The effect of semantic naming treatment on task-based neural activation and functional connectivity in aphasia

• Main Author: Johnson, Jeffrey P.
• Other Authors: Kiran, Swathi
• Language: en_US
• Published: 2018
• Subjects: Speech therapy; Anomia; Graph theory; Rehabilitation; Stroke
• Online Access: https://hdl.handle.net/2144/32731

Many individuals with chronic post-stroke aphasia respond favorably to language therapy. However, treatment outcomes are highly variable and the neural mechanisms that support recovery, and perhaps explain this variability, remain elusive. Neuroimaging studies involving patients with aphasia have implicated a number of cortical regions in both hemispheres in post-treatment language processing, which suggests that network approaches may reveal important insights into the neural bases of language recovery. This dissertation investigated how functional activation, functional connectivity, and graph theoretical measures of network topology relate to treatment-induced changes in language functions. In the first study, we used an fMRI picture-naming task to examine functional activation in 26 patients with aphasia before and after 12 weeks of naming treatment. Changes in activation were associated with treatment outcomes, such that activation increased in patients who responded best to treatment (i.e., responders) but remained largely unchanged in patients who responded less favorably (i.e., nonresponders). In the second study, we analyzed functional connectivity and graph properties of an expanded picture naming network. Relative to healthy controls, patients had reduced functional connectivity, particularly within the left hemisphere and between regions in the left and right hemisphere. As in study 1, we found differential patterns of connectivity depending on treatment outcomes, such that connectivity normalized (i.e., became more like that of healthy controls) in responders but remained abnormally low in nonresponders. Similar results were obtained via the graph analysis. Finally, in the third study, we aimed to determine if pre-treatment global and local properties reflecting integration and segregation in a task-based semantic processing network predicted patients’ response to treatment. Network strength and global efficiency were significant predictors of improvement. Additionally, responders and nonresponders showed significant differences in nodal properties in a subset of bilaterally distributed regions in the frontal and parietal lobes. The results of these studies indicate that there are critical regional and network-level differences between patients who respond well to treatment and those who respond poorly, and that some of these differences can be identified before treatment is initiated. These results provide a foundation for further investigation of network-related biomarkers for recovery and updated models of recovery that account for pre-existing differences in network topology. === 2021-12-31T00:00:00Z