Mistaking a duck for a skvader: How a conceptual form of circular analysis may taint many neuroscience studies

The Transmitter, Год журнала: 2023, Номер unknown

Опубликована: Янв. 1, 2023

Язык: Английский

Time-resolved smoothness of distributed brain activity tracks conscious states and unifies emergent neural phenomena

Research Square (Research Square), Год журнала: 2022, Номер unknown

Опубликована: Фев. 7, 2022

Abstract Much of systems neuroscience posits that emergent neural phenomena underpin important aspects brain function. Studies in the field variously emphasize importance distinct phenomena, including weakly stable dynamics, arrhythmic 1/f activity, long-range temporal correlations, and scale-free avalanche statistics. Few studies, however, have sought to reconcile these often abstract with interpretable properties activity. Here, we developed a method efficiently unbiasedly generate model data constrained by empirical features long multiregional neurophysiological recordings. We used this ground several major time-resolved smoothness, correlation distributed activity between adjacent timepoints. first found electrocorticography recordings, smoothness closely tracked transitions conscious anesthetized states. then showed minimal variance, mean, captured dynamical statistical across modalities species. Our results thus decouple from network mechanisms function, instead couple spatially nonspecific, changes These anchor theoretical frameworks single property signal and, way, ultimately help bridge theories function observed

Язык: Английский

Time-resolved smoothness of distributed brain activity tracks conscious states and unifies emergent neural phenomena

Research Square (Research Square), Год журнала: 2021, Номер unknown

Опубликована: Дек. 29, 2021

Abstract Much of systems neuroscience posits that emergent neural phenomena underpin important aspects brain function. Studies in the field variously emphasize importance distinct phenomena, including weakly stable dynamics, arrhythmic 1/f activity, long-range temporal correlations, and scale-free avalanche statistics. Few studies, however, have sought to reconcile these often abstract with interpretable properties activity. Here, we developed a method efficiently unbiasedly generate model data constrained by empirical features long neurophysiological recordings. We used this ground several major time-resolved smoothness, correlation distributed activity between adjacent timepoints. first found electrocorticography recordings, smoothness closely tracked transitions conscious anesthetized states. then showed minimal variance, mean, captured dynamical statistical across modalities species. Our results thus decouple from network mechanisms function, instead couple spatially nonspecific, changes These anchor theoretical frameworks single property signal and, way, ultimately help bridge theories function observed

Язык: Английский