Data visualization for inference in tomographic brain imaging
Bibliographic record
Abstract
Tomographic imaging (i.e.magnetic resonance imaging [MRI], positron emission tomography [PET], X-ray computed tomography [CT]) offers a unique window in understanding structure-function relationships in the living brain.Nowadays, it is standard practice to acquire entire brain volumes and perform statistical analyses at each voxel, an approach known as statistical parametric mapping (Friston, Frith, Liddle, & Frackowiak, 1991).Because it is impossible to report the statistical results in every voxel, summary tables and figures are of importance.The choice made by authors to create such figures should however not be driven by aesthetic considerations alone but also driven by the message to convey (Rougier et al., 2014).This is not to say that beautiful figures should not be used, as more appealing figures might in fact help in remembering results (Borkin et al., 2013; Madan, 2015b).At the intersection of psychology, computer vision, graphic design and statistics, there is a field of research that looks at how to represent information and what features are beneficial or harmful in figure designs.For instance, Cleveland and McGill (1983) showed that changing the axis scaling in scatter plots can alter inference on associations between variables.Siegrist (1996) show that using perspective in pie charts, lead to falsely infer magnitude differences because the slices that are closer to the reader appear to be larger than those in the back.In general, there are recommendations for plotting the data rather than summary statistics as those summary values can be obtained with very different distributions which can preclude the use of some statistical tests (see e.g.Anscombe, 1973, for correlations or Weissgerber, Milic, Winham, & Garovic, 2015, for bar graphs).Here we discuss information provided in figures when presenting tomographic data results.Many proposals have already been made by others, what we offer is a principled way chose among those proposals and apply them.A review of articles using tomographic techniques published between January 2016 and June 2018 in the European Journal of Neuroscience (N = 30 -https ://github.com/ CPern et/MRI_FaceD ata_Wakem an-Henso n/blob/maste r/DataV iz/EJN_paper_review.csv) shows that four broad types of messages are obtained from statistical parametric ORCID
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.239 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.105 | 0.026 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".