# Decoding cortical folding with deep learning: toward neurodevelopmental biomarkers of psychiatric disorders

Pierre Auriau, Antoine Grigis, Jean-François Mangin, Joël Chavas, Julien Laval, Vincent Frouin, Arnaud Cachia, Charles Laidi, Josselin Houenou, Benoit Dufumier, et al.

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Pierre Auriau, Antoine Grigis, Jean-François Mangin, Joël Chavas, Julien Laval, et al. Decoding cortical folding with deep learning: toward neurodevelopmental biomarkers of psychiatric disorders. Journal of Neural Transmission, 2026, 10.1007/s00702-026-03105-3. hal-05500148

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# Decoding cortical folding with deep learning: toward neurodevelopmental biomarkers of psychiatric disorders

Pierre Auriau<fnref n="1" /> · Antoine Grigis<fnref n="1" /> ID · Jean-François Mangin<fnref n="1" /> ID · Joël Chavas<fnref n="1" /> ID · Julien Laval<fnref n="1" /> · Vincent Frouin<fnref n="1" /> ID · Arnaud Cachia<fnref n="7" />,<fnref n="8" /> · Charles Laidi<fnref n="2" />,<fnref n="3" />,<fnref n="4" /> ID · Josselin Houenou<fnref n="2" />,<fnref n="3" />,<fnref n="9" /> ID · Benoit Dufumier<fnref n="1" /> ID · Julie Bourgin<fnref n="5" />,<fnref n="6" /> · Edouard Duchesnay<fnref n="1" /> ID

Received: 19 September 2025 / Accepted: 6 January 2026
© The Author(s) 2026

## Abstract

Cortical folding is primarily determined during prenatal and early postnatal brain development and remains relatively stable throughout life. Accumulating evidence suggests that psychiatric disorders, including schizophrenia (SCZ), Bipolar Disorder (BD), and Autism Spectrum Disorder (ASD), result from intricate interactions between early neurodevelopmental disruptions and subsequent environmental influences. Therefore, cortical folding patterns are promising candidates as stable imaging biomarkers reflecting the neurodevelopmental component of psychiatric conditions. This study aims to demonstrate that deep learning can be used to learn meaningful representations of cortical folding patterns that enable the individual-level prediction of major psychiatric disorders, namely SCZ, BD, and ASD. We introduce a dedicated deep learning architecture leveraging self-supervised pre-training on large datasets of the general population to extract subject-level representations of cortical folding from structural magnetic resonance imaging (MRI) data. We demonstrate that the learned representations allow significant single-subject prediction of clinical status for SCZ, BD, and ASD. The proposed folding-based representation presents a novel approach for identifying imaging biomarkers related to the neurodevelopmental origins of psychiatric disorders. It opens the possibility of inferring early-life brain alterations from adult MRI data, offering potential tools for stratification and precision psychiatry.

**Keywords** Brain folding · Mental disorders · Neurodevelopment · Machine learning · Autism spectrum disorder · Deep learning

<figure><img src="image_2.png" /></figure>

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<footnote>Pierre Auriau
pierreauriau@gmail.com</footnote>

<footnote>Edouard Duchesnay
edouard.duchesnay@cea.fr</footnote>

<footnote n="1">¹ Neurospin, UMR9027 BAOBAB, GAIA Laboratory, Université Paris-Saclay, CEA, CNRS, Gif-sur-Yvette 91191, France</footnote>

<footnote n="2">² Department of Translational Neuro-Psychiatry, UMR955, Université Paris Est Créteil, INSERM, Créteil, France</footnote>

<footnote n="3">³ Fondation FondaMental, Créteil, France</footnote>

<footnote n="4">⁴ Département Médico-Universitaire de Psychiatrie et d'Addictologie, Fédération Hospitalo-Universitaire de Médecine de Précision en Psychiatrie, Assistance Publique-Hôpitaux de Paris, Hôpitaux Universitaires Henri Mondor, Créteil, France</footnote>

<footnote n="5">⁵ Service de Psychiatrie de l'Enfant et de l'Adolescent, Groupe Hospitalier Nord Essonne, Bures-sur-Yvette 91440, France</footnote>

<footnote n="6">⁶ CESP UMR1018, INSERM, Université Paris-Saclay, Villejuif, France</footnote>

<footnote n="7">⁷ UMR8240, Laboratory for the Psychology of Child Development and Education, Université Paris Cité, CNRS, Paris 75005, France</footnote>

<footnote n="8">⁸ Institute of Psychiatry and Neuroscience of Paris (IPNP), UMR1266, Ima-Brain team, INSERM, Université Paris Cité, Paris 75014, France</footnote>

<footnote n="9">⁹ Department of Psychiatry, Mental Health Services, The University of Melbourne, Northern Health, Epping, Australia</footnote>
Introduction

Cortical folding begins during the last trimester of gestation, when the initially smooth cerebral surface transforms into a complex landscape of sulci and gyri (Chi et al. 1977; Dubois et al. 2008). By full-term birth, most sulci are already formed, and their overall organisation remains remarkably stable throughout life (Dubois et al. 2019; Hill et al. 2010; Meng et al. 2014). These early-emerging folding patterns are shared across humans (de Vareilles et al. 2023), reflect fundamental neurodevelopmental processes, and remain largely preserved despite environmental influences or ageing (Cachia et al. 2016).

Consequently, cortical folding provides a lifelong mor-
phological imprint of early genetic, epigenetic, or environ-
mental perturbations that shape brain maturation. It therefore
offers a unique opportunity to infer early developmental
deviations from adult MRI scans obtained after structural
maturity has been reached. This is particularly relevant in
psychiatric neuroimaging, where MRI is typically acquired
after symptom onset. In this context, cortical folding con-
stitutes a stable and retrospective marker of atypical early
neurodevelopmental trajectories, distinguishing them from
later-emerging brain alterations driven by ongoing patho-
physiological processes or environmental exposures during
subsequent stages of maturation (Casey et al. 2014; Iftimov-
ici et al. 2024; Khodosevich and Sellgren 2023; Lefrere et
al. 2023; Whittle et al. 2025).

This perspective is particularly pertinent to psychiatry,
where accumulating evidence points to a shared neurode-
velopmental continuum among mental disorders (Casey et
al. 2014; Whittle et al. 2025), driven by early disruptions
in brain maturation. The neurodevelopmental paradigm
motivates the investigation of whether alterations in corti-
cal folding can serve as predictors of psychiatric disorders
extending beyond those traditionally classified as neurode-
velopmental. Autism spectrum disorder (ASD) is classified
as a neurodevelopmental disorder in the DSM-5 (Diagnostic
and Statistical Manual of Mental Disorders, 5th Edition),
with first symptoms typically emerging in the first year of
life (Ozonoff and Iosif 2019). By contrast, schizophrenia
(SCZ) and bipolar disorder (BD) are not formally catego-
rized as neurodevelopmental, with median ages of onset at
25 and 33 years, respectively (Solmi et al. 2022). Nonethe-
less, converging evidence indicates that early developmen-
tal deviations contribute to their pathophysiology (Casey
et al. 2014; Whittle et al. 2025). The neurodevelopmental
hypothesis of SCZ posits that anomalies arising during
critical windows of early brain development predispose to
later illness (Rapoport et al. 2012; Weinberger 1987). For
BD, although no consensus exists on a distinct neurodevel-
opmental subtype, patients with early onset and psychotic

features appear more likely to present neurodevelopmental
anomalies (Kloiber et al. 2020; Lefrere et al. 2023).

There is already evidence that brain folding is associated
with psychiatric disorders (Mangin et al. 2010; Lefrere et al.
2023; Sasabayashi et al. 2021). For instance, (Cachia et al.
2015) demonstrated a reduction in brain folding in patients
with SCZ compared to healthy controls, as well as differ-
ences between patients with auditory and visual hallucina-
tions and those with only auditory hallucinations. (Penttilä
et al. 2009) showed differences in cortical folding between
patients with early-onset and intermediate-onset bipolar
disorder (for more examples, see the detailed review (Sasa-
bayashi et al. 2021) and (Lefrere et al. 2023).

The established association between cortical folding and psychiatric disorders, together with the lifelong stability of these patterns, positions them as a promising substrate for identifying markers of early neurodevelopmental alterations underlying psychiatric conditions. However, most of these studies used the gyrification index, a measure to quantify brain folding, which is the ratio between the lengths of the pial surface and the outer envelope (Zilles et al. 1988). This index can be computed at the whole-brain scale or at the regional scale, which is referred to as local gyrification indexes in this case. The gyrification index does not capture all the information about brain folding. For instance, the shape characteristics of cortical sulci, such as buried gyrus, which may be key landmarks of brain folding (Mangin et al. 2019), are not included in the gyrification index.

To address these limitations, we introduce a deep learn-
ing (DL) framework that derives rich, meaningful represen-
tations of cortical folding directly from anatomical MRI,
relying only on minimal preprocessing (sulcal skeleton
extraction) and avoiding predefined features such as the
gyrification index.

Our objective is to evaluate whether these representations
embed sufficient information to enable individual-level pre-
diction of three major psychiatric disorders (Arbabshirani
et al. 2017), ASD, BD, and SCZ, thus moving beyond the
group-level folding differences typically reported in the lit-
erature. Although previous studies have applied machine
learning to anatomical MRI for diagnostic prediction in
SCZ (de Filippis et al. 2019; Verma et al. 2023), BD (Claude
et al. 2020), and ASD (Gaur et al. 2023), our approach dif-
fers in two fundamental respects. First, because our goal is
to identify neurodevelopmental biomarkers, we restrict the
input to sulcal skeletons, representations of cortical fold-
ing that are established early in life. Second, we strive to
develop a transnosographic foundation model that captures
the shared and disorder-specific variability of cortical fold-
ing across multiple psychiatric conditions.

To achieve this goal, we leverage transfer learning:
DL models are first pre-trained on large samples from the
general population, allowing them to learn fundamental
anatomical features and providing a favourable initialisa-
tion that mitigates convergence to suboptimal local minima.
Models derived from the same pre-trained encoder, whether
fine-tuned or equipped with a new fully connected head atop
frozen weights, tend to remain within a shared optimisation
basin. This reduces variability linked to random initiali-
sation (aleatoric uncertainty; (Neyshabur et al. 2021) and
improves the stability and generalisability of the learned
representations (Dufumier et al. 2024).
Such a global deep learning model, which performs a
complex, nonlinear mapping from the entire cortical fold-
ing pattern to a single representation, inherently limits the
ability to trace back which specific folding features drive
its predictions, thereby constraining interpretability and
obscuring the capacity to link with an underlying neu-
robiological mechanism. To enhance interpretability, we
therefore complement our global modelling strategy with a
regional approach, in which multiple specialised models are
trained to focus on distinct cortical regions. This ensemble
of region-specific experts enables a more transparent map-
ping between local folding features and prediction out-
comes, offering direct insight into which areas contribute
most strongly to classification. Ensemble strategies of this
kind have been successfully applied to structural MRI for
predicting mental disorders (Gaur et al. 2023; Petiton et al.
2024), and here provide a principled way to link folding-
based representations with anatomically interpretable sig-
natures across disorders.

Such a global deep learning model, which performs a
complex, nonlinear mapping from the entire cortical fold-
ing pattern to a single representation, inherently limits the
ability to trace back which specific folding features drive
its predictions, thereby constraining interpretability and
obscuring the capacity to link with an underlying neu-
robiological mechanism. To enhance interpretability, we
therefore complement our global modelling strategy with a
regional approach, in which multiple specialised models are
trained to focus on distinct cortical regions. This ensemble
of region-specific experts enables a more transparent map-
ping between local folding features and prediction out-
comes, offering direct insight into which areas contribute
most strongly to classification. Ensemble strategies of this
kind have been successfully applied to structural MRI for
predicting mental disorders (Gaur et al. 2023; Petiton et al.
2024), and here provide a principled way to link folding-
based representations with anatomically interpretable sig-
natures across disorders.

To the best of our knowledge, none of these efforts has
explicitly focused on modelling brain folding patterns for
individual prediction. The decision to opt for Machine
Learning (ML) models presents several challenges: ML
models tend to capture image acquisition variability before
clinical relevance variability (Solanes et al. 2023). The vari-
ability of image acquisition is due to different acquisition
parameters, scanners, subject recruitment, or clinical rou-
tines regarding the platform that acquires the images, all of
which are collectively referred to as the site effect. The site
effect confound could be exacerbated for DL models as their
decision-making process is not explicit, which is why they
are often considered as "black boxes". Several interpretabil-
ity methods have been developed to understand a posteriori
how DL models make their decisions (Madapatha and Fer-
nando 2024; van der Velden et al. 2022), but these methods
often struggle to truly represent the model's choices (Ade-
bayo et al. 2020; Zhang et al. 2022).

Our contributions are twofold:

1. We developed and systematically compared three complementary deep learning strategies to derive meaningful representations of cortical folding. First, we

implemented a *baseline neural network architecture trained de novo* on clinical cohorts, establishing a reference framework for single-disorder prediction. Second, we constructed a *foundation model*, using a pre-trained neural network on a large-scale dataset from the general population (UK Biobank), to capture individual-level representations of sulcal patterns and leverage transfer learning for clinical applications. Third, to enhance interpretability and localise disorder-relevant folding anomalies, we designed an *ensemble of regional models*, in which multiple networks specialised in distinct brain regions, providing a straightforward interpretability of the predictive model.

2. We showed that brain folding shapes are predictive of clinical status for three mental disorders, namely Autism spectrum disorder (ASD), Bipolar Disorders (BD) and Schizophrenia (SCZ).

Beyond methodological advances, this work provides empirical evidence that markers rooted in the early phases of brain development, specifically cortical folding patterns, carry sufficient information to significantly discriminate at the individual level between the main clinical categories in psychiatry. These findings support the neurodevelopmental paradigm, which emphasises the role of early brain developmental perturbations in shaping vulnerability to psychiatric illness. Importantly, while categorical prediction provides a proof of concept, the next step is to extend this approach toward a dimensional framework, in line with initiatives such as the Research Domain Criteria (RDoC), to capture transdiagnostic neurodevelopmental load and stratify patients according to deviations from normative developmental trajectories rather than fixed diagnostic boundaries.

**Materials and methods**

**Datasets and pre-processing**

**Clinical datasets.** We used three datasets composed of aggregated clinical studies for each case-control classification task, previously used in (Dufumier et al. 2024). Each dataset comprises patients and their corresponding healthy controls from the same dataset, with all structural MRIs acquired at the same set of sites. The description of each dataset is available in Table 1.

**Large dataset of the general population.** We used 21,045 subjects of the UK Biobank cohort<fnref n="1" /> to pre-train deep learning models before applying them to clinical cohorts. Compared to psychiatric datasets, the UK Biobank dataset

<footnote n="1">¹ https://www.ukbiobank.ac.uk.</footnote>
<table><caption>**Table 1** Description of the clinical datasets</caption><thead><tr><th>Dataset</th><th>Clinical status</th><th>N</th><th>Age</th><th>Sex</th><th># Sites</th><th>Pooled studies</th></tr></thead><tbody><tr><td rowspan="2">ASD</td><td>HC</td><td>926</td><td>16±9</td><td>25</td><td rowspan="2">30</td><td rowspan="2">ABIDE I, ABIDE II</td></tr><tr><td>ASD</td><td>813</td><td>16±9</td><td>13</td></tr><tr><td rowspan="2">BD</td><td>HC</td><td>695</td><td>37±14</td><td>54</td><td rowspan="2">15</td><td rowspan="2">BIOBD, BSNIP1, CANDI, CNP</td></tr><tr><td>BD</td><td>469</td><td>39±12</td><td>57</td></tr><tr><td rowspan="2">SCZ</td><td>HC</td><td>761</td><td>33±12</td><td>51</td><td rowspan="2">12</td><td rowspan="2">BSNIP1, CANDI, CNP, SCHIZCONNECT</td></tr><tr><td>SCZ</td><td>532</td><td>34±12</td><td>29</td></tr></tbody></table>

The three datasets comprise healthy control (HC) subjects and patients with autism spectrum disorder (ASD), bipolar disorders (BD), and schizophrenia (SCZ). The age is described with average ± standard deviation, and the sex in percentage of females.

is composed of much older subjects (average age of 63±8). We assumed that the age gap between UK Biobank and psychiatric datasets, which can be considered as a domain gap, would not impact the transfer learning approach, as brain folding patterns can be considered independent of the subject's age. The second main difference between the datasets is the number of acquisition sites. All the acquisition parameters for the UK Biobank dataset have been homogenised, which explains that we can consider it as a single acquisition site dataset. As a result, the MRIs from the UK Biobank dataset are much more homogeneous compared to the multi-site psychiatric datasets, with a strong site effect.

**Pre-processing.** We processed the T1-weighted MR brain images with the BrainVisa/Morphologist pipeline<fnref n="2" /> to extract brain folds. The pipeline comprises several steps, including skull stripping, bias correction, brain segmentation, hemisphere segmentation, skeletonisation of the grey matter and CSF union. The pipeline provides as output the skeleton, which is a 3D image in which non-zero voxels form a negative cast of the brain's folds. In the skeleton, each sulcus is represented as a simple surface, without thickness, to remove as much as possible age-related effects on cortical sulci. In particular, cortical sulci open with age, which would result in wider sulci if we simply take the negative cast of the brain's folds (Jin et al. 2018; Kochunov et al. 2005; Shen et al. 2018). We then used the DeepFolding toolbox<fnref n="3" /> to remove ventricles from the skeletons, as their age-related enlargement can artificially increase the number of sulci detected by Morphologist. The resulting skeletons were registered to the MNI-ICBMc2009 template space with an affine registration to be able to make comparisons between subjects. The affine transformation is obtained based on the SPM12⁴ normalisation provided by the Morphologist processing. The skeletons were then resampled to an isotropic resolution of 1.5mm<fnref n="3" />. After this resampling step, we proceeded to another skeletonisation step to ensure simple surface preservation because the resampling step can introduce random thickening of sulci.

**Quality check.** As there are no quality checks in the Morphologist pipeline, we computed skeleton sizes, which are the number of non-zero voxels in the image. Skeletons with an outlier size, i.e. exceeding the cohort mean by ±3 standard deviations, were manually checked and removed to avoid computational anomalies affecting downstream learning. We also computed a density map with all the skeletons for each study. Skeletons with the highest number of voxels that were rarely found in other skeletons were manually checked. These semi-automatic quality checks discarded images for which the Morphologist had not been able to compute the skeleton correctly.

**Data splitting in training and test sets.** To evaluate classification models fairly, we split the dataset into training, validation, and test sets as is classically done in ML studies. We maintained a distribution of subjects in each set that closely matches the entire dataset. To do so, we used an iterative algorithm to attempt to keep the histograms of the selected variables unchanged between the training and test sets (Sechidis et al. 2011). We stratified on diagnosis, age, sex, and acquisition site. We maintained two independent test sets: the internal test set, which comprises images from the same acquisition sites as the train and validation sets, and the external test set, which includes images from other acquisition sites. The goal is to test how the model can generalise to unseen acquisition sites, and thus evaluate the impact of the site effect on the model's predictions. We applied a 10-fold cross-validation scheme to get 10 splits for training and validation sets and kept the test sets unchanged for each fold.

**Evaluation metrics.** To evaluate the trained models, we computed the Area Under the Receiver Operating Characteristic Curve (ROC AUC) of model predictions on internal and external test sets to assess model performance. ROC AUC scores were averaged across the 10 models, each trained on a specific fold. ROC AUC was selected because it quantifies the model's ability to discriminate patients from controls across all possible decision thresholds, providing a threshold-independent assessment of discriminative power that is well aligned with our goal of evaluating the information contained in cortical folding representations rather than defining a clinically actionable classification boundary.

<footnote n="2">² https://brainvisa.info.</footnote>

<footnote n="3">³ https://github.com/neurospin/deep_folding.</footnote>

<footnote n="4">⁴ https://www.fil.ion.ucl.ac.uk/spm.</footnote>
The statistical significance of the ROC AUC, was evaluated
with the Mann-Whitney U test (Mason and Graham 2002)
to determine whether the predicted probability distributions
for patients and healthy controls differed significantly.

Global deep learning architecture

We developed a convolutional neural network that lever-
ages a global, whole-brain representation of cortical folding
skeletons to predict the clinical status of subjects. For the
encoder, which acts like the feature extractor, we selected
the DenseNet-121 neural network architecture (Huang et al.
2017) composed of convolutional layers with skip connec-
tions. To make the final prediction, we added a multilayer
perceptron (MLP) head with ReLU activation layers and a
final sigmoid layer that outputs the predicted probability that
the skeleton belongs to a patient. We used a Binary Cross-
Entropy (BCE) loss to update the neural network weights
(encoder and head) during training. We also incorporated
an L2 regularisation on the weights to prevent overfitting.

Global model trained from scratch

Firstly, for each classification task (ASD, BD, SCZ), we ini-
tialised the weights of the global network with a random
initialisation and then trained the model from the skeletons
of the respective ASD, BD or SCZ dataset to update the
weights with the loss function (see the Fig. 1a). We updated
neural network weights with Adam optimizer and a learning
rate of 1e-4 that is divided by 5 every 10 epochs. Models
were trained for 50 epochs with a batch size of 32 and the
validation ROC AUC were checked to ensure that models
had converged. We did our best to cross validate critical
hyperparameters while keeping computational times rea-
sonable. We then evaluated the model on internal and exter-
nal test sets.

Global foundation model with pre-training

**Pre-training on a large dataset of the general population.**
We pre-trained the encoder of the global foundation model
with the Barlow Twins framework (Zbontar et al. 2021) on
the skeletons of the UK Biobank dataset (see the Fig. 1b).
For the encoder, we selected the DenseNet-121 architecture
that has a latent space size of 256. To generate two views of
the samples, we applied random cutouts to the skeletons. To
ensure that cutouts effectively remove parts of the skeletons,
we centred the patches of cutouts on non-zero values.

**Transfer learning on clinical datasets.** After pre-training on the UK Biobank cohort, the resulting encoder served as a cortical-folding foundation model. We removed its pre-trained MLP head, froze the encoder weights, and, for each classification task, attached a new randomly initialised MLP classifier. This classifier was then trained using a BCE loss to predict clinical status, while the foundation encoder remained fixed. Thus, during transfer learning, only the parameters of the newly added MLP head were updated, allowing the task-specific models to build on a shared, stable representation of cortical folding.

Feature importance for global models.

Because deep learning models establish complex non-
linear mappings from cortical-folding skeletons to abstract
sulcal representations, their capacity to yield interpretable
insights is often questioned; therefore, we performed a
feature-importance analysis on the ASD dataset to assess
whether the global model could recover brain regions previ-
ously implicated in ASD. The feature importance was con-
ducted using an occlusion-sensitivity method (Zeiler and
Fergus 2014). The technique consists of removing parts of
the input images and comparing the model’s outputs with
and without these deleted parts. In our case, we removed
patches from the skeletons and compared the ROC AUC
scores on the test set with and without occlusion. Patches for
which the ROC AUC score drops when removed may be a
brain area that the model relies on to make the decision. We
used a patch size of 16 × 16 × 16 and a stride of 4 voxels. We
chose a large patch size because skeletons were registered
using a coarse affine transformation.

Ensemble of regional models “regional approach”: combine an ensemble of local models

**Regional brain folding foundation model.** We based the regional approach on the brain folding foundation model called Champollion (Laval et al. 2025). Champollion is composed of fifty-four models specialised in a brain area defined based on cortical sulci. Each Champollion’s model was trained on the same UK Biobank dataset with the same Barlow Twins framework for each brain area. Champollion differs from the global foundation model as each of its models focuses on a specific brain area and none of them deals with the whole brain. Champollion was shown to encode critical brain folding features; in particular, relevant brain folding patterns can be linearly classified from the Champollion representations. Compared to pre-training of the global model, the data augmentation differs, and the skeletons are resampled to 2mm³ because a resolution of 1.5mm³ did
a. Global model trained from scratch

<figure><img src="image_4.png" /></figure>

b. Global foundation model with pre-training

1. Pretraining on large dataset of the general population

<figure><img src="image_5.png" /></figure>

2. Transfer learning on clinical datasets

<figure><img src="image_6.png" /></figure>

c. Ensemble of regional models “regional approach”

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>
<figure><img src="image_9.png" /></figure>

**Fig. 1** The three methods developed for the case-control classification tasks. E stands for Encoder, a 3D convolutional neural network, C for classifier, a multilayer perceptron, *p* the predicted probability outputs by the activation layer of the classifier, Chᵢ for the Champollion encoder for the brain area *i*. Snowflakes mean that the weights of the neural networks are not updated during the training process

not improve the representation quality while significantly increasing computation time (Laval et al. 2025).

**Ensemble learning.** We generated the Champollion V0 embeddings of the brain images from the three psychiatric datasets (see the Fig. 1c). We trained an MLP with one hidden layer for each brain area to predict the subject's diagnosis from the Champollion V0 embeddings with the same settings as the transfer learning step of the global model. To aggregate the predictions of the local models, we made a weighted average of the predicted probabilities of all the regional models. The weights were learned from a linear regression on the training set. The weighted average is used to select the relevant brain areas for the final prediction because not all brain areas may be impacted by mental disorders. This design ensures that representations not only capture folding features but can also be linearly mapped to meaningful clinical differences.

# Results

## Predictive performances

We compared the prediction scores of the global models trained without pre-training, with pre-training (foundation), and the regional approach in Table 2.

**Global model without pre-training.** Without pre-training, the DL models showed poor prediction performances (see Table 2). The prediction performances varied across diagnostic categories, ranked in ascending order HC vs. ASD, HC vs. BD, and HC vs. SCZ. However, the increase in prediction scores on internal test sets did not go with an increase in prediction scores on external test sets, suggesting that the model could not generalise to unseen acquisition sites.

**Global model with pre-training.** The pre-training improved prediction scores on all test sets. Specifically, on external test sets, ROC AUC scores increased by 13 points on HC vs. ASD, 7 points on HC vs. BD, and 4 points on HC vs. SCZ. All the internal test sets showed a p-value <0.05 for the statistical test. Concerning external test sets, significant predictions were obtained with HC vs. ASD, suggesting a good generalisation on unseen acquisition sites. For HC vs. BD and HC vs. SCZ, the model showed a drop of around 9 points between internal and external test sets. Transfer learning also reduced the uncertainty of the models; the 10

models from the cross-validation scheme showed reduced variability in prediction scores. These results confirm the potential of transfer learning to extract discriminant features from healthy brain images and validate our transfer learning approach.

**Regional approach.** The regional approach showed prediction scores between those without and with pre-training for the global approach.

# Feature importance and explainability for the ASD experiment

**Predictive brain regions obtained with the global pre-trained model.** To further analyse the predictions of the transfer learning approach, we computed an occlusion sensitivity map to highlight the brain areas implicated in the model's decision-making process. Feature-importance analysis was performed only for the ASD task to illustrate differences in interpretability between global and regional models. In Fig. 2, the map was projected on the sulcus template (Perrot et al. 2011) for the HC vs. ASD external test set subjects. The sensitivity map did not clearly bring out brain areas that are known to be linked to ASD.

**Predictive brain regions obtained with the regional approach.** The regional approach enables us to easily determine the brain areas that may be implicated in the disorder. Contrary to the occlusion sensitivity map, which attempts to determine the model's decision-making process a posteriori, this approach is straightforward due to the structure of the regional approach. For each brain area, we computed the corresponding ROC AUC score and projected the results onto the sulcus template of (Perrot et al. 2011), as shown in Fig. 3. The occlusion sensitivity analysis highlighted the superior temporal sulcus and the parieto-temporal junction, regions previously implicated in atypical sulcal organisation and social-communication processes in ASD.

# Discussion

We achieved single-subject predictions of clinical status from the subject's brain folding, suggesting that the brain folding shapes captured information about mental disorders. Given that patterns of cortical folding are established early during neurodevelopment. These findings are consistent with the hypothesis that cortical folding, shaped early in neurodevelopment, may encode latent vulnerabilities observable in adulthood. Notably, the highest prediction performance (ROC AUC on the external test sets) was obtained for ASD is coherent with the neurodevelopmental nature of ASD, which may yield more distinct neuroanatomical signatures
<table><caption>**Table 2** Prediction scores of the different approaches for the three classification tasks</caption><thead><tr><th rowspan="2">Task</th><th rowspan="2">Test set</th><th colspan="2">Global approach</th><th rowspan="2">Regional approach</th></tr><tr><th>no pre-training</th><th>with pre-training</th></tr></thead><tbody><tr><td>HC vs. ASD</td><td>internal</td><td>53.4±3.4</td><td><strong>63.0±1.1*</strong></td><td>58.3±2.8</td></tr><tr><td>HC vs. ASD</td><td>external</td><td>53.4±5.4</td><td><strong>66.5±1.0*</strong></td><td>56.7±3.2</td></tr><tr><td>HC vs. BD</td><td>internal</td><td>56.2±4.8</td><td><strong>68.7±1.3*</strong></td><td>61.1±2.7</td></tr><tr><td>HC vs. SCZ</td><td>internal</td><td>52.7±2.2</td><td>59.7±1.5</td><td>59.3±1.4</td></tr><tr><td>HC vs. SCZ</td><td>external</td><td>53.2±3.6</td><td>57.6±0.4</td><td>52.9±2.2</td></tr></tbody></table>

ROC AUC scores (%) for the prediction of clinical status from the subject's skeleton. The scores are averaged on the 10-fold cross-validation scheme, and the standard deviations are reported. * indicates the scores that showed a p-value < 0.05 for the U-Mann-Whitney test.

than other disorders. Indeed, to quantify the information specifically carried by cortical folding, we measured, for each disorder, the decrease in predictive performance when restricting anatomical MRI to sulcal skeletons, compared with using richer structural descriptors derived from voxel-based morphometry or from full raw structural MRI (Dufumier et al. 2024). Although folding-based classification remains significant, limiting the input to sulcal skeletons leads to marked performance drops for SCZ (57.6% vs. 77%) and BD (59.7% vs. 77%), whereas the decrease is minimal for ASD (66.5% vs. 69%). This disorder-specific

pattern indicates that folding-based descriptors retain most of the anatomically relevant information for ASD, but not for SCZ or BD. Overall, these findings support the notion that cortical folding captures a substantial proportion of ASD-related structural variability, consistent with its neurodevelopmental origins.

The proposed decoding of cortical folding align with a broader neurodevelopmental paradigm on psychiatric disorders formalised within the Research Domain Criteria (RDoC) framework (Casey et al. 2014) of the U.S. National Institute of Mental Health, which emphasises the developmental origins of psychopathology and the dimensional nature of symptom expression across diagnostic categories. We address, at least, two main axes of the RDoC:

* **Developmental Trajectory:** This involves examining whether the progression over time is linear or non-linear, determining which system develops before another, and identifying whether an observed anomaly is a delay, deviation, or regression. Because cortical folding is established early and remains stable across the lifespan, deviations captured in adult sulcal morphology offer a retrospective readout of altered developmental trajectories, allowing us to distinguish delays or atypical

Left hemisphere

<figure><img src="image_10.png" /><figcaption>**Fig. 2** Occlusion sensitivity map for the transfer learning approach on the external set for the HC vs. ASD classification task. Positive (red) values correspond to a decrease in ROC AUC scores when the area is</figcaption></figure>

Right hemisphere

<figure><img src="image_11.png" /></figure>

occluded, revealing important brain areas for the prediction. Negative (blue) values highlight an increase in ROC AUC score when the area is occluded, revealing brain areas that bring noise into the model
Left hemisphere

<figure><img src="image_12.png" /><figcaption>**Fig. 3** Prediction scores of each brain area of the regional approach for the HC vs. ASD classification task (internal test set). The brain areas coloured in red showed the highest ROC AUC scores, whereas</figcaption></figure>

deviations from normative patterns that unfolded during
critical early periods.

*   **Period of Vulnerability:** This refers to the timing of development for each functional structure. For example, disruptions during early neurogenesis are more commonly associated with ASD, whereas disruptions during adolescence, particularly in the final stages of interneuron maturation, are more often linked to schizophrenia. By isolating structural features rooted in the earliest phases of cortical maturation, folding-based representations directly index the neuroanatomical consequences of perturbations occurring within specific developmental windows, such as early neurogenesis in ASD or later maturation processes relevant to schizophrenia.

However, the explainability method used to understand the
model's decision-making process gives results that are dif-
ficult to interpret. For better interpretability, we designed a
regional approach that showed the brain areas that may be
affected by the disorder. In Fig. 2, the model highlighted
brain areas, including the sulcus temporal superior and the
parieto-temporal junction. These two brain areas are part of
the theory of mind network that may be impacted in patients
with ASD (Baron-Cohen et al. 1985; Boddaert et al. 2004;
Yirmiya et al. 1998). The regional approach showed lower

Right hemisphere

<figure><img src="image_13.png" /></figure>

the ones coloured in yellow showed the lowest scores. For readability reasons, the brain areas with a ROC AUC score lower than 0.55 were not coloured (in grey on the figure)

classification accuracies than the one obtained by the global
model at the whole-brain scale, but the regional approach is
more interpretable. We might balance between a model with
high prediction scores and one with high explainability. The
regional approach could be further improved, particularly
in the way latent brain area spaces are aggregated. In our
approach, we chose to train a linear regression to aggregate
the predicted probabilities of each brain area; however, this
method captures only linear relationships between brain
areas. To improve prediction accuracy, other strategies for
combining latent spaces could be explored, such as latent
space fusion with attention layers.

Thanks to the pre-training, we found brain folding shape
representations that are predictive of mental disorders.
However, these representations are biased by the site effect.
Indeed, for two classification tasks, we observed a drop in
prediction scores when evaluating the model on images
from sites not included in the training set, compared to eval-
uating on images coming from sites included in the training
set. This drop in prediction scores when evaluating models
on images coming from unseen acquisition sites has already
been observed for other brain features (Dufumier et al.
2024). Although brain folding is a quantitative brain feature,
skeletons are also subject to the site effect. Interestingly,
this site effect is present in the representations even if the
pre-training dataset, the UK Biobank cohort, is composed of
only one acquisition site, preventing the model from relying
on the acquisition confound to structure the latent space. It
could be worth comparing our model with one pre-trained
on a multi-site dataset and evaluating if the model can learn
site-invariant representations. Additionally, the UK Bio-
bank cohort does not accurately represent the general popu-
lation, as participants range in age from approximately 40 to
80 years, with an average age of 65. The young population
is not represented in the cohort, which introduces a domain
gap with clinical cohorts composed of young adults (period
of onset of psychiatric disorders). Even if the domain gap is
limited since skeletons are supposed to be stable with age, it
would be worth comparing our model with one pre-trained
with a dataset that comprises young adults.

As highlighted earlier, folding-based descriptors capture
only a subset of the information contained in full-brain anat-
omy, which explains why, even with further methodologi-
cal refinements, their predictive performance is expected
to remain below that of richer structural features such as
voxel-based morphometry. This limitation reflects the fact
that sulcal morphology primarily encodes early neurode-
velopmental processes and does not reflect later anatomical
changes shaped by environmental exposures. Consequently,
folding patterns may be predictive of psychiatric conditions
predominantly in a subgroup of patients with a high neu-
rodevelopmental load, serving as markers of early life per-
turbations. In contrast, more “plastic” anatomical measures,
such as grey matter volume, or cortical thickness, vary with
experience and environmental influences, whereas sulcal
organisation is established in utero and remains stable across
the lifespan. While the resulting classification performance
is not sufficient for immediate clinical application, the mod-
els nonetheless represent an initial step toward estimating
the neurodevelopmental burden in individual patients. A
promising direction for improving predictive power would
be to integrate the folding-based predictor with additional
brain-derived features that capture complementary neuro-
developmental and environmental influences. Such multi-
modal modelling could yield more robust biomarkers and
move closer to clinically meaningful stratification within
the autism spectrum.

For the clinical perspective, identifying the level of
neurodevelopmental load associated with a pathology first
allows for the personalisation of treatments. This involves
favouring lower doses and closer monitoring of side effects
due to increased cerebral vulnerability. Additionally, the
presence of neurodevelopmental elements, such as an early
age of symptom onset or minor neurological signs, could
predict a lack of response to first-line treatment in disor-
ders like schizophrenia or bipolar disorder. This could lead
to a quicker adaptation of treatment and a transition to more

effective molecules, such as clozapine (Iftimovici et al.
2024).

Conclusion

In this study, we demonstrated that deep learning models
leveraging cortical folding patterns extracted from MRI
data can predict psychiatric disorders at the individual level,
particularly Schizophrenia, Bipolar Disorders, and Autism
Spectrum Disorders. The predictive power of cortical fold-
ing, a stable marker set during early neurodevelopment,
underscores its potential as a reliable biomarker reflecting
neurodevelopmental vulnerabilities. Our results highlight
that pre-training deep learning architectures on large-scale
cohorts, such as the UK Biobank, significantly enhances
model generalizability across multi-site datasets. Never-
theless, site effects remain an essential consideration for
interpreting predictive neuroimaging models. The proposed
regional ensemble approach further enhances interpretabil-
ity by identifying specific cortical regions implicated in psy-
chiatric conditions, aligning well with established findings
in ASD. Future studies should explore integrating additional
neuroanatomical features to improve predictive accuracy
and biological interpretability. Ultimately, these brain-based
biomarkers promise advancements in precision psychiatry,
facilitating earlier diagnosis, improved patient stratification,
and tailored therapeutic interventions.

**Acknowledgements** This research was generously supported by The Robert Debré Child Brain Institute (Paris) under grant ANR-23-IAHU-0010 and the research program in precision psychiatry (PEPR PROPSY, ANR-22-EXPR-0001), both of which are funded by the France 2030 program and the French National Research Agency (ANR). Additional support was provided by two "Investissements d'Avenir" Hospital-University Research in Health initiatives: RHU-PsyCARE (ANR-18-RHUS 0014) and FAME (ANR-21-RHUS-0009).

**Author contributions** PA, AG conducted the experiments, PA, ED wrote the manuscript, ED, JFM, AG supervised the research, JC, JL, VF, BD provided technical input, AC, CL, JH, JB, provided scientific (clinical) input, All authors reviewed the manuscript.

**Funding** Open access funding provided by Commissariat à l'Énergie Atomique et aux Énergies Alternatives.

Declarations

**Competing Interests** The authors reported no biomedical financial in-
terests or potential conflicts of interest. This research study was con-
ducted retrospectively using human subject data under the local ethics
policy. The code can be found at: https://github.com/PierreAuriau/dec-
oding_cortical_folding. Part of the data are publicly available at:

- ABIDE I: http://fcon_1000.projectsnitrc.org/indi/abide/abide_I.htm

- ABIDE II: http://fcon_1000.projectsnitrc.org/indi/abide/abide_II.htm
- BIOBD: https://pubmed.ncbi.nlm.nih.gov/29981196.

- BSNIP 1: http://b-snip.org/.

- CANDI: https://www.nitrc.org/projects/candi_share.

- CNP: https://www.nitrc.org/projects/candi_share.

- SCHIZCONNECT: http://schizconnect.org/.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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