In the last decade, neuroimaging research has seen a proliferation of open tools, platforms, and standards aimed at addressing the reproducibility crisis in the field. The growing awareness on this topic is bringing about a cultural shift in the scientific community, especially among early career researchers (ECRs). As members of this demographic, we can attest to the fact that the adoption of these new tools and practices remains a challenge. This work aims to provide a practical guide for ECRs to navigate the expanding landscape of the open-science resources and make proactive decisions for their research workflows dealing with large, multiple datasets. From our own experience, we describe the common hurdles faced in typical research workflow and provide a set of solutions that could serve as a starting point for researchers looking for practical tools and protocols. Through a hypothetical scenario, we walk through the steps of curating, processing, harmonizing, and publishing a dataset while describing the tools and practices helpful for adopting FAIR (findable, accessible, interoperable, and reusable) principles. We hope this guide can help ECRs and others to simplify their daily research life as we all strive towards more open, reproducible, and translational neuroscience research.

Driven by a period of accelerated progress and recent technical breakthroughs, whole-brain functional neuroimaging in rodents offers exciting new possibilities for addressing basic questions about brain function and its alterations. In response to lessons learned from the human neuroimaging community, leading scientists and researchers in the field convened to address existing barriers and outline ambitious goals for the future. This article captures these discussions, highlighting a shared vision to advance rodent functional neuroimaging into an era of increased impact.

Sharing neuroimaging data upon a direct personal request can be challenging both for researchers who request the data and for those who agree to share their data. Unlike sharing through repositories under standardized protocols and data use/sharing agreements, each party often needs to negotiate the terms of sharing and use of data case by case. This negotiation unfolds against a complex backdrop of ethical and regulatory requirements along with technical hurdles related to data transfer and management. These challenges can significantly delay the data-sharing process, and if not properly addressed, lead to potential tensions and disputes between sharing parties. This study aims to help researchers navigate these challenges by examining what to consider during the process of data sharing and by offering recommendations and practical tips. We first divided the process of sharing data upon a direct personal request into six stages: requesting data, reviewing the applicability of and requirements under relevant laws and regulations, negotiating terms for sharing and use of data, preparing and transferring data, managing and analyzing data, and sharing the outcome of secondary analysis of data. For each stage, we identified factors to consider through a review of ethical principles for human subject research; individual institutions’ and funding agencies’ policies; and applicable regulations in the U.S. and E.U. We then provide practical insights from a large-scale ongoing neuroimaging data-sharing project led by one of the authors as a case study. In this case study, PET/MRI data from a total of 782 subjects were collected through direct personal requests across seven sites in the USA, Canada, the UK, Denmark, Germany, and Austria. The case study also revealed that researchers should typically expect to spend an average of 8 months on data sharing efforts, with the timeline extending up to 24 months in some cases due to additional data requests or necessary corrections. The current state of data sharing via direct requests is far from ideal and presents significant challenges, particularly for early career scientists, who often have a limited time frame—typically 2 to 3 years—to work on a project. The best practices and practical tips offered in this study will help researchers streamline the process of sharing neuroimaging data while minimizing friction and frustrations.

With a growing interest in personalized medicine, functional neuroimaging research has recently shifted focus from the evaluation of group-level summaries to associating individual differences in brain function with behaviors. However, this new focus brings forth challenges related to accurately measuring the sources of individual variation in functional signals. In this perspective, we highlight the impact of within-individual variations and discuss the concept of measurement reliability as a critical tool for accounting for within- and between-individual variations when measuring individual differences in brain function.

In functional MRI (fMRI), dynamic functional connectivity (dFC) typically refers to fluctuations in measured functional connectivity on a time scale of seconds. This perspective piece focuses on challenges in the measurement and interpretation of functional connectivity dynamics. Sampling error, physiological artifacts, arousal level, and task state all contribute to variability in observed functional connectivity. In our view, the central challenge in the interpretation of functional connectivity dynamics is distinguishing between these sources of variability. We believe that applications of functional connectivity dynamics to track spontaneous cognition or as a biomarker of neuropsychiatric conditions must contend with these statistical issues as well as interpretative complications. In this perspective, we include a systematic survey of the recent literature, in which sliding window analysis remains the dominant methodology (79%). We identify limitations with this approach and discuss strategies for improving the analysis and interpretation of sliding window dFC by considering the time scale of measurement and appropriate experimental controls. We also highlight avenues of investigation that could help the field to move forward.