Definitive diagnosis of Alzheimer’s disease (AD) is often unavailable, so clinical diagnoses with some degree of inaccuracy are often used in research instead. When researchers test methods that may improve clinical accuracy, the error in initial diagnosis can penalize predictions that are more accurate to true diagnoses but differ from clinical diagnoses. To address this challenge, the current study investigated the use of a simple bias adjustment for use in logistic regression that accounts for known inaccuracy in initial diagnoses.

A Bayesian logistic regression model was developed to predict unobserved/true diagnostic status given the sensitivity and specificity of an imperfect reference. This model considers cases as a mixture of true (with rate = sensitivity) and false positives (rate = 1 - specificity) while controls are mixtures of true (rate = specificity) and false negatives (rate = 1 - sensitivity). This bias adjustment was tested using Monte Carlo simulations over four conditions that varied the accuracy of clinical diagnoses. Conditions utilized 1000 iterations each generating a random dataset of n = 1000 based on a true logistic model with an intercept and three arbitrary predictors. Coefficients for parameters were randomly selected in each iteration and used to produce a set of two diagnoses: true diagnoses and observed diagnoses with imperfect accuracy. Sensitivity and specificity of the simulated clinical diagnosis varied with each of the four conditions (C): C1 = (0.77, 0.60), C2 = (0.87, 0.44), C3 = (0.71, 0.71), and C4 = (0.83, 0.55), which are derived from published values for clinical AD diagnoses against autopsy-confirmed pathology. Unadjusted and bias-adjusted logistic regressions were then fit to the simulated data to determine the models’ accuracy in estimating regression parameters and prediction of true diagnosis.

Under all conditions, the bias-adjusted logistic regression model outperformed its unadjusted counterpart. Root mean square error (the variability of estimated coefficients around their true parameter values) ranged from 0.23 to 0.79 for the unadjusted model versus 0.24 to 0.29 for the bias-adjusted model. The empirical coverage rate (the proportion of 95% credible intervals that include their true parameter) ranged from 0.00 to 0.47 for the unadjusted model versus 0.95 to 0.96 for the bias-adjusted model. Finally, the bias-adjusted model produced the best overall diagnostic accuracy with correct classification of true diagnostic values about 78% of the time versus 62-72% without adjustment.

Results of this simulation study, which used published AD sensitivity and specificity statistics, provide evidence that bias-adjustments to logistic regression models are needed when research involves diagnoses from an imperfect standard. Results showed that unadjusted methods rarely identified true effects with credible intervals for coefficients including the true value anywhere from never to less than half of the time. Additional simulations are needed to examine the bias-adjusted model’s performance under additional conditions. Future research is needed to extend the bias adjustment to multinomial logistic regressions and to scenarios where the rate of misdiagnosis is unknown. Such methods may be valuable for improving detection of other neurological disorders with greater diagnostic error as well.

A diagnosis of mild cognitive impairment (MCI) requires memory complaint and objective memory impairment. However, some individuals report subjective memory complaints (SMC) despite having intact memory performance, while others demonstrate subtle impairment on memory testing but have no memory complaints; neither case would meet criteria for MCI. This study aimed to compare memory performances over time in individuals who do not meet traditional MCI criteria to those with normal cognition and those who converted to MCI.

Diagnoses for a longitudinal sample from the Texas Alzheimer’s Research and Care Consortium were reviewed by a consensus panel of neuropsychologists and neurologists and reclassified at time of last visit. Diagnostic categories included SMC (i.e., memory complaint but no impairment on testing), objective cognitive impairment but no complaint (Impaired but not MCI), normal control (NC), MCI, and dementia. In this study, 827 participants were divided into 4 groups: 1) NC over 5 visits (n=511, 71% female; 42% Latinx/Hispanic), 2) baseline NC to amnestic MCI (n=62; 63% female; 57% Latinx/Hispanic), 3) SMC at last visit (n=133; 58% female; 70% Latinx/Hispanic), and 4) impaired but not MCI at last visit (n=121; 71% female; 60% Latinx/Hispanic). A memory composite (z-score) was created from the CERAD list-learning task (immediate, delayed, and recognition-discrimination) and Wechsler Memory Scale (Immediate and Delayed Logical Memory and Visual Reproduction) to evaluate memory performance over time. A linear mixed-model adjusting for age, education, sex, ethnicity, and number of APOE e4 alleles evaluated memory performance across 5 visits for the groups. To assess if depression followed a similar course, a linear mixed-model evaluated Geriatric Depression Scale (GDS) scores over time.

At baseline, groups differed by age (F=22.82; p<.001), education (F=8.60; p<.001), MMSE scores (F=9.38; p<.001), GDS-30 scores (F=3.56; p=.015), and memory composites (F=24.29; p<.001). A significant group X time interaction was observed (F=4.83, p<.001). Memory performance improved in both the SMC and the NC groups, remained stable in the impaired but not MCI group, and declined (as expected) in those who converted to amnestic MCI. Depression scores also showed a significant group X time interaction (F=2.43; p=.004), in which the NC to MCI group endorsed slightly more depression symptoms over time, while other groups declined or remained stable.

Memory trajectories in this diverse sample differed across groups. Individuals with SMC but without objective memory impairment and normal controls showed some improvement in memory over time, presumably due to practice effects. Those with subtle memory impairments but no complaint (i.e., did not meet MCI criteria) remained stable and those who converted to amnestic MCI had worse memory across time. The stability of memory performances in the impaired not MCI group suggests these subtle memory inefficiencies may be longstanding or unperceived. However, because our sample achieved retrospective diagnoses of SMC and impaired not MCI, it will be important for future studies to prospectively follow these groups to determine which risk factors may predict progression to MCI and what impact ethnicity may have on these trajectories.

Following the start of the SARS-COV-2 (COVID-19) pandemic there was a rapid uptake in teleneuropsychology (TeleNP). Many clinicians and researchers used videoconferencing technologies (e.g., Zoom®) to conduct remote neuropsychological assessments. Prior reviews (e.g., Marra et al., 2020) have indicated promise for the use of videoconference-based approaches to cognitive assessment under certain circumstances, though arguably nobody foresaw the widespread use of teleNP during the pandemic. Given the rapid expansion in the teleNP literature in the past couple of years, in this scoping review we specifically discuss research updates made during the COVID-19 pandemic pertaining to teleNP assessment of adults conducted via videoconferencing and their potential clinical applications.

GoogleScholar and PubMed were used to search for peer-reviewed original research articles published between January 1, 2020 (i.e., the approximate beginning of the COVID-19 pandemic) and August 1, 2022. Broad search terms were used pertaining to teleNP, remote cognitive assessment, videoconferencing, and neuropsychological assessment, resulting in 16 articles.

Though most of the included studies were based in the United States (n=5), there was international representation across studies (Chile=1; United Kingdom=1; Australia=2; New Zealand=1; France=2; Greece=1; Japan=2, Singapore=1). All of the identified articles examined TeleNP-related research questions using cognitive tests administered via videoconferencing that have been previously studied in-person to varying degrees. Several of the studies focused on psychometric characterization (i.e., reliability and validity) of the examined tests when delivered via videoconferencing, whereas others focused on demonstrating the relative equivalence of neuropsychological scores obtained via videoconferencing versus in-person evaluations.

Formal psychometric studies of traditional in-person neuropsychological tests delivered via videoconferencing since the start of the COVID-19 pandemic suggest that this remote modality of assessment is generally reliable and valid. Moreover, multiple recent studies have demonstrated relative equivalence of neuropsychological scores obtained via videoconferencing versus neuropsychological test scores obtained in-person. When considered alongside teleNP research conducted prior to the COVID-19 pandemic (e.g. Cullum et al., 2014), recent studies on videoconference-based neuropsychological assessment indicate that videoconferencing may not necessarily be a complete substitute for an in-person comprehensive evaluation given the inherent limitations of the procedure. However, teleNP via videoconferencing may be a promising tool in the neuropsychologist’s toolbox because it can help reduce common barriers to in-person neuropsychological assessment (e.g., travel time to clinics). Additional research on videoconferencing-based cognitive assessment is needed, especially in low-and-middle income countries (LMIC) and diverse populations where there may be more economic barriers to remote neuropsychological assessment relative to more economically-developed countries. Notably it is possible that research from LMIC may have been missed through the screening processes used in this review (e.g., inclusion of articles written in English).

Learning curve patterns on list-learning tasks can help clinicians determine the nature of memory difficulties, as an “impaired” score may actually reflect attention and/or executive difficulties rather than a true memory impairment. Though such pattern analysis is often qualitative, there are quantitative methods to assess these concepts that have been generally underutilized. This study aimed to develop a model that decomposes learning over repeated trials into separate cognitive processes and then include other testing data to predict performance at each trial as a function of general cognitive functioning.

Data for CVLT-II learning trials were obtained from an outpatient neuropsychology service within an academic medical center referred for clinical reasons. Participants with a cognitive diagnosis of non-demented (ND) or probable Alzheimer’s disease (AD) were included. The final sample consisted of 323 ND [Mage = 58.6 (14.8); Medu = 15.4 (2.7); 55.7% female] and 915 AD [Mage = 72.6 (9.0); Medu = 14.2 (3.1); 60.1% female cases. A Bayesian non-linear beta-binomial multilevel model was used, which uses three parameters to predict CVLT-II recall-by-trial: verbal attention span (VAS), maximal learning potential (MLP), and learning rate (LR). Briefly, VAS predicts expected first trial performance while MLP, conversely, predicts the expected best performance as trials are repeated, and LR weights the influence of VAS versus MLR over repeated trials. Predictors of these parameters included age, education, sex, race, and clinical diagnosis, in addition to raw scores on Trail Making Test Parts A and B, phonemic (FAS) fluency, animal fluency, Boston Naming Test, Wisconsin Card Sorting Test (WCST) Categories Completed, and then age-adjusted scaled scores from WAIS-IV Digit Span, Block Design, Vocabulary, and Coding. Random intercepts were included for each parameter and extracted for comparison of residual differences by diagnosis.

The model explained 84% of the variance in CVLT-II raw scores. VAS reduced with age and time-to-complete Trails B but improved with both verbal fluencies and confrontation naming. MLP increased as a function of WAIS Digit Span, animal fluency, confrontation naming, and WCST categories completed. Finally, LR was greater for females and WAIS-IV Coding and Vocabulary performances but reduced with age. Participants with AD had lower estimates of all three parameters: Cohen’s d = 2.49 (VAS) - 3.48 (LR), though including demographic and neuropsychological tests attenuated differences, Cohen’s d = 0.34 (LR) - 0.95 (MLP).

The resulting model highlights how non-memory neuropsychological deficits affect list-learning test performance. At the same time, the model demonstrated that memory patterns on the CVLT-II can still be identified beyond other confounding deficits since having AD affected all parameters independent of other cognitive impairments. The modeling approach can generate conditional learning curves for individual patient data, and when multiple diagnoses are included in the model, a person-fit statistic can be computed to return the mostly likely diagnosis for an individual. The model can also be used in research to quantify or adjust for the effect of other patient data (e.g., neuroimaging, biomarkers, medications).

Few concussion studies have investigated the psychological domain of concussions. Of the 22 postconcussion symptoms assessed on the Graded Symptom Checklist of the SCAT-5, five do not overlap with core symptoms of anxiety and depression. 43% of patients report at least one psychiatric symptom, the median is four after injury. Previous studies focus on total scores and not individual items; furthermore, few consider resilience as part of psychological factors that impact recovery. This research aims to describe general and specific characteristics of psychological functioning in males/females ages 12-18 after concussion to help guide treatment. We compared total scores for each measure between males/females and looked at the differences between the genders for individual items in each measure.

Participants were evaluated at an outpatient concussion clinic participating in the North Texas Concussion Registry (ConTex; N=1238, 53% female, mean age=15.4 years, SD=1.16 years). The Generalized Anxiety Disorder 7-item Scale (GAD-7, the Patient Health Questionnaire-8 (PHQ-8), the Brief Resilience Scale (BRS), and the Pittsburgh Sleep Quality Index (PSQI) were used to determine levels of anxiety, depression, resilience, and sleep quality.

Utilizing Mann-Whitney U tests (median, interquartile range) to examine group distributions for the GAD-7, PHQ-8, and BRS, females had significantly higher scores than males for the GAD (p<0.001; Female: 4, 1-9 v. Male: 2, 0-5) and PHQ (p<0.001; Female: 5, 210 v. Male: 3, 1-7). For the BRS, total scores for females were significantly lower than males (p<0.001; Female: 3.67, 3-4 v. Male: 3.83, 3.214.33). The PSQI media score was significantly different between males and females: item 2, p=.016 and item 4 p=.007 using an exact sampling distribution for U. Pearson Chi square tests were used to examine sex differences for each item of the psychological measures. Items 1-7 within the GAD-7 were significant between sexes (i.e. male or female). The seven items assess (1) Feelings of nervousness, (2) Inability to stop/control worry, (3) Worrying too much about different things, (4) Trouble relaxing, (5) Inability to sit still due to restlessness, (6) Irritability, and (7) Feeling afraid. Items 2-8 within the PHQ were significant between sexes. The items assess (2) Feeling down/depressed/hopeless, (3) Trouble falling/staying asleep, (4) Feeling tired/no energy, (5) Appetite changes, (6) Lowered/poor self-esteem, (7) Concentration issues, and (8) Feeling slowed down or unable to be still. There was a statistically significant difference between genders and Items 2 and 4 within the BRS were significant between sexes. The items assess (2) Difficulty surviving hard times and (4) Difficulty snapping back from something bad.

Like other studies, this study found females have higher levels of negative affect (i.e., depressive and anxious symptoms). Females displayed lower resilience and reported poorer sleep. By analyzing psychiatric measures, treatment protocols can be tailored to address specific problems, and mental health difficulties can be mitigated by teaching specific coping techniques. These results suggest clinicians should consistently be providing education on depression, anxiety, sleep, and resilience, particularly to female patients, who appear at greater risk for psychological distress.

Higher baseline dispersion (intra-individual variability) across neuropsychological test scores at a single time-point has been associated with more rapid cognitive decline, onset of Mild Cognitive Impairment (MCI) and Alzheimer’s disease (AD), faster rates of hippocampal and entorhinal atrophy, and increased AD neuropathology. Comparison between predictions made from test score dispersion within a cognitive domain versus global, cross-domain dispersion is understudied. Global dispersion may be influenced by ability-and test-specific characteristics. This study examined the performance of global versus domain-specific dispersion metrics to identify which is most predictive of cognitive decline over time.

Data for baseline and five follow-up visits of 308 participants with normal cognition (Mage=73.90, SD=8.12) were selected from the National Alzheimer’s Coordinating Center (NACC) Dataset. Participants were required to have no focal neurological deficits, or history of depression, stroke, or heart attack. Diagnoses and progression to MCI and/or dementia were determined at each visit through consensus conferences. Raw neuropsychological scores were standardized using NACC norms. Global baseline dispersion was defined as the intraindividual standard deviation (ISD) across the 10 scores in the NACC battery. Domain-specific dispersions were calculated by constructing composites and ISD was computed across tests sampling their respective domains (executive functioning/attention/processing speed [EFAS], language, and memory; see Table 1 for details on these tests). Higher values on each of these metrics reflect greater dispersion. Multinomial logistic regression model fit statistics and parameter estimates were compared across four different models (global, EFAS, Language, and Memory dispersion) covarying for age, years of education, sex, race, ethnicity, and ApoE4 status. Models were compared using the Likelihood Ratio Test (LRT) and the Akaike Information Criteria (AIC) of Models statistics.

Of the 308 participants, 70 (22.7%) progressed to MCI, and 82 (26.6%) progressed to dementia. Tables 1 and 2 show the results of the logistic regressions for the four models. All models fit the data well, with statistically significant predictions of conversion. Model 1 (global dispersion) showed a better fit than domain-specific models of dispersion per LRT and AIC values. Consistent with the results from mean differences between groups, parameter estimates showed that only global dispersion and EFAS dispersion significantly predicted conversion to dementia (when included with other covariates in models), with higher dispersion reflecting a greater risk of conversion.

In this sample, baseline global and EFAS dispersion measures significantly predicted conversion to dementia. Although global dispersion was a stronger predictor of dementia progression, findings suggest that executive functioning performance may be driving this relationship. A single index of global variability, from the calculation of standard deviation across test scores, may be supplementary for clinicians when distinguishing individuals at risk for dementia progression. None of the models were predictive of conversion to MCI. Further research is required to examine cognitive variability differences among patients who progress to MCI and patient-specific factors that may relate to test score dispersion and its utility in predicting the progression of symptoms.

Evidence-based diagnostic methods have clinical and research applications in neuropsychology. A flexible Bayesian model was developed to yield diagnostic posttest probabilities from a single person’s neuropsychological score profile by utilizing sample descriptive statistics of the test battery across diagnostic populations of interest.

Three studies examined the model’s performance. One simulation examined estimation accuracy of true z-scores. A diagnostic accuracy simulation utilized descriptive statistics from two popular neuropsychological tests, the Wechsler Adult Intelligence Scale–IV (WAIS-IV) and Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). The final simulation examined posterior predictive accuracy of scores to those reported in the WAIS manual.

The model produced minimally biased z-score estimates (root mean square errors: .02–.18) with appropriate credible intervals (95% credible interval empirical coverage rates: .94–1.00). The model correctly classified 80.87% of simulated normal, mild cognitive impairment, and Alzheimer’s disease cases using a four subtest WAIS-IV and the RBANS compared to accuracies of 60.67–65.60% from alternative methods. The posterior predictions of raw scores closely aligned to percentile estimates published in the WAIS-IV manual.

This model permits estimation of posttest probabilities for various combinations of neuropsychological tests across any number of clinical populations with the principal limitation being the accessibility of applicable reference samples. The model produced minimally biased estimates of true z-scores, high diagnostic classification rates, and accurate predictions of multiple reported percentiles while using only simple descriptive statistics from reference samples. Future nonsimulation research on clinical data is needed to fully explore the utility of such diagnostic prediction models.