The role of white matter pathways in language networks has received much attention inrecent years. This is largely due to advances in diffusion imaging techniques, which haveenabled exploration of white matter properties in vivo. The emergent model from suchwork proposes that language processing is underpinned by a dorsal and a ventral pathwayconnecting anterior and posterior regions involved in language. This thesis aimed toexplore whether consideration of white matter measures could aid understanding ofperformance profiles in chronic stroke aphasia. To this end, a group of participants withchronic stroke aphasia were recruited and their performance on a large battery oflanguage assessments was related to their neuroimaging data. The neuroimaging datacomprised high resolution T1-weighted structural scans, fractional anisotropy (FA) maps,and data generated using a tractography-based technique called Anatomical ConnectivityMapping (ACM) which provides an index of long-range connectivity that has not yetbeen applied to chronic stroke aphasia.Chapter 3 established, in a small series of case examples, that connectivityinformation from ACM can help explain variations in performance in chronic strokeaphasia. Chapter 4 extended this work to a larger group of participants. Differencesbetween aphasic participants and controls, and between groups with different aphasicsubtypes and controls, were calculated and compared across imaging methods. ACMoffered insights into connectivity differences that were complementary to informationfrom T1-weighted and FA data. In addition to revealing areas where connectivity wasreduced relative to controls, ACM revealed an increase in connectivity in the righthemisphere dorsal route homologue of aphasic participants.Chapter 5 aimed to improve our ability to capture aphasic performance and torelate it to neuroimaging data. Principal components analysis (PCA) was used to derivefactors underlying performance on the language battery. Phonological, semantic, andcognitive factors emerged from the PCA and participants’ factor scores were used ascontinuous regressors in a voxel-level analysis of their T1-weighted images. Regions thatemerged as significantly related to language abilities aligned with those found usingother methodologies. Chapter 6 brought together work from the previous chapters byrelating PCA-derived factor scores to FA maps and ACM, in order to assess therelationship between behavioural performance and the status of key white matterpathways. In line with recent characterisations of the dual route system, phonologicalperformance related to dorsal route measures and semantic performance related to ventralroute measures. Better cognitive performance was found to relate to increasedconnectivity relative to controls in the right frontal lobe. Overall these results suggest thatconsideration of white matter abnormalities, both reductions and increases, can helpexplain patterns of performance in chronic stroke aphasia and that ACM can be a usefulsource of such information given its sensitivity to connectivity remote from the lesion.These findings both provide hypotheses for future research and could be used to informtherapeutic interventions.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:588171 |
| Date | January 2013 |
| Creators | Butler, Rebecca |
| Contributors | Parker, Geoffrey; Lambon Ralph, Matthew; Woollams, Anna |
| Publisher | University of Manchester |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://www.research.manchester.ac.uk/portal/en/theses/using-diffusion-weighted-imaging-to-map-changes-in-white-matter-connectivity-in-chronic-stroke-aphasia(287f2b2a-3bdd-492a-ab90-ca9cb7c9ad90).html |
Page generated in 0.0018 seconds