Resting state fMRI implemented into a clinical picture archiving and communication system (PACS) to identify eloquent cortex in patients unable to participate in a task-based study displayed a lower failure rate than standard MRI, according to research published June 22 in PLOS One.
Unlike standard fMRI, resting state fMRI can identify areas of the brain interacting at rest without patient participation. However, post-processing resting state data is an intensive process of functional mapping and requires a high degree of advanced imaging expertise, wrote lead author Eric Leuthardt, MD, from Washington University School of Medicine in St. Louis, and colleagues.
To address this, Leuthardt and colleagues at the University of Washington created a new automated algorithm and advanced imaging IT infrastructure able to fully evaluate the clinical application and integration of resting state fMRI into standard neurosurgical operations.
Over 18 months, 191 patients (173 adults and 18 children) underwent a total of 232 resting state MRI exams between Jan. 1, 2014 and June 20, 2015—83 of whom also underwent both motor and language task-based fMRIs, according to Leuthardt and colleagues.
"Data was processed using a novel, automated, multi-layer perceptron algorithm and integrated into stereotactic navigation using a streamlined IT imaging pipe-line," the researchers wrote.
Overall, the researchers noted that resting state MRIs had failure rate of 13 percent compared to tasked-based fMRIs with a failure rate of 38.5 percent. Some 185 examinations were performed for intracranial neoplasm, 14 for refractory epilepsy and 33 for vascular malformations or other neurological disorders, according to the researchers.
"Resting state fMRI can be used in all patients, and due to its lower failure rate than task-based fMRI, it is useful for patients who are unable to cooperate with task-based studies," Leuthardt et al. wrote. "Cumulatively, the experience demonstrates robust neurosurgeon adoption, utilization for a diversity of clinical applications and a reduced failure rate when compared to standard task-based functional mapping."
A recent graduate from Dominican University (IL) with a bachelor’s in journalism, Melissa joined TriMed’s Chicago team in 2017 covering all aspects of health imaging. She’s a fan of singing and playing guitar, elephants, a good cup of tea, and her golden retriever Cooper.