Identifying Preclinical Alzheimer's Disease Using Everyday Driving Behavior: Proof of Concept

The Influence of Personality Traits on Driving Behaviors in Preclinical Alzheimer Disease

INTRODUCTION

Alzheimer disease (AD) has a long preclinical phase in which AD pathology is accumulating without detectable clinical symptoms. It is critical to identify participants in this preclinical phase as early as possible since treatment plans may be more effective in this stage. Monitoring for changes in driving behavior, as measured with GPS sensors, has been explored as a low-burden, easy-to-administer method for detecting AD risk. However, driving is a complex, multifaceted process that is likely influenced by other factors, including personality traits, that may change in preclinical AD.

METHODS

We examine the moderating influence of neuroticism and conscientiousness on longitudinal changes in driving behavior in a sample of 203 clinically normal older adults who are at varying risk of developing AD.

RESULTS

Neuroticism moderated rates of change in the frequency of speeding as well as the number of trips taken at night. Conscientiousness moderated rates of change in typical driving space.

CONCLUSIONS

Personality traits change in early AD and also influence driving behaviors. Studies that seek to utilize naturalistic driving behavior to establish AD risk need to accommodate interpersonal differences, of which personality traits are one of many possible factors. Future studies should explicitly establish how much benefit is provided by including personality traits in predictive models of AD progression.

Digital Neuropsychology beyond Computerized Cognitive Assessment: Applications of Novel Digital Technologies

Compared with other health disciplines, there is a stagnation in technological innovation in the field of clinical neuropsychology. Traditional paper-and-pencil tests have a number of shortcomings, such as low-frequency data collection and limitations in ecological validity. While computerized cognitive assessment may help overcome some of these issues, current computerized paradigms do not address the majority of these limitations. In this paper, we review recent literature on the applications of novel digital health approaches, including ecological momentary assessment, smartphone-based assessment and sensors, wearable devices, passive driving sensors, smart homes, voice biomarkers, and electronic health record mining, in neurological populations. We describe how each digital tool may be applied to neurologic care and overcome limitations of traditional neuropsychological assessment. Ethical considerations, limitations of current research, as well as our proposed future of neuropsychological practice are also discussed.

Shifting From Active to Passive Monitoring of Alzheimer Disease: The State of the Research

Most research using digital technologies builds on existing methods for staff-administered evaluation, requiring a large investment of time, effort, and resources. Widespread use of personal mobile devices provides opportunities for continuous health monitoring without active participant engagement. Home-based sensors show promise in evaluating behavioral features in near real time. Digital technologies across these methodologies can detect precise measures of cognition, mood, sleep, gait, speech, motor activity, behavior patterns, and additional features relevant to health. As a neurodegenerative condition with insidious onset, Alzheimer disease and other dementias (AD/D) represent a key target for advances in monitoring disease symptoms. Studies to date evaluating the predictive power of digital measures use inconsistent approaches to characterize these measures. Comparison between different digital collection methods supports the use of passive collection methods in settings in which active participant engagement approaches are not feasible. Additional studies that analyze how digital measures across multiple data streams can together improve prediction of cognitive impairment and early-stage AD are needed. Given the long timeline of progression from normal to diagnosis, digital monitoring will more easily make extended longitudinal follow-up possible. Through the American Heart Association-funded Strategically Focused Research Network, the Boston University investigative team deployed a platform involving a wide range of technologies to address these gaps in research practice. Much more research is needed to thoroughly evaluate limitations of passive monitoring. Multidisciplinary collaborations are needed to establish legal and ethical frameworks for ensuring passive monitoring can be conducted at scale while protecting privacy and security, especially in vulnerable populations.

Addressing the Complex Driving Needs of an Aging Population

This JAMA Insights reviews the complex driving needs of older individuals and how clinicians can help address these needs, including recommending use of supplemental technology, assessing fitness to drive, and reviewing medications that may impair driving ability.

Everyday Driving and Plasma Biomarkers in Alzheimer's Disease: Leveraging Artificial Intelligence to Expand Our Diagnostic Toolkit

BACKGROUND

Driving behavior as a digital marker and recent developments in blood-based biomarkers show promise as a widespread solution for the early identification of Alzheimer's disease (AD).

OBJECTIVE

This study used artificial intelligence methods to evaluate the association between naturalistic driving behavior and blood-based biomarkers of AD.

METHODS

We employed an artificial neural network (ANN) to examine the relationship between everyday driving behavior and plasma biomarker of AD. The primary outcome was plasma Aβ42/Aβ40, where Aβ42/Aβ40 < 0.1013 was used to define amyloid positivity. Two ANN models were trained and tested for predicting the outcome. The first model architecture only includes driving variables as input, whereas the second architecture includes the combination of age, APOE ɛ4 status, and driving variables.

RESULTS

All 142 participants (mean [SD] age 73.9 [5.2] years; 76 [53.5%] men; 80 participants [56.3% ] with amyloid positivity based on plasma Aβ42/Aβ40) were cognitively normal. The six driving features, included in the ANN models, were the number of trips during rush hour, the median and standard deviation of jerk, the number of hard braking incidents and night trips, and the standard deviation of speed. The F1 score of the model with driving variables alone was 0.75 [0.023] for predicting plasma Aβ42/Aβ40. Incorporating age and APOE ɛ4 carrier status improved the diagnostic performance of the model to 0.80 [>0.051].

CONCLUSION

Blood-based AD biomarkers offer a novel opportunity to establish the efficacy of naturalistic driving as an accessible digital marker for AD pathology in driving research.

Driving assessment in preclinical Alzheimer’s disease: progress to date and the path forward

Background

Changes in driving behaviour may start at the preclinical stage of Alzheimer’s disease (AD), where the underlying AD biological process has begun in the presence of cognitive normality. Here, we summarize the emerging evidence suggesting that preclinical AD may impact everyday driving behaviour.

Main

Increasing evidence links driving performance and behaviour with AD biomarkers in cognitively intact older adults. These studies have found subtle yet detectable differences in driving associated with AD biomarker status among cognitively intact older adults.

Conclusion

Recent studies suggest that changes in driving, a highly complex activity, are linked to, and can indicate the presence of, neuropathological AD. Future research must now examine the internal and external validity of driving for widespread use in identifying biological AD.

Lower extremities task of pressing an “accelerator” or a “brake”: association with traffic accidents in older drivers – a preliminary study

ABSTRACT Traffic accidents by older drivers are a social urgent problem. The National Police Agency (NPA) in Japan has institutionalized the Cognitive Function Test (NPA test) for renewal of a driver’s license for older adults. However, driving ability cannot be simply evaluated by usual cognitive tests on the desk. Objective: It is important to add an on-road test, but if not possible, we can use simulators. Before doing simulators, it is important to use the right foot to control the accelerator and brake pedals. We applied the Posner paradigm (visual attention test) for lower extremities. Methods: The participants were older adults. They and their families had anxiety about their driving. The 66 participants (44 men and 22 women) were divided into groups with and without experience of a traffic accident, and the following tests were examined: General cognitive and executive function tests, the NPA test, and an original Lower Extremity Reaction Test. Each participant was asked to press the “brake” or “accelerator” pedal by the right foot as quickly as possible in response to a traffic situation shown on the screen. Results: Compared to participants with favorable reactions to the Lower Extremity Reaction Test, those with poor reaction time tended to have more traffic accidents (OR=6.82), rather than the result of the NPA test. Conclusions: The results suggest that the probability of having a traffic accident can be better evaluated using the Lower Extremity Reaction Test.

Improving Our Understanding of Driving Changes in Preclinical and Early Symptomatic Alzheimer’s Disease: The Role of Naturalistic Driving Studies

Research on how preclinical and early symptomatic Alzheimer’s disease (AD) impacts driving behavior is in its infancy, with several important research areas yet to be explored. This paper identifies research gaps and suggests priorities for driving studies over the next few years among those at the earliest stages of AD. These priorities include how individual differences in demographic and biomarker measures of AD pathology, as well as differences in the in-vehicle and external driving environment, affect driving behavior. Understanding these differences is important to developing future interventions to increase driving safety among those at the earliest stages of AD.

Flavonoids from Scutellaria baicalensis Georgi Stems and Leaves Regulate the Brain Tau Hyperphosphorylation at M|ultiple Sites Induced by Composited Aβ in Rats

BACKGROUND

Neurofibrillary tangles (NFTs), formed by hyperphosphorylation of Tau protein in Alzheimer's disease (AD) are the main pathomechanisms of neuronal degeneration, which can be used as a sign of brain disorder. It is positively correlated with the degree of cognitive impairment in AD.

OBJECTIVE

The objective of this study is to investigate the effect of Scutellaria baicalensis Georgi stems and leaves flavonoids (SSF) on the hyperphosphorylated expression levels at multiple sites of Tau protein induced by β-amyloid protein 25-35 (Aβ25-35) in combined with aluminum trichloride (AlCl3) and recombinant human transforming growth factor-β1 (RHTGF-β1) (composited Aβ) in rats.

METHODS

The model of rats for AD was established by intracerebroventricular injection of Aβ25-35 and AlCl3 combined with RHTGF-β1. On day 45 after the operation, the Morris water maze was used to screen the rats' memory impairment model for AD. The successful model rats were randomly divided into the model group and three-dose of drug group. The drug group rats were daily and orally SSF administrated for 38 days. Western blotting was used to detect the protein expression of P-Tau (Thr181), P-Tau (Thr217), P-Tau (Thr231), P-Tau (Ser199), P-Tau (Ser235), P-Tau (Ser396) and P-Tau (Ser404) in the hippocampus and cerebral cortex of rats.

RESULTS

Compared with the sham group, the protein expression of P-Tau (Thr181), P-Tau (Thr217), P-Tau (Thr231), P-Tau (Ser199), P-Tau (Ser235), P-Tau (Ser396) and P-Tau (Ser404) was significantly increased in the hippocampus and cerebral cortex in the model group (P < 0.01). However, the three doses of 35, 70 and 140 mg/kg SSF regulated the expression of phosphorylated Tau protein at the above sites to varying degrees in the hippocampus and cerebral cortex (P < 0.01) induced by composited Aβ.

CONCLUSION

SSF can significantly reduce the protein expression levels of P-Tau (Thr181), P-Tau (Thr217), P-Tau (Thr231), P-Tau (Ser199), P-Tau (Ser235), P-Tau (Ser396) and P-Tau (Ser404) in rats' brain induced by the intracerebroventricular injection of composited Aβ. These results demonstrated that the neuro-protection and the impaired memory improvement of SSF were due to the inhibition for the hyperphosphorylation of Tau protein at multiple sites.

GPS driving: a digital biomarker for preclinical Alzheimer disease

BACKGROUND

Alzheimer disease (AD) is the most common cause of dementia. Preclinical AD is the period during which early AD brain changes are present but cognitive symptoms have not yet manifest. The presence of AD brain changes can be ascertained by molecular biomarkers obtained via imaging and lumbar puncture. However, the use of these methods is limited by cost, acceptability, and availability. The preclinical stage of AD may have a subtle functional signature, which can impact complex behaviours such as driving. The objective of the present study was to evaluate the ability of in-vehicle GPS data loggers to distinguish cognitively normal older drivers with preclinical AD from those without preclinical AD using machine learning methods.

METHODS

We followed naturalistic driving in cognitively normal older drivers for 1 year with a commercial in-vehicle GPS data logger. The cohort included n = 64 individuals with and n = 75 without preclinical AD, as determined by cerebrospinal fluid biomarkers. Four Random Forest (RF) models were trained to detect preclinical AD. RF Gini index was used to identify the strongest predictors of preclinical AD.

RESULTS

The F1 score of the RF models for identifying preclinical AD was 0.85 using APOE ε4 status and age only, 0.82 using GPS-based driving indicators only, 0.88 using age and driving indicators, and 0.91 using age, APOE ε4 status, and driving. The area under the receiver operating curve for the final model was 0.96.

CONCLUSION

The findings suggest that GPS driving may serve as an effective and accurate digital biomarker for identifying preclinical AD among older adults.

Using Naturalistic Driving Data to Predict Mild Cognitive Impairment and Dementia: Preliminary Findings from the Longitudinal Research on Aging Drivers (LongROAD) Study

Emerging evidence suggests that atypical changes in driving behaviors may be early signals of mild cognitive impairment (MCI) and dementia. This study aims to assess the utility of naturalistic driving data and machine learning techniques in predicting incident MCI and dementia in older adults. Monthly driving data captured by in-vehicle recording devices for up to 45 months from 2977 participants of the Longitudinal Research on Aging Drivers study were processed to generate 29 variables measuring driving behaviors, space and performance. Incident MCI and dementia cases (n = 64) were ascertained from medical record reviews and annual interviews. Random forests were used to classify the participant MCI/dementia status during the follow-up. The F₁ score of random forests in discriminating MCI/dementia status was 29% based on demographic characteristics (age, sex, race/ethnicity and education) only, 66% based on driving variables only, and 88% based on demographic characteristics and driving variables. Feature importance analysis revealed that age was most predictive of MCI and dementia, followed by the percentage of trips traveled within 15 miles of home, race/ethnicity, minutes per trip chain (i.e., length of trips starting and ending at home), minutes per trip, and number of hard braking events with deceleration rates ≥ 0.35 g. If validated, the algorithms developed in this study could provide a novel tool for early detection and management of MCI and dementia in older drivers.

News about the Role of Fluid and Imaging Biomarkers in Neurodegenerative Diseases

Biomarkers are molecules that are variable in their origin, nature, and mechanism of action; they are of great relevance in biology and also in medicine because of their specific connection with a single or several diseases. Biomarkers are of two types, which in some cases are operative with each other. Fluid biomarkers, started around 2000, are generated in fluid from specific proteins/peptides and miRNAs accumulated within two extracellular fluids, either the central spinal fluid or blood plasma. The switch of these proteins/peptides and miRNAs, from free to segregated within extracellular vesicles, has induced certain advantages including higher levels within fluids and lower operative expenses. Imaging biomarkers, started around 2004, are identified in vivo upon their binding by radiolabeled molecules subsequently revealed in the brain by positron emission tomography and/or other imaging techniques. A positive point for the latter approach is the quantitation of results, but expenses are much higher. At present, both types of biomarker are being extensively employed to study Alzheimer’s and other neurodegenerative diseases, investigated from the presymptomatic to mature stages. In conclusion, biomarkers have revolutionized scientific and medical research and practice. Diagnosis, which is often inadequate when based on medical criteria only, has been recently improved by the multiplicity and specificity of biomarkers. Analogous results have been obtained for prognosis. In contrast, improvement of therapy has been limited or fully absent, especially for Alzheimer’s in which progress has been inadequate. An urgent need at hand is therefore the progress of a new drug trial design together with patient management in clinical practice.