National Epilepsy Surgery Support Activity

While there are over one million people with drug-resistant epilepsy in India, today, there are only a handful of centers equipped to undertake presurgical evaluation and epilepsy surgery. The only solution to overcome this large surgical treatment gap is to establish comprehensive epilepsy care centers across the country that are capable of evaluating and selecting the patients for epilepsy surgery with the locally available technology and in a cost-effective manner. The National Epilepsy Surgery Support Activity (NESSA) aims to provide proper guidance and support in establishing epilepsy surgery programs across India and in neighboring resource-poor countries, and in sustaining them

Penetrating brain injury with machete, stuck to calvarium: Hurdles in imaging and solutions

Penetrating brain injury is a less common form of traumatic brain injury in civilian set up, with a higher mortality and morbidity. A detailed preoperative imaging is warranted to ascertain the extent of injury and involvement of neurovascular structures. We present a rare case of penetrating brain injury with a long machete, who underwent emergency craniotomy, removal of the weapon, debridement and evacuation of the brain contusion and dural repair. Due to the sheer size of the weapon stuck to the calvarium, only X-rays could be performed preoperatively. The difficulties posed by the case, requiring modifications in standard imaging, possible solutions to address the problem and individualized management techniques are discussed in this report

Magnetoencephalography imaging of high frequency oscillations strengthens presurgical localization and outcome prediction.

In patients with medically refractory epilepsy, resective surgery is the mainstay of therapy to achieve seizure freedom. However, ∼20-50% of cases have intractable seizures post-surgery due to the imprecise determination of epileptogenic zone. Recent intracranial studies suggest that high frequency oscillations between 80 and 200 Hz could serve as one of the consistent epileptogenicity biomarkers for localization of the epileptogenic zone. However, these high frequency oscillations are not adopted in the clinical setting because of difficult non-invasive detection. Here, we investigated non-invasive detection and localization of high frequency oscillations and its clinical utility in accurate pre-surgical assessment and post-surgical outcome prediction. We prospectively recruited 52 patients with medically refractory epilepsy who underwent standard pre-surgical workup including magnetoencephalography (MEG) followed by resective surgery after determination of the epileptogenic zone. The post-surgical outcome was assessed after 22.14 ± 10.05 months. Interictal epileptic spikes were expertly identified, and interictal epileptic oscillations across the neural activity frequency spectrum from 8 to 200 Hz were localized using adaptive spatial filtering methods. Localization results were compared with epileptogenic zone and resected cortex for congruence assessment and validated against the clinical outcome. The concordance rate of high frequency oscillations sources (80-200 Hz) with the presumed epileptogenic zone and the resected cortex were 75.0% and 78.8%, respectively, which is superior to that of other frequency bands and standard dipole fitting methods. High frequency oscillation sources corresponding with the resected cortex, had the best sensitivity of 78.0%, positive predictive value of 100% and an accuracy of 78.84% to predict the patient's surgical outcome, among all other frequency bands. If high frequency oscillation sources were spatially congruent with resected cortex, patients had an odds ratio of 5.67 and 82.4% probability of achieving a favourable surgical outcome. If high frequency oscillations sources were discordant with the epileptogenic zone or resection area, patient has an odds ratio of 0.18 and only 14.3% probability of achieving good outcome, and mostly tended to have an unfavourable outcome (χ2 = 5.22; P = 0.02; φ = -0.317). In receiver operating characteristic curve analyses, only sources of high-frequency oscillations demonstrated the best sensitivity and specificity profile in determining the patient's surgical outcome with area under the curve of 0.76, whereas other frequency bands indicate a poor predictive performance. Our study is the first non-invasive study to detect high frequency oscillations, address the efficacy of high frequency oscillations over the different neural oscillatory frequencies, localize them and clinically validate them with the post-surgical outcome in patients with medically refractory epilepsy. The evidence presented in the current study supports the fact that HFOs might significantly improve the presurgical assessment, and post-surgical outcome prediction, where it could widely be used in a clinical setting as a non-invasive biomarker

Improvement in obsessive-compulsive disorder following right anterior temporal lobectomy and amygdalohippocampectomy in a patient with refractory temporal lobe epilepsy with right mesial temporal sclerosis

There are reports of co-occurrence of obsessive–compulsive disorder (OCD) in patients with temporal lobe epilepsy (TLE). We present a report of a patient with refractory TLE due to hippocampal sclerosis with concomitant OCD on pharmacotherapy for both. She underwent surgery for standard anterior temporal lobectomy with amygdalohippocampectomy and reported improvement in obsessive–compulsive symptoms subsequently. We seek to further evidence of interaction between the two conditions and argue to undertake future research exploration on the same

Machine learning identifies “rsfMRI epilepsy networks” in temporal lobe epilepsy

Objectives Experimental models have provided compelling evidence for the existence of neural networks in temporal lobe epilepsy (TLE). To identify and validate the possible existence of resting-state “epilepsy networks,” we used machine learning methods on resting-state functional magnetic resonance imaging (rsfMRI) data from 42 individuals with TLE. Methods Probabilistic independent component analysis (PICA) was applied to rsfMRI data from 132 subjects (42 TLE patients + 90 healthy controls) and 88 independent components (ICs) were obtained following standard procedures. Elastic net-selected features were used as inputs to support vector machine (SVM). The strengths of the top 10 networks were correlated with clinical features to obtain “rsfMRI epilepsy networks.” Results SVM could classify individuals with epilepsy with 97.5% accuracy (sensitivity = 100%, specificity = 94.4%). Ten networks with the highest ranking were found in the frontal, perisylvian, cingulo-insular, posterior-quadrant, thalamic, cerebello-thalamic, and temporo-thalamic regions. The posterior-quadrant, cerebello-thalamic, thalamic, medial-visual, and perisylvian networks revealed significant correlation (r > 0.40) with age at onset of seizures, the frequency of seizures, duration of illness, and a number of anti-epileptic drugs. Conclusions IC-derived rsfMRI networks contain epilepsy-related networks and machine learning methods are useful in identifying these networks in vivo. Increased network strength with disease progression in these “rsfMRI epilepsy networks” could reflect epileptogenesis in TLE