Purpose: In this study, we sought to identify critical linguistic markers that can differentiate sentence processing of individuals with mild cognitive impairment (MCI) from the sentence processing of normal-aging populations by manipulating sentences’ linguistic complexity. We investigated whether passive sentences, as linguistically complex structures, can serve as linguistic markers that can contribute to diagnoses that distinguish MCI from normal aging. Method: In total, 52 participants, including 26 adults with amnestic MCI and 26 cognitively unimpaired adults, participated in the study. All participants were native speakers of Korean. We administered the two subsets of active and passive conditions using a sentence–picture paradigm with semantically reversible sentences to both groups. Results: A mixed-effects model using PROC NLMIXED demonstrated that the MCI group exhibited differentially greater difficulty in processing passive than active sentences compared to the normal-aging group. A logistic regression fitted with the PROC LOGISTIC model identified the sum of the passive sentences, with age and education effects as the best models to distinguish individuals with MCI from the normal-aging group. Conclusion: Sentence comprehension deficits emerged in the MCI stage when the syntactic complexity was increased. Furthermore, a passive structure was the best predictor for efficiently distinguishing the MCI group from the normal-aging group. These results are clinically and theoretically important, given that linguistic complexity can serve as a critical behavioral marker in the detection of early symptoms associated with linguistic–cognitive decline.
Bibliographical noteFunding Information:
This work was supported by Global Research Network program through the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2017S1A2A2038375; awarded to Jee Eun Sung).
The work was supported by global research network program through the ministry of education of the republic of korea and the national research foundation(NRF-2017S1A2A2;awarded to Jee Eun Sung).
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