77 Differentiating Amnestic Versus Non-Amnestic Mild Cognitive Impairment Using the NIH Toolbox Cognition Battery

• Published in: Journal of the International Neuropsychological Society Vol. 29; no. s1; pp. 380 - 381
• Main Authors: Perrin, Cameron K, Maher, Amanda Cook, Gregoire, Allyson, Reader, Jonathan, Bhaumik, Arijit, Hampstead, Benjamin M, Giordani, Bruno
• Format: Journal Article
• Language: English
• Published: New York, USA Cambridge University Press 01.11.2023
• Subjects: Alzheimer's disease; Clinical trials; Cognition & reasoning; Cognitive ability; Diagnosis; Language; MCI (Mild Cognitive Impairment); Memory; Neurodegenerative diseases; Poster Session 04: Aging | MCI; Reading; Short term memory; computerized neuropsychological testing; aging disorders; neuropsychological assessment
• ISSN: 1355-6177; 1469-7661
• DOI: 10.1017/S1355617723005131

Objective: In research, and particularly clinical trials, it is important to identify persons at high risk for developing Alzheimer’s Disease (AD), such as those with Mild Cognitive Impairment (MCI). However, not all persons with this diagnosis have a high risk of AD as MCI can be broken down further into amnestic MCI (aMCI), who have a high risk specifically for AD, and non-amnestic MCI (naMCI), who are predominantly at risk for other dementias. People with aMCI largely differ from healthy controls and naMCI on memory tasks as it is the hallmark criteria for an amnestic diagnosis. Given the growing use of the NIH Toolbox Cognition battery in research trials, this project investigated which Toolbox Cognition measures best differentiated aMCI from naMCI and in comparison to persons with normal cognition. Participants and Methods: A retrospective data analysis was conducted investigating performance on NIH Toolbox Cognition tasks among 199 participants enrolled in the Michigan Alzheimer’s Disease Research Center. All participants were over age 50 (51-89 years, M=70.64) and had a diagnosis of aMCI (N=74), naMCI (N=24), or Normal Cognition (N=101). Potential demographic differences were investigated using chi-square and ANOVAs. Repeated measure general linear model was used to look at potential group differences in Toolbox Cognition performance, covarying for age which was statistically different in aMCI versus Normal participants. Linear regression was used to determine which cognitive abilities, as measured by the Uniform Data Set-3 (UDS3), might contribute to Toolbox differences noted in naMCI versus aMCI groups. Results: As expected, aMCI had lower Toolbox memory scores compared to naMCI (p=0.007) and Normals (p<0.001). Interestingly, naMCI had lower Oral Reading scores than both aMCI (p=0.008) and Normals (p<0.001). There were no other Toolbox performance differences between the MCI groups. 19.4% of the variance in Oral Reading scores was explained by performance on the following UDS3 measures: Benson delayed recall (inverse relationship) and backward digit span and phonemic fluency (positive relationship). Conclusions: In this study, Toolbox Picture Sequence Memory and Oral Reading scores differentiated aMCI and naMCI groups. While the difference in memory was expected, it was surprising that the naMCI group performed worse than the aMCI and normal groups on the Toolbox Oral Reading task, a task presumed to reflect Crystalized abilities resistive to cognitive decline. Results suggest that Oral Reading is primarily positively associated with working memory and executive tasks from the UDS3, but negatively associated with visual memory. It is possible that the Oral Reading subtest is sensitive to domains of deficit aside from memory that can best distinguish aMCI from naMCI. A better understanding of the underlying features in the Oral Reading task will assist in better characterizing deficit patterns seen in naMCI, making selection of aMCI participants more effective in clinical trials.