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Sydney S Cash - One of the best experts on this subject based on the ideXlab platform.
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seizure onset location shapes dynamics of initiation
Clinical Neurophysiology, 2020Co-Authors: Pariya Salami, Noam Peled, Jessica K Nadalin, Louisemmanuel Martinet, Mark A Kramer, Jong W Lee, Sydney S CashAbstract:Abstract Objective Ictal electrographic patterns are widely thought to reflect underlying neural mechanisms of seizures. Here we studied the degree to which seizure patterns are consistent in a given patient, relate to particular brain regions and if two candidate biomarkers (high-Frequency oscillations, HFOs; infraslow activity, ISA) and network activity, as assessed with cross-Frequency interactions, can discriminate between seizure types. Methods We analyzed temporal changes in low and high Frequency oscillations recorded during seizures, as well as phase-amplitude coupling (PAC) to monitor the interactions between delta/theta and Ripple/fast Ripple Frequency bands at seizure onset. Results Seizures of multiple electrographic patterns were observed in a given patient and brain region. While there was an increase in HFO rate across different electrographic patterns, there are specific relationships between types of HFO activity and onset region. Similarly, changes in PAC dynamics were more closely related to seizure onset region than they were to electrographic patterns while ISA was a poor indicator for seizure onset. Conclusions Our findings suggest that the onset region sculpts neurodynamics at seizure initiation and that unique features of the cytoarchitecture and/or connectivity of that region play a significant role in determining seizure mechanism. Significance To learn how seizures are initiated, researchers would do well to consider other aspects of their manifestation, in addition to their electrographic patterns. Examination of onset pattern in conjunction with the interactions between different oscillatory frequencies in the context of different brain regions might be more informative and lead to more reliable clinical inference as well as novel therapeutic approaches.
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seizure onset location shapes dynamics of initiation
bioRxiv, 2020Co-Authors: Pariya Salami, Noam Peled, Jessica K Nadalin, Louisemmanuel Martinet, Mark A Kramer, Jong W Lee, Sydney S CashAbstract:Abstract Objective Ictal electrographic patterns are widely thought to reflect underlying neural mechanisms of seizures. Here we studied the degree to which seizure patterns are consistent in a given patient, relate to particular brain regions and if two candidate biomarkers (high-Frequency oscillations, HFOs; infraslow activity, ISA) and network activity, as assessed with cross-Frequency interactions, can discriminate between seizure types. Methods We analyzed temporal changes in low and high Frequency oscillations recorded during seizures, as well as phase-amplitude coupling (PAC) to monitor the interactions between delta/theta and Ripple/fast Ripple Frequency bands at seizure onset. Results Seizures of multiple pattern types were observed in a given patient and brain region. While there was an increase in HFO rate across different electrographic patterns, there are specific relationships between types of HFO activity and onset region. Similarly, changes in PAC dynamics were more closely related to seizure onset region than they were to electrographic patterns while ISA was a poor indicator for seizure onset. Conclusions Our findings suggest that the onset region sculpts neurodynamics at seizure initiation and that unique features of the cytoarchitecture and/or connectivity of that region play a significant role in determining seizure mechanism. Significance Clinicians should consider more than just overt electrographic patterns when considering seizure mechanisms and regions of onset. Examination of onset pattern in conjunction with the interactions between different oscillatory frequencies in the context of different brain regions might be more informative and lead to more reliable clinical inference as well as novel therapeutic approaches.
Pariya Salami - One of the best experts on this subject based on the ideXlab platform.
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seizure onset location shapes dynamics of initiation
Clinical Neurophysiology, 2020Co-Authors: Pariya Salami, Noam Peled, Jessica K Nadalin, Louisemmanuel Martinet, Mark A Kramer, Jong W Lee, Sydney S CashAbstract:Abstract Objective Ictal electrographic patterns are widely thought to reflect underlying neural mechanisms of seizures. Here we studied the degree to which seizure patterns are consistent in a given patient, relate to particular brain regions and if two candidate biomarkers (high-Frequency oscillations, HFOs; infraslow activity, ISA) and network activity, as assessed with cross-Frequency interactions, can discriminate between seizure types. Methods We analyzed temporal changes in low and high Frequency oscillations recorded during seizures, as well as phase-amplitude coupling (PAC) to monitor the interactions between delta/theta and Ripple/fast Ripple Frequency bands at seizure onset. Results Seizures of multiple electrographic patterns were observed in a given patient and brain region. While there was an increase in HFO rate across different electrographic patterns, there are specific relationships between types of HFO activity and onset region. Similarly, changes in PAC dynamics were more closely related to seizure onset region than they were to electrographic patterns while ISA was a poor indicator for seizure onset. Conclusions Our findings suggest that the onset region sculpts neurodynamics at seizure initiation and that unique features of the cytoarchitecture and/or connectivity of that region play a significant role in determining seizure mechanism. Significance To learn how seizures are initiated, researchers would do well to consider other aspects of their manifestation, in addition to their electrographic patterns. Examination of onset pattern in conjunction with the interactions between different oscillatory frequencies in the context of different brain regions might be more informative and lead to more reliable clinical inference as well as novel therapeutic approaches.
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seizure onset location shapes dynamics of initiation
bioRxiv, 2020Co-Authors: Pariya Salami, Noam Peled, Jessica K Nadalin, Louisemmanuel Martinet, Mark A Kramer, Jong W Lee, Sydney S CashAbstract:Abstract Objective Ictal electrographic patterns are widely thought to reflect underlying neural mechanisms of seizures. Here we studied the degree to which seizure patterns are consistent in a given patient, relate to particular brain regions and if two candidate biomarkers (high-Frequency oscillations, HFOs; infraslow activity, ISA) and network activity, as assessed with cross-Frequency interactions, can discriminate between seizure types. Methods We analyzed temporal changes in low and high Frequency oscillations recorded during seizures, as well as phase-amplitude coupling (PAC) to monitor the interactions between delta/theta and Ripple/fast Ripple Frequency bands at seizure onset. Results Seizures of multiple pattern types were observed in a given patient and brain region. While there was an increase in HFO rate across different electrographic patterns, there are specific relationships between types of HFO activity and onset region. Similarly, changes in PAC dynamics were more closely related to seizure onset region than they were to electrographic patterns while ISA was a poor indicator for seizure onset. Conclusions Our findings suggest that the onset region sculpts neurodynamics at seizure initiation and that unique features of the cytoarchitecture and/or connectivity of that region play a significant role in determining seizure mechanism. Significance Clinicians should consider more than just overt electrographic patterns when considering seizure mechanisms and regions of onset. Examination of onset pattern in conjunction with the interactions between different oscillatory frequencies in the context of different brain regions might be more informative and lead to more reliable clinical inference as well as novel therapeutic approaches.
Jerome Engel - One of the best experts on this subject based on the ideXlab platform.
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analysis of chronic seizure onsets after intrahippocampal kainic acid injection in freely moving rats
Epilepsia, 2005Co-Authors: Anatol Bragin, Charles L Wilson, Avetis Azizyan, Joyel Almajano, Jerome EngelAbstract:Summary: Purpose: The goal of this study was to analyze the transition period between interictal and ictal activity in freely moving rats with recurrent spontaneous seizures after unilateral intrahippocampal kainic acid (KA) injection. Methods: Pairs of tungsten electrodes (50 μm O/D) were implanted bilaterally under anesthesia at symmetrical points in the dentate gyrus (DG) and CA1 regions of anterior and posterior hippocampi and entorhinal cortex of adult Wistar rats. Stimulating electrodes were placed in the right angular bundle and KA was injected into the right posterior CA3 area of hippocampus after 1 week of baseline EEG recording. Beginning 24 h after injection, electrographic activity was recorded with video monitoring for seizures every day for 8 h/day for 60 days. Results: Seventy percent of seizures started locally in the DG ipsilateral to injection, with an increase in Frequency of interictal EEG spikes (hypersynchronous type, HYP), and 26% of seizures started with a decrease of EEG amplitude with parallel increase in Frequency (low-voltage fast type, LVF). During HYP seizures, a significant increase was observed in amplitude of beta-gamma range frequencies, Ripple Frequency, and fast Ripple (FR) Frequency, whereas during LVF seizure, an increase was noted only in the beta-gamma range. In all cases but one, an EEG wave preceded Ripple and FR oscillations. Before seizure onset, the amplitude of DG-evoked responses to single pulses decreased, whereas the amplitude of the response to the second pulse delivered at 30-ms interval increased. Conclusions: If Ripple and FR oscillations indicate the seizure-generating neuronal substrate, these areas must be small and widespread, so that the probability of recording from them directly is very low. The decreased response to electrical stimulation before seizures could indicate a protective inhibitory mechanism that contains or prevents seizure occurrence. The presence of decreased paired-pulse suppression could indicate a network predisposition to follow an external input with a certain Frequency.
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high Frequency oscillations after status epilepticus epileptogenesis and seizure genesis
Epilepsia, 2004Co-Authors: Anatol Bragin, Charles L Wilson, Joyel Almajano, Istvan Mody, Jerome EngelAbstract:Summary: Purpose: To investigate the temporal relation between high-Frequency oscillations (HFOs) in the dentate gyrus and recurrent spontaneous seizures after intrahippocampal kainite-induced status epilepticus. Methods: Recording microelectrodes were implanted bilaterally in different regions of hippocampus and entorhinal cortex. A guide cannula for microinjection of kainic acid (KA) was implanted above the right posterior CA3 area of hippocampus. After recording baseline electrical activity, KA (0.4 µg/0.2 µl) was injected. Beginning on the next day, electrographic activity was recorded with video monitoring for seizures every day for 8 h/day for ≥30 days. Results: Of the 26 rats studied, 19 revealed the appearance of sharp-wave activity and HFOs in the Frequency range of 80 to 500 Hz in the dentate gyrus ipsilateral to the KA injection. In the remaining seven rats, no appreciable activity was noted in this Frequency range. In some rats with recurrent seizures, HFOs were in the Ripple Frequency range (100‐200 Hz); in others, HFOs were in the fast Ripple Frequency range (200‐500 Hz), or a mixture of both oscillation frequencies was found. The time of detection of the first HFOs after status epilepticus varied between 1 and 30 days, with a mean of 6.3 ± 2.0 (SEM). Of the 19 rats in which HFO activity appeared, all later developed recurrent spontaneous seizures, whereas none of the rats without HFOs developed seizures. The sooner HFO activity was detected after status epilepticus, the sooner the first spontaneous seizure occurred. A significant inverse relation was found between the time to the first HFO detection and the subsequent rate of spontaneous seizures. Conclusions: A strong correlation was found between a decreased time to detection of HFOs and an increased rate of spontaneous seizures, as well as with a decrease in the duration of the latent period between KA injection and the detection of spontaneous seizures. Two types of HFOs were found after KA injection, one in the Frequency range of 100 to 200 Hz, and the other, in the Frequency range of 200 to 500 Hz, and both should be considered pathological, suggesting that both are epileptogenic. Key Words: Epileptogenesis— High-Frequency oscillations—Dentate gyrus—Kainic acid— Rat.
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interictal high Frequency oscillations 80 500hz in the human epileptic brain entorhinal cortex
Annals of Neurology, 2002Co-Authors: Anatol Bragin, Charles L Wilson, Richard J Staba, Mark A Reddick, Itzhak Fried, Jerome EngelAbstract:: Unique high-Frequency oscillations of 250 to 500 Hz, termed fast Ripples, have been identified in seizure-generating limbic areas in rats made epileptic by intrahippocampal injection of kainic acid, and in patients with mesial temporal lobe epilepsy. In the rat, fast Ripples clearly are generated by a different neuronal population than normally occurring endogenous Ripple oscillations (100-200 Hz), but this distinction has not been previously evaluated in humans. The characteristics of oscillations in the Ripple and fast Ripple Frequency bands were compared in the entorhinal cortex of patients with mesial temporal lobe epilepsy using local field potential and unit recordings from chronically implanted bundles of eight microelectrodes with tips spaced 500 microm apart. The results showed that Ripple oscillations possessed different voltage versus depth profiles compared with fast Ripple oscillations. Fast Ripple oscillations usually demonstrated a reversal of polarity in the middle layers of entorhinal cortex, whereas Ripple oscillations rarely showed reversals across entorhinal cortex layers. There was no significant difference in the amplitude distributions of Ripple and fast Ripple oscillations. Furthermore, multiunit synchronization was significantly increased during fast Ripple oscillations compared with Ripple oscillations (p < 0.001). These data recorded from the mesial temporal lobe of epileptic patients suggest that the cellular networks underlying fast Ripple generation are more localized than those involved in the generation of normally occurring Ripple oscillations. Results from this study are consistent with previous studies in the intrahippocampal kainic acid rat model of chronic epilepsy that provide evidence supporting the view that fast Ripples in the human brain reflect localized pathological events related to epileptogenesis.
Stephen L. Cowen - One of the best experts on this subject based on the ideXlab platform.
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Age Is Associated with Reduced Sharp-Wave Ripple Frequency and Altered Patterns of Neuronal Variability.
Journal of Neuroscience, 2016Co-Authors: Jean Paul L. Wiegand, Daniel T. Gray, Lesley A. Schimanski, Peter Lipa, Carol A. Barnes, Stephen L. CowenAbstract:Spatial and episodic memory performance declines with age, and the neural basis for this decline is not well understood. Sharp-wave Ripples are brief (∼70 ms) high-Frequency oscillatory events generated in the hippocampus and are associated with the consolidation of spatial memories. Given the connection between Ripple oscillations and memory consolidation, we investigated whether the structure of Ripple oscillations and Ripple-triggered patterns of single-unit activity are altered in aged rats. Local field and single-unit activity surrounding sharp-wave Ripple events were examined in the CA1 region of the hippocampus of old ( n = 5) and young ( n = 6) F344 rats during periods of rest preceding and following performance on a place-dependent eyeblink-conditioning task. Neural responses in aged rats differed from responses in young rats in several ways. First, compared with young rats, the rate of Ripple occurrence (Ripple density) is reduced in aged rats during postbehavior rest. Second, mean Ripple Frequency during prebehavior and postbehavior rest is lower in aged animals (aged: 132 Hz; young: 146 Hz). Third, single neurons in aged animals responded more consistently from Ripple to Ripple. Fourth, variability in interspike intervals was greater in aged rats. Finally, neurons were tuned to a narrower range of phases of the Ripple oscillation relative to young animals. Together, these results suggest that the CA1 network in aged animals has a reduced “vocabulary” of available representational states. SIGNIFICANCE STATEMENT The hippocampus is a structure that is critical for the formation of episodic memories. Sharp-wave Ripple events generated in the hippocampus have been implicated in memory consolidation processes critical to memory stabilization. We examine here whether these Ripple oscillations are altered over the course of the life span, which could contribute to hippocampus-dependent memory deficits that occur during aging. This experiment used young and aged memory-impaired rats to examine age-related changes in Ripple architecture, Ripple-triggered spike variance, and spike-phase coherence. We found that there are, indeed, significant changes in characteristics of Ripples in older animals that could impact consolidation processes and memory stabilization in the aged brain.
Jong W Lee - One of the best experts on this subject based on the ideXlab platform.
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seizure onset location shapes dynamics of initiation
Clinical Neurophysiology, 2020Co-Authors: Pariya Salami, Noam Peled, Jessica K Nadalin, Louisemmanuel Martinet, Mark A Kramer, Jong W Lee, Sydney S CashAbstract:Abstract Objective Ictal electrographic patterns are widely thought to reflect underlying neural mechanisms of seizures. Here we studied the degree to which seizure patterns are consistent in a given patient, relate to particular brain regions and if two candidate biomarkers (high-Frequency oscillations, HFOs; infraslow activity, ISA) and network activity, as assessed with cross-Frequency interactions, can discriminate between seizure types. Methods We analyzed temporal changes in low and high Frequency oscillations recorded during seizures, as well as phase-amplitude coupling (PAC) to monitor the interactions between delta/theta and Ripple/fast Ripple Frequency bands at seizure onset. Results Seizures of multiple electrographic patterns were observed in a given patient and brain region. While there was an increase in HFO rate across different electrographic patterns, there are specific relationships between types of HFO activity and onset region. Similarly, changes in PAC dynamics were more closely related to seizure onset region than they were to electrographic patterns while ISA was a poor indicator for seizure onset. Conclusions Our findings suggest that the onset region sculpts neurodynamics at seizure initiation and that unique features of the cytoarchitecture and/or connectivity of that region play a significant role in determining seizure mechanism. Significance To learn how seizures are initiated, researchers would do well to consider other aspects of their manifestation, in addition to their electrographic patterns. Examination of onset pattern in conjunction with the interactions between different oscillatory frequencies in the context of different brain regions might be more informative and lead to more reliable clinical inference as well as novel therapeutic approaches.
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seizure onset location shapes dynamics of initiation
bioRxiv, 2020Co-Authors: Pariya Salami, Noam Peled, Jessica K Nadalin, Louisemmanuel Martinet, Mark A Kramer, Jong W Lee, Sydney S CashAbstract:Abstract Objective Ictal electrographic patterns are widely thought to reflect underlying neural mechanisms of seizures. Here we studied the degree to which seizure patterns are consistent in a given patient, relate to particular brain regions and if two candidate biomarkers (high-Frequency oscillations, HFOs; infraslow activity, ISA) and network activity, as assessed with cross-Frequency interactions, can discriminate between seizure types. Methods We analyzed temporal changes in low and high Frequency oscillations recorded during seizures, as well as phase-amplitude coupling (PAC) to monitor the interactions between delta/theta and Ripple/fast Ripple Frequency bands at seizure onset. Results Seizures of multiple pattern types were observed in a given patient and brain region. While there was an increase in HFO rate across different electrographic patterns, there are specific relationships between types of HFO activity and onset region. Similarly, changes in PAC dynamics were more closely related to seizure onset region than they were to electrographic patterns while ISA was a poor indicator for seizure onset. Conclusions Our findings suggest that the onset region sculpts neurodynamics at seizure initiation and that unique features of the cytoarchitecture and/or connectivity of that region play a significant role in determining seizure mechanism. Significance Clinicians should consider more than just overt electrographic patterns when considering seizure mechanisms and regions of onset. Examination of onset pattern in conjunction with the interactions between different oscillatory frequencies in the context of different brain regions might be more informative and lead to more reliable clinical inference as well as novel therapeutic approaches.
