NIH Funded NITRC’s Triad of Services: Software, Data, Compute
Presented During: Poster Session 3
Friday, June 27, 2025: 01:45 PM - 03:45 PM
Friday, June 27, 2025: 01:45 PM - 03:45 PM
Presented During: Poster Session 4
Saturday, June 28, 2025: 01:45 PM - 03:45 PM
Saturday, June 28, 2025: 01:45 PM - 03:45 PM
Poster No:
1931
Submission Type:
Abstract Submission
Authors:
Nina Preuss1,2, David Kennedy3, Christian Haselgrove4
Institutions:
1NITRC, Worchester, MA, 2Preuss Enterprises, Inc., Green Cove Springs, FL, 3University of Massachusetts Chan Medical School, Worcester, MA, 4University of Massachusetts, Worchester, MA
First Author:
Co-Author(s):
Introduction:
Funded by the NIH Blueprint for Neuroscience Research, NIBIB, NIDA, NIMH, and NINDS (1), the NITRC family of services supports: MR, imaging genomics, EEG/MEG, PET/SPECT, CT, imaging genomics, optical imaging, clinical neuroimaging, and computational neuroscience communities. These services include:
NITRC Resources Repository (NITRC-R), the "go to" collaboration environment that enables the distribution, enhancement, and adoption of neuroimaging tools and resources
NITRC Image Repository (NITRC-IR), a curated repository of NIfTI-1 and DICOM imaging sessions searchable by metadata such as handedness, gender and group new datasets: OASIS, Dallas Lifespan Brain Study (DLBS), and NU Schizophrenia Data and Software Tool Federation using BIRN Infrastructure (NUSDAST), and CMI Healthy Brain Network. NITRC-IR also publishes ADHD-200, 1000 Connectomes, ABIDE, and CandiShare Schizophrenia datasets.
NITRC Computational Environment (NITRC-CE), a virtual big data compute service pre-configured with tens of popular neuroimaging software analysis tools allowing a build your own, or pay-as-you-go compute experience via commercial clouds
We encourage researchers to list their tools and resources on the NITRC website..
NITRC Resources Repository (NITRC-R), the "go to" collaboration environment that enables the distribution, enhancement, and adoption of neuroimaging tools and resources
NITRC Image Repository (NITRC-IR), a curated repository of NIfTI-1 and DICOM imaging sessions searchable by metadata such as handedness, gender and group new datasets: OASIS, Dallas Lifespan Brain Study (DLBS), and NU Schizophrenia Data and Software Tool Federation using BIRN Infrastructure (NUSDAST), and CMI Healthy Brain Network. NITRC-IR also publishes ADHD-200, 1000 Connectomes, ABIDE, and CandiShare Schizophrenia datasets.
NITRC Computational Environment (NITRC-CE), a virtual big data compute service pre-configured with tens of popular neuroimaging software analysis tools allowing a build your own, or pay-as-you-go compute experience via commercial clouds
We encourage researchers to list their tools and resources on the NITRC website..
·What we do
Methods:
NITRC-R's scientific scope continues to broaden based upon the neuroimaging research communities' needs. As the need for identifying, evaluating and repurposing others' tools and resources was successfully met, the next pent-up demand was for a searchable image repository across multiple community-generated and shared datasets.
As a result, leveraging XNAT, we developed NITRC-IR which is open to researchers as a data commons resource for their data sharing plans.
Finally, scientists and researchers are more challenged to secure sufficient computational resources to execute complex computational analysis on these large data resources. So, leveraging NeuroDebian, we developed NITRC-CE, which can be downloaded as a script to create you own virtual machine to use on your infrastructure, or you can pay-as-you-go using commercial cloud service including AWS (2).
A public Amazon Machine Instance (AMI) is also available.
We encourage the neuroinformatics community to continue providing on-line feedback on NITRC services, their design, tools, resources, and content.
We exhibit and train on the NITRC triad of services in our exhibit booths at CNS and OHBM, offer webinar training on request.
Please contact us at moderator@nitrc.org.
As a result, leveraging XNAT, we developed NITRC-IR which is open to researchers as a data commons resource for their data sharing plans.
Finally, scientists and researchers are more challenged to secure sufficient computational resources to execute complex computational analysis on these large data resources. So, leveraging NeuroDebian, we developed NITRC-CE, which can be downloaded as a script to create you own virtual machine to use on your infrastructure, or you can pay-as-you-go using commercial cloud service including AWS (2).
A public Amazon Machine Instance (AMI) is also available.
We encourage the neuroinformatics community to continue providing on-line feedback on NITRC services, their design, tools, resources, and content.
We exhibit and train on the NITRC triad of services in our exhibit booths at CNS and OHBM, offer webinar training on request.
Please contact us at moderator@nitrc.org.
·NIH funded free services
Results:
With over 18.7M page views and 5.2M sessions by 2.5M users, NITRC-R facilitates access to an ever growing number of neuroinformatics resources. Averaging 51,000 sessions and 140,000 pageviews per month, software and data from NITRC has been downloaded over 14M million times. NITRC includes 1,340 resources..
NITRC-IR offers 11,560 Subjects and 13,280 imaging sessions of searchable data across 17 studies to promote re-use and integration of these valuable shared data.
With 324 subscriptions and 700,000 compute hours,, NITRC-CE provides simplified deployment of cloud-based computation that supports FreeSurfer, FSL, AFNI and many other software resources. In real-world processing tests, a representative computation that would have taken 24 hours on a high-powered desktop took 25% of the time (8 hours) at a cost of only $2. The test was a FSL voxel-based morphometry (VBM) computation on 64 subjects from CANDIShare run on a 2.8 GhZ Intel Xeon Mac desktop versus AWS Large instance (m1.large) using SGE parallelization over 4 cores.
NITRC-IR offers 11,560 Subjects and 13,280 imaging sessions of searchable data across 17 studies to promote re-use and integration of these valuable shared data.
With 324 subscriptions and 700,000 compute hours,, NITRC-CE provides simplified deployment of cloud-based computation that supports FreeSurfer, FSL, AFNI and many other software resources. In real-world processing tests, a representative computation that would have taken 24 hours on a high-powered desktop took 25% of the time (8 hours) at a cost of only $2. The test was a FSL voxel-based morphometry (VBM) computation on 64 subjects from CANDIShare run on a 2.8 GhZ Intel Xeon Mac desktop versus AWS Large instance (m1.large) using SGE parallelization over 4 cores.
Conclusions:
Funded until 2029, NITRC now meets the FAIR sharing and open access requirements to be listed as a NIH-Supported Scientific Data Repository, a NLM Domain-Specific Repository, and a neuroscience repository on Scientific Data. A well known resource where tools and resources are presented in a coherent and synergistic environment, we encourage researchers to continue utilizing NITRC for data sharing, software dissemination and cost-effective computational performance.
Neuroinformatics and Data Sharing:
Brain Atlases
Databasing and Data Sharing
Workflows
Informatics Other 2
Novel Imaging Acquisition Methods:
Imaging Methods Other 1
Keywords:
Computational Neuroscience
Data analysis
Electroencephaolography (EEG)
FUNCTIONAL MRI
Informatics
MEG
MRI
Positron Emission Tomography (PET)
STRUCTURAL MRI
Workflows
1|2Indicates the priority used for review
Abstract Information
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Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Are you Internal Review Board (IRB) certified? Please note: Failure to have IRB, if applicable will lead to automatic rejection of abstract.
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
Honor, Leah B; Haselgrove, Christian; Frazier, Jean A et al. (2016) Data Citation in Neuroimaging: Proposed Best Practices for Data Identification and Attribution. Front Neuroinform 10:34
Bandrowski, Anita; Brush, Matthew; Grethe, Jeffery S et al. (2016) The Resource Identification Initiative: a cultural shift in publishing. Brain Behav 6:e00417
Kennedy, David N; Haselgrove, Christian; Riehl, Jon et al. (2015) The Three NITRCs: A Guide to Neuroimaging Neuroinformatics Resources. Neuroinformatics 13:383-6