Spatio-Semantic Graphs From Picture Description: Applications to Detection of Cognitive Impairment

Pranav S. Ambadi, Kristin Basche, Rebecca L. Koscik, Visar Berisha, Julie M. Liss, Kimberly D. Mueller

Research output: Contribution to journalArticlepeer-review

Abstract

Clinical assessments often use complex picture description tasks to elicit natural speech patterns and magnify changes occurring in brain regions implicated in Alzheimer's disease and dementia. As The Cookie Theft picture description task is used in the largest Alzheimer's disease and dementia cohort studies available, we aimed to create algorithms that could characterize the visual narrative path a participant takes in describing what is happening in this image. We proposed spatio-semantic graphs, models based on graph theory that transform the participants' narratives into graphs that retain semantic order and encode the visuospatial information between content units in the image. The resulting graphs differ between Cognitively Impaired and Unimpaired participants in several important ways. Cognitively Impaired participants consistently scored higher on features that are heavily associated with symptoms of cognitive decline, including repetition, evidence of short-term memory lapses, and generally disorganized narrative descriptions, while Cognitively Unimpaired participants produced more efficient narrative paths. These results provide evidence that spatio-semantic graph analysis of these tasks can generate important insights into a participant's cognitive performance that cannot be generated from semantic analysis alone.

Original languageEnglish (US)
Article number795374
JournalFrontiers in Neurology
Volume12
DOIs
StatePublished - Dec 9 2021

Keywords

  • Alzheimer's disease
  • cognition
  • cookie theft
  • dementia
  • graph theory
  • semantic analysis
  • speech biomarkers

ASJC Scopus subject areas

  • Neurology
  • Clinical Neurology

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