Reproducible Decision Making for fMRI Quality Control

Palais 

The quality control (QC) of functional magnetic resonance imaging (fMRI) data is widely performed using a range of automated tools developed to aid data quality assessments. Yet, ultimately these tools still require one or more raters to make subjective decisions about the overall quality of a subject’s data, and these decisions can vary both within and between raters. Furthermore, there is little consensus about what constitutes good or poor quality data. One way this variability in decision making can be mitigated is by using a predefined QC protocol. To illustrate this point this lecture will present results from our fMRI Open QC Project, where multiple raters reviewed the same QC reports and were asked to make decisions about data quality using a predefined QC protocol. This lecture will include exemplar QC outputs that attendees will be asked to assess with and without a QC protocol for guidance (the results of these group assessments will be shared at the end of the lecture). Lastly, this lecture will highlight common QC issues and suggest how users could generate their own QC protocols for their preferred tool.