Advanced Concepts Team, European Space Agency, Noordwijk, Netherlands
Seyed M Mirsattari
Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Canada; Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Canada; Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Canada; Department of Psychology, Western University, London, Canada
Jorge G Burneo
Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Canada; Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Canada
David A Steven
Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Canada; Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Canada
Brain and Mind Institute, Western University, London, Canada; Department of Biomedical Engineering, Western University, London, Canada; Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Canada
Ana Suller Marti
Brain and Mind Institute, Western University, London, Canada; Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Canada
Sleep is generally considered to be a state of large-scale synchrony across thalamus and neocortex; however, recent work has challenged this idea by reporting isolated sleep rhythms such as slow oscillations and spindles. What is the spatial scale of sleep rhythms? To answer this question, we adapted deep learning algorithms initially developed for detecting earthquakes and gravitational waves in high-noise settings for analysis of neural recordings in sleep. We then studied sleep spindles in non-human primate electrocorticography (ECoG), human electroencephalogram (EEG), and clinical intracranial electroencephalogram (iEEG) recordings in the human. Within each recording type, we find widespread spindles occur much more frequently than previously reported. We then analyzed the spatiotemporal patterns of these large-scale, multi-area spindles and, in the EEG recordings, how spindle patterns change following a visual memory task. Our results reveal a potential role for widespread, multi-area spindles in consolidation of memories in networks widely distributed across primate cortex.
