Temporal lobe interictal spikes disrupt encoding and retrieval of verbal memory: A subregion analysis.

OBJECTIVE: The medial temporal lobe (MTL) encodes and recalls memories and can be a predominant site for interictal spikes (IS) in patients with focal epilepsy. It is unclear whether memory deficits are due to IS in the MTL producing a transient decline. Here, we investigated whether IS in the MTL subregions and lateral temporal cortex impact episodic memory encoding and recall. METHODS: Seventy-eight participants undergoing presurgical evaluation for medically refractory focal epilepsy with depth electrodes placed in the temporal lobe participated in a verbal free recall task. IS were manually annotated during the pre-encoding, encoding, and recall epochs. We examined the effect of IS on word recall using mixed-effects logistic regression. RESULTS: IS in the left hippocampus (odds ratio [OR] = .73, 95% confidence interval [CI] = .63-.84, p < .001) and left middle temporal gyrus (OR = .46, 95% CI = .27-.78, p < .05) during word encoding decreased subsequent recall performance. Within the left hippocampus, this effect was specific for area CA1 (OR = .76, 95% CI = .66-.88, p < .01) and dentate gyrus (OR = .74, 95% CI = .62-.89, p < .05). IS in other MTL subregions or inferior and superior temporal gyrus and IS occurring during the prestimulus window did not affect word encoding (p > .05). IS during retrieval in right hippocampal (OR = .22, 95% CI = .08-.63, p = .01) and parahippocampal regions (OR = .24, 95% CI = .07-.8, p < .05) reduced the probability of recalling a word. SIGNIFICANCE: IS in medial and lateral temporal cortex contribute to transient memory decline during verbal episodic memory.

Contribution of left supramarginal and angular gyri to episodic memory encoding: An intracranial EEG study.

The role of the left ventral lateral parietal cortex (VPC) in episodic memory is hypothesized to include bottom-up attentional orienting to recalled items, according to the dual-attention model (Cabeza et al., 2008). However, its role in memory encoding could be further clarified, with studies showing both positive and negative subsequent memory effects (SMEs). Furthermore, few studies have compared the relative contributions of sub-regions in this functionally heterogeneous area, specifically the anterior VPC (supramarginal gyrus/BA40) and the posterior VPC (angular gyrus/BA39), on a within-subject basis. To elucidate the role of the VPC in episodic encoding, we compared SMEs in the intracranial EEG across multiple frequency bands in the supramarginal gyrus (SmG) and angular gyrus (AnG), as twenty-four epilepsy patients with indwelling electrodes performed a free recall task. We found a significant SME of decreased theta power and increased high gamma power in the VPC overall, and specifically in the SmG. Furthermore, SmG exhibited significantly greater spectral tilt SME from 0.5 to 1.6 s post-stimulus, in which power spectra slope differences between recalled and unrecalled words were greater than in the AnG (p = 0.04). These results affirm the contribution of VPC to episodic memory encoding, and suggest an anterior-posterior dissociation within VPC with respect to its electrophysiological underpinnings.

Ripple oscillations in the left temporal neocortex are associated with impaired verbal episodic memory encoding.

BACKGROUND: We sought to determine if ripple oscillations (80-120 Hz), detected in intracranial electroencephalogram (iEEG) recordings of patients with epilepsy, correlate with an enhancement or disruption of verbal episodic memory encoding. METHODS: We defined ripple and spike events in depth iEEG recordings during list learning in 107 patients with focal epilepsy. We used logistic regression models (LRMs) to investigate the relationship between the occurrence of ripple and spike events during word presentation and the odds of successful word recall following a distractor epoch and included the seizure onset zone (SOZ) as a covariate in the LRMs. RESULTS: We detected events during 58,312 word presentation trials from 7630 unique electrode sites. The probability of ripple on spike (RonS) events was increased in the SOZ (p < 0.04). In the left temporal neocortex, RonS events during word presentation corresponded with a decrease in the odds ratio (OR) of successful recall, however, this effect only met significance in the SOZ (OR of word recall: 0.71, 95% confidence interval (CI): 0.59-0.85, n = 158 events, adaptive Hochberg, p < 0.01). Ripple on oscillation (RonO) events that occurred in the left temporal neocortex non-SOZ also correlated with decreased odds of successful recall (OR: 0.52, 95% CI: 0.34-0.80, n = 140, adaptive Hochberg, p < 0.01). Spikes and RonS that occurred during word presentation in the left middle temporal gyrus (MTG) correlated with the most significant decrease in the odds of successful recall, irrespective of the location of the SOZ (adaptive Hochberg, p < 0.01). CONCLUSION: Ripples and spikes generated in the left temporal neocortex are associated with impaired verbal episodic memory encoding. Although physiological and pathological ripple oscillations were not distinguished during cognitive tasks, our results show an association of undifferentiated ripples with impaired encoding. The effect was sometimes specific to regions outside the SOZ, suggesting that widespread effects of epilepsy outside the SOZ may contribute to cognitive impairment.

Bimodal coupling of ripples and slower oscillations during sleep in patients with focal epilepsy.

OBJECTIVE: Differentiating pathologic and physiologic high-frequency oscillations (HFOs) is challenging. In patients with focal epilepsy, HFOs occur during the transitional periods between the up and down state of slow waves. The preferred phase angles of this form of phase-event amplitude coupling are bimodally distributed, and the ripples (80-150 Hz) that occur during the up-down transition more often occur in the seizure-onset zone (SOZ). We investigated if bimodal ripple coupling was also evident for faster sleep oscillations, and could identify the SOZ. METHODS: Using an automated ripple detector, we identified ripple events in 40-60 min intracranial electroencephalography (iEEG) recordings from 23 patients with medically refractory mesial temporal lobe or neocortical epilepsy. The detector quantified epochs of sleep oscillations and computed instantaneous phase. We utilized a ripple phasor transform, ripple-triggered averaging, and circular statistics to investigate phase event-amplitude coupling. RESULTS: We found that at some individual recording sites, ripple event amplitude was coupled with the sleep oscillatory phase and the preferred phase angles exhibited two distinct clusters (p < 0.05). The distribution of the pooled mean preferred phase angle, defined by combining the means from each cluster at each individual recording site, also exhibited two distinct clusters (p < 0.05). Based on the range of preferred phase angles defined by these two clusters, we partitioned each ripple event at each recording site into two groups: depth iEEG peak-trough and trough-peak. The mean ripple rates of the two groups in the SOZ and non-SOZ (NSOZ) were compared. We found that in the frontal (spindle, p = 0.009; theta, p = 0.006, slow, p = 0.004) and parietal lobe (theta, p = 0.007, delta, p = 0.002, slow, p = 0.001) the SOZ incidence rate for the ripples occurring during the trough-peak transition was significantly increased. SIGNIFICANCE: Phase-event amplitude coupling between ripples and sleep oscillations may be useful to distinguish pathologic and physiologic events in patients with frontal and parietal SOZ.

Graph theoretical measures of fast ripples support the epileptic network hypothesis.

The epileptic network hypothesis and epileptogenic zone hypothesis are two theories of ictogenesis. The network hypothesis posits that coordinated activity among interconnected nodes produces seizures. The epileptogenic zone hypothesis posits that distinct regions are necessary and sufficient for seizure generation. High-frequency oscillations, and particularly fast ripples, are thought to be biomarkers of the epileptogenic zone. We sought to test these theories by comparing high-frequency oscillation rates and networks in surgical responders and non-responders, with no appreciable change in seizure frequency or severity, within a retrospective cohort of 48 patients implanted with stereo-EEG electrodes. We recorded inter-ictal activity during non-rapid eye movement sleep and semi-automatically detected and quantified high-frequency oscillations. Each electrode contact was localized in normalized coordinates. We found that the accuracy of seizure onset zone electrode contact classification using high-frequency oscillation rates was not significantly different in surgical responders and non-responders, suggesting that in non-responders the epileptogenic zone partially encompassed the seizure onset zone(s) (P > 0.05). We also found that in the responders, fast ripple on oscillations exhibited a higher spectral content in the seizure onset zone compared with the non-seizure onset zone (P < 1 × 10-5). By contrast, in the non-responders, fast ripple had a lower spectral content in the seizure onset zone (P < 1 × 10-5). We constructed two different networks of fast ripple with a spectral content >350 Hz. The first was a rate-distance network that multiplied the Euclidian distance between fast ripple-generating contacts by the average rate of fast ripple in the two contacts. The radius of the rate-distance network, which excluded seizure onset zone nodes, discriminated non-responders, including patients not offered resection or responsive neurostimulation due to diffuse multifocal onsets, with an accuracy of 0.77 [95% confidence interval (CI) 0.56-0.98]. The second fast ripple network was constructed using the mutual information between the timing of the events to measure functional connectivity. For most non-responders, this network had a longer characteristic path length, lower mean local efficiency in the non-seizure onset zone, and a higher nodal strength among non-seizure onset zone nodes relative to seizure onset zone nodes. The graphical theoretical measures from the rate-distance and mutual information networks of 22 non- responsive neurostimulation treated patients was used to train a support vector machine, which when tested on 13 distinct patients classified non-responders with an accuracy of 0.92 (95% CI 0.75-1). These results indicate patients who do not respond to surgery or those not selected for resection or responsive neurostimulation can be explained by the epileptic network hypothesis that is a decentralized network consisting of widely distributed, hyperexcitable fast ripple-generating nodes.

Accuracy of high-frequency oscillations recorded intraoperatively for classification of epileptogenic regions.

To see whether acute intraoperative recordings using stereo EEG (SEEG) electrodes can replace prolonged interictal intracranial EEG (iEEG) recording, making the process more efficient and safer, 10 min of iEEG were recorded following electrode implantation in 16 anesthetized patients, and 1-2 days later during non-rapid eye movement (REM) sleep. Ripples on oscillations (RonO, 80-250 Hz), ripples on spikes (RonS), sharp-spikes, fast RonO (fRonO, 250-600 Hz), and fast RonS (fRonS) were semi-automatically detected. HFO power and frequency were compared between the conditions using a generalized linear mixed-effects model. HFO rates were compared using a two-way repeated measures ANOVA with anesthesia type and SOZ as factors. A receiver-operating characteristic (ROC) curve analysis quantified seizure onset zone (SOZ) classification accuracy, and the scalar product was used to assess spatial reliability. Resection of contacts with the highest rate of events was compared with outcome. During sleep, all HFOs, except fRonO, were larger in amplitude compared to intraoperatively (p < 0.01). HFO frequency was also affected (p < 0.01). Anesthesia selection affected HFO and sharp-spike rates. In both conditions combined, sharp-spikes and all HFO subtypes were increased in the SOZ (p < 0.01). However, the increases were larger during the sleep recordings (p < 0.05). The area under the ROC curves for SOZ classification were significantly smaller for intraoperative sharp-spikes, fRonO, and fRonS rates (p < 0.05). HFOs and spikes were only significantly spatially reliable for a subset of the patients (p < 0.05). A failure to resect fRonO areas in the sleep recordings trended the most sensitive and accurate for predicting failure. In summary, HFO morphology is altered by anesthesia. Intraoperative SEEG recordings exhibit increased rates of HFOs in the SOZ, but their spatial distribution can differ from sleep recordings. Recording these biomarkers during non-REM sleep offers a more accurate delineation of the SOZ and possibly the epileptogenic zone.

Ripples Have Distinct Spectral Properties and Phase-Amplitude Coupling With Slow Waves, but Indistinct Unit Firing, in Human Epileptogenic Hippocampus.

Ripple oscillations (80-200 Hz) in the normal hippocampus are involved in memory consolidation during rest and sleep. In the epileptic brain, increased ripple and fast ripple (200-600 Hz) rates serve as a biomarker of epileptogenic brain. We report that both ripples and fast ripples exhibit a preferred phase angle of coupling with the trough-peak (or On-Off) state transition of the sleep slow wave in the hippocampal seizure onset zone (SOZ). Ripples on slow waves in the hippocampal SOZ also had a lower power, greater spectral frequency, and shorter duration than those in the non-SOZ. Slow waves in the mesial temporal lobe modulated the baseline firing rate of excitatory neurons, but did not significantly influence the increased firing rate associated with ripples. In summary, pathological ripples and fast ripples occur preferentially during the On-Off state transition of the slow wave in the epileptogenic hippocampus, and ripples do not require the increased recruitment of excitatory neurons.

Localizing epileptogenic regions using high-frequency oscillations and machine learning.

Pathological high frequency oscillations (HFOs) are putative neurophysiological biomarkers of epileptogenic brain tissue. Utilizing HFOs for epilepsy surgery planning offers the promise of improved seizure outcomes for patients with medically refractory epilepsy. This review discusses possible machine learning strategies that can be applied to HFO biomarkers to better identify epileptogenic regions. We discuss the role of HFO rate, and utilizing features such as explicit HFO properties (spectral content, duration, and power) and phase-amplitude coupling for distinguishing pathological HFO (pHFO) events from physiological HFO events. In addition, the review highlights the importance of neuroanatomical localization in machine learning strategies.

A method for the topographical identification and quantification of high frequency oscillations in intracranial electroencephalography recordings.

OBJECTIVE: To develop a reliable software method using a topographic analysis of time-frequency plots to distinguish ripple (80-200 Hz) oscillations that are often associated with EEG sharp waves or spikes (RonS) from sinusoid-like waveforms that appear as ripples but correspond with digital filtering of sharp transients contained in the wide bandwidth EEG. METHODS: A custom algorithm distinguished true from false ripples in one second intracranial EEG (iEEG) recordings using wavelet convolution, identifying contours of isopower, and categorizing these contours into sets of open or closed loop groups. The spectral and temporal features of candidate groups were used to classify the ripple, and determine its duration, frequency, and power. Verification of detector accuracy was performed on the basis of simulations, and visual inspection of the original and band-pass filtered signals. RESULTS: The detector could distinguish simulated true from false ripple on spikes (RonS). Among 2934 visually verified trials of iEEG recordings and spectrograms exhibiting RonS the accuracy of the detector was 88.5% with a sensitivity of 81.8% and a specificity of 95.2%. The precision was 94.5% and the negative predictive value was 84.0% (N = 12). Among, 1,370 trials of iEEG recording exhibiting RonS that were reviewed blindly without spectrograms the accuracy of the detector was 68.0%, with kappa equal to 0.01 ±â€¯0.03. The detector successfully distinguished ripple from high spectral frequency 'fast ripple' oscillations (200-600 Hz), and characterize ripple duration and spectral frequency and power. The detector was confounded by brief bursts of gamma (30-80 Hz) activity in 7.31 ±â€¯6.09% of trials, and in 30.2 ±â€¯14.4% of the true RonS detections ripple duration was underestimated. CONCLUSIONS: Characterizing the topographic features of a time-frequency plot generated by wavelet convolution is useful for distinguishing true oscillations from false oscillations generated by filter ringing. SIGNIFICANCE: Categorizing ripple oscillations and characterizing their properties can improve the clinical utility of the biomarker.

Utilization of independent component analysis for accurate pathological ripple detection in intracranial EEG recordings recorded extra- and intra-operatively.

OBJECTIVE: To develop and validate a detector that identifies ripple (80-200 Hz) events in intracranial EEG (iEEG) recordings in a referential montage and utilizes independent component analysis (ICA) to eliminate or reduce high-frequency artifact contamination. Also, investigate the correspondence of detected ripples and the seizure onset zone (SOZ). METHODS: iEEG recordings from 16 patients were first band-pass filtered (80-600 Hz) and Infomax ICA was next applied to derive the first independent component (IC1). IC1 was subsequently pruned, and an artifact index was derived to reduce the identification of high-frequency events introduced by the reference electrode signal. A Hilbert detector identified ripple events in the processed iEEG recordings using amplitude and duration criteria. The identified ripple events were further classified and characterized as true or false ripple on spikes, or ripples on oscillations by utilizing a topographical analysis to their time-frequency plot, and confirmed by visual inspection. RESULTS: The signal to noise ratio was improved by pruning IC1. The precision of the detector for ripple events was 91.27 ±â€¯4.3%, and the sensitivity of the detector was 79.4 ±â€¯3.0% (N = 16 patients, 5842 ripple events). The sensitivity and precision of the detector was equivalent in iEEG recordings obtained during sleep or intra-operatively. Across all the patients, true ripple on spike rates and also the rates of false ripple on spikes, that were generated due to filter ringing, classified the seizure onset zone (SOZ) with an area under the receiver operating curve (AUROC) of >76%. The magnitude and spectral content of true ripple on spikes generated in the SOZ was distinct as compared with the ripples generated in the NSOZ (p < .001). CONCLUSIONS: Utilizing ICA to analyze iEEG recordings in referential montage provides many benefits to the study of high-frequency oscillations. The ripple rates and properties defined using this approach may accurately delineate the seizure onset zone. SIGNIFICANCE: Strategies to improve the spatial resolution of intracranial EEG and reduce artifact can help improve the clinical utility of HFO biomarkers.

Visually validated semi-automatic high-frequency oscillation detection aides the delineation of epileptogenic regions during intra-operative electrocorticography.

OBJECTIVE: To test the utility of a novel semi-automated method for detecting, validating, and quantifying high-frequency oscillations (HFOs): ripples (80-200 Hz) and fast ripples (200-600 Hz) in intra-operative electrocorticography (ECoG) recordings. METHODS: Sixteen adult patients with temporal lobe epilepsy (TLE) had intra-operative ECoG recordings at the time of resection. The computer-annotated ECoG recordings were visually inspected and false positive detections were removed. We retrospectively determined the sensitivity, specificity, positive and negative predictive value (PPV/NPV) of HFO detections in unresected regions for determining post-operative seizure outcome. RESULTS: Visual validation revealed that 2.81% of ripple and 43.68% of fast ripple detections were false positive. Inter-reader agreement for false positive fast ripple on spike classification was good (ICC = 0.713, 95% CI: 0.632-0.779). After removing false positive detections, the PPV of a single fast ripple on spike in an unresected electrode site for post-operative non-seizure free outcome was 85.7 [50-100%]. Including false positive detections reduced the PPV to 64.2 [57.8-69.83%]. CONCLUSIONS: Applying automated HFO methods to intraoperative electrocorticography recordings results in false positive fast ripple detections. True fast ripples on spikes are rare, but predict non-seizure free post-operative outcome if found in an unresected site. SIGNIFICANCE: Semi-automated HFO detection methods are required to accurately identify fast ripple events in intra-operative ECoG recordings.