Bertrand Thirion

Alexis Thual

Ana Luísa Pinho

From deep brain phenotyping to functional atlasing

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Introduction

So far, functional Magnetic Resonance Imaging (fMRI), has mostly been used in cognitive neuroscience to observe differential responses to cognitive tasks across brain regions. These differential responses, called functional contrasts, are usually reported at the population level in the literature. Representative brain maps are either described in terms of peaks of activation or shared as data derivatives in public repositories, like Neu-roVault [GVR + 15]. The accumulation of such maps brings very useful information on the neural correlates underlying cognitive operations, yet they do not allow for conclusions about the specific function of brain regions [Hen06, VSP + 18].

In the last decade, a further line of research relying on resting-state fMRI data has emerged with the main purpose of providing fine delineations of macro-scale structures (regions or networks) and hence deliver new insights on brain organization; it is framed as connectome analysis. Indeed, studies involving connectome analysis in humans have been fostered by large-scale initiatives, like the Human Connectome Project (HCP). Some of these efforts have focused on the demarcation of brain structures using information from restingstate fMRI as wall as other neuroimaging modalities [GCR + 16]. Such topographical mapping outlines brain regions [CFD + 08] or networks [SFM + 09, YKS + 11, BVG + 16] that can be grouped in brain atlases [GCR + 16, SKG + 17]. Another view on brain topography conceptualizes instead cortical organization in terms of largescale gradients [MGG + 16].

One of the current challenges is thus to bridge the information brought by both functional connectivity analysis and functional contrasts [BVG + 16]. Such integration is done much more accurately at the intrasubject level [PAF + 21], considering that large inter-individual variations related to location, magnitude or spatial organization of functional contrasts or functional connectivity can only yield blurred models at the population level.

To summarize, a functional atlas of the human brain should be informative with respect to three main features: (i) the topographical structure of the selected regions or networks; (ii) the functional identity of the extracted structures; and (iii) the connectivity underlying the signals observed among these structures.

In this paper, we outline an approach based on the accumulation of contrast maps in few individuals, wherein we discuss and illustrate the concept of functional fingerprint. This is based on the Individual Brain Charting (IBC) dataset [PAR + 18, PAG + 20], a high-resolution task-fMRI dataset acquired in a fixed environment and fixed cohort of twelve subjects. The IBC dataset provides a comprehensive collection of contrasts that aims at characterizing the cognitive components underlying a very large collection of tasks, along with high-resolution anatomical information. FMRI data are acquired at 1.5mm isotropic resolution (see [PAR + 18] for more details). This dataset currently features approximately 150 task-fMRI contrasts, together with passive watching of visual and auditory naturalistic scenes, as well as anatomical contrasts. It thus constitutes an unprecedented opportunity to test the feasibility of performing both individual-and population-based functional atlasing through the description of regional fingerprints.

From Contrast Maps to Functional Fingerprints

The accumulation of functional contrasts at the individual level allows for a subject-specific description of the functional properties of brain territories. More precisely, the functional specificity of brain regions can be captured through the conjunction of many contrasts; for instance, the visual word form area can be functionally characterized as an area which responds to visual objects in general, but more to language content than other visual categories [DC11].

The benefits resulting from the accumulation of contrast maps have been explored in few studies. Some recent large-scale mapping efforts, such as the Archi [PTM + 07, PdD + 19] and HCP datasets [BBH + 13, GCR + 16], aimed at a complete characterization of a few cognitive networks, according to their implication in task performance as well as their variability at the population level. However the cognitive coverage of such datasets is typically restricted to a handful of tasks, ultimately limiting the number of available contrasts to a few dozens at best. Acquiring naturalistic stimuli can help to broaden the scope of such studies. As an example, the StudyForrest initiative has produced a set of openly available multi-modal datasets featuring fMRI data on the continuous presentation of scenes included in the "Forrest Gump" movie. This project has thus launched sev-Figure 1: What is a functional fingerprint? A functional prototype or functional fingerprint is a vector of response to a set of contrasts in a given brain location or brain region. It characterizes the profile of functional response to a possibly wide array of experimental conditions in that particular region. In this example, we observe that the voxel considered obtains large responses to conditions involving sounds and, specifically, voice content. eral studies investigating the neurocognitive encoding of complex auditory and visual information, by modeling specific audio and visual properties of the stimuli [HBI + 14, HDH + 15, HAK + 16, SKG + 16]. Nonetheless, such approaches forgo the simplicity and the interpretability of contrast-based mapping.

On the other hand, recent neuroimaging studies have started to adopt individual analysis in order to mitigate the negative impact of both functional and anatomical inter-subject variability on the precise demarcation of brain territories [FBK11, HGC + 11, NCF12, FG12, HBI + 14, LGA + 15, HdHG + 16, HLN + 16, BB17, GLG + 17, CPM + 19]. However, they typically refer to single task studies, that only probe very specific cognitive mechanisms. Among those, the IBC dataset consists in an extensive collection of task data, targeting an exhaustive and spatially accurate characterization of individual cognitive networks. To this end, the dataset yields an extensive collection of contrasts that span a large number of cognitive components. Its successive releases [PAR + 18, PAG + 20] pertain foremost to: (i) data acquired from localizers (task batteries), whose conditions range from perception to higher-order thinking skills [PTM + 07, BBH + 13];

(ii) data from a rapid-serial-visual-presentation paradigm on language comprehension [HBML06, PAR + 18];

(iii) data on specific cognitive tasks, such as mental time and space navigation [GPvW18], reward [START_REF] Maël Lebreton | Automatic integration of confidence in the brain valuation signal[END_REF], theory-of-mind [START_REF] Dodell-Feder | fMRI item analysis in a theory of mind task[END_REF], pain [START_REF] Jacoby | Localizing Pain Matrix and Theory of Mind networks with both verbal and non-verbal stimuli[END_REF], numerosity [KPS + 14], self-reference effect [GBC + 14], and speech recognition [CSW + 15];

(iv) an auditory task, tackling different kinds of naturalistic auditory stimuli;

(v) and retinotopic mapping.

Here, we consider a collection of 149 independent contrasts that were obtained from the task data of the first three releases (see section A). With these data, we then propose to operationalize the concept of functional fingerprinting [GRL + 18] illustrated in Figure 1: fingerprints refer to a vector of activation values that define the functional prototype of a certain region of interest.

3 Discovering structure among functional prototypes Deriving a principled description of such functional signatures across brain regions is not trivial. Indeed, as noted e.g. in [START_REF] Lashkari | Discovering structure in the space of fMRI selectivity profiles[END_REF], some arbitrariness arising from modeling choices might obfuscate the intrinsic organization of the brain. Data-driven methods can thus be better suited to capturing the essence of such inner representations. They mostly rely on clustering [LVKG10, YKS + 11, YKE + 16] or decomposition methods, such as Independent Components Analysis (ICA) [SFM + 09] or Dictionary Learning [VSPT13, BVG + 16]. These approaches broadly consist in factorizing the set of functional signatures collected across locations and, potentially, across individuals. Note that all these methods entail some kind of ill-posed model selection, in order to tune the hyper-parameters. Although this issue is not discussed in detail herein, we mostly recommend avoiding overly complex models, that might induce overfit, and using methods' default parameters whenever possible. This latent-factor data model yields an efficient description of the brain maps used as inputs. The sparsity of the obtained topographies allows for a better characterization of the ensuing networks, while functional profiles yield a precise specification of their functional role. This factorization can be easily extended to multi-subjects settings [START_REF] Varoquaux | Cohort-Level Brain Mapping: Learning Cognitive Atoms to Single Out Specialized Regions[END_REF].

In this article, we focus on Dictionary Learning, which stands as an intermediate between ICA and Clustering. Similar to ICA, Dictionary Learning follows a linear decomposition approach that fits well the signal. Similar to clustering, Dictionary Learning provides clear delineations of brain structures. In recent work [BVG + 16, DMVT16], it has been shown to yield good representations for fMRI data.

We follow the functional approach to multi-subject Dictionary Learning, as described in [START_REF] Varoquaux | Cohort-Level Brain Mapping: Learning Cognitive Atoms to Single Out Specialized Regions[END_REF] and [PAF + 21]. This outputs the factorization of individual contrast maps into a dictionary of cognitive profiles common to all subjects, plus subject-specific spatial maps. Sparsity is enforced with a penalty on the loadings of the components, together with a non-negativity constraint (see Figure 2). A formal description of the procedure is drawn in section C. Given a set of input images, it returns a set of non-negative and sparse topographies associated with functional fingerprints; these jointly summarize the input data.

As shown in [PAF + 21] by a bootstrap analysis, a clear benefit of distilling functional maps into such a dictionary is that the topography of the resulting components is more stable than that of the underlying contrast maps. This makes dictionary decompositions more robust to inter-subject variability.

Topography, connectivity and functional specialization

We illustrate the result of applying Dictionary Learning to the IBC contrast maps in Figure 3. Based on a set of c = 149 contrast maps available in n = 11 subjects, we derived k = 20 networks using a dictionary-learning technique as described in [PAF + 21]. Each network is characterized by its connectivity to other networks as well as by its functional signature. Here, the functional signature is coarsely summarized by the names of the two contrasts eliciting the highest activation in the underlying network. Note that this may sometimes outline arbitrarily some contrasts that have a higher signal-to-noise ratio. Improving such descriptions is therefore a relevant topic for future research.

Moreover, the connectivity among these networks can be computed as the partial correlation between the average activity within these networks.

Overall, derived networks show good coverage not only for the sensory and motor areas-the visual system is notably divided into its ventral and dorsal pathways and different motor regions are clearly distinguishedbut also the default-mode, saliency and executive-control networks. The coverage of the prefrontal lobe is lower, indicating less consistency across subjects in the corresponding regions. Most components display crosshemisphere symmetry, except for the left-lateralized language component and the right-lateralized attentional fronto-parietal network.

The connectome among these 20 components was obtained by computing the partial correlations among the functional fingerprints. In spite of the sparse prior used for estimating partial correlations, the network is densely connected, showing a very ordered structure across networks. For instance, the bilateral network associated with tongue motion (in red) has a strong partial connectivity with networks that are involved in speech ("reversed speech" and "letters sounds", in green), listening to or reading stories ("tale" and "read words", in yellow), and response to mental time travel's events presented in auditory modality ("response to events", in pink), although those are not necessarily close in brain space.

Inter-individual variability in the obtained topographies

Connectome-type analyses enable whole-brain as well as regional brain comparisons. The individual topographies derived from these methods highlight the fact that shape, size and position of functional signatures in brain territories differ from one subject to another [GLG + 17, GLN + 18, KLO + 19, GMG + 20], although the large-scale organization of the human brain is considered to be consistent across individuals. These Nevertheless, functional mapping enables a more straightforward access to inter-individual variability, namely the one that can be identified in contrast maps. The major challenge when capturing such variability lies in teasing apart actual topographic variability from noise [START_REF] Varoquaux | Cohort-Level Brain Mapping: Learning Cognitive Atoms to Single Out Specialized Regions[END_REF]. To address this problem, functionalcorrespondence Dictionary Learning is used as it consists in the application of Dictionary Learning to data concatenated along the voxel dimension, thus enforcing functional correspondence across individuals [START_REF] Varoquaux | Cohort-Level Brain Mapping: Learning Cognitive Atoms to Single Out Specialized Regions[END_REF]. Such a model does not impose any spatial correspondence which is ideal to uncover patterns of spatial similarities/dissimilarities between subjects.

Figure 4 illustrates the brain networks obtained from the IBC dataset using the same setting as in the previous section. Although the global topographic organization is well-preserved across individuals, inter-individual variability stands out as a major feature. Imputing this variability to actual inter-individual differences or to noise constitutes a non-trivial issue and it cannot be well-addressed in a small sample. Therefore, the relevance of these maps can be assessed through the fit of other neuroimaging variables, like cortical thickness or myelin distribution, as well as non-neuroimaging ones, like behavioral scores.

The results reported in Figure 4 show a relatively ordered structure in the occipital, temporal and parietal lobes, in contrast with the large variability present in frontal regions; such variability is particularly evident in the right frontal lobe, which appears to be almost unstructured. In the left hemisphere, the consistency of the language network creates a more stable representation. As this asymmetry is, to the best of our knowledge, not fully accounted for by previous resting-state-based network studies, it suggests further comparisons between the network structure implied by such contrasts versus those observed with resting-state studies. We defer that question to future work. Moreover, as surface-based alignment is thought to provide state-of-the-art alignment of individual anatomical organization, a great deal of functional variability remains, which clearly points to variable underlying function of brain regions across individuals. Once again, this deserves more investigation.

Discussion

The dictionary-learning strategy outlined herein provides a qualitative assessment of the spatial organization of brain function: first, by providing synthetic summaries of the system-level organization of the brain; second, by outlining the differences across individuals. Bearing these intuitions in mind and proceeding toward a systematic analysis, the next step is to assess quantitatively the information conveyed by this functional fingerprint representation. This could be done by assessing external validity of the employed features, which involves typically involves some kind of generalization (see e.g. [START_REF] Varoquaux | Predictive models avoid excessive reductionism in cognitive neuroimaging[END_REF]): predicting some feature of brain organization that can be checked with unseen data gives an arguably stronger evidence for these observations. For instance, can one predict the precise location of the visual word form area in a given individual based on such functional fingerprints? If yes, this would lay the ground for automatic regions identification from fingerprints. Such approach was pursued by [GCR + 16], though the ground truth was based on non-replicable expert-supplied segmentation.

Another implication of the functional fingerprint concept is the possibility to impute non-observed contrasts, given some observed ones. This framework has been used to predict task contrasts from either resting-state topographies [TJM + 16] or other task-fMRI contrasts [TVG + 14, PAF + 21]. The interest of this type of prediction is to open the possibility to generalize the information from cohorts comprising a few densely sampled subjects to cohorts that share some common contrasts, e.g. from the IBC to the whole HCP cohort.

Here, we have considered only task-fMRI contrasts, but including more functional features from naturalistic stimuli and resting state as well as anatomical features would bring complementary information worth of further investigation. Finding the correct generalization of the fingerprint concept to different types of information, such as connectional fingerprints, is still an open question. We note that, while it is a common hypothesis that connectivity underlies function (among others, see [SOK + 12]), the link between connectivity and functional fingerprints [GRL + 18] is still elusive.

Finally, we have noticed in section 4 that it was helpful to annotate each functional fingerprint with proper cognitive terms; yet we have only relied on the contrasts referring to the largest functional responses for the considered component. A finer functional characterization asks for defining proper ontologies in cognitive neuroscience [PKK + 11, PY16]. Such formal representations can then provide annotations that better define the function of brain areas. A List of contrasts used for the IBC data functional fingerprinting

The choice of tasks and contrasts included in the analysis follow the following principles:

(i) whenever they were available, battery-structured experiments that systematically assess brain systems with a wide arrays of conditions ranging from perception to higher-order cognition have been used;

(ii) we also used tasks probing core sensory and motor systems (auditory task tackling different kinds of naturalistic auditory stimuli, retinotopic mapping, motor mapping)

(iii) we eventually reproduced domain-specific tasks have been used to map more specific aspects of cognition, such as reading, with rapid-serial-visual-presentation paradigm on language comprehension, mental time and space navigation, reward processing, theory-of-mind, pain, numerosity, self-reference effect, and speech recognition, so far; the goal was to cover as many domains as possible, in order to probe the corresponding brain systems.

Yet, the difference between domain-general tasks and domain-specific tasks should not be over-emphasized.

Localizer tasks, which are domain-general tasks, typically represent a consensual way to characterize brain functions and their neural correlates. Therefore, they tend to be less specific than those addressing a particular cognitive system. These tasks also have a battery-type organization, where multiple conditions are related in a factorial structure that helps segregating territories.

Gathering and running a large number of tasks hinges on the community ability to share existing experimental protocols. This is in general hard to achieve given the current research practices stimulus-delivery software maintenance) or labor contingencies (e.g. personnel change). Data acquisition is still ongoing, with novel protocols being added to the present IBC collection (e.g. biological motion, spatial navigation, narrative listening, movie watching, among other task batteries).

Regarding the contrasts herein employed, we have focused on the independent contrasts of the studies; they refer to the main conditions versus baseline, also including the corresponding baselines when they deliver meaningful information. A comprehensive description of the tasks and corresponding contrasts used in this study can be found in the IBC dataset documentation, which is available on https://project.inria. fr/IBC/data.

Task

B Preprocessing of the IBC data

A detailed description of the preprocessing pipeline of the IBC data is provided in [PAF + 21]. Raw data were preprocessed using PyPreprocess (https://github.com/neurospin/pypreprocess). 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. The images were corrected based on the estimated deformation model. Details about the method can be found in [START_REF] Jesper | How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging[END_REF].

Further, the 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 [START_REF] Friston | Characterizing Dynamic Brain Responses with fMRI: a Multivariate Approach[END_REF]. 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 [vEGD + 12]. The corresponding maps were then resampled to the fsaverage7 template of FreeSurfer [START_REF] Fischl | High-resolution intersubject averaging and a coordinate system for the cortical surface[END_REF]. FMRI data were analyzed using the General Linear Model. Regressors of the model were designed to capture variations in BOLD response strictly 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 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 voxels showing the 5% highest variance, were also regressed to capture physiological noise [BRLL07].

In addition, a discrete-cosine basis was included for high-pass filtering (cutoff = 1 128 Hz). Model specification was implemented using Nilearn [APE + 14], a Python library for statistical learning on neuroimaging data (https://nilearn.github.io).

C Technical description of the functional correspondence dictionary-learning method

Formally, consider the set of brain maps X s = (X s j ), j ∈ [c] obtained for c = 149 contrasts in a subject s ∈ [n]. By enumerating the values across a mesh of vertices, each X s j is a p-dimensional vector, where p is the number of vertices; X s is thus a matrix of size p × c. Functional-correspondence Dictionary Learning solves the following minimization problem for λ > 0:

min (U s )s=1...n,V∈C n s=1 X s -U s V 2 + λ U s 1 ,
where U s ≥ 0 , ∀s ∈ [n]. Here, C denotes the set of matrices with row norm smaller than 1. U s matrices have shape p × k, whereas the functional-loading matrix V has shape k × c, k being the number of components. Herein, we used k = 20. In addition, V describes the functional characteristics of the components. The estimated subject-specific spatial components (U s ), s ∈ [n] can be interpreted as individual topographies; these components may overlap, although their values are zero in most regions. This is why the median value of these components is also sparse, even without applying explicit thresholds. The λ parameter was calibrated in order to yield a sparsity of around 75%. As the estimation problem is non-convex, initialization matters; here, we created an initial V matrix by clustering the voxels across subjects into k = 20 clusters and took the normalized average of the cluster signal. This is illustrated in Figure 2. The implementation relies on the mini-batch k-means and the dictionary-learning methods of scikit-learn v0.21.3 [PVG + 11], a Python machine-learning library (https://scikit-learn.org/stable/).

Figure 2 :

 2 Figure 2: Principle of Dictionary Learning A set of 149 contrast images corresponding to distinct contrasts (left) can be factorized into a set of functional profiles multiplied by sparse non-negative topographies (right).This latent-factor data model yields an efficient description of the brain maps used as inputs. The sparsity of the obtained topographies allows for a better characterization of the ensuing networks, while functional profiles yield a precise specification of their functional role. This factorization can be easily extended to multi-subjects settings[START_REF] Varoquaux | Cohort-Level Brain Mapping: Learning Cognitive Atoms to Single Out Specialized Regions[END_REF].

  topographic differences are important, as they lay the groundwork to study inter-individual characteristics [SNV + 15, MAAB + 16, BWG + 18]. Capturing individual properties through the connectome can even lead to the unique identification of individuals [FSS + 15].

Figure 3 :

 3 Figure 3: Identifying suitable network descriptions of the brain. From 149 contrast maps available in 11 subjects, we derive k = 20 networks in a data-driven way. (left) Each network is characterized by its functional connectivity to other networks as well as by a functional signature. The functional signature is summarized by the names of the two contrasts that elicit most activation in that particular network. Using activation or resting-state data, the connectivity among these networks is captured by partial correlations among the regional signals. (right) The networks are also characterized by topographic maps, summarized herein by their cortical representation.

Figure 4 :

 4 Figure 4: Variability of individual topographies pertaining to task-elicited brain responses. Based on the decomposition of a set of contrast maps in 11 subjects, we define 20 networks through a data-driven procedure. The resulting topographies are mapped on the left and right hemispheres of the cortex. The cross-subject consensus maps are shown at the top-right corner of both panels; they are identical to those displayed on Figure 3. Individual topographies show conspicuous variability across individuals, although the large-scale organization remains consistent.

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** This work shows the importance of topographic information obtained from functional neuroimaging data: its shows that much of the variability observed in functional connectivity estimates across individuals could be explained by topographic details of the underlying brain maps. In turn, such topographic differences are predictive of inter-subject characteristics. This highlights the importance of analysis methods that explicitly account for inter-subject topographic variability. [CFD + 08] A. L. Cohen, D. A. Fair, N. U. Dosenbach, F. M. Miezin, D. Dierker, D. C. Van Essen, B. L. Schlaggar, and S. E. Petersen. Defining functional areas in individual human brains using resting functional connectivity MRI. Neuroimage, 41(1):45-57, May 2008. [CPM + 19] Nadine Chang, John Pyles, Austin Marcus, Abhinav Gupta, Michael Tarr, and Elissa Aminoff. BOLD5000, a public fMRI dataset while viewing 5000 visual images. Sci Data, 6:49, 5 2019. [CSW + 15] Karen L. Campbell, Meredith A. Shafto, Paul Wright, Kamen A. Tsvetanov, Linda Geerligs, Rhodri Cusack, Lorraine K. Tyler, Carol Brayne, Ed Bullmore, Andrew Calder, Rhodri Cusack, Tim Dalgleish, John Duncan, Rik Henson, Fiona Matthews, William Marslen-Wilson, James Rowe, Meredith Shafto, Karen Campbell, Teresa Cheung, Simon Davis, Linda Geerligs, Rogier Kievit, Anna McCarrey, Darren Price, Jason Taylor, Kamen Tsvetanov, Nitin Williams, *thy O. Laumann, Adrian W. Gilmore, Jeffrey J. Berg, Annie L. Nguyen, Donna Dierker, Andrew N. Van, Mario Ortega, Dillan J. Newbold, Jacqueline M. Hampton, Ashley N. Nielsen, Kathleen B. McDermott, Jarod L. Roland, Scott A. Norris, Steven M. Nelson, Abraham Z. Snyder, Bradley L. Schlaggar, Steven E. Petersen, and Nico U.F. Dosenbach. Integrative and Network-Specific Connectivity of the Basal Ganglia and Thalamus Defined in Individuals. Neuron, 105(4):742 -758.e6, 2020. [GPvW18] Baptiste Gauthier, Karin Pestke, and Virginie van Wassenhove. Building the Arrow of Time. . . Over Time: A Sequence of Brain Activity Mapping Imagined Events in Time and Space. Cereb Cortex, 29(10):4398-4414, 12 2018. [GRL + 18] Sarah Genon, Andrew Reid, Robert Langner, Katrin Amunts, and Simon B. Eickhoff. How to Characterize the Function of a Brain Region. Trends Cogn Sci, 22(4):350-364, Apr 2018. ** This paper discusses the epistemological underpinnings of functional brain mapping. While many brain regions have been defined, a comprehensive formalization of each region's function in relation to human behavior is still lacking. Current knowledge comes from various fields, which have diverse conceptions of 'functions'. Aggregating activation data from neuroimaging studies allows one to pinpoint the involvement of a region across a range of experimental conditions. Furthermore, largesample data can disclose covariation between brain region features and ecological behavioral phenotyping. Combining these two approaches opens a new perspective to determine the behavioral associations of a brain region, and hence its function and broader role within large-scale functional networks.

* This paper introduces several key experiments on a fraction of the IBC dataset, namely the first release. In particular, it introduces the application of dictionary learning to summarize contrast maps to topographies.

* This paper discusses functional atlasing based on large-scale data, such as those available in open repositories (OpenNeuro, NeuroVault). It shows that machine learning models can capture the link between cognitive concepts and brain regions, provided that they rely on the conjunction of two mappings: encoding (brain mapping) and decoding (predictive modeling). This also shows that cognitive ontologies provide an important resource to improve this atlasing endeavour. The paper coins the necessity of validating such models across studies, to avoid fitting the idiosyncrasies of each study.

Acknowledgements. 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).