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<!DOCTYPE HTML>
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<h1><a href="index.html">CogBases</a></h1>
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<h2>Abstracts</h2>
<p>Abstracts for the CogBases workshop</p>
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<!-- <h4>Jean-Baptiste Poline</h4>
<h3>A fully decentralized data management and processing ecosystem for neuroimaging and phenotypic data</h3>
<p>There are a number of great centralized resources for accessing data in human neuroimaging. The most prominent one is OpenNeuro, based on the BIDS standard, giving access to more than 20,000 participants with MRI data. However, internally the phenotypic variables described in the BIDS datasets on OpenNeuro are not harmonized (e.g., column names representing the same variable are coded differently) and if other datasets are available, their demographic, clinical or cognitive assessments variables will also need harmonization. Concerning the derived data, while there is already some work by the BIDS community to establish a standard representation (BIDS-derivative proposal), local neuroimaging processing are generally lacking a specification that can be followed to foster simple view and sharing of processed data with standard pipelines. In summary, there are still a lot of manual steps that are making data management and processing error prone and ineffective.
<p>We present an ecosystem consisting of NeuroBagel, a _distributed_ and scalable approach based on semantic web technologies for harmonizing and sharing phenotypic and neuroimaging variables (with a DataLad backend, work in progress), and NiPoppy, a specification for MRI processings to integrate derived data and curation information. We used NeuroBagel tools to harmonize the OpenNeuro MRI data as well as several Parkinson datasets (Quebec Parkinson Network, Parkinson Progression Marker Initiative, etc) and will demonstrate how new neuroimaging cohorts can be defined from several datasets from open or close datasets. We will show how NiPoppy can help with the standardization of the management and monitoring of neuroimaging data processing.
<p>We think - and hope - that the proposed distributed ecosystem will foster easier and more scalable neuroimaging datasharing for machine learning applications.
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