BY LISA NICKERSON
The old adage “there’s something for everyone” is an understatement when it comes to the representation of imaging data analysis techniques at the OHBM Annual Meeting. From courses and workshops on the most basic fundamentals of analysis to oral sessions and symposia highlighting work at the forefront of analytical methods development, the annual OHBM meeting is unparalleled in this regard. As a young graduate student and later as post-doc, OHBM drew me in as one of the best resources for learning about imaging data analysis. Throughout the year, I would spend countless hours, days, and even months combing through the literature and the internet trying to determine what information was reliable or most relevant for my work, scouring the SPM and FSL forums for answers to my questions, and generally being frustrated at how long it took to get the answers I needed to make headway on various analysis issues. The OHBM Educational Courses and Morning Workshops offered me an opportunity to learn from experts, meet them, and ask them my questions directly. This is the only conference I know that places such a strong emphasis on imaging data analysis, and I advise all my trainees and collaborators who are trying to learn analysis to go to OHBM to soak it in.
This year, the opportunities for learning actually begin before the OHBM meeting starts with several Satellite Meetings taking place right before the conference, including: FSL Course 2016, Pattern Recognition in Neuroimaging, Brain Connectivity, and the BrainMap/Mango Workshop. In addition, the OHBM Educational Courses take place on Sunday before the Opening Ceremonies, with several courses that are fantastic for students, post-docs, those who are new to neuroimaging, and those who just want to pick up new analysis techniques.
The Art and Pitfalls of fMRI Preprocessing is a long running OHBM Educational Course designed to expose beginners to the critical importance of key fMRI pre-processing steps for both resting state and task fMRI and, this year, covers typical pre-processing pipelines in three major software tools, FSL, SPM, and AFNI. The course on MR Diffusion Imaging: From Basics to Advanced Applications will highlight methodological considerations of both acquisition and analysis for mapping structural connectivity and white matter microstructure. More advanced statistical methods for those with some experience, or for those who are merely curious, are also represented in courses such as:
Not all of the analysis-related symposia cover connectivity though. On Monday, What Neuroimaging Can Tell Us? From Correlation to Causation and Cognitive Ontologies takes up the important issue that simply studying associations between brain function and cognitive function does not inform the causal mechanisms of how brain functions actually give rise to cognitive functions. This symposium covers causal inference, including new methods for deriving causal hypotheses from observational data and validating causal hypotheses by brain stimulation. And one of Wednesday’s symposia, Neural Nets to Neural Nets: Deep Learning Approaches to Neuroimaging, will introduce deep learning, the new area of machine learning that was used by a computer program developed by Google DeepMind to beat Lee Sedol at Go without any handicaps. These methods are also reigniting the AI community. For those interested in this exciting new area of research, also be sure to check out the Talairach Lecture by Daniel Wolpert and our interview of him to see how scientists at Cambridge are applying some of these methods to tackle neuroscience questions.
The diversity of topics covered in all of these satellite conferences, Educational Courses, and Morning Symposia is truly astounding. I find it a real challenge to keep up with new specialized techniques and evolving perspectives on established methods, and being able to drop in on these lectures at OHBM both keeps me ahead of the game for developing new research directions and keeps me doing “good science”.