Authors
Ileana Quiñones (a,b,c*†), Amaia Carrión-Castillo (a,c*†), Iñigo Diez (b†), Laura de Frutos-Sagastuy (a,d), Brendan Costello (a,c), David Carcedo (a), Lucía Manso-Ortega (a,d), Maksim Slivka (e), Abraham Sánchez (a,d), Anique Schüller (a,d), Pedro M. Paz Alonso (a,c), César Caballero-Gaudes (a,c), Manuel Carreiras (a,c,d)
Affiliations
a. Basque Center on Cognition, Brain, and Language (BCBL),20009 Donostia-San Sebastian, Spain
b. Biogipuzkoa Health Research Institute, 20014 Donostia-San Sebastian, Spain
c. IKERBASQUE. Basque Foundation for Science, 48009 Bilbao, Spain
d. University of the Basque Country, UPV/EHU, 48940 Bilbao, Spain
e. Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0313 Oslo, Norway
† Equal contribution
* Correspondence: i.quinones@bcbl.eu, a.carrion@bcbl.eu
Abstract
The neural mechanisms underlying language processing involve a well-defined brain network, including mainly left perisylvian areas. Yet, the extent of individual variability remains largely unexplored, particularly in bilingual and multilingual contexts. Differences in linguistic profiles (e.g., age of acquisition, exposure, proficiency) provide an opportunity to assess how network topology is shaped by sociolinguistic factors. To address this, we developed NEUROLINGUA, a comprehensive database of functional and structural MRI data, enriched with sociodemographic, sociolinguistic, and behavioral information. It includes 725 healthy individuals aged 18–82 immersed in a Basque-Spanish multilingual environment, ranging from near-monolinguals to highly proficient multilinguals. Participants completed a functional MRI language localizer with both auditory and visual comprehension tasks, enabling cross-modal comparisons, as well as sentences involving arithmetic problem-solving. Exploratory analyses confirmed associations between structural MRI, sociodemographic, and cognitive measures. We demonstrate that NEUROLINGUA’s functional MRI data localize the language comprehension network and thus capture linguistic profile effects. This integrative dataset offers an unparalleled resource to investigate factors influencing language network adaptability and variability in diverse sociolinguistic contexts.
Usage Notes
The dataset featured in this preprint, complete with downloadable materials and codes accessible at NeuroLingua folder on the
OpenNeuro platform, is freely available for use by academic researchers, institutions, and entities, on the condition that proper attribution is given to this work. Access to original T1-weighted images is not part of the standard OpenNeuro release and requires a separate request, scientific justification, and acceptance of a specific Data Use Agreement provided by BCBL.
It is important to note that the authors disclaim any responsibility for the appropriate or inappropriate use in clinical or other contexts. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote commercial products derived from this dataset without specific prior written permission.
Important notice regarding T1-weighted images
The vast majority of research applications can be conducted using the derivatives already provided through OpenNeuro. Therefore, researchers are strongly encouraged to use the available derivatives whenever possible.
Requests for access to the original T1-weighted images should only be submitted in exceptional circumstances, when the proposed analyses require information that is not available in the released derivatives and cannot reasonably be obtained from them.
All requests will be evaluated individually, and access may be denied if the scientific rationale is insufficiently justified.
Examples of analyses that typically do not require access to the original T1-weighted images include:
- Morphometric analyses based on the released CAT12 derivatives.
- Regional volumetric analyses.
- Cortical thickness analyses.
- ROI-based structural analyses.
- Most machine-learning applications using the provided structural features.
Requests for original T1-weighted images should be limited to analyses requiring alternative preprocessing pipelines, novel segmentation approaches, advanced image reconstruction methods, or other methodological developments that cannot be performed using the shared derivatives.
