Alzheimer's & dementia (Amsterdam, Netherlands) · 2025
Can auditory processing dysfunction indicate early cognitive decline?
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Ma Xinrong, Li Jiayu, Wang Ying, Li Shiyuan, Guo Junjie, Shen Wenxin, Yu Xiao, Dong Hongyu, Huang Shujian, Li Linpeng, Wang Jian, Yin Shankai, Wang Hui
Аннотация
INTRODUCTION: Central auditory processing (CAP) is crucial for speech perception and is also fundamental for cognitive function. This study investigated whether gap detection threshold (GDT) could serve as an early marker for identifying individuals with cognitive impairment (CI) at high risk of dementia.
METHODS: Sixty-four older adults underwent peripheral auditory, cognitive, and CAP assessments. Machine learning and resting state electroencephalography (EEG)/event-related potential (ERP) analyses explored predictors and neural correlates of CI.
RESULTS: GDT was significantly higher in those with CI (mean ± standard deviation: 8.25 ± 6.14 versus 5.98 ± 3.44 ms, respectively, p = 0.034), and negatively correlated with cognitive test scores (e.g., Addenbrooke's Cognitive Examination III: r = -0.40, p = 0.001). GDT emerged as a key predictor. EEG showed altered auditory connectivity and ERP revealed reduced N1/N2 amplitudes in high-GDT individuals (false discovery rate corrected p < 0.05).
DISCUSSION: GDT may reflect early neurophysiological changes in individuals with CI and has potential as a non-invasive biomarker.
HIGHLIGHTS: Central auditory processing (CAP) test scores were found to be significantly correlated with cognitive tests.By machine learning, the best variable gap detection threshold (GDT) for predicting cognitive impairment was screened out.GDT subgroup analysis was performed within the normal control (NC) group. Compared to the low GDT subgroup, the high GDT subgroup had lower amplitudes of the cognitive components of the event-related potential and many differences in functional connectivity, indicating that GDT has predictive value for changes in cognitive function.
METHODS: Sixty-four older adults underwent peripheral auditory, cognitive, and CAP assessments. Machine learning and resting state electroencephalography (EEG)/event-related potential (ERP) analyses explored predictors and neural correlates of CI.
RESULTS: GDT was significantly higher in those with CI (mean ± standard deviation: 8.25 ± 6.14 versus 5.98 ± 3.44 ms, respectively, p = 0.034), and negatively correlated with cognitive test scores (e.g., Addenbrooke's Cognitive Examination III: r = -0.40, p = 0.001). GDT emerged as a key predictor. EEG showed altered auditory connectivity and ERP revealed reduced N1/N2 amplitudes in high-GDT individuals (false discovery rate corrected p < 0.05).
DISCUSSION: GDT may reflect early neurophysiological changes in individuals with CI and has potential as a non-invasive biomarker.
HIGHLIGHTS: Central auditory processing (CAP) test scores were found to be significantly correlated with cognitive tests.By machine learning, the best variable gap detection threshold (GDT) for predicting cognitive impairment was screened out.GDT subgroup analysis was performed within the normal control (NC) group. Compared to the low GDT subgroup, the high GDT subgroup had lower amplitudes of the cognitive components of the event-related potential and many differences in functional connectivity, indicating that GDT has predictive value for changes in cognitive function.