![enter image description here][1] [OHBM Glasgow, June 19th, 2022][2] ============================= **An Introduction to Methods for Analyzing Large Neuroimaging Datasets** -------------------------------------------------------------------- Human brain imaging is in a period of profound change, with a growing recognition that sample size must drastically increase to achieve adequate statistical power and reproducibility. Fortunately, several large neuroimaging databases are now widely accessible (e.g., ABCD, ADNI, OASIS, UK Biobank) plus open-data platforms such as Openneuro. **Do you want the tools to work on these large neuroimaging datasets? If so, this workshop is for you!** Experts will show you how to deploy scalable methods to process neuroimaging data from structural to functional modalities and how to apply approaches such as machine learning and graph theory to large datasets. The course will also cover important general topics, including setting up a reproducible and sustainable workflow, using cloud-based software and data annotation. The educational course will showcase step-by-step concrete examples so you can translate the knowledge gained to your own research. Most of the tutorials will use data from the [AOMICS datasets][3]. Requirements (dataset and softwares) will be described within the description of each component. The talks can be found [here][4]. | Presenter(s) | Title | Time | |--------------|---|---| | Jivesh Ramduny | [Getting started][5] | 8:00-8:30 | | Tara Madhyastha | [Neuroimaging workflow in the cloud][6] | 8:30-9:10 | | Felix Hoffstaedter | [From dozens to thousands: important lessons when scaling up structural MRI processing using CAT][7] | 9:10-9:50 | | | BREAK | 9:50-10:10 | | Oscar Esteban | [Executing BIDS-Apps on large datasets and the NeuroImaging PREProcessing toolS (NiPreps) framework][8] | 10:10-10:50 | Lea Waller | [Using HALFpipe for automated data analysis and quality control][9] | 10:50-11:30 | | Sage Hahn | [A Brief Introduction to the Brain Predictability toolbox (BPt)][10] | 11:30-12:10 | | | LUNCH | 12:10-1:20 | | Yihe Weng (& Rory Boyle) | [Studying the connectome at a large scale][11] | 1:20-2:00 | | Emin Serin | [Connectome-based Machine Learning using NBS-Predict][12] | 2:00-2:40 | | | BREAK | 2:40-3:00 | | Scott Makeig | [Annotating the timeline of neuroimaging time series data using Hierarchical Event Descriptors (HED)][13] | 3:00-3:40 | | Kelly (& Mélanie Garcia) | [Establishing a reproducible and sustainable analysis workflow][14] | 3:40-4:10 | | | WRAP-UP | | For more information, email: robert.whelan@tcd.ie or herve.lemaitre@u-bordeaux.fr [1]: https://www.humanbrainmapping.org/images/header_4114.png [2]: https://www.humanbrainmapping.org/i4a/pages/index.cfm?pageid=4055 [3]: https://nilab-uva.github.io/AOMIC.github.io/ [4]: https://www.youtube.com/playlist?list=PLg2e4R8SdhpdU51uq6tccq9TYUpXTdxXF [5]: https://osf.io/teqxb/ [6]: https://osf.io/39p6e [7]: https://osf.io/fc47s [8]: https://osf.io/hu872 [9]: https://osf.io/x8stv [10]: https://osf.io/qsdv2 [11]: https://osf.io/c3kau [12]: https://osf.io/5c2vr [13]: https://osf.io/wcerp [14]: https://osf.io/97xhc