Neurologic music therapy combined with EEG-tDCS for upper motor extremity performance in patients with corticobasal syndrome: Study protocol for a novel approach.

Background: Corticobasal syndrome (CBS) is an atypical parkinsonian disorder that involves degeneration of brain regions associated with motor coordination and sensory processing. Combining transcranial direct current stimulation (tDCS) with rehabilitation training has been shown to improve upper-limb performance in other disease models. Here, we describe the protocol investigating whether tDCS with neurologic music therapy (NMT) (patterned sensory enhancement and therapeutic instrumental music performance) enhances functional arm/hand performance in individuals with CBS.

Automated seizure activity tracking and onset zone localization from scalp EEG using deep neural networks.

We propose a novel neural network architecture, SZTrack, to detect and track the spatio-temporal propagation of seizure activity in multichannel EEG. SZTrack combines a convolutional neural network encoder operating on individual EEG channels with recurrent neural networks to capture the evolution of seizure activity. Our unique training strategy aggregates individual electrode level predictions for patient-level seizure detection and localization.

Dynamic training of a novelty classifier algorithm for real-time detection of early seizure onset.

Objective: To develop an adaptive framework for seizure detection in real-time that is practical to use in the Epilepsy Monitoring Unit (EMU) as a warning signal, and whose output helps characterize epileptiform activity.

Methods: Our algorithm was tested on intracranial EEG from epilepsy patients admitted to the EMU for presurgical evaluation. Our framework uses a one-class Support Vector Machine (SVM) that is being trained dynamically according to past activity in all available channels to classify the novelty of the current activity.

Computing network-based features from intracranial EEG time series data: Application to seizure focus localization.

The surgical resection of the epileptogenic zone (EZ) is the only effective treatment for many drug-resistant epilepsy (DRE) patients, but the pre-surgical identification of the EZ is challenging. This study investigates whether the EZ exhibits a computationally identifiable signature during seizures. In particular, we compute statistics of the brain network from intracranial EEG (iEEG) recordings and track the evolution of network connectivity before, during, and after seizures.

Identification of seizure onset zone and preictal state based on characteristics of high frequency oscillations.

Objective: We investigate the relevance of high frequency oscillations (HFO) for biomarkers of epileptogenic tissue and indicators of preictal state before complex partial seizures in humans.

Methods: We introduce a novel automated HFO detection method based on the amplitude and features of the HFO events. We examined intracranial recordings from 33 patients and compared HFO rates and characteristics between channels within and outside the seizure onset zone (SOZ).

Network dynamics of the brain and influence of the epileptic seizure onset zone.

The human brain is a dynamic networked system. Patients with partial epileptic seizures have focal regions that periodically diverge from normal brain network dynamics during seizures. We studied the evolution of brain connectivity before, during, and after seizures with graph-theoretic techniques on continuous electrocorticographic (ECoG) recordings (5.

A network analysis of the dynamics of seizure.

Seizures are events that spread through the brain's network of connections and create pathological activity. To understand what is occurring in the brain during seizure we investigated the time progression of the brain's state from seizure onset to seizure suppression. Knowledge of a seizure's dynamics and the associated spatial structure is important for localizing the seizure foci and determining the optimal location and timing of electrical stimulation to mitigate seizure development.

Improving early seizure detection.

Over the last decade, the search for a method able to reliably predict seizures hours in advance has been largely replaced by the more realistic goal of very early detection of seizure onset, which would allow therapeutic or warning devices to be triggered prior to the onset of disabling clinical symptoms. We explore in this article the steps along the pathway from data acquisition to closed-loop applications that can and should be considered to design the most efficient early seizure detection. Microelectrodes, high-frequency oscillations, high sampling rate, high-density arrays, and modern analysis techniques are all elements of the recording and detection process that in combination with modeling studies can provide new insights into the dynamics of seizure onsets.

Characterization of early partial seizure onset: frequency, complexity and entropy.

Objective: A clear classification of partial seizures onset features is not yet established. Complexity and entropy have been very widely used to describe dynamical systems, but a systematic evaluation of these measures to characterize partial seizures has never been performed.

Methods: Eighteen different measures including power in frequency bands up to 300 Hz, Gabor atom density (GAD), Higuchi fractal dimension (HFD), Lempel-Ziv complexity, Shannon entropy, sample entropy, and permutation entropy, were selected to test sensitivity to partial seizure onset.

Controversies in epilepsy: debates held during the Fourth International Workshop on Seizure Prediction.

Debates on six controversial topics were held during the Fourth International Workshop on Seizure Prediction (IWSP4) convened in Kansas City, KS, USA, July 4-7, 2009. The topics were (1) Ictogenesis: Focus versus Network? (2) Spikes and Seizures: Step-relatives or Siblings? (3) Ictogenesis: A Result of Hyposynchrony? (4) Can Focal Seizures Be Caused by Excessive Inhibition? (5) Do High-Frequency Oscillations Provide Relevant Independent Information? (6) Phase Synchronization: Is It Worthwhile as Measured? This article, written by the IWSP4 organizing committee and the debaters, summarizes the arguments presented during the debates.

Partial seizures are associated with early increases in signal complexity.

Objectives: Partial seizures are often believed to be associated with EEG signals of low complexity because seizures are associated with increased neural network synchrony. The investigations reported here provide an assessment of the signal complexity of epileptic seizure onsets using newly developed quantitative measures.

Methods: Using the Gabor atom density (GAD) measure of signal complexity, 339 partial seizures in 45 patients with intracranial electrode arrays were analyzed.

Analysis of dynamics and propagation of parietal cingulate seizures with secondary mesial temporal involvement.

Cingulate-onset seizures, particularly those originating from parietal cingulate regions, are inadequately described and confounded by patterns of propagation. We analyzed scalp and depth electrode recordings in a patient whose seizures originated from a lesion in the right posterior cingulate region and produced secondary seizure activity in ipsilateral mesial temporal structures. Analyses included the matching pursuit (MP) method of time-frequency decomposition and the Gabor atom density (GAD) measure of signal complexity.

Duration of complex partial seizures: an intracranial EEG study.

Purpose: The dynamics of partial seizures originating from neocortical and mesial temporal regions are thought to differ, yet there are no quantitative comparative studies. The studies reported here investigate the duration of complex partial seizures in these populations using analyses of seizures recorded from intracranial arrays.

Methods: Data were collected from patients undergoing presurgical evaluation with intracranial electrodes.

Intrinsic ictal dynamics at the seizure focus: effects of secondary generalization revealed by complexity measures.

Purpose: Partial seizures (PSs) may be self-limited regional events or propagate further and secondarily generalize. The mechanisms and dynamics of secondarily generalized tonic-clonic seizures (GTCSs) are not well understood. Methods with which to assess the dynamic of those events are also limited.

Signal complexity and synchrony of epileptic seizures: is there an identifiable preictal period?

Objective: Epileptic seizures are characterized by increases in synchronized activity and increased signal complexity. Prediction of seizures depends upon detectable preictal changes before the actual ictal event. The studies reported here test whether two methods designed to detect changes in synchrony and complexity can identify any changes in a preictal period before visual EEG changes or clinical manifestations.

Human ECoG analysis during speech perception using matching pursuit: a comparison between stochastic and dyadic dictionaries.

We use the matching pursuit (MP) algorithm to detect induced gamma activity in human EEG during speech perception. We show that the MP algorithm is particularly useful for detecting small power changes at high gamma frequencies (> 70 Hz). We also compare the performance of the MP using a stochastic versus a dyadic dictionary and show that despite the frequency bias the time-frequency power plot (averaged over 100 trials) generated by the dyadic MP is almost identical (> 98.

Time course of arousal response during periodic leg movements in patients with periodic leg movements and restless legs syndrome.

Objective: The temporal evolution of periodic leg movements (PLM) and the relationship of their arousing effect on sleep episode has not been extensively investigated. We studied the nocturnal evolution of PLM associated or not with microarousal (MA) and associated with slow wave activity (PLM with slow wave activity) in 23 patients with PLM and/or restless legs syndrome (RLS).

Methods: All subjects had PLM associated with MA or with slow wave activity as well as without MA and all slept for 4 sleep cycles.

Characterization of epileptic seizure dynamics using Gabor atom density.

Objective: The study of epileptic electroencephalograph (EEG) dynamics can potentially provide insights into seizure onset, evolution and termination. We propose a new synthetic measure based on time-frequency decomposition to provide detailed characterization of these dynamic changes.

Methods: The matching pursuit (MP) method allows for continuous time-frequency decomposition.