Bidsme: expandable BIDS-ifier of brain imagery datasets

peer reviewedThe purpose of Bidsme is to organize a given medical image dataset following the "Brain Imaging Data Structure" (BIDS). Bidsme is an all-in-one organizer tool, that not only renames and re-structures the original data files, but also extracts and formats the necessary metadata. During the data organization, Bidsme provides the user with the full control over these processes, allowing the use of non-standard metadata and file names, as well as the addition of modalities not yet described by the BIDS. Instead of strictly imposing this structure, Bidsme allows the user to fully configure how the source dataset will be organized and what metadata will be included. Bidsme can be used both as Python package and command-line tool, and includes a tutorial with a test dataset.EO

pyActigraphy: Open-source python package for actigraphy data visualization and analysis

The possibility to continuously record locomotor movements using accelerometers (actigraphy) has allowed field studies of sleep and rest-activity patterns. It has also enabled large-scale data collections, opening new avenues for research. However, each brand of actigraph devices encodes recordings in its own format and closed-source proprietary softwares are typically used to read and analyse actigraphy data. In order to provide an alternative to these softwares, we developed a comprehensive open-source toolbox for actigraphy data analysis, pyActigraphy. It allows researchers to read actigraphy data from 7 different file formats and gives access to a variety of rest-activity rhythm variables, automatic sleep detection algorithms and more advanced signal processing techniques. Besides, in order to empower researchers and clinicians with respect to their analyses, we created a series of interactive tutorials that illustrate how to implement the key steps of typical actigraphy data analyses. As an open-source project, all kind of user’s contributions to our toolbox are welcome. As increasing evidence points to the predicting value of rest-activity patterns derived from actigraphy for brain integrity, we believe that the development of the pyActigraphy package will not only benefit the sleep and chronobiology research, but also the neuroscientific community at large.COGNA

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

The pyActigraphy toolbox, an open-source python package for actigraphy data visualisation and analysis, offers functionalities to automatise data pre-processing, read large file batches and implement various metrics and techniques for actigraphy data analysis. By developing the pyActigraphy package, we not only hope to facilitate data analysis but also foster research using actimetry and drive a community effort to improve this open-source package and develop new variables and algorithms.COGNA

Multimodal imaging of microstructural cerebral alterations and loss of synaptic density in Alzheimer's disease.

peer reviewedMultiple neuropathological events are involved in Alzheimer's disease (AD). The current study investigated the concurrence of neurodegeneration, increased iron content, atrophy, and demyelination in AD. Quantitative multiparameter magnetic resonance imaging (MRI) maps providing neuroimaging biomarkers for myelination and iron content along with synaptic density measurements using [18F] UCB-H PET were acquired in 24 AD and 19 Healthy controls (19 males). The whole brain voxel-wise group comparison revealed demyelination in the right hippocampus, while no significant iron content difference was detected. Bilateral atrophy and synaptic density loss were observed in the hippocampus and amygdala. The multivariate GLM (mGLM) analysis shows a bilateral difference in the hippocampus and amygdala, right pallidum, left fusiform and temporal lobe suggesting that these regions are the most affected despite the diverse differences in brain tissue properties in AD. Demyelination was identified as the most affecting factor in the observed differences. Here, the mGLM is introduced as an alternative for multiple comparisons between different modalities, reducing the risk of false positives while informing about the co-occurrence of neuropathological processes in AD

Multimodal imaging of microstructural cerebral changes and loss of synaptic density in Alzheimer's disease

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.V

Multimodal imaging of microstructural cerebral changes and loss of synaptic density in Alzheimer’s disease

Background: Multiple neuropathological changes are involved in Alzheimer’s disease (AD) progression. The hallmark biomarkers are amyloid-beta, tau pathology, neuronal and synaptic loss. Other potential biomarkers, such as the level of iron and myelin content in the brain, have not been thoroughly studied. Nevertheless, these can be estimated in vivo thanks to tissue magnetic resonance (MR) properties measured through quantitative MR imaging (qMRI) techniques. Aim: We aimed to assess the co-occurrence of neurodegeneration (as measured with synaptic density), increased iron content and decreased myelin content in Alzheimer’s disease. Method: Data include 24 amyloid-positive Alzheimers patients (AD-11/13 males/females) and 19 healthy controls (HC-9/11 males/females). They underwent a multiparameter qMRI protocol used to generate quantitative maps sensitive to microstructural changes in myelin, iron deposits, and water content in grey matter (GM). Synaptic density was indexed by [18F]UCB-H-PET imaging using the distribution volume density (VT) maps. First, we applied univariate statistical analyses to investigate variation between AD and HC groups for each modality individually. Then, a multivariate GLM approach was used to compare the two groups pooling all modalities. Results/Conclusions: In GM univariate analyses, there was no significant difference between the AD and HC groups in any map at corrected statistical threshold. Conversely, the multivariate analysis on GM, combining MT, R2s, and synaptic density, provided significant group differences (FWEcorr P-value < 0.05) see figure 1. These variations are observed in the right amygdala (at voxel level) and in 5 distinct clusters covering the bilateral anterior hippocampal structures. These show that patients with AD present convergence of neuropathological changes in the hippocampal area, suggesting that different pathological mechanisms co-exist in areas known to harbor early-stage neuronal death

Exploring the specificity of fatigue-related brain activity in early Multiple Sclerosis.

peer reviewe

Measurement of differential cross sections for top quark pair production using the lepton plus jets final state in proton-proton collisions at 13 TeV

National Science Foundation (U.S.

Particle-flow reconstruction and global event description with the CMS detector

The CMS apparatus was identified, a few years before the start of the LHC operation at CERN, to feature properties well suited to particle-flow (PF) reconstruction: a highly-segmented tracker, a fine-grained electromagnetic calorimeter, a hermetic hadron calorimeter, a strong magnetic field, and an excellent muon spectrometer. A fully-fledged PF reconstruction algorithm tuned to the CMS detector was therefore developed and has been consistently used in physics analyses for the first time at a hadron collider. For each collision, the comprehensive list of final-state particles identified and reconstructed by the algorithm provides a global event description that leads to unprecedented CMS performance for jet and hadronic tau decay reconstruction, missing transverse momentum determination, and electron and muon identification. This approach also allows particles from pileup interactions to be identified and enables efficient pileup mitigation methods. The data collected by CMS at a centre-of-mass energy of 8 TeV show excellent agreement with the simulation and confirm the superior PF performance at least up to an average of 20 pileup interactions