Bing Neel syndrome presenting as isolated cranial nerve palsies - a case report.

Background and aims: Waldenstroms macroglobulinemia (WM) is a low-grade B cell neoplasm. Bing Neel syndrome is a rare manifestation of WM characterized by infiltrative involvement of the central nervous system. Case report: 64-year-old man, presented with 4 years history of slowly progressive diplopia and ptosis of eyes. Examination showed left oculomotor (internal and external ophthalmoplegia), with trochlear, abducens, and right partial oculomotor and abducens nerve involvement. Evaluation showed anemia of hemoglobin 10.7 g/dL, raised erythrocyte sedimentation rate of 120 mm/h and plasma albumin:globulin reversal. Serum protein electrophoresis showed a paraprotein peak in the early gamma region with elevated IgM level (3810 mg/dL) and elevated free kappa light chain level (70.1 mg/L). Bone marrow aspiration from posterior iliac crest revealed mature small lymphocytes with positive immunohistochemical markers of CD5, CD10 negativity and MYD88 mutation positivity suggestive of WM. Patient was treated with bendamustine and rituximab regimen, with no neurological improvement at the end of one year. Conclusion: This case expands spectrum of paraproteinemic neuropathy to include cranial nerve palsy. Thus, plasma cell dyscrasias have to be considered in patients with isolated ophthalmoparesis especially in elderly patients, even with other comorbidities such as diabetes mellitus.

Sleep profile and Polysomnography in patients with drug-resistant temporal lobe epilepsy (TLE) due to hippocampal sclerosis (HS) and the effect of epilepsy surgery on sleep-a prospective cohort study.

OBJECTIVES: We analyzed changes in sleep profile and architecture of patients with drug-resistant TLE-HS using three validated sleep questionnaires- Epworth Sleepiness Scale (ESS), Pittsburgh Sleep Quality Index (PSQI), NIMHANS Comprehensive Sleep Disorders, and polysomnography (PSG). We studied the effect of epilepsy surgery in a subset of patients. METHODS: In this prospective observational cohort study, sleep profile of 40 patients with drug-resistant TLE-HS was compared to 40 healthy matched controls. Sleep architecture of 22 patients was studied by overnight PSG and compared to 22 matched controls. Sleep profile was reassessed in 20 patients after a minimum period of three months after epilepsy surgery. RESULTS: The mean PSQI was higher among patients compared to controls(P=0.0004) while mean ESS showed no difference. NCSDQ showed fewer patients feeling refreshed after a night's sleep compared to controls (p=0.006). PSG revealed a higher time in bed (p=0.0001), longer total sleep time (p=0.006) and more time spent in NREM stage 1 (p=0.001) and stage 2 (p=0.005) while spending less time in stage 3 (p=0.039) among TLE patients. Sleep efficiency was worse in patients on ≥3 ASMs compared to those on 2 ASMs (p-0.044). There was no change in mean ESS (p=0.48) or PSQI (p=0.105) after surgery. CONCLUSIONS: Patients with drug-resistant TLE-HS have an altered sleep profile and architecture. Patients on ≥3 ASMs have a lower sleep efficiency. Reassessment at short intervals after epilepsy surgery did not reveal significant changes in sleep profile.

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.

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. KEY POINTS: • ICA of resting-state fMRI carries disease-specific information about epilepsy. • Machine learning can classify these components with 97.5% accuracy. • "Subject-specific epilepsy networks" could quantify "epileptogenesis" in vivo.

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.

Magnetoencephalographic imaging of ictal high-frequency oscillations (80-200 Hz) in pharmacologically resistant focal epilepsy.

OBJECTIVE: Specificity of ictal high-frequency oscillations (HFOs) in identifying epileptogenic abnormality is significant, compared to the spikes and interictal HFOs. The objectives of the study were to detect and to localize ictal HFOs by magnetoencephalography (MEG) for identifying the seizure onset zone (SOZ), evaluate the cortical excitability from preictal to ictal transition, and establish HFO concordance rates with other modalities and postsurgical resection. METHODS: Sixty-seven patients with drug-resistant epilepsy had at least 1 spontaneous seizure each during MEG acquisition, and analysis was carried out on 20 seizures from 20 patients. Ictal MEG data were bandpass filtered (80-200 Hz) to visualize, review, and analyze the HFOs co-occurring with ictal spikes. Source montages were generated on both hemispheres, mean fast Fourier transform was computed on virtual time series for determining the preictal to ictal spectral power transition, and source reconstruction was performed with sLORETA and beamformers. The concordance rates of ictal MEG HFOs (SOZ) was estimated with 4 reference epileptogenic regions. RESULTS: In each subject, transient bursts of high-frequency oscillatory cycles, distinct from the background activity, were observed in the periictal continuum. Time-frequency analysis showed significant spectral power surge (85-160 Hz) during ictal state (P < .05) compared to preictal state, but there was no variation in the peak HFO frequencies (P > .05) for each subgroup and at each source montage. HFO source localization was consistent between algorithms (k = 0.857 ± 0.138), with presumed epileptogenic zone (EZ) comparable to other modalities. In patients who underwent surgery (n = 6), MEG HFO SOZ was concordant with the presumed EZ and the surgical resection site (100%), and all were seizure-free during follow-up. SIGNIFICANCE: HFOs could be detected in the MEG periictal state, and its sources were accurately localized. During preictal to ictal transition, HFOs exhibited dynamic augmentation in intrinsic epileptogenicity. Spatial overlap of ictal HFO sources was consistent with EZ determinants and the surgical resection area.