A statistical method for integrating functional MRI (fMRI) and PET scans may prove capable of predicting success of surgery to reduce seizures in epilepsy patients.
A recent study—from Rice University and Baylor College of Medicine in Houston, the University of California at Irvine and UCLA in this month's issue of Frontiers in Neuroscience—may be able to help neurosurgeons know whether surgery will provide seizure relief in patients.
According to a Rice University press release, the research team hopes that patients with temporal lobe epilepsy (TLE) can avoid anterior temporal lobe resection surgery if it would not help them. Patients could then choose a different, more effective surgery option.
Lead author Sharon Chiang, a MD/PhD student at Rice and Baylor College of Medicine, and Marina Vannucci, PhD, a statistician professor and department chair at Rice, used PET and resting-state fMRI data from 51 patients gathered by the UCLA Seizure Disorder Center between 2007 and 2012.
"The center worked with Rice and Baylor to investigate suspicions that failure to attain seizure freedom after resection of the anterior temporal lobe in some patients with TLE originates in tissue connected through networks to the lobe," according to the press release.
Previously used statistical techniques to analyze brain activity compared that data to controls groups to understand TLE from different perspectives. However, for this study, researchers used data to demonstrate that postoperative seizure recurrence may be due to the disruption of brain fibers connected to previously normal brain tissue or an incomplete resection of an epileptogenic network, according to the press release.
The researchers were able to identify a subgroup of patients having a 5.8 times greater risk of experiencing seizures after surgery due to what they suspect are differences in their underlying networks. Furthermore, they believe that these postoperative seizures could be accredited to epilepsy networks remaining in the brain after surgery, providing a new understanding to the brain's network interconnectivity.
“With recent studies demonstrating the efficacy of less invasive techniques such as laser ablation and neurostimulation, optimal selection of candidates for resective surgery is an important issue,” Chiang said. “Improved statistical methods for integrating the various forms of imaging acquired during the presurgical workup may help in determining appropriate candidates for open resection versus other therapies.”
This study was based on Bayesian probability, which rather than relying on definitive answers is based on strength of evidence and general probability. Additionally, researchers acquired fMRI scans of the brain at a resting-state from each study participant to increase the amount of signal extracted from PET scan data.
“We wanted to know if we could predict outcomes for one patient at a time, and it was pretty successful. Now we want to try the method on a bigger data set to see how robust the results are," Vannucci said.