VACTERL Association Etiology: The Impact of de novo and Rare Copy Number Variations.

Copy number variations (CNVs), either DNA gains or losses, have been found at common regions throughout the human genome. Most CNVs neither have a pathogenic significance nor result in disease-related phenotypes but, instead, reflect the normal population variance. However, larger CNVs, which often arise de novo, are frequently associated with human disease. A genetic contribution has long been suspected in VACTERL (Vertebral, Anal, Cardiac, TracheoEsophageal fistula, Renal and Limb anomalies) association. The anomalies observed in this association overlap with several monogenetic conditions associated with mutations in specific genes, e.g. Townes Brocks (SALL1), Feingold syndrome (MYCN) or Fanconi anemia. So far VACTERL association has typically been considered a diagnosis of exclusion. Identifying recurrent or de novo genomic variations in individuals with VACTERL association could make it easier to distinguish VACTERL association from other syndromes and could provide insight into disease mechanisms. Sporadically, de novo CNVs associated with VACTERL are described in literature. In addition to this literature review of genomic variation in published VACTERL association patients, we describe CNVs present in 68 VACTERL association patients collected in our institution. De novo variations (>30 kb) are absent in our VACTERL association cohort. However, we identified recurrent rare CNVs which, although inherited, could point to mechanisms or biological processes contributing to this constellation of developmental defects.

Intracranial EEG power and metabolism in human epilepsy.

EEG power and high frequency activity in the seizure onset zone has been increasingly considered for its relationship with seizures in animal and human studies of epilepsy. We examine the relationship between quantitative EEG measures and metabolic imaging in epilepsy patients undergoing intracranial EEG (icEEG) analysis for seizure localization. Patients with mesial temporal lobe epilepsy (MTLE) and neocortical epilepsy (NE) were studied. Metabolic imaging was performed with MR spectroscopic imaging using N-acetyl aspartate (NAA) and creatine (Cr). All data were acquired from the mesial temporal lobe such that a direct comparison of the same anatomical regions between the two groups could be performed. While no difference was seen in the total power recorded from the mesial temporal lobe, the MTLE group had significantly greater power in the high frequency bands. There was a significant positive exponential relationship between total icEEG power with NAA/Cr in MTLE, R=+0.84 and p<0.001, which was not seen in NE. There was also a significant negative relationship between fractional gamma power with NAA/Cr in MTLE, R=-0.66 and p<0.02, also not seen in NE. These data argue that within the seizure onset zone, the tight correlation between total power and NAA/Cr suggests that total electrical output is powered by available mitochondrial function. These data are also consistent with the hypothesis that high frequency activity is an abnormal manifestation of tissue injury.

Temporal distributions of seizure occurrence from various epileptogenic regions.

OBJECTIVE: The aim of this study was to determine whether seizure occurrence in partial epilepsy is under the influence of circadian rhythms and rhythmic exogenous factors, and how this influence varies according to cortical brain region. For these ends, we determined and analyzed detailed temporal distributions of seizures arising from the frontal, parietal, occipital, neocortical temporal, and mesial temporal lobes. METHODS: We retrospectively analyzed intracranial EEG recordings from 131 consecutive adult subjects whose partial epilepsy was sufficiently localized for surgical resection. In all, 669 seizures were analyzed: 132 frontal, 77 parietal, 83 occipital, 217 mesial temporal, and 160 neocortical temporal. RESULTS: Seizure distribution was dependent on brain region (p < 10(-9)). Nonuniform seizure distributions were observed in the parietal (p < 10(-4)), occipital (p < 10(-7)), mesial temporal (p < 0.02), and neocortical temporal lobes (p < 0.04). Occipital and parietal seizures occurred in strong gaussian-like distributions, 180 degrees out of phase relative to each other; occipital seizure occurrence peaked between 16:00 and 19:00, whereas parietal seizures peaked between 4:00 and 7:00. Frontal lobe seizures followed a unimodal distribution, peaking between 4:00 and 7:00. Seizures from the mesial temporal lobe were distributed bimodally, with the primary peak in the late afternoon between 16:00 and 19:00 and secondary peak in the morning between 7:00 and 10:00. Neocortical temporal seizures peaked slightly before the primary peak observed in the mesial temporal lobe; however, these distributions did not differ significantly. CONCLUSIONS: Seizure occurrence in partial epilepsy is not random. Endogenous circadian rhythms and rhythmic exogenous factors likely play substantial roles in seizure occurrence. These roles vary considerably according to brain region. Frontal and parietal lobe seizures seem most likely to occur nocturnally, whereas occipital and temporal lobe seizures seem to have strong afternoon preferences.

Coherence of the electroencephalogram during the first sleep cycle.

OBJECTIVE: The increasing amplitude of the electroencephalogram (EEG) during non-rapid eye movement sleep implies a progressive synchronization of neuronal activity. We sought to characterize the spatial relationship of cortical activity at different frequencies during the first sleep cycle, focusing on sleep stages 3 and 4 (slow wave sleep). METHODS: Sleep EEGs were obtained at home from six adults using a portable recorder. Signal power and magnitude squared coherence were measured during the first sleep cycle. Spectra obtained from bipolar and common reference derivations were compared. RESULTS: During slow wave sleep, signal power is highest in the delta frequency band and regional coherence below 5 Hz is broadly distributed. Although signal power in the alpha and sigma frequency bands is lower, peaks of regional coherence in those bands are similar to or higher than delta-band coherence. Regional coherence during slow wave sleep is differentially distributed with a 14 Hz component in central and posterior regions and a 10 Hz component in frontal and central regions. CONCLUSIONS: Ten and 14 Hz rhythms are an essential component of slow wave sleep. SIGNIFICANCE: The interpretation of scalp EEG power and coherence spectra is limited by the lack of a satisfactory recording reference. However, conclusions can be made by comparing and contrasting results from both bipolar and common reference recordings.

Distinguishing subtypes of temporal lobe epilepsy with background hippocampal activity.

PURPOSE: Two subtypes of temporal lobe epilepsy (TLE) can be defined through clinical observations and analysis of hippocampal tissue resected during surgical procedures for intractable TLE: (a) mesial temporal sclerosis (MTS), which is characterized by extensive changes to the hippocampus and good surgical outcome; and (b) paradoxical temporal lobe epilepsy (PTLE), which is characterized by minimal cell loss and comparatively poorer surgical outcome. Patients in both subtypes have seizures that appear to begin in the medial temporal lobe, but documented differences in substrate and outcome between these subtypes has defined a need to distinguish MTS and PTLE patients before surgery. This report describes a retrospective study to investigate the feasibility of doing so during intracranial monitoring. METHODS: Background EEG epochs, 5 min in duration, were recorded from the anterior hippocampus in 14 (10 MTS and four PTLE) patients with consistent localization of seizure onset to medial temporal structures. The power spectral density (PSD) of the EEG epochs was calculated by a Fourier spectral estimator, and the total signal power and power of the delta, theta, alpha, beta, and gamma frequency bands were submitted to group-to-group comparison. RESULTS: Spectral peaks were observed in the delta band in all PSD estimates and in the theta band in nine of 14 (seven MTS, two PTLE) estimates. The MTS and PTLE subtypes could be distinguished by the total signal power and delta band power. These power measurements were greater in the PTLE subtype. CONCLUSIONS: Both delta and theta spectral components are present in hippocampal background EEGs recorded from patients with TLE. The results indicate that group differences exist in spectral measures of background hippocampal signals recorded from MTS and PTLE subtypes. This suggests both that substrate differences in cellular composition and connectivity are reflected in hippocampal background EEGs and that spectral measurements of these signals may hold promise for tests to identify the group membership of individual patients.

The effect of a scalp reference signal on coherence measurements of intracranial electroencephalograms.

OBJECTIVE: To determine the effect of a scalp reference signal, such as that recorded from the mastoid or ear, on the coherence of referential intracranial electroencephalograms (EEGs). METHODS: The relationship between reference signal power and magnitude squared coherence (MSC) was determined from the theoretical expression of the coherence of referential recordings, obtained under the assumption that the reference signal is not correlated with the signals being studied. The effect of a contaminated reference signal on the coherence of intracranial EEGs was determined by measuring the MSC of both a recording of background EEGs with a simulated contaminated reference signal and a contaminated recording of a seizure. RESULTS: The MSC of referential intracranial EEGs is inflated due to the reference signal. This inflation is a function of the true MSC of the intracranial signals and the power of the reference and intracranial signals. The inflation is limited where reference signal power is smaller than the power of the intracranial signals; maximum inflation <0.1 when reference signal power=0.2xpower of intracranial EEGs and

Measuring the coherence of intracranial electroencephalograms.

OBJECTIVE: Previous coherence studies of human intracranial electroencephalograms (EEGs) can be faulted on two methodological issues: (1) coherence estimates in a majority were formed from a very small number of independent sample spectra, and (2) the statistical significance of coherence estimates was either not reported or was poorly evaluated. Coherence estimator performance may be poor when a small number of independent sample spectra are employed, and the coupling of poor estimation and statistical testing can result in inaccuracy in the measurement of coherence. The performance characteristics of the coherence estimator and statistical testing of coherence estimates are described in this manuscript. METHODS: The bias, variance, probability density functions, and confidence intervals of the estimate of magnitude squared coherence (MSC); and power analysis for the test of zero MSC were developed from the exact analytic form of the probability density function of the estimate of MSC for Gaussian random processes. The coherence of a single epoch of background EEG, recorded from a patient with intractable seizures, was evaluated with different parameter values to aid in the exposition of the concepts developed here. RESULTS: The statistical characteristics of WOSA coherence estimates are a function of a single estimator parameter, the number of independent sample spectra employed in the estimation. Bias and variance are high, confidence intervals may be large, and the probability of Type II errors is high if a small number of independent sample spectra are employed. A considerable improvement in measurement accuracy is possible with careful selection of estimator parameter values. CONCLUSIONS: Coherence measurement accuracy can be improved over previous applications by attention to estimator performance and accurate statistical testing of coherence estimates.

Time-frequency representation of electrocorticograms in temporal lobe epilepsy.

Three time-frequency distributions are evaluated in terms of their efficacy in representing nonstationary electrocorticograms (ECoG's) in human temporal lobe epilepsy. The results of a new method, the exponential distribution, are compared with those of the spectrogram and the Wigner distribution. It is shown that the exponential distribution represents a considerable improvement over the spectrogram in terms of resolution and markedly reduces cross-terms present in the Wigner distribution. Exponential distribution representations of ECoG's from different stages of an epileptic record are developed as contour maps. These high-resolution representations offer a lucid display of temporal-spectral features of the rapidly varying signals that constitute ECoG's recorded in temporal lobe epilepsy.

Phase space topography and the Lyapunov exponent of electrocorticograms in partial seizures.

Electrocorticograms (ECoG's) from 16 of 68 chronically implanted subdural electrodes, placed over the right temporal cortex in a patient with a right medial temporal focus, were analyzed using methods from nonlinear dynamics. A time series provides information about a large number of pertinent variables, which may be used to explore and characterize the system's dynamics. These variables and their evolution in time produce the phase portrait of the system. The phase spaces for each of 16 electrodes were constructed and from these the largest average Lyapunov exponents (L's), measures of chaoticity of the system (the larger the L, the more chaotic the system is), were estimated over time for every electrode before, in and after the epileptic seizure for three seizures of the same patient. The start of the seizure corresponds to a simultaneous drop in L values obtained at the electrodes nearest the focus. L values for the rest of the electrodes follow. The mean values of L for all electrodes in the postictal state are larger than the ones in the preictal state, denoting a more chaotic state postictally. The lowest values of L occur during the seizure but they are still positive denoting the presence of a chaotic attractor. Based on the procedure for the estimation of L we were able to develop a methodology for detecting prominent spikes in the ECoG. These measures (L*) calculated over a period of time (10 minutes before to 10 minutes after the seizure outburst) revealed a remarkable coherence of the abrupt transient drops of L* for the electrodes that showed the initial ictal onset. The L* values for the electrodes away from the focus exhibited less abrupt transient drops. These results indicate that the largest average Lyapunov exponent L can be useful in seizure detection as well as a discriminatory factor for focus localization in multielectrode analysis.