Periodic Reporting for period 2 - EPICONNECT (Functional brain networks in epilepsy)
Reporting period: 2017-07-01 to 2018-06-30
During the video-EEG monitoring, the patient is admitted during 5-7 days in the hospital to record EEG and video simultaneously to study the electrical field and the semiology during seizures. The recorded long-term EEG is visually analyzed by the treating physician. Epileptic spikes and seizures are marked in the EEG to get an estimate about the location of the epileptic focus. Because epilepsy is a network disorder seizures and spikes rapidly spread throughout the brain. This makes the localization of the focus from EEG recordings a challenge. In this project we studied how brain regions communicate with each other during baseline and seizures to identify the region that is causing the epilepsy.
The main outcome of the project is that we achieved to localize the region that is generating the epilepsy by studying the functional interactions between the regions with EEG. We optimized the information obtained during the video-EEG monitoring to provide the treating physician with more information about the regions causing the epilepsy in an early phase of the presurgical evaluation. During seizures and interictal epileptiform discharges the epileptic zone could be estimated from the EEG with high localization accuracy. Furthermore, even during resting state the functional brain connectivity obtained by a brief EEG test tells us which regions have been affected mostly in the patient by the epilepsy. This has the potential to ameliorate patient care. Not only could some additional more expensive tests during the presurgical evaluation become redundant, also the patients can be treated more efficiently and earlier in time, allowing faster reintegration into the society and therefore reducing costs for the society.
We investigated the functional connectome in 27 patients during 111 seizures. We compared the standard EEG-based localization technique, namely looking which brain region is most active during a seizure, with the localization technique based on functional connectome that depicts the driver of the seizure. All patients had epilepsy surgery after the presurgical evaluation and were seizure-free after surgery. This allowed us to compare the brain region we localized based on the two techniques with the resection in the patients. The brain region with maximal activity was inside the resection in 31% of the seizures and estimated within 10 mm from the border of the resection in 42%. Using the functional connectome, these numbers increased to 72% in the resection and 94% within 10 mm of the resection. Therefore, we showed that looking at the functional connectome during seizures has an added value and should be included in the presurgical evaluation.
We also investigated the functional connectome during resting state in 20 patients with left temporal lobe epilepsy, 20 patients with right temporal lobe epilepsy and 35 healthy age-matched subjects. We studied how well we can classify a person to have epilepsy or not, and if we can predict the lateralization of the epilepsy (left vs. right). The diagnosis and lateralization classifiers achieved a high accuracy (90.7% and 90.0% respectively). Meaning that based on 15min resting state EEG we can predict if the person has epilepsy or not, and if yes, the side where the epilepsy is originating from with 90% accuracy.
The project resulted in a total of 8 peer-reviewed publications: 2 in NeuroImage Clinical, 2 in Brain Topography, 1 in Epilepsia Open, 1 in Brain Stimulation, and 1 in IEEE Transactions on Biomedical Engineering and over 20 conference contributions. The results of the project have been orally presented at 10 conferences such as the European Congress on Epileptology 2016 & 2018, the International Epilepsy Congress 2017 and the International Congress of Clinical Neurophysiology 2018. The first prize of oral presentation was won at the Alpine Brain Imaging Meeting 2016.
In a last study we showed the feasibility to diagnose and lateralize epilepsy from 15 minutes of resting state EEG with accuracy around 90%. Although more research is needed to confirm these findings in a larger patient cohort, it opens the way to improved epilepsy diagnosis with an inexpensive non-invasive test. In the long run this could mean that anti-epileptic drugs are started sooner in some patients, when the epilepsy has not manifested itself completely. The sooner a patient is treated, to more chance the patient has to become seizure-free and to contribute to the society.