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BACKGROUND: Early discrimination and prediction of cognitive decline are crucial for the study of neurodegenerative mechanisms and interventions to promote cognitive resiliency. METHODS: Our research is based on resting‐state electroencephalography (EEG) and the current dataset includes 137 consensus‐diagnosed, community‐dwelling Black Americans (ages 60–90 years, 84 healthy controls [HC]; 53 mild cognitive impairment [MCI]) recruited through Wayne State University and Michigan Alzheimer's Disease Research Center. We conducted multiscale analysis on time‐varying brain functional connectivity and developed an innovative soft discrimination model in which each decision on HC or MCI also comes with a connectivity‐based score. RESULTS: The leave‐one‐out cross‐validation accuracy is 91.97% and 3‐fold accuracy is 91.17%. The 9 to 18 months' progression trend prediction accuracy over an availability‐limited subset sample is 84.61%. CONCLUSION: The EEG‐based soft discrimination model demonstrates high sensitivity and reliability for MCI detection and shows promising capability in proactive prediction of people at risk of MCI before clinical symptoms may occur. [ABSTRACT FROM AUTHOR]

Background: Brain connectivity has been proposed to identify people at risk for mild cognitive impairment (MCI). Existing work has mainly focused on functional connectivity which reflects the mutual dependence between brain regions. Effective connectivity, which represents the directed connectivity from one brain region to the other, is expected to provide more details on directed information flow in the brain network. The present study investigates how cognitive engagement changes the effective connectivity in the brain. Method: Our research focuses on task‐based EEG (64‐channel), where participants were asked to perform a motion direction discrimination task. The current dataset includes 56 consensus‐diagnosed, community‐dwelling African Americans (ages 60‐90 years, 28 healthy controls (HC) and 28 MCI patients) recruited through the Wayne State Institute of Gerontology and Michigan Alzheimer's Disease Research Center with subjective cognitive complaints. We evaluated the effective connectivity at different time periods of the motion‐detection task across all the possible EEG region pairs using causalized convergent cross‐mapping (cCCM) of the current source density. For each task trial, the successive time periods being examined included: (I) stimulus onset to Go indication, (II) Go indication to motion‐stop, and (III) the button‐press period. For each subject, we calculated the mean, maximum and minimum of the cCCM vector for each EEG region pair and each time‐period, then selected the region pairs where the cCCM‐based effective connectivity of HC and MCI show a significant difference (p‐value<0.05). Result: Our analysis indicated that typically, MCI participants show more active effective connectivity or need more effort from stimulus onset to Go indication and then to motion stop, but HC participants show higher maximum and minimum effective connectivity values during the button‐press period, where coherent coordination is required among many region pairs. Conclusion: HC and MCI do show significant differences in effective connectivity during each of the three periods of a motion detection task (marked by stimulus onset, Go indication, and button‐press) over many region pairs. Our analysis provides a massive pool of possible features for future biomarker development for effective discrimination of HC and MCI. Funding: NSF‐2032709/Li; NIH‐1R21AG046637‐01A1/Kavcic and NIH‐1R01AG054484‐01A1/Kavcic; NIH‐P30AG053760/Paulson and NIH‐P30AG072931/Paulson. [ABSTRACT FROM AUTHOR]

Background: Interest in the NIH Toolbox Cognition Battery (NIHTB‐CB) for the clinical assessment of older adults is burgeoning. Longitudinal neuropsychological assessments are the most sensitive way to establish cognitive decline and dementia risk of older adults. The psychometric property of measurement invariance is essential to valid interpretations of change. However, few studies have evaluated the stability of NIHTB‐CB scores in a sample of cognitively normal older adults with subjective cognitive decline (SCD). This study aims to analyze measurement consistency of NIHTB‐CB performance over 6 months. Method: N = 29 cognitively normal, older Black adult women (65‐81 years of age) with SCD were administered the NIHTB‐CB at baseline and six‐month follow‐up. Diagnostic status was determined via consensus conference following NACC UDS guidelines. We evaluated the mean change by matched‐pairs t‐tests, and measurement consistency by two‐way mixed effects models of intraclass correlations coefficients and Bland‐Altman plots. Uncorrected standard scores and fully corrected T scores were analyzed from NIHTB‐CB Cognition Composites of Total Cognition, Fluid Cognition, and Crystallized Cognition. Result: There were no significant mean differences from baseline to follow‐up for corrected and uncorrected Total Cognition Scores. The uncorrected Fluid Score significantly increased by 2.5 points at follow‐up (t(28) = 2.18, p =.04), and the corrected Crystallized Score increased by 1.8 points at follow‐up (t(28) = ‐2.25, p =.03). The uncorrected Crystallized Score and the corrected Fluid Score did not differ across assessments. Consistency for the uncorrected and corrected Total scores was good, (ICC(3,1) = 0.83 and 0.80, respectively). Similarly, consistency for uncorrected and corrected Fluid Scores was also good (ICC(3,1) = 0.77 and 0.78). Consistency was excellent for uncorrected Crystallized Scores (ICC(3,1) =.92), but good for corrected scores (ICC(3,1) =.87). Discrepancy scores (baseline vs follow‐up) derived from uncorrected scores for the three composites were unrelated to age and education. Bland‐Altman plots of uncorrected scores for the Composite Scores were consistent with the ICC analyses, showing good agreement between measures. Conclusion: NIHTB‐CB corrected and uncorrected composite scores have good longitudinal psychometric properties in a sample of older Black adult women with SCD, a relatively under‐investigated examinee sample. These findings also suggest that change in fluid or crystallized cognition over six months is unlikely in persons with SCD. [ABSTRACT FROM AUTHOR]

Introduction: The National Institutes of Health (NIH) Toolbox (NIHTB) provides computerized measures of cognition, emotion, sensation, and motor abilities across the lifespan. The ARMADA (Assessing Reliable Measurement in Alzheimer's Disease and Cognitive Aging) study validated the NIHTB in individuals across the cognitive aging spectrum. This article reports the characteristics of our sample of participants. Methods: Participants were recruited across nine sites and classified clinically as cognitively normal (NC), with mild cognitive impairment (MCI), or with dementia of the Alzheimer's type (DAT.) They completed the NIHTB at multiple time points and many had at least one Alzheimer's biomarker previously obtained. Results: Groups differed with respect to dementia severity levels, as anticipated, but were well‐matched across many demographic characteristics. Discussion: The ARMADA study demographics and baseline characteristics provide a suitable sample for validating the NIHTB across the cognitive aging spectrum. Other enriched samples (African American participants, Spanish NIHTB, 85+ years of age) will be reported elsewhere. Highlights: There is a need for assessments that can detect the early stages of cognitive decline in older adults.The ARMADA (Assessing Reliable Measurement in Alzheimer's Disease and Cognitive Aging) study will validate the National Institutes of Health (NIH) Toolbox across the aging spectrum, including mild cognitive impairment (MCI) and dementia of the Alzheimer's type (DAT).Here we report the characteristics of participants.Groups were well‐matched across most demographic characteristics, and clinical characteristics differed as expected.ARMADA study cohorts reflect their respective clinical syndromes for validating the NIH Toolbox. [ABSTRACT FROM AUTHOR]

Introduction: Studies investigating the relationship between blood pressure (BP) measurements from electronic health records (EHRs) and Alzheimer's disease (AD) rely on summary statistics, like BP variability, and have only been validated at a single institution. We hypothesize that leveraging BP trajectories can accurately estimate AD risk across different populations. Methods: In a retrospective cohort study, EHR data from Veterans Affairs (VA) patients were used to train and internally validate a machine learning model to predict AD onset within 5 years. External validation was conducted on patients from Michigan Medicine (MM). Results: The VA and MM cohorts included 6860 and 1201 patients, respectively. Model performance using BP trajectories was modest but comparable (area under the receiver operating characteristic curve [AUROC] = 0.64 [95% confidence interval (CI) = 0.54–0.73] for VA vs. AUROC = 0.66 [95% CI = 0.55–0.76] for MM). Conclusion: Approaches that directly leverage BP trajectories from EHR data could aid in AD risk stratification across institutions. [ABSTRACT FROM AUTHOR]

Introduction: Early detection of cognitive decline in older adults is a public health priority. Advancing Reliable Measurement in Alzheimer's Disease and Cognitive Aging (ARMADA), a multisite study, is validating cognition, emotion, motor, and sensory modules of the National Institutes of Health Toolbox for Assessment of Neurological and Behavioral Function (NIHTB) in the aging spectrum from cognitively normal to dementia of the Alzheimer's type (DAT). Methods: Participants 65 to 85 years old, in demographic groups racially proportional to the general US population, are recruited in one of three groups to validate the NIHTB: cognitively normal, amnestic mild cognitive impairment (aMCI), or mild DAT. Additional special emphasis cohorts include (1) Blacks in the three clinical groups; (2) Spanish‐speakers in the three clinical groups; (3) cognitively normal, population‐proportional, over age 85. Discussion: Longitudinal study will determine whether NIHTB can predict cognitive decline and is associated with Alzheimer's disease biomarkers. Here, we detail the methods for the ARMADA study. [ABSTRACT FROM AUTHOR]

Background: Previously, we reported that there was significant decrease in spectral power for eyes‐closed resting state EEG (rsEEG) immediately following a visual motion discrimination task as compared to before task engagement in older adults – the so‐called Task Aftereffect (TA). Further, we have also shown that rsEEG TA differentiated between older adults with mild cognitive impairment (MCI) and healthy controls (HC), with the MCI participants showing more integrated topology in post‐ as compared to pre‐cognitive task rsEEG. We now report EEG topological changes during task performance with regards to: changes in functional network organization from the beginning to the end of the task and evidence for significant differences in topology between MCI patients and HC. Method: We studied a sample of community dwelling African American older participants over age 60 years, with consensus‐based diagnosis for 51 HC and 39 MCI. EEG epochs extracted for analysis were two‐second epochs before the motion onset of each stimulus at the beginning, middle, and end of the full task. Functional connectivity between channels was assessed by the phase lag index. Minimum spanning tree (MST) was used to characterize network topology. Result: Time effect was confirmed, as network organization in all participants significantly improved with task progression. On average, brain topology became more integrated with higher leaf fraction and smaller average eccentricity. Moreover, our results show differences between groups in MST parameters over the whole task. Most differences were observed at the earliest timepoint at lower frequency range (theta and lower alpha) where average eccentricity was significantly decreased. Leaf fraction and maximal betweenness centrality were significantly increased in the MCI group as compared to HC. Conclusion: The change in network organization suggests that MCI require greater network integration across brain areas before repeated motion onset presentations. This could reflect compensatory brain mechanisms, especially as both groups did not show significant differences in task performance. These findings enhance our understanding of the functional brain network organization immediately prior to repeated responses in a visual perceptual task in MCI compared to HC. This emphasizes the potential for using graph‐theoretic measures as a valid tool for the early detection of cognitive impairment. [ABSTRACT FROM AUTHOR]

Background: Mild Cognitive Impairment (MCI) is one of the early predictors of Alzheimer's disease (AD) and dementia. Assessing comorbidities associated with an MCI diagnosis is crucial not only for diagnostic accuracy but also for understanding the role of comorbidities for evident cognitive impairment. In this study we investigated the association between MCI (amnestic and/or non‐amnestic) and primary comorbidities of hypertension (HTN), hyperlipidaemia (HLD), and Diabetes Mellitus (DM). Methods: We recruited 116 community‐dwelling older African American adults with subjective complaints (103 females, 13 males, mean age = 73.93, range = 65‐91) through the Wayne State Institute of Gerontology and Michigan Alzheimer's Disease Research Center. Participants were consensus diagnosed as 57 healthy controls (HC), 32 amnestic MCI (aMCI), and 27 nonamnestic MCI (naMCI) based on NAAC Unified Data Set3 protocol. NIH Toolbox‐Cognition battery (NIHTB‐C) also was administered, but not used for consensus. Results: Across all the participants, UDS3 forms noted 69% with hypertension (HTN), 56% hyperlipidaemia (HLD), and 28% Type II Diabetes. Among the normal group: 65% had HTN, 51% had HLD, and 28% had DM. Among those diagnosed with aMCI: 66% had HTN, 53% had HLD, and 34% had DM. Among naMCI: 81% had HTN, 63% had HLD, and 22% had DM. Comorbidities did not differ significantly across diagnostic groups. Regarding NIHTB‐C, significant ANOVA interactions did show that aMCI and naMCI with DM were lower in Fluid Intelligence than scores for controls with DM (p =.017). Also, as compared to controls with HTN, naMCI with HTN were lower on Fluid Intelligence (p =.024). Conclusion: Our findings did not demonstrate differences in cardiovascular‐ or diabetes‐related comorbidities across MCI groups and controls in this group of community‐dwelling African Americans. However, DM and HTN, but not HLD, did affect the relationship among the diagnostic groups for fluid intelligence, a summary measure of higher cognitive functioning. This suggests that comorbid medical factors can affect cognitive performance, particularly for MCI groups. Further research is needed to fully understand the scope of risk factors influencing cognitive decline in African Americans. Further analyses will assess relationships between cognitive diagnoses, race, health conditions, and demographic risk factors. [ABSTRACT FROM AUTHOR]

Background: Among cognitively normal older adults, high brain amyloid burden may signal the presence of early Alzheimer's disease processes. While research has shown subtle neuropsychological performance differences among cognitively intact older adults with and without elevated brain amyloid, early investigation has not found similar between‐group differences on the NIH Toolbox‐Cognition Battery (NIHTB‐CB; Snitz et al., 2020). Here, we utilize data from a longitudinal observational driving study to evaluate potential differences in NIHTB‐CB task performance among older adults determined to be cognitively normal via clinical research consensus, based on brain amyloid status. Method: Data from cognitively normal adults age 65+ who completed an amyloid Positron Emission Tomography scan, NIHTB‐CB tasks, and additional neuropsychological testing through the Michigan Alzheimer's Disease Research Center were analyzed. Amyloid positivity was determined based on a centiloid scale (≤10 indicating amyloid negativity; ≥20 amyloid positivity). Thirty participants were amyloid negative and 17 were amyloid positive. Nine individuals with intermediate amyloid values were excluded. Group differences in NIHTB‐CB performance were analyzed via independent sample t‐tests. Linear regressions were used to assess demographic and NIHTB‐CB task/neuropsychological performance predictors of amyloid positivity. Result: There were no demographic differences between the groups. Overall, amyloid negative participants outperformed their amyloid positive peers on NIHTB‐CB List Sorting and backward Digit Span tasks (p's<0.05), both of which are measures of working memory. There were no other group differences in cognitive performance, including episodic memory. The final NIHTB‐CB regression model only included NIHTB List Sorting, which accounted for 16.2% of the variance in amyloid status (F = 8.69, p = 0.005). Digit Span backward similarly accounted for 16.6% of the variance in amyloid status (F = 6.39, p = 0.017). Conclusion: Among cognitively normal older adults with known amyloid status, working memory performance, but not episodic memory performance, differentiated amyloid positive and negative groups. This is consistent with research demonstrating an association between elevated amyloid and executive dysfunction, but not memory impairment, in cognitively normal adults (Tideman et al., 2022). NIHTB‐CB's List Sorting Working Memory is a complex, challenging sequencing task that appears sensitive to amyloid positivity in older adults and task performance may serve as a marker for early prediction of Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Background: The tablet‐based NIH Toolbox‐Cognition Battery (NIHTB‐CB) was compared against the established NACC Uniform Data Set 3.0 (UDS 3.0) neuropsychological battery (used across Alzheimer's Disease Research Centers). NIHTB‐CB factor invariance is critical for drawing appropriate cross‐demographic conclusions. Invariance was assessed across both batteries for consensus‐driven clinical diagnosis, sex, and age in a large sample of participants from the Advancing Reliable Measurement in Alzheimer's Disease and Cognitive Aging (ARMADA) study. Goals of this project were to establish the factor relationship of these two batteries and examine their invariance across the aforementioned demographic characteristics. Method: NIHTB‐CB tasks measure Crystalized and Fluid ability. Tests were administered to 571 participants aged 65 to 99 years (mean age 77.2±7.8, 41% female) with normal cognition or amnestic mild cognitive impairment. Groups were classified for age (< = 85 and > 85), gender, and diagnosis. Exploratory Factor Analysis (EFA) was tested for each group with confirmatory factor analysis. If the model fit sufficiently between groups for the configural model, then metric, scalar, and strict invariance models were also examined. Uncorrected standard scores were used for analyses. Result: A four‐factor model emerged as the best fitting model and was invariant across all demographic groups through adjustments. Factor 1: Language (MINT Total, Oral Reading, Picture Vocabulary); Factor 2: Auditory retention (Digits Forward Total and Digits Backward Total); Factor 3: Learning/Memory (List Sorting Working Memory, Picture Sequence Memory, Craft Story 21 Immediate Recall, Craft Story 21 Delayed Recall, Benson Delayed Recall, Animals Category, Vegetables Category, and MoCA Total Score); Factor 4: Attention/Executive Functioning (Dimensional Card Sort, Flanker, Trails A, Trails B, Pattern Comparison Processing Speed, and Benson Recall). We found strong support for strict invariance across the assessed demographic characteristics. Conclusion: A four‐factor structure comprised of NIHTB‐CB and UDS 3.0 tests supports the validity of the NIHTB‐CB by matching both UDS 3.0 and NIHTB‐CB measures as expected. Strong support for strict invariance reflects the psychometric equivalence of the NIHTB‐CB construct across the groups. The psychometric properties of the NIHTB‐CB subtests therefore are generalizable across samples. By satisfying the assumption of measurement invariance, inferences made using the NIHTB‐CB about between‐group differences are presumed valid. [ABSTRACT FROM AUTHOR]

Background: Mild Cognitive Impairment (MCI) represents the phase between age‐associated cognitive decline and Alzheimer's disease and related dementias. Examination of these individuals may be especially important through computer‐based batteries such as the NIH Toolbox‐Cognition Battery (NIHTB‐CB). To inform NIHTB‐CB validity, we compared MCI patients' performance against categories of cognitive impairment defined by the consensus‐based clinical diagnosis form of the National Alzheimer's Disease Coordinating Center (NACC) Unified Data Set UDS3. We expected a correspondence between MCI consensus‐based clinical deficit areas and specific NIHTB‐CB task performance. Method: Retrospective data analysis was conducted on 65 MCI participants from the Michigan Alzheimer's Disease Research Center, with a mean age of 74±8.3, 65% female, who completed UDS3 and NIHTB‐CB measures. The UDS3 forms consist of interviews, medical/neurological examinations, and neuropsychological testing, with a clinical consensus‐based diagnosis form listing domains of cognitive impairment representing memory, language, attention, executive, and visuospatial functioning. Four domain impairment groupings with a minimum of 10 participants each were established (Executive Functioning, n = 10; Memory, n = 27; Memory+Executive Functioning, n = 17; and Memory+Language n = 11) and compared to NIHTH‐CB performance. ANOVAs adjusted for demographics and post hoc tests were conducted to differentiate across clinical consensus categories for the MCI participants. Result: Two NIHTB‐CB measures yielded significant differences across the clinical grouping: Picture Sequence Memory (PSM, F = 3.3, p = 0.03) and Pattern Comparison (PC, F = 4.1, p = 0.01). For PSM, post hoc tests demonstrated higher scores for the Executive compared to Memory+Executive (PSM: p<0.02) groups with a trend for Executive versus Memory groups (p<.07). For PC, the Memory group performed better than both the Memory+Executive (p<.02) and Memory+Language (p<.04). Conclusion: The better memory performance on PSM of the MCI Executive group compared to the two groups with memory impairment is consistent with expectation. PC, as a measure of executive ability and processing speed was sensitive to clinically diagnosed executive impairment. Lower scores for the Memory+Language group on PC were unexpected. Efficient language functioning may be necessary for complex pattern comparison as dimensions shift. Findings support the validity of NIHTB‐CB. They also suggest the need for additional research on language demands for executive measures. [ABSTRACT FROM AUTHOR]

Background: It has long been observed that the abnormal brain functions in Alzheimer's disease (AD) and mild cognitive impairment (MCI) are closely related to the weakening or loss of functional connectivity across certain brain regions. At the same time, the decreased cross‐connectivity is often accompanied by increased connectivity between some other regions, and this was considered to reflect the compensatory mechanisms of the brain. In this research, we investigate the differences in time‐varying functional connectivity between normal controls (NC) and MCI patients in motion detection tasks. Method: Our research is based on task‐based EEG (64‐channel), where the participants were asked to perform a motion direction discrimination task. The current dataset includes 60 consensus‐diagnosed, community‐dwelling African Americans (ages 60‐90 years, 30 NC and 30 MCI) recruited through the Wayne State Institute of Gerontology Healthy Black Elders Center and the Michigan Alzheimer's Disease Research Center with subjective cognitive complaints. We evaluated the time‐varying functional connectivity at different time periods of the motion‐detection task across all the possible EEG region pairs, in terms of the Pearson correlation vector of the current source density. For each task trial, the successive time periods being examined included: stimulus onset to Go/NoGo indication, Go/NoGo indication to motion‐stop, and the button‐press period. For each subject, we calculated the mean, maximum and minimum of the Pearson correlation vector for each EEG region pair and each time period as features, and then selected the features where NC and MCI show a significant difference (p‐value<0.05 in the t‐test). Result: Our analysis indicated that NC shows significantly higher connectivity across EEG region pairs Right Frontal‐Left Central (through most periods), Left Temporal‐Medial Central and Left Temporal‐Occipital (in button‐press period). On the other hand, MCI shows signigicantly high connectivity across Medial Parietal‐Occipital (through all the periods), which may reflect the compensatory mechanisms of the brain. Conclusion: HC and MCI do show significant difference in functional connectivity during the motion detection task, and the EEG regions pairs Medial Parietal‐Occipital, and Right Frontal‐Left Central shown to be excellent choices for biomarker development for the discrimination of NC and MCI. Funding: NSF‐2032709/Li; NIH‐ P30AG05376004/Paulson; NIH‐1R21AG046637‐01A1/Kavcic and NIH‐1R01AG054484‐01A1/Kavcic; [ABSTRACT FROM AUTHOR]

Background: Accurate and early detection of mild cognitive impairment (MCI) is critical for interventions to promote cognitive resiliency. EEG‐based detection of MCI is attracting considerable attention from the research community due to its accessibility, affordability and increasing accuracy. Method: Our research is based on resting‐state EEG (64‐channel, eye‐closed). The current dataset includes 90 consensus‐diagnosed, community‐dwelling African Americans (ages 60‐90 years, 51 normal cognition, NC; 39 Mild Cognitive Impairment, MCI) recruited through the Wayne State Institute of Gerontology Healthy Black Elders Center and the Michigan Alzheimer's Disease Research Center, all with subjective cognitive complaints. From an information‐theoretic perspective, causality measures the effective connectivity or directed information transfer from one brain region to another. Convergent cross‐mapping has attracted considerable attention recently since it was proven to be able to detect causal coupling in both deterministic and random settings. In this research, we propose to develop an EEG‐based MCI detection model by exploiting cross‐mapping causality analysis. We first derived the current source density (CSD) from EEG signals and performed cross‐mapping based causality analysis between the CSD signals of all the possible pairs across the selected EEG regions of interest. We then conducted joint time‐frequency‐spatial analysis on the cross‐mapping causation and selected features which are likely to reflect the differences between NC and MCI. The selected features were then fed into a machine‐learning algorithm for discrimination of NC and MCI. Result: By tuning the observing window size and the number of features used, we obtained a series of different configurations of the discrimination model between NC and MCI, each with its own estimation accuracy. The overall result is a combined output of the model under different configurations, which improve the reliability and sensitivity of the discrimination model. For the 90‐sample dataset we are working with, the leave‐one‐out cross‐validation accuracy of our model is 97.78%. Conclusion: Our analysis indicates that, potentially, EEG‐based cross‐mapping causality analysis may serve as a promising assessment tool for early detection of people at risk of MCI and dementia, even when just relying on resting state EEG. Funding: NSF‐2032709/Li; NIH‐1R21AG046637‐01A1/Kavcic and NIH‐1R01AG054484‐01A1/Kavcic; NIH‐ P30AG05376004/Paulson [ABSTRACT FROM AUTHOR]

Background: Developing economically viable assessment tools that are highly sensitive to cognitive decline and neural dysfunction is critical for the study of neurodegenerative mechanisms and interventions to promote cognitive resiliency. Due to its high time resolution, accessibility, and affordability, EEG‐based detection of cognitive impairment is attracting considerable attention from the research community. Method: Our research is based on resting‐state EEG (eye‐closed, 64‐channel), and the current dataset includes 137 consensus‐diagnosed, community‐dwelling African Americans (ages 60‐90 years, 84 Normal Cognition, NC; 53 Mild Cognitive Impairment, MCI) recruited through the Wayne State Institute of Gerontology Healthy Black Elders Center and the Michigan Alzheimer's Disease Research Center with subjective cognitive complaints. We first conducted joint time‐frequency‐spatial analysis on the time‐varying functional connectivity of all the possible pairs between the selected EEG regions of interest and generated the feature vectors for each subject. The selected features were then fed into a machine learning algorithm for the discrimination of NC and MCI. By tuning the observing window size (in time‐domain) and the number of features used, we obtained a series of different configurations of the discrimination model between NC and MCI, each with its own accuracy. The overall result is a combined output of the model under different configurations. Result: The accuracy of the discrimination model was calculated under different window sizes and numbers of features. Our result shows that there are 11 window‐size‐and‐feature configurations which can achieve an accuracy in the range of 80.29%∼86.86%. We selected 21 configurations as voters and get the final discrimination result through majority voting (+1 for NC and ‐1 for MCI). Moreover, we obtained a soft output (i.e., a likelihood score) for each subject by combining all the votes which were weighted using the accuracy of each voter. For the 137‐sample dataset we are working with, the 10‐fold cross‐validation accuracy of the model is 91.97%. Conclusion: Our analysis indicates that EEG‐based time‐varying function connectivity analysis is a promising technique for early detection of MCI. Potentially, the soft output we obtained for each subject might be used for prediction of possible cognitive decline. [ABSTRACT FROM AUTHOR]

Background: Changes in motor functioning and particularly lower motor functioning such as balance and gait have been shown to be an early predictor of dementia (Kueper et al 2017). Based on these results, it has been suggested that balance and gait measures should be a regular component of a clinical dementia risk evaluation. The NIH Toolbox (NIHTB) has developed motor‐based tasks along with its cognitive battery. The aim of this study was to evaluate the utility of several of the NIHTB‐Motor task performance to predict amyloid status in cognitively normal older adults. Method: Data from 45 cognitively normal adults age 65+ who were part of a larger study examining changes in driving behavior as predictors of cognitive decline were included in this study. Participants were divided into two groups based on PiB PET imaging and amyloid positivity determined using a centiloid scale: values ≥20 were considered positive, values ≤10 were negative; values between 10 and 20 were considered indeterminate and not included (n = 9)). Twenty nine participants were amyloid negative (OCN‐) and 16 were positive (OCN+). Data from the standing balance, grip strength and 2‐minute walking endurance tasks were analyzed using independent sample t‐tests. Result: There were no differences in age or education between the two groups. However, there was a significant correlation between age and standing balance uncorrected score. As a result, fully corrected scores were used for this analysis. There was a significant difference between the OCN‐ and OCN+ for the fully corrected standing balance standard score (t = 1.7, p<0.04) such that the OCN+ group had lower balance scores compared to the OCN‐ group. No significance differences were found for grip strength or the 2‐minute walking endurance task. Conclusion: The Standing Balance task from the NIHTB may be a sensitive marker to identify individuals who are at higher risk of developing cognitive impairment and dementia. The NIHTB tasks were designed to be easy to administer in a relatively short amount of time with normative data, and these results suggest the Standard Balance task should be included as a routine measure of dementia risk. [ABSTRACT FROM AUTHOR]

Background: Dementia is determined by underlying biological processes. There is a paucity of studies from Nigeria concerning biomarkers in dementia. We present baseline data from longitudinal study on association of lipid and metabolic biomarkers on dementia. Method: Three hundred older adults (65‐89 years; mean age: 70.30; female: 202) without stroke participated were assessed using adapted Unified Data Set (UDS 3) neuropsychological test battery. Diagnoses was made using the Jack and Bondi (1961) method (normal cognition, borderline or severe impairment/dementia). Blood samples were used to analyze lipid (total cholesterol, triglycerides, HDL, LDL, and VLDL) and sugar. Hypertension, pulse and BMI were obtained in this cross sectional study. Partial correlations controlling for age, sex, education, and marital status were used to test for associations between biomarkers and cognitive status across entire sample. MANOVA was used to analyze differences in biomarker categories on UDS3 performance. Result: There were no significant associations between dementia status and metabolic biomarkers (systolic BP, diastolic BP, diabetes, BMI and pulse), though diabetes and BMI showed small effect sizes (0.10).No significant association between lipids biomarkers (total cholesterol (TC), triglycerides (TG), HDL, LDL, VLDL) and dementia were found. Also, no significant associations were found between lipids and metabolic biomarkers on cognitive domains, except for HDL on visual spatial domain (‐0.15).There were small effect sizes (0.10 to 0.12) for TC and HDL on attention and TG and VLDL on language. There were small effect sizes (between 0.15 to 0.18) for BMI on attention; systolic BP, diabetes and BMI on visual spatial; systolic BP, diabetes, BMI and pulse on processing speed, as well as pulse on language. No significant differences were seen on cognitive measures for those diagnosed with or without diabetes or hypertention. When comparing high/low HDL, significant differences were found only for language (0.57). Conclusion: Our findings stand in contrast to prevailing western studies on the roles of metabolic syndrome and lipids on cognitive status. Though there are pockets of small effect sizes, there was little evidence for metabolic and lipid profiles as determinants of dementia and cognitive performance in older Nigerian adults. [ABSTRACT FROM AUTHOR]

Background: Given the growing population of aging adults, and subsequently individuals with dementia, there is a need for instruments that can detect cognitive impairment in early stages. The NIH Toolbox® for Assessment of Neurological and Behavioral Function (NIHTB) is a computerized battery of assessments measuring multiple domains cognition, emotion, sensation, and motor abilities. The goal of the ARMADA study is to validate the NIHTB in individuals with mild cognitive impairment (MCI) and dementia of the Alzheimer type (DAT). Here, we describe the demographic and clinical characteristics of participants ages 65 to 85 from the general population sample. Method: Normal control (NC), MCI, and DAT groups racially proportional to the US population were recruited from existing cohorts across nine Alzheimer's Disease Research Centers and sites with cognitive aging studies. Diagnoses were based on the Uniform Data Set methodology, and included multiple clinical and cognitive assessments. Participants completed the NIH Toolbox at baseline, 12‐, and 24‐month visits. Result: Groups were matched with respect to multiple demographic characteristics. There were some differences in age and sex, such that the NC group had more female participants and was the youngest. Dementia severity levels differed as expected, with the DAT group demonstrating greater cognitive and functional impairment compared to the MCI group, and the NC group demonstrating minimal cognitive or functional symptoms. Additionally, the DAT group reported more neuropsychiatric symptoms compared to the NC and MCI groups. There was no difference in reported family history of cognitive impairment across groups. Many participants across groups had at least one Alzheimer biomarker collected. Conclusion: The study cohorts included in the ARMADA study accurately reflect their respective clinical syndromes for validating the NIHTB across the cognitive aging spectrum. The goals for the ARMADA study include comparing performance on all four modules of the NIHTB across groups, evaluating the relationship between Alzheimer biomarkers and NIHTB measures, and measuring changes in performance longitudinally. Special emphasis groups, including individuals over age 85, African American participants, and Spanish‐Speaking participants, were also recruited and are reported elsewhere. [ABSTRACT FROM AUTHOR]

Background: A history of hearing loss is associated with lower scores on cognitive tests, as well as with incident Mild Cognitive Impairment and Alzheimer's disease. In order to compare hearing adaptation in older age, we used iPad‐based testing to assess older healthy controls and older persons with two distinct approaches to hearing correction: one group with cochlear implants and another comprised of individuals with hearings aids awaiting cochlear implants. Speech recognition in noise is a common measure of hearing accuracy and reflects effectiveness of both hearing aids and implants. We expected to find that better speech recognition scores would relate to higher levels of cognitive performance across the groups. Method: Participants were 54 individuals over 65 years of age (cochlear implants, n = 28; implant candidates using hearing aids n = 12; and normal hearing, n = 14), recruited through the University of Michigan Medical Center. All participants completed a speech in noise task and the NIH Toolbox‐Cognition battery (NIHTB‐CB) adapted for individuals with hearing loss. All scores from the NIHTB‐CB were corrected for education, age, race, and gender. Result: For speech recognition, all groups performed significantly differently from each other (F = 57.408, p < 0.001), with the normal hearing group performing the best and the pre‐implant group performing the worst. For NIHTB‐CB scores, the only difference found was lower performance for the cochlear implant group compared to the normal‐hearing group on the Picture Vocabulary subtest (F = 3.417, p = 0.041). Duration of hearing loss was not correlated with cognitive performance on any of the tasks. Conclusion: Overall, as expected, there were significant differences between groups on speech recognition. However, controls and all groups with hearing loss performed essentially equally well on all aspects of the adapted NIHTB‐CB. This suggests that in cases where hearing is partially corrected with a hearing aid or cochlear implant and tests are adapted for patients with hearing loss, group differences in cognition are not clearly evident, even when groups differed in speech perception. We hypothesized that duration of hearing loss may have driven the differences noted in vocabulary knowledge, however this was not supported in the results. Supported by NIH#R01DC015809‐03S1 and P30AG072931. [ABSTRACT FROM AUTHOR]

Background: Previous research has demonstrated a link between affect and cognitive functioning, particularly showing that negative affect is related to lower cognitive performance across several cognitive domains. However, the relationship between positive affect (PA) and cognitive functioning is not as thoroughly characterized, though some previous work suggests that PA may be related to improved memory consolidation and greater cognitive flexibility. There is little research, if any, on the relation between PA and cognition in older adults from diverse racial backgrounds, who are also at greater risk for Alzheimer's disease. PA may be a modifiable factor and target for intervention to deter cognitive decline. Therefore, we investigated whether PA was related to cognitive functioning in a sample of community‐dwelling, older African American adults. We predicted that greater PA would be associated with greater global cognitive functioning, immediate memory recall, and delayed memory recall while controlling for age. Method: Telephone screenings were administered to 194 older African Americans (aged 64‐94 years). Global cognitive functioning, immediate recall, and delayed recall were measured via the Telephone Interview for Cognitive Status‐Modified (TICSm). Immediate and delayed recall scores were excluded from global cognitive functioning scores to reduce conceptual overlap. PA was measured with a questionnaire of emotional functioning. We used hierarchical linear regression to determine whether PA was positively associated with cognitive functioning while controlling for age. Result: Higher PA was predictive of greater global cognitive functioning, after accounting for the effect of age (F(2,191) = 15.70, p<.001; ΔR2 =.05, b = 0.68, SE = 0.21, p =.002). Similarly, higher PA was predictive of greater immediate recall (F(2,191) = 14.10, p<.001; ΔR2 =.03, b = 0.45, SE = 0.19, p =.019) and delayed recall (F(2,191) = 7.44, p<.001; ΔR2 =.02, b = 0.48, SE = 0.23, p =.035). Conclusion: In support of our hypothesis, we have demonstrated that greater PA is associated with greater global cognitive functioning, immediate memory recall, and delayed memory recall among older African American adults. Past studies report some relationships between PA and cognition, though little, if any, work has been done with aging adults from diverse racial backgrounds. Understanding how PA relates to cognition in late life is crucial because it may be a modifiable protective factor and a target for intervention. [ABSTRACT FROM AUTHOR]

Background: There is a critical need to identify older adults in the earliest stages of cognitive decline for targeted dementia reduction interventions. Subjective memory concerns often drive older adults to seek objective memory assessments. However, there is often poor agreement between subjective complaints and objective measures. Previous research suggests that self‐appraised memory decline may be more sensitive to mild cognitive impairment than objective measures, but factors influencing the relationship between self‐perceived and actual functioning are unclear. Subjective memory appraisal may be susceptible to negative affective perceptions such as anxiety. Anxiety has increased substantially during the pandemic, suggesting the heightened potential for it to distort subjective perceptions, thereby increasing demands for cognitive assessments. This study investigated whether the discrepancy between subjective cognitive concerns and objective cognitive performance is related to anxiety in a sample of older African American adults. Method: Telephone screenings were administered to 206 older African Americans (aged 64‐94 years) during the first year of the pandemic as part of a more extensive study. Demographic data, objective memory (Telephone Interview for Cognitive Status [TICS]), subjective memory (Cognitive Change Questionnaire [CCQ]), and anxiety (Generalized Anxiety Disorder‐7 [GAD‐7]) were measured, along with survey questions about affective and stress responses to COVID‐19 experiences. Result: Objective TICS scores were predictive of subjective CCQ executive function scores (F(1, 197) = 4.37, p =.038, R2 =.022). Discrepancy scores were calculated as the standardized residual variance between objective and subjective measures. Discrepancy scores were correlated with anxiety (r =.396, p<.001), indicating that higher anxiety was related to an overestimation of cognitive difficulty relative to the level of objective cognitive resources. Conclusion: Objective and subjective measures of cognition are related. However, discrepancies exist between objectively‐measured and self‐perceived cognition. Elevation in anxiety level is associated with greater overestimation of cognitive difficulties relative to one's objective level of cognition. As pandemic‐related stressors have worsened anxiety for some, individuals with higher anxiety may also have depreciated their self‐appraisal of cognitive abilities in the present climate. Clinicians should consider anxiety as a possible contributing factor when assessing subjective cognitive difficulties. [ABSTRACT FROM AUTHOR]

Background: Estimation of premorbid IQ (PMIQ) among older adults is crucial to characterize cognitive decline properly, particularly among older adults at risk for Alzheimer's disease. Three scores from the NIH Toolbox‐Cognition Battery (NIHTB‐C) ostensibly may estimate premorbid functioning: Crystallized Cognition (CC) and its component subtests, Picture Vocabulary (PV) and Oral Reading Recognition (ORR). However, they have not yet been validated in a sample of older African American adults at risk for cognitive impairment. We investigated whether performance on the Crystallized NIHTB‐C measures was related to the WTAR, a gold standard word recognition test associated with PMIQ, in older African Americans with and without mild cognitive impairment (MCI). We also compared WTAR scores to the summary score from the NIHTB‐C Fluid Cognition (FC) battery, where no relationship was expected. Methods: The WTAR and NIHTB‐C were administered to 36 cognitively normal and 18 MCI participants (diagnoses determined by consensus conference following NACC UDS guidelines) who were community‐dwelling, older African Americans aged 65–86 years. Data analysis used Bayesian bivariate correlations and hierarchical multiple regression to adjust for education effects. Results: In the full sample, Bayesian bivariate correlations showed that WTAR performance was positively associated with performance on ORR (r =.86, BF10>100, 95%CI[.76,.92]), PV (r =.66, BF10>100, 95%CI[.47,.78]), and CC (r =.84, BF10>100, 95%CI[.72,.90]), and not associated with FC performance (r =.08, BF10 = 0.196, 95%CI[‐.19,.33]). This pattern held in analyses restricted to the cognitively normal (r =.84, BF10>100, 95%CI[.68,.91]; r =.58, BF10>100, 95%CI[.29,.75]; r =.81, BF10>100, 95%CI[.64,.90]; r =.12, BF10 = 0.22, 95%CI[‐.23,.37], respectively) and the MCI groups (r =.88, BF10>100, 95%CI[.64,.95]; r =.71, BF10 = 41.6, 95%CI[.31,.87]; r =.84, BF10>100, 95%CI[.55,.93]; r = ‐.33, BF10 = 0.70, 95%CI[‐.66,.15], respectively). Although education was positively related to the four measures (p<.05), hierarchical multiple regression revealed that education did not predict NIHTB‐C performance and did not improve model fit beyond WTAR performance alone. Conclusion: WTAR scores are strongly positively associated with NIHTB‐C CC scores and unrelated to FC scores, suggesting appropriate convergent and discriminant validity. Our findings support our hypothesis that NIHTB‐C Crystalized Cognition scores represent reasonable, valid estimates of PMIQ in older African Americans with and without MCI. [ABSTRACT FROM AUTHOR]

Background: The Advancing Reliable Measurement in Alzheimer's Disease and Cognitive Aging (ARMADA) study was designed to validate and further develop the full tablet‐based NIH Toolbox Assessment of Neurological and Behavioral Function within an older, racially diverse sample. This ARMADA cohort study of the Toolbox Cognition Battery (NIHTB‐CB) reports construct validity, assessed through its two components: convergent (high correlations to related constructs) and discriminant (low correlations with dissimilar constructs) validity. Methods: Participants (age range 65–99 years, 142 aged 85+) were consensus conference‐diagnosed with normal cognition (N = 367), amnestic mild cognitive impairment (N = 136), and Alzheimer's disease (N = 68). NIHTB‐CB assesses domains of Fluid Executive, including Set Shifting (Dimensional Card Sort/DCCS), Inhibitory Control and Attention, (Flanker) and Working Memory (List Sort Working Memory/LSWM); Fluid Processing Speed (Pattern Comparison/PCPS), Fluid Episodic Memory (Picture Sequence Memory/PSM) and Crystalized Language (Oral Reading/ORR, Vocabulary/TPVT). Participants were also given the National Alzheimer's Disease Coordinating Center Unified Data Set (UDS3) cognitive measures: Memory Composite (Benson Figure and Craft Stories Delayed Recall); Executive Composite (Trail Making, Semantic Fluency), Auditory Attention (Digit Span Forward), Figure Copy (Benson), and Language (Multi‐lingual Naming Test). Unadjusted, (uncorrected) standard scores were created based on the full Toolbox and UDS populations. Result: Based on the large sample size, most all correlations between NIHTB‐CD and UDS3 were significant, though patterns were clear. NIHTB‐CB/Fluid Executive tests had highest correlations with the related UDS3 Executive factor composite (r = 0.53‐0.60), approaching threshold for a "good" designation (>0.6), and "low" designation correlations (r<0.3) for Auditory Attention. PSM correlated highest with UDS Episodic Memory (r = 0.55), and lowest with Auditory Attention (r = 0.23). PCPS was highest with the Executive Composite (r = 0.53) and lowest with Language (r = 0.26). Crystalized NIHTB‐CB correlated highest with UDS Language (r = 0.41‐0.50) versus ORR with Memory (r = 0.22) and TPVT with Copy (r = 0.26). Conclusion: Results demonstrated that the highest correlations between NIHTB‐CD and UDS3 measures were between related constructs and lowest correlations were between dissimilar constructs. Thus, convergent/discriminant expectations were supported across NIHTB‐CB measures, reinforcing NIHTB‐CB construct validity. Consistent results across diagnoses and other analytic approaches are also presented, supporting these findings. [ABSTRACT FROM AUTHOR]

Background: NIH Toolbox‐Cognition Battery (NIHTB‐CB) is a computerized cognitive assessment battery frequently used in clinical research. However, the utility of the tablet adaptation has not been well established for clinical characterization, nor have possible racial differences been examined. We aim to identify which of the NIHTB‐CB subtests best differentiate healthy controls (HC) from those with mild cognitive impairment (MCI), as well as examine possible differences in diagnostic identification between Black/African‐American (B/AA) and white older adults. Method: Participants were community‐dwelling adults 65 years and up (HC = 96; MCI = 63), recruited through affiliated studies within the Michigan Alzheimer's Disease Research Center (NIA/NIH‐P30AG053760) and diagnosed by consensus conference through National Alzheimer's Coordinating Center criteria. They then completed the NIHTB‐CB tablet version for iPad. Discriminant function analysis was used to determine which cognitive tests best differentiated clinical diagnoses in the total sample and separately for B/AA (n = 81) and white participants (n = 78). Result: In the total sample, 78% of cases were correctly identified with Picture Sequence Memory (PSM; Standardized Coefficient (SC) = ‐.73) being the strongest predictor. The cross‐validated model correctly identified 79.2% of HCs and 60.3% of MCIs. Analyses were then stratified by race. In the B/AA sample, the model was able to correctly identify 60% of HCs and 68.4% of MCIs, with PSM (SC =.68) being the strongest predictor followed by List Sorting Working Memory (LSWM; SC =.42). In the white sample, 83% of HCs and 44% of MCIs were correctly identified and PSM (SC =.84) was the strongest predictor. Conclusion: As more treatments become available for Alzheimer's disease, it is increasingly important to identify preclinical stages, such as MCI, so that we may intervene as early as possible in the disease process. NIHTB‐CB is one tool that may aid in identification. The memory subtest PSM was the best predictor of diagnosis in each model. While the overall model was fairly accurate at predicting clinical diagnosis, the models proved less accurate when cross validated and stratified by race. Thus, our results suggest that it may be important to consider race and other aspects in identifying specific diagnostic groups. [ABSTRACT FROM AUTHOR]

Background: The search for psychophysiological markers that distinguish normal from abnormal aging has produced many candidate EEG dynamics, but relatively little research has been done (or at least reported) on the theta/beta ratio or brain wave asymmetries. Methods: We previously reported frontal asymmetries (frontal alpha and frontal beta asymmetry) originally investigated in the context of anxiety and approach‐avoidance motivation that distinguish MCI from healthy, cognitively normal persons (HC). Another potentially more cognitively‐involved marker, the ratio of theta to beta frequencies (TBR), originally investigated as a marker of attention deficits, might also usefully distinguish cognitive HC from MCI. We took advantage of a previously collected data set to conduct an exploratory analysis of the pattern of TBR in HC and MCI participants. Resting state EEG (rsEEG) was measured before and after a simple motion direction discrimination task. We investigated the standard frontal TBR as measured at frontal electrode FZ. We estimated power spectral density in the resting state (eyes closed) for both pre‐ and post‐task, extracting estimates of TBR at electrode Fz. Results: We found that MCI had significantly higher TBR than cognitively normal elderly, F(1,95)=4.98, p=0.028, with no effect of timepoint and no interaction. We then explored the possibility that TBR itself has meaningful asymmetries, calculating the TBR asymmetry at the following electrode pairs: F3‐F4, C3‐C4, CP3‐CP4, P3‐P4, PO3‐PO4. This analysis revealed an interesting pattern, with group (HC vs. MCI) interacting with location, F(4,380)=5.25, p<0.001. Relative to HC, MCI participants had higher TBR on the right (positive TBR asymmetry) in frontal electrodes (F3‐F4), but higher TBR on the left (negative TBR asymmetry) over parietal‐occipital sites (P3‐P4 and PO3‐PO4). The parietal asymmetry may reflect the early spread of lesions from inferior temporal regions to areas subserving language and visuospatial processing, but the frontal asymmetry is less obviously tied to known progression of brain lesions, and may reflect heterogeneity in the underlying pathogenesis of this sample of people with MCI. Conclusion: TBR asymmetries obtained in rsEEG can contribute significantly to successful discrimination of older persons with MCI from those with normal cognitive functioning. [ABSTRACT FROM AUTHOR]

Background: White matter (WM) measures from diffusion magnetic resonance imaging (dMRI) are sensitive to Alzheimer's disease (AD) pathology and cognitive impairment. Whereas most extant dMRI studies focused on uniformly‐oriented WM fiber tracts, variations in crossing fiber regions also show sensitivity to mild cognitive impairment (MCI) and AD diagnoses. Such crossing fiber alterations may reflect differential disease effects on constituent fibers, suggesting that quantifying crossing fibers may provide unique markers of early WM changes and neurocognitive risk in AD and MCI. Although few methods exist for quantifying crossing fibers, recent findings show older age is associated with increased microstructural complexity (CX) a novel method to characterize in crossing fibers. The present study evaluated CX in relation to variation in plasma AD biomarkers and cognitive performance in older adults without dementia. Method: Data from 48 participants included clinical evaluation, neuropsychological assessment, dMRI neuroimaging and venipuncture. SIMOA assays quantified the AD plasma biomarkers Aβ42, Aβ40, and tau phosphorylated at threonine181 (pTau‐181). Participants were clinically characterized as cognitively normal (n=19) or with MCI (amnestic: n=18; non‐amnestic: n=11). Diagnostic groups did not differ with respect to years of age and education, systolic and diastolic blood pressure, proportions of men and women, proportions of APOE ε4 allele carriers, or plasma biomarker levels. Processing of dMRI data followed the MRtrix fixel‐based analysis (FBA) framework to estimate voxelwise CX data for all participants. Result: Voxelwise regression of whole‐brain CX on pTau‐181 levels revealed a significant positive effect in the dorsal cingulum bundle and adjacent corpus callosum. Post hoc regression of mean CX sampled from significant voxels on pTau‐181 showed greater pTau‐181 level strongly predicts higher CX in this region (partial‐R=0.579, p<0.001), even while controlling for age, education, and Montreal Cognitive Assessment (MoCA) score. Path analysis linking pTau‐181 and cognition showed elevated circulating pTau‐181 levels strongly predict increased white matter complexity in mid‐cingulum bundle, which in turn predicts reduced cognitive performance on measures of working memory, list learning, and semantic fluency. Conclusion: Microstructural complexity in crossing fiber WM regions provides a sensitive marker of early WM alterations associated with elevated plasma phospho‐tau and cognitive decrements in non‐demented older adults. [ABSTRACT FROM AUTHOR]

Background: Prevalence rate of mild cognitive impairment (MCI) and Alzheimer's dementia (AD) in cART treated people living with HIV (PWH) is unknown. Method: 277 PWH and 189 HIV‐negative Ugandan adults matched by age, sex, and residence were neuropsychologically evaluated i. Impairment in seven domains ‐ including immediate recall, memory and learning (delayed recall, recognition) and others, were defined. Cognitive status – i.e., not impaired, asymptomatic impairment (ANI), minor neurocognitive disorder (MND) or HIV‐associated dementia (i.e., HAD, if HIV+), was defined according to Frascatti criteria. Cognitive dysfunction of the AD subtype was defined per Bondi et al. (2014); MCI included moderate (i.e. ≥1 SD worse in ≥2 tests) or pronounced (i.e. >2.0 SD worse in ≥1 tests) cognitive impairments without functional limitation. AD ‐ i.e., ≥1 pronounced impairment in recognition/delayed recall accompanied with functional limitation. Differences in MCI and AD by HIV status and odds ratios with 95% confidence intervals (CI) were calculated stratified by age (<60 vs. ≥60 years). Result: Among adults cognitively unimpaired (26.1%) or with ANI (27.8%), amnestic MCI rate was 28.9% (60/204) among PWH and 14.7% (21/133) among HIV‐ controls (OR = 2.18, 95%CI:1.25, 3.84). Similarly, among adults with MND/HAD, prevalent AD was 38.7% (29/75) among PWH and 23% (14/61) among HIV‐ controls (OR = 2.11, 95%CI:0.99, 4.50). Among cognitively unimpaired/ANI affected individuals <60 years old, amnestic MCI prevalence was 16.2% for community controls and 30.3% for PWH (OR = 1.9, 95%CI:1.02, 3.60). Among individuals classified as MND or HAD, prevalent AD was 16.3% for HIV‐ controls vs. 38.6% for PWH (OR = 3.23, 95% CI: 1.23, 8.52). Among adults 60+ years old with ANI/no impairment, prevalent amnestic MCI was 14.7% for community controls and 41.9% for PWH (OR = 4.04, 95%CI:1.23, 13.40) but the prevalence of AD was identical at 38.9% each for HIV+ and for HIV‐ controls 60+ years old with MND/HAD. Conclusion: Ugandan PWH are at increased risk for being diagnosed with MCI/AD, and they may be diagnosed at a younger age than HIV‐negative Ugandans. These data highlight the importance of MCI/AD screening in PWH and the need to avail PWH with interventions to mitigate MCI/AD risk. [ABSTRACT FROM AUTHOR]

Background: Age‐related changes in cognitive functions affect complex task engagement such as driving. Studies have demonstrated high occurrences of driving errors and crashes among older adults, particularly those with mild cognitive impairment (MCI). We have previously demonstrated that fatigue contributes to detriments in driving ability, especially among MCI, and that attention‐maintenance tasks (AMT) improve older cognitively normal (OCN) but not MCI performance. This study considers which cognitive deficits predict driving performance across AMT conditions. Methods: Participants included 34 OCN and 21 MCI adults age 60+ years diagnosed through consensus conference, who completed tests of memory (HVLT‐R delayed recall), executive functioning (Trail Making Test B; TMB), working memory (CogState One Back Test, ONB), simple reaction time (CogState Detection Test, DET), and choice reaction time (CogState Identification Test, IDN). Computerized driving scenarios were presented on a 45" flat screen monitor, which included a 50‐minute simulated drive designed to induce fatigue, followed by four 10‐minute sessions alternating between driving with and without AMT. Lane position, velocity, lane and velocity variability, and driving errors (e.g., leaving the road, crossing midline) were compared across 10‐minute driving segments. Cognitive measures and diagnosis were used to predict driving performance. Results: Participants with MCI 1) maintained significantly slower average speed, 2) demonstrated higher variability in lane‐keeping, and 3) had greater velocity variability, only in the non‐AMT condition. OCN participants performed significantly better on all cognitive measures, except DET. Linear regressions showed that TMB, alone, predicted average velocity during the AMT driving condition with no significant predictors for the non‐AMT. Velocity variability during the non‐AMT driving condition was predicted by the main effect of Diagnosis, TMBxDiagnosis, and ONBxDiagnosis interactions. TMB and HVLT‐R predicted velocity variability during the AMT condition. Lane variability during both driving conditions was predicted by DETxDiagnosis and IDNxDiagnosis. Conclusions: Performance on TMB and its interactions are predominantly important predictors for velocity‐related driving conditions. Reaction time decrements among those with MCI predict lane variability. This suggests that executive functions are particularly needed in maintaining velocity, while monitoring is critical for maintaining lane position. Our findings demonstrate that higher cognitive burden likely accentuates driving difficulty, especially in MCI. [ABSTRACT FROM AUTHOR]

Background: Recently, we have shown a significant decrease in resting‐state EEG (rsEEG) spectral power at post‐task as compared to pre‐task cognitive engagement in older adults. Further, older adults with mild cognitive impairment (MCI) showed greater decreases in rsEEG in temporal and central cortical regions at post‐task than healthy controls (HC). Here, we present functional connectivity (FC) and network topology derived from the same pre‐ to post‐rsEEG changes. Method: We studied a sample of community dwelling African American older participants over age 60 years, consisting of consensus‐based diagnosis for 58 HC and 41 MCI. rsEEGs were recorded before and after approximately 20 minutes of a visual motion direction discrimination task. FC between channels was assessed by the phase lag index, while minimum spanning tree (MST) was used to characterize network topology. Result: For both HC and MCI groups, when compared to pre‐task measurements, the post‐task FC was significantly increased within the middle frontal, middle central, and left temporal regions in the lower alpha frequency band. An opposite pattern with decreased FC was found within the right parietal region in the delta, theta, lower alpha, upper alpha, and beta frequency bands. In contrast, compared to HC, the MCI group did show significantly decreased FC within the middle frontal region in the delta band, following the cognitive task. All results from network topology failed to demonstrate differences between HC and MCI. On average, both groups' post‐task functional network topology showed significant decreased eccentricity and diameter in the delta and beta bands, indicating a higher global network integration. Conclusion: Our findings suggest that visual‐based cognitive engagement affects both post‐task FC and network topology similarly in both HC and MCI. For the most part, increased FC and functional topology in both groups are likely associated with after‐task effects, resulting from additional time to return to baseline, though decreased FC in MCI as compared to HC was seen within the middle frontal region in the delta. Our results demonstrate that both FC and topoloty are responsive to cognitive challenge, though frontal/executive‐driven differences in FC may be an area of future promise in distinguishing HC and MCI. [ABSTRACT FROM AUTHOR]

Background: Age‐associated impairment among older adults is a significant public health concern. The NIH Toolbox for Assessment of Neurological and Behavioral Function® (NIHTB) was developed for use in studies for which standardized, computer‐based measurement of cognitive, motor, sensory, and behavioral processes is of importance. The NIH Toolbox–Cognition battery measures both crystallized and fluid cognitive functions. The goal of this study was to compare performance of the newly available tablet‐version of the Cognition battery across healthy and impaired older adults and to analyze if race and gender effects would be evident even when using NIHTB fully‐adjusted T‐scores. Method: A total of 104 individuals (28 men, 56 women; 45% African American) completed the NIH Toolbox–Cognition as part of a National Alzheimer's Disease Coordinating Center (NACC) longitudinal study of memory and aging through the Michigan Alzheimer's Disease Research Center (Michigan ADRC). All participants received the Unified Data Set (UDS) and consensus diagnosis of normal cognition (NL), amnestic mild cognitive impairment (aMCI), or Alzheimer's dementia (AD). Univariate analyses of variance with post‐hoc comparisons were used to compare fully adjusted (age, gender, race/ethnicity, education) Toolbox T‐Score performance across the groups. Results: Univariate ANOVAs revealed significant findings for the Total and Fluid composites (both p<0.001) and Crystallized Composite (p<0.05). For Crystallized subtests, only Oral Reading demonstrated group differences, with NL>AD. Though NL and aMCI groups performed similarly, both groups outperformed AD on Dimensional Card Sort, Flanker, and Pattern Comparison Fluid subtests. NL>aMCI>AD on List Sorting Working Memory and Picture Sequence Memory. Main effects for race or gender were not seen for comparisons of NL to aMCI, but some group sizes were too small for adequate testing in the AD participants. Conclusions: As expected, NL, aMCI, and AD demonstrated significant performance differences on NIH Toolbox—Cognition composite and scale scores. As expected, Crystallized tasks were least effective, traditionally representing cognitive areas least sensitive to neurologic dysfunction. Fluid tasks were noticeably more effective, with the working memory and learning measures showing a significant tiered difference, with NL highest and AD lowest. The fully adjusted scores were successful in accounting for demographic variables. [ABSTRACT FROM AUTHOR]

Background: National Institute on Aging (NIA)‐funded Alzheimer's Disease Centers in the United States have been using a standardized neuropsychological test battery as part of the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (UDS) since 2005. Version 3 (V3) of the UDS was implemented in 2015 and included several changes to its neuropsychological battery, replacing the previous version [Version 2 (V2)]. The current study compares the V3 and V2 neuropsychological batteries with respect to their ability to distinguish among categories of cognitive impairment captured by the Clinical Dementia Rating (CDR) global scores representing either no cognitive impairment (CDR=0), questionable or mild cognitive impairment (CDR=0.5) or mild stage of dementia (CDR=1.0). Method: Data from the NACC UDS V2 and V3 neuropsychological batteries were examined. There were 16,935 unique subjects from V2 and 5022 unique subjects from V3 aged 60 years and older with CDR global score ≤ 1. To reduce the influence of practice effects, only data from their first assessment was used. To control for inequalities in sample sizes between V2 and V3, we identified an approximately equal number of subjects from V2 within each CDR group. Receiver Operating Characteristics Area under Curve (ROC‐AUC) in differentiating stages of cognitive impairment were compared and optimal cut‐points based on Youden's J scores were calculated. Result: ROC‐AUCs from all of the V3 neuropsychological tests were comparable in their ability to differentiate CDR global scores with the corresponding tests in V2, despite the fact that V3 participants included more subjects at earlier stage of CDR 0.5. UDS V3 composite scores yielded similar ROC‐AUCs to the best performing individual test within each domain, while the Montreal Cognitive Assessment (MoCA) total score yielded higher ROC‐AUCs than any individual MoCA index scores. Racial differences in differentiating between CDR=0 and CDR=0.5 were also found. Conclusion: A nonproprietary suite of neuropsychological tests in UDS V3 provided similar discriminative ability to tests in UDS V2 to distinguish categories of cognitive impairment. Optimal cut‐points calculated in this study will be useful for clinical diagnosis. [ABSTRACT FROM AUTHOR]

Background: The COVID‐19 pandemic has devastated multiple Countries targeting vulnerable populations. In Michigan there have been over 550,000 confirmed cases and over 14,000 deaths that forced health officials to rapidly develop and distribute COVID‐19 vaccines (MDHHS, 2021). As researchers study the COVID‐19 virus and vaccines, it is important to understand the educational needs of the public in deciding intent to accept the vaccine. Method: We analyzed factors correlated with generalized anxiety rates in 150 African Americans age 65 and older, recruited from Wayne State Institute of Gerontology Healthier Black Elders Center and surrounding communities. Participants completed telephone questionnaires measuring demographics, stressors associated with COVID‐19 pandemic, and emotions. A scale was designed using the Health Belief Model assessing beliefs, susceptibility, severity, conspiracy beliefs of Covid‐19 (man‐made), and intent to be vaccinated. Applying factor analysis, questions were divided into: fear of COVID‐19 (I am worried about the likelihood of getting COVID‐19, My chances of getting COVID‐19 in next few months is great, I will be very sick if I get COVID‐19, Vaccinations decrease chances of infections or complications, I will get the vaccine if I receive adequate information), vaccination safety (I am concerned the vaccine will not work, I am concerned the vaccine will not be safe, Side effects from the vaccine interfere with activities and, Vaccinations make me less worried). Participants responded to these questions on 5‐point Likert scale (from "Strongly Disagree" to "Strongly Agree"). Result: Of 150 participants, 67% expressed intention to get vaccinated while only 15% were reluctant to vaccinate. Stepwise regression analysis showed that fear of COVID‐19 and vaccination safety explained 49% variance of intent to vaccinate. Vaccination safety was predicted by age, loneliness, vulnerability, and positive emotions (R2 = 20%). Fear of COVID‐19 was predicted by anxiety and positive emotions (R2 = 8%). Conclusion: This study provides a snapshot of urban African American older adults who are generally eager to get vaccinated for COVID‐19, and predictors of intent to get vaccinated. More research is needed to understand the scope of educational needs to increase the likelihood of older African Americans to get vaccinated. [ABSTRACT FROM AUTHOR]

Background: The Covid‐19 pandemic, with its diverse effects (e.g., on quality of life, health and illness, physical and psychological well‐being, social and economic life), has also been suggested to effect cognitive functioning. To better understand the possible effect of the COVID‐19 pandemic, we examined cognitive functioning of older African Americans. Method: Study included 164 community‐dwelling older African Americans (mean age= 75.6, range=64‐94) recruited from the Wayne State Institute of Gerontology Healthier Black Elders Center and general Detroit area. Responses were gathered over the phone between 7/28/2020 to 1/14/2021. Change in cognitive functioning was evaluated with the newly developed Cognitive Change Questionnaire (CCQ) in which participants self‐evaluated their cognition related to daily activities (e.g., memorizing, paying attention, task switching) on a 5‐point scale (1–Much easier, 3–No difference, to 5–Much harder), comparing it to the pre‐pandemic time. In addition, all participants received the TICSm phone‐based cognitive screen. Responses to COVID‐19 were measured on these scales: positive emotions (e.g., gratitude, happiness), negative emotion (e.g., anger, fear), unmet needs (e.g., food, medical care), and personal issues (e.g., economic problems, reduced privacy). Result: Findings showed that 106 (65%) participants did not report any COVID‐19 related cognitive changes, 7 (4%) indicated improved cognitive functioning, and 50 (31%) indicated that their cognitive functioning was worse as compared to pre‐COVID‐19 times. Average CCQ scale score (range 8‐40) was 26.86. CCQ scale showed good psychometric properties (Cronbach alpha=.84). Significant correlations were obtained between CCQ and TICSm subscales: r=–.16 (p=.05) with total score, r=–.19 (p=.01) with immediate recall, and r=–.24 (p=.002) with delayed recall. Stepwise regression analysis showed that COVID‐19 related responses explained 24% of variability on CCQ: participants reported greater cognitive decline if they expressed greater level of negative emotions (21% explained variance) and higher anxiety (3% explained variance). Conclusion: These results demonstrate that a sizeable percentage of older community‐dwelling African Americans do self‐report cognitive decline following COVID‐19, associated with increased presence of negative emotions and anxiety. The possibility of altered cognitive function should be considered not only for quality‐of‐care issues, but also when recruiting for research trials during and after the pandemic. [ABSTRACT FROM AUTHOR]

Background: The COVID‐19 pandemic is a health emergency that has forced a change in the daily life of individuals across all nations, races and socioeconomic groups for over a year. Such a crisis has required social isolation, the closing of businesses, schools and many recreational activities. This abrupt change has had negative effects on mental health, especially in the most vulnerable populations. Method: For this study, 164 community‐dwelling older African Americans (mean age = 75.6, range = 65‐94) were recruited from the Wayne State Institute of Gerontology Healthier Black Elders Center and general Detroit area. Responses were gathered over the phone between July 28, 2020 to January 14, 2021. Generalized anxiety was measured by the GAD‐7. Responses to COVID‐19 were measured by: positive emotions (eg., gratitude, happiness), negative emotion (eg., anger, fear), unmet needs (eg., food, medical care), and personal issues (eg., economic problems, reduced privacy). Result: Of the participants, 12 reported having COVID‐19, with 48% reporting a close family member having the virus, 26% had a death in the family and 67% endorse feelings of isolation. Average anxiety on the GAD‐7 was 6.2 (SD = 5.4, range = 0‐21). Of the 163 participants that completed the scale, 46% reported no anxiety, 28% reported mild anxiety, 17% reported moderate anxiety, and 9 % reported severe anxiety. In a stepwise regression, the following factors were found to predict GAD scores: negative emotions (51% explained variance), positive emotions (3% explained variance) and daily stressors brought on by COVID‐19 (1% explained variance). Further analyses were completed to evaluate additional models to assess the interrelationships between predictors and significant COVID‐related milestones. Conclusion: This current study among older African Americans living in an urban area highlights daily‐life risk factors for anxiety during the time of COVID‐19. Negative emotions as primary predictors are factors which can be specifically addressed with current psychosocial services. More research is needed in order to fully understand the scope and correlates of anxiety predictors during these challenging times. [ABSTRACT FROM AUTHOR]