Connectomics in NeuroImaging (CNI)
Connectomics is the study of whole brain association maps, i.e., the connectome, with a focus on understanding, quantifying, and visualizing brain network organization. Connectomics research is fo interest to the neuroscientific community largely because of its potential to understand human cognition, its variation over development and aging, and its alteration in disease or injury. As such, big data in connectomics are rapidly growing with emerging international research initiatives collecting large, high quality brain images with structural, diffusion and functional imaging modalities. CNI aims to propel research which leverages this increasing wealth of connectomic data. CNI will bring together computational researchers (computer scientists, data scientists, computational neuroscientists) to discuss advancements in connectome construction, analysis, visualization and their use in clinical diagnosis and group comparison studies.
CNI aims to unify computational researchers with neuroscientists and cultivate interactions toward translational applications for the clinic. For the 3rd year, CNI will feature a single-track workshop with keynote speakers, technical paper presentations and poster sessions.
- Brain Analysis using COnnectivity Networks, MICCAI 2016. (BACON 2016)
- Connectomics in NeuroImaging, MICCAI 2018. (CNI 2018)
- Connectomics in NeuroImaging, MICCAI 2019. (CNI 2019)
Current Homepage: http://brainconnectivity.net/workshop.html
Connectomics in NeuroImaging - Transfer Learning Challenge (CNI-TLC)
Large, open source datasets, such as the Human Connectome Project (HCP) and the Autism Brain Imaging Data Exchange (ABIDE), have spurred the development of new and increasingly powerful machine learning strategies in brain connectomics. However, one key question remains: are we capturing biologically relevant and generalizable information about the brain, or are we simply overfitting to the data? The CNI Challenge 2019 will address the issues of generalizability and clinical relevance for functional connectomes. We will leverage a unique resting-state fMRI (rsfMRI) dataset of attention deficit hyperactivity disorder (ADHD) and neurotypical controls (NC). Participants will be asked to design a classification framework that can predict subject diagnosis (ADHD vs. Neurotypical Control) based on brain connectivity data. We will provide 120 examples of each class for training and validation.
We will also evaluate the classification performance on a related clinical population with an ADHD comorbidity. This challenge will allow us to assess (1) whether the method is extracting functional connectivity patterns related to ADHD symptomatology, and (2) how much of this information “transfers” between clinical populations.
Current Homepage: http://brainconnectivity.net/challenge.html
Tutorial On Publishing, GRant writing And career Development (TOPGRAD)
TOPGRAD (Tutorial On Publishing, GRant writing And career Development) aims to empower junior researchers with the confidence and skills to navigate their career, with success and fulfillment.
TOPGRAD provides advice on publishing, grant writing and career trajectories in the MICCAI field. The tutorial will consist of a number of talks and discussion on the review process, publishing strategies (including open access) and grant writing requirements. Moreover, we hold an extensive Q&A session between early career researchers and experts in the relevant fields.
- Tutorial On Publishing, GRant writing And career Development, MICCAI 2018. (TOPGRAD 2018)
- Tutorial On Publishing, GRant writing And career Development, MICCAI 2019. (TOPGRAD 2019)
Current Homepage: http://www.markus-schirmer.com/topgrad