14.2.3. Taylor et al. (2023). Highlight Results, Don’t Hide Them: Enhance interpretation, …¶
Introduction¶
Here we present commands used in the following paper:
- Taylor PA, Reynolds RC, Calhoun V, Gonzalez-Castillo J, Handwerker DA, Bandettini PA, Mejia AF, Chen G (2023). Highlight Results, Don’t Hide Them: Enhance interpretation, reduce biases and improve reproducibility. Neuroimage 274:120138. doi: 10.1016/j.neuroimage.2023.120138
Abstract: Most neuroimaging studies display results that represent only a tiny fraction of the collected data. While it is conventional to present “only the significant results” to the reader, here we suggest that this practice has several negative consequences for both reproducibility and understanding. This practice hides away most of the results of the dataset and leads to problems of selection bias and irreproducibility, both of which have been recognized as major issues in neuroimaging studies recently. Opaque, all-or-nothing thresholding, even if well-intentioned, places undue influence on arbitrary filter values, hinders clear communication of scientific results, wastes data, is antithetical to good scientific practice, and leads to conceptual inconsistencies. It is also inconsistent with the properties of the acquired data and the underlying biology being studied. Instead of presenting only a few statistically significant locations and hiding away the remaining results, we propose that studies should “highlight” the former while also showing as much as possible of the rest. This is distinct from but complementary to utilizing data sharing repositories: the initial presentation of results has an enormous impact on the interpretation of a study. We present practical examples for voxelwise, regionwise and cross-study analyses using publicly available data that was analyzed previously by 70 teams (NARPS; Botvinik-Nezer, et al., 2020), showing that it is possible to balance the goals of displaying a full set of results with providing the reader reasonably concise and “digestible” findings. In particular, the highlighting approach sheds useful light on the kind of variability present among the NARPS teams’ results, which is primarily a varied strength of agreement rather than disagreement. Using a meta-analysis built on the informative “highlighting” approach shows this relative agreement, while one using the standard “hiding” approach does not. We describe how this simple but powerful change in practice-focusing on highlighting results, rather than hiding all but the strongest ones-can help address many large concerns within the field, or at least to provide more complete information about them. We include a list of practical suggestions for results reporting to improve reproducibility, cross-study comparisons and meta-analyses.
Study keywords: FMRI, EPI, MPRAGE, human, adult, data visualization, transparent thresholding, reproducibility, interpretation, task-based FMRI, amplitude modulation, meta-analysis
Main programs:
afni_proc.py, timing_tool.py, @SSwarper, recon-all
(FS), gen_ss_review_table.py, 3dttest++, 3dttest++ -Clustsim
.., 3dttest++ -Clustsim -ETAC .., gen_group_command.py,
RBA, @chauffeur_afni
Download scripts¶
To download, either:
- visit the github page:
... or copy+paste into a terminal:
git clone https://github.com/afni/apaper_highlight_narps.git
View scripts¶
Because there are so many scripts for this project, just recommend
downloading the full set from the github pages, above. There are
helpful README* files there, as well, to describe the contents in
details.
Note that these scripts were run on the NIH’s Biowulf HPC, so some scriptiness deals with those specific features (batch/swarm submission, etc.).
We just point to a couple specific examples of the afni_proc.py
processing scripts here:
do_22_ap_task.tcsh¶
Full processing (through regression modeling) of a task-based FMRI session for a single subject. This does not include blurring, because it will be used for ROI-based analysis.
https://github.com/afni/apaper_highlight_narps/blob/main/scripts_biowulf/do_22_ap_task.tcsh
do_23_ap_task_b.tcsh¶
Full processing (through regression modeling) of a task-based FMRI session for a single subject. This does include blurring, because it will be used for voxelwise analysis.
https://github.com/afni/apaper_highlight_narps/blob/main/scripts_biowulf/do_23_ap_task_b.tcsh
