Sensorimotor network parcellation for pre-surgical patients using low-pass filtered fMRI.

Abstract

Pre-surgical mapping of sensorimotor and language functions is crucial to reduce neurological deficits in epilepsy and tumor resection surgery. As non-invasive mapping, both resting-state and task-evoked functional MRI has been explored in pre-surgical mapping. In lack of standardized test paradigm, the reliability of fMRI mapping is still a concern for clinical use. In this study, to improve the reliability of fMRI based mapping, task fMRI data from all available task paradigms (motor movement, word repeating and picture naming) were low-pass filtered in the band of resting-state fMRI (0.01-0.08Hz) and concatenated to get more time points. With K-means clustering, it was shown that the sensorimotor network could be reliably parcellated into hand and tongue sub-regions. The resulted parcellations were further verified with invasive ECoG and ECS mapping. Both the accuracy and specificity were better than using the motor-task fMRI only. Especially, for those patients who failed in task fMRI mapping, our method was able to provide accurate mapping as well. Our results also indicate that cortical sensorimotor network pattern is intrinsic and always present during various tasks, which supports the physiological link between the spontaneous and the task-evoked BOLD signals.