Andy Ho Wing Chan

Background: Traditional scalp electroencephalography (EEG), though clinically validated, presents significant limitations, including lengthy setup, patient discomfort, and limited mobility. These factors hinder its utility for long-term ambulatory monitoring. In-ear EEG offers a promising alternative by leveraging the ear canal’s anatomical proximity to the temporal lobes and natural mechanical stability. To address this, we developed SeizEAR, a standardized, three-size in-ear EEG system using paste-based electrodes with cross-ear referencing. Methods: This pilot study evaluated SeizEAR devices featuring small, medium, and large earpieces with superior and inferior paste-based electrodes in each ear, configured in a cross-ear reference scheme. It included five healthy adults who underwent recording sessions of approximately three hours (2.4 to 3.3 h). Electrode impedance was monitored against the acquisition system’s threshold indicator, with simultaneous scalp EEG from T7/T8 electrodes serving as the reference. Participants rated comfort on a 0–5 Likert scale every 30 min. Results: In-ear recordings showed moderate, band-dependent correspondence with ipsilateral temporal scalp electrodes, strongest in the delta and alpha bands and weakest in beta, and correspondence was consistently higher against a geometrically matched T7–T8 bipolar derivation than against a single referenced electrode. In-ear alpha reactivity to eye closure was present but attenuated relative to scalp. Comfort ratings remained high over the final two hours of wear, with no reported adverse events. Conclusions: SeizEAR acquired in-ear EEG that corresponded moderately with temporal scalp EEG and was well-tolerated over multi-hour wear in healthy adults. These preliminary findings support further development of a standardized in-ear EEG system and require confirmation in an adequately powered sample.

Objective: Thalamic responsive neurostimulation (RNS) is a surgical option for patients with drug‐refractory epilepsy. However, it is unclear whether thalamic connectivity with the seizure onset zone (SOZ) has a role in clinical outcomes. Here, we aim to investigate the clinical utility of the connectivity between the SOZ and the thalamus for thalamic RNS targeting. Methods: Retrospective analysis was made of 12 patients treated with thalamic RNS. Clinical features and Engel scores were recorded. Patients were divided into responders, partial responders, and nonresponders based on seizure frequency reduction at last follow‐up. Structural connectivity between the SOZ and the whole thalamus was calculated using patient‐specific tractography. RNS electrodes were used to model the volume of tissue activated (VTA) with stimulation parameters at last follow‐up based on individualized electrode locations. The patient's VTAs were then used to identify thalamic areas with high or low probability of connectivity with the SOZ and how they were associated and correlated with clinical outcomes using nonparametric Mann–Whitney U and Spearman correlation tests. Results: Seven patients were responders, three nonresponders, and two partial responders. Thalamic nuclei targeted included anterior nucleus of thalamus and centromedian nucleus. Cortical areas of the SOZs included medial prefrontal, supplementary motor, cingulate, orbitofrontal, insular, mesial temporal, and lateral temporal cortices. Stimulation of thalamic areas with higher connectivity between the SOZ and the thalamus was associated with a clinical response of >50% reduction in seizures ( p = .017). Furthermore, higher degree of tract activation between the SOZ and the thalamus was correlated with better seizure outcomes at last follow‐up ( r = .78, p = .004). Significance: Greater recruitment of white matter connections between the SOZ and thalamus is associated with clinical response and may correlate with improved seizure outcomes during thalamic RNS. Using tractography to map the patient‐specific “thalamic seizure network” and the surgical targeting of these connections may result in improved clinical outcomes in patients treated with thalamic RNS.

Background: Differentiating between motor functional dissociative seizures (FDS) and motor epileptic seizures (ES) is a common diagnostic challenge, requiring video electroencephalography (vEEG) as gold standard. However, vEEG requires specialized technicians and clinical experts to set up and interpret and oftentimes fails to capture events. We sought to develop machine-learning (ML) tools to carry out this diagnostic task independently of vEEG or human review by a neurologist. Methods: In this retrospective study, we developed two proof-of-concept ML models to differentiate motor ES from FDS based on video of FDS and ES events in patients who underwent inpatient vEEG monitoring at an academic medical center between 2012 and 2021. The first model employed a pose-estimation approach, using body landmark features that were labeled frame-by-frame by three neurologists. The second utilized an end-to-end 3D convolutional neural network (CNN), thereby learning directly from raw video frames. Using board-certified epileptologist review as a clinical gold standard, we measured model performance by area under the receiver-operating (AUROC) and precision-recall (AUPRC) curves, sensitivity, precision, and accuracy against a held-out test set of videos. Findings: We included 101 unique patients with 106 total event videos, comprising 61 (60.4%) ES and 45 (44.6%) FDS events. Both ML models distinguished both seizure types better than chance. The pose-estimation-based model achieved an AUROC of 0.71, AUPRC 0.53, sensitivity 0.90, precision 0.50, and accuracy 0.62. The CNN model exhibited superior overall performance, achieving an AUROC 0.78, AUPRC 0.84, balanced sensitivity and precision (both 0.82), and accuracy 0.80. Interpretation: Our findings demonstrate the superiority of CNN over pose-estimation models to differentiate between motor ES and FDS using video alone. Although future studies are needed, these models hold potential as adjunct diagnostic tools by enabling rapid, objective seizure evaluations without immediate neurologist involvement. Funding: CTSA grant UL1TR004419 (Kummer), NIH grants R01DK133539 and R01HL167050 (Nadkarni), NIMH T32 grant MH122394 and AAN grant 19-1120 (Chan).

Objective To describe the frequency of neuropsychiatric complications among hospitalized patients with coronavirus disease 2019 (COVID-19) and their association with pre-existing comorbidities and clinical outcomes. Methods We retrospectively identified all patients hospitalized with COVID-19 within a large multicenter New York City health system between March 15, 2020 and May 17, 2021 and randomly selected a representative cohort for detailed chart review. Clinical data, including the occurrence of neuropsychiatric complications (categorized as either altered mental status [AMS] or other neuropsychiatric complications) and in-hospital mortality, were extracted using an electronic medical record database and individual chart review. Associations between neuropsychiatric complications, comorbidities, laboratory findings, and in-hospital mortality were assessed using multivariate logistic regression. Results Our study cohort consisted of 974 patients, the majority were admitted during the first wave of the pandemic. Patients were treated with anticoagulation (88.4%), glucocorticoids (24.8%), and remdesivir (10.5%); 18.6% experienced severe COVID-19 pneumonia (evidenced by ventilator requirement). Neuropsychiatric complications occurred in 58.8% of patients; 39.8% experienced AMS; and 19.0% experienced at least one other complication (seizures in 1.4%, ischemic stroke in 1.6%, hemorrhagic stroke in 1.0%) or symptom (headache in 11.4%, anxiety in 6.8%, ataxia in 6.3%). Higher odds of mortality, which occurred in 22.0%, were associated with AMS, ventilator support, increasing age, and higher serum inflammatory marker levels. Anticoagulant therapy was associated with lower odds of mortality and AMS. Conclusion Neuropsychiatric complications of COVID-19, especially AMS, were common, varied, and associated with in-hospital mortality in a diverse multicenter cohort at an epicenter of the COVID-19 pandemic.