Maboudian Samira A, Hsu Ming, Zhang Zhihao
Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States.
Haas School of Business, University of California, Berkeley, Berkeley, CA, United States.
Front Aging Neurosci. 2022 Jul 29;14:810799. doi: 10.3389/fnagi.2022.810799. eCollection 2022.
The verbal fluency task, where participants name as many instances of a specific semantic or phonemic category as possible in a certain time limit, is widely used to probe language and memory retrieval functions in research and clinical settings. More recently, interests in using longitudinal observations in verbal fluency to examine changes over the lifespan have grown, in part due to the increasing availability of such datasets, yet quantitative methods for comparing repeated measures of verbal fluency responses remain scarce. As a result, existing studies tend to focus only on the number of unique words produced and how this metric changes over time, overlooking changes in other important features in the data, such as the identity of the words and the order in which they are produced. Here, we provide an example of how the literature of recurrence analysis, which aims to visualize and analyze non-linear time series, may present useful visualization and analytical approaches for this problem. Drawing on this literature, we introduce a novel metric (the "distance from diagonal," or DfD) to quantify semantic fluency data that incorporates analysis of the sequence order and changes between two lists. As a demonstration, we apply these methods to a longitudinal dataset of semantic fluency in people with Alzheimer's disease and age-matched controls. We show that DfD differs significantly between healthy controls and Alzheimer's disease patients, and that it complements common existing metrics in diagnostic prediction. Our visualization method also allows incorporation of other less common metrics-including the order that words are recalled, repetitions of words within a list, and out-of-category intrusions. Additionally, we show that these plots can be used to visualize and compare aggregate recall data at the group level. These methods can improve understanding of verbal fluency deficits observed in various neuropsychiatric and neurological disorders.
言语流畅性任务要求参与者在一定时间限制内尽可能多地说出特定语义或音素类别的实例，该任务在研究和临床环境中被广泛用于探究语言和记忆检索功能。最近，利用言语流畅性的纵向观察来研究生命周期内变化的兴趣有所增加，部分原因是此类数据集越来越容易获得，但用于比较言语流畅性反应重复测量的定量方法仍然稀缺。因此，现有研究往往只关注产生的独特单词数量以及该指标随时间的变化，而忽略了数据中其他重要特征的变化，比如单词的身份以及产生的顺序。在此，我们提供一个示例，说明旨在可视化和分析非线性时间序列的递归分析文献如何为这个问题提供有用的可视化和分析方法。借鉴该文献，我们引入一种新的指标（“离对角线距离”，简称DfD）来量化语义流畅性数据，该指标纳入了对两个列表之间序列顺序和变化的分析。作为演示，我们将这些方法应用于阿尔茨海默病患者和年龄匹配对照组的语义流畅性纵向数据集。我们表明，健康对照组和阿尔茨海默病患者之间的DfD存在显著差异，并且它在诊断预测中补充了现有的常用指标。我们的可视化方法还允许纳入其他不太常见的指标，包括单词被回忆的顺序、列表中单词的重复以及类别外的干扰项。此外，我们表明这些图表可用于可视化和比较组水平的总体回忆数据。这些方法可以增进对在各种神经精神和神经疾病中观察到的言语流畅性缺陷的理解。
