Epilepsy surgery for low-grade epilepsy-associated neuroepithelial tumor of temporal lobe: a single-institution experience of 61 patients

Background Low-grade epilepsy-associated neuroepithelial tumor (LEAT) is highly responsive to surgery in general. The appropriate surgical strategy remains controversial in temporal LEAT. The aim of this study is to analyze the surgical seizure outcome of temporal LEAT, focusing on the aspects of surgical strategy. Methods Sixty-one patients from a single epilepsy center with temporal LEAT underwent surgery. The surgical strategy was according to the multidisciplinary presurgical evaluation. Electrocorticogram (ECoG)-assisted resection was utilized. Surgical extent including lesionectomy and extended resection was described in detail. Seizure outcome was classified as satisfactory (Engel class I) and unsatisfactory (Engel classes II–IV). Results After a median follow-up of 36.0 (30.0) months, 83.6% of patients achieved satisfactory outcome, including 72.1% with Engel class Ia. There was 39.3% (24/61) of patients with antiepileptic drug (AED) withdrawal. Use of ECoG (χ2 = 0.000, P > 0.1), preresection spike (χ2 = 0.000, P = 0.763), or spike residue (P = 0.545) was not correlated with the seizure outcome. For lateral temporal LEAT, outcome from lesionectomy was comparable to extended resection (χ2 = 0.499, P > 0.1). For mesial temporal LEAT, 94.7% (18/19) of patients who underwent additional hippocampectomy were satisfactory, whereas only 25% (1/4) of patients who underwent lesionectomy were satisfactory (P = 0.009). Conclusion Surgical treatment was highly effective for temporal LEAT. ECoG may not influence the seizure outcome. For lateral temporal LEAT, lesionectomy with or without cortectomy was sufficient in most patients. For mesial temporal LEAT, extended resection was recommended.

Reduced Insulin-Like Growth Factor-I Effects in the Basal Forebrain of Aging Mouse

It is known that aging is frequently accompanied by a decline in cognition. Furthermore, aging is associated with lower serum IGF-I levels that may contribute to this deterioration. We studied the effect of IGF-I in neurons of the horizontal diagonal band of Broca (HDB) of young (≤6 months old) and old (≥20-month-old) mice to determine if changes in the response of these neurons to IGF-I occur along with aging. Local injection of IGF-I in the HDB nucleus increased their neuronal activity and induced fast oscillatory activity in the electrocorticogram (ECoG). Furthermore, IGF-I facilitated tactile responses in the primary somatosensory cortex elicited by air-puffs delivered in the whiskers. These excitatory effects decreased in old mice. Immunohistochemistry showed that cholinergic HDB neurons express IGF-I receptors and that IGF-I injection increased the expression of c-fos in young, but not in old animals. IGF-I increased the activity of optogenetically-identified cholinergic neurons in young animals, suggesting that most of the IGF-I-induced excitatory effects were mediated by activation of these neurons. Effects of aging were partially ameliorated by chronic IGF-I treatment in old mice. The present findings suggest that reduced IGF-I activity in old animals participates in age-associated changes in cortical activity.

Development and Evaluation of a Method for Automated Detection of Spreading Depolarizations in the Injured Human Brain

Background Spreading depolarizations (SDs) occur in some 60% of patients receiving intensive care following severe traumatic brain injury and often occur at a higher incidence following serious subarachnoid hemorrhage and malignant hemisphere stroke (MHS); they are independently associated with worse clinical outcome. Detection of SDs to guide clinical management, as is now being advocated, currently requires continuous and skilled monitoring of the electrocorticogram (ECoG), frequently extending over many days. Methods We developed and evaluated in two clinical intensive care units (ICU) a software routine capable of detecting SDs both in real time at the bedside and retrospectively and also capable of displaying patterns of their occurrence with time. We tested this prototype software in 91 data files, each of approximately 24 h, from 18 patients, and the results were compared with those of manual assessment (“ground truth”) by an experienced assessor blind to the software outputs. Results The software successfully detected SDs in real time at the bedside, including in patients with clusters of SDs. Counts of SDs by software (dependent variable) were compared with ground truth by the investigator (independent) using linear regression. The slope of the regression was 0.7855 (95% confidence interval 0.7149–0.8561); a slope value of 1.0 lies outside the 95% confidence interval of the slope, representing significant undersensitivity of 79%. R2 was 0.8415. Conclusions Despite significant undersensitivity, there was no additional loss of sensitivity at high SD counts, thus ensuring that dense clusters of depolarizations of particular pathogenic potential can be detected by software and depicted to clinicians in real time and also be archived.

A natural marmoset model of genetic generalized epilepsy.

As a common neurological disease, epilepsy has been extensively studied. Efforts have been made on rodent and other animal models to reveal the pathogenic mechanisms of epilepsy and develop new drugs as treatment . However, the features of current epilepsy models cannot fully mimic different kinds of epilepsy in human, asking for non-human primates models of epilepsy. The common marmoset (Callithrix jacchus) is a New World monkey that is widely used to study brain function. Here, we show a natural marmoset model of generalized epilepsy. In this unique marmoset family, generalized epilepsy was successfully induced by handling operation in some individuals. We mapped the marmoset family with handling-sensitive epilepsy and found that epileptic marmoset had an autosomal dominant genetic predisposition. Those marmosets were more sensitive to epilepsy inducers pentylenetetrazol (PTZ). By electrocorticogram (ECoG) recording, we detected epileptic discharge in marmoset with history of seizures. However, there was no significant change in the overall structure of epileptic marmoset brain. In summary, we report a family of marmosets with generalized seizures induced by handling operation. This epileptic marmoset family provides insights to better understand the mechanism of generalized epilepsy and helps to develop new therapeutic methods.

A Swallowing Decoder Based on Deep Transfer Learning: AlexNet Classification of the Intracranial Electrocorticogram

To realize a brain–machine interface to assist swallowing, neural signal decoding is indispensable. Eight participants with temporal-lobe intracranial electrode implants for epilepsy were asked to swallow during electrocorticogram (ECoG) recording. Raw ECoG signals or certain frequency bands of the ECoG power were converted into images whose vertical axis was electrode number and whose horizontal axis was time in milliseconds, which were used as training data. These data were classified with four labels (Rest, Mouth open, Water injection, and Swallowing). Deep transfer learning was carried out using AlexNet, and power in the high-[Formula: see text] band (75–150[Formula: see text]Hz) was the training set. Accuracy reached 74.01%, sensitivity reached 82.51%, and specificity reached 95.38%. However, using the raw ECoG signals, the accuracy obtained was 76.95%, comparable to that of the high-[Formula: see text] power. We demonstrated that a version of AlexNet pre-trained with visually meaningful images can be used for transfer learning of visually meaningless images made up of ECoG signals. Moreover, we could achieve high decoding accuracy using the raw ECoG signals, allowing us to dispense with the conventional extraction of high-[Formula: see text] power. Thus, the images derived from the raw ECoG signals were equivalent to those derived from the high-[Formula: see text] band for transfer deep learning.

An ECoG-Based Binary Classification of BCI Using Optimized Extreme Learning Machine

In order to improve the accuracy of brain signal processing and accelerate speed meanwhile, we present an optimal and intelligent method for large dataset classification application in this paper. Optimized Extreme Learning Machine (OELM) is introduced in ElectroCorticoGram (ECoG) feature classification of motor imaginary-based brain-computer interface (BCI) system, with common spatial pattern (CSP) to extract the feature. When comparing it with other conventional classification methods like SVM and ELM, we exploit several metrics to evaluate the performance of all the adopted methods objectively. The accuracy of the proposed BCI system approaches approximately 92.31% when classifying ECoG epochs into left pinky or tongue movement, while the highest accuracy obtained by other methods is no more than 81%, which substantiates that OELM is more efficient than SVM, ELM, etc. Moreover, the simulation results also demonstrate that OELM will significantly improve the performance with p value being far less than 0.001. Hence, the proposed OELM is satisfactory in addressing ECoG signal.

Electrical Stimulation in the Claustrum Area Induces a Deepening of Isoflurane Anesthesia in Rat

The role of the claustrum in consciousness and vigilance states was proposed more than two decades ago; however, its role in anesthesia is not yet understood, and this requires more investigation. The aim of our study was to assess the impact of claustrum electrical stimulation during isoflurane anesthesia in adult rats. The claustrum in the left hemisphere was electrically stimulated using a bipolar tungsten electrode inserted stereotaxically. In order to monitor the anesthetic depth, the electrocorticogram (ECoG) was recorded before, during, and after claustrum stimulation using frontal and parietal epidural electrodes placed over the left hemisphere. After reaching stabilized slow-wave isoflurane anesthesia, twenty stimuli, each of one second duration with ten seconds interstimulus duration, were applied. ECoG analysis has shown that, after a delay from the beginning of stimulation, the slow-wave ECoG signal changed to a transient burst suppression (BS) pattern. Our results show that electrical stimulation of the claustrum area during slow-wave isoflurane anesthesia induces a transitory increase in anesthetic depth, documented by the appearance of a BS ECoG pattern, and suggests a potential role of claustrum in anesthesia.

Comparison of tuning properties of gamma and high-gamma power in local field potential (LFP) versus electrocorticogram (ECoG) in visual cortex

Electrocorticogram (ECoG), obtained from macroelectrodes placed on the cortex, is typically used in drug-resistant epilepsy patients, and is increasingly being used to study cognition in humans. These studies often use power in gamma (30-70 Hz) or high-gamma (>80 Hz) ranges to make inferences about neural processing. However, while the stimulus tuning properties of gamma/high-gamma power have been well characterized in local field potential (LFP; obtained from microelectrodes), analogous characterization has not been done for ECoG. Using a hybrid array containing both micro and ECoG electrodes implanted in the primary visual cortex of two female macaques, we compared the stimulus tuning preferences of gamma/high-gamma power in LFP versus ECoG and found them to be surprisingly similar. High-gamma power, thought to index the average firing rate around the electrode, was highest for the smallest stimulus (0.3° radius), and decreased with increasing size in both LFP and ECoG, suggesting local origins of both signals. Further, gamma oscillations were similarly tuned in LFP and ECoG to stimulus orientation, contrast and spatial frequency. This tuning was significantly weaker in electroencephalogram (EEG), suggesting that ECoG is more like LFP than EEG. Overall, our results validate the use of ECoG in clinical and basic cognitive research.