Reproducible Brain Charts: An Open Data Resource for Mapping the Developing Brain and Mental Health
Presented During:
Friday, June 27, 2025: 11:30 AM - 12:45 PM
Brisbane Convention & Exhibition Centre
Room:
M3 (Mezzanine Level)
Poster No:
1816
Submission Type:
Abstract Submission
Authors:
Golia Shafiei1, Nathalia Esper2, Mauricio Hoffmann3, Lei Ai2, Andrew Chen4, Jon Cluce2, Sydney Covitz5, Steven Giavasis2, Connor Lane2, Kahini Mehta6, Tyler Moore1, Taylor Salo1, Tinashe Tapera7, Monica Calkins1, Stanley Colcombe8, Christos Davatzikos1, Raquel Gur1, Ruben Gur1, Pedro Pan9, Andrea Jackowski9, Ariel Rokem10, Luis Rohde11, Russell Shinohara1, Nim Tottenham6, Zuo Xi-Nian12, Matthew Cieslak1, Alexandre Franco2, Greg Kiar2, Giovanni Salum2, Michael Milham2, Theodore Satterthwaite1
Institutions:
1University of Pennsylvania, Philadelphia, PA, 2Child Mind Institute, New York, NY, 3Universidade Federal de Santa Maria, Santa Maria, Rio Grande do Sul, 4Medical University of South Carolina, Charleston, SC, 5Stanford University, Stanford, CA, 6Columbia University, New York, NY, 7Northeastern University, Boston, MA, 8Nathan Kline Institute, Orangeburg, NY, 9Federal University of São Paulo (UNIFESP), São Paulo, São Paulo, 10University of Washington, Seattle, CA, 11Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, 12Beijing Normal University, Beijing, Beijing
First Author:
Co-Author(s):
Introduction:
Major mental illnesses are increasingly understood as disorders of brain development [1]. Neuroimaging studies of brain development can help track healthy brain maturation and have the potential to identify deviations from normal development linked to psychopathology. However, large and diverse samples are required to capture reliable neurodevelopmental patterns on the population level [2]. While it is possible to aggregate data across multiple resources, data aggregation is not a straightforward process given the differences in neuroimaging and psychiatric phenotyping protocols used by independent studies. To this end, we introduce Reproducible Brain Charts (RBC), an open data resource that integrates several of the largest studies of brain development in youth.
Methods:
RBC includes data from five prominent neurodevelopmental studies from three continents (total N=6,346): BHRC (n=610), CCNP (n=195), HBN (n=2611), NKI (n=1329), and PNC (n=1601). Item-response theory and bifactor modeling were used to create harmonized measures of psychiatric phenotypes that capture major dimensions of psychopathology [3], including a general psychopathology factor (i.e., p-factor) as well as domain-specific factors [4]. Neuroimaging data were curated with CuBIDS [5], summarizing heterogeneity in data acquisition and facilitating metadata-based quality control (QC). Structural Magnetic Resonance Imaging (MRI) data were processed using FreeSurfer and sMRIPrep. Functional MRI (fMRI) data (resting-state and task) were processed using C-PAC [6, 7]. The "FAIRly-big" strategy [8] was adapted for reproducible image processing, ensuring that all processing steps were fully tracked via DataLad [9]. An extensive array of QC metrics and specific inclusion criteria were generated for neuroimaging data to ensure consistent quality assurance procedures.
To illustrate the utility of the aggregated RBC data, we examined: i) how imaging features were related to participant age and overall psychopathology; and ii) whether implementing RBC's QC guidelines and statistical harmonization of imaging data with CovBat-GAM [10] impacted the results. We used Generalized Additive Models (GAMs) to assess the relationship between imaging and phenotypic features, controlling for covariates such as sex and data quality. Specifically, we evaluated cortical thickness (CT) and surface area (SA) from sMRI data and examined between- and within-network connectivity from fMRI data.
To illustrate the utility of the aggregated RBC data, we examined: i) how imaging features were related to participant age and overall psychopathology; and ii) whether implementing RBC's QC guidelines and statistical harmonization of imaging data with CovBat-GAM [10] impacted the results. We used Generalized Additive Models (GAMs) to assess the relationship between imaging and phenotypic features, controlling for covariates such as sex and data quality. Specifically, we evaluated cortical thickness (CT) and surface area (SA) from sMRI data and examined between- and within-network connectivity from fMRI data.
Results:
All RBC data – including harmonized psychiatric phenotypes, unprocessed neuroimaging data, and fully processed imaging derivatives – are publicly shared without a data use agreement via the International Neuroimaging Data-sharing Initiative. The majority of participants included in RBC were 5 to 23 years old, with a relatively balanced sex distribution (45% female) (Fig. 1a). Overall, approximately 90% of imaging data passed both structural and functional QC criteria (Fig. 1b). Our initial analysis of combined RBC data without QC or statistical harmonization identified developmental effects that varied markedly between studies. However, QC and statistical harmonization effectively removed variability among datasets while retaining heterogeneity due to interindividual variations (Fig. 2a). In line with prior research, we found that CT decreased with age (p<0.0001) and SA decreased with increasing psychopathology (p<0.0001). Moreover, between-network connectivity decreased while within-network connectivity increased during development (Fig. 2b). Greater functional connectivity between the default mode and frontoparietal networks was associated with higher psychopathology (FDR-corrected p=0.001), suggesting a loss of segregation between those two networks.
Conclusions:
Taken together, RBC facilitates large-scale, robust, and reproducible research in developmental and psychiatric neuroscience. By reducing barriers to large-scale data access, RBC promotes equity and collaboration in developmental and translational brain research.
Lifespan Development:
Normal Brain Development: Fetus to Adolescence
Lifespan Development Other 2
Neuroinformatics and Data Sharing:
Databasing and Data Sharing 1
Novel Imaging Acquisition Methods:
Anatomical MRI
BOLD fMRI
Keywords:
Data analysis
Data Organization
Development
FUNCTIONAL MRI
Open Data
Open-Source Code
Psychiatric
Statistical Methods
STRUCTURAL MRI
Other - Big data
1|2Indicates the priority used for review
Abstract Information
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[2] Marek, S., Tervo-Clemmens, B., Calabro, F. J., Montez, D. F., Kay, B. P., ... & Dosenbach, N. U. (2022). Reproducible brain-wide association studies require thousands of individuals. Nature, 603(7902), 654-660.
[3] Hoffmann, M. S., Moore, T. M., Axelrud, L. K., Tottenham, N., Pan, P. M., ... & Salum, G. A. (2024). An Evaluation of Item Harmonization Strategies Between Assessment Tools of Psychopathology in Children and Adolescents. Assessment, 31(2), 502-517.
[4] Hoffmann, M. S., Moore, T. M., Axelrud, L. K., Tottenham, N., Rohde, L. A., ... & Salum, G. A. (2023). Harmonizing bifactor models of psychopathology between distinct assessment instruments: Reliability, measurement invariance, and authenticity. International Journal of Methods in Psychiatric Research, 32(3), e1959.
[5] Covitz, S., Tapera, T. M., Adebimpe, A., Alexander-Bloch, A. F., Bertolero, M. A., ... & Satterthwaite, T. D. (2022). Curation of BIDS (CuBIDS): A workflow and software package for streamlining reproducible curation of large BIDS datasets. NeuroImage, 263, 119609.
[6] Craddock, C., Sikka, S., Cheung, B., Khanuja, R., Ghosh, S., ... & Milham, M. (2013). Towards automated analysis of connectomes: The configurable pipeline for the analysis of connectomes (C-PAC). Frontiers in Neuroinformatics, 7.
[7] Li, X., Esper, N. B., Ai, L., Giavasis, S., Jin, H., ... & Milham, M. P. (2021). Moving beyond processing and analysis-related variation in neuroscience. BioRxiv, 2021-12.
[8] Wagner, A. S., Waite, L. K., Wierzba, M., Hoffstaedter, F., Waite, A. Q., ... & Hanke, M. (2022). FAIRly big: A framework for computationally reproducible processing of large-scale data. Scientific Data, 9(1).
[9] Halchenko, Y., Meyer, K., Poldrack, B., Solanky, D., Wagner, A., … & Hanke, M. (2021). DataLad: Distributed system for joint management of code, data, and their relationship. Journal of Open Source Software, 6(63), 3262.
[10] Chen, A. A., Beer, J. C., Tustison, N. J., Cook, P. A., Shinohara, R. T., Shou, H., & Alzheimer's Disease Neuroimaging Initiative. (2022). Mitigating site effects in covariance for machine learning in neuroimaging data. Human brain mapping, 43(4), 1179-1195.