Abstract
INTRODUCTION
Timely detection and tracking of Alzheimer's disease (AD) ‐related cognitive decline has become a public health priority. We investigated whether the NIH Toolbox for Assessment of Neurological and Behavioral Function—Cognition Battery (NIHTB‐CB) detects AD‐related cognitive decline.
METHODS
N = 171 participants (age 76.5 ± 8; 53% female, 34% Aβ‐positive) from the ARMADA study completed the NIHTB‐CB at baseline, 12 months, and 24 months. Linear mixed‐effect models correcting for demographics were used to examine cross‐sectional and longitudinal NIHTB‐CB scores in individuals across the clinical AD spectrum.
RESULTS
Compared to Aβ‐negative healthy controls, Aβ‐positive individuals with amnestic MCI or mild AD performed worse on all NIHTB‐CB measures and showed an accelerated decline in processing speed, working memory, and auditory word comprehension tests.
DISCUSSION
These findings support the use of the NIHTB‐CB in early AD, but also imply that the optimal NIHTB‐CB composite score to detect change over time may differ across clinical stages of AD. Future directions include replication of these findings in larger and more demographically diverse samples.
Highlights
We examined NIH Toolbox—Cognition Battery scores across the clinical AD spectrum.
All NIH Toolbox tests detected cross‐sectional cognitive impairment in MCI‐to‐mild AD.
Three NIH Toolbox tests captured further decline over time in MCI‐to‐mild AD.
The NIH Toolbox can facilitate timely detection of AD‐related cognitive decline.
Keywords: Alzheimer's disease, amyloid, cognition, computerized assessment, mild cognitive impairment
1. BACKGROUND
Alzheimer's disease (AD) pathophysiological change is the most common cause of cognitive decline and dementia. Therefore, timely detection and tracking of AD‐related cognitive decline have become a public health priority. 1 Here, timely detection refers to identifying emerging cognitive impairment at a stage when an individual is still (relatively) independent, also referred to as mild cognitive impairment (MCI), with the goal to preserve as much independence for as long as possible. 2 Subsequently, tracking cognition longitudinally in those with MCI or mild dementia due to AD is important for monitoring disease severity and optimizing patient care, as it allows for time to build a care team and prepare future planning. Finally, given currently available and upcoming disease‐modifying treatments in those with MCI or mild AD dementia, as well as emerging secondary prevention trials that target earlier, preclinical stages of AD, assessing cognition in individuals with AD pathophysiological change has become more relevant to identify treatment candidates and evaluate putative treatment effects. 3 , 4
In‐clinic paper‐and‐pencil–based neuropsychological assessments are considered the gold standard for evaluating cognition in older adults; these assessments typically comprise various individual tests that cover multiple cognitive domains. 5 However, assessing and scoring these in‐clinic assessments is time‐consuming and older adults often experience them as burdensome, which may cause them to abort the testing procedure or avoid follow‐up testing. 6 Moreover, a comprehensive neuropsychological evaluation is not always readily available due to long wait times and may not always be suitable for widescale use due to a lack of appropriate normative data, particularly in racially, ethnically, and linguistically diverse groups. 7 In addition, many different versions of neuropsychological tests are being used across research studies and clinical practices, which makes it challenging to harmonize and compare findings across studies and clinics. 8 , 9 Brief cognitive screening tests to overcome some of these logistic challenges exist; however, they tend not to be sufficiently sensitive to detect mild cognitive deficits and track cognitive change over time. 10 , 11 As such, there is a critical need for a comprehensive as well as efficient and scalable approach to identify and monitor individuals with or at risk for AD‐related cognitive decline.
The National Institutes of Health Toolbox for Assessment of Neurological and Behavioral Function (NIHTB) was designed to address this need, by providing a brief yet comprehensive instrument to facilitate uniform assessment across large‐scale research studies as well as clinical contexts. 12 The Cognition Battery of the NIHTB (NIHTB‐CB) consists of various tests covering multiple cognitive domains including episodic memory, working memory, attention and executive functions, processing speed, and language. 13 Previous studies have demonstrated the sound psychometric properties of the individual NIHTB‐CB tests, as well as convergent validity with gold‐standard paper‐pencil cognitive tests. 13 , 14 The multisite Advancing Reliable Measurement in Alzheimer's Disease and Cognitive Aging (ARMADA) study was launched to validate an iPad administration of the NIHTB (version 2.0) across the aging spectrum from cognitively normal to dementia of the Alzheimer's type (DAT) and to determine its utility in discriminating among older adults with normal or impaired cognition. 15 Therefore, the NIHTB was implemented in existing, well‐characterized, diverse cohorts of older adults across nine academic centers, of whom a subset had in‐vivo biomarker data available to assess AD pathophysiological change. 16 The purpose of the current study was to investigate whether the NIHTB‐CB can aid in the detection of early AD‐related cognitive decline. To that end, we examined cross‐sectional and longitudinal NIHTB‐CB scores in biomarker‐positive individuals across the clinical AD spectrum, ranging from cognitively normal (CN) individuals to those with MCI or mild AD dementia. 17
2. METHODS
2.1. Study design and participants
This study reports longitudinal NIHTB‐CB data from the Assessing Reliable Measurement in ARMADA project: a multi‐center longitudinal, 2‐year observational cohort study. The ARMADA study design and recruitment are described in detail elsewhere. 15 , 16 Briefly, participants were recruited across nine research centers in the United States: Northwestern University (study lead site), Mayo Jacksonville, University of Michigan, University of Wisconsin‐Madison, University of Pittsburgh, Emory University, Massachusetts General Hospital, University of California San Diego, and Columbia University. Each site recruited participants from existing cohorts of their network of National Institute on Aging (NIA) funded Alzheimer's Disease Research Centers (ADRCs) or ADRC‐affiliated longitudinal NIH‐funded studies. Participants were classified as CN, amnestic mild cognitive impairment (aMCI), or mild AD dementia, based on uniform data set (UDS) procedures and National Alzheimer's Coordinating Center (NACC) guidelines and following the 2011 NIA–Alzheimer's Association criteria. 18 , 19 For the current study, we selected all CN, aMCI, and mild AD participants who had (1) at least one NIHTB‐CB assessment available and (2) amyloid PET or amyloid‐β42/tau cerebrospinal fluid (CSF) data available.
The Institutional Review Boards of all nine research sites approved the ARMADA Study. Written informed consent was obtained from all participants prior to study procedures.
RESEARCH IN CONTEXT
Systematic review: The authors reviewed the literature on PubMed for studies comparing paper‐and‐pencil cognitive tests to computerized cognitive tests for detecting Alzheimer's disease (AD) ‐related cognitive decline, as well as studies on the NIH Toolbox (NIHTB) for use in aging and AD research and clinical contexts. Relevant citations are provided.
Interpretation: Our results suggest that the NIHTB can detect cross‐sectional and longitudinal cognitive decline in individuals with mild cognitive impairment or mild dementia due to AD. These findings provide further support for the use of the NIHTB in early AD, as a potentially useful tool for accurate, efficient, and timely detection and tracking of early AD‐related cognitive decline.
Future directions: To examine the NIHTB's sensitivity to decline in larger, more diverse samples and over longer time intervals, explore associations with other AD biomarkers, and investigate whether novel NIHTB composite measures can be derived to detect cognitive change in earlier, biomarker‐positive stages of AD.
2.2. NIHTB data collection
NIHTB version 2.0 was administered at the baseline, 12‐month, and 24‐month study visits. The NIHTB is entirely computerized including automated scoring, but administration is done by an examiner to give the task instructions, monitor compliance, and ensure valid results. Instructions are visually presented on the screen and provided orally. The cognitive battery includes five tasks measuring fluid cognitive functions (attention, executive functions, processing speed, working memory, and episodic memory) and two tasks reflecting crystallized cognition (oral word reading and auditory word comprehension). Full details on the individual tasks are described in the original publications 12 , 13 and on the NIHTB website (nihtoolbox.org), and a summary overview is provided in Table 1. For each task, raw scores are centered on a mean (M) of 100 and a standard deviation (SD) of 15. In addition to the individual task scores, the following summary scores are computed: Total Cognition Composite (including all seven tests), Fluid Composite (including Dimensional Change Card Sort, Flanker Inhibitory Control and Attention, Picture Sequence Memory, List Sorting Working Memory, and Pattern Comparison tests), and Crystallized Composite (including Picture Vocabulary and Oral Reading Recognition tests).
TABLE 1.
Overview of tasks included in the NIH Toolbox—Cognition Battery.
| Cognitive domain | Test | Description |
|---|---|---|
| Episodic memory | Picture Sequence Memory Test | A series of images depicting independent, non‐sequential activities are presented in a specific order and placed in specific, ordered locations on the screen. Participants are then asked to recall the original order and place the images accordingly. Outcome measure: number of adjacent pairs of pictures remembered correctly over two learning trials. |
| Executive functions/attention | Flanker Inhibitory Control And Attention Test | Participants have to indicate the left‐right orientation of a centrally presented stimulus while inhibiting attention to potentially incongruent stimuli that surround it. Outcome measure: score based on a combination of accuracy and reaction time. |
| Executive functions/shifting | Dimensional Change Card Sort Test | Participants are asked to sort images across two dimensions based on visual characteristics (shape and color). Outcome measure: score based on a combination of accuracy and reaction time. |
| Working memory | List Sorting Working Memory Test | A series of stimuli is presented on the computer screen visually (object) and orally (spoken name), one at a time. Participants are then instructed to repeat the stimuli in order of size, from smallest to largest: Outcome measure: total items correct across all trials. |
| Processing speed | Pattern Comparison Processing Speed Test | Participants are asked to identify whether two visual patterns are the “same” (“Yes” button) or “not the same” (“No” button). Outcome measure: number of correct items completed in 90 s. |
| Language | Oral Reading Recognition Test | Participants are asked to read aloud letters and words, using a computerized adaptive format, pronouncing them as accurately as possible. Outcome measure: IRT‐based score representing relative overall reading ability |
| Language | Picture Vocabulary Test | Participants must choose which of four pictures best represents a word presented via audio in a computerized adaptive format. Outcome measure: IRT‐based score representing relative overall vocabulary ability |
Note: Bold text highlight the primary outcome measure for each test.
Abbreviations: IRT, Item Response Theory; NIH, National Institutes of Health.
2.3. Biomarker data collection
Eight out of nine ARMADA sites collected AD biomarkers via amyloid positron emission tomography (PET) imaging or CSF lumbar puncture using previously described methods. 15 CSF assays used Luminex or Elisa methods and cutoffs were determined as an amyloid‐β42/tau ratio. Amyloid PET scans were made using either [11C]‐Pittsburgh compound B (PiB) or [18F]‐florbetapir (FBP), and cutoffs were derived from either a distribution volume ratio (PiB) or a standardized uptake value ratio (FBP) in aggregate cortical regions for frontal, precuneus, posterior cingulate, and lateral parietal regions using cerebellar grey matter as the reference region. Each site provided their PET and CSF data as a dichotomous variable (Aβ‐negative or Aβ‐positive) based on site‐specific cut‐offs, which allowed for the harmonization of various collection procedures, tracers, and biomarker types for biomarker grouping. Aβ‐negative status needed to be determined within 24 months before the first ARMADA NIHTB visit, since an Aβ‐negative status is not anticipated to change significantly within that timeframe. 15 No time limit was set for those with an Aβ‐positive status since we assume that those who were AB‐positive at any time before their NIHTB assessment would still be so at the time of their NIHTB assessment.
2.4. Statistical analysis
Analyses were done using R (v4.0.3). Statistical significance was set at p < 0.05. First, participants’ clinical status (CN or aMCI‐to‐mild AD) and AD biomarker status (Aβ‐negative or Aβ‐positive) were combined to create three groups: CN Aβ‐ (n = 113)‐; CN Aβ+ (n = 24); and aMCI‐to‐mild AD Aβ+ (n = 34). Due to sample‐size limitations, we were not able to analyze the aMCI and Mild AD groups separately, but since global Clinical Dementia Rating scale (CDR) scores of these two groups fell in the similar range (aMCI: M = 0.5 (SD = 0.14), range [0–1]; mild AD: M = 0.75 (SD = 0.26), range [0.5–1]), we considered this combined group to reflect the MCI‐to mild dementia due to AD spectrum. Demographic and clinical differences between groups were investigated using Chi‐square tests for discrete variables and one‐way analyses of variance (ANOVAs) followed by Tukey's honest significant difference (HSD) post‐hoc tests for continuous variables. We then ran a series of linear mixed‐effect (LME) models for each NIHTB‐CB test score (outcome) with time (0, 12‐month, 24‐month), group (CN Aβ‐, CN Aβ+, aMCI‐to‐mild AD Aβ+) and time*group interaction as predictors of interest, correcting for age (continuous), sex (dichotomous), and education (categorical: no college degree, college degree or graduate degree) and their interactions with time. For each outcome, estimates including 95% confidence intervals (CIs) and p‐values are reported for the CN Aβ+ and aMCI‐to‐mild AD Aβ+ group with the CN Aβ‐ group as the reference group. For estimates with a p < 0.05, a mean‐to‐standard deviation ratio (MSDR) was calculated as a standardized measure of effect size to facilitate comparisons across the different composites and individual NIHTB‐CB tests. The MSDR ratio provides a standardized signal‐to‐noise ratio, with higher values reflecting a better signal‐to‐noise. For the cross‐sectional results, the MSDR reflects the signal‐to‐noise ratio to detect differences between groups at baseline. For the longitudinal results, the MSDR reflects the signal‐to‐noise ratio to detect differences in change over time between groups.
3. RESULTS
3.1. Sample characteristics
The total sample consisted of N = 171 participants (age 76.2 ± 7.9, 53.8% female, 34% Aβ+), of whom 113 (66%) were classified as CN Aβ‐, 24 (14%) were classified as CN Aβ+, and 34 (20%) were classified as aMCI‐to‐mild AD Aβ+. Table 2 presents the sample characteristics at baseline, showing that groups differed in terms of age, education, global CDR scores, and CDR Sum of Boxes (SOB) scores. Post‐hoc comparisons revealed that the CN Aβ+ group was on average older (M = 81.5, SD = 8.7 years) than the CN Aβ‐ group (M = 75.2, SD = 7.5 years, p < 0.001) and the aMCI‐to‐mild AD Aβ+ group (M = 75.6, SD = 7, years, p = 0.01), whereas the CN Aβ‐ and aMCI‐to‐mild AD Aβ+ groups did not differ in age (p = 0.967). As expected, CDR SOB scores were highest in the aMCI‐to‐mild AD Aβ+ group (M = 2.49, SD = 1.72, p < 0.001 compared to both other groups), and CDR SOB scores did not differ between the CN Aβ‐ (M = 0.06, SD = 0.27) and CN Aβ+ group (M = 0.12, SD = 0.27, p = 0.924). Unfortunately, the study was significantly disrupted by the coronavirus pandemic in 2019, which emerged just as the baseline assessments were ending. As a result, 19.9% of the total sample was lost to follow‐up: 50.3% had at least one follow‐up visit and 29.8% had two follow‐up visits available (Table 2). Mean follow‐up time was shortest for the aMCI‐to‐mild AD Aβ+ group (M = 0.5, SD = 0.6 years, p < 0.001), but there was no difference in the follow‐up duration for the CN Aβ‐ versus CN Aβ+ groups (p = 0.125).
TABLE 2.
Demographic and clinical characteristics at baseline.
| Parameter | Total sample | CN Aβ‐ | CN Aβ+ | aMCI‐to‐mild AD Aβ+ | p‐value a | Post‐hoc group comparisons b |
|---|---|---|---|---|---|---|
| N | 171 | 113 | 24 | 34 | ||
| Age (years) | ||||||
| Mean (SD) | 76.2 (7.86) | 75.2 (7.54) | 81.5 (8.72) | 75.6 (6.95) | 0.001 | CN Aβ+ > CN Aβ‐ & aMCI‐to‐mild AD Aβ+ |
| Median [min, max] | 73.0 [63.0, 91.0] | 72.0 [66.0, 91.0] | 84.0 [66.0, 91.0] | 75.0 [63.0, 89.0] | ||
| Gender | ||||||
| Female | 92 (53.8%) | 66 (58.4%) | 13 (54.2%) | 13 (38.2%) | ||
| Male | 79 (46.2%) | 47 (41.6%) | 11 (45.8%) | 21 (61.8%) | 0.117 | |
| Education | ||||||
| No college degree | 27 (15.8%) | 12 (10.6%) | 8 (33.3%) | 7 (20.6%) | ||
| College degree | 54 (31.6%) | 33 (29.2%) | 10 (41.7%) | 11 (32.4%) | ||
| Graduate degree | 90 (52.6%) | 68 (60.2%) | 6 (25.0%) | 16 (47.1%) | 0.011 | NA |
| Ethnicity | ||||||
| Hispanic | 7 (4.1%) | 2 (1.8%) | 2 (8.3%) | 3 (8.8%) | ||
| Non‐Hispanic | 164 (95.9%) | 111 (98.2%) | 22 (91.7%) | 31 (91.2%) | 0.1 | |
| Race | ||||||
| Black | 15 (8.8%) | 12 (10.6%) | 2 (8.3%) | 1 (2.9%) | ||
| Asian | 1 (0.6%) | 0 (0%) | 0 (0%) | 1 (2.9%) | ||
| White | 151 (88.3%) | 99 (87.6%) | 21 (87.5%) | 31 (91.2%) | ||
| Other | 4 (2.3%) | 2 (1.8%) | 1 (4.2%) | 1 (2.9%) | 0.381 | |
| Clinical diagnosis | ||||||
| CN | 137 (80.1%) | 113 (100%) | 24 (100%) | 0 (0%) | ||
| aMCI | 22 (12.9%) | 0 (0%) | 0 (0%) | 22 (64.7%) | ||
| Mild AD | 12 (7.0%) | 0 (0%) | 0 (0%) | 12 (35.3%) | <0.001 | NA |
| Global CDR | ||||||
| 0 | 134 (78.4%) | 112 (99.1%) | 21 (87.5%) | 1 (2.9%) | ||
| 0.5 | 30 (17.5%) | 1 (0.9%) | 3 (12.5%) | 26 (76.5%) | ||
| 1 | 7 (4.1%) | 0 (0%) | 0 (0%) | 7 (20.6%) | <0.001 | NA |
| CDR Sum of Boxes | ||||||
| Mean (SD) | 0.55 (1.25) | 0.06 (0.27) | 0.12 (0.27) | 2.49 (1.72) | <0.001 | aMCI‐to‐mild AD Aβ+ > CN Aβ‐ & CN Aβ+. |
| Median [min, max] | 0 [0, 5.50] | 0 [0, 0.50] | 0 [0, 1.00] | 2.00 [0, 5.50] | ||
| Follow‐up visits | ||||||
| Mean (SD) | 1.10 (0.700) | 1.28 (0.590) | 1.00 (0.885) | 0.559 (0.613) | <0.001 | aMCI‐to‐mild AD Aβ+ < CN Aβ‐ & CN Aβ+. |
| Median [min, max] | 1.00 [0, 2.00] | 1.00 [0, 2.00] | 1.00 [0, 2.00] | 0.500 [0, 2.00] |
Abbreviations: AD, Alzheimer's disease; aMCI, amnestic mild cognitive impairment; ANOVA, analysis of variance; CN, cognitively normal; CDR, Clinical Dementia Rating Scale; HSD, honestly significant difference.
Group differences were tested using ANOVA for continuous variables or chi‐squared for discrete variables.
Post‐hoc group comparisons for continuous variables based on Tukey's HSD test.
3.2. Cross‐sectional group comparisons
Figure 1 shows the distribution of baseline scores by group for each NIHTB‐CB composite and individual test. Table 3 presents the estimates obtained from LME modeling NIHTB‐CB composite scores and individual test scores. Figure 2 shows the LME‐predicted trajectories on each individual test. Compared to CN Aβ‐ individuals, the CN Aβ+ group did not differ on any of the NITHB‐CB tests or composite scores at baseline. The aMCI‐to‐mild AD Aβ+ group had lower baseline scores on the Total Cognition Composite (𝛽 = −12.75, 95% CI [−15.9 to −9.6], p < 0.001, MSDR = 1.43), Fluid Cognition Composite (𝛽 = −15.84, 95% CI [−19.75 to −11.92], p < 0.001, MSDR = 1.43), and Crystallized Cognition Composite (𝛽 = −5.24, 95% CI [−7.83 to −2.65], p < 0.001, MSDR = 0.67) compared to the CN Aβ‐ group. Significant baseline differences between the CN Aβ‐ and aMCI‐to‐mild AD Aβ+ groups were detected on each individual NIHTB‐CB cognitive test (Table 3, Figure 2), albeit with different effect‐sizes and MSDRs for individual tests ranging from 0.54 to 1.23 (Figure 3).
FIGURE 1.
Histograms presenting baseline score distributions by group for each NIHTB‐CB composite and individual test. AD, Alzheimer's disease; CN, cognitively normal; MCI, mild cognitive impairment; NIHTB‐CB, National Institute of Health Toolbox—Cognition Battery.
TABLE 3.
Results obtained from LME models with NIHTB‐CB scores at baseline, 1 year, and 2 years.
| NIHTB‐CB composites | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Total cognition | Fluid cognition | Crystallized cognition | |||||||
| Parameter | Estimate | 95% CI | p‐value | Estimate | 95% CI | p‐value | Estimate | 95% CI | p‐value |
| CN Aβ+ | −0.39 | −4.17 to 3.39 | 0.842 | −1.41 | −6.11 to 3.29 | 0.562 | 0.69 | −2.42 to 3.81 | 0.668 |
| aMCI‐to‐mild AD Aβ+ | −12.75 | −15.90 to −9.60 | <0.001 | −15.84 | −19.75 to −11.92 | <0.001 | −5.24 | −7.83 to −2.65 | <0.001 |
| Time × CN Aβ+ | 0.16 | −1.19 to 1.51 | 0.823 | 0.93 | −1.16 to 3.02 | 0.391 | −0.26 | −1.42 to 0.89 | 0.659 |
| Time × aMCI‐to‐mild AD Aβ+ | −3.13 | −5.21 to −1.05 | 0.004 | −3.44 | −6.55 to −0.32 | 0.035 | −2.49 | −4.04 to −0.95 | 0.002 |
| Fluid Cognition Tests | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Flanker Inhibitory Control And Attention | Dimensional Change Card Sort | Pattern Comparison Processing Speed | |||||||
| Estimate | 95% CI | p‐value | Estimate | 95% CI | p‐value | Estimate | 95% CI | p‐value | |
| CN Aβ+ | 1.35 | −2.52 to 5.21 | 0.501 | 0.36 | −4.23 to 4.95 | 0.88 | −2.98 | −9.21 – 3.25 | 0.356 |
| aMCI‐to‐mild AD Aβ+ | −8.58 | −11.78 to −5.37 | <0.001 | −11.47 | −15.29 to −7.66 | <0.001 | −10.15 | −15.33 – ‐4.97 | <0.001 |
| Time × CN Aβ+ | −0.78 | −2.67 to 1.10 | 0.423 | 2.26 | 0.01 to 4.51 | 0.053 | 0.46 | −2.85 – 3.76 | 0.789 |
| Time × aMCI‐to‐mild AD Aβ+ | −2.56 | −5.11 to −0.01 | 0.054 | 1.14 | −1.90 to 4.18 | 0.469 | −6.17 | −10.46 – ‐1.88 | 0.006 |
| List Sorting Working Memory | Picture Sequence Memory Test | |||||
|---|---|---|---|---|---|---|
| Estimate | 95% CI | p‐value | Estimate | 95% CI | p‐value | |
| CN Aβ+ | −1.25 | −6.48 to 3.99 | 0.646 | −0.58 | −6.25 to 5.10 | 0.845 |
| aMCI‐to‐mild AD Aβ+ | −16.7 | −21.16 to −12.23 | <0.001 | −14.22 | −18.92 to −9.52 | <0.001 |
| Time × CN Aβ+ | −0.81 | −4.21 to 2.59 | 0.646 | 1.78 | −1.47 to 5.03 | 0.292 |
| Time × aMCI‐to‐mild AD Aβ+ | −7.96 | −12.63 to −3.28 | 0.001 | −0.36 | −5.14 to 4.42 | 0.885 |
| Crystallized Cognition Tests | ||||||
|---|---|---|---|---|---|---|
| Picture Vocabulary Test | Oral Reading Recognition Test | |||||
| Estimate | 95% CI | p‐value | Estimate | 95% CI | p‐value | |
| CN Aβ+ | −0.34 | −4.24 to 3.56 | 0.866 | 2.05 | −0.45 to 4.56 | 0.114 |
| aMCI‐to‐mild AD Aβ+ | −6.29 | −9.52 to −3.05 | <0.001 | −3.36 | −5.44 to −1.28 | 0.002 |
| Time × CN Aβ+ | −0.55 | −2.33 to 1.23 | 0.549 | −0.11 | −1.25 to 1.04 | 0.856 |
| Time × aMCI‐to‐mild AD Aβ+ | −3.49 | −5.81 to −1.17 | 0.004 | −1.36 | −2.88 to 0.16 | 0.085 |
Note: Estimates were obtained from LME models correcting for age, sex, education, and their interactions with time, and with the CN Aβ‐ as the reference group.
Abbreviations: AD, Alzheimer's disease; aMCI, amnestic mild cognitive impairment; CN, cognitively normal; LME, linear mixed effects; NIHTB‐CB, NIH Toolbox—Cognition Battery.
Bold text highlight p‐values < 0.05.
FIGURE 2.
LME predicted trajectories by group (CN Aβ‐, CN Aβ+, aMCI‐to‐mild AD Aβ+) for each NIHTB‐CB test. Models were run in the entire sample with group as indicator (CN Aβ‐ as reference group) and correcting for age, sex, and education. Time 0, 1, and 2 represent baseline, 12‐month follow‐up, and 24‐month follow‐up visits, respectively. AD, Alzheimer's disease; CN, cognitively normal; LME, linear mixed effect models; MCI, mild cognitive impairment; NIHTB‐CB, National Institute of Health Toolbox—Cognition Battery.
FIGURE 3.
MSDR as a measure of effect size for NIHTB‐CB test detecting cross‐sectional differences (left panel) and longitudinal change (right panel) in the aMCI‐to‐mild AD Aβ+ group compared to CN Aβ‐, after correcting for age, sex, and education. MSDRs for longitudinal change are only calculated for the three tests that captured significant change over time. AD, Alzheimer's disease; CN, cognitively normal; aMCI, amnestic mild cognitive impairment; MSDR, mean to standard deviation ratios; NIHTB‐CB, National Institute of Health Toolbox—Cognition Battery.
3.3. Longitudinal group comparisons
None of the NIHTB‐CB tests detected differential longitudinal change in the CN Aβ+ group compared to CN Aβ‐ over the course of this study (Table 3). The aMCI‐to‐mild AD Aβ+ group showed an accelerated decline over time on all NIHTB‐CB composite scores (Total Cognition: 𝛽 = −3.13, 95% CI [−5.21 to −1.05], p = 0.004, MSDR = 0.53, Fluid Cognition: = −3.44, 95% CI [−6.55 to −0.32], p = 0.035, MSDR = 0.39; Crystallized Cognition: = −2.49, 95% CI [−4.04 to −0.95], p = 0.002, MSDR = 0.54) compared to the CN Aβ‐ group. This was driven by two out of five fluid cognition tests (i.e., the Pattern Comparison Processing Speed Test and List Sorting Working Memory Test) and one crystallized cognition test (i.e., the Picture Vocabulary Test) (Table 3, Figure 2). Figure 3 presents the MSDRs for longitudinal change for these three NIHTB‐CB tests, showing similar effect sizes of change for each. In addition, the cross‐sectional and longitudinal MSDRs presented in Figure 3 reveal that the tests that detect cross‐sectional group differences are not always the same as those for detecting change over time.
4. DISCUSSION
We show that the NIHTB‐CB detects cross‐sectional impairment across multiple cognitive domains in individuals with biomarker‐confirmed aMCI‐to‐mild AD. In addition, our longitudinal analyses of the NIHTB‐CB show that measures of processing speed, working memory, and auditory word comprehension capture cognitive decline over one year in aMCI‐to‐mild AD individuals. Overall, these findings suggest that the NIHTB‐CB can detect AD‐related cognitive decline, supporting the battery's potential for use in cross‐sectional and longitudinal studies of early clinical stages of AD. However, the finding that individual NIHTB‐CB tasks are differentially sensitive to change over time suggests that the optimal NIHTB‐CB composite to monitor cognition over time in biomarker‐confirmed AD may be different than the previously developed NIHTB‐CB Fluid and Crystallized Cognitive composites.
Our finding that individuals with aMCI or mild dementia due to AD at baseline perform worse across multiple cognitive domains compared to CN Aβ‐ older adults is completely consistent with studies comparing individuals with MCI and mild AD to CN older adults using established paper‐and‐pencil cognitive tests. 11 , 20 , 21 In fact, this underlines that the NIHTB‐CB provides similar information about AD‐related cognitive impairment as what would be expected using a paper‐and‐pencil cognitive test battery, further supporting the NIHTB‐CB's validity for assessing cognition in the context of aging and AD. 13 , 14 , 22 By leveraging computerized assessment, the NIHTB‐CB might even provide benefits above and beyond conventional paper‐and‐pencil testing, by yielding standardized assessment that may result in less administrative burden, fewer scoring errors and the opportunity to harmonize assessments more easily across studies and cohorts. 23 As such, we believe that the NIHTB‐CB may facilitate a more accurate and efficient assessment of cognition in the study of AD.
Notably, only three out of the eight NIHTB‐CB tests captured an annual decline in the aMCI‐to‐mild AD group. These three tests (List Sorting Working Memory, Pattern Comparison Processing Speed, and Picture Vocabulary Test) tap into working memory, processing speed, and auditory word comprehension. At first, it might seem counterintuitive that the NIHTB‐CB test for episodic memory (Picture Sequence Memory Test) did not detect decline over time in our study, despite episodic memory impairment being the hallmark cognitive sign in early symptomatic stages of AD. In fact, a recent study applied a machine learning approach to the ARMADA data to derive the best algorithm for detecting amyloid positivity using the entire NIHTB battery, which includes cognition, motor, emotion, and sensation variables. The study identified the Picture Sequence Memory Test as one of the most important variables in detecting amyloid positivity. 24 However, this strong cross‐sectional correlation is probably also the exact reason why this test did not show sensitivity to change over time in aMCI‐to‐mild AD, as this group exhibited floor effects already at the baseline visit and thus did not have further room to decline at follow‐up visits. As AD progresses from the aMCI into the mild dementia stage, episodic memory impairment is accompanied by a decline in other cognitive domains such as executive function, working memory, and semantic abilities. 5 , 20 In that respect, declines in NIHTB‐CB tasks of working memory and processing speed in the aMCI‐to‐mild AD Aβ+ group are consistent with known disease progression.
We also found that the Picture Vocabulary Test, which is considered a measure of crystallized cognition, declined in the aMCI‐to‐mild AD group. This was in contrast with the Oral Reading Recognition Test, which did not change differently in aMCI‐to‐mild AD individuals compared to CN Aβ‐ individuals. There are several potential reasons for the dissociation in change over time on these two “crystallized” cognition tests. First, the Picture Vocabulary Test relies on intact vision as well as auditory perception, and performance on this test may be thus confounded by increased hearing loss which can happen in the context of older age as well as AD. 25 , 26 Second, it has been shown that changes in semantic abilities, such as vocabulary and word finding, are common in mild AD, which may have contributed to the worsening of Picture Vocabulary Test scores in the aMCI‐to‐mild AD group. 27 , 28 This would imply that the Picture Vocabulary Test may not be a good proxy for crystallized intelligence in more advanced symptomatic AD stages but rather provides a measure of AD disease progression instead. Thus, if both the Picture Vocabulary Test and Oral Reading Recognition Test are assessed, it would be recommended to analyze these tests separately rather than just as a composite.
None of the NIHTB‐CB tests detected cross‐sectional or longitudinal differences in the CN Aβ+ group over the course of the current study. In fact, our results suggest that CN older adults with elevated Aβ perform similarly on all NIHTB‐CB tests compared to their Aβ‐negative counterparts. This is in line with previous work that showed that none of the NIHTB‐CB measures were associated with amyloid PET positivity among CN older adults 29 as well as with other work showing that Aβ‐cognition associations in CN older adults are tenuous or small if observed at all, requiring large sample‐sizes and multi‐year follow‐up durations to detect cognitive changes. 30 Thus, our relatively small sample of Aβ+ CN participants (n = 24) and short follow‐up duration may have barred us from detecting a statistically significant difference in this group. One approach to improve the sensitivity of the NIHTB‐CB to Aβ in CN older adults would be to augment the battery with tests that have been shown to detect early Aβ‐related memory impairment, 31 such as the face‐name associative memory exam (FNAME) that was added to the NIHTB‐CB during the second year of the ARMADA study. 32 Together with previous FNAME publications, 31 , 33 first findings suggest that adding the FNAME to the NIHTB‐CB might improve the sensitivity to early Aβ‐related cognitive changes even before the onset of MCI. 30 Another opportunity to improve the detection of Aβ‐related cognitive change would be to shift from an annual administration schedule to more frequent cognitive testing, such as monthly or daily, and focus on detecting a lack of practice effects rather actual decline in test scores. 34 , 35 This paradigm seems particularly promising when using the same version of a memory test, such as the FNAME, for the detection of early decrements in learning over repeated exposures. 36
4.1. Implications
Taken together, our findings provide further support for the use of the NIHTB‐CB in the context of aging and AD research as well as clinical practice. By providing a concise yet comprehensive computerized test battery covering multiple cognitive domains, the NIHTB‐CB has great potential to facilitate a more uniform assessment of cognition in longitudinal studies of aging and AD. By yielding less administrative burden compared to a paper‐pencil cognitive test battery, the NIHTB‐CB might provide a more scalable approach to assessing cognition. This might also benefit clinical practice, by giving a clinician more time to focus on what matters for a patient and their caregiver. Early detection of Aβ‐related cognitive decline is now clinically relevant as we have disease‐modifying treatments available that target this protein. Sensitive cognitive measures that capture Aβ‐related cognitive decline can aid clinicians in early detection of treatment candidates as well as monitor treatment response. As such, it will be crucial to validate the NIHTB‐CB in larger and more demographically diverse samples, explore associations with other AD biomarkers, and investigate whether novel composite measures can be derived to detect a cognitive change in earlier, biomarker‐positive stages of AD.
Lastly, our findings imply that tests that detect cross‐sectional differences in cognition are not automatically sensitive to change over time (and vice versa), which is a vital issue to consider when selecting cognitive tests as outcome measures in research and clinical trials. 37 More specifically, our findings support the notion that the selection of cognitive outcome measures for detecting change depends on both the construct of interest (what to measure) as well as measurement properties of the instrument (how to measure) in a specific target population (who to measure). 38
4.2. Limitations and future directions
There are some limitations that should be considered when interpreting the current results. First, our study sample predominantly consists of highly educated, non‐Hispanic White participants; thereby, it is unknown how generalizable our findings are to more diverse populations. Second, the ARMADA study visit schedule was interrupted due to the coronavirus disease 2019 (COVID‐19) pandemic, which led to a substantial loss of follow‐up of particularly cognitively impaired participants. Hence, our longitudinal findings should be interpreted with slight caution, and future studies including a larger sample and longer‐follow‐up duration will be needed to confirm the sensitivity to change of the NIHTB‐CB across different clinical stages of AD and to determine the optimal NIHTB‐CB composite to detect AD‐related cognitive decline. Third, the dichotomization of Aβ‐status into ±, by aggregating together different assessment methods (i.e., PET and CSF) using site‐specific methods and cut‐offs, may have affected our ability to detect subtle associations between cognition and Aβ. Future endeavors should focus on the association between NIHTB‐CB measures and continuous Aβ levels, or potentially apply more sensitive methods to detect Aβ pathology. 39 It would also be important to assess change in the NIHTB‐CB in association with biomarkers of other AD hallmark pathologies, such as tau deposition in the medial‐temporal lobe and temporal neocortical areas. This would be particularly relevant since tau pathology in the MTL and neocortical regions is typically more proximally associated with cognitive decline than the global Aβ burden. 29
4.3. Conclusion
We sought to examine whether the NIHTB‐CB detects a cross‐sectional and longitudinal cognitive decline in biomarker‐confirmed AD. We found that the NIHTB‐CB detects cross‐sectional impairment across all measured cognitive domains as well as longitudinal change in processing speed, working memory, and auditory word comprehension in MCI and mild dementia due to AD. These findings further support the use of the NIHTB‐CB in the context of early AD. Replication of these findings in larger and more diverse samples will be needed to determine whether the NIHTB‐CB can contribute to more accurate, efficient, and thereby more timely detection and tracking of AD‐related cognitive decline.
CONFLICT OF INTEREST STATEMENT
The authors report no disclosures relevant to this manuscript. Author disclosures are available in the Supporting Information.
CONSENT STATEMENT
The Institutional Review Boards of all nine research sites approved the ARMADA Study. All human subjects provided informed consent.
Supporting information
Supporting Information
ACKNOWLEDGMENTS
We thank the study participants and their family members for their participation, along with the research staff across all sites who helped administer this study. Dr. Jutten is supported by an Alzheimer's Association Research Fellowship (AARF‐22‐967786). Funding for the ARMADA study was provided by: (1) Mayo Clinic Florida: NIA P50 AG016574 Mayo Clinic Alzheimer's Disease Research Center; Florida Department of Health Ed & Ethel Moore Alzheimer's Disease Research Program grant 8AZ08 Evaluating The Impact of a Dementia‐Caring Community Model on African Americans with Alzheimer's Disease and Their Care Partners; (2) Emory University: NIA 1P30AG066511 Goizueta Alzheimer's Disease Research Center at Emory University; (3) University of California, San Diego: NIA P30AG062429 UCSD Alzheimer's Disease Research Center; (4) NACC NIA U01 AG16976, National Alzheimer's Coordinating Center; (5) Northwestern University: NIA P30 AG013854, Northwestern Alzheimer's Disease Center; NIA R01AG045571, R56AG045571, and R01AG067781, Cognitive SuperAging studies; (6) University of Pittsburgh: NIA P50 AG005133, Alzheimer's Disease Research Center, NIA P01 AG025204, Imaging Pathophysiology in Aging and Neurodegeneration, NIA R01 AG052446, Role of Midlife Cardiovascular Disease on Alzheimer's Pathology and Cerebrovascular Reactivity in the Young‐Old; (7) Massachusetts General Hospital: NIA P30AG062421, Massachusetts ADRC; NCBI P01AG036694‐ Harvard Aging Brain Study; (8) University of Michigan: NIA P30 AG053760, Michigan Alzheimer's Disease Research Center; NIA R01 AG054484, Community Based Approach to Early Detection of Transitions to Mild Cognitive Impairment and Alzheimer's Disease in African Americans (ELECTRA); NIA R01 AG058724, Treating Mild Cognitive Impairment with High Definition Transcranial Direct Current Stimulation (STIM); NIH RF1 AG047866, Impact of Disclosing Amyloid Imaging Results to Cognitively Normal Individuals (REVEAL SCAN); Cure Alzheimer's Fund, Deep Phenotyping of Older African Americans at Risk of Dementia; (9) Columbia University: Washington Heights‐Inwood Columbia Aging Project (WHICAP), NIA PO1AG07232, R01AG037212, RF1AG054023, National Center for Advancing Translational Sciences, National Institutes of Health, through Grant Number UL1TR001873; (10) University of Wisconsin: NIA P50 AG033514 and NIA P30 AG062715 Wisconsin Alzheimer's Disease Research Center; (11) Oregon Health & Science University: Layton Aging and Alzheimer's Disease Center, NIA P30 AG066518; and (12) the ARMADA: Advancing Reliable Measurement in Alzheimer's Disease and cognitive Aging, NIA U2C AG057441.
Jutten RJ, Ho EH, Karpouzian‐Rogers T, et al. Computerized cognitive testing to capture cognitive decline in Alzheimer's disease: Longitudinal findings from the ARMADA study. Alzheimer's Dement. 2025;17:e70046. 10.1002/dad2.70046
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