Benchmarking common preprocessing strategies in early childhood functional connectivity and intersubject correlation fMRI
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Bibliographic record
Abstract
Preprocessing choices present a particular challenge for researchers working with functional magnetic resonance imaging (fMRI) data from young children. Steps which have been shown to be important for mitigating head motion, such as censoring and global signal regression (GSR), remain controversial, and benchmarking studies comparing preprocessing pipelines have been conducted using resting data from older participants who tend to move less than young children. Here, we conducted benchmarking of fMRI preprocessing steps in a population with high head-motion, children aged 4-8 years, leveraging a unique longitudinal, passive viewing fMRI dataset. We systematically investigated combinations of global signal regression (GSR), volume censoring, and ICA-AROMA. Pipelines were compared using previously established metrics of noise removal as well as metrics sensitive to recovery of individual differences (i.e., connectome fingerprinting), and stimulus-evoked responses (i.e., intersubject correlations; ISC). We found that: 1) the most efficacious pipeline for both noise removal and information recovery included censoring, GSR, bandpass filtering, and head motion parameter (HMP) regression, 2) ICA-AROMA performed similarly to HMP regression and did not obviate the need for censoring, 3) GSR had a minimal impact on connectome fingerprinting but improved ISC, and 4) the strictest censoring approaches reduced motion correlated edges but negatively impacted identifiability.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it