Na Daxiang, Yang Yidan, Xie Li, Piekna-Przybylska Dorota, Bunn Dominic, Shamambo Maleelo, White Patricia
Department of Neuroscience, Ernst J. Del Monte Institute for Neuroscience, University of Rochester Medical Center, Rochester, New York 14642.
School of Mathematical Sciences, Rochester Institute of Technology, Rochester, New York 14623.
eNeuro. 2025 Apr 23;12(4). doi: 10.1523/ENEURO.0049-25.2025. Print 2025 Apr.
Early and accurate diagnosis of Alzheimer's disease (AD) will be key for effective personalized treatment plans ( Cummings, 2023). Significant difficulties in auditory processing have been frequently reported in many patients with mild cognitive impairment, the prodromal form of AD ( Tarawneh et al., 2022), making it an outstanding candidate as AD diagnostic biomarker. However, the efficiency of diagnosis with this parameter has not been explored. Here we show that when male mice with amyloidosis begin to show memory decline, changes in the auditory brainstem response (ABR) to clicks enable the reliable diagnosis of disease using a machine learning algorithm. Interpretation of the machine learning diagnosis revealed that the upper levels of the auditory pathway, including the inferior colliculus, were the probable sources of the defects. Histological analyses show that in these locations, neuroinflammation and plaque deposition temporally correlate with behavioral changes consistent with memory loss. While these findings are tempered by the caveat that they derive from amyloidosis mice, we propose that ABR measurements be evaluated as an additional rapid, low-cost, noninvasive biomarker to assist the diagnostic testing of early-stage AD.
早期准确诊断阿尔茨海默病（AD）对于制定有效的个性化治疗方案至关重要（卡明斯，2023年）。许多轻度认知障碍患者（AD的前驱形式）经常报告存在听觉处理方面的重大困难（塔拉维等人，2022年），这使其成为AD诊断生物标志物的优秀候选对象。然而，尚未探索使用该参数进行诊断的效率。在此我们表明，当患有淀粉样变性的雄性小鼠开始出现记忆衰退时，对点击声的听觉脑干反应（ABR）变化能够通过机器学习算法可靠地诊断疾病。机器学习诊断的解读表明，听觉通路的上层结构，包括下丘，可能是缺陷的来源。组织学分析表明，在这些部位，神经炎症和斑块沉积在时间上与符合记忆丧失的行为变化相关。虽然这些发现因源于淀粉样变性小鼠而受到限制，但我们建议将ABR测量作为一种额外的快速、低成本、非侵入性生物标志物进行评估，以辅助早期AD的诊断测试。
