Neurodesk: an accessible, flexible and portable data analysis environment for reproducible neuroimaging.

Neuroimaging research requires purpose-built analysis software, which is challenging to install and may produce different results across computing environments. The community-oriented, open-source Neurodesk platform ( https://www.neurodesk.org/ ) harnesses a comprehensive and growing suite of neuroimaging software containers. Neurodesk includes a browser-accessible virtual desktop, command-line interface and computational notebook compatibility, allowing for accessible, flexible, portable and fully reproducible neuroimaging analysis on personal workstations, high-performance computers and the cloud.

The Past, Present, and Future of the Brain Imaging Data Structure (BIDS).

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS.

Neurodesk: An accessible, flexible, and portable data analysis environment for reproducible neuroimaging.

Neuroimaging data analysis often requires purpose-built software, which can be challenging to install and may produce different results across computing environments. Beyond being a roadblock to neuroscientists, these issues of accessibility and portability can hamper the reproducibility of neuroimaging data analysis pipelines. Here, we introduce the Neurodesk platform, which harnesses software containers to support a comprehensive and growing suite of neuroimaging software (https://www.neurodesk.org/). Neurodesk includes a browser-accessible virtual desktop environment and a command line interface, mediating access to containerized neuroimaging software libraries on various computing platforms, including personal and high-performance computers, cloud computing and Jupyter Notebooks. This community-oriented, open-source platform enables a paradigm shift for neuroimaging data analysis, allowing for accessible, flexible, fully reproducible, and portable data analysis pipelines.

Genome-wide epigenetic and mRNA-expression profiling followed by CRISPR/Cas9-mediated gene-disruptions corroborate the MIR141/MIR200C-ZEB1/ZEB2-FGFR1 axis in acquired EMT-associated EGFR TKI-resistance in NSCLC cells.

Background: Epithelial-mesenchymal-transition (EMT) is an epigenetic-based mechanism contributing to the acquired treatment resistance against receptor tyrosine kinase inhibitors (TKIs) in non-small cell lung cancer (NSCLC) cells harboring epidermal growth factor receptor (EGFR)-mutations. Delineating the exact epigenetic and gene-expression alterations in EMT-associated EGFR TKI-resistance (EMT-E-TKI-R) is vital for improved diagnosis and treatment of NSCLC patients. Methods: We characterized genome-wide changes in mRNA-expression, DNA-methylation and the histone-modification H3K36me3 in EGFR-mutated NSCLC HCC827 cells in result of acquired EMT-E-TKI-R. CRISPR/Cas9 was used to functional examine key findings from the omics analyses. Results: Acquired EMT-E-TKI-R was analyzed with three omics approaches. RNA-sequencing identified 2,233 and 1,972 up- and down-regulated genes, respectively, and among these were established EMT-markers. DNA-methylation EPIC array analyses identified 14,163 and 7,999 hyper- and hypo-methylated, respectively, differential methylated positions of which several were present in EMT-markers. Finally, H3K36me3 chromatin immunoprecipitation (ChIP)-sequencing detected 2,873 and 3,836 genes with enrichment and depletion, respectively, and among these were established EMT-markers. Correlation analyses showed that EMT-E-TKI-R mRNA-expression changes correlated better with H3K36me3 changes than with DNA-methylation changes. Moreover, the omics data supported the involvement of the MIR141/MIR200C-ZEB1/ZEB2-FGFR1 signaling axis for acquired EMT-E-TKI-R. CRISPR/Cas9-mediated analyses corroborated the importance of ZEB1 in acquired EMT-E-TKI-R, MIR200C and MIR141 to be in an EMT-E-TKI-R-associated auto-regulatory loop with ZEB1, and FGFR1 to mediate cell survival in EMT-E-TKI-R. Conclusions: The current study describes the synchronous genome-wide changes in mRNA-expression, DNA-methylation, and H3K36me3 in NSCLC EMT-E-TKI-R. The omics approaches revealed potential novel diagnostic markers and treatment targets. Besides, the study consolidates the functional impact of the MIR141/MIR200C-ZEB1/ZEB2-FGFR1-signaling axis in NSCLC EMT-E-TKI-R.

Dynamic causal modelling of COVID-19 and its mitigations.

This technical report describes the dynamic causal modelling of mitigated epidemiological outcomes during the COVID-9 coronavirus outbreak in 2020. Dynamic causal modelling is a form of complex system modelling, which uses 'real world' timeseries to estimate the parameters of an underlying state space model using variational Bayesian procedures. Its key contribution-in an epidemiological setting-is to embed conventional models within a larger model of sociobehavioural responses-in a way that allows for (relatively assumption-free) forecasting. One advantage of using variational Bayes is that one can progressively optimise the model via Bayesian model selection: generally, the most likely models become more expressive as more data becomes available. This report summarises the model (on 6-Nov-20), eight months after the inception of dynamic causal modelling for COVID-19. This model-and its subsequent updates-is used to provide nowcasts and forecasts of latent behavioural and epidemiological variables as an open science resource. The current report describes the underlying model structure and the rationale for the variational procedures that underwrite Bayesian model selection.

Effective immunity and second waves: a dynamic causal modelling study.

This technical report addresses a pressing issue in the trajectory of the coronavirus outbreak; namely, the rate at which effective immunity is lost following the first wave of the pandemic. This is a crucial epidemiological parameter that speaks to both the consequences of relaxing lockdown and the propensity for a second wave of infections. Using a dynamic causal model of reported cases and deaths from multiple countries, we evaluated the evidence models of progressively longer periods of immunity. The results speak to an effective population immunity of about three months that, under the model, defers any second wave for approximately six months in most countries. This may have implications for the window of opportunity for tracking and tracing, as well as for developing vaccination programmes, and other therapeutic interventions.

Dynamic causal modelling of COVID-19.

This technical report describes a dynamic causal model of the spread of coronavirus through a population. The model is based upon ensemble or population dynamics that generate outcomes, like new cases and deaths over time. The purpose of this model is to quantify the uncertainty that attends predictions of relevant outcomes. By assuming suitable conditional dependencies, one can model the effects of interventions (e.g., social distancing) and differences among populations (e.g., herd immunity) to predict what might happen in different circumstances. Technically, this model leverages state-of-the-art variational (Bayesian) model inversion and comparison procedures, originally developed to characterise the responses of neuronal ensembles to perturbations. Here, this modelling is applied to epidemiological populations-to illustrate the kind of inferences that are supported and how the model per se can be optimised given timeseries data. Although the purpose of this paper is to describe a modelling protocol, the results illustrate some interesting perspectives on the current pandemic; for example, the nonlinear effects of herd immunity that speak to a self-organised mitigation process.

Second waves, social distancing, and the spread of COVID-19 across the USA.

We recently described a dynamic causal model of a COVID-19 outbreak within a single region. Here, we combine several of these (epidemic) models to create a (pandemic) model of viral spread among regions. Our focus is on a second wave of new cases that may result from loss of immunity-and the exchange of people between regions-and how mortality rates can be ameliorated under different strategic responses. In particular, we consider hard or soft social distancing strategies predicated on national (Federal) or regional (State) estimates of the prevalence of infection in the population. The modelling is demonstrated using timeseries of new cases and deaths from the United States to estimate the parameters of a factorial (compartmental) epidemiological model of each State and, crucially, coupling between States. Using Bayesian model reduction, we identify the effective connectivity between States that best explains the initial phases of the outbreak in the United States. Using the ensuing posterior parameter estimates, we then evaluate the likely outcomes of different policies in terms of mortality, working days lost due to lockdown and demands upon critical care. The provisional results of this modelling suggest that social distancing and loss of immunity are the two key factors that underwrite a return to endemic equilibrium.

Deep brain stimulation has state-dependent effects on motor connectivity in Parkinson's disease.

Subthalamic nucleus deep brain stimulation is an effective treatment for advanced Parkinson's disease; however, its therapeutic mechanism is unclear. Previous modelling of functional MRI data has suggested that deep brain stimulation has modulatory effects on a number of basal ganglia pathways. This work uses an enhanced data collection protocol to collect rare functional MRI data in patients with subthalamic nucleus deep brain stimulation. Eleven patients with Parkinson's disease and subthalamic nucleus deep brain stimulation underwent functional MRI at rest and during a movement task; once with active deep brain stimulation, and once with deep brain stimulation switched off. Dynamic causal modelling and Bayesian model selection were first used to compare a series of plausible biophysical models of the cortico-basal ganglia circuit that could explain the functional MRI activity at rest in an attempt to reproduce and extend the findings from our previous work. General linear modelling of the movement task functional MRI data revealed deep brain stimulation-associated signal increases in the primary motor and cerebellar cortices. Given the significance of the cerebellum in voluntary movement, we then built a more complete model of the motor system by including cerebellar-basal ganglia interactions, and compared the modulatory effects deep brain stimulation had on different circuit components during the movement task and again using the resting state data. Consistent with previous results from our independent cohort, model comparison found that the rest data were best explained by deep brain stimulation-induced increased (effective) connectivity of the cortico-striatal, thalamo-cortical and direct pathway and reduced coupling of subthalamic nucleus afferent and efferent connections. No changes in cerebellar connectivity were identified at rest. In contrast, during the movement task, there was functional recruitment of subcortical-cerebellar pathways, which were additionally modulated by deep brain stimulation, as well as modulation of local (intrinsic) cortical and cerebellar circuits. This work provides in vivo evidence for the modulatory effects of subthalamic nucleus deep brain stimulation on effective connectivity within the cortico-basal ganglia loops at rest, as well as further modulations in the cortico-cerebellar motor system during voluntary movement. We propose that deep brain stimulation has both behaviour-independent effects on basal ganglia connectivity, as well as behaviour-dependent modulatory effects.

Multimodal Integration of M/EEG and f/MRI Data in SPM12.

We describe the steps involved in analysis of multi-modal, multi-subject human neuroimaging data using the SPM12 free and open source software (https://www.fil.ion.ucl.ac.uk/spm/) and a publically-available dataset organized according to the Brain Imaging Data Structure (BIDS) format (https://openneuro.org/datasets/ds000117/). The dataset contains electroencephalographic (EEG), magnetoencephalographic (MEG), and functional and structural magnetic resonance imaging (MRI) data from 16 subjects who undertook multiple runs of a simple task performed on a large number of famous, unfamiliar and scrambled faces. We demonstrate: (1) batching and scripting of preprocessing of multiple runs/subjects of combined MEG and EEG data, (2) creation of trial-averaged evoked responses, (3) source-reconstruction of the power (induced and evoked) across trials within a time-frequency window around the "N/M170" evoked component, using structural MRI for forward modeling and simultaneous inversion (fusion) of MEG and EEG data, (4) group-based optimisation of spatial priors during M/EEG source reconstruction using fMRI data on the same paradigm, and (5) statistical mapping across subjects of cortical source power increases for faces vs. scrambled faces.

Analysis of family-wise error rates in statistical parametric mapping using random field theory.

This technical report revisits the analysis of family-wise error rates in statistical parametric mapping-using random field theory-reported in (Eklund et al. []: arXiv 1511.01863). Contrary to the understandable spin that these sorts of analyses attract, a review of their results suggests that they endorse the use of parametric assumptions-and random field theory-in the analysis of functional neuroimaging data. We briefly rehearse the advantages parametric analyses offer over nonparametric alternatives and then unpack the implications of (Eklund et al. []: arXiv 1511.01863) for parametric procedures. Hum Brain Mapp, 40:2052-2054, 2019. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

MEG-BIDS, the brain imaging data structure extended to magnetoencephalography.

We present a significant extension of the Brain Imaging Data Structure (BIDS) to support the specific aspects of magnetoencephalography (MEG) data. MEG measures brain activity with millisecond temporal resolution and unique source imaging capabilities. So far, BIDS was a solution to organise magnetic resonance imaging (MRI) data. The nature and acquisition parameters of MRI and MEG data are strongly dissimilar. Although there is no standard data format for MEG, we propose MEG-BIDS as a principled solution to store, organise, process and share the multidimensional data volumes produced by the modality. The standard also includes well-defined metadata, to facilitate future data harmonisation and sharing efforts. This responds to unmet needs from the multimodal neuroimaging community and paves the way to further integration of other techniques in electrophysiology. MEG-BIDS builds on MRI-BIDS, extending BIDS to a multimodal data structure. We feature several data-analytics software that have adopted MEG-BIDS, and a diverse sample of open MEG-BIDS data resources available to everyone.

Boutiques: a flexible framework to integrate command-line applications in computing platforms.

We present Boutiques, a system to automatically publish, integrate, and execute command-line applications across computational platforms. Boutiques applications are installed through software containers described in a rich and flexible JSON language. A set of core tools facilitates the construction, validation, import, execution, and publishing of applications. Boutiques is currently supported by several distinct virtual research platforms, and it has been used to describe dozens of applications in the neuroinformatics domain. We expect Boutiques to improve the quality of application integration in computational platforms, to reduce redundancy of effort, to contribute to computational reproducibility, and to foster Open Science.

Functional Sensitivity of 2D Simultaneous Multi-Slice Echo-Planar Imaging: Effects of Acceleration on g-factor and Physiological Noise.

Accelerated data acquisition with simultaneous multi-slice (SMS) imaging for functional MRI studies leads to interacting and opposing effects that influence the sensitivity to blood oxygen level-dependent (BOLD) signal changes. Image signal to noise ratio (SNR) is decreased with higher SMS acceleration factors and shorter repetition times (TR) due to g-factor noise penalties and saturation of longitudinal magnetization. However, the lower image SNR is counteracted by greater statistical power from more samples per unit time and a higher temporal Nyquist frequency that allows for better removal of spurious non-BOLD high frequency signal content. This study investigated the dependence of the BOLD sensitivity on these main driving factors and their interaction, and provides a framework for evaluating optimal acceleration of SMS-EPI sequences. functional magnetic resonance imaging (fMRI) data from a scenes/objects visualization task was acquired in 10 healthy volunteers at a standard neuroscience resolution of 3 mm on a 3T MRI scanner. SMS factors 1, 2, 4, and 8 were used, spanning TRs of 2800 ms to 350 ms. Two data processing methods were used to equalize the number of samples over the SMS factors. BOLD sensitivity was assessed using g-factors maps, temporal SNR (tSNR), and t-score metrics. tSNR results show a dependence on SMS factor that is highly non-uniform over the brain, with outcomes driven by g-factor noise amplification and the presence of high frequency noise. The t-score metrics also show a high degree of spatial dependence: the lower g-factor noise area of V1 shows significant improvements at higher SMS factors; the moderate-level g-factor noise area of the parahippocampal place area shows only a trend of improvement; and the high g-factor noise area of the ventral-medial pre-frontal cortex shows a trend of declining t-scores at higher SMS factors. This spatial variability suggests that the optimal SMS factor for fMRI studies is region dependent. For task fMRI studies done with similar parameters as were used here (3T scanner, 32-channel RF head coil, whole brain coverage at 3 mm isotropic resolution), we recommend SMS accelerations of 4x (conservative) to 8x (aggressive) for most studies and a more conservative acceleration of 2x for studies interested in anterior midline regions.

BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods.

The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package. BIDS Apps run on all three major operating systems with no need for complex setup and configuration and thanks to the comprehensiveness of the BIDS standard they require little manual user input. Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. BIDS Apps overcome this limitation by taking advantage of the Singularity container technology. As a proof of concept, this work is accompanied by 22 ready to use BIDS Apps, packaging a diverse set of commonly used neuroimaging algorithms.

Physiological basis of vascular autocalibration (VasA): Comparison to hypercapnia calibration methods.

PURPOSE: The statistical power of functional MRI (fMRI) group studies is significantly hampered by high intersubject spatial and magnitude variance. We recently presented a vascular autocalibration method (VasA) to account for vascularization differences between subjects and hence improve the sensitivity in group studies. Here, we validate the novel calibration method by means of direct comparisons of VasA with more established measures of baseline venous blood volume (and indirectly vascular reactivity), the M-value. METHODS: Seven healthy volunteers participated in two 7 T (T) fMRI experiments to compare M-values with VasA estimates: (i) a hypercapnia experiment to estimate voxelwise M-value maps, and (ii) an fMRI experiment using visual stimulation to estimate voxelwise VasA maps. RESULTS: We show that VasA and M-value calibration maps show the same spatial profile, providing strong evidence that VasA is driven by local variations in vascular reactivity as reflected in the M-value. CONCLUSION: The agreement of vascular reactivity maps obtained with VasA when compared with M-value maps confirms empirically the hypothesis that the VasA method is an adequate tool to account for variations in fMRI response amplitudes caused by vascular reactivity differences in healthy volunteers. VasA can therefore directly account for them and increase the statistical power of group studies. The VasA toolbox is available as a statistical parametric mapping (SPM) toolbox, facilitating its general application. Magn Reson Med, 2016. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.

Sharing brain mapping statistical results with the neuroimaging data model.

Only a tiny fraction of the data and metadata produced by an fMRI study is finally conveyed to the community. This lack of transparency not only hinders the reproducibility of neuroimaging results but also impairs future meta-analyses. In this work we introduce NIDM-Results, a format specification providing a machine-readable description of neuroimaging statistical results along with key image data summarising the experiment. NIDM-Results provides a unified representation of mass univariate analyses including a level of detail consistent with available best practices. This standardized representation allows authors to relay methods and results in a platform-independent regularized format that is not tied to a particular neuroimaging software package. Tools are available to export NIDM-Result graphs and associated files from the widely used SPM and FSL software packages, and the NeuroVault repository can import NIDM-Results archives. The specification is publically available at: http://nidm.nidash.org/specs/nidm-results.html.

The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments.

The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain. Despite the fact that MRI is routinely used to obtain data for neuroscience research, there has been no widely adopted standard for organizing and describing the data collected in an imaging experiment. This renders sharing and reusing data (within or between labs) difficult if not impossible and unnecessarily complicates the application of automatic pipelines and quality assurance protocols. To solve this problem, we have developed the Brain Imaging Data Structure (BIDS), a standard for organizing and describing MRI datasets. The BIDS standard uses file formats compatible with existing software, unifies the majority of practices already common in the field, and captures the metadata necessary for most common data processing operations.

Vascular autorescaling of fMRI (VasA fMRI) improves sensitivity of population studies: A pilot study.

The blood oxygenation level-dependent (BOLD) signal is widely used for functional magnetic resonance imaging (fMRI) of brain function in health and disease. The statistical power of fMRI group studies is significantly hampered by high inter-subject variance due to differences in baseline vascular physiology. Several methods have been proposed to account for physiological vascularization differences between subjects and hence improve the sensitivity in group studies. However, these methods require the acquisition of additional reference scans (such as a full resting-state fMRI session or ASL-based calibrated BOLD). We present a vascular autorescaling (VasA) method, which does not require any additional reference scans. VasA is based on the observation that slow oscillations (<0.1Hz) in arterial blood CO2 levels occur naturally due to changes in respiration patterns. These oscillations yield fMRI signal changes whose amplitudes reflect the blood oxygenation levels and underlying local vascularization and vascular responsivity. VasA estimates proxies of the amplitude of these CO2-driven oscillations directly from the residuals of task-related fMRI data without the need for reference scans. The estimates are used to scale the amplitude of task-related fMRI responses, to account for vascular differences. The VasA maps compared well to cerebrovascular reactivity (CVR) maps and cerebral blood volume maps based on vascular space occupancy (VASO) measurements in four volunteers, speaking to the physiological vascular basis of VasA. VasA was validated in a wide variety of tasks in 138 volunteers. VasA increased t-scores by up to 30% in specific brain areas such as the visual cortex. The number of activated voxels was increased by up to 200% in brain areas such as the orbital frontal cortex while still controlling the nominal false-positive rate. VasA fMRI outperformed previously proposed rescaling approaches based on resting-state fMRI data and can be readily applied to any task-related fMRI data set, even retrospectively.