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Effective Connectivity Study Guiding the Neuromodulation Intervention in Figurative Language Comprehension Using Optical Neuroimaging

机译:使用光学神经模仿指导鉴定语言理解的神经调节干预的有效连通性研究

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The current study is aimed at establishing links between brain network examination and neural plasticity studies measured by optical neuroimaging. Sixteen healthy subjects were recruited from the University of Macau to test the Granger Prediction Estimation (GPE) method to investigate brain network connectivity during figurative language comprehension. The method is aimed at mapping significant causal relationships across language brain networks, captured by functional near-infrared spectroscopy measurements (fNIRS): (i) definition of regions of interest (ROIs) based on significant channels extracted from spatial activation maps; (ii) inspection of significant causal relationships in temporal resolution, exploring the experimental task agreement; and (iii) early identification of stronger causal relationships that guide neuromodulation intervention, targeting impaired connectivity pathways. Our results propose top-down mechanisms responsible for perceptive-attention engagement in the left anterior frontal cortex and bottom-up mechanism in the right hemispheres during the semantic integration of figurative language. Moreover, the interhemispheric directional flow suggests a right hemisphere engagement in decoding unfamiliar literal sentences and fine-grained integration guided by the left hemisphere to reduce ambiguity in meaningless words. Finally, bottom-up mechanisms seem activated by logographic-semantic processing in literal meanings and memory storage centres in meaningless comprehension. To sum up, our main findings reveal that the Granger Prediction Estimation (GPE) integrated strategy proposes an effective link between assessment and intervention, capable of enhancing the efficiency of the treatment in language disorders and reducing the neuromodulation side effects.
机译:目前的研究旨在建立通过光学神经模仿测量的脑网络检查和神经可塑性研究之间的联系。从澳门大学招募了十六个健康的科目,以测试Granger预测估计(GPE)方法,以研究比喻语言理解的脑网络连接。该方法旨在通过功能近红外光谱测量(FNIR)捕获的语言脑网络跨越语言脑网络的显着因果关系:(i)基于从空间激活图中提取的显着通道来定义感兴趣区域(ROI); (ii)探索时间决议的显着因果关系,探索实验任务协议; (iii)早期鉴定导致神经调节干预的更强的因果关系,靶向有受损的连接途径。我们的研究结果提出了自上而下的机制,负责在比喻语言的语义集成期间在右半球上的左前方皮层和自下而上机制中的感知 - 注意力。此外,互脱的方向流动表明右半球对解码不熟悉的文字句和左半球引导的细粒度集成,以减少无意义词语的模糊性。最后,在毫无意义的理解中,在文字含义和记忆存储中心中的逻辑语义处理激活了自下而上的机制。总而言之,我们的主要研究结果表明,Granger预测估计(GPE)综合策略提出了评估和干预之间有效的联系,能够提高语言障碍治疗的效率和减少神经调节副作用。

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