Movie-watching fMRI for presurgical language mapping in patients with brain tumour

The primary goal of presurgical language mapping is localising critical language areas with high sensitivity (ie, capturing areas in which resection could lead to language deficits) and specificity (ie, excluding non-language areas) and reliably determining language hemispheric dominance, on an individual basis. Language mapping is challenging due to the widely distributed functional organisation of language in the frontal, temporal and parietal lobes, and in neurosurgical patients the possibility of tumour-induced functional reorganisation. A major drawback of conventional task-based functional magnetic resonance imaging (tb-fMRI) recommended for presurgical language mapping1 is the contingency on patient performance of precisely timed tasks (eg, antonym generation—AntGen). Drawbacks of task-free resting-state fMRI (rs-fMRI) include confounding effects of ‘mind wandering’ and sensitivity to motion artefacts. In contrast, movie watching is a rich, stimulating and naturalistic activity, predicted to constrain cognitive processes and engage the distributed, multimodal neural networks supporting language function in real life.2 Our previous study demonstrated individual language mapping using movie-watching fMRI (mw-fMRI) in neurologically healthy subjects.3 Here, we examine mw-fMRI language mapping in presurgical patients with a brain tumour encroaching on putative language cortex, and varying levels of language disruption. We hypothesise that mw-fMRI versus AntGen tb-fMRI, and rs-fMRI, will provide comprehensive language mapping at reduced burden, as determined by metrics of in-scanner head motion, and mapping specificity, sensitivity and lateralisation. Mw-fMRI was compared with clinically indicated AntGen tb-fMRI in 34 patients with brain tumour undergoing presurgical language mapping, and with rs-fMRI in 22 of these patients. See online supplemental methods for exclusion criteria, and online supplemental table S1 for demographic and clinical information. Language maps were generated from tb-fMRI using a general linear model, and from mw-fMRI and rs-fMRI using independent …