Comprehensive verbal fluency features predict executive function performance.
Comprehensive verbal fluency features predict executive function performance.
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DOI：
10.1038/s41598-021-85981-1
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发表时间：
2021-03-25
影响因子：
4.6
通讯作者：
Weis S
中科院分区：
文献类型：
--
作者：
Amunts J;Camilleri JA;Eickhoff SB;Patil KR;Heim S;von Polier GG;Weis S
语义语言流畅性（sVF）任务通常用于临床诊断电池和研究环境。当执行sVF任务来评估执行功能时，正确生成单词的总和是主要的衡量标准。尽管以前的研究表明，通过使用更细粒度的sVF信息，可以更好地了解EF的性能，但这还没有得到客观的评估。为了研究使用更细粒度的sVF特征集来预测EF表现的潜力，对健康的单语德语参与者（n = 230）进行了全面的EF测试和sVF任务测试，从中提取了包括总和分数、错误类型、语音中断和语义相关性在内的特征。应用机器学习方法从以前未见过的受试者的sVF特征预测EF分数。为了研究高级sVF特征集的预测能力，我们将其与常用的和分数分析进行了比较。结果表明，综合sVF特征集对8 / 14 EF测试的预测效果显著，特别是在预测认知灵活性和抑制过程方面优于总和得分。这些发现强调了sVF任务的综合评估的预测潜力，这可能被用于诊断性eeg筛查。
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.
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DOI：
10.1590/1980-57642015dn93000008
发表时间：
2015-07
期刊：
Dementia & neuropsychologia
影响因子：
--
作者：
de Paula JJ;Paiva GCC;Costa DS
通讯作者：
Costa DS
影响因子：
5.8
作者：
Ardila, A;Galeano, LM;Rosselli, M
通讯作者：
Rosselli, M
影响因子：
1.7
作者：
Baddeley, A
通讯作者：
Baddeley, A
影响因子：
3.2
作者：
CLARK, HH
通讯作者：
CLARK, HH
影响因子：
5.4
作者：
Bedi, Gillinder;Carrillo, Facundo;Corcoran, Cheryl M.
通讯作者：
Corcoran, Cheryl M.