Background: Life expectancy (LE) following Alzheimer’s disease (AD) is highly variable. The literature to date is limited by smaller sample sizes and clinical diagnoses. Objective: No study to date has evaluated predictors of AD LE in a retrospective large autopsy-confirmed sample, which was the primary objective of this study. Methods: Participants (≥50 years old) clinically and neuropathologically diagnosed with AD were evaluated using National Alzheimer’s Coordinating Center (N = 1,401) data. Analyses focused on 21 demographic, medical, neuropsychiatric, neurological, functional, and global cognitive predictors of LE at AD dementia diagnosis. These 21 predictors were evaluated in univariate analyses. Variables found to be significant were then entered into a forward multiple regression. LE was defined as months between AD diagnosis and death. Results: Fourteen predictors were significant in univariate analyses and entered into the regression. Seven predictors explained 27% of LE variance in 764 total participants. Mini-Mental State Examination (MMSE) score was the strongest predictor of LE, followed by sex, age, race/ethnicity, neuropsychiatric symptoms, abnormal neurological exam results, and functional impairment ratings. Post-hoc analyses revealed correlations of LE were strongest with MMSE ≤12. Conclusion: Global cognitive functioning was the strongest predictor of LE following diagnosis, and AD patients with severe impairment had the shortest LE. AD patients who are older, male, white, and have more motor symptoms, functional impairment, and neuropsychiatric symptoms were also more likely have shorter LE. While this model cannot provide individual prognoses, additional studies may focus on these variables to enhance predictions of LE in patients with AD.
Abstract Background Previous research has demonstrated a link between objective socio‐economic indicators and cognitive test performance. Although some studies include subjective indicators, such as perceived neighborhood environment, little is known about which specific factors are most strongly associated with cognitive performance and whether these measures are useful beyond traditional SES proxies. Further, in addition to being disproportionately at risk of experiencing neighborhood disadvantage and lower SES, racial/ethnic minorities are more likely to be diagnosed with dementia and receive less timely care compared with White individuals. This study aims to investigate how perceived neighborhood environment and neighborhood disadvantage are related to cognitive performance within a large diverse sample. Method A probability‐based sample of participants (N = 3858; Female = 59%; Black = 51%; Hispanic = 14%) from the Dallas Heart Study Phase 2 (DHS‐2; Mean: Age = 50, Education = 13) were administered the Montreal Cognitive Assessment (MoCA), in addition to measures of perceptions of neighborhood physical environment and violence, and perceived SES. Multiple regression was used to determine associations of these variables with MoCA scores relative to traditional SES measures (i.e., income, education), controlling for demographic and relevant health factors. Post‐hoc analyses stratified by racial/ethnic group were conducted to determine whether indicators differentially influenced test scores. Result After controlling for socio‐demographic and health factors, reporting lower quality neighborhood resources and difficulty paying for “very basics like food and heating” and “medical care” were associated with lower MoCA scores in the overall sample. Post‐hoc analyses revealed significant relationships between MoCA scores and quality of neighborhood resources, “food and heating,” and “medical care” only in Black participants, while “violence” was significantly associated with lower MoCA scores in Hispanics. There were no significant relationships found in Whites. Overall, subjective measures of SES and neighborhood environment contributed modest variance in the overall model, specifically for Black and Hispanic participants ( R 2 = 3% to 5%). Conclusion Experiencing neighborhood and economic adversity was associated with lower scores on a cognitive screening measure and accounted for more variance than income or education in Black individuals and income in Hispanic individuals, while no relationship was seen in White participants. Future research is needed to determine whether these allostatic stressors influence cognitive impairment or dementia later in life.
Objective: The current study examined the potential of a novel Virtual Classroom Stroop task for assessing attentional processing. Traditional assessment of executive functions have been criticized as lacking ecological validity. The VR Classroom attempts to address this by immersing the individual in a real-world environment. Advantages include increased experimental control, improved measurement accuracy, controlled presentation of distracting stimuli, and increased enjoyment. We aimed to compare the VR-based Stroop (with and without distractions) with traditional (paper-and-pencil and computerized) versions of the Stroop. Method: Thirty-four neurotypical college students were administered two traditional versions of the Stroop task (paper-and-pencil and computerized) and the VR Classroom Stroop task. The VR Classroom presents stimuli within the environment and asks participants to click a mouse button if the color of a stimulus presented matches the color of the stimulus spoken by the “virtual teacher.” In the distraction condition, ecologically valid auditory, visual, and audio-visual distractors are presented around the environment. Results: The VR Classroom elicited an interference effect, F(1, 32) = 132.08, p < .001. Reaction times for word naming were longer than the traditional version of the Stroop. Additionally, distracting stimuli did not significantly impact reaction times. Conclusion(s): The VR Classroom successfully elicited an interference effect using Stroop stimuli, suggesting it may be used as a measure of interference control. Although distracting stimuli were not significant, clinical populations may be more likely to experience difficulty due to increased environmental complexity. Additional research is required before the Classroom can be used in clinical settings.
Objective: 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. Participants and Methods: 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. Results: 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. Conclusions: 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.
Abstract Objective Examine prediction of functional ability with neuropsychological tests using latent item response theory. Method The sample included 3155 individuals (Mage = 69.72, SD = 9.41; Median education =13.15, SD = 4.40; white = 92.81%; female = 62.03%; MCI = 25.13%; Dementia = 28.87%) from the Texas Alzheimer’s Research and Care Consortium who completed functional and cognitive assessments [Mini Mental State Examination (MMSE), Logical Memory (LM), Visual Reproduction (VR), Controlled Oral Word Association Test (COWAT), Trail Making Test (TMT), Boston Naming Test, and Digit Span]. Functional measures [Clinical Dementia Rating Scale, Physical Self Maintenance Scale, and Instrumental Activities of Daily Living)] were combined into a single outcome variable using confirmatory factor analysis. Item response theory (IRT) was used to fit the data, and latent regression to predict the latent trait score using neuropsychological data. Results All three functional scales loaded onto a single factor and demonstrated good construct coverage and measurement reliability (Supporting Figure). A graded response IRT model best fit the functional ability composite measure. MMSE (b = −1.08, p < .001), LM II (b = −0.58, p < .001), VR I and II (b = −0.09, p = .02 and b = −0.43, p < .001, respectively), COWAT (b = −0.10, p = .003), and TMT-B (b = −0.30, p < .001) all significantly predicted functional abilities, as did age (b = 0.61, p < .001) and education (b = 0.31, p < .001). Conclusions Global cognition, memory and executive function tests predicted functional abilities while attention and language tasks did not. These results suggest that certain neuropsychological tests meaningfully predict functional abilities in elderly cognitively normal and cognitively impaired individuals. Further research is needed to determine whether these cognitive domains are predictive of functional abilities in other clinical disorders.
Objective: Stroke represents a primary cause of morbidity and mortality in pregnant and postpartum people. While pregnancy-related stroke has drawn increased attention in certain domains of health research (e.g. obstetrics, neurology), neuropsychology has yet to contribute to this literature. Given neuropsychologists' crucial role in stroke evaluation and rehabilitation efforts, our field is poised to offer insights into this important topic. Method: This review presents facts about pregnancy-related stroke most relevant for neuropsychologists, including epidemiology, risk factors, and mechanisms, alongside clinical considerations and open areas of inquiry. Structured in the format of a traditional neuropsychological evaluation, we walk readers through factors to consider in record review, the clinical interview, and providing feedback and recommendations. Conclusions: Pregnancy-related stroke can be associated with marked functional disability and decreased quality of life, and it is notable that prevalence rates are increasing. Presenting at a time when people are experiencing adjustment to a new phase of life, and most commonly affecting women of color and other vulnerable populations, pregnancy-related stroke is a unique condition warranting special attention within the broader stroke discourse. This review aims to serve as a starting point for neuropsychologists to better understand the unique attributes of pregnancy-related stroke through a neuropsychology lens. Beyond that, it aims to promote broader meaningful discussion of neuropsychology's role in women's health.
Abstract Objective: Estimating when full time care will be needed in Alzheimer’s Clinical Syndrome (ACS) is difficult. This is due to limited research identifying the factors associated with loss of independent living (LOI), which may differ across dementia stages. Thus, we examined which clinical and neuropsychological factors predict LOI in the early-to-middle and late stages of ACS. Method: Using the National Alzheimer’s Coordinating Center dataset, individuals with ACS aged ≥50 years with no prior stroke were studied. LOI was based on self/informant report of progressing from requiring some assistance with complex activities at ACS diagnosis to needing assistance with basic activities. Four survival analyses were conducted to predict LOI (M time to LOI = 3.6 visits), for the early-to-middle (MMSE at diagnosis ≥20; N = 3128) and late stages of ACS (MMSE≤19; N = 737). Fifteen clinical predictors at time of ACS diagnosis were examined, followed by neuropsychological test scores added to the models. Results: In early-to-middle ACS, a faster time to LOI was predicted by incontinence, apathy, delusions, and anxiety, along with poorer processing speed, memory, category fluency, and executive function scores (p < 0.05). In the late dementia stage, incontinence, delusions, and lower processing speed predicted faster time to LOI (p < 0.05). Conclusions: Factors gathered at the time of ACS diagnosis from a clinical interview and neuropsychological assessment are predictive of time to LOI, with few differences for the early-to-middle and late stages of ACS. Developing an algorithm to estimate time to LOI will be important for future research to aid in planning future care for individuals with ACS.
Abstract Objective: Integrating multiple pieces of information is a well-known clinical challenge, especially in light of emerging technologies such as NeuroQuant, a fully-automated software used alongside magnetic resonance imaging (MRI) to quantify the volume of brain structures. Using population-based normative data, NeuroQuant compares measurements of certain neuroanatomical features to normal controls, providing metrics for structural abnormalities that may influence the concern for a suspected neurodegenerative etiology. Clinicians should be aware of potential confounding factors when incorporating this information into their clinical impression, as illustrated by this case. Method: We present a unique case study of a 71-year-old Black female who was followed with serial neurological, neuropsychological, and MRI with NeuroQuant evaluations over 10+ years. Results: The patient’s early neuropsychological testing results, in combination with NeuroQuant volumetric measurements, raised suspicion for a potential neurodegenerative process and thereby influenced treatment decisions. However, on longitudinal follow-up, all clinical evaluations were stable except NeuroQuant measurements. From ages 69 to 71, the patient’s hippocampal volumes respectively shifted from the < 5th to 61st normative percentile (see Table 1). Conclusion: Novel volumetric analyses should be interpreted with caution when formulating a clinical impression. Potential explanations for the observed change in NeuroQuant percentile include 1) an update to the normative database, 2) a software update, and 3) the patient moving normative age bands. As with neuropsychological testing, understanding the basis of scores produced by NeuroQuant and characteristics of the normative population is important, as this technology is increasingly used to direct clinical decision-making.
Telephone-based neuropsychological assessment (TeleNP) has been shown to be a valid alternative to in-person or video-based assessment. However, there is limited information regarding patients' satisfaction with TeleNP. This report presents satisfaction survey data from a diverse, clinical sample who received TeleNP during the coronavirus disease pandemic.
Abstract Objective: Neuropsychiatric, functional, motor, and demographic factors have been associated with life expectancy (LE) in those with dementia. Recent findings suggested cognition (assessed by the Mini-Mental State Exam [MMSE]) to be the strongest predictor of LE in Alzheimer’s disease (ad; Schaffert et al., 2022). We evaluated if more detailed neuropsychological scores predict LE in a larger sample of individuals with all-cause dementia. Method: Participants were 4090 deceased individuals (Mage = 74.5, Meducation = 14.8, Male = 44%, White = 90%, Non-Hispanic = 96%) with all-cause dementia (at visit 1, ad = 78%) from the National Alzheimer’s Coordinating Center. Three index scores [executive function/speed/attention (EFAS), language, memory] were calculated from NACC’s neuropsychological batteries. Variables (from visit 1) were entered into a forward regression model (p < 0.001 as point-of-entry) to predict days of LE, and included: age, gender, race (white/non-white), ethnicity (Hispanic/Non-Hispanic), diagnosis (ad/non-ad), abnormal neurological exam (yes/no), Functional Activities Questionnaire (FAQ, total score), Neuropsychiatric Inventory Questionnaire (NPI-Q, total score), MMSE, and EFAS, language, and memory composite Z-scores. Results: Performance on the EFAS composite explained the most variance in LE (R2 = 0.065), followed by age (R2 = 0.044), diagnosis (R2 = 0.023), FAQ (R2 = 0.016), gender (R2 = 0.012), abnormal neurological exam (R2 = 0.006), NPI-Q (R2 = 0.004), language abilities (R2 = 0.003), and Hispanic ethnicity (R2 = 0.003). Plus/minus one Z-score on the EFAS composite predicted 158 days of LE, and each year of age predicted 27 days of LE. Conclusions: EFAS performance and age explained >10% of LE variance. The MMSE failed to predict LE in this model that included more detailed neuropsychological data. EFAS impairment may be a more important predictor of LE compared to other neurocognitive domains and cognitive screeners.
