Science in the cloud (SIC): A use case in MRI connectomics.
2017; 6 (5): 1-10
Modern technologies are enabling scientists to collect extraordinary amounts of complex and sophisticated data across a huge range of scales like never before. With this onslaught of data, we can allow the focal point to shift from data collection to data analysis. Unfortunately, lack of standardized sharing mechanisms and practices often make reproducing or extending scientific results very difficult. With the creation of data organization structures and tools that drastically improve code portability, we now have the opportunity to design such a framework for communicating extensible scientific discoveries. Our proposed solution leverages these existing technologies and standards, and provides an accessible and extensible model for reproducible research, called 'science in the cloud' (SIC). Exploiting scientific containers, cloud computing, and cloud data services, we show the capability to compute in the cloud and run a web service that enables intimate interaction with the tools and data presented. We hope this model will inspire the community to produce reproducible and, importantly, extensible results that will enable us to collectively accelerate the rate at which scientific breakthroughs are discovered, replicated, and extended.
View details for DOI 10.1093/gigascience/gix013
View details for PubMedID 28327935
BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods
PLOS COMPUTATIONAL BIOLOGY
2017; 13 (3)
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.
View details for DOI 10.1371/journal.pcbi.1005209
View details for Web of Science ID 000398031900037
View details for PubMedID 28278228
- OpenfMRI: Open sharing of task fMRI data NEUROIMAGE 2017; 144: 259-261
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.
View details for DOI 10.1038/sdata.2016.102
View details for Web of Science ID 000390238300001
View details for PubMedID 27922621
View details for PubMedCentralID PMC5139675
The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance.
2016; 92 (2): 544-554
Higher brain function relies upon the ability to flexibly integrate information across specialized communities of brain regions; however, it is unclear how this mechanism manifests over time. In this study, we used time-resolved network analysis of fMRI data to demonstrate that the human brain traverses between functional states that maximize either segregation into tight-knit communities or integration across otherwise disparate neural regions. Integrated states enable faster and more accurate performance on a cognitive task, and are associated with dilations in pupil diameter, suggesting that ascending neuromodulatory systems may govern the transition between these alternative modes of brain function. Together, our results confirm a direct link between cognitive performance and the dynamic reorganization of the network structure of the brain.
View details for DOI 10.1016/j.neuron.2016.09.018
View details for PubMedID 27693256
View details for PubMedCentralID PMC5073034
A Practical Guide for Improving Transparency and Reproducibility in Neuroimaging Research.
2016; 14 (7)
Recent years have seen an increase in alarming signals regarding the lack of replicability in neuroscience, psychology, and other related fields. To avoid a widespread crisis in neuroimaging research and consequent loss of credibility in the public eye, we need to improve how we do science. This article aims to be a practical guide for researchers at any stage of their careers that will help them make their research more reproducible and transparent while minimizing the additional effort that this might require. The guide covers three major topics in open science (data, code, and publications) and offers practical advice as well as highlighting advantages of adopting more open research practices that go beyond improved transparency and reproducibility.
View details for DOI 10.1371/journal.pbio.1002506
View details for PubMedID 27389358
View details for PubMedCentralID PMC4936733
- Evaluation of a pre-surgical functional MRI workflow: From data acquisition to reporting INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS 2016; 86: 37-42
NeuroVault.org: A repository for sharing unthresholded statistical maps, parcellations, and atlases of the human brain.
2016; 124: 1242-1244
NeuroVault.org is dedicated to storing outputs of analyses in the form of statistical maps, parcellations and atlases, a unique strategy that contrasts with most neuroimaging repositories that store raw acquisition data or stereotaxic coordinates. Such maps are indispensable for performing meta-analyses, validating novel methodology, and deciding on precise outlines for regions of interest (ROIs). NeuroVault is open to maps derived from both healthy and clinical populations, as well as from various imaging modalities (sMRI, fMRI, EEG, MEG, PET, etc.). The repository uses modern web technologies such as interactive web-based visualization, cognitive decoding, and comparison with other maps to provide researchers with efficient, intuitive tools to improve the understanding of their results. Each dataset and map is assigned a permanent Universal Resource Locator (URL), and all of the data is accessible through a REST Application Programming Interface (API). Additionally, the repository supports the NIDM-Results standard and has the ability to parse outputs from popular FSL and SPM software packages to automatically extract relevant metadata. This ease of use, modern web-integration, and pioneering functionality holds promise to improve the workflow for making inferences about and sharing whole-brain statistical maps.
View details for DOI 10.1016/j.neuroimage.2015.04.016
View details for PubMedID 25869863
A structural and functional magnetic resonance imaging dataset of brain tumour patients.
2016; 3: 160003-?
We collected high resolution structural (T1, T2, DWI) and several functional (BOLD T2*) MRI data in 22 patients with different types of brain tumours. Functional imaging protocols included a motor task, a verb generation task, a word repetition task and resting state. Imaging data are complemented by demographics (age, sex, handedness, and pathology), behavioural results to motor and cognitive tests and direct cortical electrical stimulation data (pictures of stimulation sites with outcomes) performed during surgery. Altogether, these data are suited to test functional imaging methods for single subject analyses, in particular methods that focus on locating eloquent cortical areas, critical functional and/or structural network hubs, and predict patient status based on imaging data (presurgical mapping).
View details for DOI 10.1038/sdata.2016.3
View details for PubMedID 26836205
View details for PubMedCentralID PMC4736501
The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments.
2016; 3: 160044-?
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.
View details for DOI 10.1038/sdata.2016.44
View details for PubMedID 27326542
View details for PubMedCentralID PMC4978148
- Long-term neural and physiological phenotyping of a single human NATURE COMMUNICATIONS 2015; 6
- Estimation of dynamic functional connectivity using Multiplication of Temporal Derivatives. NeuroImage 2015; 122: 399-407
- Effects of thresholding on correlation-based image similarity metrics FRONTIERS IN NEUROSCIENCE 2015; 9
The human voice areas: Spatial organization and inter-individual variability in temporal and extra-temporal cortices
2015; 119: 164-174
fMRI studies increasingly examine functions and properties of non-primary areas of human auditory cortex. However there is currently no standardized localization procedure to reliably identify specific areas across individuals such as the standard 'localizers' available in the visual domain. Here we present an fMRI 'voice localizer' scan allowing rapid and reliable localization of the voice-sensitive 'temporal voice areas' (TVA) of human auditory cortex. We describe results obtained using this standardized localizer scan in a large cohort of normal adult subjects. Most participants (94%) showed bilateral patches of significantly greater response to vocal than non-vocal sounds along the superior temporal sulcus/gyrus (STS/STG). Individual activation patterns, although reproducible, showed high inter-individual variability in precise anatomical location. Cluster analysis of individual peaks from the large cohort highlighted three bilateral clusters of voice-sensitivity, or "voice patches" along posterior (TVAp), mid (TVAm) and anterior (TVAa) STS/STG, respectively. A series of extra-temporal areas including bilateral inferior prefrontal cortex and amygdalae showed small, but reliable voice-sensitivity as part of a large-scale cerebral voice network. Stimuli for the voice localizer scan and probabilistic maps in MNI space are available for download.
View details for DOI 10.1016/j.neuroimage.2015.06.050
View details for Web of Science ID 000361182400016
View details for PubMedID 26116964
OpenfMRI: Open sharing of task fMRI data.
OpenfMRI is a repository for the open sharing of task-based fMRI data. Here we outline its goals, architecture, and current status of the repository, as well as outlining future plans for the project.
View details for DOI 10.1016/j.neuroimage.2015.05.073
View details for PubMedID 26048618
View details for PubMedCentralID PMC4669234
NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain.
Frontiers in neuroinformatics
2015; 9: 8-?
Here we present NeuroVault-a web based repository that allows researchers to store, share, visualize, and decode statistical maps of the human brain. NeuroVault is easy to use and employs modern web technologies to provide informative visualization of data without the need to install additional software. In addition, it leverages the power of the Neurosynth database to provide cognitive decoding of deposited maps. The data are exposed through a public REST API enabling other services and tools to take advantage of it. NeuroVault is a new resource for researchers interested in conducting meta- and coactivation analyses.
View details for DOI 10.3389/fninf.2015.00008
View details for PubMedID 25914639
View details for PubMedCentralID PMC4392315
- Making big data open: data sharing in neuroimaging NATURE NEUROSCIENCE 2014; 17 (11): 1510-1517