New collection in RIO Journal devoted to neuroscience projects from 2016 Brainhack events

A new collection devoted to neuroscience projects from 2016 Brainhack events has been launched in the open access journal Research Ideas and Outcomes (RIO). At current count, the “Brainhack 2016 Project Reports” collection features eight Project Reports, whose authors are applying open science and collaborative research to advance our understanding of the brain.

Seeking to provide a forum for open, collaborative projects in brain science the Brainhack organization has found a like-minded partner in the innovative open science journal RIO. The editor of the series is Dr. R. Cameron Craddock, Computational Neuroimaging Lab, Child Mind Institute and Nathan S. Kline Institute for Psychiatric Research, USA. He is joined by co-editors Dr. Pierre Bellec, Unité de neuroimagerie fonctionnelle, Centre de recherche de l’institut de gériatrie de Montréal, Canada, Dr. Daniel S. Margulies, Max Planck Research Group “Neuroanatomy & Connectivity“, Max Planck Institute for Human Cognitive and Brain Sciences, Dr. Nolan Nichols, Genetech, USA, and Dr. Jörg Pfannmöller, University of Greifswald, Germany.

The first project description published in the collection is a Software Management Plan presenting a comprehensive set of neuroscientific software packages demonstrating the huge potential of Gentoo Linux in neuroscience. The team of Horea-Ioan Ioanas, Dr. Bechara John Saab and Prof. Dr. Markus Rudin, affiliated with ETH and University of Zürich, Switzerland, make use of the flexibility of Gentoo’s environment to address many of the challenges in neuroscience software management, including system replicability, system documentation, data analysis reproducibility, fine-grained dependency management, easy control over compilation options, and seamless access to cutting-edge software release. The packages are available for the wide family of Gentoo distributions and derivatives. “Via Gentoo-prefix, these neuroscientific software packages are, in fact, also accessible to users of many other operating systems,” explain the researchers.

While quantifying lesions in a robust manner is fundamental for studying the effects of neuroanatomical changes in the post-stroke brain while recovering, manual lesion segmentation has been found to be a challenging and often subjective process. This is where the Semi-automated Robust Quantification of Lesions (SRQL) Toolbox comes in. Developed at the University of Southern California, Los Angeles, it optimizes quantification of lesions across research sites. “Specifically, this toolbox improves the performance of statistical analysis on lesions through standardizing lesion masks with white matter adjustment, reporting descriptive lesion statistics, and normalizing adjusted lesion masks to standard space,” explain scientists Kaori L. Ito, Julia M. Anglin, and Dr. Sook-Lei Liew.

Called Mindcontrol, an open-source web-based dashboard application lets users collaboratively quality control and curate neuroimaging data. Developed by the team of Anisha Keshavan and Esha Datta, both of University of California, San Francisco, Dr. Christopher R. Madan, Boston College, and Dr. Ian M. McDonough, The University of Alabama, Mindcontrol provides an easy-to-use interface, and allows the users to annotate points and curves on the volume, edit voxels, and assign tasks to other users. “We hope to build an active open-source community around Mindcontrol to add new features to the platform and make brain quality control more efficient and collaborative,” note the researchers.

At University of California, San Francisco, Anisha Keshavan, Dr. Arno Klein, and Dr. Ben Cipollini, created the open-source Mindboggle package, which serves to improve the labeling and morphometry estimates of brain imaging data. Using inspirations and feedback from a Brainhack hackathon, they built-up on Mindboggle to develop a web-based, interactive, brain shape 3D visualization of its outputs. Now, they are looking to expand the visualization, so that it covers other data besides shape information and enables the visual evaluation of thousands of brains.

Processing neuroimaging data on the cortical surface traditionally requires dedicated heavy-weight software suites. However, a team from Max Planck Institute for Human Cognitive and Brain Sciences, Free University Berlin, and the NeuroSpin Research Institute, France, have come up with an alternative. Operating within the neuroimaging data processing toolbox Nilearn, their Python package allows loading and plotting functions for different surface data formats with minimal dependencies, along with examples of their application. “The functions are easy to use, flexibly adapt to different use cases,” explain authors Julia M. Huntenburg, Alexandre Abraham, Joao Loula, Dr. Franziskus Liem, and Dr. Gaël Varoquaux. “While multiple features remain to be added and improved, this work presents a first step towards the support of cortical surface data in Nilearn.”

To further address the increasing necessity for tools specialised to process huge high-resolution brain imaging data in their anatomical detail, Julia M. Huntenburg gathers a separate team to work on another Python-based software. Being a user-friendly standalone package, this subset of CBSTools requires no additional installations, and allows for interactive data exploration at each processing stage.

Developed at the University of California, San Francisco, Cluster-viz is a web application that provides a platform for cluster-based interactive quality control of tractography algorithm outputs, explain the team of Kesshi M. Jordan, Anisha Keshavan, Dr. Maria Luisa Mandelli, and Dr. Roland G. Henry. It.

A project from the University of Warwick, United Kingdom, aims to extend the functionalities of the FSL neuroimaging software package in order to generate and report peak and cluster tables for voxel-wise inference. Dr. Camille Maumet and Prof. Thomas E. Nichols believe that the resulting extension “will be useful in the development of standardized exports of task-based fMRI results.”

More 2016 Brainhack projects are to be added to the collection.

Open neuroscience: Collaborative Neuroimaging Lab finalist for the Open Science Prize

Despite the abundance of digital neuroimaging data, shared thanks to all funding, data collection, and processing efforts, but also the goodwill of thousands of participants, its analysis is still falling behind. As a result, the insight into both mental disorders and cognition is compromised.

The Open Neuroimaging Laboratory framework, promises a collaborative and transparent platform to optimise both the quantity and quality of this invaluable brain data, ultimately gaining a greater insight into both mental disorders and cognition.

The project was submitted for the Open Science Prize competition by Katja Heuer, Max Planck Institute for Human Cognitive and Brain Sciences, Germany, Dr Satrajit S. Ghosh, Massachusetts Institute of Technology (MIT), USA, Amy Robinson Sterling, EyeWire, USA, and Dr Roberto Toro, Institut Pasteur, France. Amongst 96 submissions from all around the globe, it was chosen as one of six teams to compete in the second and final phase of the Prize.

Simply having access and being able to download brain magnetic resonance imaging (MRI) data is not enough to reap all potential benefits. In order for it to be turned into insight and knowledge, it needs to also be queried, pre-processed and analysed, which requires a substantial amount of human curation, visual quality assessment and manual editing. With research being rather patchy, a lot of efforts are currently redundant and unreliable.

On the other hand, the Open Neuroimaging Laboratory aims to aggregate annotated brain imaging data from across various resources, thus improving its searchability and potential for reuse. It is to also develop a tool that will facilitate and encourage the creation of distributed teams of researchers to collaborate together in the analysis of this open data in real time.

“Our project will help transform the massive amount of static brain MRI data readily available online into living matter for collaborative analysis,” explain the researchers.

“We will allow a larger number of researchers to have access to this data by lowering the barriers that prevent their analysis: no data will have to be downloaded or stored, no software will have to be installed, and it will be possible to recruit a large, distributed, group of collaborators online.”

“By working together in a distributed and collaborative way, sharing our work and our analyses, we should improve transparency, statistical power and reproducibility,” they elaborate. “Our aim is to provide to everyone the means to share effort, learn from each other, and improve quality of and trust in scientific output.”Untitled

Having already developed a functional prototype of the BrainBox web application, which provides an interactive online space for collaborative data analyses and discussions, the team will now turn it into a first version with an improved user experience, stability and documentation. Planned for the Open Science Prize Phase 2 are furthering the type of analyses and exploring the development of interfaces for database-wise statistical analyses.

In the spirit of the competition, the scientists have decided to release their code open source on GitHub to facilitate bug fixes, extension and maintainability.


Original source:

Heuer K, Ghosh S, Robinson Sterling A, Toro R (2016) Open Neuroimaging Laboratory.Research Ideas and Outcomes 2: e9113. doi: 10.3897/rio.2.e9113