# Individual Brain Charting dataset extension, fourth release of high-resolution fMRI data for cognitive mapping

Juan Jesús Torre<fnref n="1" />*, Ana Luísa Pinho<fnref n="1" />, Swetha Shankar<fnref n="1" />, Alexis Amadon<fnref n="2" />, Mani Saignavongs<fnref n="3" />,<fnref n="4" />, Marcela Perrone-Bertolotti<fnref n="3" />,<fnref n="5" />, Thomas Bazeille<fnref n="1" />, Elvis Dohmatob<fnref n="1" />,<fnref n="16" />, Isabelle Denghien<fnref n="6" />, Chantal Ginisty<fnref n="7" />, Séverine Becuwe-Desmidt<fnref n="7" />, Séverine Roger<fnref n="7" />, Yann Lecomte<fnref n="7" />, Valérie Berland<fnref n="7" />, Laurence Laurier<fnref n="7" />, Véronique Joly-Testault<fnref n="7" />, Gaëlle Médiouni-Cloarec<fnref n="7" />, Christine Doublet<fnref n="7" />, Bernadette Martins<fnref n="7" />, Jean-Philippe Lachaux<fnref n="5" />, Patrick G. Bissett<fnref n="8" />, Ayse Zeynep Enkavi<fnref n="9" />, Ian Eisenberg<fnref n="8" />, Russell Poldrack<fnref n="8" />, Roberta Santoro<fnref n="10" />,<fnref n="11" />,<fnref n="12" />, Elia Formisano<fnref n="10" />,<fnref n="11" />,<fnref n="13" />, Gaël Varoquaux<fnref n="1" />, Stanislas Dehaene<fnref n="6" />,<fnref n="14" />, Lucie Hertz-Pannier<fnref n="7" />,<fnref n="15" />, and Bertrand Thirion<fnref n="1" />*

## Abstract

Functional Magnetic Resonance Imaging (fMRI) constitutes the primary imaging technique that allows us to investigate brain-functional organization at the millimeter scale. We thus present herein another extension of the *Individual Brain Charting* (IBC) dataset – a long-running project dedicated to map cognition in twelve individuals brains. In this release, we provide more multi-task fMRI data at high-spatial resolution –i.e. 1.5mm– from the same individuals. Concretely, the data refer to the performance of eighteen distinct tasks across seven fMRI sessions, comprising a wide range of cognitive processes. The data release pertains to both raw data and derived statistical maps that can be found in *OpenNeuro* plus *EBRAINS* and *NeuroVault*, respectively. In addition to fMRI, we provide paradigm descriptors of the tasks' designs compliant with the Brain Imaging data Structure (BIDS) conventions, as well as code to reproduce the tasks.

## Keywords

Brain mapping, fMRI, Neuroimaging

<footnote n="1">¹Université Paris-Saclay, Inria, CEA, Palaiseau, 91120, France</footnote>

<footnote n="2">²Université Paris-Saclay, CEA, CNRS, BAOBAB, NeuroSpin, 91191, Gif-sur-Yvette, France</footnote>

<footnote n="3">³Lyon Neuroscience Research Center, INSERM U1028 / CNRS UMR 5292, Lyon, France</footnote>

<footnote n="4">⁴Epilepsy Institute IDEE, Lyon, France</footnote>

<footnote n="5">⁵University Claude Bernard, F-69000 Lyon, France</footnote>

<footnote n="6">⁶Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin center, 91191 Gif/Yvette, France</footnote>

<footnote n="7">⁷Université Paris-Saclay, CEA, UNIACT, NeuroSpin, 91191, Gif-sur-Yvette, France</footnote>

<footnote n="8">⁸Department of Psychology, Stanford University, CA 94305, United States</footnote>

<footnote n="9">⁹Division of Humanities and Social Sciences, Caltech, CA 91125, United States</footnote>

<footnote n="10">¹⁰Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, 6200 MD Maastricht, The Netherlands</footnote>

<footnote n="11">¹¹Maastricht Brain Imaging Center, 6200 MD Maastricht, The Netherlands</footnote>

<footnote n="12">¹²Brain and Language Laboratory, Department of Clinical Neuroscience, University Medical School, University of Geneva, CH-1211 Geneva, Switzerland</footnote>

<footnote n="13">¹³Maastricht Centre for Systems Biology, Maastricht University, 6200 MD Maastricht, The Netherlands</footnote>

<footnote n="14">¹⁴Collège de France, Université Paris-Sciences-Lettres, Paris, France</footnote>

<footnote n="15">¹⁵UMR 1141, NeuroDiderot, Université de Paris, France</footnote>

<footnote n="16">¹⁶Criteo AI Lab, France</footnote>

<footnote>*Corresponding authors: juan-jesus.torre-tresols@inria.fr, bertrand.thirion@inria.fr</footnote>
Introduction

Imaging-based brain mapping is a central tool to under-
stand the principles that govern brain function, as it al-
lows the delineation of functional regions and their un-
derlying network structure.

From the wide range of imaging techniques available,
functional Magnetic Resonance Imaging (fMRI) stands out
for this endeavor; the combination of its mm-scale spatial
resolution and non-invasiveness make it the standard for
brain mapping. Although extensive literature has been
published on the functional mapping of specific brain ar-
eas, the number of studies aiming at understanding the
brain as a whole is much more scarce. Some efforts to-
wards this later objective are meta- and mega- analyses,
commonly used in many other scientific fields [14]. They
allow the study of accumulated data, by pooling together
results from different studies, which in turn facilitates
the investigation of brain systems linked to cognitive pro-
cesses. Several studies have outlined both the pros and
cons of meta-analyses [47, 5]. Potential benefits include
the reduction of possible false positives and false nega-
tives that can be found in individual studies. Others have
pointed to the lack of general guidelines for these types
of analyses in neuroimaging, and made an effort to pro-
pose such principles, [22] proposes ten guidelines for neu-
roimaging meta-analyses in particular, from being specific
with the research question to controlling for multiple con-
trasts and reporting how the data was gathered and or-
ganized. Besides, large-scale neuroimaging-data studies
have been used to address “brain signatures” of processes
such as pain [46], as well as to create brain atlases of cog-
nition [40, 39, 44].

Still, certain features of these types of analyses, such as
the underlying inter-subject and inter-site variability, limit
the generalization of such findings. Besides the variabil-
ity introduced by different cohorts, equipment plus ex-
perimental procedures across studies as well as the lack
of standardized data-processing pipelines constitute fur-
ther barriers for these methods to be used for brain atlas-
ing. Recently, many efforts have been put forward with re-
spect to overcoming the aforementioned limitations, such
as the development of large-scale brain-imaging datasets.
As not all the brain mapping limitations can be overcome
simultaneously, each project has its own scope. For in-
stance, the *CONNECT/Archi* [29, 28] and *Human Connec-
tome Project* (HCP)[3, 11] and datasets focus on sample
size and different imaging modalities, being ideal for pop-
ulation analysis. However, the number of total task-fMRI
conditions in these datasets is scarce for functional atlas-
ing (28 and 24, respectively). Another example is the
*studyforrest* dataset [17, 18, 16, 41], which focuses on
naturalistic stimuli and includes a rich variety of task data
on complex auditory and visual information. Functional
mapping of individual brains over different psychologi-
cal domains is necessary achieve a wide brain coverage
at a fine scale while maximizing the number of cogni-
tive features included. Within this context, the *Individual
Brain Charting* (IBC) project intends to develop a 1.5mm-

resolution, task-fMRI dataset acquired in a fixed environ-
ment, on a permanent cohort of twelve participants. Col-
lecting data from a broad range of tasks allows for a sharp
characterization of the neurocognitive components com-
mon to the different tasks.

The present extension of the dataset corresponds to the
fourth release of the IBC dataset, aiming to expand the
functional coverage of previous releases [30]. The first
release included modules ranging from all levels of cogni-
tion, including retinotopy, tonotopy, somatotopy, calcula-
tion, language and social reasoning [29, 3, 19]. The sec-
ond one focused on higher-level cognition, like mental-
time travel, reward, theory-of-mind, self-reference effect
and speech recognition. Still, some tasks dedicated to
lower-level processes, including pain, attention control
and numerosity, are included. The third release contains
data on retinotopy and naturalistic stimuli, namely movie-
watching protocols and visualization of real-life video
clips. This new release features two experimental batter-
ies: one of them centered on self-control and the other
one included a wide range of cognitive processes, includ-
ing elementary motor functions, object salience, verbal
and spatial working memory, and mapping of visual cate-
gories, among others. There is also an auditory task aimed
at mapping different kinds of naturalistic auditory stimuli,
with categories like animals, human voice, tools and na-
ture sounds.

In total, data from all releases are organized in 46 tasks –
most of them reproduced from previous studies– amount-
ing for 216 contrasts described on the basis of 137 cogni-
tive atoms, extracted from the *Cognitive Atlas* [35].

As we keep up our efforts to increase the functional cover-
age of the IBC dataset, more data releases are expected in
the upcoming years. In the long term, we aim at build-
ing a deep-phenotype data resource that will enable a
complete characterization of behavior and corresponding
cognitive mechanisms in terms of brain function. Such
facility can be instrumental on the development of fu-
ture human-brain-atlasing frameworks as means to fur-
ther the investigation and our understanding on the prin-
ciples governing functional organization in the human
brain. Also, we believe that openly sharing data and code
resources is key to advance together as a scientific com-
munity; we therefore focus our efforts in making these
resources available and well-documented for anyone who
might want to use them.

Materials and Methods

Participants

The present release of the IBC dataset consists of brain
fMRI data from eleven individuals (one female) with
mean age equal to 34 (SD=5). One of the eleven (par-
ticipant 1) is missing the data from the Stanford battery
and Realistic sounds task. Data from the remaining par-
ticipant are currently missing and they will be acquired
in the future and included in one of the upcoming re-
leases.
<table><caption>**Table 1.** List of the conditions for each task in the Lyon battery. The whole battery was acquired in two acquisition sessions, with four tasks per session. Each task was acquired twice to account for phase-encoding direction distortions.</caption><thead><tr><th>Task</th><th>Contrast</th><th>Description</th></tr></thead><tbody><tr><td rowspan="10">MOTO</td><td>hand_left</td><td>Left hand movement</td></tr><tr><td>hand_right</td><td>Right hand movement</td></tr><tr><td>finger_left</td><td>Left finger movement</td></tr><tr><td>finger_right</td><td>Right finger movement</td></tr><tr><td>saccade_left</td><td>Eye movement to the left</td></tr><tr><td>saccade_right</td><td>Eye movement to the right</td></tr><tr><td>foot_left</td><td>Left feet movement</td></tr><tr><td>foot_right</td><td>Right feet movement</td></tr><tr><td>tongue_left</td><td>Tongue movement to the left</td></tr><tr><td>tongue_right</td><td>Tongue movement to the right</td></tr><tr><td rowspan="4">MCSE</td><td>hi.salience_left</td><td>High salience, target on the left</td></tr><tr><td>hi.salience_right</td><td>High salience, target on the right</td></tr><tr><td>low.salience_left</td><td>Low salience, target on the left</td></tr><tr><td>low.salience_right</td><td>Low salience, target on the right</td></tr><tr><td rowspan="4">MVIS</td><td>2_dots</td><td>2 positions to remember</td></tr><tr><td>4_dots</td><td>4 positions to remember</td></tr><tr><td>6_dots</td><td>6 positions to remember</td></tr><tr><td>X_dots_control</td><td>1 position to remember</td></tr><tr><td rowspan="4">MVEB</td><td>2_letter</td><td>2 different characters</td></tr><tr><td>4_letter</td><td>4 different characters</td></tr><tr><td>6_letter</td><td>6 different characters</td></tr><tr><td>X_letter same</td><td>1 character repeated X times</td></tr><tr><td rowspan="13">AUDI</td><td>speech</td><td>Speech in French</td></tr><tr><td>reverse</td><td>French speech in reverse</td></tr><tr><td>suomi</td><td>Speech in Suomi</td></tr><tr><td>music</td><td>Real-life music sounds</td></tr><tr><td>yawn</td><td>Concatenated sounds of people yawning</td></tr><tr><td>environment</td><td>Real-life environmental sounds</td></tr><tr><td>silence</td><td>Silence, used as baseline</td></tr><tr><td>tear</td><td>Concatenated sounds of people crying</td></tr><tr><td>cough</td><td>Concatenated sounds of people coughing</td></tr><tr><td>animal</td><td>Real-life animal sounds</td></tr><tr><td>alphabet</td><td>French voice saying the alphabet</td></tr><tr><td>human</td><td>Other human sounds</td></tr><tr><td rowspan="3">LEC1</td><td>words</td><td>A word is presented</td></tr><tr><td>pseudowords</td><td>A pseudoword is presented</td></tr><tr><td>strings</td><td>A random string is presented</td></tr><tr><td rowspan="2">LEC2</td><td>attend</td><td>Word that has to be attended</td></tr><tr><td>ignore</td><td>Word that has to be ignored</td></tr><tr><td rowspan="10">VISU</td><td>character</td><td>String of random characters</td></tr><tr><td>house</td><td>Pictures of houses from the outside</td></tr><tr><td>animal</td><td>Animal pictures</td></tr><tr><td>target_fruit</td><td>Images of fruits</td></tr><tr><td>pseudoword</td><td>Strings of characters forming a pseudoword</td></tr><tr><td>face</td><td>Images of faces</td></tr><tr><td>scene</td><td>Images of natural landscapes</td></tr><tr><td>scrabled</td><td>Noisy image without any objects in it</td></tr><tr><td>tool</td><td>Pictures of tools</td></tr></tbody></table>
<table><caption>**Table 2.** List of conditions for each task of the Stanford battery and Realistic sounds task. The Stanford battery was acquired in three acquisition sessions, with three tasks per session. Each task was acquired twice to account for phase-encoding direction distortions. Regarding the Realistic sounds task, it was acquired in two acquisition sessions, with six runs of the task each. The six runs of each session were split in two phase-encoding directions to compensate for distortions.</caption><thead><tr><th>Task</th><th>Contrast</th><th>Description</th></tr></thead><tbody><tr><td rowspan="2">Stop Signal</td><td>go</td><td>Response trials</td></tr><tr><td>stop</td><td>Inhibition trials</td></tr><tr><td rowspan="3">Two-by-Two</td><td>task_stay_cue_stay</td><td>No switch in task or cue</td></tr><tr><td>task_stay_cue_switch</td><td>Only cue switches</td></tr><tr><td>task_switch_cue_switch</td><td>Both switch</td></tr><tr><td rowspan="2">Stroop</td><td>congruent</td><td>Both word and color are the same</td></tr><tr><td>incongruent</td><td>Color and word are not the same</td></tr><tr><td rowspan="4">Motor Selective SS</td><td>go_criticall</td><td>Response trials (critical side)</td></tr><tr><td>go_noncritical</td><td>Response trials (non-critical side)</td></tr><tr><td>stop</td><td>Inhibition trials</td></tr><tr><td>ignore</td><td>Ignore stop signal</td></tr><tr><td rowspan="4">Attention</td><td>spatial_congruent</td><td>The stim is cued, and flankers are congruent with target</td></tr><tr><td>spatial_incongruent</td><td>The stim is cued, and flankers are incongruent with target</td></tr><tr><td>double_congruent</td><td>The stim is not cued, flankers are congruent with target</td></tr><tr><td>double_incongruent</td><td>The stim is not cued, flankers are incongruent with target</td></tr><tr><td rowspan="2">Discount</td><td>delay</td><td>Amount of delay in days</td></tr><tr><td>amount</td><td>Amount of money offered</td></tr><tr><td rowspan="4">Ward and Aliport</td><td>PA_with_intermediate</td><td>Partially ambiguous with intermediate step</td></tr><tr><td>PAWithout_intermediate</td><td>Partially ambiguous without intermediate step</td></tr><tr><td>UA_with_intermediate</td><td>Unambiguous with intermediate step</td></tr><tr><td>UAWithout_intermediate</td><td>Unambiguous without intermediate step</td></tr><tr><td rowspan="4">Dot Patterns</td><td>correct_cue correct probe</td><td>Correct pair of cue and probe</td></tr><tr><td>correct_cue incorrect probe</td><td>Target cue with random incorrect probe</td></tr><tr><td>incorrect_cue correct probe</td><td>Random incorrect cue with target probe</td></tr><tr><td>incorrect_cue incorrect probe</td><td>Random incorrect cue and probe</td></tr><tr><td rowspan="8">Realistic Sounds</td><td>Voice</td><td>Non-language human sounds</td></tr><tr><td>Animal</td><td>Animal sounds</td></tr><tr><td>Tools</td><td>Sounds of tool usage</td></tr><tr><td>Nature</td><td>Nature sounds</td></tr><tr><td>Music</td><td>Music samples</td></tr><tr><td>Speech</td><td>Human language sounds</td></tr><tr><td>Catch</td><td>Sounds repeated from previous trial</td></tr><tr><td>Silence</td><td>No sound, used as baseline</td></tr></tbody></table>

The cohort is predominantly right-handed, measured with the *Edinburgh Handedness Inventory* [23]. For more complete information on this, as well as in age and sex of the cohort, consult [30].

## Stimuli

One unique aspect of the IBC dataset pertains to its strong emphasis in the taskwise dimension, allowing for a comprehensive brain coverage of cognitive-related mecha-

nisms. As such, data releases comprise several distinct tasks and, in the present one, we provide data acquired during the performance of eighteen tasks, covering a wide range of cognitive processes. To this end, data is typically collected from already existing protocols that have been validated in previous work.

In the next sections, we present a brief description of the tasks featuring this release.
## Lyon tasks battery

This group of tasks were originally used in a series of experiments by the Labex Cortex from the University of Lyon, France (for more information, consult https://labex-cortex.universite-lyon.fr/ labex-cortex/version-anglaise/). All of them were designed for experiments using other imaging modalities, namely intracranial electroencephalography (iEEG), regular EEG or magnetoencephalography (MEG).

**AUDI task** [27]. The audio protocol comprises several categories of sounds, that are presented to the participant in samples of 12s, with 12s of rest between presentations. The sound categories are alphabet, speech (in French), suomi, reverse speech, tear (sounds of people crying), yawn, environmental sounds, animal sounds, music, silence, cough, human (non-speech human sounds) and alphabet (female voice reciting the alphabet in French).

**LEC1 task** [36]. In this task, the participant is presented with a display of three possible rows where words appear alternately, as indicated by two columns of plus signs. At the beginning of each block, one of the three rows turns white to cue that words will appear there. The task is different depending on the rows. For the top row, the participant must answer (always using two buttons) whether the words corresponds to a living creature or not. For the middle row, several pseudowords are displayed, and the participant must answer whether they are composed of one or two syllables. Lastly, the bottom row displays random strings of letters, and the participant must determine whether they are presented in lowercase or uppercase.

**LEC2 task** [27]. For this protocol, the participant is presented with a series of words displayed on the screen one by one, which can be in one of two colors: black or white. The participants are instructed to attend only to a group of letters, and ignore the other. Both sets of words compose a complete story on their own. The interval between word presentations is jittered and centered around 700 ms.

**MCSE task** [24]. A task intended to assess visual search under both low and high salience conditions. The display is divided in four quadrants, where a group of "L" letters and a single "T" target letter are presented in various degrees of inclination. The target letter is always colored in light grey, whereas the rest of the letters can be colored in black (high salience condition) or light grey as well (low salience condition).

**MOTO task**. This is a simple motor task that maps motor responses from different body parts, alternating left and right sides. The participants are shown a display with 3 grey squares in a black background. When one of the lateral squares turns white, the participant must move the corresponding body part (hand, finger, eyes, feet or tongue) to the corresponding side (left or right).

**MVEB (Verbal Working Memory Task) task** [15]. For

this protocol, participants are presented with a string of characters that can contain up to 6 distinct letters. After a brief pause, a single letter is presented, and the participant must answer whether it was part of the aforementioned string or not. The string can be comprised of one (control), two, four or six different characters.

**MVIS (Visual Working Memory Task) task** [15]. Similarly to the MVEB task, participants are presented a 4 × 4 grid where a certain number of points (two, four or six) appear. After a short amount of time, the dots are removed and a single dot is presented somewhere in the grid. The participant's task is then to answer if the presented dot is in the same spot as any of those presented before.

In the control condition, one of the dots is highlighted, meaning that it is the only position the participant has to retain in memory.

**VISU task** [45]. This task consists in a series of images presented to the participant during 200 ms each. The participant is instructed to press a button whenever they see a target category (fruit). The other categories are: characters, house, animal, pseudoword, face, scene, scrambled, tool.

## Stanford Self Control tasks

This battery of tasks aims at assessing several processes related to self-control. As with the rest of the tasks, some adaptations were performed to ensure adaptability to our equipment and region (e.g., changed monetary amounts from dollars to euros). It was developed in Poldrack's lab from Stanford University (https://poldracklab.stanford.edu/). In this case, the tasks were specifically designed for fMRI experiments.

**Attention Network task** [9]. This protocol is a version of the classic *Flanker task* [8], where the participant has to judge the direction of a target arrow surrounded by four other flankers that can point in the same direction as the target (congruent) or not (incongruent). Additionally, two types of cues could precede the flanker stimuli: either one cue (called spatial cue) would indicate where the flanker was going to appear, or two cues would appear at the same time in the two possible locations (double cue).

**Columbia Cards task** [10]. Participants are presented with a set of cards face down. In each trial, a different number of cards appear and the participant is informed of the amount gained per gain card uncovered, the amount lost when uncovering the loss card, and the number of bad cards in the set. The participant can uncover as many cards as they want, before pressing the button that validates their decision and goes to the next trial. Uncovering a loss card automatically ends the trial. The task consists of 88 trials divided in 4 blocks of 22 trials each. The total number of cards plus the number of bad cards changes at every trial as well as the amount gained per card uncovered and the amount lost if a bad card was
uncovered. The order of the cards' presentation is pre-
determined for each trial, although the participant is not
aware of it.

**Discount task** [21]. The participant is presented with a
series of hypothetical monetary choices. They can either
accept 20 euros in the moment or rather wait a certain
amount of days in order to receive a bigger amount. Both
the second amount and the number of days to wait are
different for each trial.

**Dot Patterns expectancy task** [25]. This task presents a pair of stimuli separated by a fixation cross. The stimuli are shapes composed of small circles. The first one is the "cue", and the second one is the "probe", and is always grey. The participant has to press a button as fast as possible after the presentation of the probe, and only one specific combination of cue-probe refers to a differentiated response. This specific combination is the target pair, and it is randomly selected for each participant at the beginning of the experiment.

**Stop Signal task** [4]. The participant is trained to respond to each of four possible geometrical figures with their index or middle fingers (two figures for each finger). In some cases, a red star-shape (stop signal) appears after the presentation of the target, meaning that the motor response has to be withheld. The delay between the shape and the stop signal increases as participants successfully stop and decreases as participants fail to stop (i.e., respond on stop trials).

**Motor Selective Stop Signal** [2]. This task is mostly identical to the previously described Stop Signal task. The difference is that the participant is instructed to only stop if the stop signal occurs and they were going to make one of their two responses (critical response), but ignore the signal if they were going to make the other response (non-critical response).

**STROOP** [6]. In this task, the participant is presented one of the following words: "RED", "BLUE" or "GREEN", that can be colored in any of the three colors. The answer has to correspond to the written word, and not to the color it is presented in. The main difference with the classic STROOP task [43] is that its pace is not determined by the participant.

**Two-by-Two task** [38]. Task-switching paradigm using a 2 by 2 cue-task correspondence. The participant is presented with numbers from 1 to 9 (excluding the number 5), which can be either orange or blue. Briefly before presenting the stimulus, the type of task is revealed where the number will appear. The two possible tasks are "color" or "magnitude" (lower or higher than 5). The participants have one button assigned to one of the possible responses for each task. It is also important to note that each of the two tasks has two possible cues: the "color" task can be also be indicated by the cue "orange/blue", and the "magnitude" task can be indicated by the "high/low" cue, thus the 2 by 2 task-cue mapping.

**Ward and Alipor task** [20]. This is a digital version

of the WATT3 task [48], which is a modified version of
the classic Tower of London task [42], and its main pur-
pose is to capture activation related to planning abili-
ties. Therefore, the task uses a factorial manipulation of 2
task parameters: search depth and goal hierarchy. Search
depth involves mentally constructing the steps necessary
to reach the goal state, and the interdependency between
steps in order to do so. This is expressed by the presence
or absence of intermediate movements necessary for an
optimal solution of each problem. Goal hierarchy refers to
whether the order in which the three balls have to be put
in their goal positions can be completely extracted from
looking at the goal state or if it requires the participant
to integrate information between goal and starting states
(which result in unambiguous or partially ambiguous goal
states, respectively) Detailed descriptions together with
examples of each of the four categories can be found in
[20].

## Realistic Sounds task (Audio)

This task, developed and validated by Elia Formisano's lab (https://mbic-auditorylab.nl/) in [37], consists in the presentation of several categories of sounds. The sounds last two seconds each, and the interval between sounds can be either four or six seconds. The participant is instructed to press a button whenever they hear the same sound twice. The categories are: animal, tool, speech, voice, environment, music, silence and catch (repeated sound).

The task was acquired over 12 runs with 72 trials each, plus 5 silence trials and 5 catch trials, making a total of 82 trials per run.

## Procedures and stimulation setup

The majority of the protocols used in the IBC project are
adapted from their original studies in order to e.g. en-
sure compatibility with the acquisition setup of our fixed
environment. Additionally, we provide a comprehensive
documentation that aims to ease transparency and usage
of both neuroimaging data and task protocols (consult
https://project.inria.fr/IBC/data/). Depend-
ing on the task, they were either adapted from previously
existing protocols or re-implemented from scratch.

The list of software tools used for the tasks comprising
these data are:

1. Lyon tasks: Presentation (Version 20.1, Neurobehavioral Systems, Inc., Berkeley, CA, www.neurobs.com)

2. Stanford tasks: JavaScript, Python

3. Realistic sounds task: Python

## Functional MRI data acquisition

FMRI data were acquired using an MRI scanner
Siemens 3T Magnetom Prismafit together with a Siemens
Head/Neck 64-channel coil. Behavioral responses were
registered with two MR-compatible, optic-fiber response
devices that were used depending on the task employed: (1) a five-button ergonomic pad (Current Designs, Package 932 with Pyka HHSC-1x5-N4); and (2) a pair of in-house custom-made sticks featuring one-top button. MR-Confon package was used as audio system in the MRI environment.

All sessions were conducted at the NeuroSpin platform of the CEA Research Institute, Saclay, France.

Sequences used during the data acquisition include a Gradient-Echo (GE) pulse, whole-brain Multi-Band (MB) accelerated Echo-Planar Imaging (EPI) T2*-weighted sequence with Blood-Oxygenation-Level-Dependent (BOLD) contrasts. Consult Table 3 for a list of the different sequences employed in each acquisition session.

Two different runs for the same task were always performed using two opposite phase-encoding directions: one from Posterior to Anterior (PA) and the other from Anterior to Posterior (AP). The purpose was to guarantee within-subject replication of each task, while reducing limitations that may arise from the distortion-correction procedure. Spin-Echo (SE) EPI-2D image volumes were acquired in order to correct for spatial distortions. Like the GE-EPI sequences, two different acquisitions were also performed using PA and AP phase-encoding direction.

The parameters for all types of sequences employed are provided in [30] as well as in the documentation available on the IBC website: https://project.inria.fr/IBC/data/.

## Dataset Content

The present release of the IBC dataset refer to source (raw) MRI data concerning the tasks described in this paper, meta-data (including events files), derived statistical maps and all the stimulation material used to acquire the data (total 333 GB).

The dataset is organized following BIDS Specification version 1.4.0 [12].

### Paradigm descriptors

For each MRI session, the paradigm descriptors for GLM estimation were extracted from the log of the experiment. They were saved as event files, following BIDS Specification. Scripts to obtain these events files are provided together with the experimental protocols (see Section “Data and Software Availability: Software”).

### fMRI data

The acquired DICOM images were converted to NIfTI format using the dcm2nii tool, which can be found at https://www.nitrc.org/projects/dcm2nii. During conversion to NIfTI, all images were fully anonymized: pseudonyms were removed and images were defaced using the mri_deface command line, from the Freesurfer-6.0.0 library. Source

data were preprocessed using PyPreprocess (https://github.com/neurospin/pypreprocess).

The fMRI images for each session are available as unpreprocessed data, as well as statistical parametric maps summarizing the main effects per subject, together with the event files corresponding to each run.

## Source code

One of the key goals of the IBC project is to create open data and make experiments reproducible. For that reason, we prepare the source code used to acquire all the released data ready to be used (see “Section Data and Software Availability” for more information). The repository containing the source code is divided by tasks, and each one contains several elements:

1. protocol: Contains the scripts to run the experiment, as well as a README file explaining how to adapt and run the experiment

2. paradigm_descriptors: Here instructions are given to extract relevant information from the logfile generated by the protocols, and to obtain BIDS-formatted files like the ones we provide in the dataset

3. instructions: Files with instructions that were given to the participants before the experiment (in French)

## Dataset Validation

### Data quality

As part of the quality control of the dataset, we controlled the six rigid body motion estimates of the brain per scan. The interval ranges approximately within [-1, 1]mm/degree, and those beyond 1mm/degree motion are rare (see Figure 1 for details). No acquisition was discarded due to excessive motion (>2mm/degree).

Also, we computed the temporal SNR (tSNR), defined as the mean of each voxels' time course divided by their standard deviation, on normalized and unsmoothed data averaged across all acquisitions (Figure 1). Its values are between 35 and 70 in the cortex. Given the high resolution of the data (1.5mm isotropic), such values are indicative of a good image quality.

### Data Reproducibility

In order to assess the data quality of the Lyon and Stanford batteries, we compared group-level results obtained with the IBC data from those obtained in the original studies. When a direct comparison with the results from the original studies was not possible, we used NeuroQuery -a meta-analytic tool that produces brain maps from text queries [7] (https://neuroquery.org/query?text=salience+network). Concretely, group-level IBC contrast maps refer to the conjunction of individual z-maps with an average threshold > 25% across subjects and corrected for multiple comparisons
<table><caption>**Table 3.** Overview of the MRI sessions for the fourth release according to the imaging sequences employed for data acquisition. A BOLD-fMRI run refers to the acquisition of fMRI data for one task. Two BOLD runs, concerned with the PA- and AP- phase-encoding directions, were acquired for every task during a session. Two pairs of 2D Spin-Echo maps (PA/AP) were always acquired before and after the BOLD-fMRI runs.</caption><thead><tr><th>Session ID</th><th>Modality</th><th>Task</th><th>Duration of each run (min:sec)</th><th>Repetitions</th></tr></thead><tbody><tr><td rowspan="5">Lyon 1</td><td>2D Spin-Echo</td><td>-</td><td>00:31</td><td>PA(×2) + AP(×2)</td></tr><tr><td>BOLD fMRI</td><td>MOTO</td><td>11:57</td><td>PA + AP</td></tr><tr><td>BOLD fMRI</td><td>MCSE</td><td>05:53</td><td>PA + AP</td></tr><tr><td>BOLD fMRI</td><td>MVEB</td><td>06:45</td><td>PA + AP</td></tr><tr><td>BOLD fMRI</td><td>MVIS</td><td>05:55</td><td>PA + AP</td></tr><tr><td rowspan="5">Lyon 2</td><td>2D Spin-Echo</td><td>-</td><td>00:31</td><td>PA(×2) + AP(×2)</td></tr><tr><td>BOLD fMRI</td><td>LEC1</td><td>06:19</td><td>PA + AP</td></tr><tr><td>BOLD fMRI</td><td>LEC2</td><td>04:45</td><td>PA + AP</td></tr><tr><td>BOLD fMRI</td><td>AUDI</td><td>11:33</td><td>PA + AP</td></tr><tr><td>BOLD fMRI</td><td>VISU</td><td>05:45</td><td>PA + AP</td></tr><tr><td rowspan="3">Stanford 1</td><td>2D Spin-Echo</td><td>-</td><td>05:30</td><td>PA(×2) + AP(×2)</td></tr><tr><td>BOLD fMRI</td><td>Stop Signal</td><td>05:50</td><td>PA + AP</td></tr><tr><td>BOLD fMRI</td><td>Attention</td><td>11:20</td><td>PA + AP</td></tr><tr><td rowspan="3">Stanford 2</td><td>2D Spin-Echo</td><td>-</td><td>00:31</td><td>PA(×2) + AP(×2)</td></tr><tr><td>BOLD fMRI</td><td>Stroop</td><td>05:50</td><td>PA + AP</td></tr><tr><td>BOLD fMRI</td><td>Discount</td><td>11:20</td><td>PA + AP</td></tr><tr><td rowspan="3">Stanford 3</td><td>2D Spin-Echo</td><td>-</td><td>00:31</td><td>PA(×2) + AP(×2)</td></tr><tr><td>BOLD fMRI</td><td>Columbia Cards</td><td>08:00</td><td>PA + AP</td></tr><tr><td>BOLD fMRI</td><td>Dot Patterns</td><td>12:18</td><td>PA + AP</td></tr><tr><td rowspan="3">Audio 1</td><td>2D Spin-Echo</td><td>-</td><td>08:00</td><td>PA + AP</td></tr><tr><td>BOLD fMRI</td><td>Ward and Alipor</td><td>00:31</td><td>PA(×2) + AP(×2)</td></tr><tr><td>BOLD fMRI</td><td>Realistic Sounds</td><td>09:10</td><td>PA(×3) + AP(×3)</td></tr><tr><td rowspan="2">Audio 2</td><td>2D Spin-Echo</td><td>-</td><td>00:31</td><td>PA(×2) + AP(×2)</td></tr><tr><td>BOLD fMRI</td><td>Realistic Sounds</td><td>09:10</td><td>PA(×3) + AP(×3)</td></tr></tbody></table>

with an FDR rate set at 5%. The data analysis was performed using the NiLearn software package [1].

For the Lyon battery, all tasks were originally acquired using intra-cranial EEG. However, robust activation can be observed in pertinent brain areas for all the tasks: the MOTO, AUDI and VISU tasks, being those which map the most basic functions, show consistent activation in the different parts of the motor cortex, primary auditory cortex and visual cortex, respectively.

For the language tasks, we can observe strong activations for LEC1 in visual areas, as well as in areas of the language network, such as the left inferior frontal gyrus and basal temporal areas (i.e. Word Form Area). However, LEC2 fails to capture significant activation on inhibitory processes related to the process of ignoring one of the stories, and it does not show robust activation on the 'voice-selective' areas reported in its original work [27]. It is important to note that the modulations in activity captured in the original study are probably not strong enough to be detected by a group study in a small sample (n=11), especially after whole-brain analysis.

In the case of the MCSE task, the activations shown by our results capture, albeit with weaker intensity, the effect on areas of the dorsal attention network reported in the original work [24]. These are the Inferior Parietal Cortex (IPS), Dorsolateral Prefrontal Cortex (DLPFC) and Medial Frontal Gyrus (MFG).

Lastly, we can also observe strong parietal activation in the working memory tasks. In the map for the MVIS task, one can observe prominent activations in visual areas, accounting for the spatial nature of the task; on the other hand, activations in both frontal cortices are depicted in the map referring to the MVEB task, which reflects its verbal components. The mentioned contrasts can be seen in
<figure><img src="image_1.png" /><figcaption>**Figure 1.** Data-quality assessment of the dataset. For all tasks and subjects, (left) tSNR varies between 35 and 70 in the cortex, with higher values registered in its upper layers, and (right) density of the six rigid-body parameters concerning head motion show that they remained confined to approximately 1.25mm/1degree across 99% of the images collected.</figcaption></figure>

Figure 2.

Regarding the Stanford battery, only the original works for Motor Selective Stop Signal [2] and the Ward and Alipor [20] tasks included neuroimaging analysis. A selection of contrasts for this battery can be found in 3.

Starting with the aforementioned *Motor Selective Stop Signal*, we find clear activation for the stop-ignore condition in the right supplementary motor area, argued to process conflict resolution in the original study [2]. Weak activations are also found in some of the putative regions for the stop condition (primary sensory cortex, right inferior frontal cortex and right basal ganglia). For the very similar *Stop Signal* task, the main contrast (stop-go) fails to capture relevant activation besides the sensory activation related to the stop signal itself (presented visually). We find consistent activation for the conflict resolution between stopping a response and ignoring said stop signal, but did not capture consistently the activation for the putative stop signal effect. For the *Two-by-Two* task, although activation in putative regions of task switching (namely, supplementary motor area and pre-supplementary motor area) can be observed for the task-switch conditions, they are not kept in the contrast. In the original study [38] a 2:1 cue:task mapping is proposed, in order to assess the effect of cue and task switching separately. While the behavioral results were successful, this change turned the task into a more complex and demanding for our participants. Future studies are required to determine if this variation of the task switching paradigm is suited for fMRI mapping of the process.

Following with the *Stroop* task, no robust activation was found on putative areas for the stroop effect (anterior cingulate cortex and dorso-lateral prefrontal cortex).

For the *Attention* task, we found either very weak or absent activation for the orienting and executive control regions described in the original work [9].

The *Discount* task comes from purely behavioral research [21], and is probably more suited for parametric analysis of the conditions than for brain mapping. For the latter, no relevant activations were found in conjunction maps for our participants.

The *Ward and Alipor* task originally seeks to differentiate two different components of planning: what they call “goal hierarchy” and “search depth” [20]. They report in the original work a subtle differentiation in these two concepts in terms of activation: goal hierarchy activates more prominently the left dorso-lateral prefrontal cortex (DLPFC), and search depth activates more prominently the right DLPFC. In our case, we see strong and robust activations for goal hierarchy, with ambiguous trials eliciting far stronger activations than unambiguous ones. Also, the effect of search depth was not adequately captured, as the difference between the ‘direct’ and ‘intermediate’ maps is very minor.

The *Columbia Cards* task presents a small locus of activation in the orbitofrontal cortex, but otherwise no activations in areas putatively involved in gambling process.

Lastly, in the *Dot Patterns* task, we observe small activations in dorsal parietal areas, but fail to capture activations mentioned in the original study [25] as responsible for cognitive control in some of the conditions (namely, medial wall of the prefrontal cortex, anterior cingulate, orbitofrontal cortex, and DLPFC).

Some effects in the cognitive regions associated with the processes assumed to take place in the Stanford tasks are not statistically significant. One cannot assert they are not present, only that the small size of the cohort was too small to identify them. Another possible limitation of our analysis is also the lack of adaptations that might be required to adequately use these tasks for brain mapping.

Multi-Voxel Pattern Analysis

In order to assess the quality of the data from the *Realistic Sounds* task, we conducted a multi-voxel pattern analysis (MVPA) to see to what extent activations in the auditory cortex could be differentiated into categories as presented in the stimuli.

The input used for the model referred to the averaged images for all trials of a given condition for a particular run. Since the task was acquired in 12 runs, and there were 6 categories of interest (‘silence’ and ‘catch’ categories were
<figure><img src="image_2.png" /><figcaption>**Figure 2.** Selection of contrasts for the Lyon battery. Obtained by calculating the conjunction of 25% of the cohort for the main contrasts, robust activation can be observed for all tasks. Correction for multiple comparisons was addressed with an FDR rate set at 5%.</figcaption></figure>

excluded), there were a total of 72 images per participant. Using a Support Vector Classification with linear kernel from scikit-learn [26], the model achieved an mean accuracy of 55% using a stratified K-fold cross-validation strategy (chance=17%). The confusion matrix can be found in Figure 4 for more details on the results. The 'speech' label stands out as the easiest to predict, with a 0.74 prediction accuracy whild being confounded evenly with the rest of the labels, whereas 'animal' and 'tools' are the worst predicted, both reaching a 0.1 confounding rate with several other labels. The rest of the labels ('music', 'nature' and 'voice') have very similar accuracies (0.56, 0.56 and 0.52 respectively). The three of them are most confounded with 'animal', while 'voice' is also similarly confounded with 'speech'.

# Data and Software Availability

## Source Data

Source data of the present release (plus the former three releases) can be accessed via the public repository Open-Neuro [34] under the data accession number ds?????? as well as on EBRAINS platform (XXXA.L.: Data citations of both collections w/ DOI when available. If the fourth release is published in a new collection together with the previous releases, the list of authors shall account for all authors from all releases.) Previous collections referring

to the three first releases can be also found in the Open-Neuro under data accession number ds002685 [31] and EBRAINS [33]. This collection comprises ~1.1TB of MRI data.

The NIIfTI files as well as paradigm descriptors and imaging parameters are organized per run for each session according to BIDS Specification:

* The data repository is organized in twelve main directories sub-01 to sub-15; we underline that sub-02, sub-03 and sub-10 are not part of the data and corresponding data from sub-08 will be made available in further releases

* Data from each subject are numbered on a per-session basis, following the chronological order of the acquisitions; we also note that this order is not the same for all subjects; Table 50 of the IBC documentation can bee consulted for the exact correspondence between Session number and Session ID for every subject on https://project.inria.fr/IBC/data/ (Session ID's of the present release can be consulted on Table 3 and Session ID's of the first and second releases are respectively provided on Table 2 of [30]);

* Acquisitions are organized within session by modality;
<figure><img src="image_3.png" /><figcaption>**Figure 3.** Selection of contrasts for the Stanford battery. Obtained by calculating the conjunction of 25% of the cohort for the main contrasts, robust activation can be observed for all tasks. Correction for multiple comparisons was addressed with an FDR rate set at 5%.</figcaption></figure>

* Different identifiers are assigned to different types of data as follows:

- gzipped NIfTI 4D image volumes of BOLD fMRI data are named as sub-XX_ses-YY_task-ZZZ_dir-AA_bold.nii.gz, in which XX and YY refer respectively to the subject and session id, ZZZ refers to the name of the task, and AA can be either ‘PA’ or ‘AP’ depending on the phase-encoding direction;

- Event files are named as sub-XX_ses-YY_task-ZZZ_dir-AA_event.tsv;

- Single-band, reference images are named as sub-XX_ses-YY_task-ZZZ_dir-AASBREF.NII.gz.

Although BIDS does not yet provide support for data derivatives, a similar directory tree structure was still preserved for this content.

## Derived Statistical Maps

The unthresholded-statistic, contrast maps have been released in the public repository NeuroVault [13] with the id collection = XXXX. A former collection referring to data derivatives from the first, second and third releases is also available in the same repository (id collection = 6618, [32]).

## Software

All the protocols used to acquire the IBC dataset are publicly available on the Github repository: https://github.com/hbp-brain-charting/public-protocols.

The code used for data analysis is also publicly available on the following Github repository:

https://github.com/hbp-brain-charting/public-analysis_code.

## Consent

The experimental procedures were approved by a regional ethical committee for medical protocols in Île-de-France (“Comité de Protection des Personnes” - no. 14-031) and a committee to ensure compliance with data-protection rules (“Commission Nationale de l’Informatique et des Libertés” - DR-2016-033). They were undertaken with the informed written consent of each participant according to the Helsinki declaration and the French public health regulation. Participants were reimbursed on the basis of 80€ per MRI acquisition with extra-fees for any additional session.

## Competing interests

No competing interests were disclosed

## Grant information

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).

## Acknowledgements

We thank the Center for Magnetic Resonance Research, University of Minnesota for having kindly provided the
<figure><img src="image_4.png" /><figcaption>**Figure 4.** Confusion matrix for multi-voxel pattern analysis of the realistic sounds task. The analysis was performed between-subjects using the contrast maps for each run and each subject</figcaption></figure>

Multi-Band Accelerated EPI Pulse Sequence and Reconstruction Algorithms.

At last, we are especially thankful to all volunteers who have accepted to be part of this challenging study, with many repeated MRI scans over a long period of time.

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