Validating digital cognitive biomarkers for Alzheimer's disease: Biomarkers (non‐neuroimaging) / Novel biomarkers. (7th December 2020)

• Record Type: Journal Article
• Authors: Bock, Jason R; Lee, Michael D; Hara, Junko; Fortier, Dennis; Mangrola, Tushar; Shankle, William R
• Is Part Of: Alzheimer's & dementia. Volume 16(2020)Supplement 4
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-07
• 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.043875
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: This study sought to validate a pragmatic method to predict impending cognitive decline in Alzheimer's disease (AD) patients, prior to the development of hallmark symptoms. The method, a Hierarchical Bayesian Cognitive Process (HBCP) model, uses item responses to a wordlist memory (WLM) test to generate digital cognitive biomarkers (Table 1). We replicated our earlier study, performed on AVLT item response data from Mayo Clinic, by applying the HBCP model to distinguish non‐decliner and decliner groups of normal subjects at baseline from a novel dataset, the Alzheimer's Disease Neuroimaging Initiative (ADNI), with a distinct WLM test, ADAS‐Cog. Method: From the ADNI dataset, we classified non‐decliner subjects (n = 442) as those whose diagnosis remained normal for 3 or more years after normal baseline assessment and decliner subjects (n = 61) as those who developed amnestic MCI or AD within 3 years of normal baseline assessment. Three analytic approaches were compared: 1) Traditional summary scores per ADAS‐Cog task were assessed for group differences with a commonly‐used statistical approach, logistic regression; 2) summary scores were assessed with a Bayesian modeling approach; and 3) item response data were assessed with our HBCP model, generating digital cognitive biomarkers. Result: Logistic regression of summary scores generated β coefficients (Table 2) that did not significantly discriminate between groups. Bayes Factor assessment of fitted Gaussian distributions to the per‐task group recall by summary score measurement provided moderate evidence that the groups were measurably the same ( BF sd = 3.4, 3.1, 2.9, and 1.4, respectively). The HBCP model produced posterior distributions of group differences. Bayes Factor assessment identified three with notable group differences: Immediate Retrieval from Durable Storage, L1 ( BF ds = 11.8, strong evidence), One‐shot Learning, r ( BF ds = 4.5, moderate), and Partial Learning, a ( BF ds = 2.9, weak; Figure). Conclusion: The obtained strong evidence for L1 group differences represents successful replication of previous work with our HBCP model on this novel dataset. The ability to identify differences in subjects diagnosed cognitively normal at time of assessment demonstrates the HBCP model's advantage over summary score assessment and its applicability for detection of impending cognitive decline in asymptomatic AD patients.