Behavioural and neural structure of fluent speech production deficits in aphasia.
Behavioural and neural structure of fluent speech production deficits in aphasia.
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DOI：
10.1093/braincomms/fcac327
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发表时间：
2023
影响因子：
4.8
通讯作者：
中科院分区：
文献类型：
--
作者：
左半球中风后流利言语产生的缺陷是一个核心问题，因为它们对患者的生活产生影响，并提供了关于语言处理神经组织的见解。流利的言语产生需要语音、语义和句法处理的快速协调，因此本研究探讨了连接言语的缺陷与这些语言子系统的关系。行为数据（N = 69例左半球卒中后失语症患者）包括一组多样化和全面的叙述性言语产生措施和总体严重程度、语义缺陷和语音缺陷的措施。这些措施进入主成分分析与双因素旋转-一个潜在的结构模型，其中每个项目加载一个一般因素，反映了什么是共同的项目，和正交因素，解释方差不占一般因素。病变数据可用于58名参与者，每个因素得分进行了分析与多变量病变症状映射。连接中断的影响进行了评估，使用强大的回归与道断开或连接的图论措施作为预测。主成分分析产生了一个四因素的解决方案，占70.6%的数据中的方差，与一个一般因素对应的整体严重性和长度和复杂性的语音输出（复杂性因素），词汇句法因素，语义和语音的独立因素。言语输出的复杂性缺陷与颞顶叶区域较大有关，与总体失语症的严重程度相似。词汇句法因素与额顶叶区域中相对较小的一组损伤相关，这可能反映了控制系统的招募，以支持检索和正确使用主要服务于句法而非语义功能的词汇。在控制了总体病变体积后，基于通道的连通性中断测量与缺损评分无统计学相关性。在控制了总体病变体积后，语言网络效率和语言网络内的平均聚类系数与缺损评分显著相关。这些结果突出了整体严重程度作为中风后失语症流利言语的关键因素，与词汇句法相对应的可分离因素。Zevgolatakou等人报告称，卒中后失语症的叙述性言语测量对应于（i）一般损伤严重程度和句子/话语复杂性，这与大的颞顶叶区域的损伤相关，以及（ii）词汇句法，这与相对较小的额顶叶区域的损伤相关。
Deficits in fluent speech production following left hemisphere stroke are a central concern because of their impact on patients’ lives and the insight they provide about the neural organization of language processing. Fluent speech production requires the rapid coordination of phonological, semantic, and syntactic processing, so this study examined how deficits in connected speech relate to these language sub-systems. Behavioural data (N = 69 participants with aphasia following left hemisphere stroke) consisted of a diverse and comprehensive set of narrative speech production measures and measures of overall severity, semantic deficits, and phonological deficits. These measures were entered into a principal component analysis with bifactor rotation—a latent structure model where each item loads on a general factor that reflects what is common among the items, and orthogonal factors that explain variance not accounted for by the general factor. Lesion data were available for 58 of the participants, and each factor score was analysed with multivariate lesion–symptom mapping. Effects of connectivity disruption were evaluated using robust regression with tract disconnection or graph theoretic measures of connectivity as predictors. The principal component analysis produced a four-factor solution that accounted for 70.6% of the variance in the data, with a general factor corresponding to the overall severity and length and complexity of speech output (complexity factor), a lexical syntax factor, and independent factors for Semantics and Phonology. Deficits in the complexity of speech output were associated with a large temporo-parietal region, similar to overall aphasia severity. The lexical syntax factor was associated with damage in a relatively small set of fronto-parietal regions which may reflect the recruitment of control systems to support retrieval and correct usage of lexical items that primarily serve a syntactic rather than semantic function. Tract-based measures of connectivity disruption were not statistically associated with the deficit scores after controlling for overall lesion volume. Language network efficiency and average clustering coefficient within the language network were significantly associated with deficit scores after controlling for overall lesion volume. These results highlight overall severity as the critical contributor to fluent speech in post-stroke aphasia, with a dissociable factor corresponding to lexical syntax. Zevgolatakou et al. report that measures of narrative speech in post-stroke aphasia correspond to (i) general impairment severity and sentence/utterance complexity, which are associated with damage in a large temporo-parietal region and (ii) lexical syntax, which is associated with damage in a relatively small set of fronto-parietal regions.
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影响因子：
2.9
作者：
Basilakos A;Fillmore PT;Rorden C;Guo D;Bonilha L;Fridriksson J
通讯作者：
Fridriksson J
影响因子：
3.4
作者：
Fedorenko, Evelina;Blank, Idan Asher;Mineroff, Zachary
通讯作者：
Mineroff, Zachary
影响因子：
4.8
作者：
den Ouden, Dirk-Bart;Malyutina, Svetlana;Fridriksson, Julius
通讯作者：
Fridriksson, Julius
影响因子：
3.4
作者：
Fan JM;Gorno-Tempini ML;Dronkers NF;Miller BL;Berger MS;Chang EF
通讯作者：
Chang EF
影响因子：
10.9
作者：
Avants, Brian B.;Schoenemann, P. Thomas;Gee, James C.
通讯作者：
Gee, James C.