Low impedance electrodes improve detection of high frequency oscillations in the intracranial EEG

OBJECTIVE Epileptic fast ripple oscillations (FR, 250-500 Hz) indicate epileptogenic tissue with high specificity. However, their low amplitude makes detection demanding against noise. Since thermal noise is reduced by low impedance electrodes (LoZ), we investigate here whether this noise reduction is relevant in the FR frequency range. METHODS We analyzed intracranial electrocorticography during neurosurgery of 10 patients where a low impedance electrode was compared to a standard electrode (HiZ) with equal surface area during stimulation of the somatosensory evoked potential, which evokes a robust response in the FR frequency range. To estimate the noise level, we computed the difference between sweep 2n and sweep 2n + 1 for all sweeps. RESULTS The power spectral density of the noise spectrum improved for the LoZ over all frequencies. In the FR range, the median noise level improved from HiZ (0.153 µV) to LoZ (0.089 µV). For evoked FR, the detection rate improved (91% for HiZ vs. 100% for LoZ). CONCLUSIONS Low impedance electrodes for intracranial EEG reduce noise in the FR frequency range and may thereby improve FR detection. SIGNIFICANCE Improving the measurement chain may enhance the diagnostic value of FR as biomarkers for epileptogenic tissue

Optimization of signal-to-noise ratio in short-duration SEP recordings by variation of stimulation rate

Objective: The intraoperative averaging of the somatosensory evoked potential (SEP) requires reliable recordings within the shortest possible duration. We here systematically optimized the repetition rate of stimulus presentation. Methods: We recorded medianus and tibial nerve SEP during 22 surgeries and varied the rate of stimulus presentation between 2.7 Hz and 28.7 Hz. We randomly sampled a number of sweeps corresponding to recording durations up to 20 s and calculated the signal-to-noise ratio (SNR). Results: For the medianus nerve at 5 s recording duration, SEP stimulation rate at 12.7 Hz obtained the highest median SNR = 22.9 for the N20, which was higher than for rate 4.7 Hz (p = 1.5e-4). When increasing the stimulation rate, latency increased and amplitude decayed for cortical but not for peripheral recording sites. For the tibial nerve, the rate 4.7 Hz achieved the highest SNR for all durations. Conclusions: We determined the time-dependence of SNR for N20 and elucidated the underlying physiology. For short recordings, rapid reduction of noise through averaging at high stimulation rate outweighs the disadvantage of smaller amplitude. Significance: For a short duration of medianus nerve SEP recording only, it may be advantageous to stimulate with a repetition rate of 12.7 Hz. Keywords: Erb’s point; High frequency oscillation; Intraoperative neuromonitoring; Neurosurgery; Peripheral nerve conduction; Stimulation frequency

Information flows from hippocampus to auditory cortex during replay of verbal working memory items

The maintenance of items in working memory (WM) relies on a widespread network of cortical areas and hippocampus where synchronization between electrophysiological recordings reflects functional coupling. We investigated the direction of information flow between auditory cortex and hippocampus while participants heard and then mentally replayed strings of letters in WM by activating their phonological loop. We recorded local field potentials from the hippocampus, reconstructed beamforming sources of scalp EEG, and - additionally in four participants - recorded from subdural cortical electrodes. When analyzing Granger causality, the information flow was from auditory cortex to hippocampus with a peak in the [4 8] Hz range while participants heard the letters. This flow was subsequently reversed during maintenance while participants maintained the letters in memory. The functional interaction between hippocampus and the cortex and the reversal of information flow provide a physiological basis for the encoding of memory items and their active replay during maintenance

Using Big Data Technologies for HEP Analysis

The HEP community is approaching an era were the excellent performances of the particle accelerators in delivering collision at high rate will force the experiments to record a large amount of information. The growing size of the datasets could potentially become a limiting factor in the capability to produce scientific results timely and efficiently. Recently, new technologies and new approaches have been developed in industry to answer to the necessity to retrieve information as quickly as possible to analyze PB and EB datasets. Providing the scientists with these modern computing tools will lead to rethinking the principles of data analysis in HEP, making the overall scientific process faster and smoother. In this paper, we are presenting the latest developments and the most recent results on the usage of Apache Spark for HEP analysis. The study aims at evaluating the efficiency of the application of the new tools both quantitatively, by measuring the performances, and qualitatively, focusing on the user experience. The first goal is achieved by developing a data reduction facility: working together with CERN Openlab and Intel, CMS replicates a real physics search using Spark-based technologies, with the ambition of reducing 1 PB of public data in 5 hours, collected by the CMS experiment, to 1 TB of data in a format suitable for physics analysis. The second goal is achieved by implementing multiple physics use-cases in Apache Spark using as input preprocessed datasets derived from official CMS data and simulation. By performing different end-analyses up to the publication plots on different hardware, feasibility, usability and portability are compared to the ones of a traditional ROOT-based workflow

Blinded study: prospectively defined high-frequency oscillations predict seizure outcome in individual patients

Interictal high-frequency oscillations are discussed as biomarkers for epileptogenic brain tissue that should be resected in epilepsy surgery to achieve seizure freedom. The prospective classification of tissue sampled by individual electrode contacts remains a challenge. We have developed an automated, prospective definition of clinically relevant high-frequency oscillations in intracranial EEG from Montreal and tested it in recordings from Zurich. We here validated the algorithm on intracranial EEG that was recorded in an independent epilepsy centre so that the analysis was blinded to seizure outcome. We selected consecutive patients who underwent resective epilepsy surgery in Geneva with post-surgical follow-up > 12 months. We analysed long-term recordings during sleep that we segmented into intervals of 5 min. High-frequency oscillations were defined in the ripple (80-250 Hz) and the fast ripple (250-500 Hz) frequency bands. Contacts with the highest rate of ripples co-occurring with fast ripples designated the relevant area. As a validity criterion, we calculated the test-retest reliability of the high-frequency oscillations area between the 5 min intervals (dwell time ≥50%). If the area was not fully resected and the patient suffered from recurrent seizures, this was classified as a true positive prediction. We included recordings from 16 patients (median age 32 years, range 18-53 years) with stereotactic depth electrodes and/or with subdural electrode grids (median follow-up 27 months, range 12-55 months). For each patient, we included several 5 min intervals (median 17 intervals). The relevant area had high test-retest reliability across intervals (median dwell time 95%). In two patients, the test-retest reliability was too low (dwell time < 50%) so that outcome prediction was not possible. The area was fully included in the resected volume in 2/4 patients who achieved post-operative seizure freedom (specificity 50%) and was not fully included in 9/10 patients with recurrent seizures (sensitivity 90%), leading to an accuracy of 79%. An additional exploratory analysis suggested that high-frequency oscillations were associated with interictal epileptic discharges only in channels within the relevant area and not associated in channels outside the area. We thereby validated the automated procedure to delineate the clinically relevant area in each individual patient of an independently recorded dataset and achieved the same good accuracy as in our previous studies. The reproducibility of our results across datasets is promising for a multicentre study to test the clinical application of high-frequency oscillations to guide epilepsy surgery

Χαρακτηρισμός επιληπτικής δραστηριότητας μέσω της αποτύπωσης του λειτουργικού δικτύου σε πραγματικό μοντέλο κεφαλής από δεδομένα μαγνητοεγκεφαλογραφήματος

Summarization: Epilepsy is a complex brain disorder which affects millions of people worldwide. A significant percentage of the cases is described by drug resistance increasing this way the need for exploiting different approaches for the treatment. Such techniques incorporate invasive methods to resect the cortical tissues that are responsible for the epileptic seizures. However, these approaches require spatial accuracy in order to localize the epileptic focus as well as to avoid damaging the equivalent brain areas. An important step prior to the operative treatment is the presurgical evaluation which aims in accurate detection of the epileptic focus utilizing the Electrocorticography (ECoG). This thesis addresses the source localization/reconstruction problem from interictal epileptic spikes to improve presurgical epilepsy diagnosis with the ultimate goal to save patients from multiple repetitions of these invasive techniques. Specifically, it aims to detect the epileptic activity at source level as derived from combined electroencephalography (EEG) and magnetoencephalography (MEG) Data on a realistic head model. The source reconstruction problem is focusing on disentangling the brain sources from the activity recorded non-invasively by the sensors of the neuroimaging modalities by simulating brain anatomy and conductivities. The proposed approach includes unsupervised learning methods to sort epileptic activity using adaptive features for the spikes and comparison of algorithms such as sLORETA, eLORETA and Minimum Norm Estimate (MNE) for solving the localization problem. In the clustering model of our method we consider the problem of describing the interictal spikes with adequate features, which could be used for sorting purposes. In the source reconstruction, we solve the forward problem using a 6-compartment head model constructed with Finite Element Method (FEM). The inverse solution of the problem is being performed mainly with sLORETA algorithm but MNE and other inverse methods were also evaluated utilizing the FEM headmodel. Finally, the results obtained achieve exceptional accuracy in detecting the epileptic foci in a patient with multifocal epilepsy with the activated areas being in the vicinity of patient’s focal cortical dysplasias.Περίληψη: Η επιληψία είναι μια πολύπλοκη διαταραχή του εγκεφάλου που επηρεάζει εκατομμύρια ανθρώπους παγκοσμίως. Ένα σημαντικό ποσοστό των επιληπτικών περιπτώσεων χαρακτηρίζεται από ανοχή σε φαρμακευτικές αγωγές γεγονός που ολοένα και περισσότερο εντείνει την εξερεύνηση διαφορετικών μεθόδων θεραπείας. Τέτοιες τεχνικές περιλαμβάνουν επεμβατικές μεθόδους για αφαίρεση μέρους του φλοιού που ευθύνεται για την επιληπτογένεση. Ωστόσο, είναι απαραίτητη η χωρική ακρίβεια και ανάλυση για την αφαίρεση τέτοιων ιστών έτσι ώστε να μην προξενηθούν βλάβες σε θεμελιώδεις λειτουργίες του εγκεφάλου. Ένα σημαντικό βήμα πριν την επεμβατική θεραπεία είναι η προεγχειρητική αξιολόγηση που στοχεύει στον ακριβή εντοπισμό της επιληπτικής εστίας μέσω του ενδοκρανιακού εγκεφαλογραφήματος. Η διπλωματική αυτή εστιάζει στο πρόβλημα εντοπισμού των πηγών από συμπλέγματα αιχμών στα διαστήματα μεταξύ των επιληπτικών κρίσεων για την βελτίωση της προεγχειρητικής διάγνωσης έχοντας ως απώτερο στόχο την καταπράυνση των συμπτωμάτων και την απαλλαγή των ασθενών από επαναλαμβανόμενες επεμβάσεις. Συγκεκριμένα, αποσκοπεί στον εντοπισμό της επιληπτικής δραστηριότητας σε επίπεδο πηγών ρεύματος από συνδυασμό δεδομένων Μαγνητοεγκεφαλογραφήματος και Ηλεκτροεγκεφαλογραφήματος αξιοποιώντας ένα ρεαλιστικό μοντέλο κεφαλής. Η ανακατασκευή των πηγών είναι ένα πρόβλημα που επικεντρώνεται στην απόμιξη των εγκεφαλικών πηγών από τη δραστηριότητα που καταγράφεται μη επεμβατικά στους αισθητήρες μιας νευροαπεικονιστικής μεθόδου προσομοιώνοντας την ανατομία του εγκεφάλου αλλά και τις ηλεκτρικές του ιδιότητες. Η προτεινόμενη προσεγγιση εμπεριέχει αλγορίθμους μη εποπτευόμενης μάθησης για ταξινόμηση της επιληπτικής δραστηριότητας χρησιμοποίωντας προσαρμοστικά χαρακτηριστικά καθώς και σύγκριση ποικίλλων μεθόδων όπως το sLORETA, eLORETA και MNE για την επίλυση του αντίστροφου προβλήματος. Στο μοντέλο συσταδοποίησης εξετάζουμε το πρόβλημα της εξαγωγής αντιπροσωπευτικών χαρακτηριστικών που θα οδηγήσουν στην ομαδοποίηση των επιληπτικών φαινομένων. Σχετικά με το πρόβλημα ανακατασκευής πηγών επιλύουμε το ευθύ πρόβλημα χρησιμοποιώντας ενα μοντέλο κεφαλής απαρτιζόμενο από 6 επίπεδα/τμήματα και την μέθοδο αριθμητικής ανάλυσης Finite Element Method. Τέλος, υλοποιώντας διάφορους αλγορίθμους για την αντίστροφη λύση τα αποτελέσματα που λαμβάνουμε πετυχαίνουν εξαιρετική ακρίβεια στον εντοπισμό των επιληπτικών εστιών σε έναν ασθενή με πολυεστιακή επιληψία ανιχνεύοντας δραστηριότητα κοντά στις εστιακές δυσπλασίες του

Information flows from hippocampus to auditory cortex during replay of verbal working memory items

The maintenance of items in working memory (WM) relies on a widespread network of cortical areas and hippocampus where synchronization between electrophysiological recordings reflects functional coupling. We investigated the direction of information flow between auditory cortex and hippocampus while participants heard and then mentally replayed strings of letters in WM by activating their phonological loop. We recorded local field potentials from the hippocampus, reconstructed beamforming sources of scalp EEG , and – additionally in four participants – recorded from subdural cortical electrodes. When analyzing Granger causality, the information flow was from auditory cortex to hippocampus with a peak in the [4 8] Hz range while participants heard the letters. This flow was subsequently reversed during maintenance while participants maintained the letters in memory. The functional interaction between hippocampus and the cortex and the reversal of information flow provide a physiological basis for the encoding of memory items and their active replay during maintenance. </p

Blinded study: prospectively defined high frequency oscillations predict seizure outcome in individual patients

Interictal high frequency oscillations are discussed as biomarkers for epileptogenic brain tissue that should be resected in epilepsy surgery to achieve seizure freedom. The prospective classification of tissue sampled by individual electrode contacts remains a challenge. We have developed an automated, prospective definition of clinically relevant high frequency oscillations in intracranial EEG from Montreal and tested it in recordings from Zurich. We here validated the algorithm on intracranial EEG that was recorded in an independent epilepsy centre so that the analysis was blinded to seizure outcome. We selected consecutive patients who underwent resective epilepsy surgery in Geneva with postsurgical follow-up > 12 months. We analysed long-term recordings during sleep that we segmented into intervals of 5 minutes. High frequency oscillations were defined in the ripple (80-250 Hz) and the fast ripple (250-500 Hz) frequency bands. Contacts with the highest rate of ripples co-occurring with fast ripples designated the relevant area. As a validity criterion, we calculated the test-retest reliability of the high frequency oscillations area between the 5 min intervals (dwell time ≥50%). If the area was not fully resected and the patient suffered from recurrent seizures, this was classified as a true positive prediction. We included recordings from 16 patients (median age 32 years, range 18-53 years) with stereotactic depth electrodes and/or with subdural electrode grids (median follow-up 27 months, range 12-55 months). For each patient, we included several 5 min intervals (median 17 intervals). The relevant area had high test-retest reliability across intervals (median dwell time 95%). In two patients, the test-retest reliability was too low (dwell time < 50%) so that outcome prediction was not possible. The area was fully included in the resected volume in 2/4 patients who achieved postoperative seizure freedom (specificity 50%) and was not fully included in 9/10 patients with recurrent seizures (sensitivity 90%), leading to an accuracy of 79%. An additional exploratory analysis suggested that high frequency oscillations were associated with interictal epileptic discharges only in channels within the relevant area and not associated in channels outside the area. We thereby validated the automated procedure to delineate the clinically relevant area in each individual patient of an independently recorded dataset and achieved the same good accuracy as in our previous studies. The reproducibility of our results across datasets is promising for a multicentre study to test the clinical application of high frequency oscillations to guide epilepsy surgery