Explore the vast range of titles available.

Neurofibrillary tau tangles are a hallmark pathology of Alzheimer's disease (AD) and are more closely associated with AD-related cortical atrophy and symptom severity than amyloid-beta (Aβ). However, studies regarding the effect of tau on longitudinal cortical thinning, particularly in healthy aging and preclinical AD, have been limited in number due to the relatively recent introduction of in vivo PET tracers for imaging tau pathology. Here, we investigate [18F]-flortaucipir (FTP, a marker of paired helical filament tau) PET as a predictor of atrophy in healthy aging and preclinical AD. We examine longitudinal structural MRI brain imaging data, retrospectively and prospectively relative to FTP imaging, using piecewise linear mixed-effect models with time centered at each participant's FTP-PET session. Participants include 111 individuals from the Harvard Aging Brain Study who underwent at least three MRI sessions over an average of 4.46 years and one FTP-PET at the approximate midpoint of the observation period. Our primary analyses focus on inferior temporal (IT) FTP standardized uptake value ratios and longitudinal FreeSurfer defined cortical regions of interest. Relationships were also explored using other regional FTP measures (entorhinal, composite, and local), within high and low Pittsburgh compound-B (PiB) PET groups, and with longitudinal subcortical volume. Strong associations between IT FTP and cortical thinning were found, most notably in temporal, midline, and prefrontal regions, with stronger effects generally observed in the prospective as compared to retrospective time frame. Significant differences between prospective and retrospective rates of thinning were found in the inferior and middle temporal gyri, cingulate areas, as well as pars orbitalis such that higher IT FTP was associated with greater prospective rates of thinning. Within the high PiB group, significant differences between prospective and retrospective rates of thinning were similarly observed. However, no consistent pattern of tau-related change in cortical thickness within the low PiB group was discerned. These results provide support for the hypothesis that tau pathology is a driver of future atrophy as well as provide additional evidence for tau-PET as an effective AD biomarker for interventional clinical trials. •Tau positron emission tomography images were acquired for 111 individuals.•Magnetic resonance data were longitudinally collected (nscans = 386).•Prospective structural changes were observed with higher inferior temporal tau.•Lateral temporal regions may represent an inflection point in preclinical AD.•Findings are consistent with the hypothesis that tau is a driver of atrophy.

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 over 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 one-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 three 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 Curve 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<.001), but improvement over the first 3 months was greatest (β=0.68, 95%CI=[0.59-0.77], p<.001), suggesting stronger PE over initial repeated exposures. However, lower PE over 3 months were associated with more global amyloid burden (r=-.20, 95%CI=[-0.38 – -0.01], p=.049) and tau deposition in the entorhinal cortex (r=-.38, 95%CI=[-0.54 – -0.19], p<.001) and inferior-temporal lobe (r=-.23, 95%CI=[-0.41 – -0.02], p=.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<.001) and baseline PACC-5 scores (p=.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.

There is a growing need in clinical research domains for direct comparability between amyloid-beta (Aβ) Positron Emission Tomography (PET) measures obtained via different radiotracers and processing methodologies. Previous efforts to provide a common measurement scale fail to account for non-linearities between measurement scales that can arise from these differences. We introduce a new application of distribution mapping, based on well established statistical orthodoxy, that we call Nonlinear Distribution Mapping (NoDiM). NoDiM uses cumulative distribution functions to derive mappings between Aβ-PET measurements from different tracers and processing streams that align data based on their location in their respective distributions. Utilizing large datasets of Florbetapir (FBP) from the Alzheimer's Disease Neuroimaging Initiative (n = 349 female (%) = 53) and Pittsburgh Compound B (PiB) from the Harvard Aging Brain Study (n = 305 female (%) = 59.3) and the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (n = 184 female (%) = 53.3), we fit explicit mathematical models of a mixture of two normal distributions, with parameter estimates from Gaussian Mixture Models, to each tracer's empirical data. We demonstrate the accuracy of these fits, and then show the ability of NoDiM to transform FBP measurements into PiB-like units. A mixture of two normal distributions fit both the FBP and PiB empirical data and provides a strong basis for derivation of a transfer function. Transforming Aβ-PET data with NoDiM results in FBP and PiB distributions that are closely aligned throughout their entire range, while a linear transformation does not. Additionally the NoDiM transform better matches true positive and false positive profiles across tracers. The NoDiM transformation provides a useful alternative to the linear mapping advocated in the Centiloid project, and provides improved correspondence between measurements from different tracers across the range of observed values. This improved alignment enables disparate measures to be merged on to continuous scale, and better enables the use of uniform thresholds across tracers. •Non-linear, rank order based, radiotracer mapping method introduced called NoDiM.•Two Component GMM good fit for creating synthetic Aβ-PET CDFs.•Non linear mapping allows false positive and true negative rates to better align.•ADNI FBP successfully harmonized with HABS and AIBL PiB data.

Background Impairment in daily functioning is a clinical hallmark of dementia. Difficulties with “instrumental activities of daily living” (IADL) seem to increase gradually over the course of Alzheimer’s disease (AD), before dementia onset. However, it is currently not well established how difficulties develop along the preclinical and prodromal stages of AD. We aimed to investigate the trajectories of decline in IADL performance, as reported by a study partner, along the early stages of AD. Methods In a longitudinal multicenter study, combining data from community-based and memory clinic cohorts, we included 1555 individuals (mean age 72.5 ± 7.8 years; 50% female) based on availability of amyloid biomarkers, longitudinal IADL data, and clinical information at baseline. Median follow-up duration was 2.1 years. All amyloid-positive participants ( n  = 982) were classified into the National Institute on Aging–Alzheimer’s Association (NIA-AA) clinical stages ranging from preclinical AD (1) to overt dementia (4+). Cognitively normal amyloid-negative individuals ( n  = 573) served as a comparison group. The total scores of three study-partner reported IADL questionnaires were standardized. Results The rate of decline in cognitively normal (stage 1) individuals with and without abnormal amyloid did not differ ( p  = .453). However, from stage 2 onwards, decline was significantly faster in individuals on the AD continuum ( B [95%CI]  = − 0.32 [− 0.55, − 0.09], p  = .007). The rate of decline increased with each successive stage: one standard deviation (SD) unit per year in stage 3 (− 1.06 [− 1.27, − 0.85], p  < .001) and nearly two SD units per year in stage 4+ (1.93 [− 2.19, − 1.67], p  < .001). Overall, results were similar between community-based and memory clinic study cohorts. Conclusions Our results suggest that the rate of functional decline accelerates along the AD continuum, as shown by steeper rates of decline in each successive NIA-AA clinical stage. These results imply that incremental changes in function are a meaningful measure for early disease monitoring. Combined with the low-cost assessment, this advocates the use of these functional questionnaires for capturing the effects of early AD-related cognitive decline on daily life.

Changes in everyday functioning constitute a clinically meaningful outcome, even in the early stages of Alzheimer's disease. Performance-based assessments of everyday functioning might help uncover these early changes. We aimed to investigate how changes over time in everyday functioning relate to tau and amyloid in cognitively unimpaired older adults. Seventy-six cognitively unimpaired participants (72 ± 6 years old, 61% female) completed multiple Harvard Automated Phone Task (APT) assessments over 2.0 ± 0.9 years. The Harvard APT consists of three tasks, performed through an automated phone system, in which participants refill a prescription (APT-Script), select a new primary care physician (APT-PCP), and transfer money to pay a bill (APT-Bank). Participants underwent Pittsburgh compound-B and flortaucipir positron emission tomography scans at baseline. We computed distribution volume ratios for a cortical amyloid aggregate and standardized uptake volume ratios for medial temporal and neocortical tau regions. In separate linear mixed models, baseline amyloid by time and tau by time interactions were used to predict longitudinal changes in performance on the Harvard APT tasks. Three-way amyloid by tau by time interactions were also investigated. Lastly, we examined associations between tau and change in Harvard APT scores in exploratory voxel-wise whole-brain analyses. All models were adjusted for age, sex, and education. Amyloid [unstandardized partial regression coefficient estimate (β) = -0.007, 95% confidence interval (95% CI) = (-0.013, -0.001)], and medial temporal tau [β = -0.013, 95% CI = (-0.022, -0.004)] were associated with change over time in years on APT-PCP only, i.e., higher baseline amyloid and higher baseline tau were associated with steeper rate of decline of APT-PCP. Voxel-wise analyses showed widespread associations between tau and change in APT-PCP scores over time. Even among cognitively unimpaired older adults, changes over time in the performance of cognitively complex everyday activities relate to cortical amyloid and widespread cerebral tau burden at baseline. These findings support the link between Alzheimer's disease pathology and function and highlight the importance of measuring everyday functioning in preclinical disease stages.

Introduction It is important to study apathy in Alzheimer's disease (AD) to better understand its underlying neurobiology and develop effective interventions. In the current study, we sought to examine the relationships between longitudinal apathy and regional tau burden in cognitively impaired older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Methods Three hundred and nineteen ADNI participants with mild cognitive impairment (MCI) or AD dementia underwent flortaucipir (FTP) tau positron emission tomography (PET) imaging and clinical assessment with the Neuropsychiatric Inventory (NPI) annually. Longitudinal NPI Apathy (NPI‐A) scores were examined in relation to baseline tau PET signal in three a priori selected regions implicated in AD and AD‐related apathy (supramarginal gyrus, entorhinal cortex [EC] and rostral anterior cingulate cortex [rACC]). Secondary models were adjusted for global cognition (Mini‐Mental State Examination score) and cortical amyloid (florbetapir PET). Results Higher baseline supramarginal gyrus and EC tau burden were each significantly associated with greater NPI‐A over time, while rACC tau was associated with higher NPI‐A but did not predict its trajectory over time. These results were retained for supramarginal and EC tau after adjusting models for global cognition and cortical amyloid. Discussion Our findings suggest that baseline in vivo tau burden in parietal and temporal brain regions affected in AD, and less so in a medial frontal region involved in motivational control, is associated with increasing apathy over time in older adults with MCI and AD dementia. Future work studying emergent apathy in relation to not only core AD pathology but also circuit level dysfunction may provide additional insight into the neurobiology of apathy in AD and opportunities for intervention. Highlights Tau (Flortaucipir PET) in regions implicated in AD was associated with increasing apathy over time Cortical amyloid was also found to be a robust predictor of the trajectory of apathy Evidence of synergy between regional tau and amyloid in overall higher levels of apathy

Background Leveraging Alzheimer’s disease (AD) imaging biomarkers and longitudinal cognitive data may allow us to establish evidence of cognitive resilience (CR) to AD pathology in-vivo . Here, we applied latent class mixture modeling, adjusting for sex, baseline age, and neuroimaging biomarkers of amyloid, tau and neurodegeneration, to a sample of cognitively unimpaired older adults to identify longitudinal trajectories of CR. Methods We identified 200 Harvard Aging Brain Study (HABS) participants (mean age = 71.89 years, SD = 9.41 years, 59% women) who were cognitively unimpaired at baseline with 2 or more timepoints of cognitive assessment following a single amyloid-PET, tau-PET and structural MRI. We examined latent class mixture models with longitudinal cognition as the dependent variable and time from baseline, baseline age, sex, neocortical Aβ, entorhinal tau, and adjusted hippocampal volume as independent variables. We then examined group differences in CR-related factors across the identified subgroups from a favored model. Finally, we applied our favored model to a dataset from the Alzheimer’s Disease Neuroimaging Initiative (ADNI; n = 160, mean age = 73.9 years, SD = 7.6 years, 60% women). Results The favored model identified 3 latent subgroups, which we labelled as Normal (71% of HABS sample), Resilient (22.5%) and Declining (6.5%) subgroups. The Resilient subgroup exhibited higher baseline cognitive performance and a stable cognitive slope. They were differentiated from other groups by higher levels of verbal intelligence and past cognitive activity. In ADNI, this model identified a larger Normal subgroup (88.1%), a smaller Resilient subgroup (6.3%) and a Declining group (5.6%) with a lower cognitive baseline. Conclusion These findings demonstrate the value of data-driven approaches to identify longitudinal CR groups in preclinical AD. With such an approach, we identified a CR subgroup who reflected expected characteristics based on previous literature, higher levels of verbal intelligence and past cognitive activity.

INTRODUCTION Remote, smartphone‐based cognitive assessments such as the Mobile Toolbox (MTB) may increase the accessibility of Alzheimer's disease (AD) clinical trials. We examined the feasibility of the MTB among cognitively unimpaired (CU) older adults and investigated its associations with standardized in‐clinic cognitive testing and amyloid and tau positron emission tomography imaging. METHODS A total of 100 CU older adults self‐administered the MTB remotely on their personal devices. Linear regression models correcting for demographics investigated associations of MTB fluid and crystallized cognition composites with in‐clinic Preclinical Alzheimer's Cognitive Composite‐5 (PACC‐5) scores, global amyloid‐beta burden and tau deposition in the medial‐temporal lobe and neocortex. RESULTS Most participants completed the MTB without requiring assistance (81%) or reminders (61%). MTB fluid cognition scores were positively associated with PACC‐5 scores and negatively with tau deposition in the medial‐temporal lobe and neocortex. DISCUSSION These findings suggest that the MTB may provide a feasible approach to capture cognitive processes relevant in preclinical AD. Highlights The Mobile Toolbox (MTB) is a remote smartphone‐based cognitive assessment. We deployed the MTB in CU older adults with Alzheimer's disease (AD) biomarkers. We show how the MTB may facilitate cognitive assessment in preclinical AD research.

Introduction Non-invasive diffusion-weighted imaging (DWI) to assess brain microstructural changes via cortical mean diffusivity (cMD) has been shown to be cross-sectionally associated with tau in cognitively normal older adults, suggesting that it might be an early marker of neuronal injury. Here, we investigated how regional cortical microstructural changes measured by cMD are related to the longitudinal accumulation of regional tau as well as to episodic memory decline in cognitively normal individuals harboring amyloid pathology. Methods 122 cognitively normal participants from the Harvard Aging Brain Study underwent DWI, T1w-MRI, amyloid and tau PET imaging, and Logical Memory Delayed Recall (LMDR) assessments. We assessed whether the interaction of baseline amyloid status and cMD (in entorhinal and inferior-temporal cortices) was associated with longitudinal regional tau accumulation and with longitudinal LMDR using separate linear mixed-effects models. Results We find a significant interaction effect of the amyloid status and baseline cMD in predicting longitudinal tau in the entorhinal cortex ( p  = 0.044) but not the inferior temporal lobe, such that greater baseline cMD values predicts the accumulation of entorhinal tau in amyloid-positive participants. Moreover, we find a significant interaction effect of the amyloid status and baseline cMD in the entorhinal cortex (but not inferior temporal cMD) in predicting longitudinal LMDR ( p  < 0.001), such that baseline entorhinal cMD predicts the episodic memory decline in amyloid-positive participants. Conclusions The combination of amyloidosis and elevated cMD in the entorhinal cortex may help identify individuals at short-term risk of tau accumulation and Alzheimer’s Disease-related episodic memory decline, suggesting utility in clinical trials. Plain language summary People with Alzheimer’s disease have problems with their memory and ability to acquire and process knowledge. Understanding the earliest brain changes leading to these problems helps identify those likely to develop Alzheimer’s disease early in the disease process. This study used a marker that measures the mobility of water in the brain to investigate how these changes can predict development of a protein named tau and changes in people’s memory. The participants showed no signs of memory impairment at the beginning of the study, but some developed memory decline during follow-up. Greater mobility of water in certain brain areas predicted future increase in tau and decline in memory, indicating this measure could be used to identify people at risk of developing Alzheimer’s disease. Gagliardi, Rodriguez-Vieitez et al. image brain microstructural changes over time in cognitively-normal older adults. Cortical mean diffusivity was associated with accumulation of tau and episodic memory decline in those with amyloid deposits.

Cognitive resilience (CR) describes the phenomenon of individuals evading cognitive decline despite prominent Alzheimer’s disease neuropathology. Operationalization and measurement of this latent construct is non-trivial as it cannot be directly observed. The residual approach has been widely applied to estimate CR, where the degree of resilience is estimated through a linear model’s residuals. We demonstrate that this approach makes specific, uncontrollable assumptions and likely leads to biased and erroneous resilience estimates. This is especially true when information about CR is contained in the data the linear model was fitted to, either through inclusion of CR-associated variables or due to correlation. We propose an alternative strategy which overcomes the standard approach’s limitations using machine learning principles. Our proposed approach makes fewer assumptions about the data and CR and achieves better estimation accuracy on simulated ground-truth data.