While several studies of task-based effective connectivity of normal language processing exist, little is known about the functional reorganization of language networks in patients with stroke-induced chronic aphasia. of task-induced regional interactions between three regions (i.e., LIFG, LMFG, and LMTG) vital for picture naming. The DCM model space was organized according to exogenous input to these regions and partitioned into individual families. At the model level, random effects family wise Bayesian Model Selection revealed that models with driving input to LIFG best fit the control data whereas models with driving Rabbit Polyclonal to BCAS3 input to LMFG best fit the patient data. At the parameter level, a significant between-group difference in the connection strength from LMTG to LIFG was seen. Within the patient group, several significant relationships between network connectivity parameters, spared cortical tissue, and behavior were observed. Overall, this study provides some preliminary findings regarding how neural networks for language reorganize for individuals with aphasia and how brain connectivity relates to underlying structural integrity and task performance. lexical-phonological retrieval and semantic processing, access, and control. The third region that PWA consistently activated across studies in the Turkeltaub et al. (2011) meta-analysis was LMFG, a region that is not typically considered a classic language area like LIFG and LMTG. Like LIFG, though, regions in dorsolateral prefrontal cortex (including LMFG) have been implicated in executive control processes and are likely to be critical for picture naming, yet unlike LIFG, LMFG is usually associated with domain-general (i.e., non-language specific) cognitive control. LMFG is usually encompassed within the multiple demands network (also known as the task-positive or frontotemporal attention network) and is thought to mediate different types of behavior, including goal maintenance, selection of strategies for task completion, performance monitoring and other tasks (Fedorenko et al., 2013). In the context of language tasks, activation in LMFG has been associated with response selection or inhibition during semantically demanding tasks (Desmond et al., 1998; de Zubicaray et al., 2000; Collette et al., 2001; Jeon et al., 2009). While several regions comprise the network involved in word retrieval and picture naming, the literature has shown that LIFG and LMFG play vital roles in lexical selection and control, and LMTG plays an important role in heteromodal semantic processing. However, how these regions interact with each other after stroke has not yet been examined. Understanding this conversation is particularly important as the role of left hemisphere engagement in recovery versus compensation is not well understood. For example, we do 79551-86-3 supplier not know whether PWA network connectivity is driven by more intact, domain-general regions (such as LMFG) or by classic language regions (such as LIFG and LMTG) nor do we know if connectivity is driven by initial stages of lexical retrieval (e.g., semantic processing as mediated by LMTG) versus topCdown control processes of selection (as mediated by LIFG or LMFG). At a broader level, it is also still unknown how brain damage and behavioral deficits are related to cortical interactions for a given task. Therefore, the overall goal of this study was to examine frontotemporal effective connectivity induced by a picture naming task in PWA relative to healthy controls and to examine how connectivity parameters relate to behavioral performance and 79551-86-3 supplier cortical damage in PWA. It should be noted that it was not the goal of this study to identify if or to what extent these regions are engaged in PWA relative to controls (which they presumably are). Rather, this study aimed to examine how a subset of critical regions within the PWA language network interact in order to better understand the mechanisms of language recovery after stroke. To examine this question, we employed dynamic causal modeling (DCM), a method which can be used to 79551-86-3 supplier determine how coupling between regions and the direction of such effects are influenced by changes in the experimental tasks (Seghier et al., 2012, 2014). DCM is particularly advantageous to examine effective connectivity in stroke populations since modeling of region-specific hemodynamic response parameters can accommodate deviations from normal hemodynamic characteristics (Grefkes and Fink, 2011). DCM has been used to examine motor recovery in post-stroke patients (e.g., Grefkes et al., 2008, 2010; Rehme et al., 2011) as well as to examine changes in connectivity in aphasia as a 79551-86-3 supplier function of rehabilitation (e.g., Abutalebi et al., 2009; Kiran et al., 2015). Additionally, DCM can be used to test specific hypotheses about the causal interactions between specific regions within a larger network. Consequently, as a preliminary investigation of PWA.