# Insights on Using Graph Neural Networks for Sulcal Graphs Predictive Models

Alexis Imbert, Benoit Gaüzère, Sylvain Takerkart, Guillaume Auzias, Paul Honeine

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Alexis Imbert, Benoit Gaüzère, Sylvain Takerkart, Guillaume Auzias, Paul Honeine. Insights on Using Graph Neural Networks for Sulcal Graphs Predictive Models. 14th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition (GbRPR 2025), Jun 2025, Caen, France. pp.79-89, 10.1007/978-3-031-94139-9_8. hal-05271182

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# Insights on Using Graph Neural Networks for Sulcal Graphs Predictive Models

Alexis IMBERT<fnref n="1" />[0009-0000-4677-0981], Benoit GAÜZÈRE<fnref n="1" />[0000-0001-9980-2641], Sylvain TAKERKART<fnref n="2" />[0000-0001-8410-0962], Guillaume AUZIAS<fnref n="2" />[0000-0002-0414-5691], and Paul HONEINE<fnref n="1" />[0000-0002-3042-183X]

**Abstract.** Graph representations have emerged as a powerful tool in medical imaging to analyze complex structures such as the brain. In particular, studying the geometry of the brain surface provides valuable insights into individual characteristics. To model the brain as a graph, sulcal graphs are derived from MRI data by assigning a node to each fold of the cortical surface and connecting these nodes based on their adjacency. This approach encodes the geometry of the brain surface, making sulcal graphs a relevant data representation for predictive models. In this study, we explore the effectiveness of Graph Neural Networks (GNNs) to extract meaningful information from these graph representations, using sex classification as a pretext task. Our experiments reveal that the structural information captured by the graph is largely driven by the 3D coordinates of the nodes, raising questions about the added value of graph connectivity in these representations. Additionally, we find that conventional Multi-Layer Perceptron (MLP) models achieve comparable or superior performance to GNNs, suggesting that graph structure may not provide significant additional discriminative power in this specific task. These findings highlight the challenges of defining optimal GNN architectures for sulcal graphs and motivate further investigations into alternative representations and learning paradigms.

**Keywords:** Sulcal Graphs · Graph Neural Networks · Neuroimaging

## 1 Introduction

In the field of medical imaging, representing brains as graphs is both appealing and challenging due to the organ's complexity. Understanding this complexity by investigating the intra- and inter-invidual variations in graph representation across large samples of brains is a fundamental scientific challenge, with huge potential for applications in neuroscience and medicine.

<footnote n="1">¹ Univ Rouen Normandie, INSA Rouen Normandie, Université Le Havre Normandie, Normandie Univ, LITIS UR 4108, F-76000 Rouen, France
{alexis.imbert, benoit.gauzere}@insa-rouen.fr, paul.honeine@univ-rouen.fr</footnote>

<footnote n="2">² Institut des Neurosciences de la Timone, Marseille, France
{guillaume.auzias, sylvain.takerkart}@univ-amu.fr</footnote>
In this paper, we'd like to study the benefits of representing the brain as a graph, so we investigate how graph representations can be leveraged to extract meaningful information about the brain morphology. A long-term application of this understanding could be a better understanding of eurological disorders and psychiatric diseases. We explore state-of-the-art Graph Neural Network (GNN) architectures on graph data encoding the surface of the brain and deduce in-sights on the contributions of features describing the surface and the topological structure encoded within the graph. To assess this, we use the classification of biological sex as a pretext task. The relevance of this choice in the examination of brain morphology is supported by many studies reporting that gender shapes the spatial pattern of cortical alterations in many conditions such as autism and dyslexia.

The paper is organized as follows. In Section 2, we describe the process of constructing labeled graphs from anatomic Magnetic Resonance Imaging (MRI) data. Section 3 reviews related work and presents different modeling strategies. In Section 4, we conduct experiments to analyze the information extracted by predictive models from graph representations and identify potential biases in existing methods. Finally, we conclude with insights on the relevance of graph-based approaches for brain imaging analysis of the cortical folds.

## 2 From MRI to Sulcal Graphs

Neuroimaging data can be acquired through various techniques. MRI offers many acquisition sequences providing complementary insights: functional MRI (fMRI) captures a proxy of neuronal activity; diffusion MRI (dMRI) can be used to infer brain structural connectivity; and anatomical MRI provides detailed features of brain morphology, allowing to extract volumetric and surface representations of cerebral structures. In the present work, we focus on brain shape features extracted from T1- and T2-weighted anatomical MRI acquisitions.

End-to-end image processing software, such as Freesurfer<fnref n="3" />, can be used to automatically segment brain tissues and extract the cortical surface as a triangular mesh [15]. Dissociating normal variations across healthy individuals from abnormal variations potentially related to neurological conditions based on the features resulting from such an image processing pipeline is a classical task in the neuroimaging field. The literature of methods designed for characterizing the observed variations in brain morphology is thus huge. The potential of deep-learning models in this context started to be explored recently [17].

An increasing number of studies are confirming that the representations learnt by a Convolutional Neural Network (CNN) are highly relevant for characterizing the variations in cortical geometry. Recent studies demonstrated that CNNs can encode robust discriminative neuroimaging representations and outperform standard machine learning. In [1] for instance, supervised learning is explored to learn latent representations that are relevant for diagnostic classification and disease characterization. A limited number of studies have explored

<footnote n="3">³ https://freesurfer.net/</footnote>
the potential of deep learning methods to learn representations of cortical folding patterns, the complex spatial organization of valleys and crests on the cortical surface. In [6], CNNs are investigated for classifying folding patterns. More recently, the potential of variational autoencoder and contrastive learning for the detection of abnormal patterns was explored in [10,12]. In these studies, the representations of folding patterns are learnt using unsupervised generative methods, under the constraint of reconstructing the input data.

Given its inherent anatomo-functional organization, graphs are relevant representations for neuroimaging data. Graph representations are particularly valuable because they capture essential topological and structural properties, providing a powerful framework for analyzing brain organization and its relationship to cognitive functions or neurological disorders. Several approaches have been proposed to build graph representations from neuroimaging data. A classical graph construction approach involves projecting each subject's anatomy onto an atlas [11]. The projection is achieved through a spatial transformation that aims to map the individual anatomy onto the atlas anatomy [15]. This procedure allows one to parcellate each brain into a set of brain regions that define the nodes of the graph. Cortical measures such as sulcal depth and cortical thickness are extracted in each brain region and serve as node features. Weighted edge linking two brain regions are then defined by computing a statistical relationship between node features [4]. In the atlas-based approach used in [22], the vertices and edges from the mesh corresponding to the cortical surface of the atlas are mapped onto each individual anatomy and are used to define the nodes and edges of the corresponding brain graph. All subjects are thus represented using the same underlying mesh, the features assigned to the nodes are unique to each subject, but the number of nodes is identical, and the topology of the graph does not vary across individuals.

Therefore, atlas-based approaches harmonize brain representations by projecting a common atlas onto all individuals. While this facilitates comparisons, it comes at the expense of masking individual anatomical differences. Consequently, such methods may overlook significant individual variability. In particular, atlas-based graph representations do not respect the variations across subjects in the spatial organization of graph nodes along the cortical surface.

In the present work, we explicitly take into account the spatial relationships between cortical folds by using *sulcal graphs* to represent patterns of cortical folds. As proposed in [16], a sulcal graph is constructed by decomposing the cortical surface into sulcal basins. Fig. 1 provides a visual example of the decomposition of a brain into a sulcal graph. In this sulcal graph model, nodes correspond to sulcal basins, while edges connect adjacent basins to encode spatial relationships. Geometric features such as area, depth, and curvature are attributed to each node, while the overall graph structure reflects the topological layout of the cortical folds. Formally, each brain is thus modeled as a graph $G = (V, E)$, where nodes $V$ represent sulcal basins, each associated with an 8-dimensional feature vector that includes key attributes: the 3D coordinates of the deepest point of the basin – the sulcal pit, the surface area of the basin,
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Representation of 3 brains as sulcal graphs. Note that since the sulcal graph extraction approach is applied to each individual separately, the number of nodes and graph topology vary across individuals. The individual sulcal graphs are then projected onto a common spherical domain.</figcaption></figure>

the maximum and average depths, and 2 cortical thickness measures (thickness at the sulcal pit location and thickness averaged across all the vertices located in the sulcal basin). The graph's edges *E* define a region adjacency graph. This representation captures both local geometric details and the global organization of the cortical surface, offering a representation to analyze individual differences in folding patterns.

As illustrated in Fig. 1, individual sulcal graphs are projected onto a common spherical domain as described in [15], which allow to compute similarity measure and compare topological differences across subjects. Note that this projection does not alter inter-individual variations in the topology and features, since only the nodes' coordinates are transformed. Therefore, it does not suffer from the weakness of most approaches for designing brain graphs from the literature, where the graphs are constructed based on a predefined atlas that ensures an equal number of nodes for all subjects. Sulcal graphs offer significant advantages over the atlas-based approach. Firstly, they preserve individual differences by encoding both the topology of cortical folds and associated geometric attributes. Secondly, they enable advanced analyses using graph-based techniques, facilitating the identification of structural variations across populations and conditions.

## 3 Graph Neural Networks

Data represented as graphs have been extensively utilized in pattern recognition and machine learning. Although graphs provide a powerful and flexible representation, they pose significant challenges in terms of processing. Unlike vectors or images, graphs lack a regular structure and a fixed size. Additionally, there is no inherent ordering of nodes, leading to multiple possible representations of the same graph. To overcome these challenges, various approaches have been
proposed, including graph edit distances, graph kernels, and numerous graph embedding strategies. However, these methods often depend on domain expertise to define the features to be extracted or to determine the edit costs required for computing the graph edit distance. Such approaches may struggle to capture relevant and complex features needed for specific downstream tasks. Since 2012, deep learning approaches, particularly CNNs, have effectively addressed these challenges in image processing by learning efficient filters for specific tasks.

Following the success of CNNs in image analysis, researchers have sought to adapt CNN architectures for graph data. However, as previously discussed, graphs introduce unique challenges for differentiable learning methods. First, the number of nodes in a graph is an essential characteristic, which can vary and cannot be easily resized like image dimensions. This variation poses a challenge when applying fixed-size matrix multiplications to graph representations. Second, the lack of an inherent node order results in a combinatorial number of possible graph representations, leading to the well-known graph isomorphism problem. Similarly, the neighborhood of a node lacks a predefined order, complicating processing. These observations lead to two key properties required when designing graph-based functions. First, functions applied at the node level should be *per-mutation equivariant*, ensuring that any permutation of the graph does not alter the node-level results. Second, functions operating at the graph level should be *permutation invariant*, ensuring that different representations of the same graph produce the same output. These properties are typically enforced within graph embedding methods.

The convolution operation in CNNs has been adapted to graphs using the Message Passing Neural Network (MPNN) framework. This framework consists of two major steps: aggregation and update. Considering a node $v_i \in G$, the aggregation step consists in computing a permutation-invariant message from a set of nodes in $G$ related to $v_i$, generally the neighborhood of $v_i$. Then, the representation of $v_i$ is updated by combining the current representation of $v_i$ with the message computed in the aggregation step. Formally, considering the vectorial representation $h_i^{(l)}$ of node $v_i$ for layer $l$, updating this representation of $v_i$ can be expressed as:

$$h_i^{(l+1)} = \sigma(h_i^{(l)}, m(\{v_j | v_j \in \mathcal{N}(v_i)\})), \quad (1)$$

where the nonlinear function $\sigma(\cdot)$ is generally a ReLU, $\mathcal{N}(\cdot)$ defines the neighborhood relationship used to designate the set of nodes composing the message, and $m(\cdot)$ is a permutation invariant function resuming a set of nodes into a vectorial representation.

Several implementations of this framework have been proposed [19]. Among the most commonly used methods are Graph Convolutional Networks (GCN) [14], which consist of a node $v_i$ to aggregate the representation of each neighbor of $v_i$, as described by the adjacency relationships, weighted by the inverse of the degree of $v_i$. Such an approach allows to integrate the neighborhood information into the local representation of $v_i$. One limitation of this approach is to consider uniformly the different neighbors of a node. To allow anisotropic aggregations,
Graph Attention Networks (GAT) [20] and then GATv2 [7] introduce attention mechanisms to weight neighboring nodes based on the representation of the two incident nodes of an edge. While being widely used, these two first approaches are not able to distinguish some graph structures, which thus reduces their ability to catch the structural information. Graph Isomorphism Networks (GIN) [23] seeks to enhance the expressive power of GNNs by generalizing the Weisfeiler-Lehman test [3]. Numerous contributions have been proposed, and we refer the reader to exhaustive reviews [5,8,21]. It is established in [2] that most of the MPNN-based architectures act as low-pass filters. As a consequence, multiplying the MPNN layers leads to a flat representation for all the nodes in the graph, hence inducing a loss of information. For this reason, GNN architectures are generally limited to 4 layers.

<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Illustration of the Message Passing Neural Network (MPNN) framework on a graph. The node being updated is shown in blue, and its message-contributing neighbors are highlighted in green. Each node aggregates information from its neighbors (agg), then updates its own representation (upd). Applying this message-passing scheme to all nodes yields learned representations for the entire graph.</figcaption></figure>

The literature of applications of GNN to brain graphs is growing fast [4]. Almost all studies use the atlas-based graph construction approach, such that the challenges related to variations in the number of nodes and edges are addressed before the application of a GNN. In [22], for instance, a novel GNN is designed to identify which brain regions and their morphometric measures were most predictive of a behavioral score of fluid intelligence. The method achieves high accuracy while identifying key brain regions involved in these predictions. However, the proposed method relies on atlas-based brain graphs, such that the reported performance might be limited by the masking of inter-individual variations in the topology of the graphs during the construction of the brain graphs. To the best of our knowledge, the only previous work exploring the effectiveness of GNNs for sulcal graphs is [24]. This study focused on predicting sex from sulcal graphs using two standard GNN architectures, aiming at identifying brain regions most relevant to classification decisions. However, comparison with a
non-geometric Multi-Layer Perceptron (MLP) model showed no significant ad-
vantage for graph-based methods, raising questions about the utility of graph
representations. While providing initial insights into GNNs for sulcal graphs,
this study did not thoroughly assess the redundancy of 3D information. A more
comprehensive benchmark of recent GNN architectures may help to reveal the
information carried by the graph structure. In addition, it may be interesting
to clarify the contributions of different GNN components, including convolution
and pooling operations.

# 4 Experiments

To explore how sulcal graphs can be integrated with GNN-based approaches for predictive modeling, we design experiments to address the quantity of information encoded within the graph structure and the node features. By addressing this question, we aim to gain deeper insights into the effectiveness and relevance of graph representations for modeling the cortical surface. To this end, we use data from the Human Connectome Project (HCP) [18], which includes 1090 young adult subjects, each associated with an MRI scan of their brain along with demographic and cognitive information such as age, sex, and cognitive scores. For each subject, we construct two sulcal graphs, one per hemisphere, following the process described in Section 2. In this study, we focus on the left hemisphere and formulate a binary classification task to predict sex (male or female).

To evaluate the effectiveness of graph-based methods, we establish two baselines: a random classifier to assess the inherent difficulty of the task, and an MLP, which does not leverage the graph structure, to highlight the added value of graph representations. The input data to the MLP are the attributes of the graph nodes, and the result is reduced to a single vector after a mean pooling operation. We compare several GNN architectures, including standard graph convolution models GCN [14] and GraphSAGE [13], attention-based models GAT [20] and GATv2 [7], and the more expressive model GIN [23] designed to better distinguish different graph structures. All models are cross-validated for key hyperparameters, including the number of layers (1, 2, 4, or 8) and the hidden layer dimensions (16, 32, 64, or 128 neurons). The readout function is set to the mean for all experiments. Additionally, batch size is cross-validated with values of 32 and 64, and an early stopping strategy is applied with a patience of 100 epochs, selecting the best epoch based on validation F1-score. To ensure a fair comparison, we generate 10 random splits of the dataset using an 80% training, 10% validation, and 10% testing distribution, applying the same splits across all models. Validation and test performances are reported as the mean F1-score over the 10 folds. Experiments with failed runs, where validation/test F1-scores were zero for at least one split, are excluded from the results. While our experiments are comparable to those conducted in [24], our study differs by testing a broader range of convolutional methods, performing rigorous cross-validation to determine optimal hyperparameters and evaluating the contribution of various
<table><caption>**Table 1.** Sex prediction performance using left hemisphere sulcal graphs, evaluated via averaged F1-score on validation and test sets, ± reports the standard deviation over the 10 runs. Best results for each setup are in bold. NaN values indicate failed runs. using a random classifier trained only on the training distribution gives a test value of f1-score 69.71 ± 3.33. The list of all parameters tested, together with an indication of the best hyperparameters, can be found in the github repo.</caption><thead><tr><th rowspan="2">Model</th><th colspan="2">All-Features</th><th colspan="2">No 3D</th><th colspan="2">No Features</th></tr><tr><th>Valid</th><th>Test</th><th>Valid</th><th>Test</th><th>Valid</th><th>Test</th></tr></thead><tbody><tr><td>GAT</td><td>75.54 ± 6.39</td><td>74.91 ± 6.55</td><td>73.25 ± 4.03</td><td>73.56 ± 7.17</td><td>70.10 ± 3.41</td><td><strong>70.86 ± 3.64</strong></td></tr><tr><td>GATv2</td><td>77.84 ± 2.45</td><td>78.22 ± 4.54</td><td>75.41 ± 5.08</td><td>76.21 ± 4.59</td><td>NaN</td><td>NaN</td></tr><tr><td>GCN</td><td>77.34 ± 3.07</td><td>75.22 ± 3.97</td><td>77.49 ± 4.92</td><td>76.45 ± 3.50</td><td>NaN</td><td>NaN</td></tr><tr><td>GIN</td><td>73.59 ± 6.40</td><td>73.27 ± 4.90</td><td>73.18 ± 7.35</td><td>74.38 ± 5.21</td><td><strong>72.98 ± 2.74</strong></td><td>68.84 ± 4.50</td></tr><tr><td>GraphSAGE</td><td>77.62 ± 4.16</td><td>75.70 ± 5.38</td><td>75.61 ± 5.64</td><td><strong>76.81 ± 4.91</strong></td><td>NaN</td><td>NaN</td></tr><tr><td>MLP</td><td><strong>78.72 ± 5.10</strong></td><td><strong>79.54 ± 2.37</strong></td><td><strong>79.46 ± 3.01</strong></td><td>74.97 ± 4.86</td><td>NaN</td><td>NaN</td></tr></tbody></table>

feature sets beyond geometric attributes. The source code for reproducibility is available<fnref n="4" />.

Table 1 presents the results of the different models. The MLP outperforms all GNNs when using all features, suggesting that the graph structure may not contribute significant additional information. However, removing 3D coordinates leads to a noticeable decline in MLP performance on test set, whereas GNN models remain stable, indicating that they leverage the graph structure effectively. The geometric features introduce potential biases, as male brains tend to be larger than female brains, and basin areas may encode this distinction. When all features are removed, most models fail to learn meaningful patterns, except for GAT and GIN, which still perform above chance level. This suggests they capture some structure from the graph itself. Given the variability across models, further replication is needed. If more advanced GNNs continue to underperform compared to MLPs, the validity of using graph structures for this task should be reassessed.

# 5 Discussion and Conclusion

Based on experiments conducted with state-of-the-art and widely used GNNs, we were unable to clearly demonstrate the added value of using graphs to encode the brain surface on this dataset. However, before concluding that graphs do not encode useful information, it is crucial to consider the limitations of this study.

First, our model was trained on a dataset limited to ~1,000 subjects, while complex models with a high number of parameters, such as GNNs, typically benefit from larger datasets. To address this limitation, we could integrate additional datasets, such as ABCD [9] and those used in [22], to increase the number of samples to over 10,000. Second, our experiments suggest that most of the structural information captured by the graph is primarily carried by the nodes'

<footnote n="4">⁴ https://github.com/Alexis-IMBERT/benchmark-gnn</footnote>
3D coordinates. This observation raises questions about the role of graph connectivity in the learned representations. Moreover, certain attributes, such as zone area, are partly sufficient to discriminate among subjects. To further explore this aspect, we aim to design GNN architectures specifically tailored to sulcal graphs and their spatial properties. Incorporating such characteristics could help reduce reliance on permutation-equivariant functions for neighborhood aggregation and better account for the brain's geometric structure. Finally, although we obtained promising results on the sex prediction task, these findings need to be validated on more complex and clinically relevant tasks to assess the broader applicability of our approach.

In conclusion, designing efficient and powerful GNN architectures for modeling sulcal graphs remains a challenging task. Our results suggest that relying solely on existing methods is insufficient to develop accurate and robust models. Future work will focus on improving both the model architecture and the graph representation to better exploit the rich structural information of the brain surface.

**Acknowledgements** This work has been supported by the ANR under the grant ANR-23-CE23-0019-02. Data were provided by the Human Connectome Project, WUMinn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.

## References

1. Abrol, A., Fu, Z., Salman, M., Silva, R., Du, Y., Plis, S., Calhoun, V.: Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning. Nature Communications 12(1), 353 (Jan 2021). https://doi.org/10.1038/s41467-020-20655-6

2. Balcilar, M., Guillaume, R., Héroux, P., Gaüzère, B., Adam, S., Honeine, P.: Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective. In: International Conference on Learning Representations (ICLR) (2021), https://openreview.net/forum?id=-qh0M9XWxnv

3. Balcilar, M., Héroux, P., Gaüzère, B., Vasseur, P., Adam, S., Honeine, P.: Breaking the limits of message passing graph neural networks. In: Proceedings of the 38th International Conference on Machine Learning (ICML). vol. 139, pp. 599–608. PMLR (18 - 24 Jul 2021), http://proceedings.mlr.press/v139/balcilar21a.html

4. Bessadok, A., Mahjoub, M.A., Rekik, I.: Graph Neural Networks in Network Neuroscience. IEEE transactions on pattern analysis and machine intelligence 45(05) (May 2023). https://doi.org/10.1109/TPAMI.2022.3209686

5. Bo, D., Wang, X., Liu, Y., Fang, Y., Li, Y., Shi, C.: A Survey on Spectral Graph Neural Networks. ArXiv (2023). https://doi.org/10.48550/arXiv.2302.05631

6. Borne, L., Rivière, D., Mancip, M., Mangin, J.F.: Automatic labeling of cortical sulci using patch- or CNN-based segmentation techniques combined with bottom-up geometric constraints. Medical Image Analysis 62, 101651 (May 2020). https://doi.org/10.1016/j.media.2020.101651
7. Brody, S., Alon, U., Yahav, E.: How attentive are graph attention networks? In: International Conference on Learning Representations (2022). https://doi.org/10. 48550/arXiv.2105.14491

8. Bronstein, M.M., Bruna, J., Cohen, T., Veličković, P.: Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges (2021), http://arxiv.org/abs/2104. 13478

9. Casey, B., Cannonier, T., Conley, M.I., Cohen, A.O., Barch, D.M., Heitzeg, M.M., Soules, M.E., Teslovich, T., Dellarco, D.V., Garavan, H., Orr, C.A., Wager, T.D., Banich, M.T., Speer, N.K., Sutherland, M.T., Riedel, M.C., Dick, A.S., Bjork, J.M., Thomas, K.M., Chaarani, B., Mejia, M.H., Hagler, D.J., Daniela Cornejo, M., Sicat, C.S., Harms, M.P., Dosenbach, N.U., Rosenberg, M., Earl, E., Bartsch, H., Watts, R., Polimeni, J.R., Kuperman, J.M., Fair, D.A., Dale, A.M.: The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites. Developmental Cognitive Neuroscience **32**, 43–54 (Aug 2018). https://doi.org/10. 1016/j.dcn.2018.03.001

10. Chavas, J., Guillon, L., Pascucci, M., Dufumier, B., Rivière, D., Mangin, J.F.: Unsupervised Representation Learning of Cingulate Cortical Folding Patterns. In: International conference on medical image computing and computer-assisted intervention. pp. 77–87. Singapore, Singapore (Sep 2022). https://doi.org/10.1007/ 978-3-031-16431-6_8

11. Desikan, R., Ségonne, F., Fischl, B., Quinn, B.T., Dickerson, B.C., Blacker, D., Buckner, R.L., Dale, A.M., Maguire, R.P., Hyman, B.T., Albert, M., Killiany, R.J.: An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage **31**(3), 968–980 (Jul 2006). https://doi.org/10.1016/j.neuroimage.2006.01.021

12. Guillon, L., Chavas, J., Bénézit, A., Moutard, M.L., Roca, P., Mellerio, C., Op-
penheim, C., Rivière, D., Mangin, J.F.: Identification of rare cortical folding pat-
terns using unsupervised deep learning. Imaging Neuroscience **2**, 1–27 (02 2024).
https://doi.org/10.1162/imag_a_00084

13. Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. Advances in neural information processing systems **30** (2017)

14. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (2017), https://openreview.net/pdf?id=SJU4ayYgl

15. Lerch, J.P., van der Kouwe, A.J., Raznahan, A., Paus, T., Johansen-Berg, H., Miller, K.L., Smith, S.M., Fischl, B., Sotiropoulos, S.: Studying neuroanatomy using MRI. Nature Neuroscience **20**(3), 314–326 (Feb 2017). https://doi.org/10. 1038/nn.4501

16. Takerkart, S., Auzias, G., Brun, L., Coulon, O.: Structural graph-based morphometry: A multiscale searchlight framework based on sulcal pits. Medical Image Analysis **35**, 32–45 (Jan 2017). https://doi.org/10.1016/j.media.2016.04.011

17. Teghipco, A., Newman-Norlund, R., Fridriksson, J., Rorden, C., Bonilha, L.: Distinct brain morphometry patterns revealed by deep learning improve prediction of post-stroke aphasia severity. Communications Medicine **4**(1), 115 (2024). https://doi.org/10.1038/s43856-024-00541-8

18. Van Essen, D.C., Ugurbil, K., Auerbach, E., Barch, D.M., Behrens, T.E., Bucholz, R., Chang, A., Chen, L., Corbetta, M., Curtiss, S.W., Della Penna, S., Feinberg, D., Glasser, M.F., Harel, N., Heath, A.C., Larson-Prior, L., Marcus, D.S., Michalareas, G., Moeller, S., Oostenveld, R., Petersen, S.E., Prior, F., Schlaggar, B.L., Smith, S.M., Snyder, A.Z., Xu, J., Yacoub, E.: The Human Connec-
tome Project: A data acquisition perspective. NeuroImage 62, 2222-2231 (2012).
https://doi.org/10.1016/j.neuroimage.2012.02.018

19. Veličković, P.: Message passing all the way up. In: ICLR 2022 Workshop on Geometrical and Topological Representation Learning (2022), https://arxiv.org/pdf/2202.11097

20. Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations (2018), https://openreview.net/pdf?id=rJXMpikCZ

21. Wu, L., Cui, P., Pei, J., Zhao, L., Guo, X.: Graph Neural Networks: Foundation, Frontiers and Applications. KDD '22, Association for Computing Machinery (2022), https://doi.org/10.1145/3534678.3542609

22. Wu, Y., Besson, P., Azcona, E.A., Bandt, S.K., Parrish, T.B., Breiter, H.C., Katsaggelos, A.K.: A multicohort geometric deep learning study of age dependent cortical and subcortical morphologic interactions for fluid intelligence prediction. Scientific Reports 12(1), 17760 (Oct 2022). https://doi.org/10.1038/s41598-022-22313-x, publisher: Nature Publishing Group

23. Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How Powerful are Graph Neural Networks? (Feb 2019). https://doi.org/10.48550/arXiv.1810.00826

24. Yadav, R., Dupé, F.X., Takerkart, S., Auzias, G.: Geometric deep learning for sulcal graphs. In: IEEE International Symposium on Biomedical Imaging (ISBI 2024). Athènes, Greece (May 2024), https://hal.science/hal-04608751