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1.  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
2.  Neutral mitochondrial heteroplasmy and the influence of aging 
Human Molecular Genetics  2011;20(8):1653-1659.
The development and maintenance of mitochondrial heteroplasmy has important consequences for both health and heredity. Previous studies using pathogenic mutations have shown considerable variability between maternally related individuals and studies of several D-loop polymorphisms have suggested a relationship between heteroplasmy and somatic aging. To broadly explore the variation of human heteroplasmy and to clarify the dynamics of somatic heteroplasmy over the course of lifespan, we analyzed mitochondrial sequence variation across a range of ages. We utilized array-generated single-nucleotide polymorphism data that were well correlated with independent measures of heteroplasmy. Significant levels of heteroplasmy were identified at 0.24% of sites evaluated. By examining mother–child pairs, we found that heteroplasmy was inherited (30%) but could occur de novo in offspring or, conversely, be present in mothers but eliminated in their children (70%). Cumulatively, mitochondrial heteroplasmy across the genome increased significantly with advanced age (r = 0.224, P =8 × 10−30). Surprisingly, changes in heteroplasmy were not uniform with some sites demonstrating a loss of variation (increased homoplasmy) with aging. These data suggest that both mutation and selective pressure affect blood mitochondrial DNA sequence over the course of the human lifespan and reveal the unexpectedly dynamic nature of human heteroplasmy.
doi:10.1093/hmg/ddr043
PMCID: PMC3063991  PMID: 21296868
3.  A Stochastic Framework for Evaluating Seizure Prediction Algorithms Using Hidden Markov Models 
Journal of neurophysiology  2006;97(3):2525-2532.
Responsive, implantable stimulation devices to treat epilepsy are now in clinical trials. New evidence suggests that these devices may be more effective when they deliver therapy before seizure onset. Despite years of effort, prospective seizure prediction, which could improve device performance, remains elusive. In large part, this is explained by lack of agreement on a statistical framework for modeling seizure generation and a method for validating algorithm performance. We present a novel stochastic framework based on a three-state hidden Markov model (HMM) (representing interictal, preictal, and seizure states) with the feature that periods of increased seizure probability can transition back to the interictal state. This notion reflects clinical experience and may enhance interpretation of published seizure prediction studies. Our model accommodates clipped EEG segments and formalizes intuitive notions regarding statistical validation. We derive equations for type I and type II errors as a function of the number of seizures, duration of interictal data, and prediction horizon length and we demonstrate the model’s utility with a novel seizure detection algorithm that appeared to predicted seizure onset. We propose this framework as a vital tool for designing and validating prediction algorithms and for facilitating collaborative research in this area.
doi:10.1152/jn.00190.2006
PMCID: PMC2230664  PMID: 17021032

Results 1-3 (3)