Datasets¶

Simultaneous neural activity and cerebral blood volume recordings in awake mice, from Nunez-Elizalde et al. (2022)¹, converted to fUSI-BIDS format and hosted in the S3 collection. Total size: ~7 GB.

Use fetch_nunez_elizalde_2022 to download the dataset. Four filters narrow the download:

Angiography files are always included regardless of the tasks/acqs filters, since they are useful as anatomical references.

from confusius.datasets import fetch_nunez_elizalde_2022

# Two subjects, one task, one slice.
bids_root = fetch_nunez_elizalde_2022(
    subjects=["CR017", "CR020"],
    tasks=["spontaneous"],
    acqs=["slice03"],
)

Functional ultrasound imaging data from a rat model of neuroinflammation, from Pereira et al. (2025)⁴, re-exported to fUSI-BIDS format and hosted on the S3 collection. Total size: ~30 GB.

Use fetch_pereira_2025 to download the dataset. Three filters narrow the download:

from confusius.datasets import fetch_pereira_2025

bids_root = fetch_pereira_2025(
    subjects="r11582",
    sessions="awakebaseline",
    tasks="rest",
)

Functional ultrasound imaging data from freely-moving rats investigating vascular coding of speed in the spatial navigation system, from Cybis Pereira et al. (2026)², hosted in the S3 collection. Total size: ~13 GB.

Use fetch_cybis_pereira_2026 to download the dataset. Four filters narrow the download:

Files that lack a session or acquisition entity (e.g. subject-level statmaps in glm-speed, or decode-speed aggregates) are kept regardless of the sessions / acqs filters, since they aggregate across those entities.

The datasets filter accepts:

Derivatives are small relative to rawdata, so fetching a single derivative across all subjects is often the right first step:

from confusius.datasets import fetch_cybis_pereira_2026

# All subjects, GLM-speed derivative only.
bids_root = fetch_cybis_pereira_2026(datasets="glm-speed")

# Or raw data for a single subject, session, and acquisition slice.
bids_root = fetch_cybis_pereira_2026(
    datasets="rawdata",
    subjects="rat73",
    sessions="20220523",
    acqs="slice32",
)

Transcranial mouse resting-state fUSI recordings and derivatives, from Pepe, Mariani et al. (2026)⁷, re-exported to fUSI-BIDS format and hosted in the S3 collection. Total size: ~38 GB.

Use fetch_pepe_mariani_2026 to download the dataset. Five filters narrow the download:

The datasets filter accepts "rawdata", "registered", "preprocessed", and "Params".

from confusius.datasets import fetch_pepe_mariani_2026

# Registered fUSI derivative only.
bids_root = fetch_pepe_mariani_2026(datasets="registered", datatypes="fusi")

Functional ultrasound imaging recordings from awake, head-fixed, freely-running mice, from Landemard et al. (2026)³, re-exported to fUSI-BIDS format and hosted on the S3 collection. Total size: ~42 GB.

Use fetch_landemard_2026 to download the dataset. Four filters narrow the download:

The Landemard dataset has no session layer: every recording sits directly under sub-*/fusi/ or sub-*/susi/. Files that lack an acq- entity (e.g. sub-ALD001_scans.tsv, sub-ALD001/susi/sub-ALD001_pwd.nii.gz) are always included regardless of the acqs filter.

The datasets filter accepts:

from confusius.datasets import fetch_landemard_2026

# All subjects, atlas_mapping derivative only.
bids_root = fetch_landemard_2026(datasets="atlas_mapping")

# Or raw fUSI data for a single subject, with a single acquisition.
bids_root = fetch_landemard_2026(
    datasets="rawdata",
    subjects="ALD001",
    acqs="ref04",
    datatypes="fusi",
)

Functional ultrasound imaging data from naked mole-rats exposed to olfactory stimulation, from Khallaf et al. (2026)⁵, organised following BIDS and the proposed fUSI extension BEP-040. The original data is published on Edmond (10.17617/3.7QCU1F); ConfUSIus downloads individual files from the S3 collection rather than the source archive.

Use fetch_khallaf_2026 to download the dataset. Six filters narrow the download:

Files that lack a subject, session, or run entity (e.g. participants.tsv, or group-level GLM maps) are kept regardless of those filters, since they aggregate across entities. subjects and runs also accept integers (subjects=5622).

The datasets filter accepts:

The reconstruction filter applies only to rawdata fusi/ volumes: "raw" keeps the unregistered volumes (no rec- entity), "resampled" keeps the registered rec-resampled ones, and "both" (the default) keeps each.

The raw Iconeus acquisitions under sourcedata/ (~7.8 GB) are excluded by default. Set sourcedata=True to include the whole tree; it is never entity-filtered.

from confusius.datasets import fetch_khallaf_2026

# All subjects, GLM derivative only.
bids_root = fetch_khallaf_2026(datasets="glm")

# Or the resampled raw data for a single subject, session, and run.
bids_root = fetch_khallaf_2026(
    datasets="rawdata",
    subjects="5622",
    sessions="IPM",
    runs="1",
    reconstruction="resampled",
)

A vascular mouse fUSI template derived from Huang et al. (2025)⁶ and distributed as a NIfTI with its JSON sidecar in the S3 collection. Total size: ~16.3 MB.

Use fetch_template_huang_2025 to download and load the template directly. The Huang 2025 template is already resampled to the Allen space:

from confusius.datasets import fetch_brainglobe_atlas, fetch_template_huang_2025

template = fetch_template_huang_2025()
atlas = fetch_brainglobe_atlas("allen_mouse_50um")

A mouse fUSI template derived from Pepe, Mariani et al. (2026)⁷ and distributed as a NIfTI with its JSON sidecar in the S3 collection. Total size: ~5.5 MB.

Use fetch_template_pepe_mariani_2026 to download and load the template directly. Its native voxel grid and scanner-space qform are retained for registration. The sform encodes the full Allen atlas alignment, including shear. Use world_to_sform to resample the atlas onto the template's native grid:

from confusius.datasets import fetch_brainglobe_atlas, fetch_template_pepe_mariani_2026

template = fetch_template_pepe_mariani_2026()
atlas = fetch_brainglobe_atlas("allen_mouse_100um")
resampled_atlas = atlas.atlas.resample_like(
    template,
    template.attrs["affines"]["world_to_sform"],
)