Epilepsy is a group of neurological disorders characterized by seizures. Although the symptoms may affect any part of the body, the
electrical events that produce the symptoms occur in the brain.
A novel statistical approach to analyzing patients with epilepsy has
revealed details about their brains' internal networks. The findings may
lead to better understanding and treatment of the disease, according to
Rice University researchers.
‘A novel statistical approach to analyzing patients with epilepsy could lead to better understanding and treatment of the disease.’
Rice statistician Marina Vannucci and lead author Sharon Chiang, an
M.D./Ph.D. student at Rice and Baylor College of Medicine, and their
co-authors detailed their technique to analyze brain activity data from
patients with epilepsy and control groups to see how distinct structures
in the brain spontaneously interact.
The results showed differences in brain connectivity between the
groups. In one instance, they showed structures that plan and then
activate movement, which tend to interact in one direction in control
subjects, may have abnormal bidirectional interactions in the brains of
patients with temporal lobe epilepsy.
The study appears in the journal Human Brain Mapping
The Rice team approached its analysis of the brain in much the same
way a meteorologist uses radar to predict the weather. Rather than winds
and water, they look at the shifting circulation of blood in brain
images that depict dynamic connections between structures.
"Temporal lobe epilepsy is a form of focal epilepsy with seizures
originating from the brain's temporal lobe. A network of
regions is affected in temporal lobe epilepsy, which is evident in the research findings," said
co-author John Stern, director of the Epilepsy Clinical Program at the
University of California, Los Angeles, and co-director of the UCLA
Seizure Disorder Center.
"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," Chiang said.
The new approach is based on Bayesian probability, which does not
provide definitive answers but "degrees of belief" based on the strength
of the evidence.
The researchers used two types of data from patients with temporal
lobe epilepsy and healthy control subjects. The first, functional
magnetic resonance imaging (fMRI), detailed the brain's resting-state
networks, thought to control higher-order functions including attention,
executive control and language. Functional MRI produces maps of the
brain based on oxygenated blood flow related to neural activity.
The second, standard MRI, detailed structural connections in the
brain believed to be necessary for effective communication. Integrating
both types of data allowed for improved inference, Vannucci said.
Extended imaging sessions at UCLA allowed the statisticians to model
links between structures in epileptic patients' brains and to compare
them either individually or collectively with each other and with the
The data from scans of multiple patients and control subjects helped
piece together insights unavailable from individual techniques like
electroencephalography or positron emission tomography (PET) scans.
"The statistical approach has advantages," said Vannucci, who chairs
Rice's Department of Statistics. "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," she said. "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," she said.
Vannucci said results using fMRI data corroborated several
previously known connections found through electrocorticography. One,
for example, was the sequential activation during motor tasks of the
premotor cortex, then the primary somatosensory cortex, then the primary
motor cortex in healthy brains.
But it also revealed novel connections in patients with temporal
lobe epilepsy, including two-way communications between the premotor and
primary somatosensory cortex. It showed epileptic brains engage other
parts of the brain to handle alertness tasks. Brains of patients with
epilepsy may have smaller overall areas and intensity of activation in
their alertness networks, which keep brains ready for incoming stimuli.
The study found a different spatial pattern for effective connections
into and out of the alertness network in patients as compared to
"Currently, surgical resection is the treatment of choice for some
patients with medically refractory epilepsy," Chiang said. "However, if
drivers in these networks can be identified and possibly stimulated,
rather than completely resected, this may potentially allow a more