Deantoni, M. | bioRxiv Science

bioRxiv2020

pyActigraphy: open-source python package for actigraphy data visualisation and analysis

Over the past 40 years, actigraphy has been used to study rest-activity patterns in circadian rhythm and sleep research. Furthermore, considering its simplicity of use, there is a growing interest in the analysis of large population-based samples, using actigraphy. Here, we introduce pyActigraphy, a comprehensive toolbox for data visualization and analysis including multiple sleep detection algorithms and rest-activity rhythm variables. This open-source python package implements methods to read multiple data formats, quantify various properties of rest-activity rhythms, visualize sleep agendas, automatically detect rest periods and perform more advanced signal processing analyses. The development of this package aims to pave the way towards the establishment of a comprehensive open-source software suite, supported by a community of both developers and researchers, that would provide all the necessary tools for in-depth and large scale actigraphy data analyses. Required MetadataO_ST_ABSCurrent code versionC_ST_ABS

bioinformatics↗

bioRxiv2020

Continued topographical learning- and relearning-dependent activity in the resting state after post-training sleep and wake

Continuation of experience-dependent neural activity during offline periods of sleep and wakefulness is a critical element of memory consolidation. Using functional magnetic resonance imaging (fMRI), offline consolidation effects have been evidenced probing behavioral and neurophysiological changes during memory retrieval, i.e. in the context of task practice. Resting state fMRI (rfMRI) further allows investigating the offline evolution of recently learned information, without confounding task-related effects. In the present study, we used rfMRI to investigate offline changes in functional connectivity (FC) and Amplitude of Low Frequency Fluctuation (ALFF) associated with learning and relearning in a spatial navigation task, following an episode of post-training wake or sleep, respectively. Resting state activity was measured immediately (i) before and (ii) after topographical learning in a virtual town, (iii) 4 days later after regular sleep (RS) vs. sleep deprivation (SD) on the first post-learning night, and (iv) after topographical re-learning in an extended town encompassing the initial map. Task (navigation)-based fMRI activity was also recorded on Day 1 and Day 4 during target retrieval. Our results highlight the continuation of navigation-related activity in the subsequent resting state, as evidenced by changes in FC and ALFF in task-related neural networks. Behavioural performance was not modulated by post-training SD vs RS. However, in line with prior reports, post-training SD was associated with increased FC between navigation-related brain structures when faced to the task of learning a novel but related environment in the extended version of the city at Day 4. These results suggest the use of compensatory resources to link novel information with SD-related less efficiently consolidated memory traces.

neuroscience↗