In this episode, the motivation for a set of best reporting and analysis practices was covered, as was the history of how COBIDAS (Committee on Best Practice in Data Analysis and Sharing) started. We talk about the reproducibility crisis in fMRI and how it is being addressed. It was clear that the culture of fMRI is changing from the isolated scientists doing N of 20 studies to a connected web of researchers collecting and contributing to fMRI databases of high-quality data for the purpose of revealing ever more subtle information in the data as well as towards the goal of creating biomarkers that are clinically useful in diagnosing and treating diseases. We also discuss many of the issues and decisions made in the analysis and what causes deviations in results. Lastly, we talk about the ongoing and future global efforts to increase data transparency to make fMRI a more robust and effective tool.

Guests:

Dr. Remi Gau is currently a post doc at the Catholic University of Louvain in Belgium. He received his PhD. in 2010 in neurosciences from the University of Pierre and Marie Curie in Paris and has studied fMRI methodology at Max Planck Institute in Tuebingen and University of Birmingham, UK. He has been active over the years focusing on the infrastructure of imaging data collection and sharing as well as more widely on the culture of neuroimaging, and most recently, created the COBIDAS (Committee on Best Practice in Data Analysis and Sharing) checklist in 2019 as well as eCOBIDAS. He also does neuroscience research, focusing on laminar fMRI to explore how the brain integrates and uses information.

Dr. Tom Nichols is a Professor of Neuroimaging Statistics and a Wellcome Trust Senior Research Fellow in Basic Biomedical Science. He is a statistician with a solitary focus on modelling and inference methods for brain imaging research. He has a unique background, with both industrial and academic experience, and diverse training including computer science, cognitive neuroscience and statistics. He received his Ph.D. in Statistics from Carnegie Mellon University in 2001. After serving on the faculty of the University of Michigan's Department of Biostatistics (2000-2006) he became the Director Modelling and Genetics at GlaxoSmithKline's Clinical Imaging Centre, London. He returned to academia in 2009 moving to the University of Warwick, taking a joint position between the Department of Statistics and the Warwick Manufacturing Group. Finally, in 2017, he joined the Big Data Institute at Oxford. The focus of Dr. Nichols work is developing modelling and inference methods for brain image data. His current research involves meta-analysis of neuroimaging studies and informatics tools to make data sharing easy and pervasive.

Dr. Jack Van Horn received his Ph.D. in Psychology from the University of London, and then received his Masters of Science and Engineering from the University of Maryland. He is currently a professor in the department of Psychology at the University of Virginia. He was a staff fellow at the NIH until 2000. He moved to Dartmouth College and while there - until 2006 - was instrumental in starting their databasing and data sharing efforts. In 2006 he moved to UCLA and contributed in a large way to their data repository efforts. In 2014 he moved to USC, and finally in 2020, moved to the University of Virginia. He has been an active member of OHBM and a proponent of data sharing since the very early days.