work organization. It is, no doubt, capable of determining the extent to which an employee is engaged with the job. This study therefore investigated job concern factors (namely workload, job hazard, and interpersonal discrimination) and academic staff engagement in public universities in Lagos State, Nigeria. The descriptive survey research design was used for the study. Five null-hypotheses, tested at .05 level of significance, guided the study. Participants were 250 randomly selected academic staff from two purposively selected public universities in Lagos State. A self-constructed questionnaire was used for data collection. Data were analyzed using inferential statistics, specifically One Sample t-test, Pearson Product-Moment Correlation, Pearson Chi Square, and Independent t-test respectively. Findings showed that in public universities in Lagos State, Nigeria, the level of academic staff engagement was low [t (249) = 230.15, p>0.05]. No significant relationship was found between workload and employee engagement [r (248) = -0.611; p>0.05]. Further finding showed that employee engagement was significantly related to job hazard [r (248) = 0.502, p<0.05]. There was also significant relationship between interpersonal discrimination and employee engagement [r (248) = 0.721; p<0.05]. Finally, there was no significant difference in employee engagement among academic staff in Federal and State Universities[t (249) = 3.122, p>0.05]. It was recommended that Government and institutional administrators should ensure that the working environment is made more conducive for scholarly works, employ more qualified staff, and avoid interpersonal discrimination among academic staff

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.

BackgroundComputerized cognitive assessments may improve Alzheimer's disease (AD) secondary prevention trial efficiency and accuracy. However, they require validation against standard outcomes and relevant biomarkers.ObjectiveTo assess the feasibility and validity of the tablet-based Computerized Cognitive Composite (C3).DesignCross-sectional analysis of cognitive screening data from the A4 study (Anti-Amyloid in Asymptomatic AD).SettingMulti-center international study.ParticipantsClinically normal (CN) older adults (65-85; n=4486).MeasurementsParticipants underwent florbetapir-Positron Emission Tomography for Aβ+/- classification. They completed the C3 and standard paper and pencil measures included in the Preclinical Alzheimer's Cognitive Composite (PACC). The C3 combines memory measures sensitive to change over time (Cogstate Brief Battery-One Card Learning) and measures shown to be declining early in AD including pattern separation (Behavioral Pattern Separation Test- Object- Lure Discrimination Index) and associative memory (Face Name Associative Memory Exam- Face-Name Matching). C3 acceptability and completion rates were assessed using qualitative and quantitative methods. C3 performance was explored in relation to Aβ+/- groups (n=1323/3163) and PACC.ResultsC3 was feasible for CN older adults to complete. Rates of incomplete or invalid administrations were extremely low, even in the bottom quartile of cognitive performers (PACC). C3 was moderately correlated with PACC (r=0.39). Aβ+ performed worse on C3 compared with Aβ- [unadjusted Cohen's d=-0.22 (95%CI: -0.31,-0.13) p

BackgroundComputerized cognitive assessments may improve Alzheimer's disease (AD) secondary prevention trial efficiency and accuracy. However, they require validation against standard outcomes and relevant biomarkers.ObjectiveTo assess the feasibility and validity of the tablet-based Computerized Cognitive Composite (C3).DesignCross-sectional analysis of cognitive screening data from the A4 study (Anti-Amyloid in Asymptomatic AD).SettingMulti-center international study.ParticipantsClinically normal (CN) older adults (65-85; n=4486).MeasurementsParticipants underwent florbetapir-Positron Emission Tomography for Aβ+/- classification. They completed the C3 and standard paper and pencil measures included in the Preclinical Alzheimer's Cognitive Composite (PACC). The C3 combines memory measures sensitive to change over time (Cogstate Brief Battery-One Card Learning) and measures shown to be declining early in AD including pattern separation (Behavioral Pattern Separation Test- Object- Lure Discrimination Index) and associative memory (Face Name Associative Memory Exam- Face-Name Matching). C3 acceptability and completion rates were assessed using qualitative and quantitative methods. C3 performance was explored in relation to Aβ+/- groups (n=1323/3163) and PACC.ResultsC3 was feasible for CN older adults to complete. Rates of incomplete or invalid administrations were extremely low, even in the bottom quartile of cognitive performers (PACC). C3 was moderately correlated with PACC (r=0.39). Aβ+ performed worse on C3 compared with Aβ- [unadjusted Cohen's d=-0.22 (95%CI: -0.31,-0.13) p

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.

There has been an increased global attention on the need to make education more flexible and accessible due to the advent of modern mobile technologies. These modern mobile technologies enable people including those with disabilities to receive education anywhere and anytime. This study therefore investigated the behavioural intentions of Nigerian Students With Disabilities (SWDs) to adopt Mobile Learning (ML). The descriptive survey design was used. Data were collected through a self-structured, validated and reliable (r = 0.78) questionnaire from a total of 104 purposively selected SWDs. Two research questions and one null hypothesis guided the study. Multiple Regression (MR) and Independent t-test were used for data analysis. Findings revealed that the selected five factors namely computer self-efficacy, perceived usefulness, computing skills, ease of use, and enabling environment jointly contributed 5.3% to the variance in behavioural intentions of SWDs to adopt mobile learning. Also, the relative contributions of each of the explanatory variables in order of importance were computer self-efficacy (ß = 0.331), perceived usefulness (ß = 0.201), computing skills (ß=0.111), perceived ease of use (ß = 0.086), and enabling environment (ß = 0.012). Significant difference in behavioural intentions to adopt mobile learning among SWDs was established based on gender (t-cal = 0.303; df = 102; p<0.05). Although, mobile learning is still considered as a new technological innovation worldwide, its usage has grown tremendously. It is therefore recommended among others that ML providers should develop more friendly and easy to use ML systems and devices for the SWDs. The SWDs should develop positive computer self-efficacy beliefs and improve upon their computing skills.

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.

Background: Remote and unsupervised administration of neuropsychological tests may increase opportunities to understand cognition in everyday life compared to in‐clinic assessments. The technological sophistication and widespread use of smartphones now allows this platform to be used for remote neuropsychological testing. However, it is important to ensure that the validity of neuropsychological tests extends to remote and unsupervised administration. As the Cogstate Brief Battery (CBB) has been validated for remote use on computers, the aim of this study was to now examine whether the acceptability and validity holds when administered via smartphone to cognitively unimpaired (CU) middle‐aged and older adults. Method: CU adults (n = 173) aged 41‐75 years (M(SD) = 62.72 (7.18)) enrolled in the Healthy Brain Project completed the smartphone CBB remotely. The mean speed and accuracy of performance on the Detection (DET, psychomotor function), Identification (IDN, attention), One Back (OBK, working memory) and One Card Learning (OCL, visual learning) tests were used to define performance. Acceptability was determined by the percentage of participants completing the full CBB. Relationships between CBB performance with age and test difficulty were described to inform validity. Relationships between objective CBB performance and subjective ratings of how participants felt about their overall performance, obtained via survey, were also explored. Result: 100% of participants completed the smartphone CBB. Increasing age was associated with slower performance on all tests (Figure 1a‐1d), and reduced accuracy on the OBK (Figure 1h). Older adults (66+ years) performed slower than middle‐aged adults (40‐65 years) on all CBB tests, and with less accuracy on OBK (Table 1). Performance speed across all tests slowed with increasing test difficulty (Figure 2). Whilst a ceiling effect was observed for accuracy of simpler tests (DET, IDN, OBK), accuracy was lowest for the most difficult test (OCL) (Figure 2). Subjective ratings of overall CBB performance was significantly, albeit weakly, correlated with OCL accuracy (r = 0.23, p =.003). Conclusion: The results support the acceptability and validity of the remote, unsupervised administration of the smartphone CBB in CU middle‐aged and older adults. These results provide a foundation for gaining further understanding of the performance of the smartphone CBB in diverse and impaired populations. [ABSTRACT FROM AUTHOR]

Background: In cognitively unimpaired (CU) individuals, the PACC is widely used as a cognitive outcome measure and endpoint in observational studies and clinical trials. However, it has drawn criticism for being heavily weighted towards memory. Increasing evidence indicates a decline spanning multiple cognitive domains in CU individuals. Therefore, using principal component analysis (PCA), we derived data‐driven domain‐specific cognitive composites. And subsequently, compared them against their summed z‐score counterparts in predicting progression to mild cognitive impairment (MCI). Method: Baseline cognitive, demographic, and genotype data of 2,853 CU older adults (aged 41.6 to 98.3) was obtained from the Alzheimer's Dementia Onset and Progression in International Cohorts (ADOPIC) Consortium. Using varimax‐rotated PCA, tests significantly loading (≥ 0.5) onto each principal component were extracted to derive domain‐specific cognitive composites. The resulting domain scores were normalised to a mean of 0 and SD of 1, with a higher score indicating better cognition. Cox regression was used to assess the association between progression to MCI and baseline demographics, cognition, and APOE ε4 carriage. Akaike information criteria (AIC) was used to compare the fitness of PCA‐derived composites against the zPACC and z‐score domain‐specific composites. Result: Baseline cohort characteristics are described in Table 1. PCA explained 68% of the variance and resulted in four independent cognitive composites (Figure 1): memory; executive function; attention and processing speed; and global cognition. At 15 years from baseline, 309 participants progressed to MCI, while 2,544 remained CU. Cox regression showed that the four cognitive composites, age and APOE genotype significantly predicted progression to MCI (Concordance = 77%, p < 0.001, AIC = 4105, Table 1). Additionally, the PCA‐derived composites performed comparably, if not better than the summed z‐score counterparts, the PACC (Concordance = 74%, p < 0.001, AIC = 4163) and domain‐specific composites (Concordance = 75%, p < 0.001, AIC = 4142). Baseline older age, APOE ε4 carriage in a dose‐dependent manner (Figure 2) and poorer cognition for each PCA‐composite were independently associated with progression to MCI. Conclusion: Together with APOE ε4 carriage, our PCA‐derived domain‐specific composites performed better than their summed z‐score counterparts at predicting progression to MCI 15 years before symptom onset. [ABSTRACT FROM AUTHOR]

Background: Anxiety and stress have been identified as potential risk factors of cognitive impairment, but research examining this association in midlife has been limited. Method: The current study examined the associations between anxiety and stress symptoms, and cognition in middle‐aged cognitively unimpaired adults (n = 2,463) enrolled in the Healthy Brain Project. Exploratory factor analysis was conducted on items in the Anxiety Subscale of the Hospital Anxiety and Depression Scale, Anxiety and Stress Subscales of the Depression, Anxiety and Stress Scale, and Perceived Stress Scale. Cognition was measured using the Cogstate Brief Battery (CBB). Result: The solution that resulted in the best fit to the data comprised five factors which explained for 48% of the variance: Over‐Reactivity, Panic, Low‐Control, Tension and Intolerance. Panic‐related symptoms were significantly associated with both poorer attention and memory. Having clinically meaningful anxiety symptoms, or both anxiety and stress symptoms, was associated with poorer memory. Conclusion: Anxiety and stress symptoms may be linked to a heightened risk of developing cognitive impairment. Specifically, panic‐related symptoms may play a significant role in the relationship between anxiety symptoms and cognition. Limitations include: 1) cross‐sectional nature of the study limited ability to ascertain direction of causality; 2) individuals with major psychiatric conditions were excluded from the study, thus limiting external validity of the current findings; 3) HBP sample has a higher proportion of individuals with a family history of dementia compared to the general population, hence the sample presents with an increased risk of developing dementia and generalizability of our findings may be limited. [ABSTRACT FROM AUTHOR]

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).

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.

Learning is more personal and the responsibility rests more squarely on the shoulders of students in distance learning systems. Also, many distance learners have several other equally important commitments such as home demands, social engagements, and religious obligations that compete with their academic work. Balancing these responsibilities with academic pursuit in such a way that one does not affect the other may become problematic for the students, especially those who are not self-regulated. This may subsequently impact student academic performance. In view of this scenario, it is expected that distance learners will have good self-regulation skills in order to perform better in their academic endeavours. This study sought to discern a causal explanation of distance learners' academic performance vis-à-vis their self-regulation skills. The study utilized a descriptive, 'ex-post facto' research design. Simple random sampling technique was used to select 1,500 participants while the University of Ibadan's Distance Learning Centre was purposively selected. Data were collected through a questionnaire during the 2009 contact session. Three hypotheses were formulated and tested at the 0.05 level of significance. Pearson correlation, regression analysis, and t-tests were employed for data analysis. Students' self-regulation skills and academic performance are positively and significantly correlated. (R2 =016;P<0.5). The study however, revealed no significant difference in students' self-regulations skills on a gender basis. The need for students to have good self-regulation skills and monitor their academic progress was recommended.

With increasing numbers of people with Alzheimer's and other dementias across the globe, many countries have developed national plans to deal with the resulting challenges. In the United States, the National Alzheimer's Project Act, signed into law in 2011, required the creation of such a plan with annual updates thereafter. Pursuant to this, the US Department of Health and Human Services (HHS) released the National Plan to Address Alzheimer's Disease in 2012, including an ambitious research goal of preventing and effectively treating Alzheimer's disease by 2025. To guide investments, activities, and the measurement of progress toward achieving this 2025 goal, in its first annual plan update (2013) HHS also incorporated into the plan a set of short, medium and long-term milestones. HHS further committed to updating these milestones on an ongoing basis to account for progress and setbacks, and emerging opportunities and obstacles. To assist HHS as it updates these milestones, the Alzheimer's Association convened a National Plan Milestone Workgroup consisting of scientific experts representing all areas of Alzheimer's and dementia research. The workgroup evaluated each milestone and made recommendations to ensure that they collectively constitute an adequate work plan for reaching the goal of preventing and effectively treating Alzheimer's by 2025. This report presents these Workgroup recommendations.