Neuromodulation : journal of the International Neuromodulation Society, Jan 13, 2015
The Automatic Stimulation Mode (AutoStim) feature of the Model 106 Vagus Nerve Stimulation (VNS) ... more The Automatic Stimulation Mode (AutoStim) feature of the Model 106 Vagus Nerve Stimulation (VNS) Therapy System stimulates the left vagus nerve on detecting tachycardia. This study evaluates performance, safety of the AutoStim feature during a 3-5-day Epilepsy Monitoring Unit (EMU) stay and long- term clinical outcomes of the device stimulating in all modes. The E-37 protocol (NCT01846741) was a prospective, unblinded, U.S. multisite study of the AspireSR(®) in subjects with drug-resistant partial onset seizures and history of ictal tachycardia. VNS Normal and Magnet Modes stimulation were present at all times except during the EMU stay. Outpatient visits at 3, 6, and 12 months tracked seizure frequency, severity, quality of life, and adverse events. Twenty implanted subjects (ages 21-69) experienced 89 seizures in the EMU. 28/38 (73.7%) of complex partial and secondarily generalized seizures exhibited ≥20% increase in heart rate change. 31/89 (34.8%) of seizures were treated by Aut...
An artificial neural network (ANN) model was proposed for real-time estimation of regional cerebr... more An artificial neural network (ANN) model was proposed for real-time estimation of regional cerebral blood flow (rCBF), by given head and expired air curves obtained through 133Xe inhalation. The network was constructed according to a regression model described by a linear differential equation. Experimental results compare well with those obtained by conventional curve fitting strategies, but the parameter estimation process is much simplified. A systematic procedure in developing ANN for parametric regression analysis was introduced; networks are constructed according to the selected regression model so that the obtained weights of a trained network directly represent parameters of the regression model which best fits the observed data set. Such a design-oriented methodology extends the classification-based applications of ANN to parametric regression analysis, and therefore may have more generalized applications besides rCBF estimation.
Epileptic disorders : international epilepsy journal with videotape, 2007
We describe a patient who presented at our epilepsy-monitoring unit with myoclonic jerks, and was... more We describe a patient who presented at our epilepsy-monitoring unit with myoclonic jerks, and was diagnosed with juvenile myoclonic epilepsy (JME). Imaging of his brain revealed partial agenesis of the corpus callosum (ACC). We discuss the known genetic basis of both JME and ACC, as well as the role of the corpus callosum (CC) in primary generalized epilepsy. Both JME and ACC are associated with gene loci on chromosome 15q14. Structural brain abnormalities other than ACC, such as atrophy of the corpus callosum have been reported in patients with JME. ACC has been associated with seizures, suggesting an anti-epileptogenic role of the corpus callosum. On the other hand, corpus callosotomy is used to treat refractory idiopathic generalized epilepsy, which shows that the corpus callosum may play an epileptogenic role. The occurrence of both these conditions in one patient raises the question of whether they are purely coincidental or if there is a common basis for both. Several issues n...
Sleep and epilepsy have a dynamic interaction that presents the clinician opportunities for diagn... more Sleep and epilepsy have a dynamic interaction that presents the clinician opportunities for diagnosis and treatment. Sleep complaints are very common in patients with epilepsy and these complaints may be related to the underlying epilepsy, the treatment of epilepsy or other sleep related issues. Appropriate treatment of epilepsy may improve sleep, and treatment of sleep disorders may reduce the frequency of recurrent seizures. Sleep and sleep deprivation may provoke seizures and can provide further diagnostic information about the seizure type and location. For the clinician, understanding the relationship of sleep and epilepsy expands the diagnostic and therapeutic armamentarium.
While significant research has been done into the physiological mechanisms that underlie sleep an... more While significant research has been done into the physiological mechanisms that underlie sleep and the sleep/wake cycle, the available data regarding the nature of drowsiness is far more limited. An objective measurement of drowsiness would have clinical utility, and a precise definition of the drowsy state could offer insights into the nature and purpose of sleep. Studies utilizing fMRI have demonstrated increased area of central nervous system involvement with tasks of increasing complexity. Preliminary data from studies of magnetoencephalography (MEG) with a receptive language task have demonstrated a progressive increase in global coherence of activity between MEG sensors with increasing drowsiness. The relationship between global coherence and the level of drowsiness suggests that the former may serve as an objective measurement of the latter. If true, the relationship suggests the hypothesis that drowsiness may be defined as a progressive loss of cortical network processing efficiency, requiring the recruitment of greater amounts of cortical tissue to perform the same task.
Visually-obvious abnormalities in the resting baseline EEG--slowing, spiking and high-frequency o... more Visually-obvious abnormalities in the resting baseline EEG--slowing, spiking and high-frequency oscillations (HFOs)--are cardinal, though incompletely understood, features of the seizure onset zone in focal epilepsy. We hypothesized that evidence of cortical network dysfunction in temporal lobe epilepsy (TLE) would persist in the absence of visually-classifiable abnormalities in the baseline EEG recorded within the conventional passband, and that metrics of such dysfunction could serve as a lateralizing diagnostic in TLE. Epochs of resting EEG without significant abnormalities in light sleep over several days were compared between a group of 10 patients with proven TLE and 10 subjects without epilepsy. A novel laterality metric computed from the line length of normalized power spectra from the temporal channels was compared between the two groups. Significant group differences in spectral line length laterality metric were found between the TLE and control group. At the individual l...
ABSTRACT Rationale: Unique high-frequency oscillations (HFOs), termed fast ripples (FR), have bee... more ABSTRACT Rationale: Unique high-frequency oscillations (HFOs), termed fast ripples (FR), have been identified in seizure generating limbic areas of kainic treated rats, and in patients with mesial temporal epilepsy using depth electrodes. Results from these studies have provided evidence supporting the view that fast ripples in the human brain appear to reflect field oscillations composed of hypersynchronous action potentials of pathologically interconnected neuronal clusters related to epileptogenesis. We have previously reported on the use of magnetoencephalography (MEG) to detect HFOs. One critique of that study was that the criteria used for HFO determination were significantly different from the published studies utilizing implanted electrodes. To address that, a study specifically replicating one such methodology was performed. Methods: MEG data was recorded on an epileptic patient during the patient’s routine pre-surgical evaluation. Data was recorded on 248 MEG channels at a sample rate of 2034.51 Hz, with a high pass filter 0.2 Hz. One minute of data was recorded with the patient awake, eyes open, and one minute with the patient awake, eyes closed. The data was then transferred to MATLAB for HFO analysis. The data from each sensor was considered separately. The input signal was first filtered using a 30 order Butterworth filter with a bandpass of 151–500 Hz (for fast ripple screening) and separately with a bandpass of 80–150 Hz (for ripple screening). The filtered signal was then rectified to zero voltage. The root mean square of a moving 3 ms window was calculated sequentially over the signal. A positive HFO detection was made if a cut-off of five standard deviations above the mean RMS was exceeded for great than 6 ms. This process was repeated for each of the 248 sensors, and the sensor locations where HFOs were detected were plotted, and the specific HFO frequencies were noted. Results: Using this method, of nine patients analyzed to date, outliers consistent with HFOs have been detected in two. HFO duration ranged from 6–10 ms. Every HFO was detected in sensors over the temporal regions, unilateral in one patient, bilateral in the second. Data collected in all patients studied over a six month period will be presented, along with each patient’s underlying pathology, if known. Conclusions: HFOs can be detected using magnetoencephalography in refractory epilepsy patients using signal processing methods similar to those published for analysis of data from implanted electrodes. Whether these detections represent the same phenomenon has yet to be determined.
ABSTRACT Rationale: Unique high-frequency oscillations (HFOs), termed fast ripples (FR), have bee... more ABSTRACT Rationale: Unique high-frequency oscillations (HFOs), termed fast ripples (FR), have been identified in seizure generating limbic areas of kainic treated rats, and in patients with mesial temporal epilepsy using depth electrodes. Results from these studies have provided evidence supporting the view that fast ripples in the human brain appear to reflect field oscillations composed of hypersynchronous action potentials of pathologically interconnected neuronal clusters related to epileptogenesis. We have previously reported on the use of magnetoencephalography (MEG) to detect HFOs. One critique of that study was that the criteria used for HFO determination were significantly different from the published studies utilizing implanted electrodes. To address that, a study specifically replicating one such methodology was performed. Methods: MEG data was recorded on an epileptic patient during the patient’s routine pre-surgical evaluation. Data was recorded on 248 MEG channels at a sample rate of 2034.51 Hz, with a high pass filter 0.2 Hz. One minute of data was recorded with the patient awake, eyes open, and one minute with the patient awake, eyes closed. The data was then transferred to MATLAB for HFO analysis. The data from each sensor was considered separately. The input signal was first filtered using a 30 order Butterworth filter with a bandpass of 151–500 Hz (for fast ripple screening) and separately with a bandpass of 80–150 Hz (for ripple screening). The filtered signal was then rectified to zero voltage. The root mean square of a moving 3 ms window was calculated sequentially over the signal. A positive HFO detection was made if a cut-off of five standard deviations above the mean RMS was exceeded for great than 6 ms. This process was repeated for each of the 248 sensors, and the sensor locations where HFOs were detected were plotted, and the specific HFO frequencies were noted. Results: Using this method, of nine patients analyzed to date, outliers consistent with HFOs have been detected in two. HFO duration ranged from 6–10 ms. Every HFO was detected in sensors over the temporal regions, unilateral in one patient, bilateral in the second. Data collected in all patients studied over a six month period will be presented, along with each patient’s underlying pathology, if known. Conclusions: HFOs can be detected using magnetoencephalography in refractory epilepsy patients using signal processing methods similar to those published for analysis of data from implanted electrodes. Whether these detections represent the same phenomenon has yet to be determined.
Neuromodulation : journal of the International Neuromodulation Society, Jan 13, 2015
The Automatic Stimulation Mode (AutoStim) feature of the Model 106 Vagus Nerve Stimulation (VNS) ... more The Automatic Stimulation Mode (AutoStim) feature of the Model 106 Vagus Nerve Stimulation (VNS) Therapy System stimulates the left vagus nerve on detecting tachycardia. This study evaluates performance, safety of the AutoStim feature during a 3-5-day Epilepsy Monitoring Unit (EMU) stay and long- term clinical outcomes of the device stimulating in all modes. The E-37 protocol (NCT01846741) was a prospective, unblinded, U.S. multisite study of the AspireSR(®) in subjects with drug-resistant partial onset seizures and history of ictal tachycardia. VNS Normal and Magnet Modes stimulation were present at all times except during the EMU stay. Outpatient visits at 3, 6, and 12 months tracked seizure frequency, severity, quality of life, and adverse events. Twenty implanted subjects (ages 21-69) experienced 89 seizures in the EMU. 28/38 (73.7%) of complex partial and secondarily generalized seizures exhibited ≥20% increase in heart rate change. 31/89 (34.8%) of seizures were treated by Aut...
An artificial neural network (ANN) model was proposed for real-time estimation of regional cerebr... more An artificial neural network (ANN) model was proposed for real-time estimation of regional cerebral blood flow (rCBF), by given head and expired air curves obtained through 133Xe inhalation. The network was constructed according to a regression model described by a linear differential equation. Experimental results compare well with those obtained by conventional curve fitting strategies, but the parameter estimation process is much simplified. A systematic procedure in developing ANN for parametric regression analysis was introduced; networks are constructed according to the selected regression model so that the obtained weights of a trained network directly represent parameters of the regression model which best fits the observed data set. Such a design-oriented methodology extends the classification-based applications of ANN to parametric regression analysis, and therefore may have more generalized applications besides rCBF estimation.
Epileptic disorders : international epilepsy journal with videotape, 2007
We describe a patient who presented at our epilepsy-monitoring unit with myoclonic jerks, and was... more We describe a patient who presented at our epilepsy-monitoring unit with myoclonic jerks, and was diagnosed with juvenile myoclonic epilepsy (JME). Imaging of his brain revealed partial agenesis of the corpus callosum (ACC). We discuss the known genetic basis of both JME and ACC, as well as the role of the corpus callosum (CC) in primary generalized epilepsy. Both JME and ACC are associated with gene loci on chromosome 15q14. Structural brain abnormalities other than ACC, such as atrophy of the corpus callosum have been reported in patients with JME. ACC has been associated with seizures, suggesting an anti-epileptogenic role of the corpus callosum. On the other hand, corpus callosotomy is used to treat refractory idiopathic generalized epilepsy, which shows that the corpus callosum may play an epileptogenic role. The occurrence of both these conditions in one patient raises the question of whether they are purely coincidental or if there is a common basis for both. Several issues n...
Sleep and epilepsy have a dynamic interaction that presents the clinician opportunities for diagn... more Sleep and epilepsy have a dynamic interaction that presents the clinician opportunities for diagnosis and treatment. Sleep complaints are very common in patients with epilepsy and these complaints may be related to the underlying epilepsy, the treatment of epilepsy or other sleep related issues. Appropriate treatment of epilepsy may improve sleep, and treatment of sleep disorders may reduce the frequency of recurrent seizures. Sleep and sleep deprivation may provoke seizures and can provide further diagnostic information about the seizure type and location. For the clinician, understanding the relationship of sleep and epilepsy expands the diagnostic and therapeutic armamentarium.
While significant research has been done into the physiological mechanisms that underlie sleep an... more While significant research has been done into the physiological mechanisms that underlie sleep and the sleep/wake cycle, the available data regarding the nature of drowsiness is far more limited. An objective measurement of drowsiness would have clinical utility, and a precise definition of the drowsy state could offer insights into the nature and purpose of sleep. Studies utilizing fMRI have demonstrated increased area of central nervous system involvement with tasks of increasing complexity. Preliminary data from studies of magnetoencephalography (MEG) with a receptive language task have demonstrated a progressive increase in global coherence of activity between MEG sensors with increasing drowsiness. The relationship between global coherence and the level of drowsiness suggests that the former may serve as an objective measurement of the latter. If true, the relationship suggests the hypothesis that drowsiness may be defined as a progressive loss of cortical network processing efficiency, requiring the recruitment of greater amounts of cortical tissue to perform the same task.
Visually-obvious abnormalities in the resting baseline EEG--slowing, spiking and high-frequency o... more Visually-obvious abnormalities in the resting baseline EEG--slowing, spiking and high-frequency oscillations (HFOs)--are cardinal, though incompletely understood, features of the seizure onset zone in focal epilepsy. We hypothesized that evidence of cortical network dysfunction in temporal lobe epilepsy (TLE) would persist in the absence of visually-classifiable abnormalities in the baseline EEG recorded within the conventional passband, and that metrics of such dysfunction could serve as a lateralizing diagnostic in TLE. Epochs of resting EEG without significant abnormalities in light sleep over several days were compared between a group of 10 patients with proven TLE and 10 subjects without epilepsy. A novel laterality metric computed from the line length of normalized power spectra from the temporal channels was compared between the two groups. Significant group differences in spectral line length laterality metric were found between the TLE and control group. At the individual l...
ABSTRACT Rationale: Unique high-frequency oscillations (HFOs), termed fast ripples (FR), have bee... more ABSTRACT Rationale: Unique high-frequency oscillations (HFOs), termed fast ripples (FR), have been identified in seizure generating limbic areas of kainic treated rats, and in patients with mesial temporal epilepsy using depth electrodes. Results from these studies have provided evidence supporting the view that fast ripples in the human brain appear to reflect field oscillations composed of hypersynchronous action potentials of pathologically interconnected neuronal clusters related to epileptogenesis. We have previously reported on the use of magnetoencephalography (MEG) to detect HFOs. One critique of that study was that the criteria used for HFO determination were significantly different from the published studies utilizing implanted electrodes. To address that, a study specifically replicating one such methodology was performed. Methods: MEG data was recorded on an epileptic patient during the patient’s routine pre-surgical evaluation. Data was recorded on 248 MEG channels at a sample rate of 2034.51 Hz, with a high pass filter 0.2 Hz. One minute of data was recorded with the patient awake, eyes open, and one minute with the patient awake, eyes closed. The data was then transferred to MATLAB for HFO analysis. The data from each sensor was considered separately. The input signal was first filtered using a 30 order Butterworth filter with a bandpass of 151–500 Hz (for fast ripple screening) and separately with a bandpass of 80–150 Hz (for ripple screening). The filtered signal was then rectified to zero voltage. The root mean square of a moving 3 ms window was calculated sequentially over the signal. A positive HFO detection was made if a cut-off of five standard deviations above the mean RMS was exceeded for great than 6 ms. This process was repeated for each of the 248 sensors, and the sensor locations where HFOs were detected were plotted, and the specific HFO frequencies were noted. Results: Using this method, of nine patients analyzed to date, outliers consistent with HFOs have been detected in two. HFO duration ranged from 6–10 ms. Every HFO was detected in sensors over the temporal regions, unilateral in one patient, bilateral in the second. Data collected in all patients studied over a six month period will be presented, along with each patient’s underlying pathology, if known. Conclusions: HFOs can be detected using magnetoencephalography in refractory epilepsy patients using signal processing methods similar to those published for analysis of data from implanted electrodes. Whether these detections represent the same phenomenon has yet to be determined.
ABSTRACT Rationale: Unique high-frequency oscillations (HFOs), termed fast ripples (FR), have bee... more ABSTRACT Rationale: Unique high-frequency oscillations (HFOs), termed fast ripples (FR), have been identified in seizure generating limbic areas of kainic treated rats, and in patients with mesial temporal epilepsy using depth electrodes. Results from these studies have provided evidence supporting the view that fast ripples in the human brain appear to reflect field oscillations composed of hypersynchronous action potentials of pathologically interconnected neuronal clusters related to epileptogenesis. We have previously reported on the use of magnetoencephalography (MEG) to detect HFOs. One critique of that study was that the criteria used for HFO determination were significantly different from the published studies utilizing implanted electrodes. To address that, a study specifically replicating one such methodology was performed. Methods: MEG data was recorded on an epileptic patient during the patient’s routine pre-surgical evaluation. Data was recorded on 248 MEG channels at a sample rate of 2034.51 Hz, with a high pass filter 0.2 Hz. One minute of data was recorded with the patient awake, eyes open, and one minute with the patient awake, eyes closed. The data was then transferred to MATLAB for HFO analysis. The data from each sensor was considered separately. The input signal was first filtered using a 30 order Butterworth filter with a bandpass of 151–500 Hz (for fast ripple screening) and separately with a bandpass of 80–150 Hz (for ripple screening). The filtered signal was then rectified to zero voltage. The root mean square of a moving 3 ms window was calculated sequentially over the signal. A positive HFO detection was made if a cut-off of five standard deviations above the mean RMS was exceeded for great than 6 ms. This process was repeated for each of the 248 sensors, and the sensor locations where HFOs were detected were plotted, and the specific HFO frequencies were noted. Results: Using this method, of nine patients analyzed to date, outliers consistent with HFOs have been detected in two. HFO duration ranged from 6–10 ms. Every HFO was detected in sensors over the temporal regions, unilateral in one patient, bilateral in the second. Data collected in all patients studied over a six month period will be presented, along with each patient’s underlying pathology, if known. Conclusions: HFOs can be detected using magnetoencephalography in refractory epilepsy patients using signal processing methods similar to those published for analysis of data from implanted electrodes. Whether these detections represent the same phenomenon has yet to be determined.
Uploads
Papers by Jeremy Slater