Institute of Neuroscience and Medicine (INM-7 Brain and Behaviour), Forschungszentrum Jülich, Wilhelm-Johnen-Str, 52428, Jülich, Germany.
Institute of Systems Neuroscience, Heinrich-Heine University, Moorenstr. 5, 40225, Düsseldorf, Germany.
Sci Rep. 2021 Mar 25;11(1):6929. doi: 10.1038/s41598-021-85981-1.
Semantic verbal fluency (sVF) tasks are commonly used in clinical diagnostic batteries as well as in a research context. When performing sVF tasks to assess executive functions (EFs) the sum of correctly produced words is the main measure. Although previous research indicates potentially better insights into EF performance by the use of finer grained sVF information, this has not yet been objectively evaluated. To investigate the potential of employing a finer grained sVF feature set to predict EF performance, healthy monolingual German speaking participants (n = 230) were tested with a comprehensive EF test battery and sVF tasks, from which features including sum scores, error types, speech breaks and semantic relatedness were extracted. A machine learning method was applied to predict EF scores from sVF features in previously unseen subjects. To investigate the predictive power of the advanced sVF feature set, we compared it to the commonly used sum score analysis. Results revealed that 8 / 14 EF tests were predicted significantly using the comprehensive sVF feature set, which outperformed sum scores particularly in predicting cognitive flexibility and inhibitory processes. These findings highlight the predictive potential of a comprehensive evaluation of sVF tasks which might be used as diagnostic screening of EFs.
语义流畅性（sVF）任务通常在临床诊断工具包以及研究中使用。在进行 sVF 任务以评估执行功能（EF）时，正确生成的单词总数是主要的衡量标准。尽管先前的研究表明，通过使用更精细的 sVF 信息，可以更好地了解 EF 的表现，但这尚未得到客观评估。为了研究使用更精细的 sVF 特征集来预测 EF 表现的潜力，对 230 名健康的、只会说德语的单语母语者参与者进行了综合 EF 测试和 sVF 任务测试，从中提取了包括总分、错误类型、言语停顿和语义相关性在内的特征。应用机器学习方法从未见过的受试者的 sVF 特征中预测 EF 得分。为了研究高级 sVF 特征集的预测能力，我们将其与常用的总分分析进行了比较。结果表明，使用综合 sVF 特征集可以显著预测 8/14 项 EF 测试，特别是在预测认知灵活性和抑制过程方面，其表现优于总分。这些发现突出了综合评估 sVF 任务的预测潜力，这可能作为 EF 的诊断筛选。
