The Boston Process Approach and Digital Neuropsychological Assessment: Past Research and Future Directions.

D. Libon, R. Swenson, M. Lamar, C. Price, Ganesh Baliga, Á. Pascual-Leone, R. Au, Stephanie Cosentino, S. Andersen
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引用次数: 6

Abstract

Neuropsychological assessment using the Boston Process Approach (BPA) suggests that an analysis of the strategy or the process by which tasks and neuropsychological tests are completed, and the errors made during test completion convey much information regarding underlying brain and cognition and are as important as overall summary scores. Research over the last several decades employing an analysis of process and errors has been able to dissociate between dementia patients diagnosed with Alzheimer's disease, vascular dementia associated with MRI-determined white matter alterations, and Parkinson's disease; and between mild cognitive impairment subtypes. Nonetheless, BPA methods can be labor intensive to deploy. However, the recent availability of digital platforms for neuropsychological test administration and scoring now enables reliable, rapid, and objective data collection. Further, digital technology can quantify highly nuanced data previously unobtainable to define neurocognitive constructs with high accuracy. In this paper, a brief review of the BPA is provided. Studies that demonstrate how digital technology translates BPA into specific neurocognitive constructs using the Clock Drawing Test, Backward Digit Span Test, and a Digital Pointing Span Test are described. Implications for using data driven artificial intelligence-supported analytic approaches enabling the creation of more sensitive and specific detection/diagnostic algorithms for putative neurodegenerative illness are also discussed.
波士顿过程方法和数字神经心理学评估:过去的研究和未来的方向。
使用波士顿过程方法(BPA)的神经心理学评估表明,对完成任务和神经心理学测试的策略或过程的分析,以及在测试完成过程中所犯的错误,传达了许多关于潜在大脑和认知的信息,与总体总结分数一样重要。过去几十年的研究通过对过程和错误的分析,已经能够将诊断为阿尔茨海默病的痴呆患者、与mri确定的白质改变相关的血管性痴呆患者和帕金森病分离开来;在轻度认知障碍亚型之间。尽管如此,BPA方法的部署可能是劳动密集型的。然而,最近神经心理测试管理和评分的数字平台的可用性使得可靠、快速和客观的数据收集成为可能。此外,数字技术可以量化以前无法获得的高度细微的数据,以高精度地定义神经认知结构。本文对双酚a进行了简要的综述。描述了数字技术如何使用时钟绘制测试、向后数字广度测试和数字指向广度测试将BPA转化为特定的神经认知结构的研究。还讨论了使用数据驱动的人工智能支持的分析方法的含义,该方法能够为假定的神经退行性疾病创建更敏感和特定的检测/诊断算法。
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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