Faculty Bibliography

Interictal epileptiform discharges (IEDs) can impair memory. The properties of IEDs most detrimental to memory, however, are undefined. We studied the impact of temporal and spatial characteristics of IEDs on list learning. Subjects completed a memory task during intracranial EEG recordings including hippocampal depth and temporal neocortical subdural electrodes. Subjects viewed a series of objects, and after a distracting task, recalled the objects from the list. The impacts of IED presence, duration, and propagation to neocortex during encoding of individual stimuli were assessed. The effects of IED total number and duration during maintenance and recall periods on delayed recall performance were also determined. The influence of IEDs during recall was further investigated by comparing the likelihood of IEDs preceding correctly recalled items vs. periods of no verbal response. Across 6 subjects, we analyzed 28 hippocampal and 139 lateral temporal contacts. Recall performance was poor, with a median of 17.2% correct responses (range 10.4-21.9%). Interictal epileptiform discharges during encoding, maintenance, and recall did not significantly impact task performance, and there was no significant difference between the likelihood of IEDs during correct recall vs. periods of no response. No significant effects of discharge duration during encoding, maintenance, or recall were observed. Interictal epileptiform discharges with spread to lateral temporal cortex during encoding did not adversely impact recall. A post hoc analysis refining model assumptions indicated a negative impact of IED count during the maintenance period, but otherwise confirmed the above results. Our findings suggest no major effect of hippocampal IEDs on list learning, but study limitations, such as baseline hippocampal dysfunction, should be considered. The impact of IEDs during the maintenance period may be a focus of future research.

OBJECTIVE/UNASSIGNED:Findings highlight concerns regarding the usefulness of the RCFT in TLE lateralization, regardless of scoring approach.

PURPOSE/OBJECTIVE:A phase I feasibility study to determine the accuracy of identifying seizures based on audio recordings. METHODS:We systematically generated 166 audio clips of 30 s duration from 83 patients admitted to an epilepsy monitoring unit between 1/2015 and 12/2016, with one clip during a seizure period and one clip during a non-seizure control period for each patient. Five epileptologists performed a blinded review of the audio clips and rated whether a seizure occurred or not, and indicated the confidence level (low or high) of their rating. The accuracy of individual and consensus ratings were calculated. RESULTS:The overall performance of the consensus rating between the five epileptologists showed a positive predictive value (PPV) of 0.91 and a negative predictive value (NPV) of 0.66. The performance improved when confidence was high (PPV of 0.96, NPV of 0.70). The agreement between the epileptologists was moderate with a kappa of 0.584. Hyperkinetic (PPV 0.92, NPV 0.86) and tonic-clonic (PPV and NPV 1.00) seizures were most accurately identified. Seizures with automatisms only and non-motor seizures could not be accurately identified. Specific seizure-related sounds associated with accurate identification included disordered breathing (PPV and NPV 1.00), rhythmic sounds (PPV 0.93, NPV 0.80), and ictal vocalizations (PPV 1.00, NPV 0.97). CONCLUSION/CONCLUSIONS:This phase I feasibility study shows that epileptologists are able to accurately identify certain seizure types from audio recordings when the seizures produce sounds. This provides guidance for the development of audio-based seizure detection devices and demonstrate which seizure types could potentially be detected.

OBJECTIVE:To describe seizure outcomes in patients with medically refractory epilepsy who had evidence of bilateral mesial temporal lobe (MTL) seizure onsets and underwent MTL resection based on chronic ambulatory intracranial EEG (ICEEG) data from a direct brain-responsive neurostimulator (RNS) system. METHODS:We retrospectively identified all patients at 17 epilepsy centers with MTL epilepsy who were treated with the RNS System using bilateral MTL leads, and in whom an MTL resection was subsequently performed. Presumed lateralization based on routine presurgical approaches was compared to lateralization determined by RNS System chronic ambulatory ICEEG recordings. The primary outcome was frequency of disabling seizures at last 3-month follow-up after MTL resection compared to seizure frequency 3 months before MTL resection. RESULTS:We identified 157 patients treated with the RNS System with bilateral MTL leads due to presumed bitemporal epilepsy. Twenty-five patients (16%) subsequently had an MTL resection informed by chronic ambulatory ICEEG (mean = 42 months ICEEG); follow-up was available for 24 patients. After MTL resection, the median reduction in disabling seizures at last follow-up was 100% (mean: 94%; range: 50%-100%). Nine patients (38%) had exclusively unilateral electrographic seizures recorded by chronic ambulatory ICEEG and all were seizure-free at last follow-up after MTL resection; eight of nine continued RNS System treatment. Fifteen patients (62%) had bilateral MTL electrographic seizures, had an MTL resection on the more active side, continued RNS System treatment, and achieved a median clinical seizure reduction of 100% (mean: 90%; range: 50%-100%) at last follow-up, with eight of fifteen seizure-free. For those with more than 1 year of follow-up (N = 21), 15 patients (71%) were seizure-free during the most recent year, including all eight patients with unilateral onsets and 7 of 13 patients (54%) with bilateral onsets. SIGNIFICANCE/CONCLUSIONS:Chronic ambulatory ICEEG data provide information about lateralization of MTL seizures and can identify additional patients who may benefit from MTL resection.

Memory consolidation is hypothesized to involve the distribution and restructuring of memory representations across hippocampal and cortical regions. Theories suggest that, through extended hippocampal-cortical interactions, cortical ensembles come to represent more integrated, or overlapping, memory traces that prioritize commonalities across related memories. Sleep processes, particularly fast sleep spindles, are thought to support consolidation, but evidence for this relationship has been mostly limited to memory retention benefits. Whether fast spindles provide a mechanism for neural changes hypothesized to support consolidation, including the strengthening of hippocampal-cortical networks and integration across memory representations, remains unclear, as does the specificity of regions involved. Using functional connectivity analyses of human fMRI data (both sexes), we show that fast spindle density during overnight sleep is related to enhanced hippocampal-cortical functional connectivity the next day, when re-studying information learned before sleep. Spindle density modulated connectivity in distinct hippocampal-cortical networks depending on the category of the consolidated stimuli. Specifically, spindle density correlated with functional connectivity between anterior hippocampus and ventromedial prefrontal cortex (vmPFC) for object-word pairs, and posterior hippocampus and posteromedial cortex (PMC) for scene-word pairs. Using multivariate pattern analyses, we also show fast spindle density during post-learning sleep is associated with greater pattern similarity, or representational overlap, across individual object-word memories in vmPFC the next day. Further, the relationship between fast spindle density and representational overlap in vmPFC was mediated by the degree of anterior hippocampal-vmPFC functional connectivity. Together, these results suggest fast spindles support the network distribution of memory traces, potentially restructuring memory representations in vmPFC.SIGNIFICANCE STATEMENTHow new experiences are transformed into long-term memories remains a fundamental question for neuroscience research. Theories suggest that memories are stabilized as they are reorganized in the brain, a process thought to be supported by sleep oscillations, particularly sleep spindles. Although sleep spindles have been associated with benefits in memory retention, it is not well understood how spindles modify neural memory traces. This study found that spindles during overnight sleep correlate with changes in neural memory traces, including enhanced functional connectivity in distinct hippocampal-cortical networks and increased pattern similarity amongst memories in the cortex. The results provide critical evidence that spindles during overnight sleep may act as a physiological mechanism for the restructuring of neural memory traces.

BACKGROUND:Early accounts of forced thought were reported at the onset of a focal seizure, and characterized as vague, repetitive, and involuntary intellectual auras distinct from perceptual or psychic hallucinations or illusions. Here, we examine the neural underpinnings involved in conceptual thought by presenting a series of 3 patients with epilepsy reporting intrusive thoughts during electrical stimulation of the left lateral prefrontal cortex (PFC) during invasive surgical evaluation. We illustrate the widespread networks involved through two independent brain imaging modalities: resting state functional magnetic resonance imaging (fMRI) (rs-fMRI) and task-based meta-analytic connectivity modeling (MACM). METHODS:We report the clinical and stimulation characteristics of three patients with left hemispheric language dominance who demonstrate forced thought with functional mapping. To examine the brain networks underlying this phenomenon, we used the regions of interest (ROI) centered at the active electrode pairs. We modeled functional networks using two approaches: (1) rs-fMRI functional connectivity analysis, representing 81 healthy controls and (2) meta-analytic connectivity modeling (MACM), representing 8260 healthy subjects. We also determined the overlapping regions between these three subjects' rs-fMRI and MACM networks through a conjunction analysis. RESULTS:We identified that left PFC was associated with a large-scale functional network including frontal, temporal, and parietal regions, a network that has been associated with multiple cognitive functions including semantics, speech, attention, working memory, and explicit memory. CONCLUSIONS:We illustrate the neural networks involved in conceptual thought through a unique patient population and argue that PFC supports this function through activation of a widespread network.

Careful study of the clinical outcomes of temporal lobe epilepsy (TLE) surgery has greatly advanced our knowledge of the neuroanatomy of human memory. After early cases resulted in profound amnesia, the critical role of the hippocampus and associated medial temporal lobe (MTL) structures to declarative memory became evident. Surgical approaches quickly changed to become unilateral and later, to be more precise, potentially reducing cognitive morbidity. Neuropsychological studies following unilateral temporal lobe resection (TLR) have challenged early models, which simplified the lateralization of verbal and visual memory function. Diagnostic tests, including intracarotid sodium amobarbital procedure (WADA), structural magnetic resonance imaging (MRI), and functional neuroimaging (functional MRI (fMRI), positron emission tomography (PET), and single-photon emission computed tomography (SPECT)), can more accurately lateralize and localize epileptogenic cortex and predict memory outcomes from surgery. Longitudinal studies have shown that memory may even improve in seizure-free patients. From 70 years of experience with epilepsy surgery, we now have a richer understanding of the clinical, neuroimaging, and surgical predictors of memory decline-and improvement-after TLR. "Special Issue: Epilepsy & Behavior's 20th Anniversary".

Noninvasive transcranial brain stimulation has been widely used in experimental and clinical applications to perturb the brain activity, aiming at promoting synaptic plasticity or enhancing functional connectivity within targeted brain regions. However, there are different types of neurostimulations and various choices of stimulation parameters; how these choices influence the intermediate neurophysiological effects and brain connectivity remain incompletely understood. We propose several quantitative methods to investigate the brain connectivity of an epileptic patient before and after transcranial alternating/direct current stimulation (tACS/tDCS). The neuro-feedback derived from our analyses may provide useful cues for the effectiveness of neurostimulation.

Magnetic resonance imaging (MRI)-negative temporal lobe epilepsy (TLE) may be a distinct syndrome from TLE with mesial temporal sclerosis (TLE-MTS). Imaging and neuropsychological features of TLE-MTS are well-known; yet, these features are only beginning to be described in MRI-negative TLE. This study examined whether a quantitative measure of cortical gray and white matter blurring (GWB) was elevated in the temporal lobes ipsilateral to the seizure onset zone of individuals with MRI-negative TLE relative to TLE-MTS and healthy controls (HCs) and whether GWB elevations were associated with neuropsychological comorbidity. Gray-white matter blurring from 34 cortical regions and hippocampal volumes were quantified and compared across 28 people with MRI-negative TLE, 15 people with TLE-MTS, and 51 HCs. Declarative memory was assessed with standard neuropsychological tests and the intracarotid amobarbital procedure (IAP). In the group with MRI-negative TLE (left and right onsets combined), hippocampal volumes were within normal range but GWB was elevated, relative to HCs, across several mesial and lateral temporal lobe regions ipsilateral to the seizure onset zone. Gray-white matter blurring did not differ between the groups with TLE-MTS and HC or between the groups with TLE-MTS and MRI-negative TLE. The group with MRI-negative TLE could not be distinguished from the group with TLE-MTS on any of the standard neuropsychological tests; however, ipsilateral hippocampal volumes and IAP memory scores were lower in the group with TLE-MTS than in the group with MRI-negative TLE. The group with left MRI-negative TLE had lower general cognitive abilities and verbal fluency relative to the HC group, which adds to the characterization of neuropsychological comorbidities in left MRI-negative TLE. In addition, ipsilateral IAP memory performance was reduced relative to contralateral memory performance in MRI-negative TLE, indicating some degree of ipsilateral memory dysfunction. There was no relationship between hippocampal volume and IAP memory scores in MRI-negative TLE; however, decreased ipsilateral IAP memory scores were correlated with elevated GWB in the ipsilateral superior temporal sulcus of people with left MRI-negative TLE. In sum, GWB elevations in the ipsilateral temporal lobe of people with MRI-negative TLE suggest that GWB may serve as a marker for reduced structural integrity in regions in or near the seizure onset zone. Although mesial temporal abnormalities might be the major driver of memory dysfunction in TLE-MTS, a loss of structural integrity in lateral temporal lobe regions may contribute to IAP memory dysfunction in MRI-negative TLE.

OBJECTIVE:Sleep spindles have been implicated in memory consolidation and synaptic plasticity during NREM sleep. Detection accuracy and latency in automatic spindle detection are critical for real-time applications. APPROACH/METHODS:Here we propose a novel deep learning strategy (SpindleNet) to detect sleep spindles based on a single EEG channel. While the majority of spindle detection methods are used for off-line applications, our method is well suited for online applications. MAIN RESULTS/RESULTS:Compared with other spindle detection methods, SpindleNet achieves superior detection accuracy and speed, as demonstrated in two publicly available expert-validated EEG sleep spindle datasets. Our real-time detection of spindle onset achieves detection latencies of 150-350 ms (~2-3 spindle cycles) and retains excellent performance under low EEG sampling frequencies and low signal-to-noise ratios. SpindleNet has good generalization across different sleep datasets from various subject groups of different ages and species. SIGNIFICANCE/CONCLUSIONS:SpindleNet is ultra-fast and scalable to multichannel EEG recordings, with an accuracy level comparable to human experts, making it appealing for long-term sleep monitoring and closed-loop neuroscience experiments. &#13.