Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Jun 5;11(1):590.
doi: 10.1038/s41597-024-03390-1.

Individual Brain Charting dataset extension, third release for movie watching and retinotopy data

Ana Luísa Pinho  1   2   3 , Hugo Richard  4   5   6 , Ana Fernanda Ponce  4 , Michael Eickenberg  4   7 , Alexis Amadon  8 , Elvis Dohmatob  4   9 , Isabelle Denghien  10 , Juan Jesús Torre  4 , Swetha Shankar  4 , Himanshu Aggarwal  4 , Alexis Thual  4   10   11 , Thomas Chapalain  4 , Chantal Ginisty  12 , Séverine Becuwe-Desmidt  12 , Séverine Roger  12 , Yann Lecomte  12 , Valérie Berland  12 , Laurence Laurier  12 , Véronique Joly-Testault  12 , Gaëlle Médiouni-Cloarec  12 , Christine Doublé  12 , Bernadette Martins  12 , Gaël Varoquaux  4 , Stanislas Dehaene  10   11 , Lucie Hertz-Pannier  12   13 , Bertrand Thirion  4
Affiliations

Individual Brain Charting dataset extension, third release for movie watching and retinotopy data

Ana Luísa Pinho et al. Sci Data. .

Abstract

The Individual Brain Charting (IBC) is a multi-task functional Magnetic Resonance Imaging dataset acquired at high spatial-resolution and dedicated to the cognitive mapping of the human brain. It consists in the deep phenotyping of twelve individuals, covering a broad range of psychological domains suitable for functional-atlasing applications. Here, we present the inclusion of task data from both naturalistic stimuli and trial-based designs, to uncover structures of brain activation. We rely on the Fast Shared Response Model (FastSRM) to provide a data-driven solution for modelling naturalistic stimuli, typically containing many features. We show that data from left-out runs can be reconstructed using FastSRM, enabling the extraction of networks from the visual, auditory and language systems. We also present the topographic organization of the visual system through retinotopy. In total, six new tasks were added to IBC, wherein four trial-based retinotopic tasks contributed with a mapping of the visual field to the cortex. IBC is open access: source plus derivatives imaging data and meta-data are available in public repositories.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Global quality indices of the acquired data: tSNR map and motion magnitude distribution. (a) The tSNR map displays the average of tSNR across all tasks and subjects in the gray matter. This shows values mostly between 30 and 58, with larger tSNR in cortical regions. (b) Density of within-run motion parameters, pooled across subjects and tasks. Six distributions are plotted, for the six rigid-body parameter of head motion (translations and rotations are in mm and degrees, respectively). Each distribution is based on ∼145k EPI volumes of 12 subjects, corresponding to all time frames for all acquisitions and subjects. Bold lines below indicate the 99% coverage of all distributions and show that motion parameters mostly remain confined to 1.5 mm/1degree across 99% of all acquired images.
Fig. 2
Fig. 2
Distributions of the individual framewise displacements, in mm, across runs of each task. Framewise displacement is expressed as a scalar of the instantaneous head motion, that is described by the six rigid body-motion estimates. To facilitate visualization, the vertical axis is represented in the logarithmic scale and framewise-displacement values lower than 0.01 mm were clipped.
Fig. 3
Fig. 3
Inter-subject correlation maps for Raiders and Clips tasks. Pearson-correlation coefficient values were estimated as the mean across runs of the means, obtained for every run, of the pairwise inter-subject correlations of the individual GE-EPI data preprocessed on the surface. For visualization purposes, the maps were thresholded to 0.05. Estimations were performed separately for each task.
Fig. 4
Fig. 4
Description of the co-smoothing procedure to compute the jointly activated brain areas using FastSRM. The algorithm runs two nested CV loops. One first outer loop executes a random split of the cohort of twelve participants into a train set and a test set, respectively composed of eight and four subjects. One inner loop executes a random split of the group of runs into a train set and a test set: respectively twelve and nine for Clips and, interchangeably seven or six for Raiders. For every turn of the nested CV loops: (a) the k = 20 spatial components specific to each subject on the train runs are computed through alternate minimization together with their shared response, which is then used to compute the individual components of test subjects on the same runs; then, (b) assuming that the same features of the train runs will be found on the test runs, we fit the individual responses of the train subjects on the test runs in order to compute their shared response; (c) test runs are then predicted through their shared response computed in (b); and, (d) the vertex-wise correlation between the predicted runs and the corresponding original data is computed. For every subject, we estimated the vertex-wise median of the correlations across runs. The vertex-wise median of the correlations across all subjects was then estimated from their individual median correlations. This final coefficient represents the similarity of activated regions across subjects for each task.
Fig. 5
Fig. 5
Statistical validation of naturalistic-stimuli tasks with FastSRM. (a) Person-correlation coefficients (ρnormalized) of FastSRM prediction for Raiders task, compared to noise ceiling. The noise ceiling was estimated as correlations between run pairs 1-11, 2–12 and 3–13 that refer to the same video excerpts. The correlation between runs 1, 2 and 3 with their reconstructed runs from FastSRM are expressed as a fraction of this noise ceiling. These ratios were computed for every vertex and subject. We then took the median across subjects, normalized and took the median across runs. Results are thresholded at 0.1. (b) Group-level, Pearson-correlation coefficients (ρ) between the original and reconstructed data for Raiders and Clips tasks. Coefficients were obtained for every vertex from a double K–fold cross-validation experiment across subjects (K = 3) and runs (K = 2) for each task. Data of test subjects performing test runs were reconstructed from the projection of the shared response of train subjects while performing test runs onto the individual components of test subjects while performing train runs. Predictions between original and reconstructed data were estimated for every subject and run. To obtain the group-level estimation of the coefficients, the vertex-wise median of the coefficients were firstly taken within split-halfs, secondly between split-halfs for every subject, and finally across subjects. To assess the group-level significance of these estimates, we computed a mass-univariate non-parametric analysis, then derived an FDR-corrected p-value. Coefficients are only displayed for vertices with q ≤ 0.05. (c) Group-level z–maps displaying brain activation significantly different between Raiders and Clips tasks. Results were determined through a vertex-wise paired t–test between the individual Pearson-correlation coefficients of the two tasks and standardized afterwards. Statistical significance was assessed using an FDR-corrected threshold q = 0.05. Clusters depicted in orange/yellow represent brain activation significantly higher for Raiders than Clips, whereas those depicted in dark/light blue represent brain activation significantly higher for Clips than Raiders. Orange-yellow clusters surpass in number and size the blue clusters, highlighting the additional cognitive recruitment in Raiders related to auditory and language comprehension specific to this task.
Fig. 6
Fig. 6
Individual, flat and binary maps of retinotopy in the visual field. (top) The visual field is encoded through polar coordinates: polar angle(left) and eccentricity (right). These polar coordinates are mapped on a flattened representation of the cortical surfaces extracted from the twelve IBC subjects: sub-01, sub-04, sub-05, sub-06, sub-07 and sub-08 on the left side; sub-09, sub-11, sub-12, sub-13, sub-14 and sub-15 on the other right side. One shall note the striking similarity of these maps across individuals. Individual binary maps for fixed effects are displayed for every participant, using an FDR-corrected threshold q = 0.05.
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Genon S, Reid A, Langner R, Amunts K, Eickhoff SB. How to Characterize the Function of a Brain Region. Trends Cogn Sci. 2018;22:350–364. doi: 10.1016/j.tics.2018.01.010. - DOI - PMC - PubMed
    1. Varoquaux, G. et al. Atlases of cognition with large-scale brain mapping. PLoS computational biology14. 10.1371/journal.pcbi.1006565 (2018). - PMC - PubMed
    1. King M, Hernandez-Castillo CR, Poldrack RA, Ivry RB, Diedrichsen J. Functional boundaries in the human cerebellum revealed by a multi-domain task battery. Nat Neurosci. 2019;22:1371–1378. doi: 10.1038/s41593-019-0436-x. - DOI - PMC - PubMed
    1. Pinho AL, et al. Subject-specific segregation of functional territories based on deep phenotyping. Hum Brain Mapp. 2021;42:841–870. doi: 10.1002/hbm.25189. - DOI - PMC - PubMed
    1. Thirion B, Thual A, Pinho AL. From deep brain phenotyping to functional atlasing. Curr Opin Behav Sci. 2021;40:201–212. doi: 10.1016/j.cobeha.2021.05.004. - DOI