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Age Differences and Changes in Reaction Time: The Baltimore Longitudinal Study of Aging
Abstract
This study analyzed auditory reaction time (RT) data from 1,265 community-dwelling volunteers (833 males and 432 females) who ranged in age from 17 to 96. Cross-sectional analyses revealed slowing of simple (SRT) and relatively greater slowing of disjunctive (DRT; aka "go-no-go") reaction time across decades for both males and females. Repeated testing within participants (longitudinal analyses) over eight years showed consistent slowing and increased variability with age. Males were faster than females cross age groups, RT tasks, and visits. Beginning at about age 20, RTs increased at a rate of approximately 0.5 msec/yr for SRT and 1.6 msec/yr for DRT. Errors also increased, making unlikely a tradeoff of accuracy for faster responses. The findings are consistent with the hypotheses that slowing of behavior is: (a) a continuous process over the adult life span; (b) characterized by age-associated increases in within-participant variability; (c) a direct function of task complexity and, presumably, the degree of mediation by higher regions in the central nervous system; and (d) greater in women than men.
... Even though subjects can intervene in the dynamics of falls, it has been 14 43 estimated that the initial 300-450 milliseconds are dedicated to recognizing and attempting to prevent the fall [43,44,46]. Additionally, in elderly individuals, there is a general increase in reaction time, especially in choice reaction tasks that involve selecting an appropriate response strategy [47][48][49][50][51][52][53]. Comparative studies have estimated that this difference is in the order of hundreds of milliseconds, particularly in experimental setups that involve secondary mental tasks and considering the delay in EMG activation of the lower limbs [52,54,55]. ...
Osteoporosis represents a major healthcare concern. The development of novel treatments presents challenges due to the limited cost-effectiveness of clinical trials and ethical concerns associated with placebo-controlled trials. Computational models for the design and assessment of biomedical products ( In Silico Trials) are emerging as a promising alternative. In this study, a novel In Silico Trial technology ( BoneStrength ) was applied to replicate the placebo arms of two concluded clinical trials and its accuracy in predicting hip fracture incidence was evaluated. Two virtual cohorts (N = 1238 and 1226, respectively) were generated by sampling a statistical anatomy atlas based on CT scans of proximal femurs. Baseline characteristics were equivalent to those reported for the clinical cohorts. Fall events were sampled from a Poisson distribution. A multiscale stochastic model was implemented to estimate the impact force associated to each fall. Finite Element models were used to predict femur strength. Fracture incidence in 3 years follow-up was computed with a Markov chain approach; a patient was considered fractured if the impact force associated with a fall exceeded femur strength. Ten realizations of the stochastic process were run to reach convergence. Each realization required approximately 2500 FE simulations, solved using High-Performance Computing infrastructures. Predicted number of fractures was 12 ± 2 and 18 ± 4 for the two cohorts, respectively. The predicted incidence range consistently included the reported clinical data, although on average fracture incidence was overestimated. These findings highlight the potential of BoneStrength for future applications in drug development and assessment.
... Our architecture is built around the idea of extracting neural representations that are common across sEEG subjects. We also, however, need to take into account that performance to behavioral tasks is intrinsically different between individuals [Fozard et al., 1994, Davranche et al., 2006, Green and Bavelier, 2003, Der and Deary, 2006]. Therefore, we designed a separate task head for each individual, composed of a shallow feed-forward network, that maps the extracted neural representations F to the behavioral outcome of a given trial. ...
Deep learning based neural decoding from stereotactic electroencephalography (sEEG) would likely benefit from scaling up both dataset and model size. To achieve this, combining data across multiple subjects is crucial. However, in sEEG cohorts, each subject has a variable number of electrodes placed at distinct locations in their brain, solely based on clinical needs. Such heterogeneity in electrode number/placement poses a significant challenge for data integration, since there is no clear correspondence of the neural activity recorded at distinct sites between individuals. Here we introduce seegnificant: a training framework and architecture that can be used to decode behavior across subjects using sEEG data. We tokenize the neural activity within electrodes using convolutions and extract long-term temporal dependencies between tokens using self-attention in the time dimension. The 3D location of each electrode is then mixed with the tokens, followed by another self-attention in the electrode dimension to extract effective spatiotemporal neural representations. Subject-specific heads are then used for downstream decoding tasks. Using this approach, we construct a multi-subject model trained on the combined data from 21 subjects performing a behavioral task. We demonstrate that our model is able to decode the trial-wise response time of the subjects during the behavioral task solely from neural data. We also show that the neural representations learned by pretraining our model across individuals can be transferred in a few-shot manner to new subjects. This work introduces a scalable approach towards sEEG data integration for multi-subject model training, paving the way for cross-subject generalization for sEEG decoding.
... The young adult sample showed faster mean RT and greater consistency in their responding (as indicated by their lower RT variability) relative to the child and older adult samples. This U-shaped pattern in response speed is typical of the developmental and ageing literatures [34,35] and may be attributable to the implementation of multiple strategies in this task and/or changes in brain morphology [35]. ...
Detailed studies of the equiprobable auditory Go/NoGo task have allowed for the development of a sequential-processing model of the perceptual and cognitive processes involved. These processes are reflected in various components differentiating the Go and NoGo event-related potentials (ERPs). It has long been established that electroencephalography (EEG) changes through normal lifespan development. It is also known that ERPs and behaviour in the equiprobable auditory Go/NoGo task change from children to young adults, and again in older adults. Here, we provide a novel examination of links between in-task prestimulus EEG, poststimulus ERPs, and behaviour in three gender-matched groups: children (8–12 years), young adults (18–24 years), and older adults (59–74 years). We used a frequency Principal Component Analysis (f-PCA) to estimate prestimulus EEG components and a temporal Principal Component Analysis (t-PCA) to separately estimate poststimulus ERP Go and NoGo components in each age group to avoid misallocation of variance. The links between EEG components, ERP components, and behavioural measures differed markedly between the groups. The young adults performed best and accomplished this with the simplest EEG–ERP–behaviour brain dynamics pattern. The children performed worst, and this was reflected in the most complex brain dynamics pattern. The older adults showed some reduction in performance, reflected in an EEG–ERP–behaviour pattern with intermediate complexity between those of the children and young adults. These novel brain dynamics patterns hold promise for future developmental research.
... For all types of vehicles, the driver's age shows positive effects on injury count. The reason could be that reaction speed decreases as the driver's age increases (28). Besides, aggressive driving behavior tends to have a positive effect on injury count for EVs. ...
With an increasing market penetration of electric vehicles (EVs) in the traffic mix, it become necessary to examine crashes involving EVs. In addition, there is a need to identify differences compared with traditional internal combustion engine vehicles (ICEVs), as EVs are heavier and have different performance characteristics than ICEVs. To date, there is limited research comparing crash characteristics among EVs and ICEVs and further, differentiating among different types of EVs: battery electric vehicles (BEVs), plug-in hybrid electric vehicles (PHEVs), and hybrid electric vehicles (HEVs). To fill this research gap, this paper estimates crash injury frequency and crash severity outcomes through statistical regression analyses. The statistical models and hypothesis testing results suggest both similarities and differences in crash characteristics among BEVs, PHEVs, HEVs, and ICEVs. The similarity lies in human-related factors and traffic-related factors, and the differences come from four types of factors including vehicle, roadway, crash, and environment. The potential reasons (in terms of vehicles’ engine type, software, and hardware) that could contribute to the differences in crash characteristics among four types of vehicles are discussed. The findings of this paper can provide insights into devising safety regulations for EVs. For example, EVs equipped with advanced driving assistant technologies can help relieve crash injury counts. However, the high acceleration rate of electric motors could positively contribute to the crash severity, and the front of BEVs needs more protection since head-on crashes of BEVs cause more severe crashes.
... In one such study, recording, and analyzing surface electromyographic waveforms from biceps brachii (agonist) and pronator teres (antagonist) muscles, Lewis and Brown 8 found an age-related increase in motor response time (RT) to an auditory cue in the elderly. In a longitudinal study of aging, Fozard et al. 41 found a slowing of simple RT to a single cue and a relatively greater slowing of RT to multiple randomized cues (disjunctive RT) across decades. Their study indicated consistent slowing and increased variability of reaction time to auditory cues with age. ...
Normal aging often results in an increase in physiological tremors and slowing of the movement of the hands, which can impair daily activities and quality of life. This study, using lightweight wearable non-invasive sensors, aimed to detect and identify age-related changes in wrist kinematics and response latency. Eighteen young (ages 18–20) and nine older (ages 49–57) adults performed two standard tasks with wearable inertial measurement units on their wrists. Frequency analysis revealed 5 kinematic variables distinguishing older from younger adults in a postural task, with best discrimination occurring in the 9–13 Hz range, agreeing with previously identified frequency range of age-related tremors, and achieving excellent classifier performance (0.86 AUROC score and 89% accuracy). In a second pronation–supination task, analysis of angular velocity in the roll axis identified a 71 ms delay in initiating arm movement in the older adults. This study demonstrates that an analysis of simple kinematic variables sampled at 100 Hz frequency with commercially available sensors is reliable, sensitive, and accurate at detecting age-related increases in physiological tremor and motor slowing. It remains to be seen if such sensitive methods may be accurate in distinguishing physiological tremors from tremors that occur in neurological diseases, such as Parkinson’s Disease.
... however, the small increase in reaction time was not meaningful (less than 0.01%). No signi cant change in reaction time on the key press was observed with age even though reaction time has been shown to increase with age [22,23]. The effect of aging on maximum speed in our visually guided movement tests are visualized in Fig. 6. ...
Background: Advances in markerless motion capture (MMC) provide an opportunity to improve clinical assessments of neuromuscular health. Conventional tests are generally subjective and/or coarse, making it difficult to identify subtle deficits and track progress. As part of a larger project, we leveraged MMC to create a quantitative motor assessment informed by several commonly used evaluations. The purpose of this researchwas to 1) seed a normative database for the MMC-mediated assessment and 2) tocompare modified test results to analogous conventional tests.
Methods: The modified assessment consisted of five tests: finger oscillation, tremor, visually guided movement, reaction time, and balance. We administered it to 132 healthy individuals (64 females) between 18 and 50 years old.
Results: Descriptive statistics for measures on the MMC-mediated movement assessment from a healthy population are presented. Correlations between the modified and conventional tests were weak but followed similar trends, namely finger oscillation results depended on age and sex; reaction and movement time slowed with age; and balance sway was greatest on a soft surface with eyes closed.
Conclusions: A user-friendly, inexpensive, quantitative motor assessment is feasible with MMC; however, a new set of normative values is required for MMC-mediated tests.
Interest in how ageing affects attention is long-standing, although interactions between sensory and attentional processing in older age are not fully understood. Here, we examined interactions between peripheral hearing and selective attention in a spatialised cocktail party listening paradigm, in which three talkers spoke different sentences simultaneously and participants were asked to report the sentence spoken by a talker at a particular location. By comparing a sample of older (N = 61; age = 55–80 years) and younger (N = 58; age = 18–35 years) adults, we show that, as a group, older adults benefit as much as younger adults from preparatory spatial attention. Although, for older adults, this benefit significantly reduces with greater age-related hearing loss. These results demonstrate that older adults with excellent hearing retain the ability to direct spatial selective attention, but this ability deteriorates, in a graded manner, with age-related hearing loss. Thus, reductions in spatial selective attention likely contribute to difficulties communicating in social settings for older adults with age-related hearing loss. Overall, these findings demonstrate a relationship between mild perceptual decline and attention in older age.
Assistance systems are available in modern vehicles for a variety of situations to support the drivers. These systems can be used with the aim of increasing comfort—often they are not noticeable in normal driving operation, but are particularly important in critical driving situations.
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