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Utilizing [18F]-AV-1451 tau positron emission tomography (PET) as an Alzheimer disease (AD) biomarker will require identification of brain regions that are most important in detecting elevated tau pathology in preclinical AD. Here, we utilized an unsupervised learning, data-driven approach to identify brain regions whose tau PET is most informative in discriminating low and high levels of [18F]-AV-1451 binding. 84 cognitively normal participants who had undergone AV-1451 PET imaging were used in a sparse k-means clustering with resampling analysis to identify the regions most informative in dividing a cognitively normal population into high tau and low tau groups. The highest-weighted FreeSurfer regions of interest (ROIs) separating these groups were the entorhinal cortex, amygdala, lateral occipital cortex, and inferior temporal cortex, and an average SUVR in these four ROIs was used as a summary metric for AV-1451 uptake. We propose an AV-1451 SUVR cut-off of 1.25 to define high tau as described by imaging. This spatial distribution of tau PET is a more widespread pattern than that predicted by pathological staging schemes. Our data-derived metric was validated first in this cognitively normal cohort by correlating with early measures of cognitive dysfunction, and with disease progression as measured by β-amyloid PET imaging. We additionally validated this summary metric in a cohort of 13 Alzheimer disease patients, and showed that this measure correlates with cognitive dysfunction and β-amyloid PET imaging in a diseased population. •AV-1451 binding in four key regions identifies tau-positive individuals with preclinical AD.•The SUVR cutoff for high and low tau PET is 1.25.•Increased tau PET correlates with early cognitive impairment, and relates to β-amyloid burden in preclinical AD individuals.•The spatial pattern of AV-1451 uptake in preclinical AD is more widespread than predicted by pathological staging.

•We test a mechanistic model linking task activity and restFC dysfunction in AD.•This theorizes that altered restFC in AD disrupts activity flow across the brain.•The model transforms healthy into unhealthy activity via individual restFC patterns.•This reliably predicts task activity and related dysfunction in at-risk AD subjects.•We provide a theoretical basis for integrating restFC into AD biomarker development. Alzheimer's disease (AD) is linked to changes in fMRI task activations and fMRI resting-state functional connectivity (restFC), which can emerge early in the illness timecourse. These fMRI correlates of unhealthy aging have been studied in largely separate subfields. Taking inspiration from neural network simulations, we propose a unifying mechanism wherein restFC alterations associated with AD disrupt the flow of activations between brain regions, leading to aberrant task activations. We apply this activity flow model in a large sample of clinically normal older adults, which was segregated into healthy (low-risk) and at-risk subgroups based on established imaging (positron emission tomography amyloid) and genetic (apolipoprotein) AD risk factors. Modeling the flow of healthy activations over at-risk AD connectivity effectively transformed the healthy aged activations into unhealthy (at-risk) aged activations. This enabled reliable prediction of at-risk AD task activations, and these predicted activations were related to individual differences in task behavior. These results support activity flow over altered intrinsic functional connections as a mechanism underlying Alzheimer's-related dysfunction, even in very early stages of the illness. Beyond these mechanistic insights, this approach raises clinical potential by enabling prediction of task activations and associated cognitive dysfunction in individuals without requiring them to perform in-scanner cognitive tasks.

Investigating human cognitive faculties such as language, attention, and memory most often relies on testing small and homogeneous groups of volunteers coming to research facilities where they are asked to participate in behavioral experiments. We show that this limitation and sampling bias can be overcome by using smartphone technology to collect data in cognitive science experiments from thousands of subjects from all over the world. This mass coordinated use of smartphones creates a novel and powerful scientific \"instrument\" that yields the data necessary to test universal theories of cognition. This increase in power represents a potential revolution in cognitive science.

Situational factors can influence cognitive performance and should be considered for conducting cognitive assessments. The objective of this project was to develop a checklist for Cognitive Assessment Requirements (CARE) to identify these situational factors before conducting cognitive assessments and account for them. This study employed a four-round Delphi approach involving 22 experts to identify situational factors that can impact cognitive assessment results. The development of a robust and well-balanced checklist was guided by a consensus-driven approach, which considered metrics such as Interquartile Deviation (IQD) (> 1.00), Percentage of Positive Responses (PPR, above 60%), and mean importance ratings (< 3 on a 5-point Likert scale) to assess both degree of agreement and item importance. Consensus was reached, leading to a 14-item checklist to evaluate cognitive assessment requirements. These items were categorized into six groups: Acute Illness or Physical Discomfort, Medication Effects and Substance Use, Sleep Quality and Fatigue, Emotional State, Language factors, and Environmental factors. The CARE can be employed prior to cognitive assessments to identify situational factors of relevance to the individual client, thereby creating a more favorable environment for cognitive evaluation, and enhancing the reliability of the assessment findings. Furthermore, the CARE can help determine the level of confidence in the results by assessing whether the conditions are conducive to testing or if situational factors may undermine the validity of the evaluation.

Older adults exhibit deficits in episodic memory tasks, which have often been attributed to encoding or retrieval deficits, with little attention to consolidation mechanisms. More recently, researchers have attempted to measure consolidation in the context of a behavioral experiment using the wakeful rest paradigm (i.e., a brief, quiet period of minimal stimulation, which facilitates memory performance, compared to a distractor task). Critically, older adults might not produce this effect, given established age differences in other episodic memory processes and mind-wandering. In three experiments, we directly compared younger and older adults in modified versions of the wakeful rest paradigm. Critically, we utilized incidental encoding procedures (all experiments) and abstract shape stimuli (in Experiment 3) to limit the possibility of retrieval practice or maintenance rehearsal as potential confounding mechanisms in producing the wakeful rest effect. Wakeful rest reliably and equally benefited recall of incidentally encoded words in both younger and older adults. In contrast, wakeful rest had no benefit for standard accuracy measures of recognition performance in verbal stimuli, although there was an effect in response latencies for non-verbal stimuli. Overall, these results suggest that the benefits of wakeful rest on episodic retrieval are preserved across age groups, and hence support age-independence in potential consolidation mechanisms as measured by wakeful rest. Further, these benefits do not appear to be dependent on the intentionality of encoding or variations in distractor task types. Finally, the lack of wakeful rest benefits on recognition performance might be driven by theoretical constraints on the effect or methodological limitations of recognition memory testing in the current paradigm.

The current study examined whether healthy older adults (OA) and individuals at the earliest stages of dementia of the Alzheimer's type (DAT) differ from younger adults (YA) and from each other on a simple, extended continuous tapping task using intervals (500 ms, 1000 ms, and 1500 ms) thought to differentially engage attentional control systems. OA groups sped up their tapping at the slowest target rate compared to the YA; this pattern was magnified in the early stage DAT groups. Performance variability appeared especially sensitive to DAT-related changes, as reliable differences between healthy OA and very mild DAT individuals emerged for multiple tap rates. These differences are proposed to result from breakdowns in attentional control that disrupt error-correction processes and the ability to resolve discrepancies between internally-generated temporal expectancies and the external temporal demands of the repetitive timing task. (JINS, 2012, 18, 1–12)

Longevity is in part (25%) inherited, and genetic studies aim to uncover allelic variants that play an important role in prolonging life span. Results to date confirm only a few gene variants associated with longevity, while others show inconsistent results. However, GWAS studies concentrate on single nucleotide polymorphisms, and there are only a handful of studies investigating variable number of tandem repeat variations related to longevity. Recently, Grady and colleagues (2013) reported a remarkable (66%) accumulation of those carrying the 7 repeat allele of the dopamine D4 receptor gene in a large population of 90-109 years old Californian centenarians, as compared to an ancestry-matched young population. In the present study we demonstrate the same association using continuous age groups in an 18-97 years old Caucasian sample (N = 1801, p = 0.007). We found a continuous pattern of increase from 18-75, however frequency of allele 7 carriers decreased in our oldest age groups. Possible role of gene-environment interaction effects driven by historical events are discussed. In accordance with previous findings, we observed association preferentially in females (p = 0.003). Our results underlie the importance of investigating non-disease related genetic variants as inherited components of longevity, and confirm, that the 7-repeat allele of the dopamine D4 receptor gene is a longevity enabling genetic factor, accumulating in the elderly female population.

Personality traits such as Neuroticism and Conscientiousness are associated with Alzheimer disease (AD) pathophysiology in cognitively normal (CN) and impaired individuals, and may represent potential risk or resilience factors, respectively. This study examined the cross-sectional relationship between personality traits and regional tau deposition using positron emission tomography (PET) in cognitively normal older adults. A cohort of CN (Clinical Dementia Rating (CDR) 0, n = 128) older adults completed the NEO Five-Factor Inventory to assess traits of Neuroticism, Extroversion, Openness, Agreeableness, and Conscientiousness and underwent tau-PET and β-amyloid (Aβ)-PET imaging. We utilized linear regression models, adjusting for age, sex, geriatric depression score, and Aβ to evaluate the association between each of the personality traits and regional tau-PET accumulation. Elevated Neuroticism scores were associated with higher tau-PET accumulation in the amygdala (p = .002), entorhinal cortex (p = .012), and inferior temporal cortex (p = .016), as well as with a composite tau-PET measure (p = .002). In contrast, Extroversion, Openness, Agreeableness, and Conscientiousness were not associated with tau deposition in any of these regions (p’s > 0.160). Our results indicate that increased Neuroticism is associated with higher tau pathophysiology in regions known to be vulnerable to AD pathophysiology in CN participants. High Neuroticism scores may therefore serve as a potential risk factor for tau accumulation. Alternatively, personality can change with the onset of AD, thus increased tau levels may affect Neuroticism scores. While future longitudinal studies are needed to determine directionality, our findings suggest early associations between Neuroticism and tau accumulation in CN adults.

Although it is widely recognized that response time (RT) distributions are almost always positively skewed and that mathematical psychologists have developed straightforward procedures for capturing characteristics of RT distributions, researchers continue to rely primarily on mean performance, which can be misleading for such data. We review simple procedures for capturing characteristics of underlying RT distributions and show how such procedures have recently been useful to better understand effects from standard cognitive experimental paradigms and individual differences in performance. These well-studied procedures for understanding RT distributions indicate that effects in means can be produced by (a) shifts of RT distributions, (b) stretching of slow tails of RT distributions, or (c) some combination. Importantly, effects in means can actually be obscured by opposing influences on the modal and tail portions of RT distributions. Such disparate patterns demand novel theoretical interpretations.

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. 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. 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 ( = 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. 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.