Gordon Jean K
Department of Communication Sciences and Disorders, University of Iowa, Iowa City.
J Speech Lang Hear Res. 2020 Dec 14;63(12):4127-4147. doi: 10.1044/2020_JSLHR-20-00340. Epub 2020 Nov 16.
Purpose Spontaneous speech tasks are critically important for characterizing spoken language production deficits in aphasia and for assessing the impact of therapy. The utility of such tasks arises from the complex interaction of linguistic demands (word retrieval, sentence formulation, articulation). However, this complexity also makes spontaneous speech hugely variable and difficult to assess. The current study aimed to simplify the problem by identifying latent factors underlying performance in spontaneous speech in aphasia. The ecological validity of the factors was examined by examining how well the factor structures corresponded to traditionally defined aphasia subtypes. Method A factor analysis was conducted on 17 microlinguistic measures of narratives from 274 individuals with aphasia in AphasiaBank. The resulting factor scores were compared across aphasia subtypes. Supervised (linear discriminant analysis) and unsupervised (latent profile analysis) classification techniques were then conducted on the factor scores and the solutions compared to traditional aphasia subtypes. Results Six factors were identified. Two reflected aspects of fluency, one at the phrase level (Phrase Building) and one at the narrative level (Narrative Productivity). Two other factors reflected the accuracy of productions, one at the word level (Semantic Anomaly) and one at the utterance level (Grammatical Error). The other two factors reflected the complexity of sentence structures (Grammatical Complexity) and the use of repair behaviors (Repair), respectively. Linear discriminant analyses showed that only about two thirds of speakers were classified correctly and that misclassifications were similar to disagreements between clinical diagnoses. The most accurately diagnosed syndromes were the largest groups-Broca's and anomic aphasia. The latent profile analysis also generated profiles similar to Broca's and anomic aphasia but separated some subtypes according to severity. Conclusions The factor solution and the classification analyses reflected broad patterns of spontaneous speech performance in a large and representative sample of individuals with aphasia. However, such data-driven approaches present a simplified picture of aphasia patterns, much as traditional syndrome categories do. To ensure ecological validity, a hybrid approach is recommended, balancing population-level analyses with examination of performance at the level of theoretically specified subgroups or individuals. Supplemental Material https://doi.org/10.23641/asha.13232354.
目的 自发言语任务对于刻画失语症患者的口语表达缺陷以及评估治疗效果至关重要。此类任务的效用源于语言需求（词汇检索、句子构建、发音）的复杂相互作用。然而，这种复杂性也使得自发言语极具变异性且难以评估。当前研究旨在通过识别失语症患者自发言语表现背后的潜在因素来简化这一问题。通过考察因素结构与传统定义的失语症亚型的对应程度来检验这些因素的生态效度。
方法 对失语症语料库中274名失语症患者的叙事的17项微观语言指标进行因素分析。将所得因素得分在不同失语症亚型间进行比较。然后对因素得分进行监督分类（线性判别分析）和无监督分类（潜在剖面分析）技术，并将所得结果与传统失语症亚型进行比较。
结果 识别出六个因素。两个因素反映流畅性的不同方面，一个在短语层面（短语构建），一个在叙事层面（叙事产出）。另外两个因素反映表达的准确性，一个在词汇层面（语义异常），一个在话语层面（语法错误）。另外两个因素分别反映句子结构的复杂性（语法复杂性）和修复行为的使用（修复）。线性判别分析表明，只有约三分之二的患者被正确分类，且错误分类情况与临床诊断之间的不一致情况相似。诊断最准确的综合征是最大的两类——布罗卡失语症和命名性失语症。潜在剖面分析也生成了与布罗卡失语症和命名性失语症相似的剖面，但根据严重程度区分了一些亚型。
结论 因素分析结果和分类分析反映了一个大型且具有代表性的失语症患者样本中自发言语表现的广泛模式。然而，与传统综合征类别一样，这种数据驱动的方法呈现出的是失语症模式的简化图景。为确保生态效度，建议采用一种混合方法，平衡总体水平分析与理论上特定亚组或个体水平的表现考察。
