Graph methods to infer spatial disturbances: Application to Huntington's Disease's speech

Area of Science:

• Neuroscience
• Cognitive Science
• Linguistics

Background:

• Huntington's Disease (HD) is an inherited neurodegenerative disorder caused by Htt gene mutations.
• Cognitive impairments, particularly in spatial abilities, are common in HD but difficult to assess.
• Current assessment methods for spatial deficits in HD are time-consuming and require expert evaluation.

Purpose of the Study:

• To establish proof-of-concept that speech can be used to assess spatial deficits in Huntington's Disease.
• To investigate the utility of language-based analysis for evaluating spatial cognition in HD patients.
• To develop and validate a novel model for assessing spatial abilities through speech in HD.

Main Methods:

• Development of the Spatial Description Model (SDM) to analyze spatial relations in speech.
• Utilized the Cookie Theft Picture (CTP) task, focusing on sentences with spatial terms.
• Included 78 individuals with mutant Htt (56 manifest, 22 premanifest) and 25 healthy controls from BIOHD and Repair-HD cohorts.
• Validated the SDM's convergence and divergence using the SelfCog battery.

Main Results:

• The SDM was the only assessed model to detect significant differences in spatial abilities.
• Individuals with manifest HD described fewer spatial relations and showed less spatial exploration than controls.
• SDM-derived spatial measures correlated with visuospatial and language performance, but not motor, executive, or memory functions.

Conclusions:

• Language-based analysis, via the Spatial Description Model, can effectively detect spatial disturbances in Huntington's Disease patients.
• This approach offers a novel, potentially remote, method for assessing spatial deficits in HD.
• Integrating spatial assessment into language-based evaluations expands the functional panel for HD monitoring.