Deep Stroop: Integrating eye tracking and speech processing to characterize people with neurodegenerative disorders while performing neuropsychological tests.

IF 7 2区 医学 Q1 BIOLOGY
Computers in biology and medicine Pub Date : 2025-01-01 Epub Date: 2024-12-01 DOI:10.1016/j.compbiomed.2024.109398
Trevor Meyer, Anna Favaro, Esther S Oh, Ankur Butala, Chelsie Motley, Pedro Irazoqui, Najim Dehak, Laureano Moro-Velázquez
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Abstract

Neurodegenerative diseases (NDs) can be difficult to precisely characterize and monitor as they present complex and overlapping signs despite affecting different neural circuits. Neuropsychological tests are important tools for assessing signs, but only considering patient-generated output can limit insight. Here, we present an improvement to the neuropsychological test evaluation paradigm by deeply characterizing patient interaction and behavior during tests based on multiple perspectives alongside typically evaluated output by performing multi-modal analysis of eye movement and speech data. Using the well-known Stroop Test, we compare behaviors of healthy controls to patients with Alzheimer's Disease (AD), Mild Cognitive Impairment, Parkinson's Disease (PD), and secondary Parkinsonism. We maximize accessibility and reproducibility by automatically extracting metrics, including eye motor behavior, speech patterns, and multimodal interplay, with almost no human input required. We find many metrics including increased horizontal saccade distances sensitive to all NDs, delayed task initiation in AD, response error patterns and blinking patterns that differ between AD and PD. Our metrics show both significantly different distributions between disease groups and simultaneous correlation with the MoCA and MDS-UPDRS-III clinical rating scales. Our findings show the utility of incorporating several perspectives into one output representation, as our metric breadth creates unique sign profiles that quantify and visualize a patient's dysfunction. These metrics provide much better sign characterization between diseases and correlation with disease severity than traditional Stroop measures. This methodology offers the potential to expand its application to other traditional neuropsychological tests, shifting the paradigm in diagnostic precision for NDs and advancing patient care.

Deep Stroop:在进行神经心理测试时,将眼动追踪和语音处理结合起来,以表征患有神经退行性疾病的人。
神经退行性疾病(NDs)尽管影响不同的神经回路,但其表现出复杂和重叠的体征,因此难以准确表征和监测。神经心理学测试是评估症状的重要工具,但只考虑患者产生的输出可能会限制洞察力。在这里,我们提出了一种改进的神经心理学测试评估范式,通过对眼动和言语数据进行多模态分析,深入表征患者在测试期间的互动和行为,以及典型的评估输出。使用著名的Stroop测试,我们比较了健康对照组与阿尔茨海默病(AD)、轻度认知障碍、帕金森病(PD)和继发性帕金森病患者的行为。我们通过自动提取指标,包括眼动行为、语音模式和多模态相互作用,几乎不需要人工输入,最大限度地提高了可访问性和可重复性。我们发现了许多指标,包括对所有nd敏感的水平扫视距离增加,AD中延迟的任务启动,AD和PD之间不同的响应错误模式和闪烁模式。我们的指标显示疾病组之间的分布显著不同,同时与MoCA和MDS-UPDRS-III临床评分量表相关。我们的研究结果显示了将多个视角整合到一个输出表示中的效用,因为我们的度量宽度创建了独特的标志概况,可以量化和可视化患者的功能障碍。与传统的Stroop测量相比,这些指标提供了更好的疾病表征和疾病严重程度相关性。这种方法提供了将其应用扩展到其他传统神经心理学测试的潜力,改变了NDs诊断精度的范式,并推进了患者护理。
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来源期刊
Computers in biology and medicine
Computers in biology and medicine 工程技术-工程:生物医学
CiteScore
11.70
自引率
10.40%
发文量
1086
审稿时长
74 days
期刊介绍: Computers in Biology and Medicine is an international forum for sharing groundbreaking advancements in the use of computers in bioscience and medicine. This journal serves as a medium for communicating essential research, instruction, ideas, and information regarding the rapidly evolving field of computer applications in these domains. By encouraging the exchange of knowledge, we aim to facilitate progress and innovation in the utilization of computers in biology and medicine.
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