Funded grants in 2016
Here is the full list of grants funded in the 2016 funding cycle
NB: The 2016 funding cycle did not require the lead applicant to be based in an INCF Governing Member country.
Connectivity-based brain parcellation toolkit
INSERM U1127 - Institut du Cerveau et de la Moelle Epiniere, France
The team is composed by Leonardo Cerliani, PhD (I) and Chris Foulon, MSc (F) from the Brain and Spine Institute (ICM) in Paris, and by Daniel Margulies, PhD (US) and Marcel Falkiewicz, PhD (PL) from the Max Planck Institute (MPI) for Human Cognitive and Brain Sciences in Leipzig. The ICM and MPI groups (1) have a ~10yrs experience in studying brain connectivity, documented by several international publications; (2) elaborated different and complementary methods to analyze brain connectivity, and are at the forefront of innovation in using spectral methods for extracting gradients of brain connectivity; (3) feature complementary neuroinformatic compentences: the ICM team has released a Java GUI-based toolkit to perform disconnection analyses, aimed to be used by medical researchers of all levels of informatic knowledge (http://toolkit.bcblab.com/); the MPI team are leading experts in Python-based pipelines for processing magnetic resonance connectivity data.
Connectivity-based parcellation (CBP) has become one of the most important applications of magnetic resonance imaging approaches to brain mapping. It enables the approximation of boundaries of regions characterized by different histological features and functions, and can do so at the single-subject level. CBP allows us to study the relationship between brain connectivity and behaviour, and represents a very promising biomarker for neuropsychiatric conditions characterized by different connectional anatomy, such as Autism and Schizophrenia. Several medical researchers and practitioners — neurologists, neurosurgeons, neuropsychiatrists — are interested in applying CBP to their respective interests, and already acquire the necessary data during the clinical routine scan to do so. However, to date an accessible software for performing this analysis and aimed at these users has not yet been developed.
We will take advantage of the experience and complementary competences of our two groups at the ICM Institute in Paris, and at the MPI in Leipzig to develop a software to perform CBP, which can be used via a simple graphical user interface. Our target users are researchers working in the medical field who need to obtain valuable results in a clinically acceptable timeframe using a stable and easy-to-use software. The software will be open source to promote reproducibility and improvements, as well as the development of standards for connectivity-based parcellation methods.
International Brain Laboratory
University College London, UK
We are the International Brain Laboratory, a collaboration of 20 neuroscientists aiming to understand complex behavior. Collaboration members are loosely grouped into three categories. EXPERIMENTALISTS: D. Angelaki (Baylor College Medicine), C. Brody (Princeton), M. Carandini (UCL), A. Churchland (Cold Spring Harbor Lab), M. Hausser (UCL), S. Hofer (Basel), Z. Mainen (Champalimaud), T. Mrsic-Flogel (Basel), K. Svoboda (Janelia), D. Tank (Princeton), A. Zador (Cold Spring Harbor Lab); INTEGRATORS: J. Freeman (Janelia), K. Harris (UCL), L. Paninski (Columbia), J. Pillow (Princeton); THEORISTS: L. Abbott (Columbia), P. Dayan (UCL), S. Ganguli (Stanford), P. Latham (UCL), A. Pouget (U. Geneva)
Understanding how the brain works is one of science’s greatest challenges. A key impediment to this challenge is that we do not understand how neural systems work together to support complex behaviors. Complex behaviors involve processing sensory information, reaching decisions, acting, and learning from the results of those actions. Understanding these processes is a problem with a scale and complexity that far exceed what can be tackled by any single laboratory and that demands both computational and experimental approaches. To this end, we are creating a virtual laboratory, unifying a group of 20 highly experienced neuroscience groups across the world. Three core elements uniquely define this effort. (1) All labs will focus on the common goal of understanding a single complex behavior: foraging. (2) Groups will work together to develop and deploy standardized experimental setups, analysis pipelines, databases and other computational resources to support the sharing of data, analysis methods, and models. (3) The project will be organized as a bottom-up collaboration amongst peers, using a proven governance structure refined in the field of high-energy physics. We request funds to allow us to create the initial infrastructure that is essential for launching this collaboration. These funds will allow us to hire a part-time IT-specialist who will lay the foundation for the data and code sharing that is fundamental to this collaboration.
Linking descriptions of experiment metadata in NIDM-Experiment with BIDS supported workflows
David B. Keator
University of California, Irvine, USA
To support the deliverables proposed in this project, a team of four investigators has been compiled, each with specific expertise and experience working together on projects leading up to this proposal. The project team consists of David Keator, Ph.D. from the University of California, Irvine to support developing the software product proposed in this project, Karl Helmer, Ph.D. from Massachusetts General Hospital/Harvard Medical School to augment our existing terminology/ontology in support of the project deliverables, Krzysztof Gorgolewski, Ph.D. from Stanford University to provide consultation on the Brain Imaging Data Structure (BIDs) schema, and Jean-Baptiste Poline, Ph.D. from the University of California, Berkeley who will provide outreach to the broader INCF and neuroimaging communities in support of the proposed project deliverables.
Acceleration of scientific discovery relies on our ability to effectively use data often acquired across multiple domains. Typically, subsets of data collections are shared using lab-specific organizational schemes with little or no information provided to give the data context within the broader experimental protocol nor the ability to efficiently use the data for reproducibility outside of the originating site. To address these challenges, the INCF supported two related efforts. The Neuroimaging Data Model (NIDM; http://nidm.nidash.org/) was developed to provide a linked data format for describing all aspects of the data lifecycle, from raw data through analyses and provenance. The NIDM-Experiment component describes the experiment metadata, giving the acquired data context within the broader project protocol. The Brain Imaging Data Structure (BIDS; http://bids.neuroimaging.io/) was designed to provide software developers and the neuroimaging community with a standard for organizing the data collected in an imaging experiment to more easily automate analysis workflows. In this project we develop converters to both export imaging datasets described in NIDM-Experiment documents as BIDs-formatted data sets, providing access to BIDs supported workflows, and import of BIDs data sets to NIDM-Experiment documents, facilitating sharing and linking of BIDs data sets outside of the originating laboratory and contextualizing BIDs data sets with respect to the experiment protocols.
Enabling cellular-resolution connectomic analysis of the primate cortex
Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology, Poland
The research team is made up of members of two groups. The Laboratory of Neuroinformatics at Nencki Institute of Experimental Biology (Warsaw, Poland) is represented by the Leading Applicant, dr Piotr Majka (PM), and a Research Assistant who will be funded by this project. The Australian partners are Prof. Marcello Rosa (MR, neuroanatomist) and Mr. Shi Bai (SB, software engineer) located at the Monash University, Melbourne. The team has a necessary skillset to accomplish the proposed project. The Leading Applicant, PM has a vast experience in developing open source, web services for sharing atlas-related data and is a member of the INCF Program on Digital Brain Atlasing. MR, world expert on primate neuroanatomy, will generate and curate the data from the neuroanatomical standpoint. SB will be responsible for dissemination and sharing strategy via the web interface as well as co-development of the on-line connectome analysis framework.
We will create a publicly available, world’s most comprehensive repository of the afferent cortico-cortical connectivity of any primate species, enabling a new level of analysis and modelling. This will be achieved by releasing results of over 150 experiments involving injections of retrograde fluorescent tracers into the marmoset monkey cerebral cortex. The experiments have been already conducted, data collected, curated and preserved in a digital form. Individual injections will be mapped into the atlas space using previously established pipeline (Majka et al., 2016). To make the connectome readily accessible for exploration and visualization, an online analytical framework will be established which will enable graphical (web browser-based) or programmatic access to the data.
Releasing open access connectomes is known to trigger numerous follow-up modelling and theoretical studies (e.g. mouse brain connectivity atlas by the Allen Institute). In a longer perspective, the unique nature of data in our project will help to understand how the highly complex network of neuronal connections enable brain functions in primates, and, in general, in mammals. The computationally friendly form of the connectome will increase its interoperability with other tools and foster federating data from various experimental methods (e.g. DTI, fMRI, anterograde tracing, electrophysiology).
Implementation of a neuroimaging DOI system across major leading public neuroimaging data archives
Washington University, USA
Fred Prior (UAB, TCIA), Samir Das (McGill University, LORUS), Vince Calhoun (MIND Research Network, COINS), Christian Haselgrove (UMassMed, NITRC-IR), Clare Mackay (Oxford, Dementias Platform UK), Visakh Muraleedharan (INCF, Center-TBI)
Purpose of workshop:
With the emergence of data as a first-class outcome of the scientific process, a concomitant set of principles related to data (in terms of sharing, citation, credit, etc.) has developed that features the concept of FAIR (Findable, Accessible, Interoperable and Reusable) stewardship of our collective data resources. FAIR data must also be fairly cited, in order to assign proper attribution and credit to source (investigators, funders, etc.). Data Citation Principles have also emerged that: elaborate on the importance of data citation, provide guidelines related to how to provide credit and attribution, establishes the role of data as evidence in the scientific argument, addresses the need for unique identification of data, and supports policies related to access, persistence, specificity and verifiability, as well as interoperability and flexibility.
The next phase of this initiative is to get widespread adoption of this concept into the real-world neuroimaging data archive production systems. We propose to host a small meeting of a subset of the major leading public neuroimaging data archives to plan an implementation of the neuroimaging DOI system across these systems. While the implications for the way researchers engage in their publication and post-grant activities are not trivial, changes in these data sharing and crediting practices are necessary for the neuroscience field as a whole, in order to advance the goals of reproducible science.
A first implementation of NIDM-Workflows
UC Berkeley, USA
Satra Ghosh (MIT, lead), Tristan Glatard (Condordia), Guillaume Flandin (UCL), Tibor Auer, Theo Van Erp (UCI) J. Turner (Georgia Univ) - MIT (USA), Concordia (Canada), UCL (London, UK)
Purpose of workshop:
The Neuroimaging Data Model (NIDM), an extension of W3C PROV Semantic Web Framework, is an attempt to fill the void for a standard that connects neuroimaging resources. NIDM is composed of 3 related subunits:
- NIDM-Experiment which contains information about the subjects, experimental manipulation, data acquisition parameters, and acquired data;
- NIDM-Workflows which captures the provenance of the data analysis from initial preprocessing through statistical modeling; and
- NIDM-Results which concerns the final output of analysis.
While the focus of the group has been on NIDM-Results for activation maps, it is necessary to develop specifications for the other two components to truly enable reuse. This project proposes a first implementation of NIDM Workflow. This will extend the NIDM model that already describes fMRI activation results (ie x, y, z coordinates, estimation and inference procedure, etc.) to include standardized descriptions of anatomical, functional, and diffusion processing, and the results produced by these procedures.
We propose to proceed with the following steps:
- List the the workflows most used in the Enigma consortium Work through online calls to define a model capturing the steps, inputs and outputs of these workflows
- Implement a first version of the workflow description within the Enigma current pipelines;
- Reach out to the OHBM community and journals to propose this standard as a complement to methods description in publications.
Standardized workflow of human EEG in NIX and odML
Dynamic Brain Platform, INCF Japan Node / Kyushu Institute of Technology, Japan
Thomas Wachtler (INCF G-Node, Germany) and Roman Moucek (INCF Czech Node)
Purpose of workshop:
Making a collaborating team with J-Node platforms, G-Node, Czech Node, potentially extended to Malaysia Node in the next stage. Key persons are Hiroaki Wagatsuma, Thomas Wachtler, Director of the INCF G-Node and Roman Moucek, Node Representative, Czech Node. Supportive members are Yoshiyuki Asai (DBPF/Chief developer of PhysioDesigner), Sonja Grün (G-Node/ especially working on workflows based on odML), Yoko Yamaguchi (Director of the INCF J-Node).
In the consideration of the Electrophysiology Task Force of the INCF Data Sharing Program started from 2010, we cooperatively design the effective workflow of human EEG recording data, involving not only laboratory experiments, but also clinical treatments and industrial research investigations, based on NIX format and odML, which was an effective result from the task force provided concepts and methods for metadata design, data formats, and has led to developments of formats and tools for efficient data sharing. The requirement is annotating continuous time series into meaningful epochs depending on meta-data, like task conditions, events and coincidences with results of other simultaneous recording data. The metadata design will be extended to considerations of the machine-readable data description such as RDF/OWL format, which allows accessing the database with respect to logical reasoning process relying on the semantic web architecture. The effort will bridge a gap between time series analysis and imaging data sharing schemes.
Donders Institute, Radboud University, Nijmegen, Netherlands
Russell Poldrack, Poldracklab, Stanford University, USA
Purpose of travel:
In my PhD, I have developed a novel methodology, called task potency, which aims at quantifying how strongly a cognitive task is modulating ongoing processes in the brain relative to baseline fluctuations as seen in resting-state fMRI data. Applying a connectomic view of brain function we can define which tasks and cognitive functions most strongly potentiate a brain network and whether network involvement is specific to one task or shared between tasks. Next to development of the framework, to be openly released, I have applied this methodology to investigate how task potency develops across age, and how it is affected by ADHD-status. These investigations were conducted in locally (Nijmegen) available neuroimaging databases.
The purpose of my research visit is to evaluate the value of task potency for comparing results across tasks and datasets. This will lead to a better understanding of the relationship between cognitive functions and the brain’s functional architecture. The Poldracklab at Stanford University are the main curators of the OpenfMRI project; a large-scale effort to openly share neuroimaging datasets to promote replication of results or investigation of new questions using existing data. My visit will result in combining knowledge about how the brain departs from a resting state with their expertise in the analysis of combined independent task-based fMRI datasets to investigate the brain’s functional architecture related to a larger set of cognitive functions.
Child Mind Institute, New York, NY, USA
Fernando Barrios, Instituto de Neurobiología, Universidad Nacional Autonoma de Mexico, Queretaro, Mexico
Purpose of travel:
The Instituto de Neurobiología (INB), Universidad Nacional Autonoma de Mexico, in Queretaro Mexico has recently completed the installation of a 588-core high performance computer system for performing large-scale data analyses and simulations of neuroscience data. The students and the faculty in the labs of Dr. Fernando Barrios, Dr. Sarael Alcauter, and Dr. Erick Pasaye would like to be able to use this resource to perform neuroimaging analysis, but do not currently have the skills to configure or use the system. Dr. Barrios has asked Caroline Froehlich, from the laboratory of Dr. Cameron Craddock, and an expert in using high performance computing for neuroimaging, to come and educate the local users on how to effectively use this system.
We are applying for funds to pay for the travel and lodging of Caroline to make this trip. Having an her expertise on using HPC for neuroimaging will maximize the amount of knowledge transfer that can be performed during the trip. It will additionally help to foster this budding collaboration for large-scale neuroimaging analysis.
Rafael Neto Henriques
MRC Cognition and Brain Sciences Unit, Cambridge, UK
Ariel Rokem, University of Washington eScience Institute, Seattle, USA
Purpose of travel:
Diffusion kurtosis imaging (DKI) describes non-Gaussian characteristics of diffusion-weighted MRI data. The method enables inferences about microstructural properties of human white matter, that are not possible with other methods (e.g., DTI). Jason Yeatman and his lab have measured DKI longitudinally in children undergoing an intense summer reading program, to see how this learning environment prompts changes in brain tissue properties. This data provides a unique opportunity to understand experience-dependent plasticity.
Rafael Neto Henriques is the main developer of the diffusion kurtosis imaging (DKI) module in the open-source software library Dipy (http://dipy.org), developed in collaboration with Ariel Rokem.
Newer methods to analyze DKI have been developed and allow for biophysical interpretations of the signal (Fieremans et al., NeuroImage 2011; 58: 177-188) and extend these measures to regions with crossing fibers (Henriques et al., NeuroImage 2015; 111: 85-99). However, there is no open-source implementation of these methods. During the visit, we will implement these methods and apply them to the data that the Yeatman lab has collected. Based on these new measurements we will be able to infer how enriched learning affects biophysical properties of white matter tissue. The combination of software implementation, testing, and application require the close proximity and focus that a visit allows, and would be impossible to achieve otherwise.
University of Glasgow, UK
Jan-Mathijs Schoffelen, Donders Institute for Brain, Cognition and Behaviour, Netherlands
Purpose of travel:
The purpose of travel is to integrate a new and powerful signal analysis method, Gaussian-Copula Mutual Information (GCMI) , into the Fieldtrip package . Fieldtrip is an open-source MATLAB toolbox which offers advanced analysis methods for electrophysiological data. Fieldtrip is widely used within the neuroimaging community (cited over 1500 times ). GCMI is a recent method for estimating information theoretic quantities . It provides a general, computationally efficient, flexible, and robust multivariate statistical framework, which provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, and multi-dimensional variables, and enables a range of analyses that go beyond traditional pairwise measures of dependence.
Integrating GCMI into Fieldtrip would make the former easier to apply and allow users to harness non-parametric statistical testing in Fieldtrip. Because of the many novel analyses possible, including analyses of different types of variables and higher order quantities such as conditional mutual information and interaction information , it is not trivial to incorporate GCMI seamlessly into Fieldtrip. Therefore, a visit is crucial to provide high-bandwidth in-person interaction to address the issues that will inevitably arise and produce an efficient implementation.
 Oostenveld et al. 2010 https://goo.gl/BFHIi9
 Ince et al. 2016 https://goo.gl/AdKlR
University of Warwick, UK
Bertrand Thirion - Parietal,Inria, France / Richards Reynolds - SSCC NIMH NIH, USA / Rainer Goebel - BrainVoyager, Netherlands
Purpose of travel:
The NIDM-Results standard (http://nidm.nidash.org/specs/nidm-results.html) provides a harmonised representation of fMRI results, independant of the tool used to analyze the data. As a standardised representation of neuroimaging results, it has the potential to greatly improve neuroimaging reproducibility and meta-analysis. The standard was developed as a collaborative effort, has been recently published (doi: 10.1101/041798) and is implemented in two of the main neuroimaging software packages (SPM, FSL).
We have created a preliminary version of a NIDM-Results library (https://github.com/incf-nidash/nidmresults) in Python that would enable any fMRI software developer to easily generate a NIDM-Results document for their application. We would now like to encourage other developers involved in fMRI research to build tools to allow their users to expose their results using NIDM-Results. Natural targets for this effort are AFNI (https://afni.nimh.nih.gov/afni) and BrainVoyager (www.brainvoyager.com), the next two most widely used neuroimaging software packages, as well as nistats (https://github.com/nistats/nistats) for its strong integration with the Python neuroimaging community.
With support from the INCF, I will travel to meet and engage with the development teams of nistats and BrainVoyager. We will also organise a focused two-day meeting, next to a conference, to gather developers working on NIDM-Results as well as coordinate with other NIDM projects.
Istituto per le Applicazioni del Calcolo (IAC) "M. Picone", National Research Council, Italy
Mayrim Vega-Hernández, Neuroinformatics Department, Cuban Neuroscience Centre, Cuba
Purpose of travel:
The goal is to make a Research visit to the Istituto per le Applicazioni del Calcolo (IAC) "M. Picone", of the Italian National Research Council. The main topic of collaboration is the development of methods and neuroinformatics tools for estimating the spatio-temporal localization of (and interactions among) brain current sources of EEG and MEG measurements. This travel will build on the strengths and complementary expertise of the two institutions (Cuban Neuroscience Center, CNEURO and IAC) in the field of EEG/MEG source localization, and will serve to develop a mutually beneficial long-term collaboration in neuroinformatics. On one hand, theoretical developments will be pursued by combining the statistical approach developed by the CNEURO group with the Bayesian theory strategies worked at IAC. Due to the many different methods published, carrying out EEG/MEG source localization is usually a non-easy task for neuroscientists without a strong mathematical and physics background. Therefore, on the other hand, we plan to develop a user-friendly Matlab Toolbox (simpler than other existing tools) strictly focused on source localization analysis and oriented to researchers from application fields. The Toolbox will also include measures of functional connectivity at the source level and will be prepared to be part of neuroinformatics tools implementing processing pipelines and benchmark datasets. It will also be made freely available to the neuroscience community.
INCF provides financial support for the development of standards and best practices that comply with the FAIR principles, and for development of neuroinformatics training materials
Funding will be awarded to: