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@ARTICLE{Pinho:885844,
author = {Pinho, Ana Luísa and Amadon, Alexis and Gauthier, Baptiste
and Clairis, Nicolas and Knops, André and Genon, Sarah and
Dohmatob, Elvis and Torre, Juan Jesús and Ginisty, Chantal
and Becuwe-Desmidt, Séverine and Roger, Séverine and
Lecomte, Yann and Berland, Valérie and Laurier, Laurence
and Joly-Testault, Véronique and Médiouni-Cloarec, Gaëlle
and Doublé, Christine and Martins, Bernadette and Salmon,
Eric and Piazza, Manuela and Melcher, David and Pessiglione,
Mathias and van Wassenhove, Virginie and Eger, Evelyn and
Varoquaux, Gaël and Dehaene, Stanislas and Hertz-Pannier,
Lucie and Thirion, Bertrand},
title = {{I}ndividual {B}rain {C}harting dataset extension, second
release of high-resolution f{MRI} data for cognitive
mapping},
journal = {Scientific data},
volume = {7},
number = {1},
issn = {2052-4463},
address = {London},
publisher = {figshare},
reportid = {FZJ-2020-04130},
pages = {353},
year = {2020},
abstract = {<div>This dataset contains key characteristics about the
data described in the Data Descriptor Individual Brain
Charting dataset extension, second release of
high-resolution fMRI data for cognitive mapping.</div> <br>
<div>Contents: </div> <br> <div> 1. human readable metadata
summary table in CSV format </div> <div> 2. machine readable
metadata file in JSON format </div> <br> <br>
<div><br></div>We present an extension of the Individual
Brain Charting dataset –a high spatial-resolution,
multitask,functional Magnetic Resonance Imaging dataset,
intended to support the investigation on thefunctional
principles governing cognition in the human brain. The
concomitant data acquisition fromthe same 12 participants,
in the same environment, allows to obtain in the long run
finer cognitivetopographies, free from inter-subject and
inter-site variability. This second release provides more
datafrom psychological domains present in the first release,
and also yields data featuring new ones. Itincludes tasks on
e.g. mental time travel, reward, theory-of-mind, pain,
numerosity, self-referenceeffect and speech recognition. In
total, 13 tasks with 86 contrasts were added to the dataset
and 63new components were included in the cognitive
description of the ensuing contrasts. As the datasetbecomes
larger, the collection of the corresponding topographies
becomes more comprehensive,leading to better brain-atlasing
frameworks. This dataset is an open-access facility; raw
data andderivatives are publicly available in neuroimaging
repositories.Background $\&$ SummaryUnderstanding the
fundamental principles that govern human cognition requires
mapping the brain in terms offunctional segregation of
specialized regions. This is achieved by measuring local
differences of brain activationrelated to behavior.
Functional Magnetic Resonance Imaging (fMRI) has been used
for this purpose as an attemptto better understand the
neural correlates underlying cognition. However, while there
is a rich literature concerningperformance of isolated
tasks, little is still known about the overall functional
organization of the brain.Meta- and mega-analyses constitute
active efforts at providing accumulated knowledge on brain
systems,wherein data from different studies are pooled to
map regions consistently linked to mental functions1–9
Because1Université Paris-Saclay, Inria, CEA, Palaiseau,
91120, France. 2Université Paris-Saclay, CEA, CNRS,
BAOBAB,NeuroSpin, 91191, Gif-sur-Yvette, France. 3Cognitive
Neuroimaging Unit, INSERM, CEA, Université
Paris-Saclay,NeuroSpin center, 91191, Gif/Yvette, France.
4Laboratory of Cognitive Neuroscience, Brain Mind Institute,
School ofLife Sciences and Center for Neuroprosthetics,
Swiss Federal Institute of Technology (EPFL), Campus
Biotech, Geneva,Switzerland. 5Motivation, Brain and Behavior
(MBB) team, Institut du Cerveau (ICM), Inserm UMRS 1127, CNS
UMR7225, Sorbonne Université, Paris, France. 6Center for
Mind/Brain Sciences, University of Trento, I-38068,
Rovereto,Italy. 7LaPsyDÉ, UMR CNRS 8240, Université de
Paris, Paris, France. 8GIGA-CRC In vivo Imaging, University
of Liège,Liège, Belgium. 9Institute of Neuroscience and
Medicine, Brain $\&$ Behaviour (INM-7) Research Centre
Jülich, Jülich,Germany. 10Criteo AI Lab, Paris, France.
11Université Paris-Saclay, CEA, UNIACT, NeuroSpin, 91191
Gif-sur-Yvette,France. 12Collège de France, Université
Paris-Sciences-Lettres, Paris, France. 13UMR 1141,
NeuroDiderot, Universitéde Paris, Paris, France. ✉e-mail:
ana.pinho@inria.frData Descrip torOPENScientific Data |
(2020) 7:353 | https://doi.org/10.1038/s41597-020-00670-4
2www.nature.com/scientificdata/
www.nature.com/scientificdatadata are impacted by both
intra- and inter-subject plus inter-site variability, these
approaches still limit the exactdemarcation of functional
territories and, consequently, formal generalizations about
brain mechanisms. Severallarge-scale brain-imaging datasets
are suitable for atlasing, wherein differences can be
mitigated across subjectsand protocols together with
standardized data-processing routines. Yet, as they have
different scopes, notall requirements are met for cognitive
mapping. For instance, the Human Connectome Project
(HCP)10,11 andCONNECT/Archi12,13 datasets provide large
subject samples as they are focused in population analysis
across differentmodalities; task-fMRI data combine here 24
and 28 conditions, respectively, which is scarce for
functionalatlasing. Another example is the studyforrest
dataset14–17, that includes a variety of task data on
complex auditoryand visual information, but restricted to
naturalistic stimuli. Additionally, one shall note that
within-subject variabilityreduces task-fMRI replicability;
thus, more data per subject can in fact facilitate
reliability of group-levelresults18.To obtain as many
cognitive signatures as possible and simultaneously achieve
a wide brain coverage at a finescale, extensive functional
mapping of individual brains over different psychological
domains is necessary. Withinthis context, the Individual
Brain Charting (IBC) project pertains to the development of
a 1.5mm-resolution,task-fMRI dataset acquired in a fixed
environment, on a permanent cohort of 12 participants. Data
collectionfrom a broad range of tasks, at high spatial
resolution, yields a sharp characterization of the
neurocognitive componentscommon to the different tasks. This
extension corresponds to the second release of the IBC
dataset,meant to increase the number of psychological
domains of the first one19. It both aims at a consistent
mapping ofelementary spatial components, extracted from all
tasks, and a fine characterization of the individual
architectureunderlying this topographic information.The
first release encompassed a sample of modules ranging from
perception to higher-level cognition, e.g.retinotopy,
calculation, language and social reasoning10,12,20. The
second release refers to tasks predominantlyfocused on
higher-level functions, like mental time travel, reward,
theory-of-mind, self-reference effect andspeech recognition.
Nonetheless, a subset dedicated to lower-level processes is
also included, covering pain,action perception and
numerosity. These tasks are intended to complement those
from the first release, such thata considerable cognitive
overlap is attained, while new components are introduced.
For instance, componentsconcerning social cognition, already
found in ARCHI Standard, ARCHI Social and HCP Social tasks
from theprevious release, are now present in tasks about
theory-of-mind and self-reference effect. Likewise,
componentson incentive salience, already tackled in the HCP
Gambling task, are now included in a task battery
addressingpositive-incentive value. Yet also, a battery on
mental time travel brings in new modules pertaining to
timeorientation and cardinal-direction judgment. Data from
both releases are organized in 25 tasks –most of
themreproduced from other studies– and they amount for 205
contrasts described on the basis of 110 cognitive
atoms,extracted from the Cognitive Atlas21.Here, we give an
account –focused on the second release– of the
experimental procedures and the datasetorganization and show
that raw task-fMRI data and their derivatives represent
functional activity in directresponse to behavior. Data
collection is ongoing and more releases are planned for the
next years. Despite beinga long-term project, IBC is not
dedicated to longitudinal surveys; acquisitions of the same
tasks will not be conductedsystematically.The IBC dataset is
an open-access facility devoted to providing
high-resolution, functional maps of individualbrains as
basis to support investigations in human cognition.MethodsTo
avoid ambiguity with MRI-related terms used throughout this
manuscript, definitions of such terms follow
theBrain-Imaging-Data-Structure (BIDS) Specification version
1.2.122.Complementary information about dataset organization
and MRI-acquisition protocols can be found in theIBC
documentation available online:
https://project.inria.fr/IBC/data/},
cin = {INM-7},
ddc = {500},
cid = {I:(DE-Juel1)INM-7-20090406},
pnm = {571 - Connectivity and Activity (POF3-571)},
pid = {G:(DE-HGF)POF3-571},
typ = {PUB:(DE-HGF)16},
pubmed = {pmid:33067452},
UT = {WOS:000582128800002},
doi = {10.1038/s41597-020-00670-4},
url = {https://juser.fz-juelich.de/record/885844},
}
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