posted on 2020-11-16, 22:25authored byJean K. Gordon
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 Table S1. Spontaneous speech variables for each aphasia subtype.
Supplemental Table S2. Factor pattern matrix loadings. Loadings > .4 are shown in bold.
Supplemental Table S3. Factor structure matrix loadings. Loadings > .4 are shown in bold.
Supplemental Table S4. Post-hoc (Bonferroni) tests of ANOVAs comparing aphasia subtypes on factor scores derived from the structure matrix table. Significant differences (p < .05) are shown in bold.
Supplemental Figure S1. Intercorrelations among spontaneous speech measures. Measures are arranged in order of similarity. Blue shading represents positive correlations; red shading represents negative correlations.
Supplemental Figure S2. Parallel analysis. Blue triangles indicate eigen-values for the factor analysis when 17 factors were included (one for each variable). The red dotted line represents randomly generated data, simulated or resampled from the dataset. Six of the actual eigenvalues fall above this line and were therefore retained.
Supplemental Figure S3. Latent profile pie charts. For each identified latent profile, the numbers of speakers with each aphasia subtype are shown.
Gordon, J. K. (2020). Factor analysis of spontaneous speech in aphasia. Journal of Speech, Language, and Hearing Research. Advanced online publication. https://doi.org/10.1044/2020_JSLHR-20-00340
This study was partially supported by a New Century Scholar grant from the American Speech-Language-Hearing Foundation.