Syntactic Complexity as a Linguistic Marker to Differentiate Mild Cognitive Impairment From Normal Aging

Area of Science:

• Neuroscience
• Linguistics
• Gerontology

Background:

• Mild cognitive impairment (MCI) is an intermediate stage between normal aging and dementia.
• Early detection of MCI is crucial for timely intervention and management.
• Identifying reliable biomarkers for MCI is an ongoing research challenge.

Purpose of the Study:

• To identify linguistic markers differentiating sentence processing in individuals with MCI versus normal aging.
• To investigate if passive sentences, as linguistically complex structures, can serve as diagnostic markers for MCI.
• To explore the relationship between syntactic complexity and sentence comprehension deficits in MCI.

Main Methods:

• A study involving 52 participants (26 with amnestic MCI, 26 cognitively unimpaired).
• Utilized a sentence-picture paradigm with semantically reversible active and passive sentences in Korean.
• Employed mixed-effects and logistic regression models (PROC NLMIXED, PROC LOGISTIC) for data analysis.

Main Results:

• The MCI group demonstrated significantly greater difficulty processing passive sentences compared to active sentences, relative to the normal-aging group.
• A logistic regression model incorporating passive sentence processing, age, and education effectively distinguished individuals with MCI.
• Increased syntactic complexity, specifically through passive sentence structures, revealed comprehension deficits in the MCI group.

Conclusions:

• Sentence comprehension deficits in MCI are exacerbated by increased linguistic complexity.
• Passive sentence structures are a strong predictor for differentiating MCI from normal aging.
• Linguistic complexity offers a valuable behavioral marker for detecting early signs of cognitive decline.