| --- |
| license: cc-by-4.0 |
| tags: |
| - fmri |
| - neuroscience |
| - rdm |
| - rsa |
| --- |
| |
| # ds003604 session RDMs |
|
|
| Brain representational dissimilarity matrices for the **ds003604** auditory language fMRI |
| dataset, one per (task × session), so the fMRI preprocessing never has to be repeated. |
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| **12 of 12 cells present:** `Sem`, `Phon`, `Gram`, `Plaus` × `ses-5`, `ses-7`, `ses-9`. |
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| Each `<Task>/session_rdm_<ses>.npz` contains: |
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|
| | key | contents | |
| |---|---| |
| | `rdm` | the RDM, `(n_stim, n_stim)`, correlation distance. 72×72 for Sem/Phon, 60×60 for Gram/Plaus (perceptual-control trials excluded) | |
| | `stimuli` | stimulus **filenames**, in RDM row/column order | |
| | `stimulus_texts` | the stimulus **text**, parallel to `stimuli` — use this if you are feeding a language model, not `stimuli` | |
| | `trial_types` | condition code per stimulus (e.g. `S_H`, `P_R`, `G_G`, `SP_S`) | |
| | `semantic_categories` | coarse category per stimulus | |
| | `n_subjects`, `subject_ids` | cohort behind the RDM (38–98 depending on cell) | |
| | `metric`, `aggregation` | `correlation`, and how subjects were combined | |
|
|
| --- |
|
|
| ## ⚠️ These RDMs are dominated by a scanner-run confound |
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| **Do not use them for representational similarity analysis without correcting this first.** |
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|
| In ds003604 **each stimulus is presented in exactly one scanner run** (for Phon, run-01 |
| carries 48 of the 96 stimuli and run-02 the other 48). Run membership is therefore |
| perfectly confounded with stimulus identity, and every cross-run stimulus pair also |
| differs by that run's drift, baseline and scaling. Measured on these files: |
|
|
| ``` |
| "different run" predicts brain dissimilarity, Spearman rho: |
| Gram ses-5 +0.866 ses-7 +0.812 ses-9 +0.828 |
| Plaus ses-5 +0.741 ses-7 +0.689 ses-9 +0.761 |
| Phon ses-5 +0.562 ses-7 +0.488 ses-9 +0.624 |
| Sem ses-5 +0.511 ses-7 +0.510 ses-9 +0.601 |
| ``` |
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| Meanwhile **no stimulus property predicts them**: trial type ≈ −0.02, text length ≈ 0, |
| lexical overlap ≈ 0, in every cell. |
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| These RDMs correlate across independent subject cohorts at **rho 0.74–0.92** (different |
| sessions of the same task), which looks like excellent reliability — but run assignment is |
| fixed by the protocol and so repeats identically for every subject, meaning much of that |
| reliability is the reliability of an acquisition artefact rather than of brain |
| representation. |
|
|
| ### The fix |
|
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| Z-score each voxel **within run** before combining runs. Verified on Phon/ses-5 rebuilt |
| from 28 subjects' raw patterns: |
|
|
| ``` |
| standard RDM vs run model: rho = +0.562 |
| within-run-normed vs run model: rho = -0.041 <- confound removed |
| standard vs normed : rho = +0.287 <- they are largely different RDMs |
| ``` |
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| That last line matters: correcting the confound does not nudge the RDM, it changes it. |
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| **Cohort size is not the issue** — Sem/ses-7 rebuilt from 40 to 98 subjects agrees with |
| the 40-subject version at rho 0.928. |
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| After correction, alignment between these RDMs and a trained language model peaks at |
| **+0.022** (early layers, not significant), with a sensible layer profile but ≈0 |
| magnitude. Evidence and scripts: |
| [`BrainAlign/cdl-devai-results`](https://huggingface.co/datasets/BrainAlign/cdl-devai-results) |
| (`diagnostics_run_confound`, `diagnostics_layerwise`) and |
| [`suchirsalhan/cdl-representations-brains-babylms`](https://github.com/suchirsalhan/cdl-representations-brains-babylms) |
| (`scripts/test_within_run_norm.py`, `scripts/run_confound_check.py`). |
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| The files here are published **uncorrected**, as produced by the pipeline, so that the |
| correction is an explicit and visible choice rather than something baked in silently. |
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|
