In this episode, host Sanam Assili, a biomedical imaging scientist from ESMRMB, and co-host Dr. Gennady Roshchupkin lead an engaging discussion with Professor Christian Beckmann from Radboud University Medical Centre and Dr. Lisa Nickerson from Harvard Medical School on the transformative role of big data in neuroimaging. This episode explores the benefits and challenges of analyzing large-scale MRI and fMRI datasets, the integration of AI and multimodal data, and their impact on clinical practice and personalized medicine. Listeners will gain insights into data management, regulatory frameworks, funding disparities, and practical advice for aspiring researchers navigating this dynamic field.
Section 1: Introductions (0:00 - 4:34)
Hosts and guests introduce themselves and their expertise, setting the stage for discussing big data in neuroscience.
Section 2: Defining Big Data in Neuroimaging (4:34 - 23:43)
Panelists define "big data" in neuroimaging, covering dataset pooling, data-sharing consortia, and harmonizing multi-modal data, with examples like the Human Connectome Project and UK Biobank.
Section 3: Navigating the Data Management Challenge (23:43 - 32:40)
Focuses on managing large datasets, addressing storage, processing, and ethical/legal challenges, with emphasis on sustainable funding and UK Biobank's shift to cloud infrastructure.
Section 4: Algorithm and Data Integration: The Power of Multimodal Analysis (32:40 - 51:05)
Discussion on analytical tools, including traditional statistics, AI, and machine learning, for extracting insights, and the importance of data integration and AI's evolving role.
Section 5: The Human Element: Funding and Talent in a Competitive Landscape (35:21 - 64:53)
Explores challenges in recruiting and retaining skilled scientists, emphasizing long-term funding models beyond traditional cycles.
Section 6: Future Directions: Towards Personalized and Precision Medicine (64:53 - 71:40)
Panelists discuss the future of big data in neuroimaging, focusing on personalized medicine, biomarkers, and multidisciplinary collaboration.
Section 7: Advice for Young Researchers: Embracing the Challenges and Opportunities (71:40 - 76:04)
Panelists provide advice for aspiring researchers, highlighting funding opportunities and integrating smaller datasets with large-scale repositories.
Section 8: Conclusion and Call to Action (76:04 - 76:32)
Wraps up with key takeaways and encourages engagement with the ESMRMB community and resources.
https://pubmed.ncbi.nlm.nih.gov/32621651/
https://pmc.ncbi.nlm.nih.gov/articles/PMC8111663/
Funding Opportunities, Data Sharing Policies, and Key Neuroimaging Datasets
https://nida.nih.gov/funding/nida-funding-opportunities.
https://grants.nih.gov/grants/guide/rfa-files/RFA-DA-24-037.html.
- NIH's data sharing policy:
https://sharing.nih.gov/data-management-and-sharing-policy/about-data-management-and-sharing-policies/data-management-and-sharing-policy-overview#after.
https://new.nsf.gov/funding/data-management-plan#nsfs-data-sharing-policy-1c8.
- Large-scale datasets include:
Alzheimer's Disease Neuroimaging Initiative: https://adni.loni.usc.edu
1000 Functional Connectomes Project: http://fcon_1000.projects.nitrc.org
Human Connectome Project (Young Adult): https://www.humanconnectome.org
NIMH Data Archive: https://nda.nih.gov
UK Biobank: https://www.ukbiobank.ac.uk
Coinstac for Federated Learning: https://coinstac.org
