Background: Recent findings associated with resting state cortical networks have provided insight into the brain’s organizational structure. In addition to their neuroscientific implications, the networks identified by resting state functional MRI (rs-fMRI) may prove useful for clinical brain mapping.
Objective: To demonstrate that a data-driven approach to analyze resting state networks is useful in identifying regions classically understood to be eloquent cortex as well as other functional networks.
Methods: Study included six subjects undergoing surgical treatment for intractable epilepsy and seven subjects undergoing tumor resection. rs-fMRI data were obtained prior to surgery and seven canonical resting state networks (RSNs) were identified by an artificial neural network algorithm. Of these seven, the motor and language networks were then compared to electrocortical stimulation as the gold standard in the epilepsy patients. The sensitivity and specificity for identifying these eloquent sites was calculated at varying thresholds, which yielded receiver operating characteristic (ROC) curves and their associated area under the curve (AUC). RSN networks were plotted in the tumor subjects to observe RSN distortions in altered anatomy.
Results: The algorithm robustly identified all networks in all subjects, including those with distorted anatomy. When all ECS positive sites were considered for motor and language, rs-fMRI had AUCs of 0.80 and 0.64, respectively. When the ECS positive sites were analyzed pairwise, rs-fMRI had AUCs of 0.89 and 0.76 for motor and language, respectively.
Conclusion: A data-driven approach to rs-fMRI may be a new and efficient method for pre-operative localization of numerous functional brain regions.
From: A Novel Data Driven Approach to Preoperative Mapping of Functional Cortex Using Resting State fMRI by Leuthardt et al.