Faculty Bibliography

Hippocampal sharp-wave ripples (SPW-Rs) are high-frequency oscillations critical for memory consolidation. Despite extensive characterization in rodents, their detection in humans is limited by coarse spatial sampling, interictal epileptiform discharges (IEDs), and a lack of consensus on human ripple localization and morphology. Here, we demonstrate that mouse and human hippocampal ripples share spatial, spectral and temporal features, which are clearly distinct from IEDs. In recordings from male APP/PS1 mice, SPW-Rs were distinguishable from IEDs by multiple criteria. Hippocampal ripples recorded during NREM sleep in female and male surgical epilepsy patients exhibited similar narrowband frequency peaks and multiple ripple cycles in the CA1 and subiculum regions. Conversely, IEDs showed a broad spatial extent and wide-band frequency power. We developed a semi-automated, ripple curation toolbox (ripmap) to separate event waveforms by low-dimensional embedding to reduce false-positive rate in selected ripple channels. Our approach improves ripple detection and provides a firm foundation for future human memory research.

Electroencephalography (EEG) has been instrumental in epilepsy research for the past century, both for basic and translational studies. Its contributions have advanced our understanding of epilepsy, shedding light on the pathophysiology and functional organization of epileptic networks, and the mechanisms underlying seizures. Here we re-examine the historical significance, ongoing relevance, and future trajectories of EEG in epilepsy research. We describe traditional approaches to record brain electrical activity and discuss novel cutting-edge, large-scale techniques using micro-electrode arrays. Contemporary EEG studies explore brain potentials beyond the traditional Berger frequencies to uncover underexplored mechanisms operating at ultra-slow and high frequencies, which have proven valuable in understanding the principles of ictogenesis, epileptogenesis, and endogenous epileptogenicity. Integrating EEG with modern techniques such as optogenetics, chemogenetics, and imaging provides a more comprehensive understanding of epilepsy. EEG has become an integral element in a powerful suite of tools for capturing epileptic network dynamics across various temporal and spatial scales, ranging from rapid pathological synchronization to the long-term processes of epileptogenesis or seizure cycles. Advancements in EEG recording techniques parallel the application of sophisticated mathematical analyses and algorithms, significantly augmenting the information yield of EEG recordings. Beyond seizures and interictal activity, EEG has been instrumental in elucidating the mechanisms underlying epilepsy-related cognitive deficits and other comorbidities. Although EEG remains a cornerstone in epilepsy research, persistent challenges such as limited spatial resolution, artifacts, and the difficulty of long-term recording highlight the ongoing need for refinement. Despite these challenges, EEG continues to be a fundamental research tool, playing a central role in unraveling disease mechanisms and drug discovery.

Monitoring representative fractions of neurons from multiple brain circuits in behaving animals is necessary for understanding how different brain regions interact. Using multishank, high-density recording silicon probes (up to 1,024 sites), we describe the main characteristic LFP patterns in the hippocampus, including sharp wave-ripples (SPW-Rs), dentate spikes (DSs), theta, and gamma oscillations. Our novel observations primarily relate to the distinction between subclasses of SPW-Rs and DSs, as well as their neuronal spiking correlations. In addition to the classical SPW-Rs, initiated in the CA2-3 recurrent collateral system and characterized by a large negative sharp wave (sink) in the mid-CA1 stratum radiatum (SPW-R^Rad), a small subset of ripples, associated with a sink in CA1 str. lacunosum-moleculare was also observed (SPW-R^LM). The two types of ripple events differed in frequency, magnitude, and neuronal correlates. CA3 pyramidal neurons were strongly active during SPW-R^Rad but not during (SPW-R^LM). DSs could also be grouped further based on their excitatory inputs from the medial and lateral entorhinal cortex (DS^MEC and DS^LEC), by their impact on their physiological targets, and by the brain states into which they were embedded. Overall, our experiments demonstrate the utility and need for high-density recording of both LFP and spiking activity for the appropriate classification of seemingly similar events. These distinctions relate not only to their neurogenesis but also to their behavioral-cognitive contributions.

Humans primarily use vision to engage with and learn about the world. The hippocampus plays a crucial role in binding visual experiences of people, objects and contexts over time to create event memories. Thus, eye tracking could read out hippocampal dynamics in a precise and sensitive manner. Furthermore, eye tracking could potentially detect subjective memory decline reported by temporal lobe epilepsy patients that is missed by standardized cognitive testing. We asked whether eye movements could precisely and sensitively detect memory variability within trials and between subject cohorts. We predicted that (i) eye-tracking behaviour during visual retrieval could be validated against accuracy-based tests and that (ii) memory failures would be characterized by distinct spatiotemporal patterns of visual scanning. Fourteen healthy controls and 30 temporal lobe epilepsy patients participated in a visual object association task while eye movements and pupil size were recorded. We found a difference in accuracy during retrieval between healthy controls and temporal lobe epilepsy patients. Correct retrieval trials correlated with fewer saccades, early target preference, and a more organized search pattern. Eye-movement patterns could predict retrieval accuracy at the single trial level with outstanding performance, with percentage of gaze time on the target versus the lure as the most important features. Even during correct retrieval trials, temporal lobe epilepsy patients exhibited a more chaotic scanning pattern compared to healthy controls, suggesting a weaker memory trace. Healthy versus epilepsy diagnosis could be predicted with good performance, with trial entropy and pupillary changes as key predictive factors. Saccade patterns correlated with individual subjects' accuracy scores and performance on standardized cognitive tests but provided a greater range of performance. In summary, scanning behaviour provides a continuous measure of associative memory function that capture meaningful variability during trials, between trials, and between subjects. Thus, eye tracking could be a precise and sensitive method to detect subtle memory decline in temporal lobe epilepsy or other neuropsychiatric populations with memory impairment and may generate precise behavioural phenotyping in research settings.

In medication-resistant epilepsy, the goal of epilepsy surgery is to make a patient seizure free with a resection/ablation that is as small as possible to minimize morbidity. The standard of care in planning the margins of epilepsy surgery involves electroclinical delineation of the seizure onset zone (SOZ) and incorporation of neuroimaging findings from MRI, PET, SPECT, and MEG modalities. Resecting cortical tissue generating high-frequency oscillations (HFOs) has been investigated as a more efficacious alternative to targeting the SOZ. In this study, we used a support vector machine (SVM), with four distinct fast ripple (FR: 350-600 Hz on oscillations, 200-600 Hz on spikes) metrics as factors. These metrics included the FR resection ratio (RR), a spatial FR network measure, and two temporal FR network measures. The SVM was trained by the value of these four factors with respect to the actual resection boundaries and actual seizure free labels of 18 patients with medically refractory focal epilepsy. Leave one out cross-validation of the trained SVM in this training set had an accuracy of 0.78. We next used a simulated iterative virtual resection targeting the FR sites that were highest rate and showed most temporal autonomy. The trained SVM utilized the four virtual FR metrics to predict virtual seizure freedom. In all but one of the nine patients seizure free after surgery, we found that the virtual resections sufficient for virtual seizure freedom were larger in volume (p<0.05). In nine patients who were not seizure free, a larger virtual resection made five virtually seizure free. We also examined 10 medically refractory focal epilepsy patients implanted with the responsive neurostimulator system (RNS) and virtually targeted the RNS stimulation contacts proximal to sites generating FR at highest rates to determine if the simulated value of the stimulated SOZ and stimulated FR metrics would trend toward those patients with a better seizure outcome. Our results suggest: 1) FR measures can accurately predict whether a resection, defined by the standard of care, will result in seizure freedom; 2) utilizing FR alone for planning an efficacious surgery can be associated with larger resections; 3) when FR metrics predict the standard of care resection will fail, amending the boundaries of the planned resection with certain FR generating sites may improve outcome; and 4) more work is required to determine if targeting RNS stimulation contact proximal to FR generating sites will improve seizure outcome.

In medication-resistant epilepsy, the goal of epilepsy surgery is to make a patient seizure free with a resection/ablation that is as small as possible to minimize morbidity. The standard of care in planning the margins of epilepsy surgery involves electroclinical delineation of the seizure-onset zone and incorporation of neuroimaging findings from MRI, PET, single-photon emission CT and magnetoencephalography modalities. Resecting cortical tissue generating high-frequency oscillations has been investigated as a more efficacious alternative to targeting the seizure-onset zone. In this study, we used a support vector machine (SVM), with four distinct fast ripple (FR: 350-600 Hz on oscillations, 200-600 Hz on spikes) metrics as factors. These metrics included the FR resection ratio, a spatial FR network measure and two temporal FR network measures. The SVM was trained by the value of these four factors with respect to the actual resection boundaries and actual seizure-free labels of 18 patients with medically refractory focal epilepsy. Leave-one-out cross-validation of the trained SVM in this training set had an accuracy of 0.78. We next used a simulated iterative virtual resection targeting the FR sites that were of highest rate and showed most temporal autonomy. The trained SVM utilized the four virtual FR metrics to predict virtual seizure freedom. In all but one of the nine patients who were seizure free after surgery, we found that the virtual resections sufficient for virtual seizure freedom were larger in volume (P < 0.05). In nine patients who were not seizure free, a larger virtual resection made five virtually seizure free. We also examined 10 medically refractory focal epilepsy patients implanted with the responsive neurostimulator system and virtually targeted the responsive neurostimulator system stimulation contacts proximal to sites generating FR at highest rates to determine if the simulated value of the stimulated seizure-onset zone and stimulated FR metrics would trend towards those patients with a better seizure outcome. Our results suggest the following: (i) FR measures can accurately predict whether a resection, defined by the standard of care, will result in seizure freedom; (ii) utilizing FR alone for planning an efficacious surgery can be associated with larger resections; (iii) when FR metrics predict the standard-of-care resection will fail, amending the boundaries of the planned resection with certain FR-generating sites may improve outcome and (iv) more work is required to determine whether targeting responsive neurostimulator system stimulation contact proximal to FR generating sites will improve seizure outcome.

PURPOSE/OBJECTIVE:Brain responsive neurostimulation (NeuroPace) treats patients with refractory focal epilepsy and provides chronic electrocorticography (ECoG). We explored how machine learning algorithms applied to interictal ECoG could assess clinical response to changes in neurostimulation parameters. METHODS:We identified five responsive neurostimulation patients each with ≥200 continuous days of stable medication and detection settings (median, 358 days per patient). For each patient, interictal ECoG segments for each month were labeled as "high" or "low" to represent relatively high or low long-episode (i.e., seizure) count compared with the median monthly long-episode count. Power from six conventional frequency bands from four responsive neurostimulation channels were extracted as features. For each patient, five machine learning algorithms were trained on 80% of ECoG, then tested on the remaining 20%. Classifiers were scored by the area-under-the-receiver-operating-characteristic curve. We explored how individual circadian cycles of seizure activity could inform classifier building. RESULTS:Support vector machine or gradient boosting models achieved the best performance, ranging from 0.705 (fair) to 0.892 (excellent) across patients. High gamma power was the most important feature, tending to decrease during low-seizure-frequency epochs. For two subjects, training on ECoG recorded during the circadian ictal peak resulted in comparable model performance, despite less data used. CONCLUSIONS:Machine learning analysis on retrospective background ECoG can classify relative seizure frequency for an individual patient. High gamma power was the most informative, whereas individual circadian patterns of seizure activity can guide model building. Machine learning classifiers built on interictal ECoG may guide stimulation programming.

Due to shared hippocampal dysfunction, patients with Alzheimer's dementia and late-onset epilepsy (LOE) report memory decline. Multiple studies have described the epidemiological, pathological, neurophysiological, and behavioral overlap between Alzheimer's Disease and LOE, implying a bi-directional relationship. We describe the neurobiological decline occurring at different spatial in AD and LOE patients, which may explain why their phenotypes overlap and differ. We provide suggestions for clinical recognition of dual presentation and novel approaches for behavioral testing that reflect an "inside-out," or biologically-based approach to testing memory. New memory and language assessments could detect-and treat-memory impairment in AD and LOE at an earlier, actionable stage.

Decades of rodent research have established the role of hippocampal sharp wave ripples (SPW-Rs) in consolidating and guiding experience. More recently, intracranial recordings in humans have suggested their role in episodic and semantic memory. Yet, common standards for recording, detection, and reporting do not exist. Here, we outline the methodological challenges involved in detecting ripple events and offer practical recommendations to improve separation from other high-frequency oscillations. We argue that shared experimental, detection, and reporting standards will provide a solid foundation for future translational discovery.

Religious experiences in epilepsy patients have provoked much interest with suggestions that hyperreligiosity is associated with temporal lobe seizures. Extreme varieties of religious behavior may be more frequent in epilepsy patients during ictal activity or during post-ictal psychotic episodes. We report a 75 year-old man with epilepsy who developed a progressive decline in cognition and behavior following a religious conversion 15 years earlier. He subsequently developed religious delusions of increasing severity and symptoms of Capgras syndrome. Brain imaging revealed bilateral posterior cortical atrophy, chronic right parieto-occipital encephalomalacia, and right mesial temporal sclerosis. Electroencephalograms and neuropsychological testing revealed initial right temporal lobe abnormalities followed by progressive frontal and bilateral dysfunction. The case highlights how a history of seizures, superimposed on sensory deprivation and a progressive impairment of right posterior and bilateral anterior brain function, may have contributed to religious conversion, which was followed by dementia and delusions involving religious content.