Learn About Imaging Data Analysis at the OHBM Educational Courses and Morning Symposia

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:

• Pattern Recognition for Neuroimaging which covers how machine learning methods are adapted to investigate neuroscience questions such as disease prediction, classification, and brain decoding
  
• Graph Theoretic Models of Brain Networks highlights the application of graph theory to investigate the organizational properties of brain networks, delving into the fundamentals of network graph construction from connectivity data, different types of models and measures, and clinical applications
  
• Introduction to Imaging Genetics is another long-running course that covers approaches for modeling and understanding how genetic variation influences brain structure and function, key conceptual and methodological issues when combining two daunting – from a statistical perspective – techniques, and addresses reproducibility and validation in this field
  
• Neuroimaging Meta-Analysis reviews tools and techniques for summarizing research findings across a large number of participants and diverse experimental settings to overcome some of the typical limitations of BOLD fMRI studies, including small sample size and limited reliability of BOLD signals
  
• Practicalities for Reproducible Neuroimaging takes up the issue of reproducibility as it pertains to neuroimaging studies more generally, which will likely be of increasing importance in light of recent research conducted by the Open Science Collaboration showing poor reproducibility of psychological science research.
  

The daily Morning Symposia also offer up many opportunities to learn about both well-established methods and methods that are at the forefront of imaging research.  On Tuesday, Skeptical Connectivity: Time for Something Completely Different presents three powerful new statistical methods for investigating brain connectivity from a very different perspective than standard network analysis methods, while also discussing how these methods overcome some of the limitations of current methods. While this symposium is at the forefront of connectivity methods development, a parallel symposium on Effects of Head Motion on Structural and Functional MRI Studies will discuss the more basic topic of how motion affects resting state functional connectivity and other modalities, including methods to prospectively correct for these effects, through improved acquisition methods, modeling and other analytical strategies. For those new to connectivity methods, the latter is a must-see symposium to learn about this critical issue and methods for remediation. On Wednesday, Functional Connectivity or Causality in the Brain: How do We Know? focuses on reviewing the state of the art of functional/effective connectivity and causality mapping approaches, which will be great for novices. This symposium further explores how to experimentally validate connectivity measures and how to reconcile measures based on fMRI with those from EEG/MEG, which will surely be useful for anyone doing connectivity research to better understand their findings.​

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”.