Russell A. Poldrack

Aug 5, 2024 | nature.com | Russell A. Poldrack |Damien A. Fair |Michael P. Milham |Jon Clucas |Ting Xu |Lei Ai | +14 more

When fields lack consensus standard methods and accessible ground truths, reproducibility can be more of an ideal than a reality. Such has been the case for functional neuroimaging, where there exists a sprawling space of tools and processing pipelines. We provide a critical evaluation of the impact of differences across five independently developed minimal preprocessing pipelines for functional magnetic resonance imaging. We show that, even when handling identical data, interpipeline agreement was only moderate, critically shedding light on a factor that limits cross-study reproducibility. We show that low interpipeline agreement can go unrecognized until the reliability of the underlying data is high, which is increasingly the case as the field progresses. Crucially we show that, when interpipeline agreement is compromised, so too is the consistency of insights from brain-wide association studies. We highlight the importance of comparing analytic configurations, because both widely discussed and commonly overlooked decisions can lead to marked variation. Functional connectivity estimates vary significantly across different functional magnetic resonance imaging preprocessing pipelines. Due to these variations, using seemingly similar minimal preprocessing does not ensure consistency.

Jun 7, 2024 | thetransmitter.org | Calli McMurray |Russell A. Poldrack |Jan R. Wessel |Megan Peters

People—including researchers—make mistakes. Some errors are simple: forgetting a colon in a computer program, or copy-and-pasting the wrong value from a paper during a literature review. But when missteps evade detection and make it into a published paper, there can be “staggering” consequences, such as spurring a subfield into dead-end research questions, writes Malte Elson, professor of the psychology of digitalization at the University of Bern, in an editorial published last month in Nature.

May 5, 2024 | biorxiv.org | Michael I. Demidenko |Jeanette Alane Mumford |Russell A. Poldrack

AbstractEmpirical studies reporting low test-retest reliability of individual blood oxygen-level dependent (BOLD) signal estimates in functional magnetic resonance imaging (fMRI) data have resurrected interest among cognitive neuroscientists in methods that may improve reliability in fMRI.

Apr 21, 2024 | biorxiv.org | Rastko Ciric |ANNA XU |Russell A. Poldrack

AbstractNeuroimaging visualisations facilitate the interpretation of geometrically structured data and results. However, heterogeneous geometries--such as volumes, surfaces, and networks--have traditionally mandated different software approaches. We introduce hyve, a Python library that uses a compositional functional framework to enable parametric implementation of custom visualisations for different brain geometries.

Mar 20, 2024 | biorxiv.org | Michael I. Demidenko |Jeanette Alane Mumford |Russell A. Poldrack

AbstractEmpirical studies reporting low test-retest reliability of individual blood oxygen-level dependent (BOLD) signal estimates in functional magnetic resonance imaging (fMRI) data have resurrected interest among cognitive neuroscientists in methods that may improve reliability in fMRI.