Yanmei Tie1, Laura Rigolo1,2, Aysegul Ozdemir Ovalioglu1,3, Olutayo Olubiyi1, Kelly L Doolin1,4, Srinivasan Mukundan2,5, Alexandra J Golby1,2,5. 1. Harvard Medical School, Boston, MA, USA. 2. Departments of Neurosurgery, Brigham and Women's Hospital, Boston, MA, USA. 3. Neurosurgery Department, Haseki Education and Research Hospital, Istanbul, Turkey. 4. Trinity College Dublin, College Green, Dublin 2, Ireland. 5. Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA.
Abstract
BACKGROUND: Functional MRI (fMRI) based on language tasks has been used in presurgical language mapping in patients with lesions in or near putative language areas. However, if patients have difficulty performing the tasks due to neurological deficits, it leads to unreliable or noninterpretable results. In this study, we investigate the feasibility of using a movie-watching fMRI for language mapping. METHODS: A 7-minute movie clip with contrasting speech and nonspeech segments was shown to 22 right-handed healthy subjects. Based on all subjects' language functional regions-of-interest, 6 language response areas were defined, within which a language response model (LRM) was derived by extracting the main temporal activation profile. Using a leave-one-out procedure, individuals' language areas were identified as the areas that expressed highly correlated temporal responses with the LRM derived from an independent group of subjects. RESULTS: Compared with an antonym generation task-based fMRI, the movie-watching fMRI generated language maps with more localized activations in the left frontal language area, larger activations in the left temporoparietal language area, and significant activations in their right-hemisphere homologues. Results of 2 brain tumor patients' movie-watching fMRI using the LRM derived from the healthy subjects indicated its ability to map putative language areas; while their task-based fMRI maps were less robust and noisier. CONCLUSIONS: These results suggest that it is feasible to use this novel "task-free" paradigm as a complementary tool for fMRI language mapping when patients cannot perform the tasks. Its deployment in more neurosurgical patients and validation against gold-standard techniques need further investigation.
BACKGROUND: Functional MRI (fMRI) based on language tasks has been used in presurgical language mapping in patients with lesions in or near putative language areas. However, if patients have difficulty performing the tasks due to neurological deficits, it leads to unreliable or noninterpretable results. In this study, we investigate the feasibility of using a movie-watching fMRI for language mapping. METHODS: A 7-minute movie clip with contrasting speech and nonspeech segments was shown to 22 right-handed healthy subjects. Based on all subjects' language functional regions-of-interest, 6 language response areas were defined, within which a language response model (LRM) was derived by extracting the main temporal activation profile. Using a leave-one-out procedure, individuals' language areas were identified as the areas that expressed highly correlated temporal responses with the LRM derived from an independent group of subjects. RESULTS: Compared with an antonym generation task-based fMRI, the movie-watching fMRI generated language maps with more localized activations in the left frontal language area, larger activations in the left temporoparietal language area, and significant activations in their right-hemisphere homologues. Results of 2 brain tumorpatients' movie-watching fMRI using the LRM derived from the healthy subjects indicated its ability to map putative language areas; while their task-based fMRI maps were less robust and noisier. CONCLUSIONS: These results suggest that it is feasible to use this novel "task-free" paradigm as a complementary tool for fMRI language mapping when patients cannot perform the tasks. Its deployment in more neurosurgical patients and validation against gold-standard techniques need further investigation.
Authors: Yanmei Tie; Laura Rigolo; Isaiah H Norton; Raymond Y Huang; Wentao Wu; Daniel Orringer; Srinivasan Mukundan; Alexandra J Golby Journal: Hum Brain Mapp Date: 2013-01-03 Impact factor: 5.038
Authors: Shun Yao; Einat Liebenthal; Parikshit Juvekar; Adomas Bunevicius; Matthew Vera; Laura Rigolo; Alexandra J Golby; Yanmei Tie Journal: Front Neurosci Date: 2020-01-24 Impact factor: 4.677