Episodic memory assessment: effects of sex and age on performance and response time during a continuous recognition task.

Introduction: Continuous recognition tasks (CRTs) assess episodic memory (EM), the central functional disturbance in Alzheimer's disease and several related disorders. The online MemTrax computerized CRT provides a platform for screening and assessment that is engaging and can be repeated frequently. MemTrax presents complex visual stimuli, which require complex involvement of the lateral and medial temporal lobes and can be completed in less than 2 min. Results include number of correct recognitions (HITs), recognition failures (MISSes = 1-HITs), correct rejections (CRs), false alarms (FAs = 1-CRs), total correct (TC = HITs + CRs), and response times (RTs) for each HIT and FA. Prior analyses of MemTrax CRT data show no effects of sex but an effect of age on performance. The number of HITs corresponds to faster RT-HITs more closely than TC, and CRs do not relate to RT-HITs. RT-HITs show a typical skewed distribution, and cumulative RT-HITs fit a negative survival curve (RevEx). Thus, this study aimed to define precisely the effects of sex and age on HITS, CRs, RT-HITs, and the dynamics of RTs in an engaged population. Methods: MemTrax CRT online data on 18,255 individuals was analyzed for sex, age, and distributions of HITs, CRs, MISSes, FAs, TC, and relationships to both RT-HITs and RT-FAs. Results: HITs corresponded more closely to RT-HITs than did TC because CRs did not relate to RT-HITs. RT-FAs had a broader distribution than RT-HITs and were faster than RT-HITs in about half of the sample, slower in the other half. Performance metrics for men and women were the same. HITs declined with age as RT-HITs increased. CRs also decreased with age and RT-FAs increased, but with no correlation. The group over aged 50 years had RT-HITs distributions slower than under 50 years. For both age ranges, the RevEx model explained more than 99% of the variance in RT-HITs. Discussion: The dichotomy of HITs and CRs suggests opposing cognitive strategies: (1) less certainty about recognitions, in association with slower RT-HITs and lower HIT percentages suggests recognition difficulty, leading to more MISSes, and (2) decreased CRs (more FAs) but faster RTs to HITs and FAs, suggesting overly quick decisions leading to errors. MemTrax CRT performance provides an indication of EM (HITs and RT-HITs may relate to function of the temporal lobe), executive function (FAs may relate to function of the frontal lobe), processing speed (RTs), cognitive ability, and age-related changes. This CRT provides potential clinical screening utility for early Alzheimer's disease and other conditions affecting EM, other cognitive functions, and more accurate impairment assessment to track changes over time.

Low daily water intake profile-is it a contributor to disease?

Few previous review articles have focused on the associations between inadequate daily water intake (LOW) or urinary biomarkers of dehydration (UD; low urine volume or high urine osmolality) and multiple diseases. Accordingly, we conducted manual online searches (47 key words) of the PubMed, Embase, and Google Scholar databases with these inclusion criteria: English language, full-text, peer reviewed, no restriction on research design, and three publications minimum. Initially, 3,903 articles were identified based on their titles and abstracts. Evaluations of full length .pdf versions identified 96 studies that were acceptable for inclusion. We concluded that the evidence is insufficient or conflicting for seven disorders or diseases (i.e. suggesting the need for additional clarifying research) and it is lacking for all-cause mortality. Differential characterizations among women and men have been reported in the results of nine studies involving five diseases. Finally, the evidence for associations of LOW or UD is strong for both kidney stones and type 2 diabetes with hyperglycemia. This suggests that great public health value (i.e. reduced disease risk) may result from increased daily water intake-a simple and cost-effective dietary modification.

Toward digitally screening and profiling AD: A GAMLSS approach of MemTrax in China.

PURPOSES: To establish a normative range of MemTrax (MTx) metrics in the Chinese population. METHODS: The correct response percentage (MTx-%C) and mean response time (MTx-RT) were obtained and the composite scores (MTx-Cp) calculated. Generalized additive models for location, shape and scale (GAMLSS) were applied to create percentile curves and evaluate goodness of fit, and the speed-accuracy trade-off was investigated. RESULTS: 26,633 subjects, including 13,771 (51.71%) men participated in this study. Age- and education-specific percentiles of the metrics were generated. Q tests and worm plots indicated adequate fit for models of MTx-RT and MTx-Cp. Models of MTx-%C for the low and intermediate education fit acceptably, but not well enough for a high level of education. A significant speed-accuracy trade-off was observed for MTx-%C from 72 to 94. CONCLUSIONS: GAMLSS is a reliable method to generate smoothed age- and education-specific percentile curves of MTx metrics, which may be adopted for mass screening and follow-ups addressing Alzheimer's disease or other cognitive diseases. HIGHLIGHTS: GAMLSS was applied to establish nonlinear percentile curves of cognitive decline. Subjects with a high level of education demonstrate a later onset and slower decline of cognition. Speed-accuracy trade-off effects were observed in a subgroup with moderate accuracy. MemTrax can be used as a mass-screen instrument for active cognition health management advice.

Using MemTrax memory test to screen for post-stroke cognitive impairment after ischemic stroke: a cross-sectional study.

Background: Whereas the Montreal Cognitive Assessment (MoCA) and Addenbrooke's cognitive examination-revised (ACE-R) are commonly used tests for the detection of post-stroke cognitive impairment (PSCI), these instruments take 10-30 min to administer and do not assess processing speed, which is a critical impairment in PSCI. MemTrax (MTx) is a continuous recognition test, which evaluates complex information processing, accuracy, speed, and attention, in 2 min. Aim: To evaluate whether MTx is an effective and practical tool for PSCI assessment. Methods: This study enrolled acute ischemic stroke (AIS) patients who have assessed the cognitive status including MTx, clinical dementia rating (CDR), MoCA, Neuropsychiatric Inventory (NPI), Hamilton depression scale (HAMD), Hamilton anxiety scale (HAMA), the National Institute of Health Stroke Scale (NIHSS), modified Rankin scale (mRS), and Barthel Index of activity of daily living (BI) combined with the physical examinations of the neurologic system at the 90-day (D90) after the AIS. The primary endpoint of this study was establishing MTx cut-offs for distinguishing PSCI from AIS. Results: Of the 104 participants, 60 were classified to the PSCI group. The optimized cut-off value of MTx-%C (percent correct) was 78%, with a sensitivity and specificity for detecting PSCI from Non-PSCI of 90.0 and 84.1%, respectively, and an AUC of 0.919. Regarding the MTx-Cp (Composite score = MTx-%C/MTx-RT), using 46.3 as a cut-off value, the sensitivity and specificity for detecting PSCI from Non-PSCI were 80.0 and 93.2%, with an AUC of 0.925. Multivariate linear regression showed that PSCI reduced the MTx-%C (Coef. -14.18, 95% CI -18.41∼-9.95, p < 0.001) and prolonged the MTx-RT (response time) (Coef. 0.29, 95% CI 0.16∼0.43, p < 0.001) and reduced the MTx-CP (Coef. -19.11, 95% CI -24.29∼-13.93, p < 0.001). Conclusion: MemTrax (MTx) is valid and effective for screening for PSCI among target patients and is a potentially valuable and practical tool in the clinical follow-up, monitoring, and case management of PSCI.

Characterizing Hydration Practices in Healthy Young Recreationally Active Adults-Is There Utility in First Morning Urine Sampling?

First morning urine (FMU) assessment would be a practical and convenient solution for clinically acceptable detection of underhydration prior to competition/training, and for the general public. Thus, we thus sought to determine the diagnostic accuracy of FMU as a valid indicator of recent (previous 24 hr, 5 days average) hydration practices. For 5 consecutive days and one final morning, 67 healthy women (n = 38) and men (n = 29; age: 20 [1] years, body mass index: 25.9 [5.5]) completed 24-hr diet logs for total water intake (from beverages and foods, absolute and relative to body mass), 24-hr urine and FMU collection (last morning only) for osmolality (Osm), specific gravity (SG), and color (Col), and morning blood sampling for plasma osmolality and copeptin. Correlations determined significance and relationship strength among FMU and all other variables. Area under the receiver operating characteristic curves, sensitivity, specificity, and positive likelihood ratios were employed using previously reported values to indicate underhydration (total water intake < 30 ml/kg, osmolality > 500, and >800 mOsm/kg, specific gravity > 1.017, and copeptin > 6.93 pmol/L). FMU_Osm and FMU_SG were significantly correlated (p < .05) to all variables except the previous 5-day plasma osmolality. FMU_Col was only significantly correlated with other color time intervals and total water intake per gram. FMU_Osm held greatest utility (area under the receiver operating characteristic curve, sensitivity, and specificity >80%) overall, with the best outcome being FMU_Osm indicating a previous 24-hr osmolality threshold of 500 mOsm/kg (FMU_Osm criterion >710 mOsm/kg and positive likelihood ratio = 5.9). With less effort and cost restriction, FMU is a viable metric to assess underhydration.

Correctness and response time distributions in the MemTrax continuous recognition task: Analysis of strategies and a reverse-exponential model.

A critical issue in addressing medical conditions is measurement. Memory measurement is difficult, especially episodic memory, which is disrupted by many conditions. On-line computer testing can precisely measure and assess several memory functions. This study analyzed memory performances from a large group of anonymous, on-line participants using a continuous recognition task (CRT) implemented at https://memtrax.com. These analyses estimated ranges of acceptable performance and average response time (RT). For 344,165 presumed unique individuals completing the CRT a total of 602,272 times, data were stored on a server, including each correct response (HIT), Correct Rejection, and RT to the thousandth of a second. Responses were analyzed, distributions and relationships of these parameters were ascertained, and mean RTs were determined for each participant across the population. From 322,996 valid first tests, analysis of correctness showed that 63% of these tests achieved at least 45 correct (90%), 92% scored at or above 40 correct (80%), and 3% scored 35 correct (70%) or less. The distribution of RTs was skewed with 1% faster than 0.62 s, a median at 0.890 s, and 1% slower than 1.57 s. The RT distribution was best explained by a novel model, the reverse-exponential (RevEx) function. Increased RT speed was most closely associated with increased HIT accuracy. The MemTrax on-line memory test readily provides valid and reliable metrics for assessing individual episodic memory function that could have practical clinical utility for precise assessment of memory dysfunction in many conditions, including improvement or deterioration over time.

Effect of Concussion on Reaction Time and Neurocognitive Factors: Implications for Subsequent Lower Extremity Injury.

Background: Recent evidence has demonstrated that athletes are at greater risk for a lower extremity injury following a return-to-sport (RTS) after sport-related concussion (SRC). The reason for this is not completely clear, but it has been hypothesized that persistent deficits in neurocognitive factors may be a contributing factor. Hypothesis/Purpose: This study assessed simple reaction time, processing speed, attention, and concentration in a group of athletes, post-concussion upon clearance for RTS for potential deficits that may result in slower reaction time, processing speed, attention, and concentration. The researchers hypothesized that the concussion group would demonstrate worse scores on both assessments compared to a sex-, age-, and sport-matched cohort. Study Design: Case-controlled study. Methods: Twelve participants who had suffered a SRC and eight healthy individuals who were matched to the concussed group by age, sex, and sport were evaluated. Those with a concussion had been cleared for RTS by a licensed healthcare provider. Each participant underwent neurocognitive tests that included a simple reaction time test (SRT) and the King-Devick Test (K-D). Independent t-tests were performed to compare the groups with significance set a priori at p<0.05. Results: There was a significant difference (p =0.024) between groups for SRT with the concussed group demonstrating a better SRT than the control group. There were no significant differences (p =0.939) between the groups for the K-D. Conclusion: With no significant differences between groups in the K-D assessment and, surprisingly, the concussed group having a better SRT compared to the healthy group, our hypothesis was not supported. Clinical Relevance: These specific measures, compounded with extensive post-concussion time lapse until RTS clearance, may have limited capacity in revealing potential persistent deficits in relevant neurocognitive characteristics. Level of Evidence: Level of Evidence 3.

Now is the Time to Improve Cognitive Screening and Assessment for Clinical and Research Advancement.

Wang et al. analyze Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment accuracy as screening tests for detecting dementia associated with Alzheimer's disease (AD). Such tests are at the center of controversy regarding recognition and treatment of AD. The continued widespread use of tools such as MMSE (1975) underscores the failure of advancing cognitive screening and assessment, which has hampered the development and evaluation of AD treatments. It is time to employ readily available, efficient computerized measures for population/mass screening, clinical assessment of dementia progression, and accurate determination of approaches for prevention and treatment of AD and related conditions.

Validity of the MemTrax Memory Test Compared to the Montreal Cognitive Assessment in the Detection of Mild Cognitive Impairment and Dementia due to Alzheimer's Disease in a Chinese Cohort.

BACKGROUND: A valid, reliable, accessible, engaging, and affordable digital cognitive screen instrument for clinical use is in urgent demand. OBJECTIVE: To assess the clinical utility of the MemTrax memory test for early detection of cognitive impairment in a Chinese cohort. METHODS: The 2.5-minute MemTrax and the Montreal Cognitive Assessment (MoCA) were performed by 50 clinically diagnosed cognitively normal (CON), 50 mild cognitive impairment due to AD (MCI-AD), and 50 Alzheimer's disease (AD) volunteer participants. The percentage of correct responses (MTx-% C), the mean response time (MTx-RT), and the composite scores (MTx-Cp) of MemTrax and the MoCA scores were comparatively analyzed and receiver operating characteristic (ROC) curves generated. RESULTS: Multivariate linear regression analyses indicated MTx-% C, MTx-Cp, and the MoCA score were significantly lower in MCI-AD versus CON and in AD versus MCI-AD groups (all with p≤0.001). For the differentiation of MCI-AD from CON, an optimized MTx-% C cutoff of 81% had 72% sensitivity and 84% specificity with an area under the curve (AUC) of 0.839, whereas the MoCA score of 23 had 54% sensitivity and 86% specificity with an AUC of 0.740. For the differentiation of AD from MCI-AD, MTx-Cp of 43.0 had 70% sensitivity and 82% specificity with an AUC of 0.799, whereas the MoCA score of 20 had 84% sensitivity and 62% specificity with an AUC of 0.767. CONCLUSION: MemTrax can effectively detect both clinically diagnosed MCI and AD with better accuracy as compared to the MoCA based on AUCs in a Chinese cohort.

Overtraining Syndrome as a Complex Systems Phenomenon.

The phenomenon of reduced athletic performance following sustained, intense training (Overtraining Syndrome, and OTS) was first recognized more than 90 years ago. Although hundreds of scientific publications have focused on OTS, a definitive diagnosis, reliable biomarkers, and effective treatments remain unknown. The present review considers existing models of OTS, acknowledges the individualized and sport-specific nature of signs/symptoms, describes potential interacting predisposing factors, and proposes that OTS will be most effectively characterized and evaluated via the underlying complex biological systems. Complex systems in nature are not aptly characterized or successfully analyzed using the classic scientific method (i.e., simplifying complex problems into single variables in a search for cause-and-effect) because they result from myriad (often non-linear) concomitant interactions of multiple determinants. Thus, this review 1) proposes that OTS be viewed from the perspectives of complex systems and network physiology, 2) advocates for and recommends that techniques such as trans-omic analyses and machine learning be widely employed, and 3) proposes evidence-based areas for future OTS investigations, including concomitant multi-domain analyses incorporating brain neural networks, dysfunction of hypothalamic-pituitary-adrenal responses to training stress, the intestinal microbiota, immune factors, and low energy availability. Such an inclusive and modern approach will measurably help in prevention and management of OTS.

Utility of MemTrax and Machine Learning Modeling in Classification of Mild Cognitive Impairment.

BACKGROUND: The widespread incidence and prevalence of Alzheimer's disease and mild cognitive impairment (MCI) has prompted an urgent call for research to validate early detection cognitive screening and assessment. OBJECTIVE: Our primary research aim was to determine if selected MemTrax performance metrics and relevant demographics and health profile characteristics can be effectively utilized in predictive models developed with machine learning to classify cognitive health (normal versus MCI), as would be indicated by the Montreal Cognitive Assessment (MoCA). METHODS: We conducted a cross-sectional study on 259 neurology, memory clinic, and internal medicine adult patients recruited from two hospitals in China. Each patient was given the Chinese-language MoCA and self-administered the continuous recognition MemTrax online episodic memory test on the same day. Predictive classification models were built using machine learning with 10-fold cross validation, and model performance was measured using Area Under the Receiver Operating Characteristic Curve (AUC). Models were built using two MemTrax performance metrics (percent correct, response time), along with the eight common demographic and personal history features. RESULTS: Comparing the learners across selected combinations of MoCA scores and thresholds, Naïve Bayes was generally the top-performing learner with an overall classification performance of 0.9093. Further, among the top three learners, MemTrax-based classification performance overall was superior using just the top-ranked four features (0.9119) compared to using all 10 common features (0.8999). CONCLUSION: MemTrax performance can be effectively utilized in a machine learning classification predictive model screening application for detecting early stage cognitive impairment.

Episodic-Memory Performance in Machine Learning Modeling for Predicting Cognitive Health Status Classification.

BACKGROUND: Memory dysfunction is characteristic of aging and often attributed to Alzheimer's disease (AD). An easily administered tool for preliminary assessment of memory function and early AD detection would be integral in improving patient management. OBJECTIVE: Our primary aim was to utilize machine learning in determining initial viable models to serve as complementary instruments in demonstrating efficacy of the MemTrax online Continuous Recognition Tasks (M-CRT) test for episodic-memory screening and assessing cognitive impairment. METHODS: We used an existing dataset subset (n = 18,395) of demographic information, general health screening questions (addressing memory, sleep quality, medications, and medical conditions affecting thinking), and test results from a convenience sample of adults who took the M-CRT test. M-CRT performance and participant features were used as independent attributes: true positive/negative, percent responses/correct, response time, age, sex, and recent alcohol consumption. For predictive modeling, we used demographic information and test scores to predict binary classification of the health-related questions (yes/no) and general health status (healthy/unhealthy), based on the screening questions. RESULTS: ANOVA revealed significant differences among HealthQScore groups for response time true positive (p = 0.000) and true positive (p = 0.020), but none for true negative (p = 0.0551). Both % responses and % correct had significant differences (p = 0.026 and p = 0.037, respectively). Logistic regression was generally the top-performing learner with moderately robust prediction performance (AUC) for HealthQScore (0.648-0.680) and selected general health questions (0.713-0.769). CONCLUSION: Our novel application of supervised machine learning and predictive modeling helps to demonstrate and validate cross-sectional utility of MemTrax in assessing early-stage cognitive impairment and general screening for AD.

Machine Learning in Modeling High School Sport Concussion Symptom Resolve.

INTRODUCTION: Concussion prevalence in sport is well recognized, so too is the challenge of clinical and return-to-play management for an injury with an inherent indeterminant time course of resolve. A clear, valid insight into the anticipated resolution time could assist in planning treatment intervention. PURPOSE: This study implemented a supervised machine learning-based approach in modeling estimated symptom resolve time in high school athletes who incurred a concussion during sport activity. METHODS: We examined the efficacy of 10 classification algorithms using machine learning for the prediction of symptom resolution time (within 7, 14, or 28 d), with a data set representing 3 yr of concussions suffered by high school student-athletes in football (most concussion incidents) and other contact sports. RESULTS: The most prevalent sport-related concussion reported symptom was headache (94.9%), followed by dizziness (74.3%) and difficulty concentrating (61.1%). For all three category thresholds of predicted symptom resolution time, single-factor ANOVA revealed statistically significant performance differences across the 10 classification models for all learners at a 95% confidence interval (P = 0.000). Naïve Bayes and Random Forest with either 100 or 500 trees were the top-performing learners with an area under the receiver operating characteristic curve performance ranging between 0.656 and 0.742 (0.0-1.0 scale). CONCLUSIONS: Considering the limitations of these data specific to symptom presentation and resolve, supervised machine learning demonstrated efficacy, while warranting further exploration, in developing symptom-based prediction models for practical estimation of sport-related concussion recovery in enhancing clinical decision support.