Abstract
The human cortex exhibits intrinsic neural timescales that shape a temporal hierarchy. Whether this temporal hierarchy follows the spatial hierarchy of its topography, namely the core-periphery organization, remains an open issue. Using magnetoencephalography data, we investigate intrinsic neural timescales during rest and task states; we measure the autocorrelation window in short (ACW-50) and, introducing a novel variant, long (ACW-0) windows. We demonstrate longer ACW-50 and ACW-0 in networks located at the core compared to those at the periphery with rest and task states showing a high ACW correlation. Calculating rest-task differences, i.e., subtracting the shared core-periphery organization, reveals task-specific ACW changes in distinct networks. Finally, employing kernel density estimation, machine learning, and simulation, we demonstrate that ACW-0 exhibits better prediction in classifying a region’s time window as core or periphery. Overall, our findings provide fundamental insight into how the human cortex’s temporal hierarchy converges with its spatial core-periphery hierarchy.
Highlights
In this paper, our main aim was to investigate whether the temporal hierarchy of intrinsic neural timescales of the faster frequency (1–50 Hz) of MEG conforms to CP architecture and how they will be affected in the task state
We demonstrate that the temporal hierarchy of intrinsic neural timescales converges with the spatial topography of the CP hierarchy of the human cortex with both providing an intrinsically temporospatial hierarchy during rest and task states
We demonstrate that the temporal hierarchy of intrinsic neural timescale measured by shorter and longer autocorrelation window (ACW), i.e., ACW-50 and ACW-0 follows the spatial topography namely the CP hierarchy
Summary
The analyses involved magnetoencephalography (MEG) data of 89 subjects from the HCP WU-Minn HCP 1200 subjects data release[54]. Resting state MEG data were acquired in runs of ~6 min. The subjects were instructed to relax with eyes open and maintain fixation on a red crosshair. Following the completion of resting state MEG, subjects were asked to complete three tasks of language processing (story vs math, StoryM), motor (Motort) and working memory (Wrkmem). Task MEG data were acquired with the same parameters as the rest. Each task was approximately 7, 14 and 10 for StoryM, Motort and Wrkmem, respectively.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.