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.

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 MetadataCurrent code version

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Multiple neuropathological changes are involved in Alzheimer's disease (AD). AD hallmark biomarkers are amyloid-beta, tau pathology, and neuronal and synaptic loss. Other possible brain tissue-related biomarkers, such as iron and myelin content in the brain, are less frequently studied. Thanks to quantitative MRI (qMRI), tissue parameters such as magnetization transfer (MT), effective transverse relaxation (R2*), and proton density (PD) can be determined quantitatively, enabling the detection of microstructural tissue-related alterations in aging and neurodegenerative diseases. The current study investigated the co-occurrence of neurodegeneration (as measured with synaptic density), increased iron content, and decreased myelin content in Alzheimer's disease. The study involved 24 amyloid-positive patients (AD, 11 males) and 19 healthy controls (HC, 9 males). All participants underwent a multi-parameter mapping MRI protocol, from which quantitative maps for MTsat and R2* were estimated. Synaptic density was indexed by the total volume distribution map (Vt) derived from [18F] UCB-H PET imaging. First, groups were compared with univariate statistical analyses applied to R2*, MTsat, and Vt maps. Then multivariate General Linear Model (mGLM) was used to detect the co-occurrence of changes in R2*, MTsat, and Vt at the voxel level. Univariate GLM analysis of R2* showed no significant difference between the two groups. In contrast, the same analysis for MTsat resulted in a significant between-group difference in the right hippocampus at the cluster level with a corrected threshold (P-value < .05). The mGLM analysis revealed a significant difference in both right and left hippocampus between the AD and HC groups, as well as in the left precuneus, right middle frontal, and left superior orbitofrontal gyrus when all three modalities were present, suggesting these regions as the most affected despite the diverse changes of myelin, iron, and synapse degeneration in AD. Here, the mGLM is introduced as an alternative for multiple comparisons between different modalities, as it reduces the risk of false positives due to the multiplicity of the tests while informing about the co-occurrence of neuropathological processes in dementia.