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Despite advances in understanding the cellular and molecular processes underlying memory and cognition, and recent successful modulation of cognitive performance in brain disorders, the neurophysiological mechanisms remain underexplored. High frequency oscillations beyond the classic electroencephalogram spectrum have emerged as a potential neural correlate of fundamental cognitive processes. High frequency oscillations are detected in the human mesial temporal lobe and neocortical intracranial recordings spanning gamma/epsilon (60–150 Hz), ripple (80–250 Hz) and higher frequency ranges. Separate from other non-oscillatory activities, these brief electrophysiological oscillations of distinct duration, frequency and amplitude are thought to be generated by coordinated spiking of neuronal ensembles within volumes as small as a single cortical column. Although the exact origins, mechanisms and physiological roles in health and disease remain elusive, they have been associated with human memory consolidation and cognitive processing. Recent studies suggest their involvement in encoding and recall of episodic memory with a possible role in the formation and reactivation of memory traces. High frequency oscillations are detected during encoding, throughout maintenance, and right before recall of remembered items, meeting a basic definition for an engram activity. The temporal coordination of high frequency oscillations reactivated across cortical and subcortical neural networks is ideally suited for integrating multimodal memory representations, which can be replayed and consolidated during states of wakefulness and sleep. High frequency oscillations have been shown to reflect coordinated bursts of neuronal assembly firing and offer a promising substrate for tracking and modulation of the hypothetical electrophysiological engram. [ABSTRACT FROM AUTHOR]

Emotion is represented in limbic and prefrontal brain areas, herein termed the affective salience network (ASN). Within the ASN, there are substantial unknowns about how valence and emotional intensity are processed—specifically, which nodes are associated with affective bias (a phenomenon in which participants interpret emotions in a manner consistent with their own mood). A recently developed feature detection approach ('specparam') was used to select dominant spectral features from human intracranial electrophysiological data, revealing affective specialization within specific nodes of the ASN. Spectral analysis of dominant features at the channel level suggests that dorsal anterior cingulate (dACC), anterior insula and ventral-medial prefrontal cortex (vmPFC) are sensitive to valence and intensity, while the amygdala is primarily sensitive to intensity. Akaike information criterion model comparisons corroborated the spectral analysis findings, suggesting all four nodes are more sensitive to intensity compared to valence. The data also revealed that activity in dACC and vmPFC were predictive of the extent of affective bias in the ratings of facial expressions—a proxy measure of instantaneous mood. To examine causality of the dACC in affective experience, 130 Hz continuous stimulation was applied to dACC while patients viewed and rated emotional faces. Faces were rated significantly happier during stimulation, even after accounting for differences in baseline ratings. Together the data suggest a causal role for dACC during the processing of external affective stimuli. [ABSTRACT FROM AUTHOR]

Modulation of cognitive functions supporting human declarative memory is one of the grand challenges of neuroscience, and of vast importance for a variety of neuropsychiatric, neurodegenerative and neurodevelopmental diseases. Despite a recent surge of successful attempts at improving performance in a range of memory tasks, the optimal approaches and parameters for memory enhancement have yet to be determined. On a more fundamental level, it remains elusive how delivering electrical current in a given brain area leads to enhanced memory processing. Starting from the local and distal physiological effects on neural populations, the mechanisms of enhanced memory encoding, maintenance, consolidation, or recall in response to direct electrical stimulation are only now being unraveled. With the advent of innovative neurotechnologies for concurrent recording and stimulation intracranially in the human brain, it becomes possible to study both acute and chronic effects of stimulation on memory performance and the underlying neural activities. In this review, we summarize the effects of various invasive stimulation approaches for modulating memory functions. We first outline the challenges that were faced in the initial studies of memory enhancement and the lessons learned. Electrophysiological biomarkers are then reviewed as more objective measures of the stimulation effects than behavioral outcomes. Finally, we classify the various stimulation approaches into continuous and phasic modulation with open or closed loop for responsive stimulation based on analysis of the recorded neural activities. Although the potential advantage of closed-loop responsive stimulation over the classic open-loop approaches is inconclusive, we foresee the emerging results from ongoing longitudinal studies and clinical trials to shed light on both the mechanisms and optimal strategies for improving declarative memory. Adaptive stimulation based on the biomarker analysis over extended periods of time is proposed as a future direction for obtaining lasting effects on memory functions. Chronic tracking and modulation of neural activities intracranially through adaptive stimulation opens tantalizing new avenues to continually monitor and treat memory and cognitive deficits in a range of brain disorders. Brain co-processors created with machine-learning tools and wireless bi-directional connectivity to seamlessly integrate implanted devices with smartphones and cloud computing are poised to enable real-time automated analysis of large data volumes and adaptively tune electrical stimulation based on electrophysiological biomarkers of behavioral states. Next generation implantable devices for high-density recording and stimulation of electrophysiological activities, and technologies for distributed brain-computer interfaces are presented as selected future perspectives for modulating human memory and associated mental processes. [ABSTRACT FROM AUTHOR]

Planning surgery for patients with medically refractory epilepsy often requires recording seizures using intracranial EEG. Quantitative measures derived from interictal intracranial EEG yield potentially appealing biomarkers to guide these surgical procedures; however, their utility is limited by the sparsity of electrode implantation as well as the normal confounds of spatiotemporally varying neural activity and connectivity. We propose that comparing intracranial EEG recordings to a normative atlas of intracranial EEG activity and connectivity can reliably map abnormal regions, identify targets for invasive treatment and increase our understanding of human epilepsy. Merging data from the Penn Epilepsy Center and a public database from the Montreal Neurological Institute, we aggregated interictal intracranial EEG retrospectively across 166 subjects comprising >5000 channels. For each channel, we calculated the normalized spectral power and coherence in each canonical frequency band. We constructed an intracranial EEG atlas by mapping the distribution of each feature across the brain and tested the atlas against data from novel patients by generating a z-score for each channel. We demonstrate that for seizure onset zones within the mesial temporal lobe, measures of connectivity abnormality provide greater distinguishing value than univariate measures of abnormal neural activity. We also find that patients with a longer diagnosis of epilepsy have greater abnormalities in connectivity. By integrating measures of both single-channel activity and inter-regional functional connectivity, we find a better accuracy in predicting the seizure onset zones versus normal brain (area under the curve = 0.77) compared with either group of features alone. We propose that aggregating normative intracranial EEG data across epilepsy centres into a normative atlas provides a rigorous, quantitative method to map epileptic networks and guide invasive therapy. We publicly share our data, infrastructure and methods, and propose an international framework for leveraging big data in surgical planning for refractory epilepsy. [ABSTRACT FROM AUTHOR]

Interictal epileptiform discharges (IEDs) are a widely used biomarker in patients with epilepsy but lack specificity. It has been proposed that there are truly epileptogenic and less pathological or even protective IEDs. Recent studies suggest that highly pathological IEDs are characterized by high-frequency oscillations (HFOs). Here, we aimed to dissect these 'HFO-IEDs' at the single-neuron level, hypothesizing that the underlying mechanisms are distinct from 'non-HFO-IEDs'. Analysing hybrid depth electrode recordings from patients with temporal lobe epilepsy, we found that single-unit firing rates were higher in HFO- than in non-HFO-IEDs. HFO-IEDs were characterized by a pronounced pre-peak increase in firing, which coincided with the preferential occurrence of HFOs, whereas in non-HFO-IEDs, there was only a mild pre-peak increase followed by a post-peak suppression. Comparing each unit's firing during HFO-IEDs to its baseline activity, we found many neurons with a significant increase during the HFO component or ascending part, but almost none with a decrease. No such imbalance was observed during non-HFO-IEDs. Finally, comparing each unit's firing directly between HFO- and non-HFO-IEDs, we found that most cells had higher rates during HFO-IEDs and, moreover, identified a distinct subset of neurons with a significant preference for this IED subtype. In summary, our study reveals that HFO- and non-HFO-IEDs have different single-unit correlates. In HFO-IEDs, many neurons are moderately activated, and some participate selectively, suggesting that both types of increased firing contribute to highly pathological IEDs. [ABSTRACT FROM AUTHOR]

We describe the spatiotemporal course of cortical high-gamma activity, hippocampal ripple activity and interictal epileptiform discharges during an associative memory task in 15 epilepsy patients undergoing invasive EEG. Successful encoding trials manifested significantly greater high-gamma activity in hippocampus and frontal regions. Successful cued recall trials manifested sustained high-gamma activity in hippocampus compared to failed responses. Hippocampal ripple rates were greater during successful encoding and retrieval trials. Interictal epileptiform discharges during encoding were associated with 15% decreased odds of remembering in hippocampus (95% confidence interval 6-23%). Hippocampal interictal epileptiform discharges during retrieval predicted 25% decreased odds of remembering (15-33%). Odds of remembering were reduced by 25-52% if interictal epileptiform discharges occurred during the 500-2000 ms window of encoding or by 41% during retrieval. During encoding and retrieval, hippocampal interictal epileptiform discharges were followed by a transient decrease in ripple rate. We hypothesize that interictal epileptiform discharges impair associative memory in a regionally and temporally specific manner by decreasing physiological hippocampal ripples necessary for effective encoding and recall. Because dynamic memory impairment arises from pathological interictal epileptiform discharge events competing with physiological ripples, interictal epileptiform discharges represent a promising therapeutic target for memory remediation in patients with epilepsy. [ABSTRACT FROM AUTHOR]

The location of interictal spikes is used to aid surgical planning in patients with medically refractory epilepsy; however, their spatial and temporal dynamics are poorly understood. In this study, we analysed the spatial distribution of interictal spikes over time in 20 adult and paediatric patients (12 females, mean age = 34.5 years, range = 5-58) who underwent intracranial EEG evaluation for epilepsy surgery. Interictal spikes were detected in the 24 h surrounding each seizure and spikes were clustered based on spatial location. The temporal dynamics of spike spatial distribution were calculated for each patient and the effects of sleep and seizures on these dynamics were evaluated. Finally, spike location was assessed in relation to seizure onset location. We found that spike spatial distribution fluctuated significantly over time in 14/20 patients (with a significant aggregate effect across patients, Fisher's method: P < 0.001). A median of 12 sequential hours were required to capture 80% of the variability in spike spatial distribution. Sleep and postictal state affected the spike spatial distribution in 8/20 and 4/20 patients, respectively, with a significant aggregate effect (Fisher's method: P < 0.001 for each). There was no evidence of pre-ictal change in the spike spatial distribution for any patient or in aggregate (Fisher's method: P = 0.99). The electrode with the highest spike frequency and the electrode with the largest area of downstream spike propagation both localized the seizure onset zone better than predicted by chance (Wilcoxon signed-rank test: P = 0.005 and P = 0.002, respectively). In conclusion, spikes localize seizure onset. However, temporal fluctuations in spike spatial distribution, particularly in relation to sleep and post-ictal state, can confound localization. An adequate duration of intracranial recording-ideally at least 12 sequential hours-capturing both sleep and wakefulness should be obtained to sufficiently sample the interictal network. [ABSTRACT FROM AUTHOR]

How does the human brain's structural scaffold give rise to its intricate functional dynamics? This is a central question in translational neuroscience that is particularly relevant to epilepsy, a disorder affecting over 50 million subjects worldwide. Treatment for medication-resistant focal epilepsy is often structural-through surgery or laser ablation-but structural targets, particularly in patients without clear lesions, are largely based on functional mapping via intracranial EEG. Unfortunately, the relationship between structural and functional connectivity in the seizing brain is poorly understood. In this study, we quantify structure-function coupling, specifically between white matter connections and intracranial EEG, across pre-ictal and ictal periods in 45 seizures from nine patients with unilateral drug-resistant focal epilepsy. We use high angular resolution diffusion imaging (HARDI) tractography to construct structural connectivity networks and correlate these networks with time-varying broadband and frequency-specific functional networks derived from coregistered intracranial EEG. Across all frequency bands, we find significant increases in structure-function coupling from pre-ictal to ictal periods. We demonstrate that short-range structural connections are primarily responsible for this increase in coupling. Finally, we find that spatiotemporal patterns of structure-function coupling are highly stereotyped for each patient. These results suggest that seizures harness the underlying structural connectome as they propagate. Mapping the relationship between structural and functional connectivity in epilepsy may inform new therapies to halt seizure spread, and pave the way for targeted patient-specific interventions. [ABSTRACT FROM AUTHOR]

Accurate seizure prediction will transform epilepsy management by offering warnings to patients or triggering interventions. However, state-of-the-art algorithm design relies on accessing adequate long-term data. Crowd-sourcing ecosystems leverage quality data to enable cost-effective, rapid development of predictive algorithms. A crowd-sourcing ecosystem for seizure prediction is presented involving an international competition, a follow-up held-out data evaluation, and an online platform, Epilepsyecosystem.org, for yielding further improvements in prediction performance. Crowd-sourced algorithms were obtained via the 'Melbourne-University AES-MathWorks-NIH Seizure Prediction Challenge' conducted at kaggle.com. Long-term continuous intracranial electroencephalography (iEEG) data (442 days of recordings and 211 lead seizures per patient) from prediction-resistant patients who had the lowest seizure prediction performances from the NeuroVista Seizure Advisory System clinical trial were analysed. Contestants (646 individuals in 478 teams) from around the world developed algorithms to distinguish between 10-min inter-seizure versus pre-seizure data clips. Over 10 000 algorithms were submitted. The top algorithms as determined by using the contest data were evaluated on a much larger held-out dataset. The data and top algorithms are available online for further investigation and development. The top performing contest entry scored 0.81 area under the classification curve. The performance reduced by only 6.7% on held-out data. Many other teams also showed high prediction reproducibility. Pseudo-prospective evaluation demonstrated that many algorithms, when used alone or weighted by circadian information, performed better than the benchmarks, including an average increase in sensitivity of 1.9 times the original clinical trial sensitivity for matched time in warning. These results indicate that clinically-relevant seizure prediction is possible in a wider range of patients than previously thought possible. Moreover, different algorithms performed best for different patients, supporting the use of patient-specific algorithms and long-term monitoring. The crowd-sourcing ecosystem for seizure prediction will enable further worldwide community study of the data to yield greater improvements in prediction performance by way of competition, collaboration and synergism.10.1093/brain/awy210_video1awy210media15817489051001. [ABSTRACT FROM AUTHOR]

Semantic memory underpins our understanding of objects, people, places, and ideas. Anomia, a disruption of semantic memory access, is the most common residual language disturbance and is seen in dementia and following injury to temporal cortex. While such anomia has been well characterized by lesion symptom mapping studies, its pathophysiology is not well understood. We hypothesize that inputs to the semantic memory system engage a specific heteromodal network hub that integrates lexical retrieval with the appropriate semantic content. Such a network hub has been proposed by others, but has thus far eluded precise spatiotemporal delineation. This limitation in our understanding of semantic memory has impeded progress in the treatment of anomia. We evaluated the cortical structure and dynamics of the lexical semantic network in driving speech production in a large cohort of patients with epilepsy using electrocorticography (n = 64), functional MRI (n = 36), and direct cortical stimulation (n = 30) during two generative language processes that rely on semantic knowledge: visual picture naming and auditory naming to definition. Each task also featured a non-semantic control condition: scrambled pictures and reversed speech, respectively. These large-scale data of the left, language-dominant hemisphere uniquely enable convergent, high-resolution analyses of neural mechanisms characterized by rapid, transient dynamics with strong interactions between distributed cortical substrates. We observed three stages of activity during both visual picture naming and auditory naming to definition that were serially organized: sensory processing, lexical semantic processing, and articulation. Critically, the second stage was absent in both the visual and auditory control conditions. Group activity maps from both electrocorticography and functional MRI identified heteromodal responses in middle fusiform gyrus, intraparietal sulcus, and inferior frontal gyrus; furthermore, the spectrotemporal profiles of these three regions revealed coincident activity preceding articulation. Only in the middle fusiform gyrus did direct cortical stimulation disrupt both naming tasks while still preserving the ability to repeat sentences. These convergent data strongly support a model in which a distinct neuroanatomical substrate in middle fusiform gyrus provides access to object semantic information. This under-appreciated locus of semantic processing is at risk in resections for temporal lobe epilepsy as well as in trauma and strokes that affect the inferior temporal cortex-it may explain the range of anomic states seen in these conditions. Further characterization of brain network behaviour engaging this region in both healthy and diseased states will expand our understanding of semantic memory and further development of therapies directed at anomia. [ABSTRACT FROM AUTHOR]

High-frequency oscillations in local field potentials recorded with intracranial EEG are putative biomarkers of seizure onset zones in epileptic brain. However, localized 80-500 Hz oscillations can also be recorded from normal and non-epileptic cerebral structures. When defined only by rate or frequency, physiological high-frequency oscillations are indistinguishable from pathological ones, which limit their application in epilepsy presurgical planning. We hypothesized that pathological high-frequency oscillations occur in a repetitive fashion with a similar waveform morphology that specifically indicates seizure onset zones. We investigated the waveform patterns of automatically detected high-frequency oscillations in 13 epilepsy patients and five control subjects, with an average of 73 subdural and intracerebral electrodes recorded per patient. The repetitive oscillatory waveforms were identified by using a pipeline of unsupervised machine learning techniques and were then correlated with independently clinician-defined seizure onset zones. Consistently in all patients, the stereotypical high-frequency oscillations with the highest degree of waveform similarity were localized within the seizure onset zones only, whereas the channels generating high-frequency oscillations embedded in random waveforms were found in the functional regions independent from the epileptogenic locations. The repetitive waveform pattern was more evident in fast ripples compared to ripples, suggesting a potential association between waveform repetition and the underlying pathological network. Our findings provided a new tool for the interpretation of pathological high-frequency oscillations that can be efficiently applied to distinguish seizure onset zones from functionally important sites, which is a critical step towards the translation of these signature events into valid clinical biomarkers.awx374media15721572971001. [ABSTRACT FROM AUTHOR]

See Kleen and Kirsch (doi:10.1093/awx178) for a scientific commentary on this article.Cognitive deficits are common among epilepsy patients. In these patients, interictal epileptiform discharges, also termed spikes, are seen routinely on electroencephalography and believed to be associated with transient cognitive impairments. In this study, we investigated the effect of spikes on memory encoding and retrieval, taking into account the spatial distribution of spikes in relation to the seizure onset zone as well as anatomical regions of the brain. Sixty-seven patients with medication refractory epilepsy undergoing continuous intracranial electroencephalography monitoring engaged in a delayed free recall task to test short-term memory. In this task, subjects were asked to memorize and recall lists of common nouns. We quantified the effect of each spike on the probability of successful recall using a generalized logistic mixed model. We found that in patients with left lateralized seizure onset zones, spikes outside the seizure onset zone impacted memory encoding, whereas those within the seizure onset zone did not. In addition, spikes in the left inferior temporal gyrus, middle temporal gyrus, superior temporal gyrus, and fusiform gyrus during memory encoding reduced odds of recall by as much as 15% per spike. Spikes also reduced the odds of word retrieval, an effect that was stronger with spikes outside of the seizure onset zone. These results suggest that seizure onset regions are dysfunctional at baseline, and support the idea that interictal spikes disrupt cognitive processes related to the underlying tissue. [ABSTRACT FROM AUTHOR]

There exist significant clinical and basic research needs for accurate, automated seizure detection algorithms. These algorithms have translational potential in responsive neurostimulation devices and in automatic parsing of continuous intracranial electroencephalography data. An important barrier to developing accurate, validated algorithms for seizure detection is limited access to high-quality, expertly annotated seizure data from prolonged recordings. To overcome this, we hosted a kaggle.com competition to crowdsource the development of seizure detection algorithms using intracranial electroencephalography from canines and humans with epilepsy. The top three performing algorithms from the contest were then validated on out-of-sample patient data including standard clinical data and continuous ambulatory human data obtained over several years using the implantable NeuroVista seizure advisory system. Two hundred teams of data scientists from all over the world participated in the kaggle.com competition. The top performing teams submitted highly accurate algorithms with consistent performance in the out-of-sample validation study. The performance of these seizure detection algorithms, achieved using freely available code and data, sets a new reproducible benchmark for personalized seizure detection. We have also shared a 'plug and play' pipeline to allow other researchers to easily use these algorithms on their own datasets. The success of this competition demonstrates how sharing code and high quality data results in the creation of powerful translational tools with significant potential to impact patient care. [ABSTRACT FROM AUTHOR]

Gamma frequency activity (30-150 Hz) is induced in cognitive tasks and is thought to reflect underlying neural processes. Gamma frequency activity can be recorded directly from the human brain using intracranial electrodes implanted in patients undergoing treatment for drug-resistant epilepsy. Previous studies have independently explored narrowband oscillations in the local field potential and broadband power increases. It is not clear, however, which processes contribute to human brain gamma frequency activity, or their dynamics and roles during memory processing. Here a large dataset of intracranial recordings obtained during encoding of words from 101 patients was used to detect, characterize and compare induced gamma frequency activity events. Individual bursts of gamma frequency activity were isolated in the time-frequency domain to determine their spectral features, including peak frequency, amplitude, frequency span, and duration. We found two distinct types of gamma frequency activity events that showed either narrowband or broadband frequency spans revealing characteristic spectral properties. Narrowband events, the predominant type, were induced by word presentations following an initial induction of broadband events, which were temporally separated and selectively correlated with evoked response potentials, suggesting that they reflect different neural activities and play different roles during memory encoding. The two gamma frequency activity types were differentially modulated during encoding of subsequently recalled and forgotten words. In conclusion, we found evidence for two distinct activity types induced in the gamma frequency range during cognitive processing. Separating these two gamma frequency activity components contributes to the current understanding of electrophysiological biomarkers, and may prove useful for emerging neurotechnologies targeting, mapping and modulating distinct neurophysiological processes in normal and epileptogenic brain. [ABSTRACT FROM AUTHOR]

Despite many advances in the study of large-scale human functional networks, the question of timing, stability, and direction of communication between cortical regions has not been fully addressed. At the cellular level, neuronal communication occurs through axons and dendrites, and the time required for such communication is well defined and preserved. At larger spatial scales, however, the relationship between timing, direction, and communication between brain regions is less clear. Here, we use a measure of effective connectivity to identify connections between brain regions that exhibit communication with consistent timing. We hypothesized that if two brain regions are communicating, then knowledge of the activity in one region should allow an external observer to better predict activity in the other region, and that such communication involves a consistent time delay. We examine this question using intracranial electroencephalography captured from nine human participants with medically refractory epilepsy. We use a coupling measure based on time-lagged mutual information to identify effective connections between brain regions that exhibit a statistically significant increase in average mutual information at a consistent time delay. These identified connections result in sparse, directed functional networks that are stable over minutes, hours, and days. Notably, the time delays associated with these connections are also highly preserved over multiple time scales. We characterize the anatomic locations of these connections, and find that the propagation of activity exhibits a preferred posterior to anterior temporal lobe direction, consistent across participants. Moreover, networks constructed from connections that reliably exhibit consistent timing between anatomic regions demonstrate features of a small-world architecture, with many reliable connections between anatomically neighbouring regions and few long range connections. Together, our results demonstrate that cortical regions exhibit functional relationships with well-defined and consistent timing, and the stability of these relationships over multiple time scales suggests that these stable pathways may be reliably and repeatedly used for large-scale cortical communication. [ABSTRACT FROM AUTHOR]

SEE EISSA AND SCHEVON DOI101093/AWW332 FOR A SCIENTIFIC COMMENTARY ON THIS ARTICLE: Surgery can be a last resort for patients with intractable, medically refractory epilepsy. For many of these patients, however, there is substantial risk that the surgery will be ineffective. The prediction of who is likely to benefit from a surgical approach is crucial for being able to inform patients better, conduct principled prospective clinical trials, and ultimately tailor therapeutic approaches to these patients more effectively. Dynamical computational models, informed with patient data, can be used to make predictions and give mechanistic insight. In this study, we develop patient-specific dynamical network models of epileptogenic cortex. We infer the network connectivity matrix from non-seizure electrographic recordings of patients and use these connectivity matrices as the network structure in our model. The model simulates the dynamics of a bi-stable switch at every node in this network, meaning that every node starts in a background state, but has the ability to transit to a co-existing seizure state. Whether a transition happens in a node is partly determined by the stochastic nature of the input to the node, but also by the input the node receives from other connected nodes in the network. By conducting simulations with such a model, we can detect the average transition time for nodes in a given network, and therefore define nodes with a short transition time as highly epileptogenic. In a retrospective study, we found that in some patients the regions with high epileptogenicity in the model overlap with those identified clinically as the seizure onset zone. Moreover, it was found that the resection of these regions in the model reduces the overall likelihood of a seizure. Following removal of these regions in the model, we predicted surgical outcomes and compared these to actual patient outcomes. Our predictions were found to be 81.3% accurate on a dataset of 16 patients with intractable epilepsy. Intriguingly, in patients with unsuccessful outcomes, the proposed computational approach is able to suggest alternative resection sites. The model presented here gives mechanistic insight as to why surgery may be unsuccessful in some patients. This may aid clinicians in presurgical evaluation by providing a tool to explore various surgical options, offering complementary information to existing clinical techniques. [ABSTRACT FROM AUTHOR]

Gamma oscillations play a pivotal role in multiple cognitive functions. They enable coordinated activity and communication of local assemblies, while abnormalities in gamma oscillations exist in different neurological and psychiatric diseases. Thus, a specific rectification of gamma synchronization could potentially compensate the deficits in pathological conditions. Previous experiments have shown that animals can voluntarily modulate their gamma power through operant conditioning. Using a closed-loop experimental setup, we show in six intracerebrally recorded epileptic patients undergoing presurgical evaluation that intracerebral power spectrum can be increased in the gamma frequency range (30-80 Hz) at different fronto-temporal cortical sites in human subjects. Successful gamma training was accompanied by increased gamma power at other cortical locations and progressively enhanced cross-frequency coupling between gamma and slow oscillations (3-12 Hz). Finally, using microelectrode targets in two subjects, we report that upregulation of gamma activities is possible also in spatial micro-domains, without the spread to macroelectrodes. Overall, our findings indicate that intracerebral gamma modulation can be achieved rapidly, beyond the motor system and with high spatial specificity, when using micro targets. These results are especially significant because they pave the way for use of highresolution therapeutic approaches for future clinical applications. [ABSTRACT FROM AUTHOR]

Multiple seizure foci, seizure propagation and epileptic spasms complicate presurgical seizure localization in tuberous sclerosis. Furthermore, controversy exists about the contribution of tubers, perituberal cortex and the underlying genetic abnormality to epileptogenesis. We aimed to determine the epileptogenic substrate in tuberous sclerosis by studying spatio-temporal patterns of seizure onset and propagation on intracranial EEG recordings in which multiple depth and surface electrodes sampled multiple tubers and perituberal cortex. Ten intracranial EEG recordings (seven extraoperative, three intraoperative) from 10 children with tuberous sclerosis were analysed. Notable thickening and signal abnormality in the centre of many tubers on magnetic resonance imaging led to tuber centres being recorded with depth electrodes. Spatially-meaningful bipolar montages were reformatted incorporating channels recording only from the tuber centre, tuber rim and perituberal cortex. Interictal epileptiform discharges and ictal rhythms were analysed visually for location, field, morphology, frequency, latency and temporal dispersion. Fifteen electroclinically distinct seizures were recorded in the 10 patients. Seizure onset was recorded in tubers in all 15 electroclinically distinct seizures; in 9/10 electroclinically distinct seizures recorded with optimal spatial sampling, seizure onset was recorded in the tuber centre, with or without involvement of the tuber rim but not perituberal cortex. Quantitative electroencephalography analysis by pairwise cross-correlation confirmed that the tuber centre led the tuber rim and perituberal cortex during interictal, preictal and ictal spike trains. Seizure propagation was observed in 10/15 electroclinically distinct seizures, being tuber-to-tuber in all. Seven of the 17 tubers showing seizure propagation activated an independent ictal rhythm, morphologically distinct from that seen in seizure onset region (intra-ictal activation). Of the total 48 tubers sampled, 16 exhibited seizure onset, 17 were involved in seizure propagation and 40 exhibited interictal epileptiform discharges, 33 independent and seven propagated. Seizure onsets were recorded in 16/33 tubers with independent interictal epileptiform discharges, but 0/7 tubers with only propagated epileptiform discharges or 0/8 tubers with no epileptiform discharges (P = 0.003). Seizure onsets were recorded from 4/7 tubers with and 0/10 tubers without intra-ictal activation (P = 0.015). Thus, focal seizures and interictal epileptiform discharges in tuberous sclerosis arise in the centre of epileptogenic tubers and propagate to the tuber rim, perituberal cortex and other epileptogenic tubers. Rhythmic interictal epileptiform discharges and intra-ictal activation of propagated ictal rhythms are potential biomarkers of epileptogenic tubers. Interictal and ictal EEG features of epileptogenic tubers have similarities to focal cortical dysplasia type II, consistent with the reported imaging, histological and molecular similarities. [ABSTRACT FROM AUTHOR]

Decades of experience with intracranial recordings in patients with epilepsy have demonstrated that seizures can occur in deep cortical regions such as the mesial temporal lobes without showing any obvious signs of seizure activity on scalp electroencephalogram. Predicated on the idea that these seizures are purely focal, currently, the only way to detect these 'scalp-negative seizures' is with intracranial recordings. However, intracranial recordings are only rarely performed in patients with epilepsy, and are almost never performed outside of the context of epilepsy. As such, little is known about scalp-negative seizures and their role in the natural history of epilepsy, their effect on cognitive function, and their association with other neurological diseases. Here, we developed a novel approach to non-invasively identify scalp-negative seizures arising from the mesial temporal lobe based on scalp electroencephalogram network connectivity measures. We identified 25 scalp-negative mesial temporal lobe seizures in 10 patients and obtained control records from an additional 13 patients, all of whom underwent recordings with foramen ovale electrodes and scalp electroencephalogram. Scalp data from these records were used to train a scalp-negative seizure detector, which consisted of a pair of logistic regression classifiers that used scalp electroencephalogram coherence properties as input features. On cross-validation performance, this detector correctly identified scalp-negative seizures in 40% of patients, and correctly identified the side of seizure onset for each seizure detected. In comparison, routine clinical interpretation of these scalp electroencephalograms failed to identify any of the scalp-negative seizures. Among the patients in whom the detector raised seizure alarms, 80% had scalp-negative mesial temporal lobe seizures. The detector had a false alarm rate of only 0.31 per day and a positive predictive value of 75%. Of the 13 control patients, false seizure alarms were raised in only one patient. The fact that our detector specifically recognizes focal mesial temporal lobe seizures based on scalp electroencephalogram coherence features, lends weight to the hypothesis that even focal seizures are a network phenomenon that involve widespread neural connectivity. Our scalp-negative seizure detector has clear clinical utility in patients with temporal lobe epilepsy, and its potential easily translates to other neurological disorders, such as Alzheimer's disease, in which occult mesial temporal lobe seizures are suspected to play a significant role. Importantly, our work establishes a novel approach of using computational approaches to non-invasively detect deep seizure activity, without the need for invasive intracranial recordings. [ABSTRACT FROM AUTHOR]

Because seizures originate from different pathological substrates, the question arises of whether distinct or similar mechanisms underlie seizure generation across different pathologies. Better defining intracranial electroencephalographic morphological patterns at seizure-onset could improve the understanding of such mechanisms. To this end, we investigated intracranial electroencephalographic seizure-onset patterns associated with different epileptogenic lesions, and defined high-frequency oscillation correlates of each pattern. We analysed representative seizure types from 33 consecutive patients with drug-resistant focal epilepsy and a structural magnetic resonance imaging lesion (11 mesial temporal sclerosis, nine focal cortical dysplasia, six cortical atrophy, three periventricular nodular heterotopia, three polymicrogyria, and one tuberous sclerosis complex) who underwent depth-electrode electroencephalographic recordings (500 Hz filter, 2000 Hz sampling rate). Patients were included only if seizures arose from contacts located in lesional/peri-lesional tissue, and if clinical manifestations followed the electrographic onset. Seizure-onset patterns were defined independently by two reviewers blinded to clinical information, and consensus was reached after discussion. For each seizure, pre-ictal and ictal sections were selected for high-frequency oscillation analysis. Seven seizure-onset patterns were identified across the 53 seizures sampled: low-voltage fast activity (43%); low-frequency high-amplitude periodic spikes (21%); sharp activity at ≤13 Hz (15%); spike-and-wave activity (9%); burst of high-amplitude polyspikes (6%); burst suppression (4%); and delta brush (4%). Each pattern occurred across several pathologies, except for periodic spikes, only observed with mesial temporal sclerosis, and delta brush, exclusive to focal cortical dysplasia. However, mesial temporal sclerosis was not always associated with periodic spikes nor focal cortical dysplasia with delta brush. Compared to other patterns, low-voltage fast activity was associated with a larger seizure-onset zone (P = 0.04). Four patterns, sharp activity at ≤13 Hz, low-voltage fast activity, spike-and-wave activity and periodic spikes, were also found in regions of seizure spread, with periodic spikes only emerging from mesial temporal sclerosis. Each of the seven patterns was accompanied by a significant increase in high-frequency oscillations upon seizure-onset. Overall, our data indicate that: (i) biologically-distinct epileptogenic lesions share intracranial electroencephalographic seizure-onset patterns, suggesting that different pathological substrates can affect similarly networks or mechanisms underlying seizure generation; (ii) certain pathologies are associated with intracranial electroencephalographic signatures at seizure-onset, e.g. periodic spikes which may reflect mechanisms specific to mesial temporal sclerosis; (iii) some seizure-onset patterns, including periodic spikes, can also be found in regions of spread, which cautions against relying on the morphology of the initial discharge to define the epileptogenic zone; and (iv) high-frequency oscillations increase at seizure-onset, independently of the pattern. [ABSTRACT FROM PUBLISHER]

Surgical treatment of epilepsy is a challenge for patients with non-contributive brain magnetic resonance imaging. However, surgery is feasible if the seizure-onset zone is precisely delineated through intracranial electroencephalography recording. We recently described a method, volumetric imaging of epileptic spikes, to delineate the spiking volume of patients with focal epilepsy using magnetoencephalography. We postulated that the extent of the spiking volume delineated with volumetric imaging of epileptic spikes could predict the localizability of the seizure-onset zone by intracranial electroencephalography investigation and outcome of surgical treatment. Twenty-one patients with non-contributive magnetic resonance imaging findings were included. All patients underwent intracerebral electroencephalography investigation through stereotactically implanted depth electrodes (stereo-electroencephalography) and magnetoencephalography with delineation of the spiking volume using volumetric imaging of epileptic spikes. We evaluated the spatial congruence between the spiking volume determined by magnetoencephalography and the localization of the seizure-onset zone determined by stereo-electroencephalography. We also evaluated the outcome of stereo-electroencephalography and surgical treatment according to the extent of the spiking volume (focal, lateralized but non-focal or non-lateralized). For all patients, we found a spatial overlap between the seizure-onset zone and the spiking volume. For patients with a focal spiking volume, the seizure-onset zone defined by stereo-electroencephalography was clearly localized in all cases and most patients (6/7, 86%) had a good surgical outcome. Conversely, stereo-electroencephalography failed to delineate a seizure-onset zone in 57% of patients with a lateralized spiking volume, and in the two patients with bilateral spiking volume. Four of the 12 patients with non-focal spiking volumes were operated upon, none became seizure-free. As a whole, patients having focal magnetoencephalography results with volumetric imaging of epileptic spikes are good surgical candidates and the implantation strategy should incorporate volumetric imaging of epileptic spikes results. On the contrary, patients with non-focal magnetoencephalography results are less likely to have a localized seizure-onset zone and stereo electroencephalography is not advised unless clear localizing information is provided by other presurgical investigation methods. [ABSTRACT FROM PUBLISHER]

Transient high-frequency (100–500 Hz) oscillations of the local field potential have been studied extensively in human mesial temporal lobe. Previous studies report that both ripple (100–250 Hz) and fast ripple (250–500 Hz) oscillations are increased in the seizure-onset zone of patients with mesial temporal lobe epilepsy. Comparatively little is known, however, about their spatial distribution with respect to seizure-onset zone in neocortical epilepsy, or their prevalence in normal brain. We present a quantitative analysis of high-frequency oscillations and their rates of occurrence in a group of nine patients with neocortical epilepsy and two control patients with no history of seizures. Oscillations were automatically detected and classified using an unsupervised approach in a data set of unprecedented volume in epilepsy research, over 12 terabytes of continuous long-term micro- and macro-electrode intracranial recordings, without human preprocessing, enabling selection-bias-free estimates of oscillation rates. There are three main results: (i) a cluster of ripple frequency oscillations with median spectral centroid = 137 Hz is increased in the seizure-onset zone more frequently than a cluster of fast ripple frequency oscillations (median spectral centroid = 305 Hz); (ii) we found no difference in the rates of high frequency oscillations in control neocortex and the non-seizure-onset zone neocortex of patients with epilepsy, despite the possibility of different underlying mechanisms of generation; and (iii) while previous studies have demonstrated that oscillations recorded by parenchyma-penetrating micro-electrodes have higher peak 100–500 Hz frequencies than penetrating macro-electrodes, this was not found for the epipial electrodes used here to record from the neocortical surface. We conclude that the relative rate of ripple frequency oscillations is a potential biomarker for epileptic neocortex, but that larger prospective studies correlating high-frequency oscillations rates with seizure-onset zone, resected tissue and surgical outcome are required to determine the true predictive value. [ABSTRACT FROM PUBLISHER]

Focal seizures appear to start abruptly and unpredictably when recorded from volumes of brain probed by clinical intracranial electroencephalograms. To investigate the spatiotemporal scale of focal epilepsy, wide-bandwidth electrophysiological recordings were obtained using clinical macro- and research microelectrodes in patients with epilepsy and control subjects with intractable facial pain. Seizure-like events not detectable on clinical macroelectrodes were observed on isolated microelectrodes. These ‘microseizures’ were sparsely distributed, more frequent in brain regions that generated seizures, and sporadically evolved into large-scale clinical seizures. Rare microseizures observed in control patients suggest that this phenomenon is ubiquitous, but their density distinguishes normal from epileptic brain. Epileptogenesis may involve the creation of these topographically fractured microdomains and ictogenesis (seizure generation), the dynamics of their interaction and spread. [ABSTRACT FROM PUBLISHER]

Interictal high-frequency oscillations over 200 Hz have been recorded with microelectrodes in the seizure onset zone of epileptic patients suffering from mesial temporal lobe epilepsy. Recent work suggests that similar high-frequency oscillations can be detected in the seizure onset zone using standard diagnostic macroelectrodes. However, only a few channels were examined in these studies, so little information is available on the spatial extent of high-frequency oscillations. Here, we present data on high-frequency oscillations recorded from a larger number of intracerebral contacts spatial (mean 38) in 16 patients. Data were obtained from 1 h of interictal recording sampled at 1024 Hz and was analysed using a new semi-automatic detection procedure based on a wavelet decomposition. A detailed frequency analysis permitted a rapid and reliable discrimination of high-frequency oscillations from other high-frequency events. A total of 1932 high-frequency oscillations were detected with an average frequency of 261 ± 53 Hz, amplitude of 11.9 ± 6.7 μV and duration of 22.7 ± 11.6 ms. Records from a patient often showed several different high-frequency oscillation patterns. We classified 24 patterns from 11 patients. Usually (20/24 patterns) high-frequency oscillations were nested in an epileptic paroxysm, such as a spike or a sharp wave, and typically high-frequency oscillations (19/24) were recorded from just one recording contact. Unexpectedly in other cases, high-frequency oscillations (5/24) were detected simultaneously on two or three contacts, sometimes separated by large distances. This large spatial extent suggests that high-frequency oscillations may sometimes result from a neuronal synchrony manifest on a scale of centimetres. High-frequency oscillations were almost always recorded in seizure-generating structures of patients suffering from mesial (9/9) or polar (1/3) temporal lobe epilepsy. They were never found in the epileptic or healthy basal, lateral temporal or extra temporal neocortex nor in the healthy amygdalo–hippocampal complex. These findings confirm that the generation of oscillations at frequencies higher that 200 Hz is, at this scale, a specific, intrinsic property of seizure-generating networks in medial and polar temporal lobes, which have a common archaic phylogenetic origin. We show that this activity can be detected and its spatial extent determined with conventional intracranial electroencephalography electrodes in records from patients with temporal lobe epilepsy. It is a reliable marker of the seizure onset zone that should be considered in decisions on surgical treatment. [ABSTRACT FROM PUBLISHER]

Interictal high frequency oscillations (HFOs), in particular those with frequency components in excess of 200 Hz, have been proposed as important biomarkers of epileptic cortex as well as the genesis of seizures. We investigated the spatial extent, classification and distribution of HFOs using a dense 4 × 4 mm2 two dimensional microelectrode array implanted in the neocortex of four patients undergoing epilepsy surgery. The majority (97%) of oscillations detected included fast ripples and were concentrated in relatively few recording sites. While most HFOs were limited to single channels, ∼10% occurred on a larger spatial scale with simultaneous but morphologically distinct detections in multiple channels. Eighty per cent of these large-scale events were associated with interictal epileptiform discharges. We propose that large-scale HFOs, rather than the more frequent highly focal events, are the substrates of the HFOs detected by clinical depth electrodes. This feature was prominent in three patients but rarely seen in only one patient recorded outside epileptogenic cortex. Additionally, we found that HFOs were commonly associated with widespread interictal epileptiform discharges but not with locally generated ‘microdischarges’. Our observations raise the possibility that, rather than being initiators of epileptiform activity, fast ripples may be markers of a secondary local response. [ABSTRACT FROM PUBLISHER]