Since Lynn and colleagues first described the use of focused ultrasound (FUS) waves for intracranial ablation in 1942, many strides have been made toward the treatment of several brain pathologies using this novel technology. In the modern era of minimal invasiveness, high-intensity focused ultrasound (HIFU) promises therapeutic utility for multiple neurosurgical applications, including treatment of tumors, stroke, epilepsy, and functional disorders. Although the use of HIFU as a potential therapeutic modality in the brain has been under study for several decades, relatively few neuroscientists, neurologists, or even neurosurgeons are familiar with it. In this extensive review, the authors intend to shed light on the current use of HIFU in different neurosurgical avenues and its mechanism of action, as well as provide an update on the outcome of various trials and advances expected from various preclinical studies in the near future. Although the initial technical challenges have been overcome and the technology has been improved, only very few clinical trials have thus far been carried out. The number of clinical trials related to neurological disorders is expected to increase in the coming years, as this novel therapeutic device appears to have a substantial expansive potential. There is great opportunity to expand the use of HIFU across various medical and surgical disciplines for the treatment of different pathologies. As this technology gains recognition, it will open the door for further research opportunities and innovation.

Study Objectives: Sleep is reflected not only in the electroencephalogram but also in heart rhythms and breathing patterns. We hypothesized that it is possible to accurately stage sleep based on the electrocardiogram (ECG) and respiratory signals. Methods: Using a dataset including 8682 polysomnograms, we develop deep neural networks to stage sleep from ECG and respiratory signals. Five deep neural networks consisting of convolutional networks and long- and short-term memory networks are trained to stage sleep using heart and breathing, including the timing of R peaks from ECG, abdominal and chest respiratory effort, and the combinations of these signals. Results: ECG in combination with the abdominal respiratory effort achieved the best performance for staging all five sleep stages with a Cohen’s kappa of 0.585 (95% confidence interval ±0.017); and 0.760 (±0.019) for discriminating awake vs. rapid eye movement vs. nonrapid eye movement sleep. Performance is better for younger ages, whereas it is robust for body mass index, apnea severity, and commonly used outpatient medications. Conclusions: Our results validate that ECG and respiratory effort provide substantial information about sleep stages in a large heterogeneous population. This opens new possibilities in sleep research and applications where electroencephalography is not readily available or may be infeasible.