# Should one go for individual or group-level brain parcellations? A deep-phenotyping benchmark

Bertrand Thirion, Himanshu Aggarwal, Ana Fernanda Ponce, Ana Luísa Pinho, Alexis Thual

► To cite this version:

Bertrand Thirion, Himanshu Aggarwal, Ana Fernanda Ponce, Ana Luísa Pinho, Alexis Thual. Should one go for individual or group-level brain parcellations? A deep-phenotyping benchmark. Brain Structure and Function, 2023, 10.1007/s00429-023-02723-x. http://dx.doi.org/10.1007/s00429-023-02718-6

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# Should one go for individual or group-level brain parcellations ? A deep-phenotyping benchmark

Bertrand Thirion<fnref n="1" />, Himanshu Aggarwal<fnref n="1" />, Ana Fernanda Ponce<fnref n="1" />, Ana Luísa Pinho<fnref n="3" />,<fnref n="4" />, and Alexis Thual<fnref n="1" />,<fnref n="2" />

## Abstract

The analysis and understanding of brain characteristics often require considering regions-level information rather than voxel-sampled data. Subject-specific parcellations have been put forward in recent years, as they can adapt to individual brain organization and thus offer more accurate individual summaries than standard atlases. But the price to pay for adaptability is the lack of group-level consistency of the data representation. Here, we investigate whether the good representations brought by individualized models is merely an effect of circular analysis, in which individual brain features are better represented by subject-specific summaries, or whether this carries over to new individuals, i.e. whether one can actually adapt an existing parcellation to new individuals and still obtain good summaries in these individuals. For this, we adapt a dictionary learning method to produce brain parcellations. We use it on a deep phenotyping dataset to assess quantitatively the patterns of activity obtained under naturalistic and controlled-task-based settings. We show that the benefits of individual parcellations are substantial, but that they vary a lot across brain systems.

## Highlights

* Individualized parcellations schemes learned on naturalistic data generalize to task data, not only within, but also across individuals

* Individualized parcellations better represent functional signal than fixed parcellations in most brain regions

* Parcellations obtained from naturalistic data fail to capture structure in motor and somato-sensory cortex

* Data-driven parcellations make it possible to adapt resolution and better fit brain signals.

* Some brain systems require higher resolution for accurate representations than that of standard atlases

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

<footnote n="2">²Inserm, Collège de France, Paris, France</footnote>

<footnote n="3">³Department of Computer Science, Western University, London, Ontario, Canada</footnote>

<footnote n="4">⁴Western Institute for Neuroscience, Western University, London, Ontario, Canada</footnote>
# 1 Introduction

**Brain atlases and brain parcellations** Brain-image analysis relies extensively on intermediate-scale representations of the brain volume, such as parcellations and atlases. Here, we consider *analysis tasks* such as connectivity analysis [Dadi et al., 2019, Pervaiz et al., 2020, Bryce et al., 2021, Bijsterbosch et al., 2018], brain activity decoding [Menuet et al., 2022, Mensch et al., 2021, Thomas et al., 2022], the study of regional physiological parameters [Badillo et al., 2013] or the impact of lesions [Kasties et al., 2021]. Among such intermediate representations, *atlases* typically refer to a given ontology of brain structures, that is based on core knowledge on brain organization, such as cyto-architecture or connectivity [Amunts et al., 2014]. Other representations, such as so-called *spatial gradients*, do not benefit from such clear semantics [Kong et al., 2023]. *Parcellations* are related to atlases. The key difference is that they are defined as a data-driven object, i.e. they are built to summarize some image-derived information [Thirion et al., 2006, Tzourio-Mazoyer et al., 2002]. Atlases and Parcellations can be specified in many ways, depending on whether they are defined in the brain volume or on the cortical surface (see e.g. [Glasser et al., 2016]), or whether they are deterministic —leading to a strict partition of the voxels into components— or probabilistic [Schaefer et al., 2017, Dadi et al., 2020], i.e. with overlapping, fuzzy components. Parcellation definition hinges on some perspective of the features that are deemed important in a particular type of brain-imaging analysis (cyto-architecture, anatomical connectivity, functional connectivity, vascular structures etc.)

In a data-analysis context, atlas-based and parcellation-based representations are designed to meet two objectives: i) compress the data by replacing a voxel-sampled image by a region-level representation, which is then typically used to reduce the multiple comparison burden in brain imaging group analyses, and ii) name the regions, i.e. provide anatomical labels to identify brain features, which is important to put the results of neuroimaging data analyses in context. From a data-analytic perspective, atlases face two issues: first, they are not a very efficient data-reduction approach, as data-driven approaches perform better [Schaefer et al., 2017, Dadi et al., 2020] when it comes to maximizing representation accuracy. Specifically, when considering functional brain organization, fixed atlases do not fit well brain topographic organization, such as peak regions of brain maps, or boundaries between functional regions [Gordon et al., 2017]. An obvious reason is that most atlases have not been defined to account for function; a second reason is that they do not adapt to individual characteristics. The second major issue is that region naming is known to be inconsistent [Bohland et al., 2009], weakening the usefulness of structure naming and making it ultimately hard for the community to build a consistent ontology of brain structures, though integration efforts are under way [Lawrence et al., 2021, Uddin et al., 2023].

In this paper, we thus turn to a probabilistic-parcellation approach, which has gained popularity in the recent years [Harrison et al., 2020, Amunts et al., 2020, Dadi et al., 2020]. Probabilistic parcellations give a non-negative weight to voxels to belong to the model components. A given voxel may have non-zero weight for several components, illustrating the uncertainty of voxel-to-component assignments, which in turn reflects noise in the data, poor model fit, or inter-subject variability. Individualized parcellations are parcellation schemes that adapt to each individual in a given dataset, as opposed to a fixed population-level model [Thirion et al., 2006, Glasser et al., 2016, Gordon et al., 2017, Gratton et al., 2018, Kong et al., 2019, Greene et al., 2020, Zhao et al., 2020, Harrison et al., 2020, Pinho et al., 2021]. They represent a desirable step toward the personalization of brain models. Our aim is to investigate whether it is actually *useful* to individualize brain parcellations.
**Population-level versus individual parcellations**
The recent trend in developing individual parcellations leads to some questions regarding their cost/benefit trade-off. On the one hand, individualized parcellations have been reported to better represent individual signal than population-level parcellations [Glasser et al., 2016, Gordon et al., 2017, Braga and Buckner, 2017, Kong et al., 2019]. On the other hand, the use of such parcellations schemes raises difficult questions, such as:

i) How to ensure consistency of the parcellation across individuals? A purely bottom-up approach that would parcel each individual's dataset independently does not yield corresponding parcellations schemes, which makes it useless in practice. A constrained approach, where parcellations are obtained simultaneously across individuals leads to complex algorithmic procedures to ensure parcellation consistency [Thirion et al., 2006, Gordon et al., 2017], or to arbitrary choices, where parcellations end up being defined manually [Glasser et al., 2016].

ii) Keeping in mind that the generalizability of findings is key to validity [Varoquaux and Poldrack, 2019], how can one generalize scientific results obtained in a given sample of individuals using a subject-adaptive parcellation to a wider group? This at least requires that the learned parcellation scheme can be generalized to new individuals. But the concrete mechanisms to perform such generalization are still elusive, if not lacking.

These two concerns have not been dealt satisfactorily so far. In [Glasser et al., 2016] the generalization of parcellation schemes to new data was performed, but the end validation was restricted to the consistency with hand-drawn delineations, which does not grant predictive validity. Overall, the quantification of parcellation goodness of fit is still lacking, making it impossible to decide how good a given representation scheme is. The evaluation of parcellation schemes is thus an important and urgent practical question. It has been partly addressed in the realm of functional connectivity analysis by [Bijsterbosch et al., 2018], however, due to the very structure of connectivity, it is still a challenge to unambiguously tease out the spatial variability from connectivity variability across individuals. Activation mapping represents a simpler framework where the functional properties of brain are probed, irrespective of other regions, that can also be hard to characterize. Other indirect evidence comes from the work of [Tavor et al., 2016, Dohmatob et al., 2021], that has shown that probabilistic parcellations derived from resting-state accurately predict brain responses to tasks, but these rely on the dual regression scheme [Nickerson et al., 2017, Harrison et al., 2020] that breaks the nature of parcellation, as it leads to non-sparse and potentially non-positive spatial loadings for the components. A better-defined approach instead relies on naturalistic fMRI, that does not suffer from the indeterminacy of resting-state fMRI [Nastase et al., 2020].

**Our contribution**
In this work, we tackle the question of the goodness of fit of individualized parcellations, with a focus on *predictive accuracy*: if brain functional characteristics can be associated with the components of a given parcellation, how do these characteristics generalize to new individuals? We thus propose a metric to measure the *predictive accuracy* of parcellation/atlasing schemes, i.e. the reconstruction quality of individual data in participants that have not been included in the initial definition of the parcellation scheme.

To this end, we leverage *Individual Brain Charting* (IBC), a deep-phenotyping dataset [Pinho et al., 2018, Pinho et al., 2020] comprising naturalistic data and more than a hundred condition-derived functional contrasts obtained from forty tasks, in a fixed cohort of twelve participants. We propose to learn individual-parcellation schemes from naturalistic stimuli (mostly movie-watching data) and then assess whether the learned parcellation scheme performs well on *new individuals* in the reconstruction of brain maps for *new contrasts*, i.e.
imaging contrasts that were not used to build the parcellation. It may well be that the gain obtained on in-
dividual parcellation fitting vanishes when one tries to generalize the learned representation scheme to new
individuals. This could make fixed dictionaries, or even standard atlases, a safer choice.

We compare the accuracy of three types of parcellations:

* atlases, i.e. a priori defined parcellations schemes. We consider the MultiModal Parcellation (MMP) 1.0 atlas [Glasser et al., 2016], which is a reference among cortical atlases. We also consider the Schaefer parcellation scheme [Schaefer et al., 2017]

* fixed dictionary, a probabilistic parcellation scheme that is inferred from neuroimaging data, but common to a population of individuals

* individual dictionary, a probabilistic parcellation scheme that is inferred from neuroimaging data, and specific to each individual.

We also take this opportunity to assess the impact of resolution on data driven parcellation —*resolution* being implicitly defined by the number of components in the parcellation: we measure the impact of doubling the number of components extracted in a given brain system on the accuracy of the reconstruction.

It is also important to observe that evaluating parcellation schemes on noisy data can lead to some biases; for instance, methods that shrink signal estimates will generalize better than methods that don't. To avoid such biases, we carefully select contrast maps that display the highest reproducibility level across individuals, within each brain system considered, so that the analysis relies on signal, not noise.

In the next sections, we first describe in detail our proposed method; then we present results comparing the goodness of fit of individual parcellations *versus* group level parcellations and fixed atlases. We particularly outline regional differences, noticing that the most accurate approach is not the same one across brain systems.

2 Methods

## 2.1 Dataset used

**Data acquisition and pre-processing** A detailed description of the preprocessing pipeline of the IBC data is provided in [Pinho et al., 2021]. All fMRI images, i.e. GE-EPI volumes, were collected twice with reversed phase-encoding directions, resulting in pairs of images with distortions going in opposite directions. Susceptibility-induced off-resonance field was estimated from the two Spin-Echo EPI volumes in reversed phase-encoding directions. Images were corrected based on the estimated deformation model. Details about the method can be found in [Andersson et al., 2003].

Further, all GE-EPI volumes were aligned to each other within every participant. A rigid-body transformation was employed, in which the average volume of all images was used as reference [Friston et al., 1995]. Raw data were preprocessed with *PyPreprocess* <fnref n="1" />, using SPM12 routines for the main steps. The anatomical and motion-corrected fMRI images were given as input to *FreeSurfer* v6.0.0, in order to extract meshes of the tissue interfaces and the sampling of functional activation on these meshes, as described in [van Essen et al., 2012]. The corresponding maps were then resampled to the *fsaverage7* template of *FreeSurfer* [Fischl et al., 1999].

<footnote n="1">¹https://github.com/neurospin/pypreprocess</footnote>
<table><caption>Table 1: List of the naturalistic tasks used in the experiments. Note that time is total acquisition time summed across runs.</caption><thead><tr><td>Task name</td><td>task performed</td><td>duration (#runs / time, min)</td><td>reference</td></tr></thead><tbody><tr><td>Clips</td><td>Watching and fixating</td><td>21 / 210</td><td>[Nishimoto et al., 2011]</td></tr><tr><td>Bang</td><td>Watching and listening</td><td>1 / 8</td><td>[Campbell et al., 2015]</td></tr><tr><td>Raiders of the lost Ark</td><td>Watching and listening</td><td>13 / 120</td><td>[Haxby et al., 2011]</td></tr><tr><td>The Good, the Bad and the Ugly</td><td>Watching and listening</td><td>21 / 180</td><td>[Mantini et al., 2012]</td></tr><tr><td>The Little Prince</td><td>Listening</td><td>9 / 90</td><td>[Hale et al., 2022]</td></tr></tbody></table>

**Task data** FMRI data were analyzed using the *General Linear Model*. Regressors of the model were designed to capture variations in BOLD response following stimulus timing specifications. They were estimated through the convolution of boxcar functions, that represent per-condition stimulus occurrences, with the canonical *Hemodynamic Response Function* (HRF). To build such models, paradigm descriptors grouped in triplets (i.e. onset time, duration and trial type) according to BIDS specification were determined from the log files' registries generated by the stimulus-delivery software. To account for small fluctuations in the latency of the HRF peak response, additional regressors were computed based on the convolution of the same task-conditions profile with the time derivative of the HRF. Nuisance regressors were also added to the design matrix in order to minimize the final residual error. To remove signal variance associated with spurious effects arising from movements, six temporal regressors were defined for the motion parameters. Further, the first five principal components of the signal, extracted from the 5% voxels with highest variance, were also regressed to capture physiological noise [Behzadi et al., 2007].

In addition, a discrete-cosine basis was included for high-pass filtering ($cutoff=\frac{1}{128}$ Hz). Model specification was implemented using *Nilearn* [Abraham et al., 2014], a Python library for statistical learning on neuroimaging data<fnref n="2" />.

The ensuing set of contrasts is listed in section A. Let $n$ denote the number of participants. Contrasts acquired in each participant result in spatial maps, noted ($Z^s$), $s = 1 \cdots n$, where $Z^s \in \mathbb{R}^{v \times c}$, $v$ is the number of vertices across the two hemispheres and $c$ the number of contrasts.

**Naturalistic stimuli** We considered five naturalistic datasets. Corresponding acquisitions are briefly described in Table 1, together with the original reference. These datasets were acquired and preprocessed with the same pipeline as the task data and resulted in time courses sampled on the mesh of each individual brain, finally registered to fsaverage7 space.

A dimension reduction was performed on the different datasets using a *Shared Response Model* (SRM, [Chen et al., 2015, Richard et al., 2021]). We used the FastSRM implementation of the method<fnref n="3" />. SRM yields subject-specific maps associated with common temporal components elicited by some naturalistic content. Twenty components were retained for each task, except the *Bang* task, for which ten components were retained. The reason is that *Bang* contains less data (see Table 1). These components were concatenated to form a naturalistic response signature in each vertex, so that the whole naturalistic dataset was reduced to $q = 90$ spatial maps in each individual. These spatial maps are noted ($X^s$), $s = 1 \cdots n$, where $X^s \in \mathbb{R}^{v \times q}$, $v$ is the number of vertices across the two hemispheres, and $q = 90$ represents the set of SRM components.

<footnote n="2">²https://nilearn.github.io</footnote>

<footnote n="3">³https://github.com/hugorichard/FastSRM</footnote>
**Data availability** The raw data are available on the OpenNeuro<fnref n="4" /> and Ebrains platforms<fnref n="5" />. Preprocessed data are available on Ebrains<fnref n="6" /> and contrast maps are available on NeuroVault<fnref n="7" /> and Ebrains<fnref n="8" />. A comprehensive documentation of the data is available online<fnref n="9" />.

## 2.2 Modeling

**Terminology** We call dictionary a set of brain components associated with some functional characteristics; such an object is called a probabilistic data-driven parcellation in other circumstances [Dadi et al., 2020]. We follow the methodology developed in [Thirion et al., 2021, Varoquaux et al., 2013]. The only difference is that it is applied on a subset of the brain domain, in sub-systems defined in the left or right hemisphere respectively. See Section 2.3 for details.

**Dictionary estimation** Formally, consider the set of brain maps $X^s$ obtained for $q = 90$ maps in a subject $s \in 1, \dots, n$ and a given brain region. To avoid cluttering notations, we omit the dependence on the region, and simply consider that the data matrices $X^s, s = 1 \dots n$ and $Z^s, s = 1 \dots n$ are available for a set of voxels or vertices under consideration. To get more compact notations, the $1 \dots n$ set will be denoted $\llbracket n \rrbracket$ in this paper. By enumerating the values across a mesh of vertices, for $j \in \llbracket q \rrbracket$, each column $x_j^s$ of $X^s$ is a $p$-dimensional vector, where $p$ is the number of vertices (since we consider a brain system, we have $p < q$); $X^s$ is thus a matrix of size $p \times q$. Individual dictionary learning build on functional correspondences [Thirion et al., 2021, Varoquaux et al., 2013]; it comes from solving the following minimization problem for a given $\lambda > 0$:

$$ \min_{(\mathbf{U}^s)_{s=1 \dots n} \in \mathcal{V}_{\mathbf{X}} \in \mathcal{C}} \sum_{s=1}^{n} \left( \|\mathbf{X}^s - \mathbf{U}^s \mathbf{V}_{\mathbf{X}}\|_F^2 + \lambda \|\mathbf{U}^s\|_1 \right), \quad (1) $$

where $\mathbf{U}^s \ge 0, \forall s \in \llbracket n \rrbracket$, i.e. the spatial components are forced to be non-negative in all subjects. Here, $\mathcal{C}$ denotes the set of matrices with row norm smaller than 1. Note that the $\mathbf{V}_{\mathbf{X}} \in \mathcal{C}$ constraint is only a technical one that is needed to make the problem well-posed. It does not change the structure of the solution. $\mathbf{U}_s$ matrices have shape $p \times k$, whereas the functional-loading matrix $\mathbf{V}_{\mathbf{X}}$ has shape $k \times q, k$ being the number of components. $\mathbf{V}_{\mathbf{X}}$ describes the functional characteristics of the components. $\|\cdot\|_F$ and $\|\cdot\|_1$ stand for the Frobenius and $\ell_1$ norm on matrices respectively. The estimated subject-specific spatial components ($\mathbf{U}^s$), $s \in \llbracket n \rrbracket$ can be interpreted as individual topographies; these non-negative components may overlap, although their values are zero in most vertices. The $\lambda$ parameter was calibrated in order to yield a sparsity of around 50%.

In this work, we also explore alternatives to this individualized schemes. In particular, we consider a fixed dictionary approach. It is obtained by concatenating all training subjects along the contrast dimension, effectively assuming that the spatial organization is common across all individuals. This leads to $\tilde{\mathbf{U}}$ components that are common across individuals.

$$ \min_{\tilde{\mathbf{U}} \in \mathbb{R}^{p \times k} \atop \mathbf{V}_{\mathbf{X}} \in \mathcal{C}} \sum_{s=1}^{n} \|\mathbf{X}^s - \tilde{\mathbf{U}} \mathbf{V}_{\mathbf{X}}\|_F^2 + \lambda \|\tilde{\mathbf{U}}\|_1, \quad (2) $$

<footnote n="4">⁴https://openneuro.org/datasets/ds002685</footnote>

<footnote n="5">⁵https://search.kg.ebrains.eu/instances/8ddf749f-fb1d-4d16-acc3-fbde91b90e24</footnote>

<footnote n="6">⁶https://search.kg.ebrains.eu/instances/3ca4f5a1-647b-4829-8107-588a699763c1</footnote>

<footnote n="7">⁷https://neurovault.org/collections/6618</footnote>

<footnote n="8">⁸https://search.kg.ebrains.eu/instances/07ab1665-73b0-40c5-800e-557bc319109d</footnote>

<footnote n="9">⁹https://individual-brain-charting.github.io/docs</footnote>
As the estimation problem (in both individual and fixed dictionary settings) is non-convex, initialization matters; here, we informally controlled that results would be stable when training data is varied. We found that the variance of the ensuing results is small (see e.g. in Figure 5). As this variance accounts in part for the initialization effect, we can conclude that the impact of initialization is negligible with respect to the effects reported in the results section. The number k of components depends on the region considered (see below).

**Representing new fMRI contrasts in a given dictionary** We now consider c novel contrasts observed in the same individuals, resulting in matrices $Z^s = (z_j^s), j \in [c]$. What we need to do is to estimate the functional loadings $V_Z$ associated with the components $U$ of the dictionary outline above. Following the model in equation 1, $V_Z$ is defined as

$$V_Z = \underset{V \in \mathbb{R}^{k \times c}}{\operatorname{argmin}} \sum_{s=1}^{n} \|Z^s - U^s V\|_F^2, \quad (3)$$

The only difference is that the technical constrain $V_Z \in C$ is no longer necessary, since $(U^s)_{s \in [n]}$ are fixed. This estimation is readily solved by ordinary least squares. Interestingly, this fitting procedure is analogous to the one described in [Tavor et al., 2016, Dohmatob et al., 2021].

**Generalization to new individuals** Assume now that we have data from new subjects $(X^s, Z^s), s \in [n+1, \dots, n+m]$. Generalizing the model to these individuals means learning the spatial components for these new individuals. This is solved, again, by re-writing equation 1: for each individual $s \in [n+1, \dots, n+m]$,

$$U^s = \underset{U}{\operatorname{argmin}} \|X^s - U V_X\|_F^2 + \lambda \|U\|_1, \quad (4)$$

using the values of $V_X$ and $\lambda$ learned from Eq. 1. Then, one can try and predict what would be the response of these new subjects to the contrasts under consideration:

$$\hat{Z}^s = U^s V_Z \quad (5)$$

$\hat{Z}^s$ represents the expected per-vertex amount of activity in subject $s$ for $c$ contrasts, assuming that the topographical information learned from naturalistic data is consistent with the topographic organization of task data, and that the amount of activity in movie and task are consistent across individuals.

Method evaluation proceeds by computing the discrepancy between observed $Z^s$ and predicted $\hat{Z}^s$. Since $Z^s = (z_1^s, \dots, z_c^s)$ represents $c$ contrast maps, we propose to take the cosine distance between observed and predicted maps and average them to get an overall similarity across contrasts:

$$\Delta(s) = \frac{1}{c} \sum_{d=1}^{c} \left( 1 - \frac{\langle z_d^s, \hat{z}_d^s \rangle}{|z_d^s| |\hat{z}_d^s|} \right)$$

This distance represents how strongly predicted contrasts match observed ones in the region under consideration, on average. Lower values are better.
## 2.3 Experiments

The experiment consisted of studying functional organization in twenty-one coarse-grain brain systems, namely those used in [Glasser et al., 2016] to describe the MMP1.0 atlas regions. These large-scale systems are described in Table 3. We only made one exception to the organization described in [Glasser et al., 2016]: V1 was included in the same set as the other primary visual regions. Left and right hemispheres were fitted separately. We performed the prediction experiment in three settings:

* using the MMP1.0 atlas (which are fixed, non-overlapping spatial components). Concretely, this means that in equation 3 and 4, $U^s$ was replaced by a region indicator read from the MMP1.0 parcellation.

* using a fixed dictionary inferred from the data through dictionary learning; the number of components was taken equal to the number of MMP1.0 regions in the brain system. Concretely, this means that in equation 3 and 4, $U^s$ was replaced by the group average model $\tilde{U}$ from Eq. 2.

* using an individual dictionary, i.e. a set of overlapping components inferred from the data through dictionary learning; the number of components was taken equal to the number of MMP1.0 regions in the brain system.

In concrete terms, we performed a randomized cross-validation loop, where six subjects were used to train the model and five were used to assess the model, in twenty random splits of the data. Computing the distance 2.2 and averaging across test subjects leads to distances $\Delta_{atlas}$, $\Delta_{fixed}$ and $\Delta_{individual}$ respectively, for each split. Such values are defined at the level of brain systems, that are assumed to be defined consistently across subjects. We observed that the fluctuation of these $\Delta$ values was very narrow across splits (see Figure 5 below), meaning that the data was sufficient to obtain reliable estimates.

We also performed a control experiment, in which we directly measured the cosine distance between all maps of the test subjects and the average map among training subjects:

$$ \text{distance}(s) = \frac{1}{c} \sum_{d=1}^{c} \left( 1 - \frac{\langle \mathbf{z}_{d}^{s}, \text{mean}_{t \in \text{training set}} \mathbf{z}_{d}^{t} \rangle}{|\mathbf{z}_{d}^{s}| |\text{mean}_{t \in \text{training set}} \mathbf{z}_{d}^{t}|} \right) \quad (6) $$

The whole training and test procedure is illustrated in Figure 1. In a second experiment, we repeated the same procedure as above, but taking twice the number of components present in the MMP1.0 atlas.

In order to ensure that results were not tied to the peculiar choice of the MMP1.0 atlas, we reproduced them with one of Schaefer's parcellation [Schaefer et al., 2017], namely a parcellation into 400 regions (200 in each hemisphere), that subdivides the Yeo17 atlas [Yeo et al., 2011]. In this experiment, system definition corresponds to the 17 large-scale Yeo17 networks.

Finally, we also performed a whole-brain comparison of the three models to study how the three types of atlases behave in a large-scale setting, dealing with 160k vertices and 181 regions, which renders the estimation procedure more challenging.

**Implementation** The implementation relies on the dictionary-learning methods of scikit-learn v1.0 [Pedregosa et al., 2011], a Python machine-learning library<fnref n="10" />. Dictionary fitting took 15s to 90s on a computer desktop, depending on the brain system considered.

<footnote n="10">¹⁰https://scikit-learn.org/stable/</footnote>
<figure><img src="image_2.png" /><figcaption>Figure 1: **Representation of the individualized parcellation benchmark done in this work.** (*left*) Dictionary learning is performed on naturalistic fMRI (mostly movie watching, see Table 1), resulting into individual-specific spatial components. From these components, one can learn to predict fMRI contrasts, using a regression approach. (*right*) Then, we transfer the dictionary onto new participants, obtaining a subject-specific dictionary, based on which we can then predict the amount of activation for the task fMRI contrasts observed in the training set. This is used to compute a prediction error that quantifies the overall goodness of fit of the model.</figcaption></figure>
**Reproducibility** The analysis code for data preprocessing is available on our Github repository <fnref n="11" />. Scripts used to obtain results and figures are available <fnref n="12" />.

# 3 Results

An instance of the dictionary learning procedure is displayed for one brain system, namely the left Temporal-Parietal-Occipital Junction (TPJ) in Figure 2. It shows in particular that the estimated components differ markedly from the MMP1.0 atlas boundaries in that region. Still, it remains possible to find a one-to-one relationship between MMP1.0 regions and the obtained components, so that we can name the components using the atlas names. Components learned by dictionary learning can be characterized by different functional properties, as shown in Figure 2, left: the TPOJ3 region shows more activity for pain-related contrasts, TPOJ2 for visual contrasts, TPOJ1 for auditory descriptions, STV for Story listening and PSL for general auditory conditions. In terms of topography the TPOJ2/TPOJ3 and STV/PSL boundaries are oriented differently from the MMP1.0 atlas. Another example is available in appendix, see Figure 9.

We mapped the distance gain $\Delta_{atlas} - \Delta_{individual}$ measured on functional contrasts in novel participants, in which the parcellation scheme was applied and individual activation was predicted from that of the training subjects. This difference is considered for the twenty one systems listed in Table 3, and represented on a brain map in Figure 3 (top). Obtained values are the result of a twenty-fold random splitting of the data into train and test. This shows that individual parcellations provide a better generalization in a majority of systems (14 over 21, left or right hemisphere), and particularly so in the Temporal-Parietal-Occipital Junction (TPJ) bilaterally and the right Inferior Frontal Gyrus. By contrast, individual parcellations yield less accurate mappings in the motor regions and the insula bilaterally. In Figure 3 (bottom), one can see the same map, but evaluated on the subjects of the training set, i.e. in the absence of cross-validation. The figure displays overall the same region effects, but all values are larger, showing that individual parcellations outperform systematically. But without proper validation, this superiority is spurious.

In Fig.4, one can see the difference in $\Delta_{fixed} - \Delta_{individual}$ between individual dictionary and fixed dictionary models, within the above brain systems, across twenty random cross-validation folds. Again we observe very clear regional differences: the bilateral Temporal-Parietal-Occipital Junction and right Inferior Frontal Gyrus again display a clear advantage for the individual parcellation, scheme, but motor and insular regions display the opposite trend.

To provide a precise quantitative account of these difference across systems, distances $\Delta_{atlas}$, $\Delta_{fixed}$ and $\Delta_{individual}$ are displayed in Figure 5 for all twenty one systems, in the left (top) and right (bottom) hemisphere, respectively. These are displayed with bars. The accuracy obtained without atlases, simply using voxel-level signal averages as explained in Eq. 6, are represented by an horizontal line. This leads to several observations: i) in the most general case (13/21 systems in the left hemisphere, 14/21 in the right hemisphere), we see that individual dictionary outperforms fixed dictionary, which in turn outperforms the atlas; however, ii) there are several exceptions to this general trend: for instance, in the somatosensory regions, the reverse pattern is obtained, where the atlas performs better than both individual and fixed dictionaries; iii) these quantitative results are very consistent across hemispheres, and come with small error bars across validation folds; iv) in some regions, a standard voxel-level mapping yields a better prediction than the parcellation (early visual cortex, somatosensory and motor regions), while it performs worse in most others (Dorsal and Ventral Streams,

<footnote n="11">¹¹https://github.com/individual-brain-charting/public_analysis_code</footnote>

<footnote n="12">¹²https://github.com/bthirion/deep_parcellation_benchmark</footnote>
<table><thead><tr><th></th><th>TPOJ3</th><th>TPOJ2</th><th>STV</th><th>PSL</th><th>TPOJ1</th></tr></thead><tbody><tr><td>visual-auditory</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>no talk</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>talk-no talk</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>recognition other hit</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>enumeration constant</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>physical pain</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>photo</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>sn average event</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>we average event</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>word-consonant string</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>sentence-jabberwocky</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>random</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>mental-random</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>story-math</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>triangle random</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>reading-listening</td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

<figure><img src="image_3.png" /><figcaption>Figure 2: **Example of an individualized-parcellation scheme in the Temporal-Parietal-Occipital Junction (TPJ).** (left) For five-components parcellation of the TPJ, one can characterize their response to 160 contrasts; sixteen are displayed for readability, and are related to cognitive operations (mathematics, language, theory of mind, see Table 2 for a description of these contrasts). (right) Each vertex of the TPJ can be assigned to these regions by considering the loadings in each individual (bottom), or some summary of these loadings across individuals. (top) The resulting parcellation is superimposed with the original MMP1.0 parcellation. It clearly outlines a different spatial structure, where the TPOJ2/TPOJ3 and STV/PSL boundaries are oriented differently from the MMP1.0 atlas.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>Figure 3: **Comparison of functional parcellations *versus* atlas-based parcellation.** (Top) This map shows $\Delta_{atlas} - \Delta_{individual}$, the distance difference between individualized parcellations and a population-level atlas for 160 functional contrasts, in each of the twenty one systems listed in Table 3. (Bottom) This map represents the same quantity, but without cross-validation: the model is trained and evaluated on the same subjects. In both cases, positive values mean that individualized parcellations outperform the atlas, and negative values, the converse.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Figure 4: **Comparison of individual dictionary versus atlas-based representations.** (Top) The map displays the $\Delta_{fixed} - \Delta_{individual}$ distance difference between individualized parcellations and a fixed parcellation, for 160 functional contrasts, averaged in each of the twenty one systems listed in Table 3. (Bottom) The map represents the same thing, but without cross-validation: the model is trained and evaluated on the same subjects. In both cases, positive values mean that individualized parcellations outperform the fixed one, and negative values, the converse.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>Figure 5: **Quantitative comparison of the parcellations fit per large-scale brain system.** The quality metric obtained for three spatial models is compared in twenty-one brain systems, across twenty validation folds (lower is better). The three spatial models are the MMP1.0 atlas, a fixed dictionary and a subject-specific dictionary; $\Delta_{\text{atlas}}$, $\Delta_{\text{fixed}}$ and $\Delta_{\text{individual}}$ are displayed with bars representing between-fold variations. The horizontal line represents the predictive accuracy of a voxel level model as defined in Eq. 6.</figcaption></figure>

Superior plus Inferior Parietal Sulci, and Temporal-Parietal-Occipital Junction). We hypothesize that regions where voxel-level fit outperforms parcellation-based fit are affected from an overly coarse parcellation scheme, and they would thus require a finer scheme, with more regions to capture subtle functional differences.

To check whether allowing a larger number of regions changes the capacity of parcellation schemes to fit brain data, we reproduced all experiments while doubling the number of components estimated by the dictionary fitting. Results are shown in Figure 6. Increasing the number of regions improves the goodness of fit of the parcellation models, see e.g. in the early visual cortex —yet it does not change the main trends observed previously. It enhances the performance of parcellation-based methods with respect to simple voxel-level modeling.

**Reproduction on Schaefer atlas** In this experiment, system definition corresponds to the 17 large-scale Yeo17 networks, and we compare individual and fixed dictionaries against Schaefer parcels in Fig. 7.

In that case, we observe a systematic lead of data-driven parcellations over the fixed atlas, and minor differences between the individual and the fixed parcellation. These results illustrate the benefit of data-driven parcellations, though not necessarily individualized parcellations. When compared to MMP1.0 parcellation, results are overall worse, outlining that the Schaefer atlas captures less well brain characteristics than MMP1.0.
<figure><img src="image_7.png" /><figcaption>Figure 6: **Quantitative comparison of the atlas fit per large-scale brain system, with a dictionary having twice the number of components of the atlas.** The quality metric obtained for three spatial models is compared in twenty-one brain systems, across twenty validation folds (lower is better). The three spatial models are the MMP1.0 atlas, a fixed dictionary and a subject-specific dictionary; Δ<sub>atlas</sub>, Δ<sub>fixed</sub> and Δ<sub>individual</sub> are displayed with bars representing between-fold variations. The horizontal line represents the predictive accuracy of a voxel level model as defined in Eq. 6. The difference with respect to Fig. 5 is that the dictionaries have been fitted with a number of components equal to twice the number of regions in the atlas.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Figure 7: **Quantitative comparison of the atlas fit per large-scale brain system of the Schaefer400 sub-division of Yeo17 atlas.** The quality metric obtained for three spatial models is compared in seventeen brain systems, across twenty validation folds (lower is better). The three spatial models are the Schaefer400 atlas, a fixed dictionary and a subject-specific dictionary; Δ<sub>atlas</sub>, Δ<sub>fixed</sub> and Δ<sub>individual</sub> are displayed with bars representing between-fold variations. The horizontal line represents the predictive accuracy of a voxel level model as defined in Eq. 6.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>Figure 8: **Quantitative comparison of the atlas fit at the whole-brain level.** The quality metric obtained for three spatial models at the whole brain scale, across twenty validation folds. The three spatial models are the MMP1.0 atlas, a fixed dictionary and a subject-specific dictionary; $\Delta_{\text{atlas}}$, $\Delta_{\text{fixed}}$ and $\Delta_{\text{individual}}$ are displayed with bars representing between-fold variations. The horizontal lines represent the predictive accuracy of a voxel level model as defined in Eq. 6.</figcaption></figure>

**Whole-brain parcellation** We also considered whole brain parcellation schemes. We fixed the number of components to 181 and compare the average distances $\Delta_{\text{atlas}}$, $\Delta_{\text{fixed}}$ and $\Delta_{\text{individual}}$ across systems, still using only contrasts that are relevant in each brain system. The results are shown in Fig. 8. Interestingly it shows that individual parcellations slightly outperform fixed atlas, such as MMP1.0, but perform less well than the fixed parcellations scheme. A likely reason is that the optimization problem underlying dictionary estimation becomes harder at a whole brain scale, with a large number if components involved. Hence the fixed model fitted on more data is more robust to overfitting and has better generalization than the individualized model.

# 4 Discussion

When creating a reduced representation of a dataset, should one rely on traditional brain atlases, a data-driven but fixed parcellation, or an individual parcellation? Each solution has its pros and cons: atlases come with semantics about regions identity and make it possible to query information on regions from the literature (see e.g. [Yarkoni et al., 2011]). Fixed dictionaries allow overlapping regions and are more adaptive to the signal, leading to better fit [Dadi et al., 2020]. Yet, it is sometimes considered that individualized dictionaries better capture brain organization, and thus should be preferred. This view relies on the observation that inter-subject variability is partly not only noise but that it also contains meaningful information about individual characteristics. In short, individual dictionaries represent an appealing opportunity to personalize brain models. In the present article, we have shown that it is possible to create a reliable parcellation scheme on new subjects based on the individual parcellation of other subjects.

More in detail, if one simply considers the representation capacity of parcellations within individuals, the fact that individual dictionaries better capture individual brain organization than fixed dictionaries or atlases is a self-fulfilling prophecy: by construction, it is designed to provide the best summary of the input data, under relatively limited constraints. This phenomenon is illustrated in Fig. 3 (bottom), where the individual dictionary systematically outperforms the atlas, when it is evaluated on the subjects that it was trained on. This difference
in accuracy between training and testing data is exactly what is called overfit. To a lesser extent, individualized dictionaries show some overfit with respect to fixed dictionaries, as seen in Fig. 4(bottom). Overfitting is even more pronounced when the parcellations’ goodness of fit is measured on the imaging contrasts that were used to create those parcellation (which are certainly affected by circularity, in such a case.

By contrast, generalizing parcellation schemes to new individuals provides a stricter and arguably more reliable test of the parcellations representation abilities. Indeed, such generalization-based framework penalizes methods that yield a considerable amount of variability, as they will generalize poorly to new individuals. For instance, when considering whole-brain parcellations, we found that individualized parcellations outperform a standard atlas, but did less well than a fixed parcellation scheme. This shows that the estimation of components across the whole brain remains a hard task, and that overfitting may well occur. This picture is however more interesting when analysis is carried out per brain system.

The present study leads to nuanced conclusions regarding the benefit of individualized parcellations. For instance, Figure 2 displays a region, the TPJ, in which functional organization is poorly captured by current atlases and calls for data-driven and individualized models. However, the gain is less obvious in other brain systems. More generally, in two thirds of such regions, like the Temporal-Parietal-Occipital Junction, the Inferior Parietal system, the MT+ Complex and Dorso-Lateral Prefrontal Cortex, there is a clear advantage of learning the atlas and its study at the individual level. The reason is probably that these regions exhibit substantial functional variability across individuals, that is not well accounted for by anatomical registration. On the other hand, in a few regions such as somato-sensory region or the insula, the reverse patterns were found: classical atlases outperform individual parcellations.

Moreover, we found that in most regions, atlas-based representations better generalize to new individuals than vertex-level representations, with two main exceptions: primary visual cortex and somato-sensory regions (see in Fig. 5 and 6, the error bar is higher for the individual dictionary than for the atlas, for the *somato-sensory and motor* and *early visual* systems; it is visible in both left and right hemispheres).

There are different possible causes for the diversity of results. First, it might be simply difficult to learn stable organization from the input data in some regions, such as a parcellation of the motor cortex from movie watching that does not involve motor actions. In that case, data-driven parcellations are simply ineffective: a standard atlas is more useful. Second, there might be a resolution effect: some systems, such as the visual cortex, require finer parcellations to obtain good results. Figure 6 depicts this situation, where doubling the number of parcels wrt Fig.5 improves accuracy in these ill-fitted regions. Overall, data-driven parcellations retain the advantage of adapting the resolution to better fit the signal.

However, the proposed approach has some limitations. We used a single metric, namely cosine distance [Manning et al., 2008], averaged over a selection of contrasts that display sufficient within-subject test-retest reliability. In particular, the metric is insensitive to signal magnitude, but in many context, such magnitude matters less than the topographic organization of the brain map. Following [Thirion et al., 2014], we could have also considered parcellation stability across individuals, but we believe that this type of analysis only leads to ambiguous results: what level of variability can indeed be considered to be tolerable? Does increased stability imply less sensitivity to the training data? In [Dadi et al., 2020], other criteria were taken into account, such as connectivity-based prediction, which is not addressed here. The relevance is less clear than the accuracy criterion we used, and more importantly, [Bijsterbosch et al., 2018] have shown that the interpretation of differences in connectome-based prediction is quite complex, as it is hard to separate the connectivity estimates from the underlying atlas definition.

Finally, the work presented here relied quite extensively on a deep phenotyping approach, i.e., a collection
that yields abundant subject-specific data. In the present, we leveraged part of the IBC data, that provides a large variety of activation maps (160 contrasts were used here), highlighting functional characteristics of many brain systems. The price to pay is that data are obtained from a limited sample of participants, which may reduce the statistical validity of the results. We however observe that the results are very stable across 20 cross-validation folds, indicating that the conclusions are not too sensitive to sampling effects. This dataset also contains naturalistic data, that are extremely useful for parcellation purposes. This type of data can provide useful quantitative information on brain organization, and thus guide methodology for larger-scale studies, where many more individuals are sampled from a more limited set of experimental contexts. The present work highlights that parcellations learned from naturalistic stimuli (particularly, movie watching) are not well defined in somatosensory regions. We thus conjecture that learning them from dedicated task data, such as a motor localizer, would lead to a better result. Nevertheless, deep phenotyping allows us to address such questions, that we leave for future work. Another valuable direction of research is to compare parcellations learned from resting-state data, naturalistic data and functional contrasts. Finally, we expect that functional alignment could be used to enhance the similarity across participants, as demonstrated in [Thual et al., 2022], but this goes beyond the present work.

**Conclusion** This study provides evidence that individual data can advantageously be used to parcel brain imaging data, and that the resulting component definitions can be propagated to new individuals with some partial success: hotspots of brain functional organization variability will exhibit clear benefits, while other systems, prominently primary systems, do not. The nuances in our results imply that in practical situations, parcellations users will have to clearly assess the benefit of these representations. By default, relying on fixed scheme is a safe choice.

**Acknowledgments.** This project/research has received funding from the European Union's Horizon 2020 Framework Program for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3). Bertrand Thirion is supported the KARAIB AI chair (ANR-20-CHIA-0025-01). Ana Luísa Pinho is the recipient of a BrainsCAN Postdoctoral Fellowship at Western University, funded by the Canada First Research Excellence Fund (CFREF).

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## A List of the task contrasts used in the presented experiments

We list in the Table 2 the list of functional contrasts that were considered to evaluate parcellation accuracy<fnref n="13" />.

<table><caption>Table 2: List of functional tasks and contrasts of the IBC dataset considered in this study.</caption><thead><tr><td>Task</td><td>Contrast</td><td>Description</td></tr></thead><tbody><tr><td>archi standard</td><td>left-right button press</td><td>left vs. right hand button press</td></tr><tr><td>archi standard</td><td>horizontal-vertical</td><td>horizontal vs. vertical checkerboard</td></tr><tr><td>archi standard</td><td>computation-sentences</td><td>mental subtraction vs. sentence reading</td></tr><tr><td>archi standard</td><td>reading-listening</td><td>reading sentence vs. listening to sentence</td></tr><tr><td>archi standard</td><td>reading-checkerboard</td><td>read sentence vs. checkerboard</td></tr><tr><td>archi standard</td><td>motor-cognitive</td><td>button presses vs. narrative/computation</td></tr><tr><td>archi spatial</td><td>saccades</td><td>saccade vs. fixation</td></tr><tr><td>archi spatial</td><td>rotation side</td><td>hand palm or back vs. fixation</td></tr></tbody></table>

<footnote n="13">¹³see the documentation for details on these contrasts.</footnote>
<table><caption>Table 2: List of functional tasks and contrasts of the IBC dataset considered in this study.</caption><thead><tr><th>Task</th><th>Contrast</th><th>Description</th></tr></thead><tbody><tr><td>archi spatial</td><td>hand-side</td><td>left or right hand vs. hand palm or back</td></tr><tr><td>archi spatial</td><td>object orientation</td><td>image orientation reporting</td></tr><tr><td>archi spatial</td><td>grasp-orientation</td><td>object grasping vs. orientation reporting</td></tr><tr><td>archi social</td><td>triangle random</td><td>randomly drifting triangle</td></tr><tr><td>archi social</td><td>triangle mental-random</td><td>mental motion vs. random motion</td></tr><tr><td>archi social</td><td>mechanistic video</td><td>reading a mechanistic story</td></tr><tr><td>archi social</td><td>mechanistic audio</td><td>listening to a mechanistic tale</td></tr><tr><td>archi social</td><td>false belief-mechanistic video</td><td>false-belief story vs. mechanistic story</td></tr><tr><td>archi social</td><td>false belief-mechanistic audio</td><td>false-belief tale vs. mechanistic tale</td></tr><tr><td>archi social</td><td>non speech sound</td><td>listen to natural sound</td></tr><tr><td>archi social</td><td>speech-non speech</td><td>listen to voice sound vs. natural sound</td></tr><tr><td>archi emotional</td><td>face gender-control</td><td>guess the gender from face image</td></tr><tr><td>archi emotional</td><td>face trusty-gender</td><td>assess face trustfulness vs. gender</td></tr><tr><td>archi emotional</td><td>expression gender-control</td><td>guess the gender from eyes image vs. view scrambled image</td></tr><tr><td>archi emotional</td><td>expression intention-gender</td><td>guess intention vs. gender from eyes image</td></tr><tr><td>hcp emotion</td><td>shape</td><td>shape comparison</td></tr><tr><td>hcp emotion</td><td>face-shape</td><td>emotional face comparison vs. shape comparison</td></tr><tr><td>hcp gambling</td><td>reward</td><td>gambling with positive outcome</td></tr><tr><td>hcp gambling</td><td>punishment-reward</td><td>negative vs. positive gambling outcome</td></tr><tr><td>hcp motor</td><td>tongue-avg</td><td>move tongue vs. hands and feet</td></tr><tr><td>hcp language</td><td>math</td><td>mental additions</td></tr><tr><td>hcp language</td><td>story-math</td><td>listening to tale vs. mental additions</td></tr><tr><td>hcp relational</td><td>match</td><td>visual feature matching vs. fixation</td></tr><tr><td>hcp relational</td><td>relational-match</td><td>relational comparison vs. matching</td></tr><tr><td>hcp social</td><td>random</td><td>random motion vs. fixation</td></tr><tr><td>hcp social</td><td>mental-random</td><td>mental motion vs. random motion</td></tr><tr><td>hcp WM</td><td>2back-0back</td><td>2-back vs. 0-back</td></tr><tr><td>rsvp language</td><td>consonant string</td><td>read and encode consonant strings vs. fixation</td></tr><tr><td>rsvp language</td><td>word-consonant string</td><td>read words vs. consonant strings</td></tr><tr><td>rsvp language</td><td>pseudo-consonant string</td><td>read pseudowords vs. consonant strings</td></tr><tr><td>rsvp language</td><td>word-pseudo</td><td>read words vs. pseudowords</td></tr><tr><td>rsvp language</td><td>complex-simple</td><td>read sentence with complex vs. simple syntax</td></tr><tr><td>rsvp language</td><td>sentence-word</td><td>read sentence vs. words</td></tr><tr><td>rsvp language</td><td>jabberwocky-pseudo</td><td>read jabberwocky vs. pseudowords</td></tr></tbody></table>
<table><caption>Table 2: List of functional tasks and contrasts of the IBC dataset considered in this study.</caption><thead><tr><th>Task</th><th>Contrast</th><th>Description</th></tr></thead><tbody><tr><td>rsvp language</td><td>sentence-jabberwocky</td><td>read sentence vs. jabberwocky</td></tr><tr><td>mtt we</td><td>we average reference</td><td>updating ones position in space and time in west-east island</td></tr><tr><td>mtt we</td><td>we all space cue</td><td>spatial cue of the next event in west-east island</td></tr><tr><td>mtt we</td><td>we all time cue</td><td>time cue of the next event in west-east island</td></tr><tr><td>mtt we</td><td>we all space-time cue</td><td>spatial vs. time cues in west-east island</td></tr><tr><td>mtt we</td><td>we all time-space cue</td><td>time vs. spatial cues in west-east island</td></tr><tr><td>mtt we</td><td>we average event</td><td>figuring out the space or time of an event in west-east island</td></tr><tr><td>mtt we</td><td>we space event</td><td>figuring out the position of an event in west-east island</td></tr><tr><td>mtt we</td><td>we time event</td><td>figuring out the time of an event in west-east island</td></tr><tr><td>mtt we</td><td>we space-time event</td><td>event in space vs. event in time in west-east island</td></tr><tr><td>mtt we</td><td>we time-space event</td><td>event in time vs. event in space in west-east island</td></tr><tr><td>mtt we</td><td>westside-eastside event</td><td>events occuring westside vs. eastside</td></tr><tr><td>mtt we</td><td>eastside-westside event</td><td>events occuring eastside vs. westside</td></tr><tr><td>mtt we</td><td>we before-after event</td><td>events occuring before vs. after in west-east island</td></tr><tr><td>mtt we</td><td>we after-before event</td><td>events occuring after vs. before in west-east island</td></tr><tr><td>mtt we</td><td>we all event response</td><td>motor responses performed after every event condition in the west-east island</td></tr><tr><td>mtt sn</td><td>sn average reference</td><td>updating ones position in space and time in south-north island</td></tr><tr><td>mtt sn</td><td>sn all space cue</td><td>spatial cue of the next event in south-north island</td></tr><tr><td>mtt sn</td><td>sn all time cue</td><td>time cue of the next event in south-north island</td></tr><tr><td>mtt sn</td><td>sn all space-time cue</td><td>spatial vs. time cues in south-north island</td></tr><tr><td>mtt sn</td><td>sn all time-space cue</td><td>time vs. spatial cues in south-north island</td></tr><tr><td>mtt sn</td><td>sn average event</td><td>figuring out the space or time of an event in south-north island</td></tr><tr><td>mtt sn</td><td>sn space event</td><td>figuring out the position of an event in south-north island</td></tr><tr><td>mtt sn</td><td>sn time event</td><td>figuring out the time of an event in south-north island</td></tr><tr><td>mtt sn</td><td>sn space-time event</td><td>event in space vs. event in time in south-north island</td></tr><tr><td>mtt sn</td><td>sn time-space event</td><td>event in time vs. event in space in south-north island</td></tr><tr><td>mtt sn</td><td>southside-northside event</td><td>events occuring southside vs. northside</td></tr><tr><td>mtt sn</td><td>northside-southside event</td><td>events occuring northside vs. southside</td></tr><tr><td>mtt sn</td><td>sn before-after event</td><td>events occuring before vs. after in south-north island</td></tr><tr><td>mtt sn</td><td>sn after-before event</td><td>events occuring after vs. before in south-north island</td></tr></tbody></table>
<table><caption>Table 2: List of functional tasks and contrasts of the IBC dataset considered in this study.</caption><thead><tr><th>Task</th><th>Contrast</th><th>Description</th></tr></thead><tbody><tr><td>mtt sn</td><td>sn all event response</td><td>motor responses performed after all event condition in the south-north island</td></tr><tr><td>preference food</td><td>food constant</td><td>evaluation of food</td></tr><tr><td>preference food</td><td>food linear</td><td>linear effect of food preference</td></tr><tr><td>preference food</td><td>food quadratic</td><td>quadratic effect of food preference</td></tr><tr><td>preference paintings</td><td>painting constant</td><td>evaluation of paintings</td></tr><tr><td>preference paintings</td><td>painting linear</td><td>linear effect of paintings preference</td></tr><tr><td>preference paintings</td><td>painting quadratic</td><td>quadratic effect of paintings preference</td></tr><tr><td>preference faces</td><td>face constant</td><td>evaluation of faces</td></tr><tr><td>preference faces</td><td>face linear</td><td>linear effect of face preference</td></tr><tr><td>preference faces</td><td>face quadratic</td><td>quadratic effect of face preference</td></tr><tr><td>preference houses</td><td>house constant</td><td>evaluation of houses</td></tr><tr><td>preference houses</td><td>house linear</td><td>linear effect of houses preference</td></tr><tr><td>preference houses</td><td>house quadratic</td><td>quadratic effect of houses preference</td></tr><tr><td>theory of mind</td><td>photo</td><td>manipulation of fact judgments</td></tr><tr><td>theory of mind</td><td>belief-photo</td><td>belief vs. factual judgments</td></tr><tr><td>emotional pain</td><td>physical pain</td><td>reading physical pain story</td></tr><tr><td>emotional pain</td><td>emotional-physical pain</td><td>emotional vs. physical pain story</td></tr><tr><td>pain movie</td><td>movie pain</td><td>movie with physically painful events</td></tr><tr><td>pain movie</td><td>movie mental-pain</td><td>mental events vs. physically painful events</td></tr><tr><td>self</td><td>instructions</td><td>read instruction in form of a question</td></tr><tr><td>bang</td><td>talk-no talk</td><td>speech vs. non-speech sections in movie watching</td></tr><tr><td>bang</td><td>no talk</td><td>non-speech section in movie watching</td></tr><tr><td>lyon lec2</td><td>attend</td><td>response to attended text</td></tr><tr><td>lyon lec2</td><td>unattend</td><td>response to unattended text</td></tr><tr><td>lyon lec2</td><td>attend-unattend</td><td>response to attended vs. unattended text</td></tr><tr><td>lyon audi</td><td>silence</td><td>listen to silence</td></tr><tr><td>lyon audi</td><td>tear-silence</td><td>listen to tears</td></tr><tr><td>lyon audi</td><td>suomi-silence</td><td>listen to unknown language</td></tr><tr><td>lyon audi</td><td>yawn-silence</td><td>listen to yawning</td></tr><tr><td>lyon audi</td><td>human-silence</td><td>listen to human sounds</td></tr><tr><td>lyon audi</td><td>music-silence</td><td>listen to music</td></tr><tr><td>lyon audi</td><td>reverse-silence</td><td>listen to reversed speech</td></tr><tr><td>lyon audi</td><td>speech-silence</td><td>listen to speech</td></tr><tr><td>lyon audi</td><td>alphabet-silence</td><td>listen to letters</td></tr><tr><td>lyon audi</td><td>cough-silence</td><td>listen to coughing</td></tr><tr><td>lyon audi</td><td>environment-silence</td><td>listen to environment sounds</td></tr><tr><td>lyon audi</td><td>laugh-silence</td><td>listen to laugh</td></tr></tbody></table>
<table><caption>Table 2: List of functional tasks and contrasts of the IBC dataset considered in this study.</caption><thead><tr><th>Task</th><th>Contrast</th><th>Description</th></tr></thead><tbody><tr><td>lyon audi</td><td>animals-silence</td><td>listen to animals</td></tr><tr><td>lyon visu</td><td>scrambled</td><td>view a scrambled image</td></tr><tr><td>lyon visu</td><td>face-scrambled</td><td>view a face image</td></tr><tr><td>lyon visu</td><td>characters-scrambled</td><td>view a characters</td></tr><tr><td>lyon visu</td><td>scene-scrambled</td><td>view a scene</td></tr><tr><td>lyon visu</td><td>house-scrambled</td><td>view a house</td></tr><tr><td>lyon visu</td><td>animal-scrambled</td><td>view an animal</td></tr><tr><td>lyon visu</td><td>pseudoword-scrambled</td><td>view a pseudoword</td></tr><tr><td>lyon visu</td><td>tool-scrambled</td><td>view a tool</td></tr><tr><td>lyon lec1</td><td>random string</td><td>read a random string</td></tr><tr><td>lyon lec1</td><td>word-random string</td><td>read a word vs. a random string</td></tr><tr><td>lyon lec1</td><td>word-pseudoword</td><td>read a word vs. a pseudoword</td></tr><tr><td>lyon lec1</td><td>pseudoword-random string</td><td>read a pseudoword vs. a random string</td></tr><tr><td>lyon mveb</td><td>2 letters different-same</td><td>maintaining two letters vs. one</td></tr><tr><td>lyon mveb</td><td>4 letters different-same</td><td>maintaining four letters vs. one</td></tr><tr><td>lyon mveb</td><td>6 letters different-same</td><td>maintaining six letters vs. one</td></tr><tr><td>lyon mveb</td><td>6 letters different-2 letters different</td><td>maintaining six letters vs. two</td></tr><tr><td>lyon mvis</td><td>2 dots-2 dots control</td><td>maintain position of two dots vs. one</td></tr><tr><td>lyon mvis</td><td>4 dots-4 dots control</td><td>maintain position of four dots vs. one</td></tr><tr><td>lyon mvis</td><td>6 dots-6 dots control</td><td>maintain position of six dots vs. one</td></tr><tr><td>lyon mvis</td><td>6 dots-2 dots</td><td>maintain position of six dots vs. two</td></tr><tr><td>lyon moto</td><td>instructions</td><td>read instructions</td></tr><tr><td>lyon moto</td><td>finger right-fixation</td><td>right finger tapping vs. any movement</td></tr><tr><td>lyon moto</td><td>finger left-fixation</td><td>left finger tapping vs. any movement</td></tr><tr><td>lyon moto</td><td>foot left-fixation</td><td>move left foot vs. any movement</td></tr><tr><td>lyon moto</td><td>foot right-fixation</td><td>move right foot vs. any movement</td></tr><tr><td>lyon moto</td><td>hand left-fixation</td><td>move left hand vs. any movement</td></tr><tr><td>lyon moto</td><td>hand right-fixation</td><td>move right hand vs. any movement</td></tr><tr><td>lyon moto</td><td>saccade-fixation</td><td>saccade vs. any movement</td></tr><tr><td>lyon moto</td><td>tongue-fixation</td><td>move tongue vs. any movement</td></tr><tr><td>lyon mcse</td><td>salience left-right</td><td>looking for a symbol in left vs. right visual field</td></tr><tr><td>lyon mcse</td><td>low-high salience</td><td>looking for a low-salient symbol</td></tr><tr><td>audio</td><td>music-silence</td><td>listen to music vs. silence</td></tr><tr><td>audio</td><td>speech-silence</td><td>listen to speech vs. silence</td></tr></tbody></table>
**B List of the brain systems considered**

The list of brain systems considered in our experiments is given in Table 3.

Table 3: List of the systems used to parcel the cortical surface. These systems are those described in [Glasser et al., 2016], with the exception that V1 is not handled as an independent region but associated with other primary visual areas.
<table><thead><tr><th>System name</th><th>MMP1.0 region names</th><th>MMP1.0 regions indices</th><th>size (ver- tices)<br>left/right</th></tr></thead><tbody><tr><td>early visual<br>dorsal stream</td><td>V1, V2, V3, V4</td><td>1,4,5,6</td><td>10714/10455</td></tr><tr><td>ventral stream</td><td>V3A, V3B, V6, V6A, V7, IPS1</td><td>3,13,16,17,19,152</td><td>2692/2774</td></tr><tr><td></td><td>V8, VVC, PIT, FFC, VMV1,<br>VMV2,VMV3</td><td>7, 18, 22, 153, 154, 160, 163</td><td>3117/3351</td></tr><tr><td>MT+ Complex</td><td>V3CD, LO1, LO2, LO3, V4t, FST,<br>MT, MST, PH</td><td>2, 20, 21, 23, 138, 156, 157, 158, 159</td><td>3795/3243</td></tr><tr><td>somatosensory and<br>motor</td><td>4, 3a, 3b, 1, 2</td><td>8, 9, 51, 52, 53</td><td>13388/13135</td></tr><tr><td>paracentral lobular<br>and mid cingulate</td><td>24dd, 24dv, 6mp, 6ma, SCEF, 5m, 5L,<br>5mv</td><td>36, 37, 39, 40, 41, 43, 44, 55</td><td>7665/8240</td></tr><tr><td>premotor</td><td>55b, 6d, 6a, FEF, 6v, 6r, PEF</td><td>10, 11, 12, 54, 56, 78, 96</td><td>6184/6338</td></tr><tr><td>posterior opercular</td><td>43, FOP1, OP4, OP1, OP2-3, PFcm</td><td>99, 100, 101, 102, 105, 113</td><td>3868/3913</td></tr><tr><td>early auditory</td><td>A1, MBelt, LBelt, Pbelt, RI</td><td>24, 104, 124, 173, 174</td><td>3245/2558</td></tr><tr><td>association audi- tory</td><td>A4, A5, STSdp, STSda, STSvp,<br>STSva, STGa, TA2</td><td>107, 123, 125, 128, 129, 130, 175, 176</td><td>5640/5961</td></tr><tr><td>insular and frontal<br>opercular</td><td>52, PI, Ig, PoI1, PoI2, FOP2, FOP3,<br>MI, AVI, AAIC, Pir,FOP4, FOP5</td><td>103, 106, 108, 109, 110, 111, 112, 114,<br>115, 167, 168, 169, 178</td><td>8445/8305</td></tr><tr><td>medial temporal</td><td>H, PreS, EC, PeEc, PHA1, PHA2,<br>PHA3</td><td>118, 119, 120, 122, 126, 127, 155</td><td>5660/4398</td></tr><tr><td>lateral temporal</td><td>PHT, TE1p, TE1m, TE1a, TE2p, TE2a,<br>TGv, TGd, TF</td><td>131, 132, 133, 134, 135, 136, 137, 172,<br>177</td><td>9284/8305</td></tr><tr><td>temporal-parietal- occipital junction</td><td>TPOJ1, TPOJ2, TPOJ3, STV, PSL</td><td>25, 28, 139, 140, 141</td><td>3406/4769</td></tr><tr><td>superior parietal<br>and IPS</td><td>LIPv, LIPd, VIP, AIP, MIP, 7PC, 7AL,<br>7Am, 7PL, 7Pm</td><td>29, 42, 45, 46, 47, 48, 49, 50, 95, 117</td><td>8552/8125</td></tr><tr><td>inferior parietal</td><td>PGp, PGs, PGi, PFm, PF, PFt, PFop,<br>IP0, IP1, IP2</td><td>116, 143, 144, 145, 146, 147, 148, 149,<br>150, 151</td><td>12814/12469</td></tr><tr><td>posterior cingulate</td><td>DVT, ProS, POS1, POS2, RSC, v23ab,<br>d23ab, 31pv, 31pd, 31a, 23d, 23c, PCV,<br>7m</td><td>14, 15, 27, 30, 31, 32, 33, 34, 35, 38,<br>121, 142, 161, 162</td><td>10082/10304</td></tr><tr><td>anterior cingu- late and medial<br>prefrontal</td><td>33pr, p24pr, a24pr, p24, a24, p32pr,<br>a32pr, d32, p32, s32, 8BM, 9m, 10v,<br>10r, 25</td><td>57, 58, 59, 60, 61, 62, 63, 64, 65, 69,<br>88, 164, 165, 179, 180</td><td>7874/8736</td></tr><tr><td>orbital and polar<br>frontal</td><td>47s, 47m, a47r, 111, 13l, a10p, p10p,<br>10pp, 10d., OFC, pOFC</td><td>66, 72, 77, 89, 90, 91, 92, 93, 94, 166,<br>170</td><td>7299/7772</td></tr><tr><td>inferior frontal</td><td>44, 45, IFJp, IFJa, IFSp, IFSa, 47l,<br>p47r</td><td>74, 75, 76, 79, 80, 81, 82, 171</td><td>5163/4617</td></tr><tr><td>dorsolateral pre- frontal</td><td>8C, 8Av, i6-8, s6-8, SFL, 8BL, 9p, 9a,<br>8Ad, p9-46v, a9-46v, 46, 9-46d</td><td>26, 67, 68, 70, 71, 73, 83, 84, 85, 86,<br>87, 97, 98</td><td>11087/11163</td></tr></tbody></table>
## C Contrasts selected to test

In each brain system studied, the parcellation model was tested on a subset of the contrasts that achieve high test-retest reproducibility in average across subjects: the criterion is that the correlation of the maps restricted to a given brain system across runs has to be greater than 0.5. For completeness we list in Table 4 the contrasts used in each system in the left hemisphere. Accordingly, we list in Table 5 the contrasts used in each system in the right hemisphere.

<table><caption>Table 4: Contrasts used in each system of the left hemisphere for performance assessment. These contrasts have been selected so that the correlation of system-level activity maps is greater than .5 in average across runs.</caption><tr><td>early visual</td><td>reading-listening, reading-checkerboard, horizontal-vertical, rotation side, object orientation, triangle random, mechanistic video, mental-random, random, reward, shape, face-shape, match, face-avg, place-avg, consonant string, we average event, house constant, face constant, food constant, photo, physical pain, enumeration constant, encode other, recognition other hit, correct rejection, attend, scrambled, random string, word-pseudoword, word-random string, pseudoword-random string, letter occurrence response</td></tr><tr><td>dorsal stream</td><td>reading-checkerboard, saccades, rotation side, object orientation, triangle random, mental-random, random, reward, face-avg, pseudo-consonant string, consonant string, we average event, sn average event, house constant, food constant, photo, physical pain, movie pain, vstm constant, enumeration constant, encode other, recognition other hit, random string, word-pseudoword, letter occurrence response, low-high salience</td></tr><tr><td>ventral stream</td><td>reading-listening, rotation side, object orientation, mechanistic video, random, reward, face-shape, face-avg, place-avg, house constant, face constant, food constant, photo, physical pain, vstm constant, enumeration constant, attend, random string</td></tr><tr><td>MT+ Complex</td><td>reading-listening, saccades, rotation side, object orientation, triangle random, mechanistic video, story-math, mental-random, random, reward, face-shape, body-avg, face-avg, place-avg, pseudo-consonant string, consonant string, we average event, sn average event, house constant, face constant, food constant, photo, physical pain, vstm constant, enumeration constant, encode other, recognition other hit, unattend, attend, random string, letter occurrence response</td></tr><tr><td>somatosensory and motor</td><td>motor-cognitive, object orientation, random, left hand-avg, right hand-avg, right foot-avg, tongue-avg, we average event, sn average event, house constant, face constant, food constant, enumeration constant, tongue-fixation</td></tr><tr><td>paracentral lobular and mid cingulate</td><td>object orientation, right hand-avg, right foot-avg, 2back-0back, we average event, sn average event, food constant, photo, physical pain, enumeration constant</td></tr></table>
<table><tr><td>premotor</td><td>motor-cognitive, computation-sentences, saccades, rotation side, object orientation, story-math, random, reward, right hand-avg, tongue-avg, 2back-0back, we average event, sn average event, house constant, face constant, food constant, photo, physical pain, enumeration constant, attend, random string, pseudoword-random string, 6 letters different-same, 6 dots-2 dots</td></tr><tr><td>posterior opercular</td><td>motor-cognitive, object orientation, random, left hand-avg, tongue-avg, we average event, sn average event, house constant, face constant, food constant, enumeration constant, alphabet-silence</td></tr><tr><td>early auditory</td><td>reading-listening, mechanistic audio, non speech sound, story-math, we average event, sn average reference, sn average event, enumeration constant, tear-silence, suomi-silence, yawn-silence, human-silence, silence, reverse-silence, alphabet-silence, cough-silence, environment-silence, laugh-silence, animals-silence</td></tr><tr><td>association auditory</td><td>reading-listening, mechanistic audio, mechanistic video, non speech sound, expression intention-gender, story-math, sentence-jabberwocky, sentence-word, word-consonant string, we average reference, we average event, sn average reference, sn average event, photo, physical pain, emotional-physical pain, enumeration constant, tear-silence, suomi-silence, human-silence, reverse-silence, alphabet-silence</td></tr><tr><td>insular and frontal opercular</td><td>story-math, 2back-0back, we average event, sn average event, face constant, photo, physical pain, enumeration constant</td></tr><tr><td>medial temporal</td><td>story-math, face-shape, face-avg, place-avg, tools-avg, house constant, emotional-physical pain, enumeration constant</td></tr><tr><td>lateral temporal</td><td>story-math, 2back-0back, sentence-jabberwocky, we average event, sn average event, photo, physical pain, emotional-physical pain, enumeration constant</td></tr><tr><td>temporal-parietal-occipital junction</td><td>reading-listening, saccades, rotation side, object orientation, mechanistic audio, mechanistic video, non speech sound, expression intention-gender, story-math, mental-random, random, reward, sentence-jabberwocky, word-consonant string, we average event, sn average event, face constant, photo, physical pain, enumeration constant, attend, suomi-silence, alphabet-silence</td></tr><tr><td>superior parietal and IPS</td><td>reading-listening, motor-cognitive, computation-sentences, saccades, hand-side, rotation side, object orientation, story-math, random, reward, 2back-0back, word-consonant string, pseudo-consonant string, consonant string, we average event, sn average event, house constant, face constant, food constant, photo, physical pain, enumeration constant, pseudoword-random string, 6 letters different-same, 6 dots-2 dots</td></tr><tr><td>inferior parietal</td><td>motor-cognitive, computation-sentences, rotation side, object orientation, expression intention-gender, story-math, random, reward, 2back-0back, sentence-jabberwocky, we average event, sn average event, house constant, face constant, food constant, photo, physical pain, emotional-physical pain, movie pain, movie mental-pain, enumeration constant, encode other</td></tr><tr><td>posterior cingulate</td><td>story-math, random, reward, we average event, sn average event, photo, physical pain, enumeration constant</td></tr></table>
<table><caption>Table 5: Contrasts used in each system of the right hemisphere for performance assessment. These contrasts have been selected so that the correlation of system-level activity maps is greater than .5 in average across runs.</caption><tr><td>anterior cingulate and medial prefrontal orbital and polar frontal</td><td>expression intention-gender, story-math, 2back-0back, we average event, sn average event, physical pain, enumeration constant, encode other story-math, reward, we average event, photo, physical pain, enumeration constant, enumeration linear, attend</td></tr><tr><td>inferior frontal</td><td>computation-sentences, grasp-orientation, object orientation, mechanistic video, expression intention-gender, story-math, reward, 2back-0back, sentence-jabberwocky, word-consonant string, we average event, sn average event, photo, physical pain, enumeration constant, enumeration linear, attend, word-pseudoword, word-random string, pseudoword-random string</td></tr><tr><td>dorsolateral prefrontal</td><td>story-math, reward, 2back-0back, we average event, sn average event, photo, physical pain, enumeration constant</td></tr></table>

<table><tr><td>early visual</td><td>reading-listening, reading-checkerboard, horizontal-vertical, rotation side, object orientation, triangle random, mechanistic video, mental-random, random, reward, shape, face-shape, match, face-avg, place-avg, consonant string, we average event, house constant, face constant, food constant, photo, physical pain, vstm constant, enumeration constant, encode other, recognition other hit, correct rejection, attend, random string, word-pseudoword, word-random string, pseudoword-random string, letter occurrence response</td></tr><tr><td>dorsal stream</td><td>reading-listening, saccades, rotation side, object orientation, story-math, mental-random, random, reward, face-avg, place-avg, pseudo-consonant string, consonant string, we average event, sn average event, house constant, food constant, photo, physical pain, vstm constant, enumeration constant, random string, word-pseudoword</td></tr><tr><td>ventral stream</td><td>reading-listening, rotation side, object orientation, random, reward, face-shape, face-avg, place-avg, house constant, face constant, food constant, photo, physical pain, vstm constant, enumeration constant, letter occurrence response</td></tr><tr><td>MT+ Complex</td><td>reading-listening, reading-checkerboard, saccades, rotation side, object orientation, triangle random, mechanistic video, mental-random, random, reward, face-shape, body-avg, face-avg, place-avg, word-consonant string, pseudo-consonant string, consonant string, we average event, sn average event, house constant, face constant, food constant, photo, physical pain, movie pain, vstm constant, enumeration constant, animal-scrambled, random string, word-pseudoword</td></tr><tr><td>somatosensory and motor</td><td>left-right button press, motor-cognitive, left hand-avg, left foot-avg, tongue-avg, we average event, sn average event, enumeration constant</td></tr></table>
<table>
  <tr>
    <td>paracentral lobular and mid cingulate premotor</td>
    <td>object orientation, random, left foot-avg, we average event, sn average event, photo, physical pain, enumeration constant<br>[motor-cognitive, saccades, object orientation, story-math, random, reward, tongue-avg, 2back-0back, we average event, sn average event, house constant, face constant, food constant, photo, physical pain, vstm constant, enumeration constant</td>
  </tr>
  <tr>
    <td>posterior opercular</td>
    <td>motor-cognitive, tongue-avg, we average event, sn average event, enumeration constant, tear-silence, reverse-silence, alphabet-silence</td>
  </tr>
  <tr>
    <td>early auditory</td>
    <td>reading-listening, mechanistic audio, non speech sound, left foot-avg, we average event, sn average event, enumeration constant, tear-silence, suomi-silence, yawn-silence, human-silence, silence, reverse-silence, alphabet-silence, cough-silence, environment-silence, laugh-silence, animals-silence</td>
  </tr>
  <tr>
    <td>association auditory</td>
    <td>reading-listening, mechanistic audio, non speech sound, story-math, mental-random, we average reference, we average event, sn average reference, sn average event, physical pain, enumeration constant, tear-silence, suomi-silence, human-silence, reverse-silence, alphabet-silence, environment-silence, laugh-silence</td>
  </tr>
  <tr>
    <td>insular and frontal opercular</td>
    <td>story-math, 2back-0back, we average event, sn average event, photo, physical pain, enumeration constant, 6 letters different-same</td>
  </tr>
  <tr>
    <td>medial temporal</td>
    <td>story-math, face-shape, body-avg, face-avg, place-avg, house constant, photo, enumeration constant</td>
  </tr>
  <tr>
    <td>lateral temporal</td>
    <td>story-math, random, reward, face-shape, 2back-0back, we average event, sn average event, enumeration constant</td>
  </tr>
  <tr>
    <td>temporal-parietal-occipital junction</td>
    <td>reading-listening, motor-cognitive, saccades, object orientation, mechanistic audio, story-math, mental-random, random, we average reference, we average event, sn average reference, sn average event, physical pain, enumeration constant, alphabet-silence</td>
  </tr>
  <tr>
    <td>superior parietal and IPS</td>
    <td>reading-listening, saccades, hand-side, rotation side, object orientation, triangle random, story-math, random, reward, 2back-0back, word-consonant string, pseudo-consonant string, consonant string, we average event, sn average event, house constant, face constant, food constant, photo, physical pain, movie pain, enumeration constant, word-pseudoword, low-high salience</td>
  </tr>
  <tr>
    <td>inferior parietal</td>
    <td>motor-cognitive, object orientation, story-math, random, reward, match, 2back-0back, we average event, sn average event, house constant, face constant, food constant, photo, physical pain, emotional-physical pain, movie pain, movie mental-pain, enumeration constant, encode other</td>
  </tr>
  <tr>
    <td>posterior cingulate</td>
    <td>story-math, random, we average event, sn average event, photo, physical pain, movie mental-pain, enumeration constant</td>
  </tr>
  <tr>
    <td>anterior cingulate and medial prefrontal orbital and polar frontal</td>
    <td>story-math, 2back-0back, we average event, sn average event, photo, physical pain, enumeration constant, encode other<br>story-math, reward, 2back-0back, we average event, sn average event, photo, physical pain, enumeration constant</td>
  </tr>
</table>
<figure><img src="image_10.png" /><figcaption>Figure 9: Example of an individualized parcellation scheme in the somato-sensory cortex. (left) For 5-components parcellation of the somato-sensory cortex, one can characterize their response to 160 contrasts; 16 are displayed for readability, and are related to various motor or non-motor tasks. The resulting group parcellation (top right) is superimposed with the original MMP1.0 parcellation. Individual parcellations are displayed on the bottom right.</figcaption></figure>

<table><tr><td>inferior frontal</td><td>story-math, random, reward, 2back-0back, we average event, sn average event, photo, physical pain, enumeration constant, enumeration linear</td></tr><tr><td>dorsolateral prefrontal</td><td>story-math, reward, 2back-0back, we average event, sn average event, photo, enumeration constant, encode other</td></tr></table>

## D Additional regional results

We provide in Fig.9 a view of the parcellation in the motor system of the MMP1.0 atlas, where data-driven parcellations achieve a poor fit. Indeed the parcellation does not seem to match somatotopic structures; this could be due to the fact that movie watching does not provide enough relevant information in the motor cortex.