Abstract
The Individual Brain Charting project focuses on collecting functional Magnetic Resonance Imaging data across a large set of cognitive tasks from a fixed cohort of participants, within a standardized environment. This approach seeks to obtain refined cognitive phenotyping of individual brains, uncovering details of their functional organization. We present an extension to the dataset, integrating data from eleven participants obtained at 3T, from a fixed environment to minimize inter-site and inter-subject variability. This release further enriches the cumulative coverage of psychological domains, while introducing new concepts. It includes tasks on mathematical processing, spatial navigation, emotion recognition and memory, proactive control, oddball detection, reward processing, reaction time, biological motion perception, gambling, scene processing and working memory. In total, 18 tasks with 180 contrasts were added, and 54 cognitive components were included in the description of the ensuing contrasts. As the dataset becomes larger, the collection of the corresponding topographies becomes more comprehensive, leading to enhanced brain-atlasing frameworks. Aligned with open-access and data-sharing standards, this dataset emphasizes transparency and collaborative research.
Similar content being viewed by others
Data availability
The IBC dataset is publicly available through the EBRAINS Knowledge Graph platform71,72,73,74. As of this release, the IBC project has incorporated 67 tasks, encompassing over 340 unique conditions and 530 independent contrasts, resulting in approximately 40 hours of fMRI data per participant. Data collection concluded in Fall 2023, with the final data release anticipated by the end of 2026. This forthcoming release aims to expand the dataset to 50 hours of fMRI data per participant, featuring 80 diverse tasks and over 700 independent contrasts. It will include new paradigms focusing on color perception, abstraction, tactile stimulation, and video gaming, among other cognitive domains.
Code availability
We have developed three main repositories: one for the protocols’ routines, one for the data processing code, and one for a tool to access the data. Users are encouraged to open issues for any questions related to the code.
References
Naselaris, T., Allen, E. & Kay, K. Extensive sampling for complete models of individual brains. Current Opinion in Behavioral Sciences 40, 45–51, https://doi.org/10.1016/j.cobeha.2020.12.008 (2021).
Wang, M.-Y., Korbmacher, M., Eikeland, R. & Specht, K. Deep brain imaging of three participants across 1 year: The Bergen breakfast scanning club project. Frontiers in Human Neuroscience, 16, https://doi.org/10.3389/fnhum.2022.1021503 (2022).
Elam, J. S. et al. The Human Connectome Project: A retrospective. NeuroImage 244, 118543, https://doi.org/10.1016/j.neuroimage.2021.118543 (2021).
Varoquaux, G. et al. Atlases of cognition with large-scale brain mapping. PLoS Computational Biology, 14(11) https://doi.org/10.1371/journal.pcbi.1006565 (2018).
Gurevitch, J., Koricheva, J., Nakagawa, S. & Stewart, G. Meta-analysis and the science of research synthesis. Nature 555, 175–182, https://doi.org/10.1038/nature25753 (2018).
Müller, V. I. et al. Ten simple rules for neuroimaging meta-analysis. Neuroscience and Biobehavioral Reviews 84, 151–161, https://doi.org/10.1016/j.neubiorev.2017.11.012 (2018).
Poldrack, R. A. et al. Long-term neural and physiological phenotyping of a single human. Nature Communications 6, 8885, https://doi.org/10.1038/ncomms9885 (2015).
Pinho, A. L. et al. Subject-specific segregation of functional territories based on deep phenotyping. Human Brain Mapping 42, 841–870, https://doi.org/10.1002/hbm.25189 (2021).
Nee, D. E. fMRI replicability depends upon sufficient individual-level data. Communications Biology 2, 1–4, https://doi.org/10.1038/s42003-018-0073-z (2019).
Michon, K. J., Khammash, D., Simmonite, M., Hamlin, A. M. & Polk, T. A. Person-specific and precision neuroimaging: Current methods and future directions. NeuroImage 263, 119589, https://doi.org/10.1016/j.neuroimage.2022.119589 (2022).
Pinho, A. L. et al. Individual Brain Charting, a high-resolution fMRI dataset for cognitive mapping. Scientific Data 5, 180105, https://doi.org/10.1038/sdata.2018.105 (2018).
Pinho, A. L. et al. Individual Brain Charting dataset extension, second release of high-resolution fMRI data for cognitive mapping. Scientific Data, 7(1). https://doi.org/10.1038/s41597-020-00670-4 (2020).
Pinel, P. et al. Fast reproducible identification and large-scale databasing of individual functional cognitive networks. BMC Neuroscience 8, 91, https://doi.org/10.1186/1471-2202-8-91 (2007).
Barch, D. M. et al. Function in the human connectome: Task-fMRI and individual differences in behavior. NeuroImage 80, 169–189, https://doi.org/10.1016/j.neuroimage.2013.05.033 (2013).
Humphries, C., Binder, J. R., Medler, D. A. & Liebenthal, E. Syntactic and semantic modulation of neural activity during auditory sentence comprehension. Journal of Cognitive Neuroscience 18, 665–679, https://doi.org/10.1162/jocn.2006.18.4.665 (2006).
Binder, J. R. et al. Mapping anterior temporal lobe language areas with fMRI: A multicenter normative study. NeuroImage 54, 1465–1475, https://doi.org/10.1016/j.neuroimage.2010.09.048 (2011).
Gauthier, B. & van Wassenhove, V. Cognitive mapping in mental time travel and mental space navigation. Cognition 154, 55–68, https://doi.org/10.1016/j.cognition.2016.05.015 (2016).
Gauthier, B. & van Wassenhove, V. Time is not space: Core computations and domain-specific networks for mental travels. Journal of Neuroscience 36, 11891–11903, https://doi.org/10.1523/JNEUROSCI.1400-16.2016 (2016).
Gauthier, B., Pestke, K. & van Wassenhove, V. Building the arrow of time………over time: A sequence of brain activity mapping imagined events in time and space. Cerebral Cortex 29, 4398–4414, https://doi.org/10.1093/cercor/bhy320 (2018).
Lebreton, M., Abitbol, R., Daunizeau, J. & Pessiglione, M. Automatic integration of confidence in the brain valuation signal. Nature Neuroscience 18, 1159–1167, https://doi.org/10.1038/nn.4064 (2015).
Dodell-Feder, D., Koster-Hale, J., Bedny, M. & Saxe, R. fMRI item analysis in a theory of mind task. NeuroImage 55, 705–712, https://doi.org/10.1016/j.neuroimage.2010.12.040 (2011).
Jacoby, N., Bruneau, E., Koster-Hale, J. & Saxe, R. Localizing Pain Matrix and Theory of Mind networks with both verbal and non-verbal stimuli. NeuroImage 126, 39–48, https://doi.org/10.1016/j.neuroimage.2015.11.025 (2016).
Genon, S. et al. Cognitive and neuroimaging evidence of impaired interaction between self and memory in Alzheimer’s disease. Cortex 51, 11–24, https://doi.org/10.1016/j.cortex.2013.06.009 (2014).
Pinho, A. L. et al. Individual Brain Charting third release, probing brain activity during movie watching and retinotopic mapping. Scientific Data 11, 590, https://doi.org/10.1038/s41597-024-03390-1 (2024).
Nishimoto, S. et al. Reconstructing visual experiences from brain activity evoked by natural movies. Current Biology 21, 1641–1646, https://doi.org/10.1016/j.cub.2011.08.031 (2011).
Sereno, M. I. et al. Borders of multiple visual areas in humans revealed by functional magnetic resonance imaging. Science 268, 889–893, https://doi.org/10.1126/science.7754376 (1995).
Haxby, J. V. et al. A common, high-dimensional model of the representational space in human ventral temporal cortex. Neuron 72, 404–416, https://doi.org/10.1016/j.neuron.2011.08.026 (2011).
Eisenberg, I. W. et al. Applying novel technologies and methods to inform the ontology of self-regulation. Behav. Res. Ther 101, 46–57, https://doi.org/10.1016/j.brat.2017.09.014 (2017).
Santoro, R. et al. Reconstructing the spectrotemporal modulations of real-life sounds from fMRI response patterns. Proc. Natl. Acad. Sci. USA 114, 4799–4804, https://doi.org/10.1073/pnas.1617622114 (2017).
Perrone-Bertolotti, M. et al. How silent is silent reading? Intracerebral evidence for top-down activation of temporal voice areas during reading. J. Neurosci 32, 17554–17562, https://doi.org/10.1523/JNEUROSCI.2982-12.2012 (2012).
Vidal, J. R. et al. Category-specific visual responses: an intracranial study comparing gamma, beta, alpha, and ERP response selectivity. Front. Hum. Neurosci 4, 195, https://doi.org/10.3389/fnhum.2010.00195 (2010).
Saignavongs, M. et al. Neural activity elicited by a cognitive task can be detected in single-trials with simultaneous intracerebral EEG-fMRI recordings. Int. J. Neural Syst 27, 1750001, https://doi.org/10.1142/S0129065717500010 (2017).
Hamamé, C. M. et al. Reading the mind’s eye: online detection of visuo-spatial working memory and visual imagery in the inferior temporal lobe. Neuroimage 59, 872–879, https://doi.org/10.1016/j.neuroimage.2011.07.087 (2012).
Ossandón, T. et al. Efficient “pop-out” visual search elicits sustained broadband gamma activity in the dorsal attention network. J. Neurosci 32, 3414–3421, https://doi.org/10.1523/JNEUROSCI.6048-11.2012 (2012).
Amalric, M. & Dehaene, S. Origins of the brain networks for advanced mathematics in expert mathematicians. Proc. Natl. Acad. Sci. USA 113, 4909–4917, https://doi.org/10.1073/pnas.1603205113 (2016).
Diersch, N., Valdes-Herrera, J. P., Tempelmann, C. & Wolbers, T. Increased hippocampal excitability and altered learning dynamics mediate cognitive mapping deficits in human aging. J. Neurosci 41, 3204–3221, https://doi.org/10.1523/JNEUROSCI.0528-20.2021 (2021).
Shafto, M. A. et al. The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study protocol: a cross-sectional, lifespan, multidisciplinary examination of healthy cognitive ageing. BMC Neurol 14, 204, https://doi.org/10.1186/s12883-014-0204-1 (2014).
O’Doherty, J., Kringelbach, M. L., Rolls, E. T., Hornak, J. & Andrews, C. Abstract reward and punishment representations in the human orbitofrontal cortex. Nat. Neurosci 4, 95–102, https://doi.org/10.1038/82959 (2001).
Botvinik-Nezer, R. et al. fMRI data of mixed gambles from the Neuroimaging Analysis Replication and Prediction Study. Sci. Data 6, 106, https://doi.org/10.1038/s41597-019-0113-7 (2019).
Stigliani, A., Weiner, K. S. & Grill-Spector, K. Temporal processing capacity in high-level visual cortex is domain specific. J. Neurosci 35, 12412–12424, https://doi.org/10.1523/JNEUROSCI.4822-14.2015 (2015).
Douglas, D. et al. Perception of Impossible Scenes Reveals Differential Hippocampal and Parahippocampal Place Area Contributions to Spatial Coherency. Hippocampus 27, 61–76, https://doi.org/10.1002/hipo.22673 (2017).
Keator, D. B. et al. The Function Biomedical Informatics Research Network Data Repository. NeuroImage 124, 1074–1079, https://doi.org/10.1016/j.neuroimage.2015.09.003 (2016).
Kuo, B.-C., Nobre, A. C., Scerif, G. & Astle, D. E. Top–Down activation of spatiotopic sensory codes in perceptual and working memory search. J. Cogn. Neurosci 28, 996–1009, https://doi.org/10.1162/jocn_a_00952 (2016).
Poldrack, R. et al. The cognitive atlas: toward a knowledge foundation for cognitive neuroscience. Front. Neuroinform 5, 17, https://doi.org/10.3389/fninf.2011.00017 (2011).
Gordon, E. M. et al. Precision functional mapping of individual human brains. Neuron 95, 791–807.e7, https://doi.org/10.1016/j.neuron.2017.07.011 (2017).
Chang, N. et al. BOLD5000, a public fMRI dataset while viewing 5000 visual images. Sci. Data 6, 49, https://doi.org/10.1038/s41597-019-0052-3 (2019).
Allen, E. J. et al. A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence. Nat. Neurosci 25, 116–126, https://doi.org/10.1038/s41593-021-00962-x (2022).
Bellec, P. & Boyle, J., Bridging the gap between perception and action: the case for neuroimaging, AI and video games https://doi.org/10.31234/osf.io/3epws (2019).
Gorgolewski, K. et al. The Brain Imaging Data Structure: a standard for organizing and describing outputs of neuroimaging experiments. Sci. Data 3, 160044, https://doi.org/10.1038/sdata.2016.44 (2016).
Oldfield, R. C. The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9, 97–113, https://doi.org/10.1016/0028-3932(71)90067-4 (1971).
Chang, D. H. F., Ban, H., Ikegaya, Y., Fujita, I. & Troje, N. F. Cortical and subcortical responses to biological motion. NeuroImage 174, 87–96, https://doi.org/10.1016/j.neuroimage.2018.03.013 (2018).
Johansson, G. Visual perception of biological motion and a model for its analysis. Percept. Psychophys 14, 201–211, https://doi.org/10.3758/BF03212378 (1973).
Amalric, M. & Dehaene, S. A distinct cortical network for mathematical knowledge in the human brain. NeuroImage 189, 19–31, https://doi.org/10.1016/j.neuroimage.2019.01.001 (2019).
O’Doherty, J., Critchley, H., Deichmann, R. & Dolan, R. J. Dissociating valence of outcome from behavioral control in human orbital and ventral prefrontal cortices. J. Neurosci. 23, 7931–7939, https://doi.org/10.1523/JNEUROSCI.23-21-07931.2003 (2003).
Tom, S. M., Fox, C. R., Trepel, C. & Poldrack, R. A. The neural basis of loss aversion in decision-making under risk. Science 315, 515–518, https://doi.org/10.1126/science.1134239 (2007).
Endo, N., Saiki, J., Nakao, Y. & Saito, H. Perceptual judgments of novel contour shapes and hierarchical descriptions of geometrical properties. Japanese Journal of Psychology https://doi.org/10.4992/jjpsy.74.346 (2003).
Moeller, S. et al. Multiband multislice GE-EPI at 7 Tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI. Magn Reson Med 63, 1144–1153, https://doi.org/10.1002/mrm.22361 (2010).
Feinberg, D. A. et al. Multiplexed echo planar imaging for sub-second whole brain fMRI and fast diffusion imaging. PLoS One 5, e15710, https://doi.org/10.1371/journal.pone.0015710 (2010).
Li, X., Morgan, P. S., Ashburner, J., Smith, J. & Rorden, C. The first step for neuroimaging data analysis: DICOM to NIfTI conversion, J. Neurosci. Methods volume264 https://doi.org/10.1016/j.jneumeth.2016.03.001 (2016).
Bischoff-Grethe, A. et al. A technique for the deidentification of structural brain MR images. Hum Brain Mapp 28, 892–903, https://doi.org/10.1002/hbm.20312 (2007).
Smith, S. et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23, S208–S219, https://doi.org/10.1016/j.neuroimage.2004.07.051 (2004).
Andersson, J. L. R., Skare, S. & Ashburner, J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage 20, 870–888, https://doi.org/10.1016/S1053-8119(03)00336-7 (2003).
Friston, K. J., Frith, C. D., Frackowiak, R. S. J. & Turner, R. Characterizing dynamic brain responses with fMRI: a multivariate approach. Neuroimage 2, 166–172, https://doi.org/10.1006/nimg.1995.1019 (1995).
Ashburner, J. & Friston, K. Multimodal image coregistration and partitioning–a unified framework. Neuroimage 6, 209–217, https://doi.org/10.1006/nimg.1997.0290 (1997).
Ashburner, J. & Friston, K. J. Unified segmentation. Neuroimage 26, 839–851, https://doi.org/10.1016/j.neuroimage.2005.02.018 (2005).
Friston, K. J. et al. Event-Related fMRI: Characterizing Differential Responses. Neuroimage 7, 30–40, https://doi.org/10.1006/nimg.1997.0306 (1998).
Friston, K. J., Josephs, O., Rees, G. & Turner, R. Nonlinear event-related responses in fMRI. Magn Reson Med 39, 41–52, https://doi.org/10.1002/mrm.1910390109 (1998).
Abraham, A. et al. Machine learning for neuroimaging with scikit-learn. Front Neuroinform 8, 14, https://doi.org/10.3389/fninf.2014.00014 (2014).
Amunts, K.et al. Linking Brain Structure, Activity, and Cognitive Function through Computation., eNeuro9(2) https://doi.org/10.1523/ENEURO.0316-21.2022 (2022).
Wilkinson, M. D. et al. The Fair Guiding Principles for scientific data management and stewardship. Scientific Data 3, 160018, https://doi.org/10.1038/sdata.2016.18 (2016).
Aggarwal, H., Ponce, A.F. & Thirion, B., Individual Brain Charting (IBC) (v4.0). EBRAINS https://doi.org/10.25493/S7GR-CT0 (2025).
Aggarwal, H., Ponce, A.F. & Thirion, B., Preprocessed data from the Individual Brain Charting (IBC) project (v2.0). EBRAINS https://doi.org/10.25493/4ZZ6-5S5 (2025).
Aggarwal, H., Ponce, A.F. & Thirion, B., 3mm-preprocessed data from the Individual Brain Charting (IBC) project (v2.1). EBRAINS https://doi.org/10.25493/DTEX-HWU (2025).
Aggarwal, H., Ponce, A.F. & Thirion, B., Contrast maps obtained from Individual Brain Charting (v3.0). EBRAINS https://doi.org/10.25493/873R-QK2 (2024).
Ponce, A.F., Supplementary tables for individual brain charting data descriptor (v5). figshare https://doi.org/10.6084/m9.figshare.30061798.v1 (2025).
Murphy, K., Bodurka, J. & Bandettini, P. A. How long to scan? The relationship between fMRI temporal signal to noise ratio and necessary scan duration. Neuroimage 34, 565–574, https://doi.org/10.1016/j.neuroimage.2006.09.032 (2007).
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L. & Petersen, S. E. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59, 2142–2154, https://doi.org/10.1016/j.neuroimage.2011.10.018 (2012).
Lazar, N. A., Luna, B., Sweeney, J. A. & Eddy, W. F. Combining Brains: A Survey of Methods for Statistical Pooling of Information. NeuroImage 16, 538–550, https://doi.org/10.1006/nimg.2002.1107 (2002).
Acknowledgements
We thank extensively the IBC project participants for their time and commitment to this long-term project, their disposition to perform many hours of MRI scanning over the years is what makes this project possible. We thank colleagues from the Cambridge Centre for Ageing and Neuroscience CamCAN for the availability of their protocols and their collaboration on having them reproduced in the IBC project. We thank colleagues from the Aging and Cognition Research Group at the German Center for Neurodegenerative Diseases (DZNE) for their collaboration on the implementation of the Spatial Navigation protocol. We thank colleagues from the Wellcome Centre for Human Neuroimaging at University College London for their collaboration on the integration of the Reward Processing protocol. We thank colleagues from the Schonberg Laboratory at Tel Aviv University for their support and collaboration on the implementation of the NARPS protocol on the IBC project. We thank colleagues from the Department of Psychology and the Stanford Neurosciences Institute at Stanford University for their collaboration on having the FaceBody protocol reproduced in the IBC project. We thank colleagues from the Department of Psychology at the University of Toronto for the collaboration on having the Scene protocol reproduced in the IBC project. We thank the Department of Psychiatry and Human Behavior at the University of California for their collaboration on the implementation of the FBIRN battery on the IBC project. We thank colleagues from the University of Rochester and the University of London for their collaboration on having the Visual Search protocol reproduced in the IBC project. We thank the Center for Magnetic Resonance Research, University of Minnesota for having kindly provided the Multi-Band Accelerated EPI Pulse Sequence and Reconstruction Algorithms. This project has received funding from the European Union’s Horizon 2020 Framework Program for Research and Innovation under Grant Agreement No 720270 (Human Brain Project SGA1) and 785907 (Human Brain Project SGA2). Ana Luísa Pinho is the recipient of a BrainsCAN Postdoctoral Fellowship at Western University, funded by the Canada First Research Excellence Fund (CFREF).
Author information
Authors and Affiliations
Contributions
A.F.P. performed MRI acquisitions, produced task annotations, contributed to the development of tools for the dataset, wrote the documentation and wrote this manuscript. H.A. performed MRI acquisitions, set task protocols, produced annotations, wrote the documentation, developed tools for the dataset and designed some analyses. S.S. performed MRI acquisitions, set task protocols, produced annotations and wrote the documentation. J.J.T. performed MRI acquisitions, set task protocols, produced annotations and wrote the documentation. A.L.P. performed MRI acquisitions, set task protocols, produced annotations, wrote the documentation and took part in the general design of the study. A.T. contributed to the design of the study, the development of tools and data analyses. C.G., Y.L., V.B., and L.B. performed the MRI acquisitions plus visual inspection of the neuroimaging data for quality-checking. L.L., V.J.-T. and G.M.-C. recruited the participants and managed routines related to appointment scheduling and ongoing medical assessment. L.H.-P. conceived the general design of the study and wrote the ethical protocols. C.D. recruited the participants and managed the scientific communication of the project with them. B.M. managed regulatory issues. M.A. and S.D. conceived the general design of the study and made the MathLanguage protocol integration into IBC possible. N.D. and T.W. assisted with the integration of the SpatialNavigation protocol. M.A.S. supervised integration of the CamCAN battery: EmoMem, EmoReco, StopNogo, Catell, FingerTapping and VSTMC protocols. J.P.OD., V.M. and R.J.D. supervised integration of the Reward Processing protocol. R.A.P. supervised implementation of the gambling protocols, later incorporated into the NARPS protocol. A.S. and K.G.S. supervised integration of the FaceBody protocol. D.D. and A.C.H.L supervised integration of the Scene protocol. D.B.K. and S.G.P. supervised integration of the FBIRN battery: ItemRecognition, BreathHolding, Checkerboard and FingerTap protocols. D.H.F. and N.F.T. made the BiologicalMotion protocol integration into IBC possible. B.-C.K. and D.E.A. made the VisualSearch protocol integration into IBC possible. B.T. conceived the general design of the study, managed the project, wrote the ethical protocols, pre-processed fMRI data, performed the analysis of the fMRI data, developed FastSRM, developed Nilearn, developed Pypreprocess, developed the IBC public analysis pipeline, uploaded IBC collections on EBRAINS and contributed to the IBC documentation.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Ponce, A.F., Aggarwal, H., Shankar, S. et al. Individual Brain Charting: fifth release of high-resolution fMRI data for cognitive mapping. Sci Data (2026). https://doi.org/10.1038/s41597-026-06869-1
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41597-026-06869-1
