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White matter basis for the hub-and-spoke semantic representation: evidence from semantic dementia.
Brain ( IF 14.5 ) Pub Date : 2020-04-01 , DOI: 10.1093/brain/awaa057 Yan Chen 1, 2 , Lin Huang 3 , Keliang Chen 4 , Junhua Ding 1 , Yumei Zhang 5 , Qing Yang 4 , Yingru Lv 6 , Zaizhu Han 1 , Qihao Guo 3
Brain ( IF 14.5 ) Pub Date : 2020-04-01 , DOI: 10.1093/brain/awaa057 Yan Chen 1, 2 , Lin Huang 3 , Keliang Chen 4 , Junhua Ding 1 , Yumei Zhang 5 , Qing Yang 4 , Yingru Lv 6 , Zaizhu Han 1 , Qihao Guo 3
Affiliation
The hub-and-spoke semantic representation theory posits that semantic knowledge is processed in a neural network, which contains an amodal hub, the sensorimotor modality-specific regions, and the connections between them. The exact neural basis of the hub, regions and connectivity remains unclear. Semantic dementia could be an ideal lesion model to construct the semantic network as this disease presents both amodal and modality-specific semantic processing (e.g. colour) deficits. The goal of the present study was to identify, using an unbiased data-driven approach, the semantic hub and its general and modality-specific semantic white matter connections by investigating the relationship between the lesion degree of the network and the severity of semantic deficits in 33 patients with semantic dementia. Data of diffusion-weighted imaging and behavioural performance in processing knowledge of general semantic and six sensorimotor modalities (i.e. object form, colour, motion, sound, manipulation and function) were collected from each subject. Specifically, to identify the semantic hub, we mapped the white matter nodal degree value (a graph theoretical index) of the 90 regions in the automated anatomical labelling atlas with the general semantic abilities of the patients. Of the regions, only the left fusiform gyrus was identified as the hub because its structural connectivity strength (i.e. nodal degree value) could significantly predict the general semantic processing of the patients. To identify the general and modality-specific semantic connections of the semantic hub, we separately correlated the white matter integrity values of each tract connected with the left fusiform gyrus, with the performance for general semantic processing and each of six semantic modality processing. The results showed that the hub region worked in concert with nine other regions in the semantic memory network for general semantic processing. Moreover, the connection between the hub and the left calcarine was associated with colour-specific semantic processing. The observed effects could not be accounted for by potential confounding variables (e.g. total grey matter volume, regional grey matter volume and performance on non-semantic control tasks). Our findings refine the neuroanatomical structure of the semantic network and underline the critical role of the left fusiform gyrus and its connectivity in the network.
中文翻译:
轮辐式语义表示的白质基础:语义痴呆的证据。
轮辐式语义表示理论认为,语义知识是在神经网络中处理的,该神经网络包含一个无模式的轮毂,特定于感觉运动模态的区域以及它们之间的连接。枢纽,区域和连接性的确切神经基础仍不清楚。语义痴呆症可能是构建语义网络的理想病变模型,因为该疾病同时表现出无模态和特定于情态的语义处理(例如颜色)缺陷。本研究的目的是通过研究网络的病变程度和语义缺陷严重程度之间的关系,使用无偏数据驱动方法来识别语义中心及其一般和特定于形式的语义白质连接。 33例语义性痴呆患者。从每个受试者中收集扩散加权成像和行为表现数据,以处理一般语义和六种感觉运动模态(即对象形式,颜色,运动,声音,操纵和功能)的知识。具体来说,为了识别语义中心,我们将自动解剖标记集中的90个区域的白质结度值(图形理论指标)映射到患者的一般语义能力。在这些区域中,只有左梭状回被确定为枢纽,因为其结构连接强度(即结度值)可以显着预测患者的一般语义处理。为了识别语义中心的一般和特定于形式的语义连接,我们分别将与左侧梭状回相关的每个区域的白质完整性值与一般语义处理的性能和六个语义模态处理的性能相关联。结果表明,中心区域与语义存储网络中的其他九个区域协同工作,以进行一般的语义处理。此外,轮毂和左钙cal碱之间的连接与特定颜色的语义处理有关。潜在的混淆变量(例如,总灰质量,区域灰质量和非语义控制任务的性能)无法解释观察到的效果。我们的发现完善了语义网络的神经解剖结构,并强调了左梭状回及其在网络中的连通性的关键作用。
更新日期:2020-04-21
中文翻译:
轮辐式语义表示的白质基础:语义痴呆的证据。
轮辐式语义表示理论认为,语义知识是在神经网络中处理的,该神经网络包含一个无模式的轮毂,特定于感觉运动模态的区域以及它们之间的连接。枢纽,区域和连接性的确切神经基础仍不清楚。语义痴呆症可能是构建语义网络的理想病变模型,因为该疾病同时表现出无模态和特定于情态的语义处理(例如颜色)缺陷。本研究的目的是通过研究网络的病变程度和语义缺陷严重程度之间的关系,使用无偏数据驱动方法来识别语义中心及其一般和特定于形式的语义白质连接。 33例语义性痴呆患者。从每个受试者中收集扩散加权成像和行为表现数据,以处理一般语义和六种感觉运动模态(即对象形式,颜色,运动,声音,操纵和功能)的知识。具体来说,为了识别语义中心,我们将自动解剖标记集中的90个区域的白质结度值(图形理论指标)映射到患者的一般语义能力。在这些区域中,只有左梭状回被确定为枢纽,因为其结构连接强度(即结度值)可以显着预测患者的一般语义处理。为了识别语义中心的一般和特定于形式的语义连接,我们分别将与左侧梭状回相关的每个区域的白质完整性值与一般语义处理的性能和六个语义模态处理的性能相关联。结果表明,中心区域与语义存储网络中的其他九个区域协同工作,以进行一般的语义处理。此外,轮毂和左钙cal碱之间的连接与特定颜色的语义处理有关。潜在的混淆变量(例如,总灰质量,区域灰质量和非语义控制任务的性能)无法解释观察到的效果。我们的发现完善了语义网络的神经解剖结构,并强调了左梭状回及其在网络中的连通性的关键作用。