Identification of cognitive resilience through neuropsychological tests in the Framingham Heart Study. (20th December 2022)

• Record Type: Journal Article
• Authors: Hwang, Phillip H; Amini, Samad; Karjadi, Cody; Lin, Honghuang; Andersen, Stacy L.; Paschalidis, Ioannis; Au, Rhoda; Mez, Jesse B.
• Is Part Of: Alzheimer's & dementia. Volume 18(2022)Supplement 7
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-20
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.059082
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: To examine the predictive abilities of 1) traditional neuropsychological (NP) test summary scores, and 2) error and process scores in separating cognitively resilient individuals from those who exhibit cognitive decline. Method: A total of 69 participants from the Framingham Heart Study (FHS) Offspring and Omni 1 cohorts who had ≥1 NP assessment from 2011–2014 and at high risk for dementia were included in the sample. Participants also had to be ≥60 years of age and dementia‐free at the time of NP assessment. Being at high risk for dementia was determined using a cutoff score of ≥23 based on a published FHS dementia risk score algorithm. Dementia diagnosis was adjudicated based on DSM‐IV criteria by consensus. Participants completed 10 NP tests, which were scored using 11 traditional scores and 75 error and process scores. Supervised machine learning algorithms were used to classify individuals who were cognitively resilient, defined as not progressing to dementia within ≥5 years after the NP evaluation. Result: Among the 69 participants in our sample, 10 were cognitively resilient while 59 developed dementia. Average area under the curve when using traditional NP metrics to predict cognitive resilience ranged from 90.5% to 95.7% across each supervised machine learning algorithm (logistic regression, random forest, support vector machines, and light gradient boosting machine) (Figure 1). Average accuracy ranged from 85.3% to 92.6%, while average sensitivity was 100% across all models and average specificity ranged from 82.7% to 91.4% (Table 1). Average area under the curve when using the error and process scores to predict cognitive resilience ranged from 93.2% to 98.3% across each supervised machine learning algorithm (Figure 2). Average accuracy ranged from 89.9% to 97.0%, while average sensitivity was also 100% across all models and average specificity ranged from 88.2% to 96.5% (Table 2). All performance metrics were calculated based on five‐fold cross‐validation. Conclusion: Results from this pilot study suggest that use of NP tests may serve as an accurate method to identify individuals who are cognitive resilient, allowing for the study of genetic, lifestyle, and other factors that confer resilience among individuals with an elevated risk for dementia.