Picture Sequence Memory Research Articles

White matter correlates of cognition: A diffusion magnetic resonance imaging study

BackgroundOur comprehension of the interplay of cognition and the brain remains constrained. While functional imaging studies have identified cognitive brain regions, structural correlates of cognitive functions remain underexplored. Advanced methods like Diffusion Magnetic Resonance Imaging (DMRI) facilitate the exploration of brain connectivity and White Matter (WM) tract microstructure. Therefore, we conducted connectometry method on DMRI data, to reveal WM tracts associated with cognition. Methods125 healthy participants from the National Institute of Mental Health Intramural Healthy Volunteer Dataset were recruited. Multiple regression analyses were conducted between DMRI-derived Quantitative Anisotropy (QA) values within WM tracts and scores of participants in Flanker Inhibitory Control and Attention Test (attention), Dimensional Change Card Sort (executive function), Picture Sequence Memory Test (episodic memory), and List Sorting Working Memory Test (working memory) tasks from National Institute of Health toolbox. The significance level was set at False Discovery Rate (FDR)<0.05. ResultsWe identified significant positive correlations between the QA of WM tracts within the left cerebellum and bilateral fornix with attention, executive functioning, and episodic memory (FDR=0.018, 0.0002, and 0.0002, respectively), and a negative correlation between QA of WM tracts within bilateral cerebellum with attention (FDR=0.028). Working memory demonstrated positive correlations with QA of left inferior longitudinal and left inferior fronto-occipital fasciculi (FDR=0.0009), while it showed a negative correlation with QA of right cerebellar tracts (FDR=0.0005). ConclusionOur results underscore the intricate link between cognitive performance and WM integrity in frontal, temporal, and cerebellar regions, offering insights into early detection and targeted interventions for cognitive disorders.

Identification of amnestic mild cognitive impairment among Black and White community-dwelling older adults using NIH Toolbox Cognition tablet battery.

Identify which NIH Toolbox Cognition Battery (NIHTB-CB) subtest(s) best differentiate healthy controls (HC) from those with amnestic mild cognitive impairment (aMCI) and compare the discriminant accuracy between a model using a priori "Norm Adjusted" scores versus "Unadjusted" standard scores with age, sex, race/ethnicity, and education controlled for within the model. Racial differences were also examined. Participants were Black/African American (B/AA) and White consensus-confirmed (HC = 96; aMCI = 62) adults 60-85 years old that completed the NIHTB-CB for tablet. Discriminant function analysis (DFA) was used in the Total Sample and separately for B/AA (n = 80) and White participants (n = 78). Picture Sequence Memory (an episodic memory task) was the highest loading coefficient across all DFA models. When stratified by race, differences were noted in the pattern of the highest loading coefficients within the DFAs. However, the overall discriminant accuracy of the DFA models in identifying HCs and those with aMCI did not differ significantly by race (B/AA, White) or model/score type (Norm Adjusted versus Unadjusted). Racial differences were noted despite the use of normalized scores or demographic covariates-highlighting the importance of including underrepresented groups in research. While the models were fairly accurate at identifying consensus-confirmed HCs, the models proved less accurate at identifying White participants with an aMCI diagnosis. In clinical settings, further work is needed to optimize computerized batteries and the use of NIHTB-CB norm adjusted scores is recommended. In research settings, demographically corrected scores or within model correction is suggested.

Body Water Compartment and Sympathovagal Balance: Association with Visuomotor Retention in Young Adults

Cognitive functions are important for visuomotor learning and are associated with physiological factors such as body fluid compartment and cardiovascular reactivity. Previous studies found that body fluid compartment is associated with brain blood flow, which is regulated via sympathovagal balance. However, more evidence is needed to determine whether body fluid compartment and cardiovascular reactivity are associated with visuomotor learning. The purpose of this study was to explore the relationship of body fluid compartment and cardiovascular reactivity to visuomotor learning. We hypothesized that these physiological factors are associated with overall learning retention using upper extremity (including feedforward, volitional, and feedback conditions). Methods: Thirty young adults (12 males, 24.6 ± 3.9 years, right hand dominant) participated in this study. Participants tracked a sinusoidal wave with left arm at pre-test, learning, and post-test sessions on the first visit and at retention test session on the second visit seven days later. Participants performed one set of nine trials during each test and five sets of three trials during learning. Resistance and wave velocity were different in each trial. Learning error was calculated by the averaged absolute distance between the tracking line and the target line. Heart rate (HR), systolic blood pressure (SBP), and low to high frequency power ratio of heart rate variability (LF/HF) were continuously recorded during baseline before pre-test and during tests and learning. We analyzed participants’ extracellular to intracellular body water ratio (ECW/ICW) using an InBody body composition analyzer and tested their cognitive function with Picture Sequence Memory (PSMT), Pattern Comparison Processing Speed, Dimensional Change Card Sort, Flanker Inhibitory Control and Attention, and List Sorting Working Memory Tests from NIH Cognitive Toolbox. Data were statistically analyzed using linear regression analysis and quartile analysis. Results: ECW/ICW was associated with young adults’ ability to retain visuomotor learning after one week (R2 = 0.138, p = 0.043), whereas HR, SBP, and LF/HF at each session were not significantly associated with visuomotor retention. No differences were found in pre-test error among quartiles of five cognitive tests. Significant differences in retention error among quartiles were only found in PSMT, with participants scoring in the highest quartile of PSMT having lower SBP during the first visit and lower retention error compared to the lowest quartile. This finding suggests that young adults with better episodic memory were less stressed during learning and had better visuomotor retention. Conclusion: These findings support that ECW/ICW is associated with not only cognitive function but also visuomotor retention. In addition, cardiovascular reactivity during visuomotor learning may provide information for retention behavior. Relationship of body water compartment and sympathovagal balance to feedforward and feedback conditions in learning will be further discussed. Research Support: This study was funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development: R01HD084645, R01HD082109. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Development and validation of an episodic memory measure in the Mobile Toolbox (MTB): Arranging Pictures

ABSTRACT Introduction Arranging Pictures is a new episodic memory test based on the NIH Toolbox (NIHTB) Picture Sequence Memory measure and optimized for self-administration on a personal smartphone within the Mobile Toolbox (MTB). We describe evidence from three distinct validation studies. Method In Study 1, 92 participants self-administered Arranging Pictures on study-provided smartphones in the lab and were administered external measures of similar and dissimilar constructs by trained examiners to assess validity under controlled circumstances. In Study 2, 1,021 participants completed the external measures in the lab and self-administered Arranging Pictures remotely on their personal smartphones to assess validity in real-world contexts. In Study 3, 141 participants self-administered Arranging Pictures remotely twice with a two-week delay on personal iOS smartphones to assess test-retest reliability and practice effects. Results Internal consistency was good across samples (ρxx = .80 to .85, p < .001). Test-retest reliability was marginal (ICC = .49, p < .001) and there were significant practice effects after a two-week delay (ΔM = 3.21 (95% CI [2.56, 3.88]). As expected, correlations with convergent measures were significant and moderate to large in magnitude (ρ = .44 to .76, p < .001), while correlations with discriminant measures were small (ρ = .23 to .27, p < .05) or nonsignificant. Scores demonstrated significant negative correlations with age (ρ = -.32 to −.21, p < .001). Mean performance was slightly higher in the iOS compared to the Android group (MiOS = 18.80, NiOS = 635; MAndroid = 17.11, NAndroid = 386; t(757.73) = 4.17, p < .001), but device type did not significantly influence the psychometric properties of the measure. Indicators of potential cheating were mixed; average scores were significantly higher in the remote samples (F(2, 850) = 11.415, p < .001), but there were not significantly more perfect scores. Conclusion The MTB Arranging Pictures measure demonstrated evidence of reliability and validity when self-administered on personal device. Future research should examine the potential for cheating in remote settings and the properties of the measure in clinical samples.

Neurocognition and its predictors in a linguistically and culturally diverse cohort of people with HIV

Objective: HIV/AIDS disproportionately affects Black and Latino people in the United States, yet there is a lack of research on predictors of neurocognitive outcomes in these groups. We examined neurocognitive performance and its key predictors across White, Black, and Latino people with HIV (PWH). Method: Participants included 586 PWH of White, Black, and Latino (English- and Spanish-speaking) background. Neurocognition was assessed via demographically-adjusted Fluid Cognition Composite T-scores from the NIH-Toolbox cognition battery, and individual tests comprising this composite. Predictors examined included sociodemographic and HIV disease characteristics, and medical, psychiatric and substance comorbidities. Results: Compared to White PWH, English-speaking Latino PWH had lower T-scores on the Fluid Cognition Composite, as well as Flanker Inhibition and Picture Sequence Memory tests. While there were no other significant group differences on Fluid Cognition, both Latino PWH language groups performed worse than Black PWH on Flanker Inhibition, and Black PWH performed worse than White PWH on List Sorting. Separate multivariable linear regression models by ethnic/racial/language group showed that significant correlates of worse Fluid Cognition included depressive symptoms among White PWH; hepatitis C co-infection among Black PWH; hypertension among English-speaking Latino PWH; and higher estimated duration of HIV disease and depressive symptoms in Spanish-speaking Latino PWH. Conclusions: Findings suggest worse neurocognition among English-speaking Latino PWH compared to Whites. Predictors of neurocognitive function among PWH differ across ethnic/racial and language groups. Consideration of these HIV disease characteristics and comorbidities may be valuable in developing targeted culturally-relevant interventions aimed at ameliorating neurocognitive dysfunction among diverse PWH.

Prediction of cognitive impairment using higher order item response theory and machine learning models.

Timely detection of cognitive impairment (CI) is critical for the wellbeing of elderly individuals. The MyCog assessment employs two validated iPad-based measures from the NIH Toolbox® for Assessment of Neurological and Behavioral Function (NIH Toolbox). These measures assess pivotal cognitive domains: Picture Sequence Memory (PSM) for episodic memory and Dimensional Change Card Sort Test (DCCS) for cognitive flexibility. The study involved 86 patients and explored diverse machine learning models to enhance CI prediction. This encompassed traditional classifiers and neural-network-based methods. After 100 bootstrap replications, the Random Forest model stood out, delivering compelling results: precision at 0.803, recall at 0.758, accuracy at 0.902, F1 at 0.742, and specificity at 0.951. Notably, the model incorporated a composite score derived from a 2-parameter higher order item response theory (HOIRT) model that integrated DCCS and PSM assessments. The study's pivotal finding underscores the inadequacy of relying solely on a fixed composite score cutoff point. Instead, it advocates for machine learning models that incorporate HOIRT-derived scores and encompass relevant features such as age. Such an approach promises more effective predictive models for CI, thus advancing early detection and intervention among the elderly.

Test-Retest Reliability and Reliable Change on the NIH Toolbox Cognition Battery.

Researchers and practitioners can detect cognitive improvement or decline within a single examinee by applying a reliable change methodology. This study examined reliable change through test-retest data from the English-language National Institutes of Health Toolbox Cognition Battery (NIHTB-CB) normative sample. Participants included adults (n = 138; age: M ± SD = 54.8±20.0, range: 18-85; 51.4% men; 68.1% White) who completed test-retest assessments about a week apart on five fluid cognition tests, providing raw scores, age-adjusted standard scores (SS), and demographic-adjusted T-scores (T). The Fluid Cognition Composite (SS: ICC = 0.87; T-score: ICC = 0.84) and the five fluid cognition tests had good test-retest reliability (SS: ICC range = 0.66-0.85; T-score: ICC range = 0.64-0.86). The lower and upper bounds of 70%, 80%, and 90% confidence intervals (CIs) were calculated around change scores, which serve as cutoffs for determining reliable change. Using T-scores, 90% CI, and adjustment for practice effects, 32.3% declined on one or more tests, 9.7% declined on two or more tests, 36.6% improved on one or more tests, and 5.4% improved on two or more tests. It was common for participants to show reliable change on at least one test score, but not two or more test scores. Per an 80% CI, test-retest difference scores beyond these cutoffs would indicate reliable change: Dimensional Change Card Sort (SS ≥ 14/T ≥ 10), Flanker (SS ≥ 12/T ≥ 8), List Sorting (SS ≥ 14/T ≥ 10), Picture Sequence Memory (SS ≥ 19/T ≥ 13), Pattern Comparison (SS ≥ 11/T ≥ 8), and Fluid Cognition Composite (SS ≥ 10/T ≥ 7). The reliable change cutoffs could be applied in research or practice to detect within-person change in fluid cognition at the individual level.

Maternal Tobacco Use During Pregnancy and Child Neurocognitive Development

Maternal tobacco use during pregnancy (MTDP) persists across the globe. Longitudinal assessment of the association of MTDP with neurocognitive development of offspring at late childhood is limited. To examine whether MTDP is associated with child neurocognitive development at ages 9 to 12 years. This cohort study included children aged 9 and 10 years at wave 1 (October 2016 to October 2018) and aged 11 to 12 years at a 2-year follow-up (wave 2, August 2018 to January 2021) across 21 US sites in the Adolescent Brain Cognitive Development (ABCD) Study. Data were analyzed from June 2022 to December 2023. MTDP. Outcomes of interest were neurocognition, measured by the National Institutes of Health (NIH) Toolbox Cognition Battery, and morphometric brain measures through the region of interest (ROI) analysis from structural magnetic resonance imaging (sMRI). Among 11 448 children at wave 1 (mean [SD] age, 9.9 [0.6] years; 5990 [52.3%] male), 1607 children were identified with MTDP. In the NIH Toolbox Cognition Battery, children with MTDP (vs no MTDP) exhibited lower scores on the oral reading recognition (mean [SE] B = -1.2 [0.2]; P < .001), picture sequence memory (mean [SE] B = -2.3 [0.6]; P < .001), and picture vocabulary (mean [SE] B = -1.2 [0.3]; P < .001) tests and the crystallized cognition composite score (mean [SE] B = -1.3 [0.3]; P < .001) at wave 1. These differential patterns persisted at wave 2. In sMRI, children with MTDP (vs no MTDP) had smaller cortical areas in precentral (mean [SE] B = -104.2 [30.4] mm2; P = .001), inferior parietal (mean [SE] B = -153.9 [43.4] mm2; P < .001), and entorhinal (mean [SE] B = -25.1 [5.8] mm2; P < .001) regions and lower cortical volumes in precentral (mean [SE] B = -474.4 [98.2] mm3; P < .001), inferior parietal (mean [SE] B = -523.7 [136.7] mm3; P < .001), entorhinal (mean [SE] B = -94.1 [24.5] mm3; P < .001), and parahippocampal (mean [SE] B = -82.6 [18.7] mm3; P < .001) regions at wave 1. Distinct cortical volume patterns continued to be significant at wave 2. Frontal, parietal, and temporal lobes exhibited differential ROI, while there were no notable distinctions in the occipital lobe and insula cortex. In this cohort study, MTDP was associated with enduring deficits in childhood neurocognition. Continued research on the association of MTDP with cognitive performance and brain structure related to language processing skills and episodic memory is needed.

Nocturnal blood pressure dipping, blood pressure variability, and cognitive function in early and middle-aged adults.

Higher nighttime blood pressure (BP), less BP dipping, and higher BP variability have been linked with worse cognitive function in the elderly. The goal of this study is to explore whether this relationship already exists in early and middle adulthood. We further examined whether ethnic differences between African Americans and European Americans in BP parameters can explain ethnic differences in cognitive function. 24-h ambulatory BP monitoring and cognitive function were obtained from 390 participants (average age: 37.2 years with a range of 25-50; 54.9% African Americans; 63.6% females). We observed that higher nighttime BP, decreased dipping, and higher variability were significantly associated with lower scores on the Picture Sequence Memory Test. Significant negative associations between variability and overall composite scores were also observed. No significant associations between average 24-h or daytime BP and cognitive function were observed. Ethnic differences in nighttime diastolic pressures and dipping can explain 6.81% to 10.8% of the ethnicity difference in the score of the Picture Sequence Memory Test (ps<.05). This study suggests that the associations of nighttime BP, dipping, and variability with cognitive function already exist in young and middle-aged adults. Ethnic differences in nighttime BP and dipping can at least partially explain ethnic differences in cognitive function. The stronger association of these parameters with cognitive function than daytime or average BP in this age range raises the importance of using ambulatory BP monitoring for more precise detection of abnormal BP patterns in young adulthood.

Predicting Brain Amyloid Status Using the National Institute of Health Toolbox (NIHTB) for Assessment of Neurological and Behavioral Function.

Amyloid-beta (Aβ) plaque is a neuropathological hallmark of Alzheimer's disease (AD). As anti-amyloid monoclonal antibodies enter the market, predicting brain amyloid status is critical to determine treatment eligibility. To predict brain amyloid status utilizing machine learning approaches in the Advancing Reliable Measurement in Alzheimer's Disease and Cognitive Aging (ARMADA) study. ARMADA is a multisite study that implemented the National Institute of Health Toolbox for Assessment of Neurological and Behavioral Function (NIHTB) in older adults with different cognitive ability levels (normal, mild cognitive impairment, early-stage dementia of the AD type). Participants across various sites were involved in the ARMADA study for validating the NIHTB. 199 ARMADA participants had either PET or CSF information (mean age 76.3 ± 7.7, 51.3% women, 42.3% some or complete college education, 50.3% graduate education, 88.9% White, 33.2% with positive AD biomarkers). We used cognition, emotion, motor, sensation scores from NIHTB, and demographics to predict amyloid status measured by PET or CSF. We applied LASSO and random forest models and used the area under the receiver operating curve (AUROC) to evaluate the ability to identify amyloid positivity. The random forest model reached AUROC of 0.74 with higher specificity than sensitivity (AUROC 95% CI:0.73 - 0.76, Sensitivity 0.50, Specificity 0.88) on the held-out test set; higher than the LASSO model (0.68 (95% CI:0.68 - 0.69)). The 10 features with the highest importance from the random forest model are: picture sequence memory, cognition total composite, cognition fluid composite, list sorting working memory, words-in-noise test (hearing), pattern comparison processing speed, odor identification, 2-minutes-walk endurance, 4-meter walk gait speed, and picture vocabulary. Overall, our model revealed the validity of measurements in cognition, motor, and sensation domains, in associating with AD biomarkers. Our results support the utilization of the NIH toolbox as an efficient and standardizable AD biomarker measurement that is better at identifying amyloid negative (i.e., high specificity) than positive cases (i.e., low sensitivity).

Nicotine use during late adolescence and young adulthood is associated with changes in hippocampal volume and memory performance.

With the advent of electronic nicotine delivery systems, the use of nicotine and tobacco products (NTPs) among adolescents and young adults remains high in the US. Use of e-cigarettes additionally elevates the risk of problematic use of other substances like cannabis, which is often co-used with NTPs. However, their effects on brain health, particularly the hippocampus, and cognition during this neurodevelopmental period are poorly understood. Healthy late adolescents/young adults (N = 223) ages 16-22 completed a structural MRI to examine right and left hippocampal volumes. Memory was assessed with the NIH Toolbox Picture Sequence Memory Test (PSMT) and Rey Auditory Verbal Learning Test (RAVLT). Cumulative 6-month NTP and cannabis episodes were assessed and modeled continuously on hippocampal volumes. Participants were then grouped based on 6-month NTP use to examine relationships with the hippocampus and memory: current users (CU) endorsed weekly or greater use; light/abstinent users (LU) endorsed less than weekly; and never users (NU). NTP use predicted larger hippocampal volumes bilaterally while cannabis use had no impact nor interacted with NTP use. For memory, larger left hippocampal volumes were positively associated with PSMT performance, RAVLT total learning, short delay and long delay recall for the NU group. In contrast, there was a negative relationship between hippocampal volumes and performances for LU and CU groups. No differences were detected between NTP-using groups. These results suggest that the hippocampus is sensitive to NTP exposure during late adolescence/young adulthood and may alter typical hippocampal morphometry in addition to brain-behavior relationships underlying learning and memory processes.

Cannabis use and neurocognitive performance at 13–14 Years-Old: Optimizing assessment with hair toxicology in the Adolescent brain cognitive development (ABCD) study

ObjectiveCannabis is widely used, including in early adolescence, with prevalence rates varying by measurement method (e.g., toxicology vs. self-report). Critical neurocognitive development occurs throughout adolescence. Given conflicting prior brain-behavior results in cannabis research, improved measurement of cannabis use in younger adolescents is needed. MethodsData from the Adolescent Brain Cognitive Development (ABCD) Study Year 4 follow-up (participant age: 13–14 years-old) included hair samples assessed by LC-MS/MS and GC–MS/MS, quantifying THCCOOH (THC metabolite), THC, and cannabidiol concentrations, and the NIH Toolbox Cognitive Battery. Youth whose hair was positive for cannabinoids or reported past-year cannabis use were included in a Cannabis Use (CU) group (n = 123) and matched with non-using Controls on sociodemographics (n = 123). Standard and nested ANCOVAs assessed group status predicting cognitive performance, controlling for family relationships. Follow-up correlations assessed cannabinoid hair concentration, self-reported cannabis use, and neurocognition. ResultsCU scored lower on Picture Memory (p = .03) than Controls. Within the CU group, THCCOOH negatively correlated with Picture Vocabulary (r = -0.20, p = .03) and Flanker Inhibitory Control and Attention (r = -0.19, p = .04), and past-year cannabis use was negatively associated with List Sorting Working Memory (r = -0.33, p = .0002) and Picture Sequence Memory (r = -0.19, p = .04) performances. ConclusionsYouth who had used cannabis showed lower scores on an episodic memory task, and more cannabis use was linked to poorer performances on verbal, inhibitory, working memory, and episodic memory tasks. Combining hair toxicology with self-report revealed more brain-behavior relationships than self-report data alone. These youth will be followed to determine long-term substance use and neurocognition trajectories.

Comparing Clinical Diagnosis of Domains of Impairment in individuals with Mild Cognitive Impairment with the NIH Toolbox‐Cognitive Battery Performance

AbstractBackgroundMild 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.MethodRetrospective 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.ResultTwo 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&lt;0.02) groups with a trend for Executive versus Memory groups (p&lt;.07). For PC, the Memory group performed better than both the Memory+Executive (p&lt;.02) and Memory+Language (p&lt;.04).ConclusionThe 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.

Predicting Brain Amyloid Status Using the NIH Toolbox for Assessment of Neurological and Behavioral Function (NIHTB)

AbstractBackgroundAmyloid‐beta (Ab) plaque is a pathophysiological hallmark of Alzheimer’s disease (AD). As anti‐amyloid monoclonal antibodies enter the market, predicting brain amyloid status is critical to determine treatment eligibility. Here, we predicted brain amyloid status in the Advancing Reliable Measurement in Alzheimer’s Disease and Cognitive Aging (ARMADA) study using machine learning.MethodsARMADA, a multisite study, aimed to validate the National Institute of Health Toolbox for Assessment of Neurological and Behavioral Function (NIHTB) in older adults with different cognitive abilities (normal, MCI, early AD dementia). We used cognition, emotion, motor, sensation scores from NIHTB, and demographics to predict amyloid status measured by PET or CSF. Each site used its own criteria to define the amyloid PET positivity (Positive for AD biomarkers, Negative, Visual reading unavailable) or CSF result (Consistent with AD, Not consistent with AD, Borderline, Indeterminate). Amyloid positivity was defined as PET being “Positive for AD biomarkers” or CSF being “Consistent with AD.” We applied LASSO and random forest models and used the area under the receiver operating curve (AUROC) to evaluate the ability to identify amyloid positivity.ResultsOf 462 ARMADA participants, 199 had either PET or CSF information (mean age 76.3 ± 7.7, 51.3% women, 42.3% some or complete college education, 50.3% graduate education, 88.9% White, 33.2% with positive AD biomarkers). The random forest model reached AUROC of 0.74 with higher specificity than sensitivity (AUROC 95% CI:0.73 – 0.76, Sensitivity 0.50, Specificity 0.88) on the held‐out test set; higher than the LASSO model (0.68 (95% CI:0.68 – 0.69)). The 10 features with the highest importance from the random forest model are: picture sequence memory, cognition total composite, cognition fluid composite, list sorting working memory, words‐in‐noise test (hearing), pattern comparison processing speed, odor identification, 2‐minutes‐walk endurance, 4‐meter walk gait speed, and picture vocabulary. Overall, our model revealed the validity of measurements in cognition, motor, and sensation domains, in associating with AD biomarkers.ConclusionOur results support the utilization of the NIH toolbox as an efficient and standardizable AD biomarker measurement that is better at identifying amyloid negative (i.e., high specificity) than positive (i.e., low sensitivity) cases.

Measurement Invariance of the NIH Toolbox‐Cognition Battery Across Demographic Characteristics

AbstractBackgroundThe 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.MethodNIHTB‐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 (&lt; = 85 and &gt; 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.ResultA 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.ConclusionA 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.

The Mobile Toolbox for assessing cognition in older adults: associations with standardized cognitive testing and amyloid and tau PET

AbstractBackgroundRemote, smartphone‐based cognitive assessments such as the Mobile Toolbox (MTB) can improve our ability to detect subtle, yet meaningful cognitive changes that are affected early on by Alzheimer’s disease (AD) pathology. The goal of our study was to investigate which MTB tests are sensitive to subtle cognitive deficits due to early amyloid and tau deposition.MethodThe MTB will be administered to N = 100 clinically normal (CN) older adults who have retrospective and prospective clinical, cognitive and AD biomarker data from three well‐characterized prospective cohort‐studies of CN older adults: the Harvard Aging Brain Study and the affiliated Subjective Cognitive Decline and Instrumental Activities of Daily Living cohorts. The MTB includes six measures of fluid cognition, including episodic memory (Picture Sequence Memory and Face‐Name Associations), executive functions (Dimensional Change Card Sort, Flanker), working memory (Memory for Sequence), processing speed (Number‐Symbol Match), and two measures of crystallized cognition (Spelling and Vocabulary). Using confirmatory factor analysis, MTB baseline data will be used to determine which MTB tests are most strongly associated with established clinical assessments (i.e., measures included in the Preclinical Alzheimer’s Cognitive Composite‐5 [PACC5]) as well as amyloid and tau biomarkers on positron emission tomography (PET) imaging.ResultPreliminary MTB data was available on 15 participants (age 74.3±8.2, 63% female, 16.6±2.9 years of education, MMSE 29±1.5). Table 1 shows exploratory correlation analyses, revealing that both MTB crystallized measures were associated with education but not age or sex. As expected, worse performance on MTB memory measures were associated with lower PACC5 scores, and greater amyloid and tau burden, albeit at trend‐level. Executive function and processing speed measures were not related to amyloid but did show associations with entorhinal tau.ConclusionWhile preliminary, these findings suggest that the MTB may help elucidate the subtle cognitive deficits that are associated with early AD pathology. In addition to memory measures, the MTB fluid measures capture various aspects of cognitive function that are also relevant to preclinical AD. Data collection is ongoing, and next steps include examining which (combination of) MTB measures are most sensitive for detecting emerging AD pathology and to track cognitive change over time.

Associations between NIH‐Toolbox Flanker test and the Picture Sequence Memory Test and multimodal Alzheimer’s disease biomarkers in cognitively unimpaired individuals

AbstractBackgroundAlzheimer’s disease (AD) pathologic changes start years before cognitive symptoms appear; however, some early cognitive changes can be detected. We aimed to investigate the association between performance changes in two NIH‐Toolbox tests, Flanker test and Picture Sequence Memory Test (PSMT), and baseline AD biomarkers in cognitively unimpaired (CU) individuals.MethodWe studied 356 ALFA+ participants (mean [SD] age = 60.8 [4.72] years). The rate of cognitive change after 3 years was measured through delta scores (Follow‐up ‐ Baseline). Biomarker data included cerebrospinal fluid (CSF) β‐amyloid (Aβ) 42/40 ratio, phosphorylated‐tau‐181 (p‐tau), neurofilament light (NfL), and neuroimaging (MRI‐based cortical thickness AD signature, hippocampal volumes, and FDG‐PET‐SUVR (fluorodeoxyglucose ‐ positron emission tomography ‐ standardized uptake value ratio) metaROI) measures (Table 1). CSF Aβ40, Aβ42 and NfL were measured with the exploratory NTK robust immunoassays, while CSF p‐tau181 was measured using the electrochemiluminescence Elecsys® Phospho‐Tau (181P) CSF immunoassays (Roche Diagnostics International Ltd). AT groups were defined according to published cut‐offs (A+ Aβ 42/40 &lt;0.071; T+: p‐tau &gt; 24 pg/ml) (Milà‐Alomà et al., 2020). Baseline biomarker levels were used as predictors of cognitive change in linear models. Interactions and stratified analyses were also explored.ResultAT groups significantly differed in the change in the Flanker Test (p = 0.002), driven by poorer performance in A+T+ vs A‐T‐ groups (p = 0.001) (Figure1). Higher cortical thickness predicted better longitudinal cognitive performance in the Flanker Test in the whole group (p = 0.020; β = 0.983). We found a significant interaction between AT group and FDG‐PET‐SUVR (p = 0.042), and the right hippocampal volume (p = 0.001), but no significant associations were observed in analyses stratified by AT (Figure2A). A negative association between right hippocampal volume and Flanker test change was observed in A+T‐ (p = 0.026) and A+T+ (p = 0.029) (Figure2B). No associations were observed between the PSMT and AD biomarkers (Figure 1).ConclusionOur findings support the hypothesis that executive function show a steeper decline than episodic memory in CU individuals with evidence of pathologic AD biomarkers levels. We observed a negative association between executive change and neuroimaging neurodegeneration markers in A+T‐ and A+T+ groups. This is in accordance with previous studies that found non‐linear associations in incipient AD pathology.

An exploratory analysis of the relationship between cerebral flood flow, mood, and fluid cognition in older adults

AbstractBackgroundASL, a common MRI measure of cerebral blood flow (CBF), is thought to be a potential tool in the detection of preclinical Alzheimer’s disease. This exploratory analysis examined the relationship between CBF, mood, and fluid cognitive performance on the NIH Toolbox Cognition Battery (NIHTB‐CB) as part of an ongoing triglyceride intervention study.Method20 female older adults (age 65.6 ± 7.6) underwent ASL MRI when fasting and two hours after a triglyceride intervention. 3D pCASL MRI with background suppression was used to measure CBF in ml/100g/min using label duration = 1.8 s, post‐labeling delay = 2 s, labeling offset = 25‐30 mm, slices = 30, resolution = 2×2×4 mm3, SENSE‐factor = 2, TR/TE = 5000/18 ms. We also performed a reference scan (M0, 1 minute) identical to the one above but with no labeling or background suppression and TR = 10,000 ms. CBF was measured globally and in four regions of interest: angular gyrus (AG), posterior cingulate (PC), temporal lobe, and the hippocampus. Subjects completed the Generalized Anxiety Disorder Scale (GAD‐7) and MoCA at screening. The NIHTB‐CB was completed following the second MRI, and age‐adjusted scores were used in all analyses.ResultBaseline global CBF, AG CBF, and hippocampal CBF negatively correlated with GAD‐7 scores (r = ‐.797, p&lt;.0001; r = ‐.635, p = .0026; r = ‐.620, p = .0036, respectively). In contrast, global CBF and hippocampal CBF positively correlated with performance on the MoCA (r = .501, p = .0243; r = .546, p = .0126, respectively). Post‐intervention CBF in 3 of the 4 areas of interest positively correlated with fluid measures on the NIHTB‐CB, including the Flanker (AG: r = .528, p = .0166), Picture Sequence Memory Test (AG: r = .501, p = .0245; temporal lobe: r = .578, p = .0077), List Sorting Test (PC: r = .508, p = .0241), and fluid composite scores (AG: r = .610, p = .0043).ConclusionGreater anxiety correlated with lower baseline CBF overall and in the AG and hippocampus. Post‐lipid CBF correlated with better performance in working memory, executive function, and episodic memory. These results align with our predictions based on each of these regions’ functions. Further research exploring the relationship between mood and CBF is warranted, as well as investigating the effects of APOE4 status on these associations.

The risk of sleep‐related cognitive impairment increases in middle‐age to older adulthood

AbstractBackgroundThe association between sleep quality and cognition is widely established but the role of aging in this relationship has been unclear due to inconsistent findings with heterogeneous methods and a lack of studies in wide age ranges. The current study examined how age impacts the sleep‐cognition relationship using a large‐scale dataset with adults from young adulthood to older adulthood. Furthermore, we tested non‐linear effect of age to investigate the presence of age ranges in which the association between sleep and cognition is modified.MethodSample included 711 individuals (ages 36.00‐89.83, 59.66 ± 14.91, 55.7% female) from the Human Connectome Project‐Aging (HCP‐A). Sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI), and cognitive outcome measures included the NIH Toolbox fluid cognition (Dimensional change Card Sort Test, List Sorting Working Memory Test, Picture Sequence Memory Test, Pattern comparison Processing Speed Test, Flanker Inhibitory Control and Attention Test) and crystallized cognition composite scores (Oral Reading Recognition Test and Picture Vocabulary Test), the Trail Making Test (TMT) A and B, and the Rey Auditory Verbal Learning Test (RAVLT). The association between sleep quality and cognitive performance was measured using linear regression models, with sex, race, use of sleep medication, and years of education as covariates. Age moderation was tested via the interaction between sleep and age on cognition, with age as linear and non‐linear (quadratic) terms.ResultThere was a significant interaction term between the PSQI and non‐linear age term (age2) on TMT‐B (p = 0.02) and NIH Toolbox crystallized cognition (p = 0.02), indicating that the association between poor sleep quality and cognitive performance was moderated by age. Further analyses indicated that there were “critical age ranges” in which the sleep‐cognition association was more salient (ages 50‐75 for TMT‐B and 66‐70 for crystallized cognition).ConclusionThe association between sleep quality and cognitive performance may be modified by age. Individuals of the middle‐age to early older adulthood may be more vulnerable to sleep‐related cognitive impairment, indicating the clinical importance of detection and treatment of sleep disturbance in this particular age group.

A psychometric evaluation of the NIH Toolbox fluid cognition tests adapted for Swahili and Dholuo languages in Kenyan children and adolescents.

Our objective was to evaluate the psychometric properties of the culturally adapted NIH Toolbox African Languages® when used in Swahili and Dholuo-speaking children in western Kenya. Swahili-speaking participants were recruited from Eldoret and Dholuo-speaking participants from Ajigo; all were <14 years of age and enrolled in primary school. Participants completed a demographics questionnaire and five fluid cognition tests of the NIH Toolbox® African Languages program, including Flanker, Dimensional Change Card Sort (DCCS), Picture Sequence Memory, Pattern Comparison, and List Sorting tests. Statistical analyses examined aspects of reliability, including internal consistency (in both languages) and test-retest reliability (in Dholuo only). Participants included 479 children (n = 239, Swahili-speaking; n = 240, Dholuo-speaking). Generally, the tests had acceptable psychometric properties for research use within Swahili- and Dholuo-speaking populations (mean age = 10.5; SD = 2.3). Issues related to shape identification and accuracy over speed limited the utility of DCCS for many participants, with approximately 25% of children unable to match based on shape. These cultural differences affected outcomes of reliability testing among the Dholuo-speaking cohort, where accuracy improved across all five tests, including speed. There is preliminary evidence that the NIH Toolbox ® African Languages potentially offers a valid assessment of development and performance using tests of fluid cognition in Swahili and Dholuo among research settings. With piloting underway across other diverse settings, future research should gather additional evidence on the clinical utility and acceptability of these tests, specifically through the establishment of norming data among Kenyan regions and evaluating these psychometric properties.