NeuroVLM

NeuroVLM#

NeuroVLM maps between neuroimaging activation maps and neuroscience text.

Installation

Install the package and run a minimal quickstart.

Installation
Tutorials

Step-by-step notebooks to learn the workflow.

Tutorials
API Reference

Function and class reference for the public package API.

API Documentation

Install#

Minimal, inference-only installation:

pip install neurovlm

With optional dependencies needed to train and reproduce analyses:

pip install "neurovlm[full]"

Quickstart#

Fetch NeuroVLM’s datasets:

from neurovlm.data import fetch_data
fetch_data()

Use the NeuroVLM object for text-to-brain and brain-to-text:

from neurovlm import NeuroVLM
from neurovlm.data import load_latent

# Text-to-brain: generation
nvlm = NeuroVLM()
result = nvlm.text(["vision", "default mode network"]).to_brain(head="mse")
result.to_nifti() # returns list of nib.Nifti1Image
result.plot(0, threshold=0.25) # plot image for vision
result.plot(1, threshold=0.15) # plot image for DMN

# Text-to-brain: ranking and retrieval
nvlm = NeuroVLM()
result = nvlm.text("motor").to_brain(head='infonce')
top = result.top_k(2) # each row pairs to a neuorimage that is similar to the text query
top.plot_row(1, threshold=0.1) # WashU atlas
top.plot_row(2, threshold=2.5) # NeuroVault
top.plot_row(4, threshold=0.1) # PubMed

# Brain-to-text: ranking and retrieval
nvlm = NeuroVLM()
result = nvlm.brain(load_latent("networks_neuro")["Du"]["AUD"]).to_text()
result.top_k(5).query("cosine_similarity > 0.4") # return up to 5 examples per dataset

Documentation#

See the docs for the API and tutorials.

License#

Apache-2.0 (LICENSE).

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