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1.  Mapping and mining interictal pathological gamma (30–100 Hz) oscillations with clinical intracranial EEG in patients with epilepsy 
Expert systems with applications  2012;39(8):7355-7370.
Localizing an epileptic network is essential for guiding neurosurgery and antiepileptic medical devices as well as elucidating mechanisms that may explain seizure-generation and epilepsy. There is increasing evidence that pathological oscillations may be specific to diseased networks in patients with epilepsy and that these oscillations may be a key biomarker for generating and indentifying epileptic networks. We present a semi-automated method that detects, maps, and mines pathological gamma (30–100 Hz) oscillations (PGOs) in human epileptic brain to possibly localize epileptic networks. We apply the method to standard clinical iEEG (<100 Hz) with interictal PGOs and seizures from six patients with medically refractory epilepsy. We demonstrate that electrodes with consistent PGO discharges do not always coincide with clinically determined seizure onset zone (SOZ) electrodes but at times PGO-dense electrodes include secondary seizure-areas (SS) or even areas without seizures (NS). In 4/5 patients with epilepsy surgery, we observe poor (Engel Class 4) post-surgical outcomes and identify more PGO-activity in SS or NS than in SOZ. Additional studies are needed to further clarify the role of PGOs in epileptic brain.
doi:10.1016/j.eswa.2012.01.071
PMCID: PMC3480232  PMID: 23105174
Epileptic network; Interictal epileptic discharge; Pathological gamma oscillation; Detection; Mapping; Data-mining
2.  Data mining neocortical high-frequency oscillations in epilepsy and controls 
Brain  2011;134(10):2948-2959.
Transient high-frequency (100–500 Hz) oscillations of the local field potential have been studied extensively in human mesial temporal lobe. Previous studies report that both ripple (100–250 Hz) and fast ripple (250–500 Hz) oscillations are increased in the seizure-onset zone of patients with mesial temporal lobe epilepsy. Comparatively little is known, however, about their spatial distribution with respect to seizure-onset zone in neocortical epilepsy, or their prevalence in normal brain. We present a quantitative analysis of high-frequency oscillations and their rates of occurrence in a group of nine patients with neocortical epilepsy and two control patients with no history of seizures. Oscillations were automatically detected and classified using an unsupervised approach in a data set of unprecedented volume in epilepsy research, over 12 terabytes of continuous long-term micro- and macro-electrode intracranial recordings, without human preprocessing, enabling selection-bias-free estimates of oscillation rates. There are three main results: (i) a cluster of ripple frequency oscillations with median spectral centroid = 137 Hz is increased in the seizure-onset zone more frequently than a cluster of fast ripple frequency oscillations (median spectral centroid = 305 Hz); (ii) we found no difference in the rates of high frequency oscillations in control neocortex and the non-seizure-onset zone neocortex of patients with epilepsy, despite the possibility of different underlying mechanisms of generation; and (iii) while previous studies have demonstrated that oscillations recorded by parenchyma-penetrating micro-electrodes have higher peak 100–500 Hz frequencies than penetrating macro-electrodes, this was not found for the epipial electrodes used here to record from the neocortical surface. We conclude that the relative rate of ripple frequency oscillations is a potential biomarker for epileptic neocortex, but that larger prospective studies correlating high-frequency oscillations rates with seizure-onset zone, resected tissue and surgical outcome are required to determine the true predictive value.
doi:10.1093/brain/awr212
PMCID: PMC3187540  PMID: 21903727
high-frequency oscillations; epilepsy; intracranial EEG
3.  Interobserver reproducibility of electroencephalogram interpretation in critically ill children 
Purpose
Correct outcome prediction after cardiac arrest in children may improve clinical decision making and family counseling. Various investigators have used EEG to predict outcome with varying success, but one limiting issue is the potential lack of reproducibility of EEG interpretation. Therefore, we aimed to evaluate interobserver agreement using standardized terminology in the interpretation of EEG tracings obtained from critically ill children following cardiac arrest.
Methods
3 pediatric neurophysiologists scored 74 EEG samples using standardized categories, terminology, and interpretation rules. Interobserver agreement was evaluated using kappa and intra-class correlation coefficients.
Results
Agreement was substantial for the categories of continuity, burst suppression, sleep architecture, and overall rating. Agreement was moderate for seizure occurrence and inter-ictal epileptiform discharge type. Agreement was fair for inter-ictal epileptiform discharge presence, beta activity, predominant frequency, and fastest frequency. Agreement was slight for maximum voltage and focal slowing presence.
Conclusions
The variability of inter-rater agreement suggests that some EEG features are superior to others for use in a predictive algorithm. Using only reproducible EEG features is needed to ensure the most accurate and consistent predictions. Since even seizure identification had only moderate agreement, studies of non-convulsive seizures in critically ill patients must be conducted and interpreted cautiously.
doi:10.1097/WNP.0b013e3182051123
PMCID: PMC3107383  PMID: 21221016
Electroencephalogram; Interobserver variability; Seizure; Pediatric; Hypoxic Ischemic Encephalopathy; Cardiac Arrest
4.  Targeted loss of Arx results in a developmental epilepsy mouse model and recapitulates the human phenotype in heterozygous females 
Brain  2009;132(6):1563-1576.
Mutations in the X-linked aristaless-related homeobox gene (ARX) have been linked to structural brain anomalies as well as multiple neurocognitive deficits. The generation of Arx-deficient mice revealed several morphological anomalies, resembling those observed in patients and an interneuron migration defect but perinatal lethality precluded analyses of later phenotypes. Interestingly, many of the neurological phenotypes observed in patients with various ARX mutations can be attributed, in part, to interneuron dysfunction. To directly test this possibility, mice carrying a floxed Arx allele were generated and crossed to Dlx5/6CRE-IRES-GFP(Dlx5/6CIG) mice, conditionally deleting Arx from ganglionic eminence derived neurons including cortical interneurons. We now report that Arx−/y;Dlx5/6CIG (male) mice exhibit a variety of seizure types beginning in early-life, including seizures that behaviourally and electroencephalographically resembles infantile spasms, and show evolution through development. Thus, this represents a new genetic model of a malignant form of paediatric epilepsy, with some characteristics resembling infantile spasms, caused by mutations in a known infantile spasms gene. Unexpectedly, approximately half of the female mice carrying a single mutant Arx allele (Arx−/+;Dlx5/6CIG) also developed seizures. We also found that a subset of human female carriers have seizures and neurocognitive deficits. In summary, we have identified a previously unrecognized patient population with neurological deficits attributed to ARX mutations that are recapitulated in our mouse model. Furthermore, we show that perturbation of interneuron subpopulations is an important mechanism underling the pathogenesis of developmental epilepsy in both hemizygous males and carrier females. Given the frequency of ARX mutations in patients with infantile spasms and related disorders, our data unveil a new model for further understanding the pathogenesis of these disorders.
doi:10.1093/brain/awp107
PMCID: PMC2685924  PMID: 19439424
Epilepsy; development; conditional knockout; genetic model; interneurons
5.  Human and Automated Detection of High-Frequency Oscillations in Clinical Intracranial EEG Recordings 
Objective
Recent studies indicate that pathologic high-frequency oscillations (HFOs) are signatures of epileptogenic brain. Automated tools are required to characterize these events. We present a new algorithm tuned to detect HFOs from 30 – 85 Hz, and validate it against human expert electroencephalographers.
Methods
We randomly selected 28 3-minute single-channel epochs of intracranial EEG (IEEG) from two patients. Three human reviewers and three automated detectors marked all records to identify candidate HFOs. Subsequently, human reviewers verified all markings.
Results
A total of 1,330 events were collectively identified. The new method presented here achieved 89.7% accuracy against a consensus set of human expert markings. A one-way ANOVA determined no difference between the mean F-measures of the human reviewers and automated algorithm. Human Kappa statistics (mean κ = 0.38) demonstrated marginal identification consistency, primarily due to false negative errors.
Conclusions
We present an HFO detector that improves upon existing algorithms, and performs as well as human experts on our test data set. Validation of detector performance must be compared to more than one expert because of interrater variability.
Significance
This algorithm will be useful for analyzing large EEG databases to determine the pathophysiological significance of HFO events in human epileptic networks.
doi:10.1016/j.clinph.2006.12.019
PMCID: PMC2020804  PMID: 17382583
high-frequency oscillation; HFO; intracranial EEG; epilepsy

Results 1-5 (5)