Scientists from the University of Nottingham have found that the way neurons are connected within different brain regions can indicate disease progression and treatment outcomes for people with neurological conditions like epilepsy.
For the study, the researchers recruited 33 patients with temporal lobe epilepsy, and 36 age and sex-matched healthy subjects. Each underwent a high-resolution non-invasive structural MRI imaging technique called diffusion tensor imaging. This allowed the researchers to divide the surface of the brain into 50,000 network nodes of comparable size. Each brain region could thus be studied from a local network of 100-500 nodes.
In the end, the researchers found distinct changes between the control group and those with epilepsy. In particular, they found that those with epilepsy had reduced connectivity between brain regions. They also found that connectivity within brain regions was a better predictor of surgical outcomes leading to a reduced number of seizures than connectivity between brain regions.
The researchers also noted that whereas connectivity between regions could identify 90% of cases when surgery would not improve the incidence of seizures, connectivity within brain regions was able to identify 95% of such cases.
“When someone has an epileptic seizure, it ‘spreads’ through the brain. We found that local network changes occurred for regions along the main spreading pathways for seizures.” says Dr Marcus Kaiser, one of the researchers behind the study. “Importantly, regions far away from the starting point of the seizure, for example in the opposite brain hemisphere, were involved.”
“This indicates that the increased brain activity during seizures leads to changes in a wide range of brain regions. Furthermore, the longer patients suffered, the more regions showed local changes and the more severe were these changes.”
The researchers say that their software can easily be employed in hospitals for clinical use. They say it can also be combined with other data from genetics and other imaging techniques including PET, CT and EEG to optimize surgical outcome predictions.