Academic Journal

Waveform detection by deep learning reveals multi-area spindles that are selectively modulated by memory load

التفاصيل البيبلوغرافية
العنوان: Waveform detection by deep learning reveals multi-area spindles that are selectively modulated by memory load
المؤلفون: Maryam H Mofrad, Greydon Gilmore, Dominik Koller, Seyed M Mirsattari, Jorge G Burneo, David A Steven, Ali R Khan, Ana Suller Marti, Lyle Muller
المصدر: eLife, Vol 11 (2022)
بيانات النشر: eLife Sciences Publications Ltd
سنة النشر: 2022
المجموعة: Directory of Open Access Journals: DOAJ Articles
مصطلحات موضوعية: memory, sleep spindles, memory consolidation, deep learning, Medicine, Science, Biology (General), QH301-705.5
الوصف: 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.
نوع الوثيقة: article in journal/newspaper
اللغة: English
تدمد: 2050-084X
Relation: https://elifesciences.org/articles/75769; https://doaj.org/toc/2050-084X; e75769; https://doaj.org/article/2a2c2c59492b4bb4ab3e25bd5c6cb08e
DOI: 10.7554/eLife.75769
الاتاحة: https://doi.org/10.7554/eLife.75769
https://doaj.org/article/2a2c2c59492b4bb4ab3e25bd5c6cb08e
رقم الانضمام: edsbas.B36A7853
قاعدة البيانات: BASE
الوصف
تدمد:2050084X
DOI:10.7554/eLife.75769