Discovering EEG biomarkers of Lennox-Gastaut syndrome through unsupervised time-frequency analysis.

Objective: The discovery and validation of electroencephalography (EEG) biomarkers often rely on visual identification of waveforms. However, bias toward visually striking events restricts the search space for new biomarkers, and low interrater reliability can limit rigorous validation. We present a data-driven approach to biomarker discovery called scalp EEG Pattern Identification and Categorization (s-EPIC), which enables automated, unsupervised identification of EEG waveforms.

Electrode Surface Area Impacts Measurement of High Frequency Oscillations in Human Intracranial EEG.

Objective: High-frequency oscillations (HFOs) are a promising prognostic biomarker of surgical outcome in patients with epilepsy. Their rates of occurrence and morphology have been studied extensively using recordings from electrodes of various geometries. While electrode size is a potential confounding factor in HFO studies, it has largely been disregarded due to a lack of consistent evidence.

Optimizing automated detection of high frequency oscillations using visual markings does not improve SOZ localization.

Objective: High frequency oscillations (HFOs) are a biomarker of the seizure onset zone (SOZ) and can be visually or automatically detected. In theory, one can optimize an automated algorithm's parameters to maximize SOZ localization accuracy; however, there is no consensus on whether or how this should be done. Therefore, we optimized an automated detector using visually identified HFOs and evaluated the impact on SOZ localization accuracy.

Computational EEG attributes predict response to therapy for epileptic spasms.

Objective: We set out to evaluate whether response to treatment for epileptic spasms is associated with specific candidate computational EEG biomarkers, independent of clinical attributes.

Methods: We identified 50 children with epileptic spasms, with pre- and post-treatment overnight video-EEG. After EEG samples were preprocessed in an automated fashion to remove artifacts, we calculated amplitude, power spectrum, functional connectivity, entropy, and long-range temporal correlations (LRTCs).

Interrater reliability of interictal EEG waveforms in Lennox-Gastaut Syndrome.

Objective: Identification of EEG waveforms is critical for diagnosing Lennox-Gastaut Syndrome (LGS) but is complicated by the progressive nature of the disease. Here, we assess the interrater reliability (IRR) among pediatric epileptologists for classifying EEG waveforms associated with LGS.

Methods: A novel automated algorithm was used to objectively identify epochs of EEG with transient high power, which were termed events of interest (EOIs).

Evolution of Cortical Functional Networks in Healthy Infants.

During normal childhood development, functional brain networks evolve over time in parallel with changes in neuronal oscillations. Previous studies have demonstrated differences in network topology with age, particularly in neonates and in cohorts spanning from birth to early adulthood. Here, we evaluate the developmental changes in EEG functional connectivity with a specific focus on the first 2 years of life.

A novel method for dynamically altering the surface area of intracranial EEG electrodes.

Intracranial electroencephalogram (iEEG) plays a critical role in the treatment of neurological diseases, such as epilepsy and Parkinson's disease, as well as the development of neural prostheses and brain computer interfaces. While electrode geometries vary widely across these applications, the impact of electrode size on iEEG features and morphology is not well understood. Some insight has been gained from computer simulations, as well as experiments in which signals are recorded using electrodes of different sizes concurrently in different brain regions.

EEG biomarkers for the diagnosis and treatment of infantile spasms.

Early diagnosis and treatment are critical for young children with infantile spasms (IS), as this maximizes the possibility of the best possible child-specific outcome. However, there are major barriers to achieving this, including high rates of misdiagnosis or failure to recognize the seizures, medication failure, and relapse. There are currently no validated tools to aid clinicians in assessing objective diagnostic criteria, predicting or measuring medication response, or predicting the likelihood of relapse.

Beyond rates: time-varying dynamics of high frequency oscillations as a biomarker of the seizure onset zone.

. High frequency oscillations (HFOs) recorded by intracranial electrodes have generated excitement for their potential to help localize epileptic tissue for surgical resection. However, the number of HFOs per minute (i.

Scalp EEG markers of normal infant development using visual and computational approaches.

The infant brain is rapidly developing, and these changes are reflected in scalp electroencephalography (EEG) features, including power spectrum and sleep spindle characteristics. These biomarkers not only mirror infant development, but they are also altered by conditions such as epilepsy, autism, developmental delay, and trisomy 21. Prior studies of early development were generally limited by small cohort sizes, lack of a specific focus on infancy (0-2 years), and exclusive use of visual marking for sleep spindles.

An Effortful Approach to Social Affiliation in Schizophrenia: Preliminary Evidence of Increased Theta and Alpha Connectivity during a Live Social Interaction.

People with schizophrenia often experience a profound lack of motivation for social affiliation-a facet of negative symptoms that detrimentally impairs functioning. However, the mechanisms underlying social affiliative deficits remain poorly understood, particularly under realistic social contexts. Here, we investigated subjective reports and electroencephalography (EEG) functional connectivity in schizophrenia during a live social interaction.

Computational characteristics of interictal EEG as objective markers of epileptic spasms.

Objective: Favorable neurodevelopmental outcomes in epileptic spasms (ES) are tied to early diagnosis and prompt treatment, but uncertainty in the identification of the disease can delay this process. Therefore, we investigated five categories of computational electroencephalographic (EEG) measures as markers of ES.

Methods: We measured 1) amplitude, 2) power spectra, 3) Shannon entropy and permutation entropy, 4) long-range temporal correlations, via detrended fluctuation analysis (DFA) and 5) functional connectivity using cross-correlation and phase lag index (PLI).

Infant functional networks are modulated by state of consciousness and circadian rhythm.

Functional connectivity networks are valuable tools for studying development, cognition, and disease in the infant brain. In adults, such networks are modulated by the state of consciousness and the circadian rhythm; however, it is unknown if infant brain networks exhibit similar variation, given the unique temporal properties of infant sleep and circadian patterning. To address this, we analyzed functional connectivity networks calculated from long-term EEG recordings (average duration 20.

Overnight Caloric Restriction Prior to Cardiac Arrest and Resuscitation Leads to Improved Survival and Neurological Outcome in a Rodent Model.

While interest toward caloric restriction (CR) in various models of brain injury has increased in recent decades, studies have predominantly focused on the benefits of chronic or intermittent CR. The effects of ultra-short, including overnight, CR on acute ischemic brain injury are not well studied. Here, we show that overnight caloric restriction (75% over 14 h) prior to asphyxial cardiac arrest and resuscitation (CA) improves survival and neurological recovery as measured by, behavioral testing on neurological deficit scores, faster recovery of quantitative electroencephalography (EEG) burst suppression ratio, and complete prevention of neurodegeneration in multiple regions of the brain.

Automated detection of ripple oscillations in long-term scalp EEG from patients with infantile spasms.

Scalp high-frequency oscillations (HFOs) are a promising biomarker of epileptogenicity in infantile spasms (IS) and many other epilepsy syndromes, but prior studies have relied on visual analysis of short segments of data due to the prevalence of artifacts in EEG. Here we set out to robustly characterize the rate and spatial distribution of HFOs in large datasets from IS subjects using fully automated HFO detection techniques.We prospectively collected long-term scalp EEG data from 12 subjects with IS and 18 healthy controls.

Amplitude of high frequency oscillations as a biomarker of the seizure onset zone.

Objective: Studies of high frequency oscillations (HFOs) in epilepsy have primarily tested the HFO rate as a biomarker of the seizure onset zone (SOZ), but the rate varies over time and is not robust for all individual subjects. As an alternative, we tested the performance of HFO amplitude as a potential SOZ biomarker using two automated detection algorithms.

Method: HFOs were detected in intracranial electroencephalogram (iEEG) from 11 patients using a machine learning algorithm and a standard amplitude-based algorithm.

Trends in the use of automated algorithms for the detection of high-frequency oscillations associated with human epilepsy.

High-frequency oscillations (HFOs) in intracranial electroencephalography (EEG) are a promising biomarker of the epileptogenic zone and tool for surgical planning. Many studies have shown that a high rate of HFOs (number per minute) is correlated with the seizure-onset zone, and complete removal of HFO-generating brain regions has been associated with seizure-free outcome after surgery. In order to use HFOs as a biomarker, these transient events must first be detected in electrophysiological data.

Schizotypy dimensions are associated with altered resting state alpha connectivity.

The disconnection hypothesis of schizophrenia says that symptoms are explained by dysfunctional connections across a wide range of brain networks. Despite some support for this hypothesis, there have been mixed findings. One reason for these may be the multidimensional nature of schizophrenia symptoms.

Detection of anomalous high-frequency events in human intracranial EEG.

Objective: High-frequency oscillations (HFOs) are a promising biomarker for the epileptogenic zone. However, no physiological definition of an HFO has been established, so detection relies on the empirical definition of an HFO derived from visual observation. This can bias estimates of HFO features such as amplitude and duration, thereby hindering their utility as biomarkers.

Effect of interictal epileptiform discharges on EEG-based functional connectivity networks.

Objective: Functional connectivity networks (FCNs) based on interictal electroencephalography (EEG) can identify pathological brain networks associated with epilepsy. FCNs are altered by interictal epileptiform discharges (IEDs), but it is unknown whether this is due to the morphology of the IED or the underlying pathological activity. Therefore, we characterized the impact of IEDs on the FCN through simulations and EEG analysis.

Emerging roles of network analysis for epilepsy.

In recent years there has been increasing interest in applying network science tools to EEG data. At the 2018 American Epilepsy Society conference in New Orleans, LA, the yearly session of the Engineering and Neurostimulation Special Interest Group focused on emerging, translational technologies to analyze seizure networks. Each speaker demonstrated practical examples of how network tools can be utilized in clinical care and provide additional data to help care for patients with intractable epilepsy.

Inference on Long-Range Temporal Correlations in Human EEG Data.

Detrended Fluctuation Analysis (DFA) is a statistical estimation algorithm used to assess long-range temporal dependence in neural time series. The algorithm produces a single number, the DFA exponent, that reflects the strength of long-range temporal correlations in the data. No methods have been developed to generate confidence intervals for the DFA exponent for a single time series segment.

Automated Detection of High Frequency Oscillations in Human Scalp Electroencephalogram.

High frequency oscillations (HFOs) > 80 Hz are a promising biomarker of epileptic tissue. Recent evidence has shown that spontaneous HFOs can be recorded from the scalp, but detection of these electrographic events remains a challenge. Here, we modified a simple automatic detector, used originally for intracranial EEG (iEEG) recordings, to detect ripples and fast ripples in scalp EEG.