osl-dynamics, a toolbox for modeling fast dynamic brain activity

Neural activity contains rich spatiotemporal structure that corresponds to cognition. This includes oscillatory bursting and dynamic activity that span across networks of brain regions, all of which can occur on timescales of tens of milliseconds. While these processes can be accessed through brain recordings and imaging, modeling them presents methodological challenges due to their fast and transient nature. Furthermore, the exact timing and duration of interesting cognitive events are often a priori unknown. Here, we present the OHBA Software Library Dynamics Toolbox (osl-dynamics), a Python-based package that can identify and describe recurrent dynamics in functional neuroimaging data on timescales as fast as tens of milliseconds. At its core are machine learning generative models that are able to adapt to the data and learn the timing, as well as the spatial and spectral characteristics, of brain activity with few assumptions. osl-dynamics incorporates state-of-the-art approaches that can be, and have been, used to elucidate brain dynamics in a wide range of data types, including magneto/electroencephalography, functional magnetic resonance imaging, invasive local field potential recordings, and electrocorticography. It also provides novel summary measures of brain dynamics that can be used to inform our understanding of cognition, behavior, and disease. We hope osl-dynamics will further our understanding of brain function, through its ability to enhance the modeling of fast dynamic processes

osl-dynamics: a toolbox for modelling fast dynamic brain activity

Neural activity contains rich spatio-temporal structure that corresponds to cognition. This includes oscillatory bursting and dynamic activity that span across networks of brain regions, all of which can occur on timescales of a tens of milliseconds. While these processes can be accessed through brain recordings and imaging, modelling them presents methodological challenges due to their fast and transient nature. Furthermore, the exact timing and duration of interesting cognitive events is often a priori unknown. Here we present the OHBA Software Library Dynamics Toolbox (osl-dynamics), a Python-based package that can identify and describe recurrent dynamics in functional neuroimaging data on timescales as fast as tens of milliseconds. At its core are machine learning generative models that are able to adapt to the data and learn the timing, as well as the spatial and spectral characteristics, of brain activity with few assumptions. osl-dynamics incorporates state-of-the-art approaches that can be, and have been, used to elucidate brain dynamics in a wide range of data types, including magneto/electroencephalography, functional magnetic resonance imaging, invasive local field potential recordings and electrocorticography. It also provides novel summary measures of brain dynamics that can be used to inform our understanding of cognition, behaviour and disease. We hope osl-dynamics will further our understanding of brain function, through its ability to enhance the modelling of fast dynamic processes

The GLM-spectrum:A multilevel framework for spectrum analysis with covariate and confound modelling

The frequency spectrum is a central method for representing the dynamics within electrophysiological data. Some widely used spectrum estimators make use of averaging across time segments to reduce noise in the final spectrum. The core of this approach has not changed substantially since the 1960s, though many advances in the field of regression modelling and statistics have been made during this time. Here, we propose a new approach, the General Linear Model (GLM) Spectrum, which reframes time averaged spectral estimation as multiple regression. This brings several benefits, including the ability to do confound modelling, hierarchical modelling, and significance testing via non-parametric statistics. We apply the approach to a dataset of EEG recordings of participants who alternate between eyes-open and eyes-closed resting state. The GLM-Spectrum can model both conditions, quantify their differences, and perform denoising through confound regression in a single step. This application is scaled up from a single channel to a whole head recording and, finally, applied to quantify age differences across a large group-level dataset. We show that the GLM-Spectrum lends itself to rigorous modelling of within- and between-subject contrasts as well as their interactions, and that the use of model-projected spectra provides an intuitive visualisation. The GLM-Spectrum is a flexible framework for robust multilevel analysis of power spectra, with adaptive covariate and confound modelling

osl-dynamics, a toolbox for modeling fast dynamic brain activity

Neural activity contains rich spatiotemporal structure that corresponds to cognition. This includes oscillatory bursting and dynamic activity that span across networks of brain regions, all of which can occur on timescales of tens of milliseconds. While these processes can be accessed through brain recordings and imaging, modeling them presents methodological challenges due to their fast and transient nature. Furthermore, the exact timing and duration of interesting cognitive events are often a priori unknown. Here, we present the OHBA Software Library Dynamics Toolbox (osl-dynamics), a Python-based package that can identify and describe recurrent dynamics in functional neuroimaging data on timescales as fast as tens of milliseconds. At its core are machine learning generative models that are able to adapt to the data and learn the timing, as well as the spatial and spectral characteristics, of brain activity with few assumptions. osl-dynamics incorporates state-of-the-art approaches that can be, and have been, used to elucidate brain dynamics in a wide range of data types, including magneto/electroencephalography, functional magnetic resonance imaging, invasive local field potential recordings, and electrocorticography. It also provides novel summary measures of brain dynamics that can be used to inform our understanding of cognition, behavior, and disease. We hope osl-dynamics will further our understanding of brain function, through its ability to enhance the modeling of fast dynamic processes

Mixtures of large-scale dynamic functional brain network modes

Accurate temporal modelling of functional brain networks is essential in the quest for understanding how such networks facilitate cognition. Researchers are beginning to adopt time-varying analyses for electrophysiological data that capture highly dynamic processes on the order of milliseconds. Typically, these approaches, such as clustering of functional connectivity profiles and Hidden Markov Modelling (HMM), assume mutual exclusivity of networks over time. Whilst a powerful constraint, this assumption may be compromising the ability of these approaches to describe the data effectively. Here, we propose a new generative model for functional connectivity as a time-varying linear mixture of spatially distributed statistical “modes”. The temporal evolution of this mixture is governed by a recurrent neural network, which enables the model to generate data with a rich temporal structure. We use a Bayesian framework known as amortised variational inference to learn model parameters from observed data. We call the approach DyNeMo (for Dynamic Network Modes), and show using simulations it outperforms the HMM when the assumption of mutual exclusivity is violated. In resting-state MEG, DyNeMo reveals a mixture of modes that activate on fast time scales of 100–150 ms, which is similar to state lifetimes found using an HMM. In task MEG data, DyNeMo finds modes with plausible, task-dependent evoked responses without any knowledge of the task timings. Overall, DyNeMo provides decompositions that are an approximate remapping of the HMM’s while showing improvements in overall explanatory power. However, the magnitude of the improvements suggests that the HMM’s assumption of mutual exclusivity can be reasonable in practice. Nonetheless, DyNeMo provides a flexible framework for implementing and assessing future modelling developments

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

Dynamic Imperfections in the Compact Linear Collider

The Compact Linear Collider (CLIC) is a proposed TeV-scale electron-positron collider under development at the European Organization for Nuclear Research (CERN). CLIC adopts a staged approach with three centre-of-mass energies: 380 GeV, 1.5 TeV and 3 TeV. This work focuses on the first stage, which has been optimised for studies of the Higgs boson and top-quark physics. A high luminosity is achieved by targeting ultra-small beam sizes at the collision point. Realising these beam sizes relies on the production and transport of ultra-low emittance beams. The preservation of emittance is important in three sections: the Ring to Main Linac (RTML), the Main Linac (ML) and the Beam Delivery System (BDS). Typically, each section is studied individually. In this work, they are integrated into a single simulation, referred to as an ‘integrated simulation'. In an integrated simulation, particles are tracked through the RTML, ML and BDS to reach the collision point. The luminosity is calculated with a full simulation of the collision including beam-beam effects. Imperfections lead to emittance growth and degrade luminosity. Integrated simulations are performed to evaluate the impact of static and dynamic imperfections. The impact of static imperfections is mitigated with well known beam-based tuning procedures. This work focuses on the impact of dynamic imperfections and their mitigation. A well studied dynamic imperfection is ground motion. Integrated simulations in this work show ground motion can be mitigated with a feedback system that corrects the beam trajectory and a stabilisation system for quadrupole magnets. Much of this work is devoted to a newly considered dynamic imperfection, that is stray magnetic fields (SFs). CLIC is sensitive to sub-nT SFs. The typical amplitude of SFs found in an accelerator environment is several orders of magnitude larger than this. Therefore, SFs are a serious consideration in the design and operation of CLIC. %This work examines particular SF sources: natural sources, such as the Earth's magnetic field; environmental sources, such as electrical power infrastructure and technical sources, such as magnets, power cables, ventilation systems, etc. A dedicated mitigation system is needed to ensure SFs do not significantly impact luminosity. A passive shielding technique is investigated. Measurements of the shielding provided by mu-metal for small-amplitude external magnetic fields are performed. With these measurements, a magnetic shielding model is validated. The proposed mitigation strategy for CLIC is to surround sensitive regions of the beamline with a 1 mm mu-metal layer. SFs at two accelerator facilities at CERN are surveyed. With these measurements, a model for SFs is developed. Integrated simulations including SFs are performed and show luminosity loss is effectively mitigated with a beam trajectory feedback system and mu-metal shield

OSL Dynamics Toolbox

Example data and models to illustrate the use of osl-dynamics (https://github.com/OHBA-analysis/osl-dynamics)

Integrated simulation of dynamic effects for the 380 GeV CLIC design

Integrated simulations of the Ring to Main Linac, Main Linac and Beam Delivery System of the 380 GeV CLIC design are presented. The performance of a perfect lattice and the effect of dynamic imperfections are studied. The dynamic effects investigated were ground motion, stray magnetic fields and longitudinal stability. The effectiveness of different mitigation methods for ground motion and stray magnetic fields was also studied

Stray magnetic field tolerances for the 380 GeV CLIC design

The amplitude of external dynamic magnetic fields that can be tolerated by the 380 GeV CLIC design are presented along with the effect of different correction techniques. Different spatial distributions for the magnetic fields are considered. This includes a sinusoidal spatial dependence, a constant valued magnetic field across the entire accelerator and a random variation of the magnetic field from point to point