SELECTION IN WRITTEN LANGUAGE PRODUCTION: EVIDENCE FROM APHASIA

Most models of word production assume that in the process of producing a target word, multiple distractors also get activated, both other words (at the lexical level) and other phonemes/letters (at the segmental level). Thus, a selection mechanism is needed to select the targets at each level. While selection is one of the mechanisms that has received a considerable amount of attention in the spoken production literature, minimal amount of research has been dedicated to understanding this mechanism in written production. In fact, although written language is an integral part of our everyday life, written language production is the most under-researched language domain. The work presented in this dissertation enhances our understanding of the selection mechanisms involved in written word production. Specifically, we investigated whether the selection mechanism(s) are: (1) shared across cognitive domains (language vs non-language), (2) shared across levels of processing (lexical vs segmental), and (3) internal or external to the network that supports the mapping of representations across levels of processing. To this end, we collected behavioral data from a group of individuals with post-stroke aphasia and a group of aged-matched healthy control individuals, using two experimental tasks: the written Blocked Cyclic Naming task and the Simon visual-spatial compatibility task. Structural (gray matter) neuroimaging data were also collected for the post-stroke aphasia group. The results of the data analyses undertaken provide no evidence of a shared mechanism between the language and non-language domains investigated. With respect to levels of processing, the findings reveal that selection at the lexical and segmental levels is supported by distinct mechanisms. Finally, the evidence indicates that selection processes involved in written word production are supported by a mechanism that is external to the representational mapping system, and that this external mechanism relies on left-hemisphere inferior frontal and orbitofrontal regions of the brain. These findings significantly advance our understanding of the selection mechanisms involved in written language production, which has important theoretical implications for understanding the writing system and for theories of word production more generally, as well as having translational implications related to naming therapies in aphasia and beyond

Structural and effective connectivity of lexical-semantic and naming networks in patients with chronic aphasia

Given the difficulty in predicting outcomes in persons with stroke-induced aphasia (PWA), neuroimaging-based biomarkers of recovery could provide invaluable predictive power to stroke models. However, the neural patterns that constitute beneficial neural organization of language in PWA remain debated. Thus, in this work, we propose a novel network theory of aphasia recovery and test our overarching hypothesis, i.e., that task-specific language processing in PWA requires the dynamic engagement of intact tissue within a bilateral network of anatomically-segregated but functionally and structurally connected language-specific and domain-general brain regions. We first present two studies in which we examined left frontotemporal connectivity during different language tasks (i.e., picture naming and semantic feature verification). Results suggest that PWA heavily rely on left middle frontal gyrus (LMFG)-driven connectivity for tasks requiring lexical-semantic processing and semantic control whereas controls prefer models with input to either LMFG or left inferior frontal gyrus (LIFG). Both studies also revealed several significant associations between spared tissue, connectivity and language skills in PWA. In the third study, we examined bilateral frontotemporoparietal connectivity and tested a lesion- and connectivity-based hierarchical model of chronic aphasia recovery. Between-group comparisons showed controls exhibited stronger left intra-hemispheric task-modulated connectivity than did PWA. Connectivity and language deficit patterns most closely matched predictions for patients with primarily anterior damage whereas connectivity results for patients with other lesion types were best explained by the nature of the semantic task. In the last study, we investigated the utility of lesion classification based on gray matter (GM) only versus combined GM plus white matter (WM) metrics. Results suggest GM only classification was sufficient for characterizing aphasia and anomia severity but the GM+WM classification better predicted naming treatment outcomes. We also found that fractional anisotropy of left WM association tracts predicted baseline naming and treatment outcomes independent of total lesion volume. Finally, results of a preliminary multimodal prediction analysis suggest that combined structural and functional metrics reflecting the integrity of regions and connections comprise optimal predictive models of behavior in PWA. To conclude this dissertation, we discuss how multimodal network models of aphasia recovery can guide future investigations.2020-10-23T00:00:00