Improving our understanding of speech and language outcome in neurosurgery patients

Malignant gliomas remain incurable and result in more years of life lost than any other tumours. Surgical resection is strongly recommended but carries a risk of causing functional impairment. This thesis aims to demonstrate how state-of-the-art functional magnetic resonance imaging (fMRI) language paradigms can contribute to neurosurgical planning. The first three experiments use a multitask fMRI language paradigm to functionally segregate left posterior temporal and left posterior frontal regions involved in the perception and production of speech. Experiment 1 demonstrated three functionally distinct responses in the left posterior superior temporal sulcus (STS), left temporo-parietal junction and anterior ascending terminal branch of the left STS. Experiment 2 validates these findings in an independent group of participants, increasing confidence that they are robust. Experiment 3 dissociates the response of three different parts of the left premotor cortex during speech production. Experiment 4 shows that left posterior temporal regions are more consistently activated, in neurotypical controls, when a picture naming task presents pairs of objects rather than single objects. Further work could therefore test whether paired object naming is a more sensitive task for pre- and intra-operative language mapping. Finally, Experiment 5 found that successful reading before and after surgery, in two patients with gliomas affecting the left temporo-parietal junction, enhanced activation in bilateral perirhinal regions that were associated with semantic identification of visually presented objects in neurotypical controls. Future studies can now test whether patients who undergo resection of the left temporo-parietal junction have better reading, post-surgery, when bilateral perirhinal activation is enhanced prior to surgery. Taken together, this work expands our knowledge of the functional anatomy of language, proposes a new way of utilising fMRI data from neurotypical controls to tailor pre- and intra-operative language mapping strategies and provides an insight into how the reading system reorganises itself after brain damage

LIPIcs, Volume 244, ESA 2022, Complete Volume

LIPIcs, Volume 244, ESA 2022, Complete Volum

Neuroinformatics in Functional Neuroimaging

This Ph.D. thesis proposes methods for information retrieval in functional neuroimaging through automatic computerized authority identification, and searching and cleaning in a neuroscience database. Authorities are found through cocitation analysis of the citation pattern among scientific articles. Based on data from a single scientific journal it is shown that multivariate analyses are able to determine group structure that is interpretable as particular “known ” subgroups in functional neuroimaging. Methods for text analysis are suggested that use a combination of content and links, in the form of the terms in scientific documents and scientific citations, respectively. These included context sensitive author ranking and automatic labeling of axes and groups in connection with multivariate analyses of link data. Talairach foci from the BrainMap ™ database are modeled with conditional probability density models useful for exploratory functional volumes modeling. A further application is shown with conditional outlier detection where abnormal entries in the BrainMap ™ database are spotted using kernel density modeling and the redundancy between anatomical labels and spatial Talairach coordinates. This represents a combination of simple term and spatial modeling. The specific outliers that were found in the BrainMap ™ database constituted among others: Entry errors, errors in the article and unusual terminology