Description: Unthresholded NIFTI files for the project 'Mapping lesion, structural disconnection, and functional disconnection to symptoms in semantic aphasia'. Here, we observe typical patterns of lesion, structural disconnection, and functional disconnection in a sample of 23 patients with semantic aphasia following left hemisphere stroke. We observe degrees of damage to relevant functional networks, and map damage onto both semantic impairment and dysexecutive symptoms as measured by the Brixton Spatial Anticipation Test. This collection first includes overlap maps for the different measures of damage: - Lesion Overlap - an overlay of all patients' lesions (binary files manually drawn in MRICron) - SDC Overlap - an overlay of all patients' structural disconnection (SDC) files. These files were generated using the Disconnectome function of the BCB Toolkit, reflecting probabilistic white matter disconnection on the basis of lesion location. Input files used a threshold of 50% probability of disconnection. For the purpose of this overlay, these files were binarised. - FDC Overlap - an overlay of all patients' functional disconnection (FDC) files. These files were generated using patients' lesion files in seed-based functional connectivity analysis in CONN, to determine areas likely functionally disconnected by lesion. Again, these files were binarised in order to generate this overlay. We next include output files of our symptom mapping for both semantic and executive performance, for each measure of damage. Symptoms were mapped using non-parametric two-sample t-tests in Randomise using threshold-free cluster enhancement (with 5,000 permutations). For interpretation we threshold these files at a t-value of 2.6, but they are presented uncorrected here. Files are named according to their measure of damage, behavioural scores used, and the direction of the effect. 'Lesion semantic positive' would reflect lesioned clusters associated with less poor semantic performance. 'SDC Brixton negative' would reflect structurally disconnected clusters associated with poorer executive performance.
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