The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services

Avesani, Paolo; McPherson, Brent; Hayashi, Soichi; Caiafa, César F.; Henschel, Robert; Garyfallidis, Eleftherios; Kitchell, Lindsey; Bullock, Daniel; Patterson, Andrew; Olivetti, Emanuele; Sporns, Olaf; Saykin, Andrew J.; Wang, Lei; Dinov, Ivo D.; Hancock, David Y.; Caron, Bradley; Qian, Yiming; Pestilli, Franco

doi:10.31234/osf.io/y82n7

Cited by 27 publications

(44 citation statements)

References 84 publications

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“…Although considerable effort has been devoted to open science and data sharing ( MacWhinney, 2014 ; Gilmore et al, 2016 ; Foster and Deardorff, 2017 ; Frank et al, 2017 ), researchers often neglect “method sharing.” As pointed out by Caiafa and Pestilli (2017) , in this new data-intensive era, effectively every experiment is the convergence of three key dimensions: data , analytics , and computing . Platforms such as OpenNeuro ( Gorgolewski et al, 2017 ) and brainlife ( Hayashi et al, 2017 ; Avesani et al, 2019 ) have successfully achieved this vision of sharing data, analysis methods and computing resources in neuroscience. As psychologists begin to grapple with novel big-data techniques for studying behavior, similar platforms could unite researchers with different expertise to enhance scientific communication and discovery while reducing cost of conducting novel and interdisciplinary research.…”

Section: Overall Discussionmentioning

confidence: 99%

Finding Structure in Time: Visualizing and Analyzing Behavioral Time Series

Barbaro

Abney

et al. 2020

Front. Psychol.

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The temporal structure of behavior contains a rich source of information about its dynamic organization, origins, and development. Today, advances in sensing and data storage allow researchers to collect multiple dimensions of behavioral data at a fine temporal scale both in and out of the laboratory, leading to the curation of massive multimodal corpora of behavior. However, along with these new opportunities come new challenges. Theories are often underspecified as to the exact nature of these unfolding interactions, and psychologists have limited ready-to-use methods and training for quantifying structures and patterns in behavioral time series. In this paper, we will introduce four techniques to interpret and analyze high-density multi-modal behavior data, namely, to: (1) visualize the raw time series, (2) describe the overall distributional structure of temporal events (Burstiness calculation), (3) characterize the non-linear dynamics over multiple timescales with Chromatic and Anisotropic Cross-Recurrence Quantification Analysis (CRQA), (4) and quantify the directional relations among a set of interdependent multimodal behavioral variables with Granger Causality. Each technique is introduced in a module with conceptual background, sample data drawn from empirical studies and ready-to-use Matlab scripts. The code modules showcase each technique's application with detailed documentation to allow more advanced users to adapt them to their own datasets. Additionally, to make our modules more accessible to beginner programmers, we provide a "Programming Basics" module that introduces common functions for working with behavioral timeseries data in Matlab. Together, the materials provide a practical introduction to a range of analyses that psychologists can use to discover temporal structure in high-density behavioral data.

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Section: Overall Discussionmentioning

confidence: 99%

Finding Structure in Time: Visualizing and Analyzing Behavioral Time Series

Barbaro

Abney

et al. 2020

Front. Psychol.

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“…Advancing scientific understanding of repetitive head impact exposure using open science methods and data sharing. In addition to advancing scientific understanding of participating in elite collegiate athletics and repetitive head impact exposure, we embrace an open science approach and use the recently developed cloud-computing platform brainlife.io to share the full research assets developed for the study -data and reproducible analyses methods (Avesani et al, 2019;Stewart et al, 2015;Towns et al, 2014) . This investigation is the first in the field of traumatic brain injury (TBI) to use a fully-automated open science data processing platform to process, store, and release data.…”

Section: Discussionmentioning

confidence: 99%

“…We investigated how the microstructural properties of the cortical and subcortical white matter might differ among football players, cross-country runners and non-athletes. To do so, we developed a reproducible data processing workflow using the cloud computing platform brainlife.io (Avesani et al, 2019) . Our workflow was comprised of 16 brainlife.io Apps (see also Table 1 ).…”

Section: Methodsmentioning

confidence: 99%

“…The data processing pipeline ( Table 1) outlined in the Methods section of this paper can be accessed as computable applications via open services hosted at brainlife.io (Avesani et al, 2019;Stewart et al, 2015;Towns et al, 2014) , or as static code. The services allow processing of data in the same fashion outlined below .…”

Section: Availability Of Data and Open Services For Reproducibility Omentioning

confidence: 99%

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Advanced mapping of the human white matter microstructure better separates elite sports participation

Caron¹,

Bullock²,

Kitchell³

et al. 2020

Preprint

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Collision-sport athletes, especially football players, are exposed to a higher number of repetitive head impacts. Little is known, however, regarding the effects of long-term exposure to repetitive head impacts on brain tissue structure and the locations (i.e. superficial or deep tissue structures) affected. On top of this, little is known about the effects of highly competitive athletics on brain tissue structure. We investigated this relationship, including the baseline effect of collegiate athletic participation, by mapping measures of microstructure to cortical and subcortical white matter parcels and major white matter tracts of varsity IU football players, cross-country runners, and non-athlete students using advanced microstructural mapping techniques and machine-learning. We tested a series of hypotheses on the differences between the subject groups. To do so, we implemented an innovative approach to statistical hypothesis testing combining advanced white matter tissue properties and machine-learning classifiers. We used cross-validation to select the best subject group model and best machine-learning classifier. Wide-spread differences in brain tissue microstructure across cortical, subcortical, and major white matter structures were documented. The tissue properties of the major white matter tracts were found to best predict collision-sport participation and sports participation in general, however, cortical and subcortical white matter parcels were also found to predict collision-sports participation. The biggest differences in brain tissue microstructure is between the athletes (football and cross country combined) and the non-athletes students. The rewards and risks of playing competitive sports at the highest collegiate level may account for the differences in brain microstructure. This was the first investigation into the effects of repetitive head impacts to use an open-source data processing platform brainlife.io. The data and code for these analyses are available via brainlife.io.

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“…CBRAIN is a cloud service to process data, similar to Brainlife. In Brainlife, some pipeline exist to process tractography [Avesani et al, 2018].…”

Section: Discussionmentioning

confidence: 99%

TractoFlow: A robust, efficient and reproducible diffusion MRI pipeline leveraging Nextflow & Singularity

Theaud

Houde

Boré

et al. 2019

Preprint

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A diffusion MRI (dMRI) tractography processing pipeline should be: i) reproducible in immediate test-test, ii) reproducible in time, iii) efficient and iv) easy to use. Two runs of the same processing pipeline with the same input data should give the same output today, tomorrow and in 2 years. However, processing dMRI data requires a large number of steps (20+ steps) that, at this time, may not be reproducible between runs or over time. If parameters such as the number of threads or the random number generator are not carefully set in the brain extraction, registration and fiber tracking steps, the end tractography results obtained can be far from reproducible and limit brain connectivity studies. Moreover, processing can take several hours to days of computation for a large database, even more so if the steps are running sequentially.To handle these issues, we present TractoFlow, a fully automated pipeline that processes datasets from the raw diffusion weighted images (DWI) to tractography. It also outputs classical diffusion tensor imaging measures (fractional anisotropy (FA) and diffusivities) and several HARDI measures (Number of Fiber Orientation (NuFO), Apparent Fiber Density (AFD)). The pipeline requires a DWI and T1-weighted image as NIfTI files and b-values/b-vectors in FSL format. An optional reversed phase encoded b=0 image can also be used. This pipeline is based on two technologies: Nextflow and Singularity, as well as recommended pre-processing and processing steps from the dMRI community. In this work, the TractoFlow pipeline is evaluated on three databases and shown to be efficient and reproducible from 98% to 100% depending on parameter choices. For example, 105 subjects from the Human Connectome Project (HCP) were fully ran in twenty-five (25) hours to produce, for each subject, a whole-brain tractogram with 4 million streamlines. The contribution of this paper is to introduce the importance of a robust pipeline in terms of runtime and reproducibility over time. In the era of open data and open science, efficiency and reproducibility is critical in neuroimaging projects. Our TractoFlow pipeline is publicly available for academic research and is an important step forward for better structural brain connectivity mapping.Diffusion magnetic resonance imaging (dMRI) is the main technique to non-invasively 2 obtain information about the white matter. Diffusion MRI is currently at the core of 3 structural connectivity or white matter brain mapping [Van Essen et al., 2012], using 4 dMRI tractography [Descoteaux et al., 2009; Girard et al., 2014] to reconstruct and vi-5 sualize the white matter architecture [Maier-Hein et al., 2017; Jeurissen et al., 2017]. 6 However, there could be from 20 to 25 processing steps involved to perform dMRI trac-7 tography from a raw dMRI data. 8The data processing depends on many tools in different packages with many parame-9 ters. Setting up the environment and installing all dependencies to process the data can 10 be long, tedious and difficult, even more so for a be...

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The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services

Cited by 27 publications

References 84 publications

Finding Structure in Time: Visualizing and Analyzing Behavioral Time Series

Finding Structure in Time: Visualizing and Analyzing Behavioral Time Series

Advanced mapping of the human white matter microstructure better separates elite sports participation

TractoFlow: A robust, efficient and reproducible diffusion MRI pipeline leveraging Nextflow & Singularity

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