MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.
Department of Psychiatry, University of Cambridge, Cambridge, UK.
Brain. 2023 May 2;146(5):1950-1962. doi: 10.1093/brain/awac388.
Focal brain damage caused by stroke can result in aphasia and advances in cognitive neuroscience suggest that impairment may be associated with network-level disorder rather than just circumscribed cortical damage. Several studies have shown meaningful relationships between brain-behaviour using lesions; however, only a handful of studies have incorporated in vivo structural and functional connectivity. Patients with chronic post-stroke aphasia were assessed with structural (n = 68) and functional (n = 39) MRI to assess whether predicting performance can be improved with multiple modalities and if additional variance can be explained compared to lesion models alone. These neural measurements were used to construct models to predict four key language-cognitive factors: (i) phonology; (ii) semantics; (iii) executive function; and (iv) fluency. Our results showed that each factor (except executive ability) could be significantly related to each neural measurement alone; however, structural and functional connectivity models did not explain additional variance above the lesion models. We did find evidence that the structural and functional predictors may be linked to the core lesion sites. First, the predictive functional connectivity features were found to be located within functional resting-state networks identified in healthy controls, suggesting that the result might reflect functionally specific reorganization (damage to a node within a network can result in disruption to the entire network). Second, predictive structural connectivity features were located within core lesion sites, suggesting that multimodal information may be redundant in prediction modelling. In addition, we observed that the optimum sparsity within the regularized regression models differed for each behavioural component and across different imaging features, suggesting that future studies should consider optimizing hyperparameters related to sparsity per target. Together, the results indicate that the observed network-level disruption was predicted by the lesion alone and does not significantly improve model performance in predicting the profile of language impairment.
中风引起的局部脑损伤可导致失语症，认知神经科学的进展表明，损伤可能与网络级别的紊乱有关，而不仅仅是局限于皮质损伤。 几项研究已经表明，使用病变可以在大脑行为之间建立有意义的关系； 然而，只有少数研究结合了体内结构和功能连接。 对慢性中风后失语症患者进行了结构（n = 68）和功能（n = 39）MRI 评估，以评估是否可以通过多种方式改善预测性能，并且与单独使用病变模型相比是否可以解释更多的差异。 这些神经测量结果用于构建模型，以预测四个关键的语言认知因素：（i）语音学；（ii）语义学；（iii）执行功能；（iv）流畅度。 我们的结果表明，除了执行能力外，每个因素都可以与单独的每个神经测量值显著相关； 然而，结构和功能连接模型并未解释超过病变模型的额外差异。 我们确实发现了证据表明结构和功能预测因子可能与核心病变部位有关。 首先，发现预测功能连接特征位于健康对照组中确定的功能静息状态网络内，这表明结果可能反映了功能特异性的重组（网络内节点的损伤会导致整个网络的中断）。 其次，预测结构连接特征位于核心病变部位，这表明多模态信息在预测模型中可能是冗余的。 此外，我们观察到正则化回归模型中的最佳稀疏度因行为成分和不同的成像特征而异，这表明未来的研究应该考虑针对每个目标优化与稀疏度相关的超参数。 总之，结果表明，观察到的网络级别的中断仅由病变预测，并且不会显著改善预测语言损伤特征的模型性能。
