Brain imaging is an increasingly interdisciplinary field, encompassing multiple data types and multiple analytic traditions. Projects typically involve many moving parts, such as building customized preprocessing pipelines, transforming between data formats, preparing datasets for analysis and, ultimately, displaying results. The field is conventionally built on highly specialized software packages that solve these individual challenges well but are not necessarily designed to be interoperable.
Trainees new to the field are therefore often left to come up with isolated heuristics and workarounds to complete a project. Here we present a set of tools to navigate the increasingly complex informatics ecosystem of brain imaging. netneurotools is a Python toolkit that has been continuously developed and maintained by this laboratory’s trainees. The philosophy of the toolkit is that it should be the Swiss army knife of the laboratory: functions and routines that we often use but that are not part of any established pipeline or package.
Since its inception, the toolkit has been open and welcomes contributions from neuroscientists across the globe. netneurotools presents a necessary counterweight to out-of-the-box software packages and highlights the importance of smaller, ad hoc functions for implementing projects. By opening a window into the inner workings of a laboratory, netneurotools also presents an opportunity to begin a new type of discourse among groups and establish tangible links within the community. This is a preview of subscription content, access via your institution Access Nature and 54 other Nature Portfolio journals Get Nature+, our best-value online-access subscription Receive 12 print issues and online access Prices may be subject to local taxes which are calculated during checkout Bazinet, V. et al.
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