Responsible Use of Large-Scale Neuroimaging Datasets

Large, open datasets have emerged as important neuroimaging resources that offer exciting opportunities for innovative discoveries. Before engaging in secondary data analyses, it is essential that researchers consider relevant ethical issues for responsible data use.

Training Considerations for Responsible Conduct of Research

Training in responsible conduct of research typically emphasizes important topics related to the protection of human subjects, animal welfare, and laboratory safety. However, responsible conduct of research also extends to data management, sharing and ownership, scientific rigor and reproducibility, and responsible authorship and publication.

Increased sharing of large, open datasets must be accompanied by heightened attention to ensuring the protection of participant identity, including individuals from more vulnerable populations, such as patients with clinical disorders and/or from historically underrepresented groups.

Responsible Data Analyses Require Advance Planning

Beyond concerns about participant privacy, responsible data analyses require advance planning, becoming familiar with the data acquisition protocols, and understanding the limitations of the acquired data.

Preventing Stigmatizing Research

Finally, prior to any data analysis or interpretation, researchers must engage responsibly and fully consider the psychological, social, economic, and any other potentially harmful impacts their research could have on individuals, communities, and society. Specifically, this means that responsible use of variables related to race, ethnicity, gender, and sex must be thoughtfully considered prior to conducting analyses of neuroimaging data. Comparisons across participants who are grouped by race and/or ethnicity can potentially be interpreted as evidence of biomarkers that explain neurobiological mechanisms through which some communities experience lower rates of achievement and poorer life outcomes. To discourage continuation of this biological deficits framework, it is imperative that data analysts recognize that ethical conduct in research includes ensuring that analyses prevent further stigmatization, marginalization, and injustice toward individuals because of racial, ethnic, or gender status.

Additional discussion of these issues can be found in two recent publications, including a review article and a practical guide for analyzing data from the ABCD Study.

Guidelines for Preventing Stigmatizing Research have been adapted from the Responsible Conduct of Research developed by All of Us, the NIH’s Precision Medicine Initiative.

– Angie Laird, Florida International University


Data Highlight: High-quality diffusion-weighted imaging of Parkinson's disease

The demonstration of neuropathological disturbances in nigrostriatal and extranigral brain pathways using magnetic resonance imaging in Parkinson's disease remains a challenge. This NITRC-IR project provides access to the data that accompanies research where the authors applied a novel diffusion-weighted imaging approach: track density imaging (TDI) to identify brainstem and nigrostriatal pathways. Their research has since been cited 58 times. View the Data >

Ziegler, at al. Mapping track density changes in nigrostriatal and extranigral pathways in Parkinson's disease. Neuroimage. 2014 Oct 1;99:498-508


Batch Processing on NITRC-CE

NITRC-CE can now be used for batch processing using NeuroStack, a community-developed system for AWS. After setting up NeuroStack and defining your processing stream, any data you upload to an AWS S3 bucket is automatically processed by a NITRC-CE instance and output appears in another S3 bucket. Learn More >

Say Hello at OHBM in Glasgow!

We are looking forward to our first in-person conference since 2019! We will be staffing Booth 22 in the exhibit hall; stop by to learn more about NITRC, ask questions, or just say hello! In addition, we'll be presenting a NITRC poster #1003 on Wednesday, June 22 and Thursday, June 23. More Info About OHBM >