Berry, Brent

AbstractObjectiveCognitive deficits are one of the most debilitating comorbidities in epilepsy and other neurodegenerative, neuropsychiatric, and neurodevelopmental brain disorders. Current diagnostic and therapeutic options are limited and lack objective measures of the underlying neural activities. In this study, electrophysiological biomarkers that reflect cognitive functions in clinically validated batteries were determined to aid diagnosis and treatment in specific brain regions.MethodsWe employed the Cambridge Neuropsychological Test Automated Battery (CANTAB) tasks to probe memory and executive functions in 86 patients with epilepsy undergoing clinical electroencephalography (EEG) monitoring. EEG electrode signals during performance of particular battery tasks were decomposed to identify specific frequency bands and cortical areas that differentiated patients with impaired, normal, and good standardized performance according to their age and gender.ResultsThe anterior prefrontal cortical EEG power in the theta frequency band was consistently lower in patients with impaired memory and executive function performance (z‐score < −1). This effect was evident in all four behavioral measures of executive, visual, spatial, and working memory functions and was confined to the cortical area of all four frontal pole electrodes (Nz, Fpz, Fp1, and Fp2).SignificanceTheta EEG power in the anterior prefrontal cortex provides simple, accessible, and objective electrophysiological measure of memory and executive functions in epilepsy. Our results suggest a feasible clinical biomarker for diagnosis, monitoring, and treatment of cognitive deficits with emerging targeted neuromodulation approaches.

Low frequency brain rhythms facilitate communication across large spatial regions in the brain and high frequency rhythms are thought to signify local processing among nearby assemblies. A heavily investigated mode by which these low frequency and high frequency phenomenon interact is phase-amplitude coupling (PAC). This phenomenon has recently shown promise as a novel electrophysiologic biomarker, in a number of neurologic diseases including human epilepsy. In 17 medically refractory epilepsy patients undergoing phase-2 monitoring for the evaluation of surgical resection and in whom temporal depth electrodes were implanted, we investigated the electrophysiologic relationships of PAC in epileptogenic (seizure onset zone or SOZ) and non-epileptogenic tissue (non-SOZ). That this biomarker can differentiate seizure onset zone from non-seizure onset zone has been established with ictal and pre-ictal data, but less so with interictal data. Here we show that this biomarker can differentiate SOZ from non-SOZ interictally and is also a function of interictal epileptiform discharges. We also show a differential level of PAC in slow-wave-sleep relative to NREM1-2 and awake states. Lastly, we show AUROC evaluation of the localization of SOZ is optimal when utilizing beta or alpha phase onto high-gamma or ripple band. The results suggest an elevated PAC may reflect an electrophysiology-based biomarker for abnormal/epileptogenic brain regions.

Eidetic memory has been reported in children and in patients with synesthesia but is otherwise thought to be a rare phenomenon. Presented herein is a patient with right-sided language dominance, as proven via multiple functional imaging and neuropsychometric methods, who has a seizure onset zone in the right temporo-parietal-occipital cortex. This patient’s medically refractory epilepsy and thus hyperactive cortex could possibly contribute to near eidetic ability with paired-associates learning tasks (in both short-term and long-term retention). There are reports of epilepsy negatively affecting memory, but as far as the authors are aware to date, there is limited evidence of any lesion enhancing cognitive functions (whether through direct lesion or via compensatory mechanism) that would be localized to a seizure onset zone in the dominant temporo-parietal-occipital junction.

Background: The Coronavirus Disease 2019 (COVID-19) is a global pandemic that has killed over 1.5 million people worldwide. A constellation of multisystem involvement with SARS-CoV-2 has been reported. COVID-19 has been shown to affect the human nervous system, however, both the extent and severity of involvement have yet to be fully elucidated. In this manuscript, we aimed to better understand the effect of COVID-19 on neuro-respiratory status by studying COVID-19 patients who presented with central apnea. Methodology: We analyzed patient characteristics, clinical outcomes, laboratory results, and imaging results of three patients with symptomatic, PCR-proven COVID-19 and episodes of central apnea. Results: Of the three patients included in this study, two patients developed new central apnea, and one patient developed an exacerbation of underlying central apnea despite COVID-19 treatments with systemic steroids and remdesivir. All occurred, on average, 15 days after the onset of COVID-19 symptoms. At 1-year follow-up, all patients experienced complete resolution of apneic breathing. Conclusions: Physicians should be vigilant for the presentation of COVID-19 with central apnea. Central apnea may be a complication in patients with severe COVID-19 infection. More research is warranted to further understand this association.

Clinical trials are conducted to evaluate the efficacy of new treatments. Clinical trials involving multiple treatments utilize the randomization of treatment assignments to enable the evaluation of treatment efficacies in an unbiased manner. Such evaluation is performed in post hoc studies that usually use supervised-learning methods that rely on large amounts of data collected in a randomized fashion. That approach often proves to be suboptimal in that some participants may suffer and even die as a result of having not received the most appropriate treatments during the trial. Reinforcement-learning methods improve the situation by making it possible to learn the treatment efficacies dynamically during the course of the trial, and to adapt treatment assignments accordingly. Recent efforts using multi-arm bandits, a type of reinforcement-learning method, have focused on maximizing clinical outcomes for a population that was assumed to be homogeneous. However, those approaches have failed to account for the variability among participants that is becoming increasingly evident as a result of recent clinical-trial-based studies. We present a contextual-bandit-based online treatment optimization algorithm that, in choosing treatments for new participants in the study, takes into account not only the maximization of the clinical outcomes as well as the patient characteristics. We evaluated our algorithm using a real clinical trial dataset from the International Stroke Trial. We simulated the online setting by sequentially going through the data of each participant admitted to the trial. Two bandits (one for each context) were created, with four choices of treatments. For a new participant in the trial, depending on the context, one of the bandits was selected. Then, we took three different approaches to choose a treatment: (a) a random choice (i.e., the strategy currently used in clinical trial settings), (b) a Thompson sampling-based approach, and (c) a UCB-based approach. Success probabilities of each context were calculated separately by considering the participants with the same context. Those estimated outcomes were used to update the prior distributions within the bandit corresponding to the context of each participant. We repeated that process through the end of the trial and recorded the outcomes and the chosen treatments for each approach. We also evaluated a context-free multi-arm-bandit-based approach, using the same dataset, to showcase the benefits of our approach. In the context-free case, we calculated the success probabilities for the Bernoulli sampler using the whole clinical trial dataset in a context-independent manner. The results of our retrospective analysis indicate that the proposed approach performs significantly better than either a random assignment of treatments (the current gold standard) or a multi-arm-bandit-based approach, providing substantial gains in the percentage of participants who are assigned the most suitable treatments. The contextual-bandit and multi-arm bandit approaches provide 72.63% and 64.34% gains, respectively, compared to a random assignment.

Abstract Introduction Low Frequency brain rhythms facilitate communication across large spatial regions in the brain and high frequency rhythms are thought to signify local processing among nearby assemblies. A heavily investigated mode by which these low frequency and high frequency phenomenon interact is Phase-Amplitude Coupling )PAC). This phenomenon has recently shown promise as a novel electrophysiologic biomarker, in a number of neurologic diseases. In 10 patients undergoing phase-2 monitoring for the evaluation of surgical resection and in whom temporal depth electrodes were implanted, we investigated electrophysiologic relationships of PAC in epileptogenic (seizure onset zone or SOZ) and non-epileptogenic tissue (non-SOZ). That this biomarker can differentiate pathologic from non-pathologic brain and has been established with ictal and pre-ictal data, but less so with interictal data. Here we show that this biomarker can differentiate interictally. We also show PAC activity is related to interictal epileptiform discharges and high frequency activity. Importantly, we also show a differential level of PAC in slow-wave-sleep from NREM1-2 and awake. And finally we show that localization of pathologic tissue sensitivity and specificity is optimal when utilizing beta or alpha phase onto high-gamma or Ripple with knowledge of the sleep stage. Illustrating some of the physiologic nature of this biomarker in human epilepsy will provide a basis for understanding the mechanism of neurologic disease and normal physiology of brain communication, details which are at this point ready to be utilized in neurotechnological therapies to treat and understand both. Methods Per Institutional Review Board protocol, 10 patients who were under evaluation for resective surgery for MRE at Mayo Clinic in Rochester MN were included in this study. IRB approved the study and necessary consenting procedures were followed prior to any data acquisition. All subjects had bilaterally placed intracranial depth electrodes with usually 8 contacts. In some cases not all contacts could be used for data acquisition (hardware or recording problems). 6 Subjects of this cohort had scalp and EMG recordings concurrently placed for the purposes of sleep scoring. Subject recordings were ignored for POD-1 as anesthetics were dissipating. Subjects then stayed in the ICU ranging from 3-12 days before explanation.Pathological tissue identified as seizure onset zone (SOZ) was determined from phase II monitoring and determined by a trained neurologist. Sleep staging was done with expert-in-the-loop semi automated methods described elsewhere but overseen by a trained neurologist. Behavioral state was determined with scalp EEG signals and verified by a neurologist board certified in sleep medicine. All EEG recordings were bandpass filtered 0.3-75Hz and 60Hz notch filtered for scoring. Visual sleep scoring was in accordance with standard methods with modification for replacing the electrooculogram (EOG) recording with FP1, FP2, FPZ scalp electrodes. Wakefulness was determined by the presence of eye blinks visualized in fronto-parietal scalp leads, accompanied by posteriorly dominant alpha rhythms (8 - 12 Hz) comprising &gt;50% of the epoch. Slow-wave sleep (N3) was scored when high-voltage (&gt;75 uV) delta (0.5 - 3 Hz) frequency scalp EEG activity was present in at least 20% of the epoch (i.e., at least 6 s within a 30 s epoch) in the frontal derivations using conventional International 10-20 System electrode placements (FP1, FP2, FZ, F3, F4, CZ, C3, C4, O1, O2, and Oz). Phase Amplitude Coupling with Coherency Angle CFC. A Hanning taper n points is the length of the sliding time window. Next, the coherency CFC(fmodulating,fmodulated) was estimated between signal {Xt} and the estimate of the time-course of power{Pt(fmodulated)} for a given frequency fmodulating. The coherence was the absolute value of the CFC . The phase difference between the signal at fmodualting and the power at fmodulated is given by the angle of the coherency arg(CFC). In this case γ refer to a 1024 points Hanning window and * to the complex conjugate. This allowed us to characterize the phase-to-power cross-frequency interaction with respect to f and fmodulated sensor by sensor. The spike detection algorithm was utlized to evaluated successive 1 minute blocks of iEEG and removes artifact channels. Individual channels were defined as average slope greater than 10 SD outside of mean slopes of all channels. Second, iEEG was bandpass filtered 20-50Hz to identify possible spikes, where a sharp discharge must last between 20-70ms. Absolute amplitudes of peaks greater than 4SD of channel mean amplitude were noted as potential spike locations for further consideration. Third, raw iEEG was bandpass filtered (2nd order Butterworth) 1-35Hz. A scaling factor is determined by finding a value that will bring the median of all channel amplitudes to 70uV. All channels are multiplied by this scaling factor. Once the data have been scaled, the amplitude and slope of each half-wave of the potential spikes identified previously in step 2 are calculated and the values are compared to static thresholds (Total amplitude of both half-waves &gt; 600μV, slope of each half-wave &gt; 7μV/ms, duration of each half-wave &gt; 10ms). Potential spikes with half-waves that exceed these thresholds are marked as interictal spikes.HFOs were detected using a Hilbert transform-based method, as previously reported. Here, the discrete time series is transformed into an analytic signal, where the real part is the original signal, and the imaginary part is the Hilbert transform of the original, x(t). Results Holding constant the frequency for amplitude to include all high activity (30-175Hz), beta is the best localizing (not statistically significant from alpha) band. Holding the frequency for phase, constant, there is no statistical difference between LG, HG, and ripple in terms of pathological bain tissue localization. Examining different frequencies for phase in varied behavioral/sleep states using 12 minute awake segments were used in all 10 patients, localization is best via AUROC when delta is the frequency band used to calculate PAC in Slow Wave Sleep / NREM3.Interictal Epileptiform spiking is seen with much higher regularity, although correlated and seen in the SOZ. A great deal of the associated elevations in wide-spectrum (0.5-30 modulating 65-175Hz) PAC (here defined as &gt;2 SD across the entire 2 hour period and assessed across all channels) occurred with IEDs. Since 3 second segments were used to calculate PAC values, sometimes 2 IEDs were within a single epoch although in a small minority of observations. PAC elevations are seen with IEDs and with HFO, although to a greater extend with spikes. However, some IEDs are not associated with elevations in PAC. In fact most detected IEDs were not associated with elevated PAC values or with HFOs for that matter. A minority of IEDs are associated with elevated PAC values and HFOs. At the group and individual levels the average spike count per patient, grouping all SOZ and non-SOZ electrodes together, there is not a statistically significant effect, but if the average count is taken per electrode within a single patient, a significant difference was noted at p = .0031. Subjects on average had 15 (SEM 0.15) electrodes and SOZ electrode counts of 3 (SEM 0.71). When analyzing IED + elevated PAC, grouped SOZ electrodes among patients and within individuals show a strongly significant effect (.0004). When taking into account electrode numbers within each group this significance only increases (.0001). At the individual level, significance of p&lt;0.05 is seen for all but one patient in this cohort. Conclusion Sleep Stage is critical in the analysis of pathological brain from non-pathological brain and electrophysiologic biomarkers behave differently in the different behavioral states. Here we found evidence to support his proposition in the following ways: low frequency delta phase modulates a broad high frequency amplitude in N3 and has relevance for brain pathology. Phase-Amplitude Coupling is increased in pathologic tissue. Peaks in PAC occur sporadically and infrequently in these patients. PAC is correlated with Interictal Epileptiform Discharges and High Frequency Oscillations however most Interictal Epileptiform Discharges are unrelated to peaks in Phase-Amplitude Coupling. Low Frequency Activity in Beta-band modulates broad high frequency power across low gamma, high gamma, and ripple bands. Support (If Any) Neurophysiological mapping and stimulation of the human brain for memory enhancement. Funded by EUO - PIS via BioTechMed Center, Department of Multimedia Systems, Faculty of Electronics, Telecommunication and Informatics, Gdansk University of Technology

Many autoimmune diseases can affect the central nervous system, and their varying clinical presentations often confound a straightforward diagnosis. In this report, we describe a unique presentation of CLIPPERS syndrome. To our knowledge, this is the first case to demonstrate significant supratentorial involvement with symmetric and non-confluent lesions in the medial orbitofrontal cortex; additionally, this is the second case to describe an association between diagnoses of hypothyroidism and CLIPPERS.

Abstract Routine scalp EEG is essential in the clinical diagnosis and management of epilepsy. However, a normal scalp EEG (based on expert visual review) recorded from a patient with epilepsy can cause delays in diagnosis and clinical care delivery. Here, we investigated whether normal EEGs might contain subtle electrophysiological clues of epilepsy. Specifically, we investigated (i) whether there are indicators of abnormal brain electrophysiology in normal EEGs of epilepsy patients, and (ii) whether such abnormalities are modulated by the side of the brain generating seizures in focal epilepsy. We analysed awake scalp EEG recordings of age-matched groups of 144 healthy individuals and 48 individuals with drug-resistant focal epilepsy who had normal scalp EEGs. After preprocessing, using a bipolar montage of eight channels, we extracted the fraction of spectral power in the alpha band (8–13 Hz) relative to a wide band of 0.5–40 Hz within 10-s windows. We analysed the extracted features for (i) the extent to which people with drug-resistant focal epilepsy differed from healthy subjects, and (ii) whether differences within the drug-resistant focal epilepsy patients were related to the hemisphere generating seizures. We then used those differences to classify whether an EEG is likely to have been recorded from a person with drug-resistant focal epilepsy, and if so, the epileptogenic hemisphere. Furthermore, we tested the significance of these differences while controlling for confounders, such as acquisition system, age and medications. We found that the fraction of alpha power is generally reduced (i) in drug-resistant focal epilepsy compared to healthy controls, and (ii) in right-handed drug-resistant focal epilepsy subjects with left hemispheric seizures compared to those with right hemispheric seizures, and that the differences are most prominent in the frontal and temporal regions. The fraction of alpha power yielded area under curve values of 0.83 in distinguishing drug-resistant focal epilepsy from healthy and 0.77 in identifying the epileptic hemisphere in drug-resistant focal epilepsy patients. Furthermore, our results suggest that the differences in alpha power are greater when compared with differences attributable to acquisition system differences, age and medications. Our findings support that EEG-based measures of normal brain function, such as the normalized spectral power of alpha activity, may help identify patients with epilepsy even when an EEG does not contain any epileptiform activity, recorded seizures or other abnormalities. Although alpha abnormalities are unlikely to be disease-specific, we propose that such abnormalities may provide a higher pre-test probability for epilepsy when an individual being screened for epilepsy has a normal EEG on visual assessment.