Can auditory processing dysfunction indicate early cognitive decline?

IF 4.4 Q1 CLINICAL NEUROLOGY

Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring Pub Date : 2025-09-26 eCollection Date: 2025-10-01 DOI:10.1002/dad2.70189

Xinrong Ma, Jiayu Li, Ying Wang, Shiyuan Li, Junjie Guo, Wenxin Shen, Xiao Yu, Hongyu Dong, Shujian Huang, Linpeng Li, Jian Wang, Shankai Yin, Hui Wang

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Abstract

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.

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听觉处理功能障碍是否预示着早期认知能力下降？

中央听觉加工（Central auditory processing， CAP）是语音感知的关键，也是认知功能的基础。本研究探讨间隙检测阈值（GDT）是否可以作为识别认知障碍（CI）高危痴呆个体的早期标记。方法：64名老年人接受外周听觉、认知和CAP评估。机器学习和静息状态脑电图(EEG)/事件相关电位（ERP）分析探讨了CI的预测因素和神经相关性。结果：CI组GDT显著高于对照组（平均±标准差分别为8.25±6.14 ms和5.98±3.44 ms, p = 0.034），且GDT与认知测试成绩呈负相关（如Addenbrooke's cognitive Examination III: r = -0.40, p = 0.001）。GDT成为关键的预测因子。脑电图显示听觉连通性改变，ERP显示高GDT个体的N1/N2振幅降低（错误发现率纠正）。讨论：GDT可能反映CI个体的早期神经生理变化，具有作为非侵入性生物标志物的潜力。重点：中枢性听觉处理（CAP）测试成绩与认知测试显著相关。通过机器学习，筛选出预测认知障碍的最佳变量间隙检测阈值（GDT）。正常对照（NC）组进行GDT亚组分析。与低GDT亚组相比，高GDT亚组的事件相关电位的认知成分振幅更低，功能连通性也存在许多差异，表明GDT对认知功能的变化具有预测价值。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring Medicine-Psychiatry and Mental Health

CiteScore

7.80

自引率

7.50%

发文量

101

审稿时长

8 weeks

期刊介绍： Alzheimer''s & Dementia: Diagnosis, Assessment & Disease Monitoring (DADM) is an open access, peer-reviewed, journal from the Alzheimer''s Association® that will publish new research that reports the discovery, development and validation of instruments, technologies, algorithms, and innovative processes. Papers will cover a range of topics interested in the early and accurate detection of individuals with memory complaints and/or among asymptomatic individuals at elevated risk for various forms of memory disorders. The expectation for published papers will be to translate fundamental knowledge about the neurobiology of the disease into practical reports that describe both the conceptual and methodological aspects of the submitted scientific inquiry. Published topics will explore the development of biomarkers, surrogate markers, and conceptual/methodological challenges. Publication priority will be given to papers that 1) describe putative surrogate markers that accurately track disease progression, 2) biomarkers that fulfill international regulatory requirements, 3) reports from large, well-characterized population-based cohorts that comprise the heterogeneity and diversity of asymptomatic individuals and 4) algorithmic development that considers multi-marker arrays (e.g., integrated-omics, genetics, biofluids, imaging, etc.) and advanced computational analytics and technologies.