MRC Cognition & Brain Sciences Unit, University of Cambridge, Cambridge, CB2 7EF, United Kingdom.
Neuroscience and Aphasia Research Unit (NARU), School of Biological Sciences, The University of Manchester, Manchester, United Kingdom; Division of Psychiatry and Applied Psychology, School of Medicine, University of Nottingham, Nottingham, United Kingdom.
Cortex. 2022 Oct;155:333-346. doi: 10.1016/j.cortex.2022.07.014. Epub 2022 Aug 14.
Multi-assessment batteries are necessary for diagnosing and quantifying the multifaceted deficits observed post-stroke. Extensive batteries are thorough but impractically long for clinical settings or large-scale research studies. Clinically-targeted "shallow" batteries superficially cover a wide range of language skills relatively quickly but can struggle to identify mild deficits or quantify the impairment level. Our aim was to compare these batteries across a large group of chronic stroke aphasia and to test a novel data-driven reduced version of an extensive battery that maintained sensitivity to mild impairment, ability to grade deficits and the underlying component structure.
We tested 75 chronic left-sided stroke participants, spanning global to mild aphasia. The underlying structure of these three batteries was analysed using cross-validation and principal component analysis, in addition to univariate and multivariate lesion-symptom mapping.
This revealed a four-factor solution for the extensive and data-reduced batteries, identifying phonology, semantic skills, fluency and executive function in contrast to a two-factor solution using the shallow battery (language severity and cognitive severity). Lesion symptom mapping using participants' factor scores identified convergent neural structures for phonology (superior temporal gyrus), semantics (inferior temporal gyrus), speech fluency (precentral gyrus) and executive function (lateral occipitotemporal cortex). The two shallow battery components converged with the phonology and executive function clusters. In addition, we show that multivariate models could predict the component scores using neural data, however not for every component.
Overall, the data-driven battery appears to be an effective way to save time yet retain maintained sensitivity to mild impairment, ability to grade deficits and the underlying component structure observed in post-stroke aphasia.
多评估电池对于诊断和量化中风后观察到的多方面缺陷是必要的。广泛的电池虽然全面,但在临床环境或大规模研究中不切实际地冗长。针对临床的“浅层”电池可以相对快速地涵盖广泛的语言技能,但可能难以识别轻度缺陷或量化损伤程度。我们的目的是在一大组慢性中风失语症患者中比较这些电池,并测试一种广泛电池的新型数据驱动的简化版本,该版本保持对轻度损伤的敏感性、分级损伤的能力以及潜在的组成结构。
我们测试了 75 名慢性左侧中风患者,涵盖了从全局到轻度失语症的范围。使用交叉验证和主成分分析,以及单变量和多变量损伤-症状映射,对这三种电池的潜在结构进行了分析。
这揭示了广泛和数据简化电池的四因素解决方案,确定了语音、语义技能、流畅性和执行功能,而使用浅层电池则确定了两个因素解决方案(语言严重程度和认知严重程度)。使用参与者的因子得分进行损伤-症状映射,确定了语音(颞上回)、语义(颞下回)、言语流畅性(中央前回)和执行功能(外侧枕颞皮质)的收敛神经结构。两个浅层电池的组件与语音和执行功能聚类相重合。此外,我们表明,多元模型可以使用神经数据预测组件得分,但并非对每个组件都如此。
总体而言,数据驱动的电池似乎是一种节省时间的有效方法,同时保持对轻度损伤的敏感性、分级损伤的能力以及中风后失语症中观察到的潜在组成结构。