To Smooth or not to Smooth: Enhancing Specificity While Maintaining Sensitivity
Bibliographic record
Abstract
Abstract Traditionally, when conducting voxel- or vertex-wise analyses in neuroimaging studies, it seemed imperative that brain data are convoluted with a Gaussian kernel, a procedure known as “spatial smoothing”. However, we suggest that – under certain conditions – smoothing may be omitted for the benefit of an improved regional specificity. We demonstrate the suitability of this omission by combining high-dimensional spatial registration and threshold-free cluster enhancement (TFCE) in a sample of 754 brains. Our findings revealed that, without smoothing, it is possible to capture brain atrophy within the hippocampal complex while dissociating neighboring areas (cornu ammonis, dentate gyrys, subiculum, and amygdala). In contrast, the traditional smoothing step would result in a single hippocampal cluster (the larger the smoothing kernel, the lower the specificity). Supplemental analyses not only varying the size of the smoothing kernel, but also the size of the sample, the signal-to-noise ratio, as well as the accuracy of the spatial registration confirm that no smoothing (or less smoothing) leads to increased specificity while maintaining sensitivity, at least for small-scale structures (e.g., hippocampus and amygdala). Nevertheless, classic analyses based on smoothed data will continue to provide important insights, especially for large-scale structures (e.g., cortical regions).
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".