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Changes in conversational pattern as a clinical trial outcome: a proof-of-concept study using the I-CONECT data
Liu Chen1, Chao-Yi Wu1, Hiroko H Dodge1
1Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, United States.
Innovation in Aging
|July 6, 2026
Summary
Natural language processing can detect cognitive decline. A 6-month conversational intervention significantly reduced semantic noise in individuals with mild cognitive impairment, bringing their scores closer to those with normal cognition.
Area of Science:
- Natural Language Processing
- Cognitive Science
- Computational Linguistics
Background:
- Natural language processing (NLP) offers novel linguistic measures for detecting subtle cognitive decline.
- Current NLP analyses often rely on constrained tasks like picture descriptions, limiting real-world applicability.
- Semantic noise (SN) presents a novel approach to quantify conversational patterns and monitor cognitive changes.
Purpose of the Study:
- To investigate the efficacy of a behavioral intervention in altering semantic noise (SN) in individuals with mild cognitive impairment (MCI).
- To assess the sensitivity of SN as a linguistic measure for tracking cognitive changes over time.
- To explore the potential of using spontaneous conversation analysis for cognitive monitoring.
Main Methods:
- Analysis of text from semi-structured conversations within the I-CONECT project.
- Participants included cognitively normal (NC) and mild cognitive impairment (MCI) individuals in an intervention group.
- Mean semantic noise (MSN) was measured at baseline (week 2) and 6 months (week 24) of a 6-month conversational intervention (4 sessions/week).
Main Results:
- Baseline MSN was significantly higher in the MCI group (2.09) compared to the NC group (1.87) (p=.024).
- After 6 months, MSN in the MCI group significantly decreased (p=.009), approaching levels observed in the NC group (1.95).
- No significant change in MSN was observed in the NC group (p=.426), and post-intervention MCI and NC groups did not differ significantly (p=.280).
Conclusions:
- A 6-month conversational intervention normalized semantic noise (SN) in individuals with mild cognitive impairment (MCI), making their conversational patterns resemble those of cognitively normal (NC) individuals.
- Linguistic measures derived from naturalistic conversations are sensitive to cognitive changes.
- Further research into extracting linguistic measurements from spontaneous conversations can aid in daily cognitive monitoring.
Keywords:
Conversational engagementDigital linguistic biomarkerMild cognitive impairmentNatural language processingSpontaneous conversationMore Related Videos
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