Varied Performance of Picture Description Task as a Screening Tool Across MCI Subtypes

Authors

Joan A. Mefford, University of California, Los Angeles
Zilong Zhao, Miro Health, Inc.
Leah Heilier, Miro Health, Inc.
Man Xu, Miro Health, Inc.
Guifeng Zhou, Miro Health, Inc.
Rachel Mace, Johns Hopkins University
Kelly L. Sloane, University of Pennsylvania
Shannon M. Sheppard, Chapman UniversityFollow
Shenly Glenn, Miro Health, Inc.

Document Type

Article

Publication Date

3-13-2023

Abstract

A picture description task is a component of Miro Health’s platform for self-administration of neurobehavioral assessments. Picture description has been used as a screening tool for identification of individuals with Alzheimer’s disease and mild cognitive impairment (MCI), but currently requires in-person administration and scoring by someone with access to and familiarity with a scoring rubric. The Miro Health implementation allows broader use of this assessment through self-administration and automated processing, analysis, and scoring to deliver clinically useful quantifications of the users’ speech production, vocal characteristics, and language. Picture description responses were collected from 62 healthy controls (HC), and 33 participants with MCI: 18 with amnestic MCI (aMCI) and 15 with non-amnestic MCI (naMCI). Speech and language features and contrasts between pairs of features were evaluated for differences in their distributions in the participant subgroups. Picture description features were selected and combined using penalized logistic regression to form risk scores for classification of HC versus MCI as well as HC versus specific MCI subtypes. A picture-description based risk score distinguishes MCI and HC with an area under the receiver operator curve (AUROC) of 0.74. When contrasting specific subtypes of MCI and HC, the classifiers have an AUROC of 0.88 for aMCI versus HC and and AUROC of 0.61 for naMCI versus HC. Tests of association of individual features or contrasts of pairs of features with HC versus aMCI identified 20 features with p-values below 5e-3 and False Discovery Rates (FDRs) at or below 0.113, and 61 contrasts with p-values below 5e-4 and FDRs at or below 0.132. Findings suggest that performance of picture description as a screening tool for MCI detection will vary greatly by MCI subtype or by the proportion of various subtypes in an undifferentiated MCI population.

Comments

This article was originally published in PLoS Digital Health, volume 2, issue 3, in 2023. https://doi.org/10.1371/journal.pdig.0000197

Recommended Citation

Mefford JA, Zhao Z, Heilier L, Xu M, Zhou G, Mace R, et al. (2023) Varied performance of picture description task as a screening tool across MCI subtypes. PLOS Digit Health 2(3): e0000197. https://doi.org/10.1371/journal.pdig.0000197

Peer Reviewed

1

Copyright

The authors

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.