Machine learning for (non-)epileptic tissue detection from the intraoperative electrocorticogram.

Objective: Clinical visual intraoperative electrocorticography (ioECoG) reading intends to localize epileptic tissue and improve epilepsy surgery outcome. We aimed to understand whether machine learning (ML) could complement ioECoG reading, how subgroups affected performance, and which ioECoG features were most important.

Methods: We included 91 ioECoG-guided epilepsy surgery patients with Engel 1A outcome.

Robust compression and detection of epileptiform patterns in ECoG using a real-time spiking neural network hardware framework.

Interictal Epileptiform Discharges (IED) and High Frequency Oscillations (HFO) in intraoperative electrocorticography (ECoG) may guide the surgeon by delineating the epileptogenic zone. We designed a modular spiking neural network (SNN) in a mixed-signal neuromorphic device to process the ECoG in real-time. We exploit the variability of the inhomogeneous silicon neurons to achieve efficient sparse and decorrelated temporal signal encoding.

The effect of propofol on effective brain networks.

Objective: We compared the effective networks derived from Single Pulse Electrical Stimulation (SPES) in intracranial electrocorticography (ECoG) of awake epilepsy patients and while under general propofol-anesthesia to investigate the effect of propofol on these brain networks.

Methods: We included nine patients who underwent ECoG for epilepsy surgery evaluation. We performed SPES when the patient was awake (SPES-clinical) and repeated this under propofol-anesthesia during the surgery in which the ECoG grids were removed (SPES-propofol).

ER-detect: a pipeline for robust detection of early evoked responses in BIDS-iEEG electrical stimulation data.

Human brain connectivity can be measured in different ways. Intracranial EEG (iEEG) measurements during single pulse electrical stimulation provide a unique way to assess the spread of electrical information with millisecond precision. To provide a robust workflow to process these cortico-cortical evoked potential (CCEP) data and detect early evoked responses in a fully automated and reproducible fashion, we developed Early Response (ER)-detect.

Developmental trajectory of transmission speed in the human brain.

The structure of the human connectome develops from childhood throughout adolescence to middle age, but how these structural changes affect the speed of neuronal signaling is not well described. In 74 subjects, we measured the latency of cortico-cortical evoked responses across association and U-fibers and calculated their corresponding transmission speeds. Decreases in conduction delays until at least 30 years show that the speed of neuronal communication develops well into adulthood.

Accurate differentiation between physiological and pathological ripples recorded with scalp-EEG.

Objective: To compare scalp-EEG recorded physiological ripples co-occurring with vertex waves to pathological ripples co-occurring with interictal epileptiform discharges (IEDs).

Methods: We marked ripples in sleep EEGs of children. We compared the start of ripples to vertex wave- or IED-start, and duration, frequency, and root mean square (RMS) amplitude of physiological and pathological ripples using multilevel modeling.

High-resolution electric source imaging for presurgical evaluation of tuberous sclerosis complex patients.

Objective: We retrospectively assessed the localizing value of patient-history-based semiology (PHS), video-based semiology (VS), long-term monitoring video electroencephalography (LTM-VEEG) and interictal high resolution electric source imaging (HR-ESI) in the presurgical workup of patients with tuberous sclerosis complex (TSC).

Methods: Data from 24 consecutive TSC surgical candidates who underwent both HR-ESI and LTM-VEEG was retrospectively collected. PHS and VS were analyzed to hypothesize the symptomatogenic zone localization.

Sleep slow-wave homeostasis and cognitive functioning in children with electrical status epilepticus in sleep.

Study Objectives: Encephalopathy with electrical status epilepticus in sleep (ESES) is characterized by non-rapid eye movement (non-REM)-sleep-induced epileptiform activity and acquired cognitive deficits. The synaptic homeostasis hypothesis describes the process of daytime synaptic potentiation balanced by synaptic downscaling in non-REM-sleep and is considered crucial to retain an efficient cortical network. We aimed to study the overnight decline of slow waves, an indirect marker of synaptic downscaling, in patients with ESES and explore whether altered downscaling relates to neurodevelopmental and behavioral problems.

A skew-based method for identifying intracranial EEG channels with epileptic activity without detecting spikes, ripples, or fast ripples.

Objective: To develop a method for identifying intracranial EEG (iEEG) channels with epileptic activity without the need to detect spikes, ripples, or fast ripples.

Methods: We compared the skew of the distribution of power values from five minutes non-rapid eye movement stage N3 sleep for the 5-80 Hz, 80-250 Hz (ripple), and 250-500 Hz (fast ripple) bands of epileptic (located in seizure-onset or irritative zone) and non-epileptic iEEG channels recorded in patients with drug-resistant focal epilepsy. We optimized settings in 120 bipolar channels from 10 patients, compared the results to 120 channels from another 10 patients, and applied the method to channels of 12 individual patients.

Pathological responses to single-pulse electrical stimuli in epilepsy: The role of feedforward inhibition.

Delineation of epileptogenic cortex in focal epilepsy patients may profit from single-pulse electrical stimulation during intracranial EEG recordings. Single-pulse electrical stimulation evokes early and delayed responses. Early responses represent connectivity.

Simultaneous MEG and EEG to detect ripples in people with focal epilepsy.

Objective: We studied ripples (80-250 Hz) simultaneously recorded in electroencephalography (EEG) and magnetoencephalography (MEG) to evaluate the differences.

Methods: Simultaneous EEG and MEG were recorded in 30 patients with drug resistant focal epilepsy. Ripples were automatically detected and visually checked in virtual channels throughout the cortex.

A Comparison of Evoked and Non-evoked Functional Networks.

The growing interest in brain networks to study the brain's function in cognition and diseases has produced an increase in methods to extract these networks. Typically, each method yields a different network. Therefore, one may ask what the resulting networks represent.

Ripples in scalp EEGs of children: co-occurrence with sleep-specific transients and occurrence across sleep stages.

Study Objectives: A dialogue between hippocampal ripples (80-250 Hz) and neocortical sleep-specific transients is important for memory consolidation. Physiological neocortical ripples can be recognized in scalp EEGs of children. We investigated how often scalp-EEG recorded ripples co-occur with different types of sleep-specific transients, the distribution and spatial extent of ripples with and without co-occurring sleep-specific transients, and the occurrence of ripples across sleep stages.

Evoked directional network characteristics of epileptogenic tissue derived from single pulse electrical stimulation.

We investigated effective networks constructed from single pulse electrical stimulation (SPES) in epilepsy patients who underwent intracranial electrocorticography. Using graph analysis, we compared network characteristics of tissue within and outside the epileptogenic area. In 21 patients with subdural electrode grids (1 cm interelectrode distance), we constructed a binary, directional network derived from SPES early responses (<100 ms).

Neocortical electrical stimulation for epilepsy: Closed-loop versus open-loop.

The aim of this review is to evaluate whether open-loop or closed-loop neocortical electrical stimulation should be the preferred approach to manage seizures in intractable epilepsy. Twenty cases of open-loop neocortical stimulation with an implanted device have been reported, in 5 case studies. Closed-loop stimulation with an implanted device has been investigated in a larger number of patients in the RNS System clinical trials.

Beamforming applied to surface EEG improves ripple visibility.

Objective: Surface EEG can show epileptiform ripples in people with focal epilepsy, but identification is impeded by the low signal-to-noise ratio of the electrode recordings. We used beamformer-based virtual electrodes to improve ripple identification.

Methods: We analyzed ten minutes of interictal EEG of nine patients with refractory focal epilepsy.

Phenomenological network models: Lessons for epilepsy surgery.

The current opinion in epilepsy surgery is that successful surgery is about removing pathological cortex in the anatomic sense. This contrasts with recent developments in epilepsy research, where epilepsy is seen as a network disease. Computational models offer a framework to investigate the influence of networks, as well as local tissue properties, and to explore alternative resection strategies.

Tailoring epilepsy surgery with fast ripples in the intraoperative electrocorticogram.

Objective: Intraoperative electrocorticography (ECoG) can be used to delineate the resection area in epilepsy surgery. High-frequency oscillations (HFOs; 80-500 Hz) seem better biomarkers for epileptogenic tissue than spikes. We studied how HFOs and spikes in combined pre- and postresection ECoG predict surgical outcome in different tailoring approaches.

Evoked versus spontaneous high frequency oscillations in the chronic electrocorticogram in focal epilepsy.

Objective: Spontaneous high frequency oscillations (HFOs; ripples 80-250Hz, fast ripples (FRs) 250-500Hz) are biomarkers for epileptogenic tissue in focal epilepsy. Single pulse electrical stimulation (SPES) can evoke HFOs. We hypothesized that stimulation distinguishes pathological from physiological ripples and compared the occurrence of evoked and spontaneous HFOs within the seizure onset zone (SOZ) and eloquent functional areas.