Preprint Figure Reproduction

This directory contains the evaluation notebooks used to reproduce figures in NeuroVLM preprint v3: https://www.biorxiv.org/content/10.64898/2026.02.06.704508v3

The canonical pipeline is 02_data/ -> 03_models/ -> figures/. Data and model notebooks remain in their upstream directories so that each notebook has one canonical location.

Core dependencies

• Data preprocessing: ../02_data/

• Model training and model analyses: ../03_models/

• Installation and data access: ../installation.md

Most evaluation notebooks load released datasets, embeddings, and pretrained models through the neurovlm package. Rerun upstream preprocessing or training only when the required artifact is unavailable.

Figure map

Figure	Canonical notebook	Purpose and dependency notes
Fig. 2	../03_models/14_qformer_anat.ipynb	Anatomical Query-Former model analysis; retained with the canonical model notebooks.
Fig. 3	12_neurovault_decoding.ipynb	NeuroVault text-brain decoding examples and cached generated descriptions.
Fig. 4	20_pubmed_cv.ipynb	PubMed cross-validation, retrieval evaluation, and training-run outputs.
Fig. 4	22_text_to_brain_metrics.ipynb	Text-to-brain generation metrics; currently missing from the repository.
Fig. 4	24_brain_to_text_pubmed.ipynb	Brain-to-text PubMed generation and metric summaries.
Fig. 5	11_autoencoder.ipynb	Autoencoder reconstruction metrics for PubMed and NeuroVault data.
Fig. 5	13_network_labeling.ipynb	Network-label confusion and one-vs-rest evaluation.
Fig. 6	16_qualatative_auto.ipynb	Qualitative autoencoder reconstructions across network, PubMed, and NeuroVault inputs.
Fig. S2	../02_data/01_coordinate.ipynb	Coordinate smoothing and DiFuMo projection; retained with canonical data preprocessing.
Fig. S3	23_versus_others.ipynb	Recall comparison with external NiCLIP, NeuroConText, and language-model baselines.
Fig. S4	19_ica_networks.ipynb	ICA network labeling analyses for HCP and UK Biobank maps.

The table records notebook-level provenance. It does not assign individual panels where the notebook does not identify them explicitly.

Recommended execution order

1. Install NeuroVLM and make the packaged or downloaded data available.

2. Run ../02_data/01_coordinate.ipynb only if the coordinate-derived artifacts needed for Fig. S2 are missing.

3. Load released model artifacts; run ../03_models/14_qformer_anat.ipynb when reproducing the Fig. 2 model analysis.

4. Run the notebooks in this directory for the desired figures.

5. Collect generated caches, metric tables, and figures from docs/figures/outputs/ when running from the repository root, or from a local outputs/ directory when running inside docs/figures/.

20_pubmed_cv.ipynb performs cross-validation training and is substantially more expensive than notebooks that only load released artifacts. 19_ica_networks.ipynb and 24_brain_to_text_pubmed.ipynb currently select CUDA explicitly. The decoding, generation, and external-baseline notebooks may also require a GPU, model downloads, or separately installed baseline projects.

Missing or external dependencies

TODO: Fig. 4 references 22_text_to_brain_metrics.ipynb, but that notebook was not found anywhere in the repository during this refactor. It must be recovered from the original analysis source before Fig. 4 is fully reproducible from this tree.

23_versus_others.ipynb expects external NiCLIP and NeuroConText resources; their locations can be configured with the environment variables documented in that notebook. Some generation notebooks also cache outputs from large language models rather than retraining those models from scratch.