Adverse effects of template-based warping on spatial fMRI analysis
Bibliographic record
Abstract
Conventional voxel-based group analysis of functional magnetic resonance imaging (fMRI) data typically requires warping each subject's brain images onto a common template to create an assumed voxel correspondence. The implicit assumption is that aligning the anatomical structures would correspondingly align the functional regions of the subjects. However, due to anatomical and functional inter-subject variability, mis-registration often occurs. Moreover, wholebrain warping is likely to distort the spatial patterns of activation, which have been shown to be important markers of task-related activation. To reduce the amount of mis-registration and distortions, warping at the brain region level has recently been proposed. In this paper, we investigate the effects of both whole-brain and region-level warping on the spatial patterns of activation statistics within certain regions of interests (ROIs). We have chosen to examine the bilateral thalami and cerebellar hemispheres during a bulb-squeezing experiment, as these regions are expected to incur taskrelated activation changes. Furthermore, the appreciable size difference between the thalamus and cerebellum allows for exploring the effects of warping on various ROI sizes. By applying our recently proposed 3D moment-based invariant spatial features to characterize the spatial pattern of fMRI activation statistics, we demonstrate that whole-brain warping generally reduced discriminability of task-related activation differences. Applying the same spatial analysis to ROIs warped at the region level showed some improvements over whole-brain warping, but warp-free analysis resulted in the best performance. We hence suggest that spatial analysis of fMRI data that includes spatial warping to a common space must be interpreted with caution.
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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 | 0.021 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".