Researchers from Rice University (TX, USA) and UCLA (CA, USA) have observed differences in brain connectivity between epilepsy patients and healthy controls. The study has been published in the journal Human Brain Mapping and used a novel statistical approach to reveal details of the internal networks of the participants’ brains. Using two imaging techniques, the team was able to analyze brain activity data to see how separate anatomical regions spontaneously interact.
By looking at changes in blood circulation in images of the brain, the authors reported differences in brain connectivity between the two groups. One part of the study showed that structures that plan and then activate movement may have abnormal bidirectional interactions in the brains of patients with temporal lobe epilepsy, compared with controls.
John Stern (UCLA), one of the study authors, explained: “Temporal lobe epilepsy is a form of focal epilepsy with seizures originating from the brain’s temporal lobe. However, a network of regions is affected, which is evident in the research findings.” Sharon Chiang (Rice University), another of the study authors, continued: “The idea is that, with better understanding of drivers in these networks, down the line, future treatments may be able to disrupt these networks and prevent epileptic seizures.”
In the study, the researchers used fMRI to image the brain’s resting-state networks, thought to control higher-order functions such as attention, executive control and language. In addition, standard MRI was used to detail structural connections in the brain believed to be necessary for effective communication. Statisticians could then model links between brain structures of the epilepsy patients and compare them either individually or collectively with each other, as well as controls.
Marina Vanucci (Rice University), senior author of the study, commented on the statistical technique: “The statistical approach has advantages. One is that we use data from multiple subjects. Rather than estimating networks from individuals and then averaging them, we estimate networks at the epileptic and control group levels by using all the data at once. Then we can look for differences between the two networks and across time. We take into account what we call heterogeneity, accounting for variations between one individual and another. It allows us to get better estimations. At the end of the day we have fewer false positives, so the network we are able to construct is more reliable. Ultimately, we want to understand what is different about that connectivity and the effect of epilepsy on the connections across the whole brain.”
Results using fMRI data corroborated previous findings using other imaging techniques. For example, the sequential activation during motor tasks of the premotor cortex, then the primary somatosensory cortex, then the primary motor cortex in healthy brains. However, it also revealed novel connections such as two-way communications between the premotor and primary somatosensory cortex. In addition the brain of epilepsy patients may have smaller overall areas and intensity of activation in their alertness networks, which keep brains ready for incoming stimuli. A different spatial pattern for effective connections into and out of the alertness network in epilepsy patients was also observed.
Chiang commented on the medical implications of the study: “Currently, surgical resection is the treatment of choice for some patients with medically refractory epilepsy. However, if drivers in these networks can be identified and possibly stimulated, rather than completely resected, this may potentially allow a more targeted treatment.”
Sources: Chiang S, Guindani M, Yeh HJ, Haneef Z, Stern JM, Vannucci M. Bayesian vector autoregressive model for multi-subject effective connectivity inference using multi-modal neuroimaging data. Hum. Brain Mapp. 38(3), 1311–1332 (2017); http://news.rice.edu/2017/03/06/statistics-method-shows-networks-differ-in-epileptic-brains