Scanning the horizon: towards transparent and reproducible neuroimaging research

By Russell A. Poldrack, Chris L. Baker, Joke Durnez, Krzysztof J. Gorgolewski, Paul M. Matthews, Marcus Munafò, Thomas E. Nichols, Jean-Baptiste Poline, E. Vul, Tal Yarkoni. Nature Reviews Neuroscience, 2016

1,305 citations recorded by the source corpus.

Functional neuroimaging techniques have transformed our ability to probe the neurobiological basis of behaviour and are increasingly being applied by the wider neuroscience community. However, concerns have recently been raised that the conclusions that are drawn from some human neuroimaging studies are either spurious or not generalizable. Problems such as low statistical power, flexibility in data analysis, software errors and a lack of direct replication apply to many fields, but perhaps particularly to functional MRI. Here, we discuss these problems, outline current and suggested best practices, and describe how we think the field should evolve to produce the most meaningful and reliable answers to neuroscientific questions.

Subfields: Cognitive Neuroscience, Health, Toxicology and Mutagenesis

View Paper: https://www.semanticscholar.org/paper/7e07d99521053a210897153ef4c4cf36f9fadefc

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Citations in the corpus

Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation (2019, background): “It extends the efforts initiated in both the neuroimaging (Gorgolewski and Poldrack, 2016; Poldrack et al., 2017) and ML (Sonnenburg et al.”
Reduced default mode network functional connectivity in patients with recurrent major depressive disorder (2019, neutral): “Finally, our findings require independent replication (11).”
Cross‐validation failure: Small sample sizes lead to large error bars (2017, background): “Overall, exploring larger datasets is a promising future for neuroimaging (Poldrack et al., 2017).”
Variability in the analysis of a single neuroimaging dataset by many teams (2019, background): “We do not, however, believe that there is a single (or even a few) best analysis pipeline across studies 52,53 .”
Influences on the Test–Retest Reliability of Functional Connectivity MRI and its Relationship with Behavioral Utility (2017, background): “Accordingly, the field of neuroimaging has begun to compile best practices to increase reliability, reproducibility, and validity (Nichols et al. 2017; Poldrack et al. 2017).”
The relation between statistical power and inference in fMRI (2017, background): “Next, we aimed to investigate which of the two scenarios is more comparable to empirical data (See reference [6] for a similar approach).”
Don't fear ‘fear conditioning’: Methodological considerations for the design and analysis of studies on human fear acquisition, extinction, and return of fear (2017, neutral): “…Balaban, 1999; havioral Reviews 77 (2017) 247–285 261 startle: Blumenthal et al., 2005; SCR: Boucsein et al., 2012; HR: Jennings et al., 1981; fMRI: Poldrack et al., 2016, 2008) for recommendations with respect to equipment, electrode placement, data recording, response quantification, and data…”
From human writing to artificial intelligence generated text: examining the prospects and potential threats of ChatGPT in academic writing (2023, positive): “For instance, ChatGPT can be utilized to analyse academic papers by scanning them and extracting important details such as the author(s), publication date, and significant findings [11, 13].”