During the prodromal stage of Alzheimer’s disease (AD), neurodegenerative changes can be identified by measuring volumetric loss in AD-prone brain regions on MRI. Cognitive assessments sensitive enough to measure the early brain-behavior manifestations of AD and correlate with biomarkers of neurodegeneration are needed to identify and monitor individuals at risk for dementia. Weak sensitivity to early cognitive change has been one major limitation of traditionally used assessment tools. In this study, we focused on expanding our previous work by determining whether a digitized cognitive stress test, the Loewenstein-Acevedo Scales for Sematic Interference and Learning, Brief Computerized Version (LASSI-BC) could differentiate between Cognitively Unimpaired (CU) and amnestic Mild Cognitive Impairment (aMCI) groups. A second focus was to correlate LASSI-BC performance to volumetric reductions in AD-prone regions. Data was gathered from 127 older adults who were comprehensively evaluated and administered the LASSI-BC. Eighty-seven of these participants (51 CU; 36 aMCI) underwent MRI imaging. The volumes of 12 AD-prone brain regions were related to LASSI-BC and other memory indices correcting for False Discovery Rate (FDR). Results indicated that, even after adjusting for initial learning ability, the failure to recover from proactive semantic interference (frPSI) on the LASSI-BC differentiated between CU and aMCI groups. An optimal combination of frPSI and initial learning strength on the LASSI-BC yielded an area under the ROC curve of .876 (76.1% sensitivity, 82.7% specificity). Further, LASSI-BC measures of frPSI were associated with volumetric reductions in AD prone regions including the hippocampus, amygdala, inferior temporal lobes, precuneus and posterior cingulate.
Research Article
A novel computerized cognitive test for the detection of MCI and its association with neurodegeneration in Alzheimer’s disease prone brain regions
https://doi.org/10.21203/rs.3.rs-1498453/v1
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Mild Cognitive Impairment
proactive semantic interference
MRI volume
cortical thickness
LASSI-L
There have been remarkable advances in identifying biological markers associated with preclinical Alzheimer’s disease (AD) and AD-related dementias (ADRD) using neuroimaging and fluid-based markers (Zetterberg & Blennow, 2021; Johnson, Fox, Sperling, & Klunk, 2012). During the prodromal stage of AD, also known as Mild Cognitive Impairment (MCI), neurodegenerative changes can be identified by measuring volumetric loss in AD-prone brain regions on MRI (Frisoni et al., 2017; Kantarci et al., 2013). Despite these biomarker advances, the manner in which early cognitive decline is measured remains largely unchanged (Curiel & Loewenstein, 2022) and traditional cognitive assessment measures are insensitive to early cognitive loss (Tang, Albanese, & Stephan, 2015; Bondi & Smith, 2014). Although historically useful for clinical practice and longitudinal research, the utility of conventional neuropsychological testing needs to be questioned as the field advances its efforts to measure pre-clinical manifestations that correlate with biomarkers (Rentz et al., 2013). Identifying these subtle changes is of paramount importance to prevention efforts, as early interventions are likely to delay the clinical onset of impending disease (Brooks & Loewenstein, 2010).
The Loewenstein-Acevedo Scale of Semantic Interference and Learning (LASSI-L) has shown great utility in detecting cognitive changes during preclinical stages of AD (Loewenstein et al., 2016; Curiel-Cid et al., 2020) and has outperformed widely used memory measures in both English and Spanish (Curiel Cid et al., 2019). This cognitive stress paradigm employs controlled learning and cued recall to maximize the storage of 15 words (List A) belonging to three semantic categories (fruits, musical instruments, and articles of clothing) followed by the administration of different targets representing these same categories to elicit proactive semantic interference (PSI: old learning interfering with new learning). Unlike traditional paradigms, the LASSI-L then facilitates the assessment of an individual’s ability to recover from PSI through an additional learning trial of the competing list. A growing body of evidence indicates that PSI, frPSI, and semantic intrusion errors on the LASSI-L are sensitive in discriminating between the cognitively unimpaired and those with Pre-MCI or MCI due to AD with amyloid PET biomarker positivity (Crocco et al., 2021; Loewenstein et al., 2018a, Kitaigorodsky et al., 2021a). LASSI-L deficits, particularly frPSI, are related to volumetric reductions in AD-vulnerable brain regions (Loewenstein et al., 2017a; Loewenstein et al., 2017b; Curiel-Cid et al., 2020; Zheng et al., 2021). Even in older adults with normal performance on a traditional neuropsychological battery, these AD-salient deficits were associated with increased amyloid load (Loewenstein et al., 2016).
Most paper-and-pencil tests are lengthy, vulnerable to human error (i.e., administration/scoring), and prone to practice effects (Loewenstein et al., 2018b). Moreover, most of these measures have not been subjected to examination for cultural and language biases (Manly, 2005; Babulal et al., 2019). To mitigate limitations, computer technologies have been adopted as a suitable alternative, offering advantages such as cost/time savings, remote administration, uniform and standardized administration, and automated scoring. Systematic reviews have identified computerized measures designed to detect dementia or MCI (Aslam et al., 2018; Tsoy, Zygouris, & Possin, 2021), with most being adaptations of traditional paradigms. In a meta-analysis, Chan, and colleagues (2018) compared the performance of computerized and paper-and-pencil memory tests and concluded that the psychometric properties of computerized instruments, such as reliability and validity, have varied, and many have lacked the sensitivity and specificity needed to identify and discriminate early stages of MCI (Chan et al., 2018; Aslam et al., 2018). Considering the above, computerized tests that are sensitive to early changes and converge with biomarkers of AD remain sorely needed.
Given the promising results of the LASSI-L, Curiel-Cid and colleagues (2021) developed the LASSI-BC, a brief computerized version that incorporates all essential elements of the original test. The LASSI-BC does not require a skilled examiner, runs on most browser-capable devices, and is appropriate for use among older adults from varying ethnic/cultural backgrounds (Curiel et al., 2020; Capp et al., 2020). The LASSI-BC has good test-retest reliability in aMCI and high discriminant validity between aMCI and controls (Curiel et. al., 2021). The aims of this investigation are to expand our previous findings that the LASSI-BC can differentiate CU from aMCI and examine whether performance is associated with MRI volumes in AD-prone brain regions. Associations with common memory measures were also examined.
Participants
One hundred and twenty-seven community-dwelling older adults from an NIA-funded R01 study were included in this IRB-approved investigation (Demographics Table 1). All participants were independent in activities of daily living, had knowledgeable informants, and did not meet DSM-V criteria for Major Neurocognitive Disorder, an active Mood or Psychotic Disorder, or any other DSM-V Axis I neuropsychiatric disorder (American Psychiatric Association, 2013). Participants were evaluated by clinicians blind to neuropsychological test performance and used the Clinical Dementia Rating Scale (CDR; Morris, 1993), and the Mini-mental State Examination (MMSE; Folstein, Folstein, & McHugh, 1975) to assess memory and other clinical and cognitive complaints. In cases where there was memory decline by history and/or clinical examination, a Global CDR score of 0.5 was given and a probable diagnosis of aMCI was assigned, pending the results of formal neuropsychological testing. Next, a standard neuropsychological battery was administered independently of the clinical examination in the participants’ dominant and/or preferred language by bilingual (English/Spanish) psychometrists.
Amnestic MCI Group (aMCI; n = 46)
Classification as aMCI was due to a single amnestic deficit, or with an amnestic plus additional non-amnestic deficits if there were: a) subjective memory complaints by participant and/or collateral informant; b) evidence of memory and/or other cognitive decline; c) Global Clinical Dementia Rating scale of 0.5; d) one or more memory measures fell below 1.5 SD or more relative to adjusted normative data. Mean age of the aMCI sample was 73.8 (SD = 8.5 years); average level of education was 14.3 (SD = 4.3 years). Mean MMSE score was 26.5 (SD = 2.2).
Cognitively Unimpaired Group (CU; n = 81)
Participants were classified as CU if all the following criteria were met: a) no subjective cognitive complaints by the participant or collateral informant; b) no evidence of memory and/or other cognitive decline; c) Global CDR score of 0.0; d) performance on all traditional neuropsychological tests was not more than 1.0 SD relative to adjusted normative data. CU were slightly younger 69.8 (SD = 6.1 years) and more educated than aMCI [16.2 (SD = 2.6 years)]. The mean MMSE score was 28.9 (SD = 1.2). There were no statistically significant differences in race/ethnicity and language of testing between the two groups.
Neuropsychological Measures
The LASSI-BC is a remotely accessible test available in both English and Spanish that can be run on devices that operate Google Chrome. While the LASSI-BC can be self-administered and scored by the computer, a psychometrician was present to record responses. A description of the test and its psychometric properties can be found in Curiel-Cid et al., (2021). Primary measures include the second cued recall score for List A (maximum learning), first cued recall score for List B (susceptibility to PSI) and second cued recall score of List B (frPSI). Semantic intrusion errors were also examined as these have shown to be related to amyloid pathology (Loewenstein et al., 2018a).
The neuropsychological battery included the Hopkins Verbal Learning Test (HVLT-R; Benedict, Schretlen, Groninger, & Brandt, 1998), delayed paragraph recall of the National Alzheimer’s Coordinating Center Uniform Data Set (NACC UDS; Beekly et al., 2007), Controlled Oral Word Association Test: Category Fluency (Binetti et al., 1995), Block Design subtest of the Wechsler Adult Intelligence Scale, Fourth Edition (WAIS-IV; Wechsler, 2008), and the Trail Making Test (Parts A and B; Reitan, 1958). LASSI-BC was not used for diagnostic determination to avoid circularity.
MRI Measurements
51 CU and 36 aMCI participants underwent neuroimaging using a GE Discovery MR750 3T (GE, Waukesha, WI, USA) MRI scanner. Brain parcellation was obtained using a 3D T1-weighted sequence (MPRAGE) with 1.0 mm isotropic resolution. FreeSurfer Version 6.0 software (http://surfer.nmr.mgh.harvard.edu) was employed to assess volumes in AD signature regions (Dickerson et al., 2011) used in our previous work (Loewenstein et al., 2016; Zheng et al., 2021). Regions included the hippocampus, entorhinal cortex, amygdala, para-hippocampal gyrus, inferior temporal lobule, temporal pole, supramarginal, superior parietal, precuneus, rostral middle frontal and superior frontal areas. All volumes of the brain regions were normalized by dividing by the total intracranial volume.
Statistical Analysis
The distribution of demographic factors and neuropsychological measures were calculated and compared between the two diagnostics groups using χ2 test for categorical variables and T-test for continuous variables. Comparative scores for each diagnostic group were adjusted for statistically significant demographic variables using a one-way analysis of covariance. Binary logistic regression was performed to examine the ability of LASSI-BC to differentiate between groups. The outcome variable of the logistic regression was the binary cognitive diagnosis (CU vs. aMCI) and the predictors were the LASSI-BC measures (controlling for age, sex, education, testing language, and global cognitive performance measured by MMSE). Odds ratio (OR) and corresponding 95% confidence interval (CI) of aMCI diagnosis was reported with an OR of less than one, indicating less likely to be aMCI. Receiver Operator Characteristic (ROC) curves were calculated for each LASSI-BC measure to determine their ability to classify aMCI cases from controls. The area under the ROC curve and 95% confidence interval were reported. The Youden Index, which identifies the optimal cutoff and corresponding sensitivity, and specificity was also reported. A combination of LASSI-BC subscales measuring maximum learning and frPSI were also examined under the ROC curve.
The association between LASSI-BC and traditional neuropsychological measures with 13 different AD-prone brain regions using structural MRI was also examined. Based on our previous work, we had an a priori hypothesis that performance on A2 cued recall (maximum learning), B1 cued recall, and B2 Cued recall would be related to AD regions among participants with aMCI. We examined the normality of each variable through normal probability plot and the Shapiro-Wilk normality test. Pearson correlation coefficients within groups were computed separately, and the correlation coefficient matrices were constructed. To adjust for multiple comparisons, FDR analyses were performed for each test-wise contrast to adjust the p-values. Only p-values corrected for FDR using methods by Benjamini and Hochberg (1995) that were < 0.05 were considered. We further calculated the Pearson correlation coefficients while controlling for maximum learning capacity (Cued A2 recall) to determine whether performance on Cued B1 or Cued B2 had independent explanatory power beyond initial learning capacity (Cued A2 recall).
CU participants scored higher on the MMSE (28.9 vs. 26.5, p < 0.001). Unsurprisingly, the CU group also scored higher on the HVLT-R total recall (adjusted mean 24.1 vs. 17.7, p < 0.001) and NACC delayed passages (adjusted mean 12.4 vs. 7.7, p < 0.001), as these measures were used in diagnostic determination. LASSI-BC measures were not employed in the diagnostic process. Participants with aMCI scored lower on A2 cued recall (maximum learning) (covariate adjusted mean 10.7 vs. 13.1, p < 0.001), B1 cued recall and intrusion errors (PSI) (adjusted mean 5.4 vs. 7.5 recall, 4.0 vs. 1.7 intrusion, both p < 0.001) and B2 cued recall and intrusion errors (frPSI) (adjusted mean 7.3 vs. 10.9 recall, 3.5 vs. 1.3 intrusion, both p < 0.001). The means of Cued B1 and Cued B2 recalls and intrusions after further adjusting for Cued A2 recall (maximum learning) were also reported. Table 2 shows the mean values of both Cued B2 recall and intrusions (frPSI) remained statistically different between CU and aMCI after adjusting for maximum learning and demographic covariates (10.4 vs. 8.8 Cued B2 recall, and 1.5 vs. 3.1 Cued B2 intrusions, both p < 0.01). The mean differences also held for measures of PSI (7.2 vs 6.0, p < 0.01 Cued B1 recall, and Cued B1 intrusions 2.1 vs. 3.4, p < 0.05).
A logistic regression model controlling for age, sex, education, testing language, and MMSE showed that LASSI-BC measures effectively differentiated groups (Table 3). A one-point increase on Cued A2 recall (maximum learning) was associated with 48% less likelihood of an aMCI diagnosis (OR 0.48, 95% CI [0.33, 0.70], p < 0.001). We further adjusted the regression model by Cued A2 recall to show the discriminating ability of frPSI (Cued B2) in relation to initial learning (Table 3). Cued B2 recall and intrusion errors remained statistically significant in differentiating groups after adjusting for initial learning (Cued A2 recall). The odds ratios for B2 cued recall and intrusions were 0.74 [0.58, 0.94] and 1.51[1.11, 2.08] respectively, both p < 0.01.
ROC analysis for LASSI-BC A2 Cued Recall (maximum learning) yielded an area under the ROC curve of 0.85 and 95% CI of [0.78 to 0.92] (p < 0.001). A cutoff of > 11 by the maximum Youden J index value of 0.56 yielded a sensitivity of 67% and specificity of 89%. For Cued B2 Recall (frPSI), the area under the ROC curve was 0.82 and 95% CI [0.75, 0.89], p < 0.001. A cutoff of > 10 was associated with the maximum Youden J index value of 0.55 and yielded a sensitivity of 87% and specificity of 68%. A combination of these measures [A2 Cued recall (maximum learning) and Cued B2 recall (frPSI)] yielded an area under ROC curve of 0.876 with 95% CI of [0.82, 0.94] and 76.1% sensitivity and 82.7% specificity (Fig. 1).
LASSI-BC Measures and Brain Volumes on MRI
The associations between LASSI-BC and brain volumes were examined for 51 CU and 36 aMCI participants separately; distribution of age, sex, race, and testing language was similar (all p > 0.05). Pearson Correlation analyses were conducted separately for each LASSI-BC and standard memory measures. Normality assumption was met, and p-values were adjusted for FDR. For aMCI A2 Cued Recall (maximum learning) was associated with volumes in hippocampus (r = 0.31; p = 0.049), precuneus (r = 0.52; p = 0.013), inferior temporal lobule (r = 0.41; p = 0.013), superior frontal lobule (r = 0.56; p = 0.003), amygdala (r = 0.43; p = 0.007), posterior cingulate (r = 0.42; p = 0.013), superior parietal lobule (r = 0.41; p = 0.013), rostral middle frontal (r = 0.41, p = 0.013), and supramarginal (r = 0.34, p = 0.039) regions (Table 4).
In those with aMCI, frPSI (B2 Cued Recall) was associated with 11 of the 13 brain regions examined: hippocampus (r = 0.62; p = 0.001), entorhinal cortex (r = 0.33, p = 0.036), precuneus (r = 0.51; p = 0.003), inferior temporal lobule (r = 0.51; p = 0.003), superior frontal lobule (r = 0.49; p = 0.003), amygdala (r = 0.61; p = 0.003), posterior cingulate (r = 0.45; p = 0.007), superior parietal (r = 0.31, p = 0.040), para-hippocampal (r = 0.33, p = 0.036), rostral middle frontal (r = 0.35, p = 0.033), and supra marginal (r = 0.37, p = 0.027) regions (Table 4). HVLT-R immediate recall and NACC passages were not significant. HVLT-R delayed recall alone was associated with the posterior cingulate area (r = 0.45; p = 0.034). Pearson correlation coefficients (controlling for maximum learning; Cued A2 recall) in the MCI cohort were calculated and adjusted for FDR. Results indicated that B2 Cued Recall (frPSI) remained highly associated with hippocampal volume (r = 0.57, p = 0.003) and amygdala volume (r = 0.51, p = 0.007). There was no association with neuropsychological measures in CU group.
This study did not replicate findings of a relationship between frPSI and volumetric reductions in the entorhinal cortex; an interesting result given the significant correlation between other circuits involving medial temporal lobe structures. However, the current sample was predominantly community-based, whereas our previous work exploring MRI neurodegeneration with LASSI-L, had a greater admixture of clinic and community-based samples. A higher base rate that accompanies underlying AD in those seeking clinical evaluation for memory disorders, may account for stronger associations previously between the LASSI-L and the ERC. Because the presence of aMCI and volume loss signaling neurodegeneration does not confirm underlying AD pathology, we plan to obtain amyloid PET scans and/or fluid-based markers to examine the LASSI-BC as it relates to specific etiology. The computerized LASSI-BC showed that frPSI and maximum learning were strongly associated with brain volumes in the hippocampus, amygdala, inferior temporal, precuneus, and other regions affected early in AD. Associations were stronger with maximum learning when compared to frPSI, which raised the question of whether performance deficits captured by frPSI were related to an underlying memory deficit in aMCI or, whether frPSI independently explained the association. To examine this, posthoc comparisons of Cued B2 recall measures and MRI regions were conducted using partial correlation coefficients. This showed that indeed, volumetric reductions in aMCI participants were related or independently associated to frPSI.
It is important to develop paradigms that can detect subtle deficits among those at-risk yet with little or no cognitive impairment. Emerging cognitive tests should also strive to exhibit sensitivity to biomarkers such as amyloid, tau, and neurodegeneration. Doing so will help the field address critical challenges including proper selection of at-risk participants and monitoring cognitive change over time. A unique aspect of the LASSI-BC, relative to other computerized measures, is the use of a sensitive semantic interference paradigm that has been shown repeatedly to be a salient marker in prodromal AD and related to multiple AD biomarkers. Being remotely deliverable, the LASSI-BC offers an added advantage, as telemedicine is now becoming a modal form of healthcare delivery to reach older adults who lack transportation, are socially isolated, have physical impediments, or are immunologically vulnerable (Kitaigorodsky et al., 2021b). One of the strengths of the current study includes the replication of many of our previous findings in a different sample of aMCI participants using similar diagnostic criteria. In addition, we employed methods to control for FDR and to minimize the possibility of family-wise Type 1 errors. Potential weaknesses include a modest number of aMCI participants with neuroimaging. Also, while this sample was highly diverse, subsequent work is required to determine the generalizability of this finding to persons from other cultural groups. Finally, future studies should explore whether susceptibility to frPSI is predictive of longitudinal changes in cognition and AD-specific biomarkers.
Maximum learning capacity and frPSI are uniquely and significantly associated with volumetric loss in AD prone areas (hippocampus, precuneus, inferior temporal lobules, rostral middle frontal areas and temporal pole). Beyond this, only the HVLT delayed recall score was associated with one brain region. Among CU, no significant correlations with brain volumes were observed on LASSI-BC or traditional measures.
Authors' Contributions: Curiel-Cid, Kitaigorodsky and Loewenstein developed the study concept and design. Data analysis was completed by Curiel-Cid, Zheng, and Loewenstein.
All other authors aided in drafting the manuscript and revising it critically for important intellectual content. All the authors stated their approval of the final version to be published and agreement to be accountable for the integrity and accuracy of all aspects of the work.
Corresponding author: Correspondence to Rosie E. Curiel Cid, Psy.D.
Funding: This research was funded by the National Institute of Aging Grant 1 R01 AG047649-01A1 (Rosie Curiel-Cid, PI), and 1 R01 AG047649-01A1 (David Loewenstein, PI). The sponsors had no role in the design and conduct of the study; in the collection analysis, and interpretation of data; in the preparation of the manuscript; or in the review or approval of the manuscript.
Conflicts of interest: The LASSI-BC measure was developed by and is intellectual property held by Drs. Loewenstein and Curiel-Cid at the University of Miami. For all other authors, there is no conflict of interest.
Ethics approval: This study is an IRB-approved investigation at the University of Miami Miller School of Medicine. All procedures performed met all national and international standards for the protection of human subjects.
Informed consent: Informed consent was collected from all participants, who were compensated for their participation.
Consent to publish: The submitted work has not been published previously and is not being considered for publication elsewhere. All authors consent to publish this study.
Data availability: Material has not been reproduced from prior publications, whether by the same or different authors. Any previously published material is explicitly quoted and referenced.
Code availability: Not applicable.
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Table 1. Comparison between CU and aMCI participants
Table 2. Mean difference between cognitively unimpaired and aMCI adjusting for maximum learning and covariates.
Table 3. Logistic regression of LASSI-BC measures in differentiating CU vs. aMCI.
Table 4. The associations between MRI volumes in AD prone regions and performance on the LASSI-BC and standard memory measures in participants with aMCI
