Increased Fast ripple to ripple Ratios Correlate with Reduced Hippocampal Volumes and Neuron Loss in Temporal Lobe Epilepsy Patients

Staba, Richard J.; Frighetto, Leonardo; Behnke, Eric; Mathern, Gary W.; Fields, Tony; Bragin, Anatol; Ogren, Jennifer A.; Fried, Itzhak; Wilson, Charles L.; Engel, Jerome

doi:10.1111/j.1528-1167.2007.01225.x

Cited by 125 publications

(127 citation statements)

References 63 publications

(76 reference statements)

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“…2D). Fast ripples appear to correlate with reduced hippocampal volumes and neuronal loss both in human TLE (Ogren et al, 2009;Staba et al, 2007) and in experimental models (Bragin et al, 2002a;, but can occur in the absence of neuronal death (Jiruska et al, 2010b). Here too, different recruitment delays of several pools of neurons determine a sequential out-of-phase activation resulting in the emergent extracellular fast ripple oscillations.…”

Section: Fast Ripples and The Epileptic Hippocampus: The Out-of-phasementioning

confidence: 99%

Mechanisms of physiological and epileptic HFO generation

Jefferys

Prida

Wendling

et al. 2012

Progress in Neurobiology

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High frequency oscillations (HFO) have a variety of characteristics: band-limited or broad-band, transient burst-like phenomenon or steady-state. HFOs may be encountered under physiological or under pathological conditions (pHFO). Here we review the underlying mechanisms of oscillations, at the level of cells and networks, investigated in a variety of experimental in vitro and in vivo models. Diverse mechanisms are described, from intrinsic membrane oscillations to network processes involving different types of synaptic interactions, gap junctions and ephaptic coupling. HFOs with similar frequency ranges can differ considerably in their physiological mechanisms. The fact that in most cases the combination of intrinsic neuronal membrane oscillations and synaptic circuits are necessary to sustain network oscillations is emphasized. Evidence for pathological HFOs, particularly fast ripples, in experimental models of epilepsy and in human epileptic patients is scrutinized. The underlying mechanisms of fast ripples are examined both in the light of animal observations, in vivo and in vitro, and in epileptic patients, with emphasis on single cell dynamics. Experimental observations and computational modeling have led to hypotheses for these mechanisms, several of which are considered here, namely the role of out-ofphase firing in neuronal clusters, the importance of strong excitatory AMPA-synaptic currents and recurrent inhibitory connectivity in combination with the fast time scales of IPSPs, ephaptic coupling and the contribution of interneuronal coupling through gap junctions. The statistical behaviour of fast ripple events can provide useful information on the underlying mechanism and can help to further improve classification of the diverse forms of HFOs.

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Section: Fast Ripples and The Epileptic Hippocampus: The Out-of-phasementioning

confidence: 99%

Mechanisms of physiological and epileptic HFO generation

Jefferys

Prida

Wendling

et al. 2012

Progress in Neurobiology

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261

237

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“…HFOs were successively described in stereo-EEG (SEEG) of epileptic patients acquired using clinical macroelectrodes (surface contact: 0.8 mm 2 ), occurring during the ictal phase particularly in the seizure onset zone (SOZ) . Further studies showed that HFOs can be recorded in the interictal phase, still more commonly in the SOZ (Urrestarazu et al, 2007;Jacobs et al, 2008;Worrell et al, 2008); they can be linked to epileptogenic lesions (Staba et al, 2007), being non-specific to a particular type of lesion, representing epileptogenicity per se ; they appear to predict the outcome after epilepsy surgery, in adults (Jacobs et al, 2010) as in children (Wu et al, 2010). HFOs are not confined to mesial temporal structures, occurring also in neocortical regions, usually at lower rates (Urrestarazu et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Continuous High Frequency Activity: A peculiar SEEG pattern related to specific brain regions

Melani

Zelmann

Mari

et al. 2013

Clinical Neurophysiology

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Objective-While visually marking the high frequency oscillations in the stereo-EEG of epileptic patients, we observed a continuous/semicontinuous activity in the ripple band (80-250 Hz), which we defined continuous High Frequency Activity (HFA). We aim to analyze in all brain regions the occurrence and significance of this particular pattern.Methods-Twenty patients implanted in mesial temporal and neocortical areas were studied. One minute of slow-wave sleep was reviewed. The background was classified as continuous/ semicontinuous, irregular, or sporadic based on the duration of the fast oscillations. Each channel was classified as inside/outside the seizure onset zone (SOZ) or a lesion.Results-The continuous/semicontinuous HFA occurred in 54 of the 790 channels analyzed, with a clearly higher prevalence in hippocampus and occipital lobe. No correlation was found with the SOZ or lesions. In the occipital lobe the continuous/semicontinuous HFA was present independently of whether eyes were open or closed. Conclusions-We describe what appears to be a new physiological High Frequency Activity, independent of epileptogenicity, present almost exclusively in the hippocampus and occipital cortex but independent of the alpha rhythm.Significance-The continuous HFA may be an intrinsic characteristic of specific brain regions, reflecting a particular type of physiological neuronal activity.

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“…Both ripple and fast ripple rates are higher in seizure onset zones, confirmed by surgical outcome . Other studies have suggested that HFOs should be divided into two categories based on frequency (Bragin et al 1999;Staba et al 2002), where ripples are considered to be physiological events and fast ripples are associated with epileptogenicity (Staba et al 2007). However, to differentiate between pathological and physiological HFOs, frequency alone is insufficient (Matsumoto et al 2013).…”

Section: Discussionmentioning

confidence: 99%

“…Finally, to be qualified as HFOs, the events were required to have at least six rectified peaks above a second threshold, which was three standard deviations above the mean of the rectified filtered signal. Originally, the detector was designed to identify HFOs in hippocampus and entorhinal cortex microwire recordings in humans (Staba et al 2002), and it was used in the studies of microelectrode recordings in temporal regions (Staba et al 2004;Staba et al 2007). …”

Section: Rms Detectormentioning

confidence: 99%

A Simple Statistical Method for the Automatic Detection of Ripples in Human Intracranial EEG

Charupanit

Lopour

2017

Brain Topogr

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Keywords:HFO Epilepsy Ripple Electrocorticogram Depth electrode Highlights  New automatic HFO detection algorithm requires optimization of only one tunable parameter that is related to the number of allowable false positive events.  The detector uses an iterative estimate of the background amplitude distribution to choose the threshold.  Cross-validation tests indicate that sensitivity is superior to a commonly used automated detector based on RMS amplitude. AbstractHigh frequency oscillations (HFOs) are a promising biomarker of epileptic tissue, but detection of these electrographic events remains a challenge. Automatic detectors show encouraging results, but they typically require optimization of multiple parameters, which is a barrier to good performance and broad applicability. We therefore propose a new automatic HFO detection algorithm, focusing on simplicity and ease of implementation. It requires tuning of only an amplitude threshold, which can be determined by an iterative process or directly calculated from statistics of the rectified filtered data (i.e. mean plus standard deviation). The iterative approach uses an estimate of the amplitude probability distribution of the background activity to calculate the optimum threshold for identification of transient high amplitude events. We tested both the iterative and non-iterative approaches using a dataset of visually marked HFOs, and we compared the performance to a commonly used detector based on the root-mean-square. When the threshold was optimized for individual channels via ROC curve, all three methods were comparable. The iterative detector achieved a sensitivity of 99.6%, false positive rate (FPR) of 1.1%, and false detection rate (FDR) of 37.3%. However, in an eight-fold cross-validation test, the iterative method had better sensitivity than the other two methods (80.0% compared to 64.4% and 65.8%), with FPR and FDR of 1.3%, and 49.4%, respectively. The simplicity of this algorithm, with only a single parameter, will enable consistent application of automatic detection across research centers and recording modalities, and it may therefore be a powerful tool for the assessment and localization of epileptic activity.2

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Increased Fast ripple to ripple Ratios Correlate with Reduced Hippocampal Volumes and Neuron Loss in Temporal Lobe Epilepsy Patients

Abstract: SUMMARY

Cited by 125 publications

References 63 publications

Mechanisms of physiological and epileptic HFO generation

Mechanisms of physiological and epileptic HFO generation

Continuous High Frequency Activity: A peculiar SEEG pattern related to specific brain regions

A Simple Statistical Method for the Automatic Detection of Ripples in Human Intracranial EEG

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