Periodic Reporting for period 3 - COGNAP (To nap or not to nap? Why napping habits interfere with cognitive fitness in ageing)
Okres sprawozdawczy: 2021-01-01 do 2022-06-30
Identifying novel factors which associate with inter-individual variability in cognitive decline is a promising area in ageing research. Considering its strong implication in neuroprotective function, the proposal predicts that variability in circadian rhythmicity explains a significant part of the age-related changes in human cognition. Circadian rhythms -one of the most fundamental processes of living organisms- are present throughout the nervous system and act on cognitive brain function. Circadian rhythms shape the temporal organization of sleep and wakefulness to achieve human diurnality, characterized by a consolidated bout of sleep during night-time and a continuous period of wakefulness during the day. Of prime importance is that the temporal organization of sleep and wakefulness evolves throughout the adult lifespan, leading to higher sleep-wake fragmentation with ageing. The increasing occurrence of daytime napping is the most visible manifestation of this fragmentation. Contrary to the common belief, napping stands as a health risk factor in seniors in epidemiological data. We posit that chronic napping in older people primarily reflects circadian disruption. Based on preliminary findings, we predict that this disruption will lead to lower cognitive fitness. We further hypothesize that a re-stabilization of circadian sleep-wake organization through a nap prevention intervention will reduce age-related cognitive decline. Characterizing the link between cognitive ageing and the temporal distribution of sleep and wakefulness will not only bring ground-breaking advances at the scientific level, but is also timely in the ageing society. Cognitive decline, as well as inadequately timed sleep, represent dominant determinants of the health span of our fast ageing population and easy implementable intervention programs are urgently needed.
The main focus of the work performed the last 18 months comprised the built-up of the team needed to perform the project, preparing the study environnment, proceed to data acquisition and start to build the analysis pipelines. During the first 5 months, the team was recruited and the study organized. From April 2018 to August 2018 pilot measurements were performed to set up the questionnaire battery, cognitive testing, the constant routine (WP01) as well as the working memory task in the fMRI environment (part of WP02). In parallel the first recruitment wave was launched (e.g. construction of an internet site for the project, presentation of the project at open public seminars). The entire study procedure was tested for its feasibility in a comprehensive pilot study in August 2018. We started the study with the first volunteer in September 2018. Until now, we screened around 600 individuals for exclusion criteria. Out of this pool and until June 2020, 115 underwent a screening night and 60 entered the study procedure (so far we count 3 withdrawals during the intervention part of WP03). During the pandemics (data acquisition stop between March 10th and beginning of June 2020), the group focused his work on data analysis, but also in proceeding to data quality check (log listing) and appropriate storage (mainly apply BIDS format to the electrophysiological and MRI data)
In parallel to data acquisition, the team participated to several congresses (see list below) and developed tools for data processing, the first of which was recently submitted for publication in a peer reviewed journal (paper on the PyActigraphy Toolbox: http://doi.org/10.5281/zenodo.2537921).
Even though our project is based on group comparisons which should be computed at final sample size, we started to run first proof-of concept analyses
WP01. Circadian ranking of the participants. As expected we observe a clear circadian profile of average time course of subjective sleepiness, psychomotor vigilance performance (all participants averaged) and melatonin data (available for first 10 participants so far). To proceed to ranking as explained in the project, melatonin data need to be analyzed for the entire participant pool.
WP2: Phenotyping cognitive and brain fitness. By waiting access to the full-set of in lab measures in the lab and before being able to proceed to group comparisons, we estimated circadian disruption using field actimetry indices and observed that these indices significantly explain variance on a memory and attention performance composite score, such that lower performance is associated with higher sleep-wake fragmentation. In that sense, our data corroborate an association between sleep-wake cycle fragmentation and cognition in the aged.
Functional imaging: By waiting access to the full-set of in lab measures in the lab, we estimated circadian disruption using a field actimetry indices. Preliminary analyses suggest under-recruitment in task-relevant superior parietal regions in individuals characterized by higher wake fragmentation.
WP3. Interventional approach. Nap suppression will effectively improve circadian organization and thereby optimize cognitive fitness in the aged. These data have not yet been processed in depth. Nevertheless, a preliminary analysis indicates that that after 6 month follow-up, the nap intervention group felt less negative affect as assessed by the PANAS (positive-negative affect) scale at follow up compared to their baseline measurement.
These preliminary outcomes will be presented at the 25th Congress of the European Sleep Research Society, which will be held from September 22th-25th 2020.
In parallel to data acquisition, the team participated to several congresses (see list below) and developed tools for data processing, the first of which was recently submitted for publication in a peer reviewed journal (paper on the PyActigraphy Toolbox: http://doi.org/10.5281/zenodo.2537921).
Even though our project is based on group comparisons which should be computed at final sample size, we started to run first proof-of concept analyses
WP01. Circadian ranking of the participants. As expected we observe a clear circadian profile of average time course of subjective sleepiness, psychomotor vigilance performance (all participants averaged) and melatonin data (available for first 10 participants so far). To proceed to ranking as explained in the project, melatonin data need to be analyzed for the entire participant pool.
WP2: Phenotyping cognitive and brain fitness. By waiting access to the full-set of in lab measures in the lab and before being able to proceed to group comparisons, we estimated circadian disruption using field actimetry indices and observed that these indices significantly explain variance on a memory and attention performance composite score, such that lower performance is associated with higher sleep-wake fragmentation. In that sense, our data corroborate an association between sleep-wake cycle fragmentation and cognition in the aged.
Functional imaging: By waiting access to the full-set of in lab measures in the lab, we estimated circadian disruption using a field actimetry indices. Preliminary analyses suggest under-recruitment in task-relevant superior parietal regions in individuals characterized by higher wake fragmentation.
WP3. Interventional approach. Nap suppression will effectively improve circadian organization and thereby optimize cognitive fitness in the aged. These data have not yet been processed in depth. Nevertheless, a preliminary analysis indicates that that after 6 month follow-up, the nap intervention group felt less negative affect as assessed by the PANAS (positive-negative affect) scale at follow up compared to their baseline measurement.
These preliminary outcomes will be presented at the 25th Congress of the European Sleep Research Society, which will be held from September 22th-25th 2020.
We decided to include a modelling approach for WP0 1by reasoning that fully grasping the modulatory potential of sleep phenotypes on cognitive aging needs a transition from mechanistic in-lab approaches to larger scale population-wide real life data acquisitions (field actimetry also acquired in the frame of the COGNAP project). To do so, we initiateed a collaboration with the Brain Dynamics Group of the University of Sydney (Dr Postnova) to bring new expertise. Dr. Postnova's key interest is in application of physics and mathematics methods to understanding how brain systems across different levels interact to produce the dynamics on the higher-level of functioning such as alertness, sleep and performance. The team developped a physiology-based model or arousal state dynamics which will be applied to the COGNAP data. We aim at assess whether mathematical lab-proofed modelling of physiologically meaningful parameters of sleep-wake regulation can explain rest-activity phenotypes observed in daily life through actigraphy and whether actigraphy-derived sleep phenotypes have a predictive value for the individual’s cognitive and brain aging short-term trajectory.