Direct Cortical Stimulation to Probe the Ictogenicity of the Epileptogenic Nodes in Temporal Lobe Epilepsy

Accurate mapping of the seizure onset zone (SOZ) is critical to the success of epilepsy surgery outcomes. Epileptogenicity index (EI) is a statistical method that delineates hyperexcitable brain regions involved in the generation and early propagation of seizures. However, EI can overestimate the SOZ for particular electrographic seizure onset patterns. Therefore, using direct cortical stimulation (DCS) as a probing tool to identify seizure generators, we systematically evaluated the causality of the high EI nodes (>0.3) in replicating the patient's habitual seizures. Specifically, we assessed the diagnostic yield of high EI nodes, i.e., the proportion of high EI nodes that evoked habitual seizures. A retrospective single-center study that included post-stereo encephalography (SEEG) confirmed TLE patients (n = 37) that had all high EI nodes stimulated, intending to induce a seizure. We evaluated the nodal responses (true and false responder rate) to stimulation and correlated with electrographic seizure onset patterns (hypersynchronous-HYP and low amplitude fast activity patterns-LAFA) and clinically defined SOZ. The ictogenicity (i.e., the propensity to induce the patient's habitual seizure) of a high EI node was only 44.5%. The LAFA onset pattern had a significantly higher response rate to DCS (i.e., higher evoked seizures). The concordance of an evoked habitual seizure with a clinically defined SOZ with good outcomes was over 50% (p = 0.0025). These results support targeted mapping of SOZ in LAFA onset patterns by performing DCS in high EI nodes to distinguish seizure generators (true responders) from hyperexcitable nodes that may be involved in early propagation.

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The Connectivity Index: An Effective Metric for Grading Epileptogenicity

10.1101/635664 ◽

2019 ◽

Author(s):

Qi Yan ◽

Nicolas Gaspard ◽

Hitten P Zaveri ◽

Hal Blumenfeld ◽

Lawrence J. Hirsch ◽

...

Keyword(s):

Average Distance ◽

Single Pulse ◽

Brain Regions ◽

Control Site ◽

Connectivity Index ◽

Evoked Responses ◽

Seizure Onset ◽

Seizure Onset Zone ◽

Site Of Stimulation ◽

Stimulation Of

AbstractObjectiveTo investigate the performance of a metric of functional connectivity to classify and grade the excitability of brain regions based on evoked potentials to single pulse electrical stimulation (SPES).MethodsPatients who received 1-Hz frequency stimulation between 2003 and 2014 at Yale at prospectively selected contacts were included. The stimulated contacts were classified as seizure onset zone (SOZ), highly irritative zone (IZp) or control. Response contacts were classified as seizure onset zone (SOZ), active interictal (IZp), quiet or other. The normalized number of responses was defined as the number of contacts with any evoked responses divided by the total number of recorded contacts, and the normalized distance is the ratio of the average distance between the site of stimulation and sites of evoked responses to the average distances between the site of stimulation and all other recording contacts. A new metric we labeled the connectivity index (CI) is defined as the product of the two values.Results57 stimulation-sessions in 22-patients were analyzed. The connectivity index (CI) of the SOZ was higher than control (median CI of 0.74 vs. 0.16, p = 0.0002). The evoked responses after stimulation of SOZ were seen at further distance compared to control (median normalized distance 0.96 vs. 0.62, p = 0.0005). It was 1.8 times more likely to record a response at SOZ than in non-epileptic contacts after stimulation of a control site. Habitual seizures were triggered in 27% of patients and 35 % of SOZ contacts (median stimulation intensity 4 mA) but in none of the control or IZp contacts. Non-SOZ contacts in multifocal or poor surgical outcome cases had a higher CI than non-SOZ contacts in those with localizable onsets (medians CI of 0.5 vs. 0.12, p = 0.04). There was a correlation between the stimulation current intensity and the normalized number of evoked responses (r = + 0.49, p 0.01) but not with distance (r = + 0.1, p 0.64)ConclusionsWe found enhanced connectivity when stimulating the SOZ compared to stimulating control contacts; responses were more distant as well. Habitual auras and seizures provoked by SPES were highly predictive of brain sites involved in seizure generation.

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Spatial variation in high-frequency oscillation rates and amplitudes in intracranial EEG

Neurology ◽

10.1212/wnl.0000000000004998 ◽

2018 ◽

Vol 90 (8) ◽

pp. e639-e646 ◽

Cited By ~ 23

Author(s):

Hari Guragain ◽

Jan Cimbalnik ◽

Matt Stead ◽

David M. Groppe ◽

Brent M. Berry ◽

...

Keyword(s):

High Frequency ◽

Anatomic Variation ◽

Brain Regions ◽

Brain Atlas ◽

Reproducible Research ◽

High Frequency Oscillation ◽

Seizure Onset ◽

Frequency Oscillation ◽

Intracranial Eeg ◽

Seizure Onset Zone

ObjectiveTo assess the variation in baseline and seizure onset zone interictal high-frequency oscillation (HFO) rates and amplitudes across different anatomic brain regions in a large cohort of patients.MethodsSeventy patients who had wide-bandwidth (5 kHz) intracranial EEG (iEEG) recordings during surgical evaluation for drug-resistant epilepsy between 2005 and 2014 who had high-resolution MRI and CT imaging were identified. Discrete HFOs were identified in 2-hour segments of high-quality interictal iEEG data with an automated detector. Electrode locations were determined by coregistering the patient's preoperative MRI with an X-ray CT scan acquired immediately after electrode implantation and correcting electrode locations for postimplant brain shift. The anatomic locations of electrodes were determined using the Desikan-Killiany brain atlas via FreeSurfer. HFO rates and mean amplitudes were measured in seizure onset zone (SOZ) and non-SOZ electrodes, as determined by the clinical iEEG seizure recordings. To promote reproducible research, imaging and iEEG data are made freely available (msel.mayo.edu).ResultsBaseline (non-SOZ) HFO rates and amplitudes vary significantly in different brain structures, and between homologous structures in left and right hemispheres. While HFO rates and amplitudes were significantly higher in SOZ than non-SOZ electrodes when analyzed regardless of contact location, SOZ and non-SOZ HFO rates and amplitudes were not separable in some lobes and structures (e.g., frontal and temporal neocortex).ConclusionsThe anatomic variation in SOZ and non-SOZ HFO rates and amplitudes suggests the need to assess interictal HFO activity relative to anatomically accurate normative standards when using HFOs for presurgical planning.

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Weighted and directed interactions in evolving large-scale epileptic brain networks

Scientific Reports ◽

10.1038/srep34824 ◽

2016 ◽

Vol 6 (1) ◽

Cited By ~ 23

Author(s):

Henning Dickten ◽

Stephan Porz ◽

Christian E. Elger ◽

Klaus Lehnertz

Keyword(s):

Large Scale ◽

Complex Dynamics ◽

Brain Networks ◽

Population Sample ◽

Brain Regions ◽

Seizure Onset ◽

Dynamical Disease ◽

Seizure Onset Zone ◽

Promising Line ◽

And Function

Abstract Epilepsy can be regarded as a network phenomenon with functionally and/or structurally aberrant connections in the brain. Over the past years, concepts and methods from network theory substantially contributed to improve the characterization of structure and function of these epileptic networks and thus to advance understanding of the dynamical disease epilepsy. We extend this promising line of research and assess—with high spatial and temporal resolution and using complementary analysis approaches that capture different characteristics of the complex dynamics—both strength and direction of interactions in evolving large-scale epileptic brain networks of 35 patients that suffered from drug-resistant focal seizures with different anatomical onset locations. Despite this heterogeneity, we find that even during the seizure-free interval the seizure onset zone is a brain region that, when averaged over time, exerts strongest directed influences over other brain regions being part of a large-scale network. This crucial role, however, manifested by averaging on the population-sample level only – in more than one third of patients, strongest directed interactions can be observed between brain regions far off the seizure onset zone. This may guide new developments for individualized diagnosis, treatment and control.

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The seizure onset zone drives state-dependent epileptiform activity in susceptible brain regions

Clinical Neurophysiology ◽

10.1016/j.clinph.2019.05.032 ◽

2019 ◽

Vol 130 (9) ◽

pp. 1628-1641 ◽

Cited By ~ 2

Author(s):

Joshua M. Diamond ◽

Julio I. Chapeton ◽

William H. Theodore ◽

Sara K. Inati ◽

Kareem A. Zaghloul

Keyword(s):

Epileptiform Activity ◽

Brain Regions ◽

Seizure Onset ◽

State Dependent ◽

Seizure Onset Zone

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Stable high frequency background EEG activity distinguishes epileptic from healthy brain regions

Brain Communications ◽

10.1093/braincomms/fcaa107 ◽

2020 ◽

Vol 2 (2) ◽

Author(s):

Annika Minthe ◽

Wibke G Janzarik ◽

Daniel Lachner-Piza ◽

Peter Reinacher ◽

Andreas Schulze-Bonhage ◽

...

Keyword(s):

High Frequency ◽

Background Activity ◽

Sleep Stage ◽

Strong Association ◽

Brain Regions ◽

High Frequency Oscillation ◽

Seizure Onset ◽

High Frequency Oscillations ◽

Seizure Onset Zone ◽

Low Entropy

Abstract High-frequency oscillations are markers of epileptic tissue. Recently, different patterns of EEG background activity were described from which high-frequency oscillations occur: high-frequency oscillations with continuously oscillating background were found to be primarily physiological, those from quiet background were linked to epileptic tissue. It is unclear, whether these interactions remain stable over several days and during different sleep-wake stages. High-frequency oscillation patterns (oscillatory vs. quiet background) were analysed in 23 patients implanted with depth and subdural grid electrodes. Pattern scoring was performed on every channel in 10 s intervals in three separate day- and night-time EEG segments. An entropy value, measuring variability of patterns per channel, was calculated. A low entropy value indicated a stable occurrence of the same pattern in one channel, whereas a high value indicated pattern instability. Differences in pattern distribution and entropy were analysed for 143 280 10 s intervals with allocated patterns from inside and outside the seizure onset zone, different electrode types and brain regions. We found a strong association between high-frequency oscillations out of quiet background activity, and channels of the seizure onset zone (35.2% inside versus 9.7% outside the seizure onset zone, P < 0.001), no association was found for high-frequency oscillations from continuous oscillatory background (P = 0.563). The type of background activity remained stable over the same brain region over several days and was independent of sleep stage and recording technique. Stability of background activity was significantly higher in channels of the seizure onset zone (entropy mean value 0.56 ± 0.39 versus 0.64 ± 0.41; P < 0.001). This was especially true for the presumed epileptic high-frequency oscillations out of quiet background (0.57 ± 0.39 inside versus 0.72 ± 0.37 outside the seizure onset zone; P < 0.001). In contrast, presumed physiological high-frequency oscillations from continuous oscillatory backgrounds were significantly more stable outside the seizure onset zone (0.72 ± 0.45 versus 0.48 ± 0.53; P < 0.001). The overall low entropy values suggest that interactions between high-frequency oscillations and background activity are a stable phenomenon specific to the function of brain regions. High-frequency oscillations occurring from a quiet background are strongly linked to the seizure onset zone whereas high-frequency oscillations from an oscillatory background are not. Pattern stability suggests distinct underlying mechanisms. Analysing short time segments of high-frequency oscillations and background activity could help distinguishing epileptic from physiologically active brain regions.

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A Novel Method of Seizure Onset Zone Localization by Serial Tc-99m ECD Brain Perfusion SPECT Clearance Patterns

10.21203/rs.3.rs-672209/v1 ◽

2021 ◽

Author(s):

Chanan Sukprakun ◽

Chusak Limotai ◽

Kitiwat Khamwan ◽

Panya Pasawang ◽

Supatporn Tepmongkol

Keyword(s):

Brain Perfusion ◽

Brain Regions ◽

Diagnostic Tools ◽

Seizure Onset ◽

Visual Grading ◽

Seizure Onset Zone ◽

Significant Difference ◽

Perfusion Spect ◽

Novel Method ◽

Drug Resistant Epilepsy

Abstract In this prospective study, we postulate that there is a difference between clearance of [99mTc]Tc- ethyl cysteinate dimer (ECD) in the seizure onset zone (SOZ) and other brain areas and thus SOZ localization by clearance patterns might become a potential novel method for SOZ localization in epilepsy. The parametric images of brain ECD clearance were generated by linear regression model analysis from serial brain SPECT scans from 30 minutes to 240 minutes after ECD injection (7-times point) in 7 patients with drug-resistant epilepsy and 3 normal volunteers. Clearance patterns of the SOZ confirmed by good surgical outcome or consensus with other investigations were analyzed quantitatively and semi-quantitatively by visual grading (slower or faster washout than contralateral brain regions). The average [99mTc]Tc-ECD clearance rates of SOZs were + 1.08 % ± 2.57 %/hr (wash in), -7.02 % ± 2.56 %/hr (washout), and − 5.37% ± 1.71 %/hr (washout) in ictal, aura and interictal states, respectively. Paired t-tests between the SOZ and contralateral regions showed statistically significant difference (p = 0.039 in interictal state). Clearance patterns that can define the SOZs were 1) wash in and slow washout on ictal slope, 2) fast washout on aura slope and interictal slope with 100% (6/6), 100% (2/2) and 75% (6/8) localization using ictal, aura, and interictal slope maps, respectively. Our study provided the evidence that clearance pattern methods are potential additive diagnostic tools for SOZ localization when routine one-time point SPECT are unable to define the SOZ.

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Synchrony in Normal and Focal Epileptic Brain: The Seizure Onset Zone is Functionally Disconnected

Journal of Neurophysiology ◽

10.1152/jn.00368.2010 ◽

2010 ◽

Vol 104 (6) ◽

pp. 3530-3539 ◽

Cited By ~ 101

Author(s):

Christopher P. Warren ◽

Sanqing Hu ◽

Matt Stead ◽

Benjamin H. Brinkmann ◽

Mark R. Bower ◽

...

Keyword(s):

Focal Epilepsy ◽

Brain Regions ◽

Seizure Onset ◽

Neuronal Synchrony ◽

Seizure Onset Zone ◽

Paradoxical Finding ◽

Eeg Recordings ◽

Learning And Cognition ◽

Patients With Epilepsy ◽

And Control

Synchronization of local and distributed neuronal assemblies is thought to underlie fundamental brain processes such as perception, learning, and cognition. In neurological disease, neuronal synchrony can be altered and in epilepsy may play an important role in the generation of seizures. Linear cross-correlation and mean phase coherence of local field potentials (LFPs) are commonly used measures of neuronal synchrony and have been studied extensively in epileptic brain. Multiple studies have reported that epileptic brain is characterized by increased neuronal synchrony except possibly prior to seizure onset when synchrony may decrease. Previous studies using intracranial electroencephalography (EEG), however, have been limited to patients with epilepsy. Here we investigate neuronal synchrony in epileptic and control brain using intracranial EEG recordings from patients with medically resistant partial epilepsy and control subjects with intractable facial pain. For both epilepsy and control patients, average LFP synchrony decreases with increasing interelectrode distance. Results in epilepsy patients show lower LFP synchrony between seizure-generating brain and other brain regions. This relative isolation of seizure-generating brain underlies the paradoxical finding that control patients without epilepsy have greater average LFP synchrony than patients with epilepsy. In conclusion, we show that in patients with focal epilepsy, the region of epileptic brain generating seizures is functionally isolated from surrounding brain regions. We further speculate that this functional isolation may contribute to spontaneous seizure generation and may represent a clinically useful electrophysiological signature for mapping epileptic brain.

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