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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.
**12 of 12 cells present:** `Sem`, `Phon`, `Gram`, `Plaus` × `ses-5`, `ses-7`, `ses-9`.
Each `<Task>/session_rdm_<ses>.npz` contains:
| 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
**Do not use them for representational similarity analysis without correcting this first.**
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
```
Meanwhile **no stimulus property predicts them**: trial type ≈ −0.02, text length ≈ 0,
lexical overlap ≈ 0, in every cell.
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
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
```
That last line matters: correcting the confound does not nudge the RDM, it changes it.
**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.
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`).
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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