Broadening learning communities during COVID-19: developing a curricular framework for telemedicine education in neurology.

BACKGROUND: In response to the cancellation of clinical clerkships due to COVID-19, the Johns Hopkins (JH) Neurology Education Team developed a virtual elective to enhance medical students' clinical telemedicine skills and foster community between academic institutions. METHODS: This two-week clinical elective, entitled "Virtual Patient Rounds in Neurology," was administered once in April 2020 and once in May 2020. The curriculum included attending/fellow-led Virtual Rounds, Student Presentations, and Asynchronous Educational Activities. We also developed a new lecture series entitled JHNeuroChats, which consisted of live synchronous lectures presented by JH faculty and Virtual Visiting Professors. Trainees and faculty from outside institutions were invited to participate in the JHNeuroChats. Students and faculty completed pre- and post-elective surveys to assess the educational impact of the elective. Student's t-tests were used to compare scores between pre- and post-elective surveys. RESULTS: Seven JH medical students enrolled in each iteration of the elective, and an additional 337 trainees and faculty, representing 14 different countries, registered for the JHNeuroChats. We hosted 48 unique JHNeuroChats, 32 (66.7%) of which were led by invited Virtual Visiting Professors. At the end of the elective, students reported increased confidence in virtually obtaining a history (P < 0.0001) and performing a telehealth neurological physical exam (P < 0.0001), compared to the start of the course. In addition, faculty members reported increased confidence in teaching clinical medicine virtually, although these findings were not statistically significant (P = 0.15). CONCLUSIONS: Despite the constraints imposed by COVID-19, this virtual Neurology elective increased medical students' confidence in certain telemedicine skills and successfully broadened our learning community to encompass learners from around the world. As virtual medical education becomes more prevalent, it is important that we are intentional in creating opportunities for shared learning across institutions. We believe that this elective can serve as a model for these future educational collaborations.

Identifying Sleep Biomarkers to Evaluate Cognition in HIV.

Sleep disturbance and cognitive impairment represent two of the most common and debilitating conditions facing seropositive (HIV+) individuals who are otherwise well controlled with antiretroviral therapy. Sleep-assessment-based biomarkers represent an important step towards improving our understanding of the unique mechanistic features that may link sleep disruption and cognition in HIV+ individuals, ultimately leading to advancements in treatment and management options. In this study, a risk score was computed via a generalized linear model (GLM), which optimally combines polysomnography (PSG) features extracted from EEG, EMG, and EOG signals, to distinguish 18 HIV+ Black male individuals with and without cognitive impairment. The optimal set of features was identified via the least absolute shrinkage and selection operator (LASSO) approach, and the risk separation between the two groups, i.e., cognitively normal and cognitive impaired, was significant (and has a P-value < .001). The optimal set of predictive features were all EEG derived and sleep stage-specific. These preliminary findings suggest that sleep-based EEG features may be used as both diagnostic and prognostic biomarkers for cognition in HIV+ subjects.

A Novel Sleep Stage Scoring System: Combining Expert-Based Rules with a Decision Tree Classifier.

Overnight polysomnography (PSG) is the gold standard tool used to characterize sleep and for diagnosing sleep disorders. PSG is a non-invasive procedure that collects various physiological data which is then scored by sleep specialists who assign a sleep stage to every 30-second window of the data according to predefined scoring rules. In this study, we aimed to automate the process of sleep stage scoring of overnight PSG data while adhering to expert-based rules. We developed an algorithm utilizing a likelihood ratio decision tree classifier and extracted features from EEG, EMG and EOG signals based on predefined rules of the American Academy of Sleep Medicine Manual. Specifically, features were computed in 30-second epochs in the time and the frequency domains of the signals and used as inputs to the classifier which assigned each epoch to one of five possible stages: N3, N2, N1, REM or Wake. The algorithm was trained and tested on PSG data from 38 healthy individuals with no reported sleep disturbances. The overall scoring accuracy was 80.70% on the test set, which was comparable to the training set. Our results imply that the automatic classification is highly robust, fast, consistent with visual scoring and is highly interpretable.

A look at the strength of micro and macro EEG analysis for distinguishing insomnia within an HIV cohort.

HIV patients are often plagued by sleep disorders and suffer from sleep deprivation. However, there remains a wide gap in our understanding of the relationship between HIV status, poor sleep, overall function and future outcomes; particularly in the case of HIV patients otherwise well controlled on cART (combined anti-retroviral therapy). In this study, we compared two groups: 16 non-HIV subjects (seronegative controls) and 12 seropositive HIV patients with undetectable viral loads. We looked at sleep behavioral (macro-sleep) features and sleep spectral (micro-sleep) features obtained from human-scored overnight EEG recordings to study whether the scored EEG data can be used to distinguish between controls and HIV subjects. Specifically, the macro-sleep features were defined by sleep stages and included sleep transitions, percentage of time spent in each sleep stage, and duration of time spent in each sleep stage. The micro-sleep features were obtained from the power spectrum of the EEG signals by computing the total power across all channels and frequencies, as well as the average power in each sleep stage and across different frequency bands. While the macro features do not distinguish between the two groups, there is a significant difference and a high classification accuracy for the scoring-independent micro features. This spectral separation is interesting because evidence suggests a relationship between sleep complaints and cognitive dysfunction in HIV patients stable on cART. Furthermore, there are currently no biomarkers that predict the early development of cognitive decline in HIV patients. Thus, a micro-sleep architectural approach could serve as a biomarker to identify HIV patients vulnerable to cognitive decline, providing an avenue to explore the utility of early intervention.

To Score or Not to Score? A look at the distinguishing power of micro EEG analysis on an annotated sample of PSG studies conducted in an HIV cohort.

In this study, we used the Pittsburgh Sleep Quality Index to divide the subjects into two groups, good sleepers and bad sleepers. We computed sleep behavioral (macro-sleep architectural) features and sleep spectral (micro-sleep architectural) features in order to observe if the annotated EEG data can be used to distinguish between good and bad sleepers in a more quantitative manner. Specifically, the macro-sleep features were defined by sleep stages and included sleep transitions, percentage of time spent in each sleep stage, and duration of time spent in each sleep stage. The micro-sleep features were obtained from the power spectrum of the EEG signals by computing the total power across all channels and all frequencies, as well as the average power in each sleep stage and across different frequency bands. We found that while the scoring-independent micro features are significantly different between the two groups, the macro features are not able to significantly distinguish the two groups. The fact that the macro features computed from the scoring files cannot pick up the expected difference in the EEG signals raises the question as to whether human scoring of EEG signals is practical in assessing sleep quality.