背景:失语症是一种通常由左半球损伤引起的言语语言障碍。支持不同类型失语症及其症状的神经机制仍未完全了解。本研究旨在确定通过静息态功能磁共振成像(rs-fMRI)测量的失语症和Broca失语症之间静息态功能连接的差异。
方法:我们使用了基于网络的统计(NBS)方法,以及基于体素和连接体的病变症状映射(V-,CLSM),确定不同的神经相关的组和Broca的组。为了控制病变效应,我们在NBS方法和LSM方法中均纳入了病变体积作为协变量.
结果:NBS确定了一个位于背侧语言流两侧的子网络,包括颈上回,初级感官,电机,和听觉皮层,和脑岛。Broca组的子网络中的连接比anomic组弱。通过复杂的网络度量检查了子网络的属性,这表明右下额沟区域,右中央小叶,双侧颞上回表现出强烈的相互作用。左颞上回,右中央后回,左缘上回在信息流和整体沟通效率中起着重要作用。该网络的破坏是与Broca失语症相关的一系列症状的基础。全脑CLSM没有检测到任何重要的连接,当考虑数千个连接时,这表明了NBS的优势。然而,当分析仅限于一个假设的感兴趣的网络时,CLSM确定了将Broca\与无意义性失语症区分开的连接。
结论:我们发现了一组有失语症和Broca失语症的个体之间的静息状态脑网络差异的新特征。我们确定了一个连接子网络,该连接子网络在统计学上区分了两组的静息状态大脑网络,与产生隔离连接的标准CLSM结果相比。网络水平分析是研究中风后语言障碍的神经相关性的有用工具。
BACKGROUND: Aphasia is a speech-language impairment commonly caused by damage to the left hemisphere. The neural mechanisms that underpin different types of aphasia and their symptoms are still not fully understood. This study aims to identify differences in resting-state functional connectivity between anomic and Broca\'s aphasia measured through resting-state functional magnetic resonance imaging (rs-fMRI).
METHODS: We used the network-based statistic (NBS) method, as well as voxel- and connectome-based lesion symptom mapping (V-, CLSM), to identify distinct neural correlates of the anomic and Broca\'s groups. To control for lesion effect, we included lesion volume as a covariate in both the NBS method and LSM.
RESULTS: NBS identified a subnetwork located in the dorsal language stream bilaterally, including supramarginal gyrus, primary sensory, motor, and auditory cortices, and insula. The connections in the subnetwork were weaker in the Broca\'s group than the anomic group. The properties of the subnetwork were examined through complex network measures, which indicated that regions in right inferior frontal sulcus, right paracentral lobule, and bilateral superior temporal gyrus exhibit intensive interaction. Left superior temporal gyrus, right postcentral gyrus, and left supramarginal gyrus play an important role in information flow and overall communication efficiency. Disruption of this network underlies the constellation of symptoms associated with Broca\'s aphasia. Whole-brain CLSM did not detect any significant connections, suggesting an advantage of NBS when thousands of connections are considered. However, CLSM identified connections that differentiated Broca\'s from anomic aphasia when analysis was restricted to a hypothesized network of interest.
CONCLUSIONS: We identified novel signatures of resting-state brain network differences between groups of individuals with anomic and Broca\'s aphasia. We identified a subnetwork of connections that statistically differentiated the resting-state brain networks of the two groups, in comparison with standard CLSM results that yielded isolated connections. Network-level analyses are useful tools for the investigation of the neural correlates of language deficits post-stroke.