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 magnetic resonance imaging (rs-fMRI) may prove useful for clinical brain mapping.
OBJECTIVE: To demonstrate that a data-driven approach to analyze resting-state networks (RSNs) is useful in identifying regions classically understood to be eloquent cortex as well as other functional networks.
METHODS: This study included 6 patients undergoing surgical treatment for intractable epilepsy and 7 patients undergoing tumor resection. rs-fMRI data were obtained before surgery and 7 canonical RSNs were identified by an artificial neural network algorithm. Of these 7, the motor and language networks were then compared with electrocortical stimulation (ECS) as the gold standard in the epilepsy patients. The sensitivity and specificity for identifying these eloquent sites were calculated at varying thresholds, which yielded receiver-operating characteristic (ROC) curves and their associated area under the curve (AUC). RSNs were plotted in the tumor patients to observe RSN distortions in altered anatomy.
RESULTS: The algorithm robustly identified all networks in all patients, 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 preoperative localization of numerous functional brain regions.
ABBREVIATIONS: AUC, area under the curve
BA, Brodmann area
BOLD, blood oxygen level dependent
ECS, electrocortical stimulation
fMRI, functional magnetic resonance imaging
ICA, independent component analysis
MLP, multilayer perceptron
MP-RAGE, magnetization-prepared rapid gradient echo
ROC, receiver-operating characteristic
rs-fMRI, resting-state functional magnetic resonance imaging
RSN, resting-state network
Departments of *Neurological Surgery,
§Biomedical Engineering, and
¶Mechanical Engineering and Material Sciences,
‖Mallinckrodt Institute of Radiology,
#Center for Innovation in Neuroscience and Technology, Washington University School of Medicine, St. Louis, Missouri
Correspondence: Eric C. Leuthardt, MD, Department of Neurosurgery, Washington University in St. Louis, School of Medicine, Campus Box 8057, 660 South Euclid, St. Louis, MO 63130. E-mail: firstname.lastname@example.org
Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (www.neurosurgery-online.com).
Received November 27, 2012
Accepted August 13, 2013