Exploring the Complexity of Aphasia With Network Analysis

Comparing the Clique Percolation algorithm to other overlapping community detection algorithms in psychological networks: A Monte Carlo simulation study

In psychological networks, one limitation of the most used community detection algorithms is that they can only assign each node (symptom) to a unique community, without being able to identify overlapping symptoms. The clique percolation (CP) is an algorithm that identifies overlapping symptoms but its performance has not been evaluated in psychological networks. In this study, we compare the CP with model parameters chosen based on fuzzy modularity (CPMod) with two other alternatives, the ratio of the two largest communities (CPRat), and entropy (CPEnt). We evaluate their performance to: (1) identify the correct number of latent factors (i.e., communities); and (2) identify the observed variables with substantive (and equally sized) cross-loadings (i.e., overlapping symptoms). We carried out simulations under 972 conditions (3x2x2x3x3x3x3): (1) data categories (continuous, polytomous and dichotomous); (2) number of factors (two and four); (3) number of observed variables per factor (four and eight); (4) factor correlations (0.0, 0.5, and 0.7); (5) size of primary factor loadings (0.40, 0.55, and 0.70); (6) proportion of observed variables with substantive cross-loadings (0.0%, 12.5%, and 25.0%); and (7) sample size (300, 500, and 1000). Performance was evaluated through the Omega index, Mean Bias Error (MBE), Mean Absolute Error (MAE), sensitivity, specificity, and mean number of isolated nodes. We also evaluated two other methods, Exploratory Factor Analysis and the Walktrap algorithm modified to consider overlap (EFA-Ov and Walk-Ov, respectively). The Walk-Ov displayed the best performance across most conditions and is the recommended option to identify communities with overlapping symptoms in psychological networks.

Defining aphasia: Content analysis of six aphasia diagnostic batteries

Clear operational definitions of constructs are necessary to ensure that research findings are meaningful and interpretable. In the field of aphasiology, aphasia is often defined to the effect of "aphasia is an acquired language disorder often due to brain injury that affects expressive and receptive language." To contribute to our understanding of the construct of aphasia, we conducted a content analysis of six diagnostic aphasia tests: the Minnesota Test for the Differential Diagnosis of Aphasia, the Porch Index of Communicative Ability, the Boston Diagnostic Aphasia Examination, the Western Aphasia Battery, the Comprehensive Aphasia Test, and the Quick Aphasia Battery. These chosen tests have historical prominence, with several in regular clinical and research use today. We hypothesized that the content of the aphasia tests should be very similar since they all purport to identify and characterize (if present) aphasia, with recognition that there may be some subtle differences in test content stemming in large part to epistemological differences in the test makers' views of aphasia. Instead, we found predominantly weak Jaccard indices, a similarity correlation coefficient, between test targets. Only five test targets were found in all six aphasia tests: auditory comprehension of words and sentences, repetition of words, confrontation naming of nouns, and reading comprehension of words. The qualitative and quantitative results suggest that the content across aphasia tests may be more disparate than expected. We conclude by discussing the implication of our results for the field, including the importance of updating, if necessary, the operational definition of aphasia through conversation with a broad audience of interested and affected people.

The Resilience of the Phonological Network May Have Implications for Developmental and Acquired Disorders

A central tenet of network science states that the structure of the network influences processing. In this study of a phonological network of English words we asked: how does damage alter the network structure (Study 1)? How does the damaged structure influence lexical processing (Study 2)? How does the structure of the intact network "protect" processing with a less efficient algorithm (Study 3)? In Study 1, connections in the network were randomly removed to increasingly damage the network. Various measures showed the network remained well-connected (i.e., it is resilient to damage) until ~90% of the connections were removed. In Study 2, computer simulations examined the retrieval of a set of words. The performance of the model was positively correlated with naming accuracy by people with aphasia (PWA) on the Philadelphia Naming Test (PNT) across four types of aphasia. In Study 3, we demonstrated another way to model developmental or acquired disorders by manipulating how efficiently activation spread through the network. We found that the structure of the network "protects" word retrieval despite decreases in processing efficiency; words that are relatively easy to retrieve with efficient transmission of priming remain relatively easy to retrieve with less efficient transmission of priming. Cognitive network science and computer simulations may provide insight to a wide range of speech, language, hearing, and cognitive disorders.