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Coronavirus disease 2019 (COVID-19) led to two nationwide lockdowns in Israel, reducing both supply and demand for medical services in the Israel Defense Force (IDF). IDF soldiers serve on bases within Israel, and most of them return home at the end of the day, similar to other armies in the world.To analyze the health services provided by the IDF with regard to policy changes during lockdowns.We compared medical encounters between different services provided by the IDF Medical Corps. We related them to specific time periods: pre-first lockdown, first lockdown (and corresponding timeframes of the previous 3 years), between lockdowns, second lockdown, and post-second lockdown.Compared to past periods, we found a similar reduction of 27-30% in primary care medical encounter rates during the two lockdowns: 42-43% in sick days and 50-54% in referrals to the emergency department. Referral rates to all specialist medical encounters and elective surgeries decreased significantly during the first lockdown period and increased 1.2-3.5 times during the second lockdown.A continuance of the shift to telehealth is required to withstand a future lockdown, with a full supply of secondary medical services attuned to core medical issues relevant for combat personnel. A liberal sick leave policy is required to eliminate unnecessary in-person visits, thus reducing the risk of infection.

Objective. The goal of this study is to decode the electrical activity of single neurons in the human subthalamic nucleus (STN) to infer the speech features that a person articulated, heard or imagined. We also aim to evaluate the amount of subthalamic neurons required for high accuracy decoding suitable for real-life speech brain-machine interfaces (BMI). Approach. We intraoperatively recorded single-neuron activity in the STN of 21 neurosurgical patients with Parkinson’s disease undergoing implantation of deep brain stimulator while patients produced, perceived or imagined the five monophthongal vowel sounds. Our decoder is based on machine learning algorithms that dynamically learn specific features of the speech-related firing patterns. Main results. In an extensive comparison of algorithms, our sparse decoder (‘SpaDe’), based on sparse decomposition of the high dimensional neuronal feature space, outperformed the other algorithms in all three conditions: production, perception and imagery. For speech production, our algorithm, Spade, predicted all vowels correctly (accuracy: 100%; chance level: 20%). For perception accuracy was 96%, and for imagery: 88%. The accuracy of Spade showed a linear behavior in the amount of neurons for the perception data, and even faster for production or imagery. Significance. Our study demonstrates that the information encoded by single neurons in the STN about the production, perception and imagery of speech is suitable for high-accuracy decoding. It is therefore an important step towards BMIs for restoration of speech faculties that bears an enormous potential to alleviate the suffering of completely paralyzed (‘locked-in’) patients and allow them to communicate again with their environment. Moreover, our research indicates how many subthalamic neurons may be necessary to achieve each level of decoding accuracy, which is of supreme importance for a neurosurgeon planning the implantation of a speech BMI.