Spoken Language Biomarker of Cognitive Impairment in PD
Overview
- Phase
- N/A
- Intervention
- Not specified
- Conditions
- Parkinson Disease
- Sponsor
- Northwestern University
- Enrollment
- 140
- Locations
- 1
- Primary Endpoint
- Discourse Composite Variable
- Status
- Completed
- Last Updated
- 2 years ago
Overview
Brief Summary
The purpose of this study is to identify unique profiles of speech and language changes that distinguish individuals with Parkinson's disease from adults without Parkinson's disease and individuals with Parkinson's disease with cognitive (e.g., memory, thinking skills) impairment from those without cognitive impairment.
Detailed Description
Aim 1 will characterize PD-MCI (Parkinson's disease mild cognitive impairment), PDN (Parkinson's disease without cognitive impairment), and HA (healthy adult) spoken discourse, cognitive, and motor speech profiles. Phase 2 biomarker development requires robustly characterized cohorts in which to test candidate biomarkers. Using a standardized battery of cognitive, language, and motor speech tests PD participants will be assigned to PD-MCI (single/multi-domain) or PDN groups. The investigators propose collecting spoken discourse samples using standardized elicitation protocols. The same tasks will be extracted from the extant HA database. Researchers will transcribe, code, and analyze discourse samples. Group differences (including sub-analyses for single and multi-domain MCI subtypes), elicitation stimuli effects, and group x stimuli interactions will be examined using multivariate and mixed-design ANOVA procedures. Aim 2 will develop and evaluate the classification accuracy of an optimally weighted discourse classification function for PD-MCI and PDN. The investigators propose using discriminant function analysis to identify an optimized composite variable that best predicts PD-MCI, PDN, and HA group membership. Sensitivity/specificity analyses, positive/negative predictive values, and receiver operating characteristic curves will be used to evaluate the discourse classification function properties. The primary endpoint is an optimally weighted discourse function that can classify PD-MCI with \> 80% sensitivity/specificity.
Investigators
Angela Roberts
Principal Investigator
Northwestern University
Eligibility Criteria
Inclusion Criteria
- •Only individuals with PD will be newly recruited and enrolled as part of the study. Healthy adult data will be extracted from extant databases (publicly available) from NIH-funded studies.
- •Inclusion Criteria Person with Parkinson's disease without cognitive impairment
- •Age 50-90 years
- •Diagnosis of idiopathic Parkinson's disease (UK Brain Bank criteria) made by a movement disorders specialist
- •Under the care of a movement disorders specialist for a minimum of 1-year duration
- •Native monolingual English speaker
- •Hoehn \& Yahr score between 1.5 and 4
- •Grade 10 education, or higher
- •Sufficient vision and hearing (aided or unaided) for all experiment tasks
- •Montreal Cognitive Assessment (or MoCA-converted MMSE score) greater than or equal to 25
Exclusion Criteria
- •Exclusion Criteria Neurological injury or disease (other than PD for the PD cohort)
- •History of unmanaged or untreated depression or major psychiatric illness
- •History of deep brain stimulation surgery (DBS)
- •Diagnosis of Dementia with Lewy Bodies
Outcomes
Primary Outcomes
Discourse Composite Variable
Time Frame: baseline
Weighted variable comprised of discourse features extracted from spoken discourse samples across productivity, lexical retrieval, verbal fluency, syntax complexity, grammatical accuracy, narrative coherence, correct information units, and main event accuracy. Raw data from each measure are multiplied by a specified weight and summed to generate a single composite discourse score.
Secondary Outcomes
- Correct information units (CIUs)(baseline)
- CIUs/min(baseline)
- % Main Events(baseline)
- Moving-average type-token ratio (MATTR)(baseline)
- % Grammatical(baseline)
- Words per minute(baseline)
- % CIUs(baseline)
- Mean length of utterance (MLU)(baseline)
- Number of clauses/content units(baseline)
- Percent maze words/total words(baseline)
- Subordination index (SI)(baseline)
- Number of pauses/content units(baseline)
- Word-level dysfluencies/content units(baseline)