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A crucial aspect of any clinical trial is using the right outcome measure to assess treatment efficacy. Compared to the rapidly evolved understanding and measurement of pathophysiology in preclinical and early symptomatic stages of Alzheimer's disease (AD), relatively less progress has been made in the evolution of clinical outcome assessments (COAs) for those stages. The current paper aims to provide a benchmark for the design and evaluation of COAs for use in early AD trials. We discuss lessons learned on capturing cognitive changes in predementia stages of AD, including challenges when validating novel COAs for those early stages and necessary evidence for their implementation in clinical trials. Moving forward, we propose a multi-step framework to advance the use of more effective COAs to assess clinically meaningful changes in early AD, which will hopefully contribute to the much-needed consensus around more appropriate outcome measures to assess clinical efficacy of putative treatments. HIGHLIGHTS: We discuss lessons learned on capturing cognitive changes in predementia stages of AD. We propose a framework for the design and evaluation of performance based cognitive tests for use in early AD trials. We provide recommendations to facilitate the implementation of more effective cognitive outcome measures in AD trials.

Objective:Smartphones have the potential for capturing subtle changes in cognition that characterize preclinical Alzheimer’s disease (AD) in older adults. The Ambulatory Research in Cognition (ARC) smartphone application is based on principles from ecological momentary assessment (EMA) and administers brief tests of associative memory, processing speed, and working memory up to 4 times per day over 7 consecutive days. ARC was designed to be administered unsupervised using participants’ personal devices in their everyday environments.Methods:We evaluated the reliability and validity of ARC in a sample of 268 cognitively normal older adults (ages 65–97 years) and 22 individuals with very mild dementia (ages 61–88 years). Participants completed at least one 7-day cycle of ARC testing and conventional cognitive assessments; most also completed cerebrospinal fluid, amyloid and tau positron emission tomography, and structural magnetic resonance imaging studies.Results:First, ARC tasks were reliable as between-person reliability across the 7-day cycle and test-retest reliabilities at 6-month and 1-year follow-ups all exceeded 0.85. Second, ARC demonstrated construct validity as evidenced by correlations with conventional cognitive measures (r = 0.53 between composite scores). Third, ARC measures correlated with AD biomarker burden at baseline to a similar degree as conventional cognitive measures. Finally, the intensive 7-day cycle indicated that ARC was feasible (86.50% approached chose to enroll), well tolerated (80.42% adherence, 4.83% dropout), and was rated favorably by older adult participants.Conclusions:Overall, the results suggest that ARC is reliable and valid and represents a feasible tool for assessing cognitive changes associated with the earliest stages of AD.

Balancing the evolution of longitudinal cognitive assessment with practical considerations like maintaining legacy measures presents a vexing problem for many Alzheimer's Disease Research Centers (ADRCs) in the US. Changes to the Uniform Data Set (UDS) have left many ADRCs with heterogeneous cognitive data that can be challenging to harmonize. Using longitudinal data from the Knight ADRC, we used equipercentile equating to combine cognitive data collected across UDS versions 2 and 3. The combined data was then used to assess whether equated variables could create stable and informative domain-specific and general cognitive composite measures. Exploratory factor analysis (EFA) revealed four underlying clusters of tests that corresponded to episodic memory, semantic memory, working memory, and attention/processing speed. These factors appeared stable across test forms, with equivalent factors observed when EFA was conducted on UDS2 and UDS3 independently, and replicated when UDS2 and UDS3 were harmonized using equated variables. To understand the utility of these domain-specific factors, we also computed a commonly used preclinical Alzheimer cognitive composite (ADCS-PACC), a novel PACC (Knight-PACC), and a global composite consisting of 13 cognitive tasks. Prior work has proposed that these PACCs are sensitive to preclinical changes in AD, while the global composite would reflect general cognitive ability. We compared the relationships between each composite and neuroimaging metrics of amyloid, tau, brain structure, and clinical symptoms, cross-sectionally and longitudinally. We also simulated clinical trials using each composite and compared the sample size estimates needed to power a study expecting to observe a moderate effect size. Overall, global and PACC-based metrics of cognition slightly outperformed domain-specific composites, showing better sensitivity to AD biomarkers, although the working memory composite consistently performed poorly. We also found that data-driven, domain-specific composites can complement already-established general cognitive composites, with the exception of the present working memory composite. Power analyses revealed that the global, Knight-PACC, and attention and processing speed composites would require the smallest sample sizes to detect cognitive change in a clinical trial, while the ADCS-PACC required two to three times as many participants. Importantly, this work demonstrated the stability of cognitive domains across test versions. Finally, in line with prior work, we also highlight that attention and processing speed may be sensitive metrics of cognitive change in the earliest stages of AD.

Background: Obesity is an increasingly recognized modifiable risk factor for Alzheimer’s disease (AD). Increased body mass index (BMI) is related to distinct changes in white matter (WM) fiber density and connectivity. Objective: We investigated whether sex differentially affects the relationship between BMI and WM structural connectivity. Methods: A cross-sectional sample of 231 cognitively normal participants were enrolled from the Knight Alzheimer Disease Research Center. Connectome analyses were done with diffusion data reconstructed using q-space diffeomorphic reconstruction to obtain the spin distribution function and tracts were selected using a deterministic fiber tracking algorithm. Results: We identified an inverse relationship between higher BMI and lower connectivity in the associational fibers of the temporal lobe in overweight and obese men. Normal to overweight women showed a significant positive association between BMI and connectivity in a wide array of WM fibers, an association that reversed in obese and morbidly obese women. Interaction analyses revealed that with increasing BMI, women showed higher WM connectivity in the bilateral frontoparietal and parahippocampal parts of the cingulum, while men showed lower connectivity in right sided corticostriatal and corticopontine tracts. Subgroup analyses demonstrated comparable results in participants with and without positron emission tomography or cerebrospinal fluid evidence of brain amyloidosis, indicating that the relationship between BMI and structural connectivity in men and women is independent of AD biomarker status. Conclusion: BMI influences structural connectivity of WM differently in men and women across BMI categories and this relationship does not vary as a function of preclinical AD.

Although often unmeasured in studies of cognition, many older adults possess Alzheimer disease (AD) pathologies such as beta-amyloid (Aβ) deposition, despite being asymptomatic. We were interested in examining whether the behavior-structure relationship observed in later life was altered by the presence of preclinical AD pathology. 511 cognitively unimpaired adults completed magnetic resonance imaging (MRI) and three attentional control tasks; a subset (n = 396) also underwent Aβ-positron emissions tomography (PET). A vertex-wise model was conducted to spatially represent the relationship between cortical thickness and average attentional control accuracy, while moderation analysis examined whether Aβ deposition impacted this relationship. First, we found that reduced cortical thickness in temporal, medial- and lateral-parietal, and dorsolateral prefrontal cortex, predicted worse performance on the attention task composite. Subsequent moderation analyses observed that levels of Aβ significantly influence the relationship between cortical thickness and attentional control. Our results support the hypothesis that preclinical AD, as measured by Aβ deposition, is partially driving what would otherwise be considered general aging in a cognitively normal adult population.

Longitudinal changes in Alzheimer's disease (AD) biomarkers, including cerebrospinal fluid (CSF) analytes, amyloid uptakes from positron emission tomography (PET), structural outcomes from magnetic resonance imaging (MRI), and cognition, have not been compared between Blacks and Whites.A total of 179 Blacks and 1180 Whites who were cognitively normal at baseline and had longitudinal data from at least one biomarker modality were analyzed for the annual rates of change.CSF amyloid beta (Aβ)42/Aβ40 declined more slowly (P = .0390), and amyloid (PET) accumulated more slowly (P = .0157), in Blacks than Whites. CSF Aβ42 changed in opposite directions over time between Blacks and Whites (P = .0039). The annual increase in CSF total tau and phosphorylated tau181 for Blacks was about half of that for Whites.Longitudinal racial differences in amyloid biomarkers are observed. It will be important to comprehensively and prospectively examine the effects of apolipoprotein E genotype and sociocultural factors on these differences.

Background:Estimates of ‘brain-predicted age’ quantify apparent brain age compared to normative trajectories of neuroimaging features. The brain age gap (BAG) between predicted and chronological age is elevated in symptomatic Alzheimer disease (AD) but has not been well explored in presymptomatic AD. Prior studies have typically modeled BAG with structural MRI, but more recently other modalities, including functional connectivity (FC) and multimodal MRI, have been explored.Methods:We trained three models to predict age from FC, structural (S), or multimodal MRI (S+FC) in 390 amyloid-negative cognitively normal (CN/A−) participants (18–89 years old). In independent samples of 144 CN/A−, 154 CN/A+, and 154 cognitively impaired (CI; CDR > 0) participants, we tested relationships between BAG and AD biomarkers of amyloid and tau, as well as a global cognitive composite.Results:All models predicted age in the control training set, with the multimodal model outperforming the unimodal models. All three BAG estimates were significantly elevated in CI compared to controls. FC-BAG was significantly reduced in CN/A+ participants compared to CN/A−. In CI participants only, elevated S-BAG and S+FC BAG were associated with more advanced AD pathology and lower cognitive performance.Conclusions:Both FC-BAG and S-BAG are elevated in CI participants. However, FC and structural MRI also capture complementary signals. Specifically, FC-BAG may capture a unique biphasic response to presymptomatic AD pathology, while S-BAG may capture pathological progression and cognitive decline in the symptomatic stage. A multimodal age-prediction model improves sensitivity to healthy age differences.Funding:This work was supported by the National Institutes of Health (P01-AG026276, P01- AG03991, P30-AG066444, 5-R01-AG052550, 5-R01-AG057680, 1-R01-AG067505, 1S10RR022984-01A1, and U19-AG032438), the BrightFocus Foundation (A2022014F), and the Alzheimer’s Association (SG-20-690363-DIAN).

Older adults with lower education are at greater risk for dementia. It is unclear which brain changes lead to these outcomes. Longitudinal imaging-based measures of brain structure and function were examined in adult individuals (baseline age, 45–86 years; two to five visits per participant over 1–9 years). College degree completion differentiates individual-based and neighborhood-based measures of socioeconomic status and disadvantage. Older adults (~65 years and over) without a college degree exhibit a pattern of declining large-scale functional brain network organization (resting-state system segregation) that is less evident in their college-educated peers. Declining brain system segregation predicts impending changes in dementia severity, measured up to 10 years past the last scan date. The prognostic value of brain network change is independent of Alzheimer’s disease (AD)-related genetic risk (APOE status), the presence of AD-associated pathology (cerebrospinal fluid phosphorylated tau, cortical amyloid) and cortical thinning. These results demonstrate that the trajectory of an individual’s brain network organization varies in relation to their educational attainment and, more broadly, is a unique indicator of individual brain health during older age. A brain-imaging study in humans demonstrates that older age brain network decline varies in relation to an individual’s education and predicts future dementia severity beyond other markers of Alzheimer’s disease-related genetic risk and pathology.

Decades of research has established a shift from an “eveningness” preference to a “morningness” preference with increasing age. Accordingly, older adults typically have better cognition in morning hours compared to evening hours. We present the first known attempt to capture circadian fluctuations in cognition in individuals at risk for Alzheimer disease (AD) using a remotely administered smartphone assessment that samples cognition rapidly and repeatedly over several days. Older adults (N = 169, aged 61–94 years; 93% cognitively normal) completed four brief smartphone-based testing sessions per day for 7 consecutive days at quasi-random time intervals, assessing associate memory, processing speed, and visual working memory. Scores completed during early hours were averaged for comparison with averaged scores completed during later hours. Mixed effects models evaluated time of day effects on cognition. Additional models included clinical status and cerebrospinal fluid (CSF) biomarkers for beta amyloid (Aβ42) and phosphorylated tau181 (pTau). Models with terms for age, gender, education, APOE ε4 status, and clinical status revealed significantly worse performance on associate memory in evening hours compared to morning hours. Contemporaneously reported mood and fatigue levels did not moderate relationships. Using CSF data to classify individuals with and without significant AD pathology, there were no group differences in performance in morning hours, but subtle impairment emerged in associate memory in evening hours in those with CSF-confirmed AD pathology. These findings indicate that memory is worse in evening hours in older adults, that this pattern is consistent across several days, and is independent of measures of mood and fatigue. Further, they provide preliminary evidence of a “cognitive sundowning” in the very earliest stages of AD. Time of day may be an important consideration for assessments in observational studies and clinical trials in AD populations.

Background Comprehensive testing of cognitive functioning is standard practice in studies of Alzheimer disease (AD). Short-form tests like the Montreal Cognitive Assessment (MoCA) use a “sampling” of measures, administering key items in a shortened format to efficiently assess cognition while reducing time requirements, participant burden, and administrative costs. We compared the MoCA to a commonly used long-form cognitive battery in predicting AD symptom onset and sensitivity to AD neuroimaging biomarkers. Methods Survival, area under the receiver operating characteristic (ROC) curve (AUC), and multiple regression analyses compared the MoCA and long-form measures in predicting time to symptom onset in cognitively normal older adults (n = 6230) from the National Alzheimer’s Coordinating Center (NACC) cohort who had, on average, 2.3 ± 1.2 annual assessments. Multiple regression models in a separate sample (n = 416) from the Charles F. and Joanne Knight Alzheimer Disease Research Center (Knight ADRC) compared the sensitivity of the MoCA and long-form measures to neuroimaging biomarkers including amyloid PET, tau PET, and cortical thickness. Results Hazard ratios suggested that both the MoCA and the long-form measures are similarly and modestly efficacious in predicting symptomatic conversion, although model comparison analyses indicated that the long-form measures slightly outperformed the MoCA (HRs > 1.57). AUC analyses indicated no difference between the measures in predicting conversion (DeLong’s test, Z = 1.48, p = 0.13). Sensitivity to AD neuroimaging biomarkers was similar for the two measures though there were only modest associations with tau PET (rs = − 0.13, ps < 0.02) and cortical thickness in cognitively normal participants (rs = 0.15–0.16, ps < 0.007). Conclusions Both test formats showed weak associations with symptom onset, AUC analyses indicated low diagnostic accuracy, and biomarker correlations were modest in cognitively normal participants. Alternative assessment approaches are needed to improve how clinicians and researchers monitor cognitive changes and disease progression prior to symptom onset.

Dominantly inherited Alzheimer's disease (DIAD) causes predictable biological changes decades before the onset of clinical symptoms, enabling testing of interventions in the asymptomatic and symptomatic stages to delay or slow disease progression. We conducted a randomized, placebo-controlled, multi-arm trial of gantenerumab or solanezumab in participants with DIAD across asymptomatic and symptomatic disease stages. Mutation carriers were assigned 3:1 to either drug or placebo and received treatment for 4-7 years. The primary outcome was a cognitive end point; secondary outcomes included clinical, cognitive, imaging and fluid biomarker measures. Fifty-two participants carrying a mutation were assigned to receive gantenerumab, 52 solanezumab and 40 placebo. Both drugs engaged their Aβ targets but neither demonstrated a beneficial effect on cognitive measures compared to controls. The solanezumab-treated group showed a greater cognitive decline on some measures and did not show benefits on downstream biomarkers. Gantenerumab significantly reduced amyloid plaques, cerebrospinal fluid total tau, and phospho-tau181 and attenuated increases of neurofilament light chain. Amyloid-related imaging abnormalities edema was observed in 19.2% (3 out of 11 were mildly symptomatic) of the gantenerumab group, 2.5% of the placebo group and 0% of the solanezumab group. Gantenerumab and solanezumab did not slow cognitive decline in symptomatic DIAD. The asymptomatic groups showed no cognitive decline; symptomatic participants had declined before reaching the target doses.

Glial fibrillary acidic protein (GFAP) is a promising candidate blood-based biomarker for Alzheimer’s disease (AD) diagnosis and prognostication. The timing of its disease-associated changes, its clinical correlates, and biofluid-type dependency will influence its clinical utility. We evaluated plasma, serum, and CSF GFAP in families with autosomal dominant AD (ADAD), leveraging the predictable age at symptom onset to determine changes by stage of disease. Plasma GFAP elevations appear a decade before expected symptom onset, after β-amyloid accumulation and prior to neurodegeneration and cognitive decline. Plasma GFAP distinguished β-amyloid-positive from β-amyloid-negative ADAD participants and showed a stronger relationship with β-amyloid load in asymptomatic than symptomatic ADAD. Higher plasma GFAP was associated with the degree and rate of neurodegeneration and cognitive impairment. Serum GFAP showed similar relationships, but these were less pronounced for CSF GFAP. Our findings support a role for plasma GFAP as a clinical biomarker for β-amyloid-associated cognitive deterioration in AD.

Neurodegenerative disease is highly prevalent among older adults and, if undetected, may obscure estimates of cognitive change among aging samples. Our aim in this study was to determine the nature and magnitude of cognitive change in the absence of common neuropathologic markers of neurodegenerative disease. Cognitively normal older adults (ages 65–89 years, N = 199) were classified as normal or abnormal using neuroimaging and cerebrospinal-fluid biomarkers of β-amyloid, tau, and neurodegeneration. When cognitive change was modeled without accounting for biomarker status, significant decline was evident for semantic memory, processing speed, and working memory. However, after adjusting for biomarker status, we found that the rate of change was attenuated and that the biomarker-normal group demonstrated no decline for any cognitive domain. These results indicate that estimates of cognitive change in otherwise healthy older adults will be biased toward decline when the presence of early neurodegenerative disease is not accounted for.

Background Major prevention trials for Alzheimer’s disease (AD) are now focusing on multidomain lifestyle interventions. However, the exact combination of behavioral factors related to AD pathology remains unclear. In 2 cohorts of cognitively unimpaired individuals at risk of AD, we examined which combinations of personality traits, neuropsychiatric symptoms, and cognitive lifestyle (years of education or lifetime cognitive activity) related to the pathological hallmarks of AD, amyloid-β, and tau deposits. Methods A total of 115 older adults with a parental or multiple-sibling family history of sporadic AD (PREVENT-AD [PRe-symptomatic EValuation of Experimental or Novel Treatments for AD] cohort) underwent amyloid and tau positron emission tomography and answered several questionnaires related to behavioral attributes. Separately, we studied 117 mutation carriers from the DIAN (Dominant Inherited Alzheimer Network) study group cohort with amyloid positron emission tomography and behavioral data. Using partial least squares analysis, we identified latent variables relating amyloid or tau pathology with combinations of personality traits, neuropsychiatric symptoms, and cognitive lifestyle. Results In PREVENT-AD, lower neuroticism, neuropsychiatric burden, and higher education were associated with less amyloid deposition (p = .014). Lower neuroticism and neuropsychiatric features, along with higher measures of openness and extraversion, were related to less tau deposition (p = .006). In DIAN, lower neuropsychiatric burden and higher education were also associated with less amyloid (p = .005). The combination of these factors accounted for up to 14% of AD pathology. Conclusions In the preclinical phase of both sporadic and autosomal dominant AD, multiple behavioral features were associated with AD pathology. These results may suggest potential pathways by which multidomain interventions might help delay AD onset or progression.

Background: The ultimate validation of a clinical marker for Alzheimer’s disease (AD) is its association with AD neuropathology. Objective: To identify clinical measures that predict pathology, we evaluated the relationships of the picture version of the Free and Cued Selective Reminding Test (pFCSRT + IR), the Mini-Mental State Exam (MMSE), and the Clinical Dementia Rating scale Sum of Boxes (CDR-SB) to Braak stage. Methods: 315 cases from the clinicopathologic series at the Knight Alzheimer’s Disease Research Center were classified according to Braak stage. Boxplots of each predictor were compared to identify the earliest stage at which decline was observed and ordinal logistic regression was used to predict Braak stage. Results: Looking at the assessment closest to death, free recall scores were lower in individuals at Braak stage III versus Braak stages 0 and I (combined) while MMSE and CDR scores for individuals did not differ from Braak stages 0/I until Braak stage IV. The sum of free recall and total recall scores independently predicted Braak stage and had higher predictive validity than MMSE and CDR-SB in models including all three. Conclusion: pFCSRT + IR scores may be more sensitive to early pathological changes than either the CDR-SB or the MMSE.

INTRODUCTION: Machine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease. METHODS: Longitudinal structural magnetic resonance imaging, amyloid positron emission tomography (PET), and fluorodeoxyglucose PET were acquired in 131 mutation carriers and 74 non-carriers from the Dominantly Inherited Alzheimer Network; the groups were matched for age, education, sex, and apolipoprotein e4 (APOE e4). A deep neural network was trained to predict disease progression for each modality. Relief algorithms identified the strongest predictors of mutation status. RESULTS: The Relief algorithm identified the caudate, cingulate, and precuneus as the strongest predictors among all modalities. The model yielded accurate results for predicting future Pittsburgh compound B (R2 = 0.95), fluorodeoxyglucose (R2 = 0.93), and atrophy (R2 = 0.95) in mutation carriers compared to non-carriers. DISCUSSION: Results suggest a sigmoidal trajectory for amyloid, a biphasic response for metabolism, and a gradual decrease in volume, with disease progression primarily in subcortical, middle frontal, and posterior parietal regions.

BACKGROUND Precision medicine, optimized interventions, and access to care are catchphrases for the future of behavioral treatments. Progress has been slow due to the dearth of clinical trials that (i) optimize interventions’ benefits; (ii) individually tailor interventions to meet individual patients’ needs and preferences; (iii) lead to rapid implementation after effectiveness is demonstrated. Two innovations have emerged to meet these challenges: fully remote trials and precision clinical trials. OBJECTIVE This paper provides a detailed description of an early-phase clinical trial, Mindful MyWay, designed to test online mindfulness training in older adults with depression. A smartphone app was developed to help coordinate the study, deliver the intervention, and evaluate the acceptability of the intervention, as well as predictors and outcomes of it. Consistent with the concept of fully remote trials, the study requires no in-person contact and can be conducted with participants anywhere in the US. Consistent with a precision medicine framework, the study assesses patients using high-frequency assessments of symptoms, cognitive performance, and patient preferences to both understand the individualized nature of treatment response and help individually tailor the intervention. METHODS The Mindful MyWay study delivers mindfulness education to older adults with depression. The curriculum parallels the Mindfulness-Based Stress Reduction (MBSR) curriculum, a structured group-based mindfulness training that is typically provided in person. The Healthy Mind Lab mHealth smartphone application (app) is downloaded from the Apple App Store. Participants receive reminders 4 times each day to complete brief smartphone-based assessments of depressive symptoms and cognitive performance, for 4 weeks both prior to and after completing the intervention. They also receive reminders each week to complete their web-based mindfulness video sessions. The intervention consists of an introduction video and 10 weekly mindfulness training sessions, with the expectation to practice mindfulness at home daily. The app also collects participant preference data throughout the 10-week intervention period; these high-frequency assessments identify participants’ individually dynamic preferences, towards a goal of individually optimizing the intervention in future iterations. RESULTS Participant recruitment and data collection began in March 2019. The final endpoint assessments will be collected in May 2022. The paper describes lessons learned regarding the critical role of early-phase testing prior to moving to a randomized trial. CONCLUSIONS The Mindful MyWay study is an exemplar of innovative clinical trial designs that use smartphone technology in behavioral and neuropsychiatric conditions. These include fully remote studies that can recruit throughout the US including hard-to-access areas and high-frequency data collection which is ideal for idiographic assessment and individualized intervention optimization. Our findings will be used to modify our methods and inform future randomized controlled trials within a precision medicine framework. CLINICALTRIAL NCT03922217; https://clinicaltrials.gov/ct2/show/NCT03922217 Clinicaltrials.gov (Initial release date 3/13/2019)

Objectives To determine the extent to which cognitive domain scores moderate change in driving behavior in cognitively healthy older adults using naturalistic (Global Positioning System-based) driving outcomes and to compare against self-reported outcomes using an established driving questionnaire. Methods We analyzed longitudinal naturalistic driving behavior from a sample (N = 161, 45% female, mean age = 74.7 years, mean education = 16.5 years) of cognitively healthy, nondemented older adults. Composite driving variables were formed that indexed “driving space” and “driving performance.” All participants completed a baseline comprehensive cognitive assessment that measured multiple domains as well as an annual self-reported driving outcomes questionnaire. Results Across an average of 24 months of naturalistic driving, our results showed that attentional control, broadly defined as the ability to focus on relevant aspects of the environment and ignore distracting or competing information as measured behaviorally with tasks such as the Stroop color naming test, moderated change in driving space scores over time. Specifically, individuals with lower attentional control scores drove fewer trips per month, drove less at night, visited fewer unique locations, and drove in smaller spaces than those with higher attentional control scores. No cognitive domain predicted driving performance such as hard braking or sudden acceleration. Discussion Attentional control is a key moderator of change over time in driving space but not driving performance in older adults. We speculate on mechanisms that may relate attentional control ability to modifications of driving behaviors.

Life-long engagement in cognitively demanding activities may mitigate against declines in cognitive ability observed in healthy or pathological aging. However, the “mental costs” associated with completing cognitive tasks also increases with age and may be partly attributed to increases in preclinical levels of Alzheimer disease (AD) pathology. We test whether cognitive costs increase in a domain-general fashion in older adults, and further, whether such age-related increases in cognitive effort cost are associated with amyloid burden, a signature pathology of AD. In two experiments, we administered an objective measure of cognitive effort (Cognitive Effort Discounting: COG-ED) to a sample of older adults recruited from online sources (Experiment 1) or from ongoing longitudinal studies of aging and dementia (Experiment 2). Experiment 1 compared age-related differences in cognitive effort across two distinct domains, working memory and speech comprehension. Experiment 2 compared cognitive costs between a group of participants who were rated positive for amyloid burden relative to those who had no evidence of amyloid. Results showed age-related increases in cognitive effort cost were evident in both domains. Cost estimates were highly correlated between the working memory and speech comprehension tasks and were present even when controlling for overall working memory capacity. In addition, older adults who were amyloid positive had higher cognitive cost estimates than those who were amyloid negative. Cognitive effort costs may index a domain-general trait that consistently increases in healthy aging. Differences in amyloid burden suggest a potential neurobiological mechanism for age-related increases in cost.

Important insights into the early pathogenesis of Alzheimer's disease can be provided by studies of autosomal dominant Alzheimer's disease and Down syndrome. However, it is unclear whether the timing and spatial distribution of amyloid accumulation differs between people with autosomal dominant Alzheimer's disease and those with Down syndrome. We aimed to directly compare amyloid changes between these two groups of people.In this cross-sectional study, we included participants (aged ≥25 years) with Down syndrome and sibling controls who had MRI and amyloid PET scans in the first data release (January, 2020) of the Alzheimer's Biomarker Consortium-Down Syndrome (ABC-DS) study. We also included carriers of autosomal dominant Alzheimer's disease genetic mutations and non-carrier familial controls who were within a similar age range to ABC-DS participants (25-73 years) and had MRI and amyloid PET scans at the time of a data freeze (December, 2020) of the Dominantly Inherited Alzheimer Network (DIAN) study. Controls from the two studies were combined into a single group. All DIAN study participants had genetic testing to determine PSEN1, PSEN2, or APP mutation status. APOE genotype was determined from blood samples. CSF samples were collected in a subset of ABC-DS and DIAN participants and the ratio of amyloid β42 (Aβ42) to Aβ40 (Aβ42/40) was measured to evaluate its Spearman's correlation with amyloid PET. Global PET amyloid burden was compared with regards to cognitive status, APOE ɛ4 status, sex, age, and estimated years to symptom onset. We further analysed amyloid PET deposition by autosomal dominant mutation type. We also assessed regional patterns of amyloid accumulation by estimated number of years to symptom onset. Within a subset of participants the relationship between amyloid PET and CSF Aβ42/40 was evaluated.192 individuals with Down syndrome and 33 sibling controls from the ABC-DS study and 265 carriers of autosomal dominant Alzheimer's disease mutations and 169 non-carrier familial controls from the DIAN study were included in our analyses. PET amyloid centiloid and CSF Aβ42/40 were negatively correlated in carriers of autosomal dominant Alzheimer's disease mutations (n=216; r=-0·565; p0·0001) and in people with Down syndrome (n=32; r=-0·801; p0·0001). There was no difference in global PET amyloid burden between asymptomatic people with Down syndrome (mean 18·80 centiloids [SD 28·33]) versus asymptomatic mutation carriers (24·61 centiloids [30·27]; p=0·11) and between symptomatic people with Down syndrome (77·25 centiloids [41·76]) versus symptomatic mutation carriers (69·15 centiloids [51·10]; p=0·34). APOE ɛ4 status and sex had no effect on global amyloid PET deposition. Amyloid deposition was elevated significantly earlier in mutation carriers than in participants with Down syndrome (estimated years to symptom onset -23·0 vs -17·5; p=0·0002). PSEN1 mutations primarily drove this difference. Early amyloid accumulation occurred in striatal and cortical regions for both mutation carriers (n=265) and people with Down syndrome (n=128). Although mutation carriers had widespread amyloid accumulation in all cortical regions, the medial occipital regions were spared in people with Down syndrome.Despite minor differences, amyloid PET changes were similar between people with autosomal dominant Alzheimer's disease versus Down syndrome and strongly supported early amyloid dysregulation in individuals with Down syndrome. Individuals with Down syndrome aged at least 35 years might benefit from early intervention and warrant future inclusion in clinical trials, particularly given the relatively high incidence of Down syndrome.The National Institute on Aging, Riney and Brennan Funds, the Eunice Kennedy Shriver National Institute of Child Health and Human Development, the German Center for Neurodegenerative Diseases, and the Japan Agency for Medical Research and Development.

Background Cerebrospinal fluid tau and neurofilament light (NfL) are two biomarkers of neurodegeneration in Alzheimer's disease. Previous reports have shown that the influence of tau on cognitive decline depends on levels of amyloid burden whereas NfL predicts decline independently of amyloid. Most studies use a global cognitive composite as the primary outcome, and it is unknown if critical cognitive domain scores are similarly sensitive to rates of decline due to neurodegeneration. Objective To examine the unique contribution of amyloid, tau, and NfL to rates of cognitive decline in multiple cognitive composites in a cognitively healthy, middle-aged to older adult cohort. Methods A total of 255 participants (55% female; mean age = 66.2 years, range = 42.5-86.7 years) completed CSF studies and serial cognitive assessments to measure global cognition, episodic memory, and attentional control. Linear mixed effects models were used to examine rates of change on each composite score as a function of baseline biomarker levels. Results Total tau predicted decline in attention regardless of amyloid status, but the relationship to global cognition and episodic memory was dependent on amyloid, replicating prior literature. NfL predicted decline in attention and global cognition, but not memory, and this effect was independent of amyloid status. Conclusions These findings suggest that NfL can be used to monitor cognitive decline in aging and Alzheimer's disease and that an attentional control composite may be a better outcome for tracking general neurodegenerative effects on cognition.

Primary age-related tauopathy is increasingly recognized as a separate neuropathological entity different from Alzheimer’s disease. Both share the neuropathological features of tau aggregates and neuronal loss in the temporal lobe, but primary age-related tauopathy lacks the requisite amyloid plaques central to Alzheimer’s disease. While both have similar clinical presentations, individuals with symptomatic primary age-related tauopathy are commonly of more advanced ages with milder cognitive dysfunction. Direct comparison of the neuropsychological trajectories of primary age-related tauopathy and Alzheimer’s disease has not been thoroughly evaluated and thus, our objective was to determine how cognitive decline differs longitudinally between these two conditions after the onset of clinical symptoms. Data were obtained from the National Alzheimer’s Coordinating Center on participants with mild cognitive impairment at baseline and either no neuritic plaques (i.e. primary age-related tauopathy) or moderate to frequent neuritic plaques (i.e. Alzheimer neuropathological change) at subsequent autopsy. For patients with Alzheimer’s disease and primary age-related tauopathy, we compared rates of decline in the sum of boxes score from the CDR® Dementia Staging Instrument and in five cognitive domains (episodic memory, attention/working memory, executive function, language/semantic memory, and global composite) using z-scores for neuropsychological tests that were calculated based on scores for participants with normal cognition. The differences in rates of change were tested using linear mixed-effects models accounting for clinical centre clustering and repeated measures by individual. Models were adjusted for sex, age, education, baseline test score, Braak stage, apolipoprotein ε4 (APOE ε4) carrier status, family history of cognitive impairment, and history of stroke, hypertension, or diabetes. We identified 578 participants with a global CDR of 0.5 (i.e. mild cognitive impairment) at baseline, 126 with primary age-related tauopathy and 452 with Alzheimer’s disease. Examining the difference in rates of change in CDR sum of boxes and in all domain scores, participants with Alzheimer’s disease had a significantly steeper decline after becoming clinically symptomatic than those with primary age-related tauopathy. This remained true after adjusting for covariates. The results of this analysis corroborate previous studies showing that primary age-related tauopathy has slower cognitive decline than Alzheimer’s disease across multiple neuropsychological domains, thus adding to the understanding of the neuropsychological burden in primary age-related tauopathy. The study provides further evidence to support the hypothesis that primary age-related tauopathy has distinct neuropathological and clinical features compared to Alzheimer’s disease.

Introduction: Although some members of families with autosomal dominant Alzheimer's disease mutations learn their mutation status, most do not. How knowledge of mutation status affects clinical disease progression is unknown. This study quantifies the influence of mutation awareness on clinical symptoms, cognition, and biomarkers. / Methods: Mutation carriers and non‐carriers from the Dominantly Inherited Alzheimer Network (DIAN) were stratified based on knowledge of mutation status. Rates of change on standard clinical, cognitive, and neuroimaging outcomes were examined. / Results: Mutation knowledge had no associations with cognitive decline, clinical progression, amyloid deposition, hippocampal volume, or depression in either carriers or non‐carriers. Carriers who learned their status mid‐study had slightly higher levels of depression and lower cognitive scores. / Discussion: Knowledge of mutation status does not affect rates of change on any measured outcome. Learning of status mid‐study may confer short‐term changes in cognitive functioning, or changes in cognition may influence the determination of mutation status.

While the Dominantly Inherited Alzheimer Network Trials Unit (DIAN-TU) was ongoing, external data suggested higher doses were needed to achieve targeted effects; therefore, doses of gantenerumab were increased 5-fold, and solanezumab was increased 4-fold. We evaluated to what extent mid-trial dose increases produced a dose-dependent treatment effect.Using generalized linear mixed effects (LME) models, we estimated the annual low- and high-dose treatment effects in clinical, cognitive, and biomarker outcomes.Both gantenerumab and solanezumab demonstrated dose-dependent treatment effects (significant for gantenerumab, non-significant for solanezumab) in their respective target amyloid biomarkers (Pittsburgh compound B positron emission tomography standardized uptake value ratio and cerebrospinal fluid amyloid beta 42), with gantenerumab demonstrating additional treatment effects in some downstream biomarkers. No dose-dependent treatment effects were observed in clinical or cognitive outcomes.Mid-trial dose escalation can be implemented as a remedy for an insufficient initial dose and can be more cost effective and less burdensome to participants than starting a new trial with higher doses, especially in rare diseases.We evaluated the dose-dependent treatment effect of two different amyloid-specific immunotherapies.Dose-dependent treatment effects were observed in some biomarkers.No dose-dependent treatment effects were observed in clinical/cognitive outcomes, potentially due to the fact that the modified study may not have been powered to detect such treatment effects in symptomatic subjects at a mild stage of disease exposed to high (or maximal) doses of medication for prolonged durations.