Localisation of eloquent cortex using magnetoencephalography and its clinical implications.

OBJECTIVES: This study aimed to localise the eloquent cortex and measure evoked field (EF) parameters using magnetoencephalography in patients with epilepsy and tumours near the eloquent cortex. METHODS: A total of 41 patients (26 with drug-refractory epilepsy and 15 with tumours), with a mean age of 33 years, were recruited. Visual evoked field (VEF), auditory evoked field (AEF), sensory evoked field (SSEF), and motor-evoked field (MEF) latencies, amplitudes, and localisation were compared with those of a control population. Subgroup analyses were performed based on lobar involvement. Evoked Field parameters on the affected side were compared with those on the opposite side. The effect of distance from the lesion on nearby and distant evoked fields was evaluated. RESULTS: AEF and VEF amplitudes and latencies were reduced bilaterally (p < 0.05). Amplitude in the ipsilateral SSEF was reduced by 29.27% and 2.16% in the AEF group compared to the contralateral side (p = 0.02). In patients with temporal lobe lesions, the SSEF amplitude was reduced bilaterally (p < 0.02), and latency was prolonged compared with controls. The MEF amplitude was reduced and latency was prolonged in patients with frontal lobe lesions (p = 0.01). EF displacement was 32%, 57%, 21%, and 16% for AEF, MEF, VEF, and SSEF respectively. Patients in the epilepsy group had distant EF abnormalities. CONCLUSIONS: EF amplitude was reduced and latency was prolonged in the involved hemisphere. Distant EF amplitudes were more affected than latencies in epilepsy. Amplitude and distance from the lesion had negative correlation for all EF. EF changes indicated eloquent cortical displacement which may not be apparent on MRI.

Seizure detection and epileptogenic zone localisation on heavily skewed MEG data using RUSBoost machine learning technique.

Background: Epilepsy is a neurological disorder which is characterised by recurrent and involuntary seizures. Magnetoencephalography (MEG) is clinically used as a presurgical tool in locating the epileptogenic zone by localising either interictal epileptic discharges (IEDs) or ictal activities. The localisation of ictal onset provides reliable and more accurate seizure onset zones rather than localising the IEDs. Ictals or seizures are presently detected during MEG analysis by manually inspecting the recorded data. This is laborious when the duration of recordings is longer. Methods: We propose a novel method which uses statistical features such as short-time permutation entropy (STPE), gradient of STPE (GSTPE), short-time energy (STE) and short-time mean (STM) extracted from the ictal and interictal MEG data of drug resistant epilepsy patients group. Since the data is heavily skewed, the RUSBoost algorithm with k-fold cross-validation is used to classify the data into ictal and interictal by using the four feature vectors. This method is further used for localising the epileptogenic region using region-specific classifications by means of the RUSBoost algorithm. Results: The accuracy obtained for seizure detection is 93.4%. The specificity and sensitivity for the same are 93%. The localisation accuracies for each lobe are in the range of 88.1-99.1%. Discussion: Through this ictus detection method, the current scenario of laborious inspection of the ictal MEG can be reduced. The proposed system, thus, can be implemented in real-time as a better and more efficient method for seizure detection and further it can prove to be highly beneficial for patients and health-care professionals during real-time MEG recording. Furthermore, the identification of the epileptogenic lobe can provide clinicians with useful insights, and a pre-cursor for source localisation.

Content-Based Estimation of Brain MRI Tilt in Three Orthogonal Directions.

In a general scenario, the brain images acquired from magnetic resonance imaging (MRI) may experience tilt, distorting brain MR images. The tilt experienced by the brain MR images may result in misalignment during image registration for medical applications. Manually correcting (or estimating) the tilt on a large scale is time-consuming, expensive, and needs brain anatomy expertise. Thus, there is a need for an automatic way of performing tilt correction in three orthogonal directions (X, Y, Z). The proposed work aims to correct the tilt automatically by measuring the pitch angle, yaw angle, and roll angle in X-axis, Z-axis, and Y-axis, respectively. For correction of the tilt around the Z-axis (pointing to the superior direction), image processing techniques, principal component analysis, and similarity measures are used. Also, for correction of the tilt around the X-axis (pointing to the right direction), morphological operations, and tilt correction around the Y-axis (pointing to the anterior direction), orthogonal regression is used. The proposed approach was applied to adjust the tilt observed in the T1- and T2-weighted MR images. The simulation study with the proposed algorithm yielded an error of 0.40 ± 0.09°, and it outperformed the other existing studies. The tilt angle (in degrees) obtained is ranged from 6.2 ± 3.94, 2.35 ± 2.61, and 5 ± 4.36 in X-, Z-, and Y-directions, respectively, by using the proposed algorithm. The proposed work corrects the tilt more accurately and robustly when compared with existing studies.

Role of magnetoencephalography and stereo-electroencephalography in the presurgical evaluation in patients with drug-resistant epilepsy.

In selected patients with drug-resistant focal epilepsies (DRFE), who otherwise are likely to be excluded from epilepsy surgery (ES) because of the absence of a magnetic resonance imaging (MRI)-demonstrable lesion or discordant anatomo-electro-clinical (AEC) data, magnetoencephalography (MEG) may help to generate an AEC hypothesis and stereo-electroencephalography (SEEG) may help to verify the hypothesis and proceed with ES. The sensitivity of MEG is much better in localizing the spiking zone in relation to lateral temporal and extratemporal cortical regions compared to the mesial temporal structures. MEG has a dominant role in the presurgical evaluation of patients with MRI-negative DRFEs, insular epilepsies, and recurrent seizures after failed epilepsy surgeries, and in guiding placement of invasive electrodes. Moreover, postoperative seizure freedom is better if MEG spike source localized cortical region is included in the resection. When compared to subdural grid electrode recording, SEEG is less invasive and safer. Those who are otherwise destined to suffer from uncontrolled seizures and their consequences, SEEG guided ES is a worthwhile and a cost-effective option. Depending on the substrate pathology, there is > 80-90% chance of undergoing ES and 60-80% chance of becoming seizure-free following SEEG. Recent noninvasive techniques aimed at better structural imaging, delineating brain connectivity and recording specific intracerebral EEG patterns such as high frequency oscillations might decrease the need for SEEG; but more importantly, make SEEG exploration more goal-directed and hypothesis-driven.

Combined MEG-EEG source localisation in patients with sub-acute sclerosing pan-encephalitis.

To study the genesis and propagation patterns of periodic complexes (PCs) associated with myoclonic jerks in sub-acute sclerosing pan-encephalitis (SSPE) using magnetoencephalography (MEG) and electroencephalography (EEG). Simultaneous recording of MEG (306 channels) and EEG (64 channels) in five patients of SSPE (M:F = 3:2; age 10.8 ± 3.2 years; symptom-duration 6.2 ± 10 months) was carried out using Elekta Neuromag(®) TRIUX™ system. Qualitative analysis of 80-160 PCs per patient was performed. Ten isomorphic classical PCs with significant field topography per patient were analysed at the 'onset' and at 'earliest significant peak' of the burst using discrete and distributed source imaging methods. MEG background was asymmetrical in 2 and slow in 3 patients. Complexes were periodic (3) or quasi-periodic (2), occurring every 4-16 s and varied in morphology among patients. Mean source localization at onset of bursts using discrete and distributed source imaging in magnetic source imaging (MSI) was in thalami and or insula (50 and 50 %, respectively) and in electric source imaging (ESI) was also in thalami and or insula (38 and 46 %, respectively). Mean source localization at the earliest rising phase of peak in MSI was in peri-central gyrus (49 and 42 %) and in ESI it was in frontal cortex (52 and 56 %). Further analysis revealed that PCs were generated in thalami and or insula and thereafter propagated to anterolateral surface of the cortices (viz. sensori-motor cortex and frontal cortex) to same side as that of the onset. This novel MEG-EEG based case series of PCs provides newer insights for understanding the plausible generators of myoclonus in SSPE and patterns of their propagation.

Insights from Magnetic Evoked Field Analysis in Patients with Wilson's Disease.

Aims: To study the latency, amplitude, and source localization of magnetic evoked field (MEF) responses to visual, auditory, and somatosensory stimuli in Wilson's disease (WD) using magnetoencephalography (MEG) and compare it with "healthy" controls, and correlate the observations with disease severity and brain MRI. Methods: MEF of 28 patients with neurological WD (age: 22.82 ± 5.8 years; M:F = 12:16) and 21 matched controls (age: 25.0 ± 4.6 years; M:F = 10:11) were recorded using MEG. Source localization was performed using standard models on the components of M100, M20, and M100 for visual, somatosensory, and auditory evoked fields, respectively and its latency/amplitude was correlated with disease severity. Results: There were significant differences in source location between control and WD during visual evoked field (VEF) and auditory evoked field (AEF) studies. Latencies of M20 (right-p = 0.02; left-p = 0.04) and M32 (right-p = 0.01) components of SSEF were significantly prolonged. The amplitude of M20 was significantly reduced in patients bilaterally (P = 0.001). There was a trend for the prolonged latency of M100 of VEF in patients (P = 0.09). Five patients had reduced right M145 compared to 8 controls. The left somatosensory evoked fields (SSEF) latency correlated with disease severity (P = 0.04). There was no significant correlation between major components of other MEF with disease severity or MRI score. Conclusions: This study, first of its kind to use MEF analysis in a large cohort of patients with WD, detected subclinical but a variable degree of abnormalities, most consistently of SSEF. It provides valuable insights of functioning and localization of various pathways in a disease known to have protean clinical manifestations and widespread MRI changes.

A Combined Methodology to Eliminate Artifacts in Multichannel Electrogastrogram Based on Independent Component Analysis and Ensemble Empirical Mode Decomposition.

Cutaneous measurements of electrogastrogram (EGG) signals are heavily contaminated by artifacts due to cardiac activity, breathing, motion artifacts, and electrode drifts whose effective elimination remains an open problem. A common methodology is proposed by combining independent component analysis (ICA) and ensemble empirical mode decomposition (EEMD) to denoise gastric slow-wave signals in multichannel EGG data. Sixteen electrodes are fixed over the upper abdomen to measure the EGG signals under three gastric conditions, namely, preprandial, postprandial immediately, and postprandial 2 h after food for three healthy subjects and a subject with a gastric disorder. Instantaneous frequencies of intrinsic mode functions that are obtained by applying the EEMD technique are analyzed to individually identify and remove each of the artifacts. A critical investigation on the proposed ICA-EEMD method reveals its ability to provide a higher attenuation of artifacts and lower distortion than those obtained by the ICA-EMD method and conventional techniques, like bandpass and adaptive filtering. Characteristic changes in the slow-wave frequencies across the three gastric conditions could be determined from the denoised signals for all the cases. The results therefore encourage the use of the EEMD-based technique for denoising gastric signals to be used in clinical practice.

NREM Sleep and Antiepileptic Medications Modulate Epileptiform Activity by Altering Cortical Synchrony.

INTRODUCTION: The activating role of non-rapid eye movement (NREM) sleep on epileptic cortex and conversely, the seizure remission brought about by antiepileptic medications, has been attributed to their effects on neuronal synchrony. This study aims to understand the role of neural synchrony of NREM sleep in promoting interictal epileptiform discharges (IEDs) in patients with epilepsy (PWE) by assessing the peri-IED phase synchrony during awake and sleep states. It also studies the role played by antiepileptic drugs (AEDs) on EEG desynchronization in the above cohort. METHODS: A total of 120 PWE divided into 3 groups (each n = 40; juvenile myoclonic epilepsy [JME], temporal lobe epilepsy [TLE]. and extratemporal lobe epilepsy [Ex-TLE]) were subjected to overnight polysomnography. Each patient group was subdivided into drug-naive and on treatment (Each n = 20). EEG phase synchronization analysis was performed to compare peri-IED phase synchronization indices (SI) during awake and sleep stages and between drug naïve and on treatment groups in 4 frequency bands, namely delta, theta, alpha, and beta. The mean ± SD of peri-IED SI among various subgroups was compared employing a multilevel mixed effects modeling approach. RESULTS: Patients with JME had increased peri-IED cortical synchrony in N3 sleep stage, whereas patients with partial epilepsy had increased IED cortical synchrony in N1 sleep stage. On the other hand, peri-IED synchrony was lower during wake and REM sleep. We also found that peri-IED synchronization in patients with JME was higher in drug-naive patients compared with those on sodium valproate monotherapy in theta, alpha, and beta bands. CONCLUSION: The findings of this study suggest that sleep stages can alter cortical synchrony in patients with JME and focal epilepsy, with NREM IEDs being more synchronized and wake/REM IEDs being less synchronized. Furthermore, it also suggests that AEDs alleviate seizures in PWE by inhibiting cortical synchrony.

Heightened Background Cortical Synchrony in Patients With Epilepsy: EEG Phase Synchrony Analysis During Awake and Sleep Stages Using Novel Ensemble Measure.

INTRODUCTION: Excessive cortical synchrony within neural ensembles has been implicated as an important mechanism driving epileptiform activity. The current study measures and compares background electroencephalographic (EEG) phase synchronization in patients having various types of epilepsies and healthy controls during awake and sleep stages. METHODS: A total of 120 patients with epilepsy (PWE) subdivided into 3 groups (juvenile myoclonic epilepsy [JME], temporal lobe epilepsy [TLE], and extra-temporal lobe epilepsy [Ex-TLE]; n = 40 in each group) and 40 healthy controls were subjected to overnight polysomnography. EEG phase synchronization (SI) between the 8 EEG channels was assessed for delta, theta, alpha, sigma, and high beta frequency bands using ensemble measure on 10-second representative time windows and compared between patients and controls and also between awake and sleep stages. Mean ± SD of SI was compared using 2-way analysis of variance followed by pairwise comparison ( P ≤ .05). RESULTS: In both delta and theta bands, the SI was significantly higher in patients with JME, TLE, and Ex-TLE compared with controls, whereas in alpha, sigma, and high beta bands, SI was comparable between the groups. On comparison of SI between sleep stages, delta band: progressive increase in SI from wake ⇒ N1 ⇒ N2 ⇒ N3, whereas REM (rapid eye movement) was comparable to wake; theta band: decreased SI during N2 and increase during N3; alpha band: SI was highest in wake and lower in N1, N2, N3, and REM; and sigma and high beta bands: progressive increase in SI from wake ⇒ N1 ⇒ N2 ⇒ N3; however, sigma band showed lower SI during REM. CONCLUSION: This study found an increased background cortical synchronization in PWE compared with healthy controls in delta and theta bands during wake and sleep. This background hypersynchrony may be an important property of epileptogenic brain circuitry in PWE, which enables them to effortlessly generate a paroxysmal EEG depolarization shift.

Ensemble Empirical Mode Decomposition based methodology for ultrasonic testing of coarse grain austenitic stainless steels.

A signal processing methodology is proposed in this paper for effective reconstruction of ultrasonic signals in coarse grained high scattering austenitic stainless steel. The proposed methodology is comprised of the Ensemble Empirical Mode Decomposition (EEMD) processing of ultrasonic signals and application of signal minimisation algorithm on selected Intrinsic Mode Functions (IMFs) obtained by EEMD. The methodology is applied to ultrasonic signals obtained from austenitic stainless steel specimens of different grain size, with and without defects. The influence of probe frequency and data length of a signal on EEMD decomposition is also investigated. For a particular sampling rate and probe frequency, the same range of IMFs can be used to reconstruct the ultrasonic signal, irrespective of the grain size in the range of 30-210 µm investigated in this study. This methodology is successfully employed for detection of defects in a 50mm thick coarse grain austenitic stainless steel specimens. Signal to noise ratio improvement of better than 15 dB is observed for the ultrasonic signal obtained from a 25 mm deep flat bottom hole in 200 µm grain size specimen. For ultrasonic signals obtained from defects at different depths, a minimum of 7 dB extra enhancement in SNR is achieved as compared to the sum of selected IMF approach. The application of minimisation algorithm with EEMD processed signal in the proposed methodology proves to be effective for adaptive signal reconstruction with improved signal to noise ratio. This methodology was further employed for successful imaging of defects in a B-scan.

Baseline drift removal and denoising of MCG data using EEMD: role of noise amplitude and the thresholding effect.

We adopt the Ensemble Empirical Mode Decomposition (EEMD) method, with an appropriate thresholding on the Intrinsic Mode Functions (IMFs), to denoise the magnetocardiography (MCG) signal. To this end, we discuss the two associated problems that relate to: (i) the amplitude of noise added to the observed signal in the EEMD method with a view to prevent mode mixing and (ii) the effect of direct thresholding that causes discontinuities in the reconstructed denoised signal. We then denoise the MCG signals, having various signal-to-noise ratios, by using this method and compare the results with those obtained by the standard wavelet based denoising method. We also address the problem of eliminating the high frequency baseline drift such as the sudden and discontinuous changes in the baseline of the experimentally measured MCG signal using the EEMD based method. We show that the EEMD method used for denoising and the elimination of baseline drift is superior in performance to other standard methods such as wavelet based techniques and Independent Component Analysis (ICA).