BrainLat (EEG modality)

Description

BrainLat is the public multimodal neuroimaging dataset released by the Latin American Brain Health Institute (BrainLat, Universidad Adolfo Ibáñez, Santiago, Chile) to address the under-representation of Latin American populations in dementia and neurodegeneration research (Prado et al., 2023). The full collection comprises 780 participants — 530 patients with neurodegenerative disease (Alzheimer’s disease, behavioral-variant frontotemporal dementia, multiple sclerosis, and Parkinson’s disease) plus 250 healthy controls — recruited through a multicentric effort across five Latin American countries. It is described as the first regional collection to combine clinical and cognitive assessments, anatomical (T1) MRI, resting-state functional MRI, diffusion-weighted MRI, and high-density resting-state EEG in dementia patients, assembled to enable affordable, scalable biomarker research in regions with larger health inequities.

Citation

• Paper DOI: 10.1038/s41597-023-02806-8
• Dataset DOI: 10.7303/syn51549340
• Reference: Prado, P., Medel, V., Gonzalez-Gomez, R., et al. (2023). The BrainLat project, a multimodal neuroimaging dataset of neurodegeneration from underrepresented backgrounds. Scientific Data 10, 889.

Catalog citation block

Prado, P., Medel, V., Gonzalez-Gomez, R., Sainz-Ballesteros, A., Vidal, V., Santamaría-García, H., Moguilner, S., Mejía, J., Slachevsky, A., Behrens, M. I., Aguillon, D., Lopera, F., Parra, M. A., Matallana, D., Maito, M. A., Garcia, A. M., Custodio, N., Funes, A. Á., Piña-Escudero, S. D., … Ibáñez, A. (2023). The BrainLat project, a multimodal neuroimaging dataset of neurodegeneration from underrepresented backgrounds. Scientific Data, 10, 889. DOI: 10.1038/s41597-023-02806-8.

Author Correction: Prado, P., et al. (2024). Scientific Data, 11, 19. DOI: 10.1038/s41597-023-02870-0. (Author-name spelling correction only.)

Dataset: BrainLat-dataset, Synapse syn51549340, DOI: 10.7303/syn51549340, CC BY 4.0.

Download and access

Field	Value
Official source	https://www.synapse.org/#!Synapse:syn51549340
Access class	registration
Size	TODO(confirm)
Format	eeglab_set
BIDS	true
Mirrors	none recorded

Access steps:

1. Register a Synapse account
2. Accept the data-use terms on the BrainLat-dataset project (syn51549340)
3. Download the EEG modality from Synapse

Source & access

The dataset is hosted on Synapse under the project “BrainLat-dataset”, Synapse ID syn51549340, DOI 10.7303/syn51549340 (Prado et al., 2023). It is distributed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license; access requires registering a Synapse account and accepting the data-use terms. The data-descriptor paper is open access in Scientific Data: 10.1038/s41597-023-02806-8 (nature.com/articles/s41597-023-02806-8, PMC10710425). A subsequent Author Correction (10.1038/s41597-023-02870-0, PMC10761837) corrected only an author-name spelling (Maria Isabel Behrens, previously “Beherens”); it did not change any data, demographics, or acquisition values.

Data structure

EEG data are organized in EEG-BIDS format (Prado et al., 2023). Original recordings were stored as Biosemi *.bdf files and converted/distributed with the *.set extension (EEGLAB), with accompanying .tsv/.csv sidecar and participant/metadata files. Distribution is via Synapse (not OpenNeuro/DataLad); no git-annex layer is involved.

License

• Name: CC-BY-4.0
• Note (verbatim from the directory): CC BY 4.0, but access requires a Synapse account and acceptance of data-use terms the site cannot pass on
• Snippets on this site: no — the site ships no snippet or precomputed product from this dataset; lessons link the official access page.

Acquisition

Field	Value
Device	128-channel Biosemi ActiveTwo, pin-type active sintered Ag-AgCl electrodes
Device class	research-cap
Channels	128
Sampling rate (Hz)	512
Online filters	0.03–100 Hz
Reference	linked-mastoid online (re-referenced to average offline in the published preprocessing)
Mains frequency (Hz)	TODO(confirm)
Paradigms	rest-ec
Participants	162 (157 EEG participants per the paper (Table 4); 162 recordings in the released EEG set)
Population	157 with EEG of 780: AD 35, bvFTD 19, PD 29, MS 32, controls 42; five Latin American countries
Clinical groups	AD, bvFTD, PD, MS, HC
Sessions	1
Durations	eyes-closed rest, ~10 min, one run per participant

Acquisition

Resting-state EEG was recorded with a single amplifier model used across all centers: a 128-channel Biosemi ActiveTwo system with pin-type active, sintered Ag-AgCl electrodes (Prado et al., 2023). Recordings used linked-mastoid online reference (re-referenced to average reference offline) with analog/online filters set at 0.03—100 Hz, and data were acquired/stored at 512 Hz. Each session was a single ~10-minute continuous run. Recordings were monitored online for drowsiness and for myogenic and sweat artifacts. Published preprocessing applied a 0.5—40 Hz digital band-pass, interpolated bad channels (3.2 ± 1.1 channels per recording on average), and used ICA to remove blink and eye-movement components.

Participants

The full multimodal dataset has 780 participants (453 female / 327 male; mean age 62.7 ± 9.5 years, range 21—89), of whom 530 are patients and 250 healthy controls (Prado et al., 2023). The resting-state EEG subset comprises 157 participants (Prado et al., 2023, Table 4): Alzheimer’s disease (AD) 35, behavioral-variant frontotemporal dementia (bvFTD) 19, Parkinson’s disease (PD) 29, multiple sclerosis (MS) 32, and healthy controls (HC) 42. Not every participant has every modality — the authors note “EEG only” cases and that coverage gaps stem from the differing original study objectives, technological constraints, and varied storage formats across the contributing centers. Diagnoses were made by expert clinicians following standard criteria for each condition.

Tasks / conditions

A single condition: ongoing, eyes-closed resting-state EEG, ~10 minutes, one run per participant (Prado et al., 2023). No task or stimulus paradigm is included in the EEG modality.

Used on this site

L7.6

Loader

Fetches only the stated subset. Recorded loader: TODO(confirm).

TODO(confirm) — the catalog records no loader for this dataset.

Caveats worth knowing

• Mains frequency differs by country within one dataset (50 Hz in Argentina, Chile, Peru; 60 Hz in Mexico, Colombia).

Notable caveats

• EEG cohort is complete. The dataset-intrinsic EEG N is 157 (Prado et al., 2023, Table 4).
• Line noise. EEG was collected across five countries; mains frequency in this region is 50 Hz (Argentina, Chile, Peru) and 60 Hz (Mexico, Colombia), so line-noise frequency is not uniform across recordings — relevant for notch choices. The descriptor does not report an explicit notch filter on the raw EEG.
• Multicentric heterogeneity. Although the same Biosemi amplifier model was used at all sites, recruitment, demographics, and ancillary protocols differ by center; treat site as a potential confound. Diagnostic-group sizes are unbalanced (HC 42, AD 35, MS 32, PD 29, bvFTD 19).
• Missing/partial modality coverage. Many participants lack one or more modalities; do not assume EEG and MRI are paired for a given subject.

Related datasets

• ERP CORE (Compendium of Open Resources and Experiments)
• EEG Motor Movement/Imagery Dataset (EEGMMIDB)
• LEMON (MPI-Leipzig Mind-Brain-Body)
• Dortmund Vital Study resting EEG
• SRM Resting-state EEG

# Loader: TODO(confirm) — the catalog records no loader for this dataset.
# Download a small subset from the official source: https://www.synapse.org/#!Synapse:syn51549340