ESMRMB Podcast

Patricia and Francesco join Sanam to talk about Project Abstracts, one of the new submission formats for the ESMRMB 2025 Annual Meeting. Designed for early-stage ideas, this format invites feedback and fosters interdisciplinary collaboration—whether you’re developing a new tool, launching a clinical concept, or exploring a novel direction. 🔗 Submission details & guidelines: https://wiki.esmrmb.org/index.php/Project_Abstracts https://esmrmb2025.org/late-abstract-submission/

In this episode, Johanna and Maria join Sanam to introduce Registered Report Abstracts, a new submission format for the ESMRMB 2025 Annual Meeting. Learn how this format supports hypothesis-driven research by encouraging early feedback, transparency, and scientific rigor—especially valuable for early-career researchers. 🔗 Submission details & guidelines: https://wiki.esmrmb.org/index.php/Registered_Reports https://esmrmb2025.org/late-abstract-submission/

In this episode, we speak with Annelia Haek, a master’s student at Ghent University, about her first experience attending and presenting at an international academic conference. She shares the emotional rollercoaster of preparing for her power pitch, the surprising intensity of constant socializing, and the transformative perspective she gained on research as a collaborative pursuit. Her story is an inspiring reminder that even nerve-wracking moments can lead to growth, connection, and renewed motivation for a career in science.

In this episode, Dr. Moss Zhao speaks with Dr. Beatrice Lena about ultra low field MRI.

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. Chapters 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. Articles: https://pubmed.ncbi.nlm.nih.gov/32621651/ [https://pubmed.ncbi.nlm.nih.gov/32621651/] https://pmc.ncbi.nlm.nih.gov/articles/PMC8111663/ [https://pmc.ncbi.nlm.nih.gov/articles/PMC8111663/] Funding Opportunities, Data Sharing Policies, and Key Neuroimaging Datasets - Funding:  https://nida.nih.gov/funding/nida-funding-opportunities [https://nida.nih.gov/funding/nida-funding-opportunities].  https://grants.nih.gov/grants/guide/rfa-files/RFA-DA-24-037.html [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://sharing.nih.gov/data-management-and-sharing-policy/about-data-management-and-sharing-policies/data-management-and-sharing-policy-overview#after]. - NSF guidelines:  https://new.nsf.gov/funding/data-management-plan#nsfs-data-sharing-policy-1c8 [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 [https://adni.loni.usc.edu/] * 1000 Functional Connectomes Project: http://fcon_1000.projects.nitrc.org [http://fcon_1000.projects.nitrc.org/] * Human Connectome Project (Young Adult): https://www.humanconnectome.org [https://www.humanconnectome.org/] * NIMH Data Archive: https://nda.nih.gov [https://nda.nih.gov/] * UK Biobank: https://www.ukbiobank.ac.uk [https://www.ukbiobank.ac.uk/] * Coinstac for Federated Learning: https://coinstac.org [https://coinstac.org/]