This is an exciting time to be working at the intersection of neuroimaging and data science. On the one hand, we have more and more neuroimaging data. Even if you don’t have access to a brain scanner yourself, you can gain access to a variety of datasets online and – it seems – start working on your own brain data analysis almost immediately. Some people believe that the mountains of data that we can all now access will yield the kinds of insights that will relieve the pain and suffering due to mental health disorders and neurological diseases. Some even suggest that a better understanding of the biology of differences between individuals will help address the societal inequities that give rise to these differences. These are big hopes, and we carefully share them. But as anyone who has started wading into neuroimaging data knows, the reality is often much more mundane. It entails wrangling data between obscure data formats; making different pieces of software work in harmony with each other; it means struggling to make sure that the analysis that you did today still replicates when a collaborator runs it tomorrow, or even when you run it next week; it means trying to stay up-to-date with the flurry of methods and new findings that are published almost every day.
How do we start to wrap our heads around these challenges? We wrote this book in large part because we wish a book like this had existed when we were coming to terms with these challenges ourselves, during our development as scientists. Fortunately for us, we had mentors and collaborators who could teach us many of the things that are now in this book, but the process of learning was often more roundabout than we wish it had been. As we’ve started teaching some of these things to others, we once again wished that we had all these pieces of information in one place, and here we are. We sincerely hope that this book will serve to make the path through some of these challenges more direct, or at least a bit more well-illuminated.
We are grateful for the inspiration and help that we received in putting this book together. Some of the material in this book evolved from materials that we put together as part of organizing and teaching the NeuroHackademy Summer Institute in Neuroimaging and Data Science. The book and its contents are inspired by the participants and instructors that have come through the school over the years we have run it, in person (2016-2019, and 2022) and online (2020-2021). We learned a great deal about neuroimaging and data science from them all. We are grateful for the support that NeuroHackademy has received from the US National Institute of Mental Health. We are also immensely grateful for the financial, physical, and intellectual infrastructure provided by the University of Washington eScience Institute and the wonderful team there. We would also like to thank Teresa Gomez, Sam Johnson, and McKenzie Hagen, who read early versions of the book and spotted various errors and typos. We’d also like to thank four anonymous reviewers for many suggestions on how to improve the content of our book and make it more helpful.
We are grateful to the developers of the many excellent open-source software tools that are featured in this book, as well as the communities and institutions that support the development and maintenance of these tools. This book was written almost entirely using Jupyter and the Jupyterbook toolchain. We’d like to particularly thank the developers of these tools for enabling new ways of computational expression.