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The theory of transient cognitive impairment in epilepsy posits that lapses in attention result from ephemeral disruption of attentional circuitry by interictal events. Eye movements are intimately associated with human attention and can be monitored in real time using eye-tracking technologies. Here, we sought to characterize the associations between interictal epileptiform discharges (IEDs), gaze, and attentional behavior in children with epilepsy.Eleven consecutive children undergoing invasive monitoring with stereotactic electrodes for localization-related epilepsy performed an attentional set-shifting task while tandem intracranial electroencephalographic signals and eye-tracking data were recorded. Using an established algorithm, IEDs were detected across all intracranial electrodes on a trial-by-trial basis. Hierarchical mixed-effects modeling was performed to delineate associations between trial reaction time (RT), eye movements, and IEDs.Hierarchical mixed-effects modeling revealed that both the presence of an IED (β ± SE = 72.74 ± 24.21 ms, p = .003) and the frequency of epileptiform events (β ± SE = 67.54 ± 17.30 ms, p .001) were associated with prolonged RT on the attentional set-shifting task. IED occurrence at the time of stimulus presentation was associated with delays in gaze initiation toward the visual targets (p = .017).The occurrence of epileptiform activity in close temporal association with stimulus presentation is associated with delays in target-directed gaze and prolonged response time, hallmarks of momentary lapses in attention. These findings provide novel insights into the mechanisms of transient impairments in children and support the use of visual tracking as a correlate of higher order attentional behavior.

Although vagus nerve stimulation (VNS) is a common procedure, seizure outcomes are heterogeneous, with few available means to preoperatively identify the ideal surgical candidate. Here, we perform a scoping review of the literature to identify biomarkers of VNS response in patients with drug-resistant epilepsy. Several databases (Ovid MEDLINE, Ovid Embase, BIOSIS Previews, and Web of Science) were searched for all relevant articles that reported at least one biomarker of VNS response following implantation for intractable epilepsy. Patient demographics, seizure data, and details related to biomarkers were abstracted from all studies. From the 288 records screened, 28 articles reporting on 16 putative biomarkers were identified. These were grouped into four categories: network/connectomic-based biomarkers, electrophysiological signatures, structural findings on neuroimaging, and systemic assays. Differences in brain network organization, connectivity, and electrophysiological synchronicity demonstrated the most robust ability to identify VNS responders. Structural findings on neuroimaging yielded inconsistent associations with VNS responsiveness. With regard to systemic biomarkers, heart rate variability was shown to be an independent marker of VNS response, whereas inflammatory markers were not useful. There is an unmet need to preoperatively identify candidates who are likely to benefit from VNS. Several biomarkers demonstrate promise in predicting seizure responsiveness to VNS, particularly measures of brain network connectivity. Further efforts are required to validate existing biomarkers to inform clinical decision-making.

Working memory deficits are prevalent in childhood epilepsy. Working memory processing is thought to be supported by the phase of hippocampal neural oscillations. Disruptions in working memory have previously been linked to the occurrence of transient epileptic activity. This study aimed to resolve the associations between oscillatory neural activity, transient epileptiform events, and working memory in children with epilepsy.Intracranial recordings were acquired from stereotactically implanted electrodes in the hippocampi, epileptogenic zones, and working memory-related networks of children with drug-resistant epilepsy during a 1-back working memory task. Interictal epileptic activity was captured using automated detectors. Hippocampal phase and interregional connectivity within working memory networks were indexed by Rayleigh Z and the phase difference derivative, respectively. Trials with and without transient epileptiform events were compared.Twelve children (mean age = 14.3 ± 2.8 years) with drug-resistant epilepsy were included in the study. In the absence of transient epileptic activity, significant delta and theta hippocampal phase resetting occurred in response to working memory stimulus presentation (Rayleigh z-score = 9, Rayleigh z-score = 8). Retrieval trials that were in phase with the preferred phase angle were associated with faster reaction times (p = .01, p = .03). Concurrently, delta and theta coordinated interactions between the hippocampi and working memory-related networks were enhanced (phase difference derivative [PDD] z-scores = 6-11). During retrieval trials with pre-encoding or pre-retrieval transient epileptic activity, phase resetting was attenuated (Rayleigh z-score = 5, Rayleigh z-score = 1), interregional connectivity was altered (PDD z-scores = 1-3), and reaction times were prolonged (p = .01, p = .03).This work highlights the role of hippocampal phase in working memory. We observe poststimulus hippocampal phase resetting coincident with enhanced interregional connectivity. The precision of hippocampal phase predicts optimal working memory processing, and transient epileptic activity prolongs working memory processing. These findings can help guide future treatments aimed at restoring memory function in this patient population.

BackgroundCognitive flexibility encompasses the ability to efficiently shift focus and forms a critical component of goal-directed attention. The neural substrates of this process are incompletely understood in part due to difficulties in sampling the involved circuitry.MethodsStereotactic intracranial recordings that permit direct resolution of local-field potentials from otherwise inaccessible structures were employed to study moment-to-moment attentional activity in children with epilepsy during the performance of an attentional set-shifting task. A combined deep learning and model-agnostic feature explanation approach was used to analyze these data and decode attentionally-relevant neural features. Connectomic profiling of highly predictive attentional nodes was further employed to examine task-related engagement of large-scale functional networks.ResultsThrough this approach, we show that beta/gamma power within executive control, salience, and default mode networks accurately predicts single-trial attentional performance. Connectomic profiling reveals that key attentional nodes exclusively recruit dorsal default mode subsystems during attentional shifts.ConclusionsThe identification of distinct substreams within the default mode system supports a key role for this network in cognitive flexibility and attention in children. Furthermore, convergence of our results onto consistent functional networks despite significant inter-subject variability in electrode implantations supports a broader role for deep learning applied to intracranial electrodes in the study of human attention.FundingNo funds supported this specific investigation. Awards and grants supporting authors include: Canadian Institutes of Health Research (CIHR) Vanier Scholarship (NMW, HY); CIHR Frederick Banting and Charles Best Canada Graduate Scholarship Doctoral Award (SMW); CIHR Canada Graduate Scholarship Master’s Award (ONA); and a CIHR project grant (GMI).

The thalamus is a densely connected collection of nuclei that play a critical role in gating information flow across the neocortex. Through diffuse reciprocal cortico-thalamo-cortical connectivity, the anterior and centromedian nuclei exert remarkable control over cortically expressed activity. Consequently, mounting evidence implicates these thalamic centres in both the genesis and propagation of aberrant epileptiform activity across the brain. The present work reviews existing literature with regards to the anatomy, function, and dysfunction of the anterior and centromedian thalamic nuclei as they relate to epileptogenesis and ictal dynamics in humans. A confluence of electrophysiological, anatomical, and neuromodulatory evidence links these thalamic hubs to a variety of epilepsy syndromes. These data are discussed as they relate to targeted thalamic neuromodulation.

Decelerated resting cortical oscillations, high-frequency activity, and enhanced cross-frequency interactions are features of focal epilepsy. The association between electrophysiological signal properties and neurocognitive function, particularly following resective surgery, is, however, unclear. In the current report, we studied intraoperative recordings from intracranial electrodes implanted in seven children with focal epilepsy and analyzed the spectral dynamics both before and after surgical resection of the hypothesized seizure focus. The associations between electrophysiological spectral signatures and each child's neurocognitive profiles were characterized using a partial least squares analysis. We find that extent of spectral alteration at the periphery of surgical resection, as indexed by slowed resting frequency and its acceleration following surgery, is associated with baseline cognitive deficits in children. The current report provides evidence supporting the relationship between altered spectral properties in focal epilepsy and neuropsychological deficits in children. In particular, these findings suggest a critical role of disrupted thalamocortical rhythms, which are believed to underlie the spectral alterations we describe, in both epileptogenicity and neurocognitive function.

The neural mechanisms that underlie selective attention in children are poorly understood. By administering a set-shifting task to children with intracranial electrodes stereotactically implanted within anterior cingulate cortex (ACC) for epilepsy monitoring, we demonstrate that selective attention in a set-shifting task is dependent upon theta-band phase resetting immediately following stimulus onset and that the preferred theta phase angle is predictive of reaction time during attentional shift. We also observe selective enhancement of oscillatory coupling between the ACC and the dorsal attention network and decoupling with the default mode network during task performance. When transient focal epileptic activity occurs around the time of stimulus onset, phase resetting is impaired, connectivity changes with attentional and default mode networks are abolished, and reaction times are prolonged. The results of the present work highlight the fundamental mechanistic role of oscillatory phase in ACC in supporting attentional circuitry and present novel opportunities to remediate attention deficits in children with epilepsy.

Purpose High frequency oscillations (HFOs) are putative biomarkers of epileptogenicity. These electrophysiological phenomena can be effectively detected in electroencephalography using automated methods. Nonetheless, the implementation of these methods into clinical practice remains challenging as significant variability exists between algorithms and their characterizations of HFOs. Here, we perform a scoping review of the literature pertaining to automated HFO detection methods. In addition, we propose a framework for defining and detecting HFOs based on a simplified single-stage time-frequency based detection algorithm with clinically-familiar parameters. Methods Several databases (OVID Medline, Web of Science, PubMed) were searched for articles presenting novel, automated HFO detection methods. Details related to the algorithm and various stages of data acquisition, pre-processing, and analysis were abstracted from included studies. Results From the 261 records screened, 57 articles presented novel, automated HFO detection methods and were included in the scoping review. These algorithms were categorized into 3 groups based on their most salient features: energy thresholding, time-frequency analysis, and data mining/machine learning. Algorithms were optimized for specific datasets and suffered from low specificity. A framework for user-constrained inputs is proposed to circumvent some of the weaknesses of highly performant detectors. Conclusions Further efforts are required to optimize and validate existing automated HFO detection methods for clinical utility. The proposed framework may be applied to understand and standardize the variations in HFO definitions across institutions.