Generation of induced pluripotent stem cell line, NIMHi009-A, from PBMCs of an adult healthy male

Human induced pluripotent stem cells provide an exceptional platform for studying pathogenesis in vitro. We, therefore, have generated and characterized human induced pluripotent stem cell (iPSC) line NIMHi009-A derived from peripheral blood mononuclear cells (PBMCs) of healthy adult male control for an epileptic patient carrying voltage gated sodium channel mutation, using Sendai virus-based reprogramming. The generated iPSCs express pluripotency genes and can spontaneously differentiate into three germ layers. These cells display a normal karyotype and are free of mycoplasma. The iPSC line NIMHi009-A can be used as healthy control for modelling various diseases and screening for drugs

Generation and characterisation of a human induced pluripotent stem cell line, NIMHi007-A, from peripheral blood mononuclear cells derived from an adult healthy female

We report the generation and characterisation of a human induced pluripotent stem cell (iPSC) line, NIMHi007-A, derived from peripheral blood mononuclear cells (PBMCs) of a healthy female adult individual. PBMCs were reprogrammed using the non-integrating Sendai virus consisting of Yamanaka reprogramming factors- SOX2, cMYC, KLF4, and OCT4. The iPSCs displayed a normal karyotype, express pluripotency markers, and could generate into three germ layers, endoderm, mesoderm, and ectoderm, in-vitro. This iPSC line, NIMHi007-A, can be used as a healthy control for various in-vitro disease models and study their underlying pathophysiological mechanisms

Sub-lobar dysplasia — A comprehensive evaluation with neuroimaging, magnetoencephalography and histopathology

Sublobar dysplasia, a rare cortical malformation has been defined in only 8 patients to date. It was identified on the basis of histopathological features and MRI findings. We report a right temporal sublobar dysplasia, with detailed evaluation including neuroimaging, magnetoencephalography and histopathology to further characterize the pathology. Additional pathological features included a deep collateral sulcus in the basal right temporal lobe, thinned out right corticospinal tract, and bilateral asymmetric basal ganglia changes. Magnetoencephalograpy localized the seizure focus to the posterior margin of the dysplasia. Histopathological evaluation helped exclude other types of dysplasia. Similar to a previous study, the child had Engel 1a outcome. Keywords: Sublobar dysplasia, Temporal lobe epilepsy, Tractography, Magnetoencephalography, Histopatholog

Functional network connectivity imprint in febrile seizures.

peer reviewedComplex febrile seizures (CFS), a subset of paediatric febrile seizures (FS), have been studied for their prognosis, epileptogenic potential and neurocognitive outcome. We evaluated their functional connectivity differences with simple febrile seizures (SFS) in children with recent-onset FS. Resting-state fMRI (rs-fMRI) datasets of 24 children with recently diagnosed FS (SFS-n = 11; CFS-n = 13) were analysed. Functional connectivity (FC) was estimated using time series correlation of seed region-to-whole-brain-voxels and network topology was assessed using graph theory measures. Regional connectivity differences were correlated with clinical characteristics (FDR corrected p < 0.05). CFS patients demonstrated increased FC of the bilateral middle temporal pole (MTP), and bilateral thalami when compared to SFS. Network topology study revealed increased clustering coefficient and decreased participation coefficient in basal ganglia and thalamus suggesting an inefficient-unbalanced network topology in patients with CFS. The number of seizure recurrences negatively correlated with the integration of Left Thalamus (r = - 0.58) and FC of Left MTP to 'Right Supplementary Motor and left Precentral' gyrus (r = - 0.53). The FC of Right MTP to Left Amygdala, Putamen, Parahippocampal, and Orbital Frontal Cortex (r = 0.61) and FC of Left Thalamus to left Putamen, Pallidum, Caudate, Thalamus Hippocampus and Insula (r 0.55) showed a positive correlation to the duration of the longest seizure. The findings of the current study report altered connectivity in children with CFS proportional to the seizure recurrence and duration. Regardless of the causal/consequential nature, such observations demonstrate the imprint of these disease-defining variables of febrile seizures on the developing brain

Machine learning detects EEG microstate alterations in patients living with temporal lobe epilepsy

Purpose Quasi-stable electrical distribution in EEG called microstates could carry useful information on the dynamics of large scale brain networks. Using machine learning techniques we explored if abnormalities in microstates can identify patients with Temporal Lobe Epilepsy (TLE) in the absence of an interictal discharge (IED). Method 4 Classes of microstates were computed from 2 min artefact free EEG epochs in 42 subjects (21 TLE and 21 controls). The percentage of time coverage, frequency of occurrence and duration for each of these microstates were computed and redundancy reduced using feature selection methods. Subsequently, Fishers Linear Discriminant Analysis (FLDA) and logistic regression were used for classification. Result FLDA distinguished TLE with 76.1% accuracy (85.0% sensitivity, 66.6% specificity) considering frequency of occurrence and percentage of time coverage of microstate C as features. Conclusion Microstate alterations are present in patients with TLE. This feature might be useful in the diagnosis of epilepsy even in the absence of an IED

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