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# Cortical sulci are regional neurodevelopmental biomarkers of schizophrenia diagnosis

Aymeric Gaudin, Pierre Auriau, Edouard Duchesnay, Denis Riviere, Joel

Chavas, Jean-François Mangin

► To cite this version:

Aymeric Gaudin, Pierre Auriau, Edouard Duchesnay, Denis Riviere, Joel Chavas, et al. Cortical sulci are regional neurodevelopmental biomarkers of schizophrenia diagnosis. 2024. hal-04592457

HAL Id: hal-04592457

https://hal.science/hal-04592457

Preprint submitted on 29 May 2024

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# Cortical sulci are regional neurodevelopmental biomarkers of schizophrenia diagnosis

Aymeric Gaudin, Pierre Auriau, Edouard Duchesnay, Denis Riviere, Joel

Chavas, Jean-François Mangin

► To cite this version:

Aymeric Gaudin, Pierre Auriau, Edouard Duchesnay, Denis Riviere, Joel Chavas, et al. Cortical sulci are regional neurodevelopmental biomarkers of schizophrenia diagnosis. 2024. hal-04592457

HAL Id: hal-04592457

https://hal.science/hal-04592457v1

Preprint submitted on 29 May 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Cortical sulci are regional neurodevelopmental biomarkers of schizophrenia diagnosis

Aymeric Gaudin

Pierre Auriau

Edouard Duchesnay

Denis Rivière

Joël Chavas

Jean-François Mangin

NeuroSpin, CEA Saclay, Université Paris-Saclay, France

AYMERIC.GAUDIN@CEA.FR

PIERRE.AURIAU@CEA.FR

EDOUARD.DUCHESNAY@CEA.FR

DENIS.RIVIERE@CEA.FR

JOEL.CHAVAS@CEA.FR

JEAN-FRANCOIS.MANGIN@CEA.FR

## Abstract

The human cortical brain is folded and made of bumps called gyri, separated by grooves called sulci. Folding is highly variable among individuals, and making sense of this variability has been a long-standing goal. Part of this variability can be described by clinically relevant parameters. Here, we learn to predict a mental disorder with a likely neurodevelopmental imprint—schizophrenia—from the cortical folds by using as volume inputs cortical skeletons, which are negative casts of the brain. We are looking for local folding descriptors, not global ones; thus, we scatter the learning over 24 sulcal bilateral regions on the two hemispheres and apply an ensemble method to each region. We found significant correlations between cortical folding and schizophrenia in the precentral, temporal, and occipital regions and the collateral fissure.

**Keywords:** deep learning, cortical sulci, folding patterns, schizophrenia

## 1. Introduction

The cortical brain is folded, with a high interindividual variability. Neuroanatomists have succeeded in deciphering part of this variability by defining a nomenclature of the sulci (Ono et al., 1990; Borne et al., 2020). However, links to clinically relevant parameters, like schizophrenia (a psychiatric disease with a neurodevelopmental imprint), are often related not to the presence of these identified sulci but to their particular shape or folding pattern.

Up to now, looking systematically for a correlate of disease in cortical folding patterns could only be tackled using simple geometric features like the length or depth of cortical sulci of the standard nomenclature (Cachia et al., 2008; Chilla et al., 2022). We here build a supervised benchmark on schizophrenia using all the information from the cortical folds. As most cortical folding patterns are likely local geometric entities, we run supervised deep learning algorithms and an ensemble method for schizophrenia

detection, not on the whole brain directly, but on 24 bilateral sulcal regions.

<figure><img src="image_3.png" /><figcaption>Figure 1: Visualization of the method.</figcaption></figure>
# Cortical sulci are regional neurodevelopmental biomarkers of schizophrenia diagnosis

Aymeric Gaudin

Pierre Auriau

Edouard Duchesnay

Denis Rivière

Joël Chavas

Jean-François Mangin

NeuroSpin, CEA Saclay, Université Paris-Saclay, France

AYMERIC.GAUDIN@CEA.FR

PIERRE.AURIAU@CEA.FR

EDOUARD.DUCHESNAY@CEA.FR

DENIS.RIVIERE@CEA.FR

JOEL.CHAVAS@CEA.FR

JEAN-FRANCOIS.MANGIN@CEA.FR

## Abstract

The human cortical brain is folded and made of bumps called gyri, separated by grooves called sulci. Folding is highly variable among individuals, and making sense of this variability has been a long-standing goal. Part of this variability can be described by clinically relevant parameters. Here, we learn to predict a mental disorder with a likely neurodevelopmental imprint—schizophrenia—from the cortical folds by using as volume inputs cortical skeletons, which are negative casts of the brain. We are looking for local folding descriptors, not global ones; thus, we scatter the learning over 24 sulcal bilateral regions on the two hemispheres and apply an ensemble method to each region. We found significant correlations between cortical folding and schizophrenia in the precentral, temporal, and occipital regions and the collateral fissure.

**Keywords:** deep learning, cortical sulci, folding patterns, schizophrenia

## 1. Introduction

The cortical brain is folded, with a high interindividual variability. Neuroanatomists have succeeded in deciphering part of this variability by defining a nomenclature of the sulci (Ono et al., 1990; Borne et al., 2020). However, links to clinically relevant parameters, like schizophrenia (a psychiatric disease with a neurodevelopmental imprint), are often related not to the presence of these identified sulci but to their particular shape or folding pattern.

Up to now, looking systematically for a correlate of disease in cortical folding patterns could only be tackled using simple geometric features like the length or depth of cortical sulci of the standard nomenclature (Cachia et al., 2008; Chilla et al., 2022). We here build a supervised benchmark on schizophrenia using all the information from the cortical folds. As most cortical folding patterns are likely local geometric entities, we run supervised deep learning algorithms and an ensemble method for schizophrenia

detection, not on the whole brain directly, but on 24 bilateral sulcal regions.

<figure><img src="image_2.png" /><figcaption>Figure 1: Visualization of the method.</figcaption></figure>
## 2. Methods and Experiments

**Datasets and stratification.** We use a schizophrenia dataset called hereafter SCZ, which is an aggregation of five datasets (Ambite et al., 2015; Tamminga et al., 2014; Gorgolewski et al., 2017; NITRC, 2010). SCZ contains twelve acquisition sites, 760 control subjects, and 532 schizophrenia subjects (Dufumier, 2022). We stratify the sets according to diagnosis, age, sex, and site. We keep subjects from one acquisition site as an external test set to measure the model's generalization capability to an unseen acquisition site. The final split for train/validation/internal-test/external-test is 913/102/114/163 subjects.

**Description of the inputs.** All subjects' brain magnetic resonance images (MRIs) were processed with the Morphologist pipeline from the BrainVISA software <fnref n="1" /> to get their so-called cortical sulcal skeleton—a negative cast of the brain folds—. Cortical skeletons are then affinely normalized to a standard brain referential (ICBMc2009) and resampled with a 2mm cubic voxel size.

**Sulcal region selection and masking.** Borne et al. (2020) gives a nomenclature of 62 sulci per hemisphere. We define 24 overlapping sulcal regions from these sulci in the standard ICBM referential (Fig. 2A).

Using a database manually labeled for this nomenclature of sulci, we construct a localization map for each sulcus (Chavas et al., 2022). To define the mask of each sulcal region crop, we take the union of the binarized maps of sulci included in the definition of the sulcal region and dilate it by 5 mm <fnref n="2" />. Using these masks, we finally compute 2 × 24 regional crops (24 crops per hemisphere) for each subject of the SCZ dataset. Software is publicly available <fnref n="3" />.

**Hyperparameter search and model evaluation** The network is composed of a convolutional network that takes as inputs two bilateral regional crops (crops of the same region, respectively, from the left and right hemispheres). Outputs are then concatenated, pooled, and fed into a 2-layer dense projection head with 10 neurons each (we performed a structural gridsearch with 150 models starting from the backbone of Gaudin et al. (2024)) (Fig. 1).

For each couple of regions, 60 models are run for a maximum of 100 epochs with a ReLu projection head activation over a learning rate on [10⁻⁴, 10⁻<fnref n="2" />] (log-uniform), over the drop rate on [0,0.2], over a sigma noise on [0,0.5], and a maximum rotation angle on {0,1,...,6}°. We evaluate our model using the Area Under Curve (AUC). The epoch with the smallest criterion = 1 - AUCₗᵃ + 1/2|AUCₗᵃ - AUCᵣᵃ| is kept. This criterion maximizes the validation AUC and encourages consistency between training and validation. As the results are consistent among models, all 60 models are used for evaluation.

To improve the classification, we turn to the ensemble averaging model. We report the AUC over the average softmax prediction for all selected models. We compute the ensemble standard deviation as the standard deviation of 100 bootstrap samples (Fig 2).

<footnote n="1">1. https://brainvisa.info</footnote>

<footnote n="2">2. except for the cingulate region, for which we take the intersection of the paracingulate and the F.C.M.ant. sulcal masks Chavas et al. (2022); Gaudin et al. (2024)</footnote>

<footnote n="3">3. Crop generation uses the deep_folding toolbox: https://github.com/neurospin/deep_folding. The deep learning model used in this paper can be found on GitHub at https://github.com/neurospin-projects/2023_agaudin_jchavas_folding_supervised</footnote>
## 2. Methods and Experiments

**Datasets and stratification.** We use a schizophrenia dataset called hereafter SCZ, which is an aggregation of five datasets (Ambite et al., 2015; Tamminga et al., 2014; Gorgolewski et al., 2017; NITRC, 2010). SCZ contains twelve acquisition sites, 760 control subjects, and 532 schizophrenia subjects (Dufumier, 2022). We stratify the sets according to diagnosis, age, sex, and site. We keep subjects from one acquisition site as an external test set to measure the model's generalization capability to an unseen acquisition site. The final split for train/validation/internal-test/external-test is 913/102/114/163 subjects.

**Description of the inputs.** All subjects' brain magnetic resonance images (MRIs) were processed with the Morphologist pipeline from the BrainVISA software <fnref n="1" /> to get their so-called cortical sulcal skeleton—a negative cast of the brain folds—. Cortical skeletons are then affinely normalized to a standard brain referential (ICBMc2009) and resampled with a 2mm cubic voxel size.

**Sulcal region selection and masking.** Borne et al. (2020) gives a nomenclature of 62 sulci per hemisphere. We define 24 overlapping sulcal regions from these sulci in the standard ICBM referential (Fig. 2A).

Using a database manually labeled for this nomenclature of sulci, we construct a localization map for each sulcus (Chavas et al., 2022). To define the mask of each sulcal region crop, we take the union of the binarized maps of sulci included in the definition of the sulcal region and dilate it by 5 mm <fnref n="2" />. Using these masks, we finally compute 2 × 24 regional crops (24 crops per hemisphere) for each subject of the SCZ dataset. Software is publicly available <fnref n="3" />.

**Hyperparameter search and model evaluation** The network is composed of a convolutional network that takes as inputs two bilateral regional crops (crops of the same region, respectively, from the left and right hemispheres). Outputs are then concatenated, pooled, and fed into a 2-layer dense projection head with 10 neurons each (we performed a structural gridsearch with 150 models starting from the backbone of Gaudin et al. (2024)) (Fig. 1).

For each couple of regions, 60 models are run for a maximum of 100 epochs with a ReLu projection head activation over a learning rate on [10⁻⁴, 10⁻<fnref n="2" />] (log-uniform), over the drop rate on [0,0.2], over a sigma noise on [0,0.5], and a maximum rotation angle on {0,1,...,6}°. We evaluate our model using the Area Under Curve (AUC). The epoch with the smallest criterion = 1 - AUCₗᵃ + 1/2|AUCₗᵃ - AUCₜₐᵢ| is kept. This criterion maximizes the validation AUC and encourages consistency between training and validation. As the results are consistent among models, all 60 models are used for evaluation.

To improve the classification, we turn to the ensemble averaging model. We report the AUC over the average softmax prediction for all selected models. We compute the ensemble standard deviation as the standard deviation of 100 bootstrap samples (Fig 2).

<footnote n="1">1. https://brainvisa.info</footnote>

<footnote n="2">2. except for the cingulate region, for which we take the intersection of the paracingulate and the F.C.M.ant. sulcal masks Chavas et al. (2022); Gaudin et al. (2024)</footnote>

<footnote n="3">3. Crop generation uses the deep_folding toolbox: https://github.com/neurospin/deep_folding. The deep learning model used in this paper can be found on GitHub at https://github.com/neurospin-projects/2023_agaudin_jchavas_folding_supervised</footnote>
3. Results

<figure><img src="image_4.png" /><figcaption>Figure 2: *AUC results for schizophrenia detection per sulcal region.* The AUC is computed on the external test dataset. **A** gives the AUC results per sulcal region. The bar plot is the average of the AUCs for all 60 models. The scatterplot and the error bars represent the mean ensemble AUC and the ensemble bootstrap standard deviation. **B** gives the visualization of the same ensemble AUC results on the sulcus SPAM (Statistical Probabilistic Anatomy Map (Perrot et al., 2011)) model of the right hemisphere <fnref n="4" />.</figcaption></figure>

The ensemble mean AUC for schizophrenia detection is the highest for the central-
precentral region (AUC goes up to 0.59 ± 0.02), the collateral fissure, the temporal lobe,
particularly the Inferior Temporal Sulcus, and the occipital region (Fig. 2). There is also
a significative prediction in the parietal lobe, the STS branches, the anterior cingulate,
the central-postcentral region, and the frontal sulci. Conversely, there is no prediction in
the orbital region, internal parietal region, frontal marginal region, and interior frontal
sulcus. Our findings in the cingulate, temporal, and occipital regions align with known
other neuroanatomical correlates (Bora et al., 2011; Maller et al., 2017).

4. Conclusions and perspectives

Using supervised deep learning and ensemble methods, we found sulcal regions that signif-
icantly predict schizophrenia. Moreover, the strength of the prediction is not distributed
equally among all regions, as some sulcal regions have no prediction power. This shows
that what is found is not a global feature, as would be, for instance, a global change in
sulcal depth. Thus, for each bilateral couple of sulcal regions, these results can be used as
supervised benchmarks for schizophrenia prediction from cortical folding.

<footnote n="4">4. We have grayed the regions when AUC=0.5 is within 2.7 bootstrap standard deviations of the ensemble AUC</footnote>
3. Results

<figure><img src="image_3.png" /><figcaption>Figure 2: *AUC results for schizophrenia detection per sulcal region.* The AUC is computed on the external test dataset. **A** gives the AUC results per sulcal region. The bar plot is the average of the AUCs for all 60 models. The scatterplot and the error bars represent the mean ensemble AUC and the ensemble bootstrap standard deviation. **B** gives the visualization of the same ensemble AUC results on the sulcus SPAM (Statistical Probabilistic Anatomy Map (Perrot et al., 2011)) model of the right hemisphere <fnref n="4" />.</figcaption></figure>

The ensemble mean AUC for schizophrenia detection is the highest for the central-
precentral region (AUC goes up to 0.59 ± 0.02), the collateral fissure, the temporal lobe,
particularly the Inferior Temporal Sulcus, and the occipital region (Fig. 2). There is also
a significative prediction in the parietal lobe, the STS branches, the anterior cingulate,
the central-postcentral region, and the frontal sulci. Conversely, there is no prediction in
the orbital region, internal parietal region, frontal marginal region, and interior frontal
sulcus. Our findings in the cingulate, temporal, and occipital regions align with known
other neuroanatomical correlates (Bora et al., 2011; Maller et al., 2017).

4. Conclusions and perspectives

Using supervised deep learning and ensemble methods, we found sulcal regions that signif-
icantly predict schizophrenia. Moreover, the strength of the prediction is not distributed
equally among all regions, as some sulcal regions have no prediction power. This shows
that what is found is not a global feature, as would be, for instance, a global change in
sulcal depth. Thus, for each bilateral couple of sulcal regions, these results can be used as
supervised benchmarks for schizophrenia prediction from cortical folding.

<footnote n="4">4. We have grayed the regions when AUC=0.5 is within 2.7 bootstrap standard deviations of the ensemble AUC</footnote>
## Acknowledgments

This work was funded by the grants ANR-19-CE45-0022-01IFOPASUBA and ANR-20-CHIA-0027-01FOLDDICO.

## References

Jose Luis Ambite, Marcelo Tallis, Kathryn Alpert, David B. Keator, Margaret King, Drew Landis, George Konstantinidis, Vince D. Calhoun, Steven G. Potkin, Jessica A. Turner, and Lei Wang. SchizConnect: Virtual Data Integration in Neuroimaging. In *Data Integration in the Life Sciences*, pages 37–51, 2015.

Emre Bora, Alex Fornito, Joaquim Radua, Mark Walterfang, Marc Seal, Stephen J. Wood, Murat Yücel, Dennis Velakoulis, and Christos Pantelis. Neuroanatomical abnormalities in schizophrenia: A multimodal voxelwise meta-analysis and meta-regression analysis. *Schizophrenia Research*, 127(1):46–57, April 2011.

Léonie Borne, Denis Rivière, Martial Mancip, and Jean-François Mangin. 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.

Arnaud Cachia, Marie-Laure Paillère-Martinot, André Galinowski, Dominique Januel, Renaud de Beaurepaire, Frank Bellivier, Eric Artiges, Jamila Andoh, David Bartrés-Faz, Edouard Duchesnay, Denis Rivière, Marion Plaze, Jean-François Mangin, and Jean-Luc Martinot. Cortical folding abnormalities in schizophrenia patients with resistant auditory hallucinations. *NeuroImage*, 39(3):927–935, February 2008.

Joël Chavas, Louise Guillon, Marco Pascucci, Benoît Dufumier, Denis Rivière, and Jean-François Mangin. Unsupervised Representation Learning of Cingulate Cortical Folding Patterns. In *MICCAI 2022*, pages 77–87, 2022.

Geetha Soujanya Chilla, Ling Yun Yeow, Qian Hui Chew, Kang Sim, and K. N. Bhanu Prakash. Machine learning classification of schizophrenia patients and healthy controls using diverse neuroanatomical markers and Ensemble methods. *Scientific Reports*, 12(1): 2755, February 2022.

Benoît Dufumier. *Representation learning in neuroimaging : transferring from big healthy data to small clinical cohorts*. PhD thesis, https://www.theses.fr/2022UPASG093, université Paris-Saclay, December 2022.

Aymeric Gaudin, Louise Guillon, Clara Fischer, Arnaud Cachia, Denis Rivière, Jean-François Mangin, and Joël Chavas. Optimizing contrastive learning for cortical folding pattern detection. *SPIE 2024, in press*, 2024.

Krzysztof J. Gorgolewski, Joke Durnez, and Russell A. Poldrack. Preprocessed Consortium for Neuropsychiatric Phenomics dataset. *F1000Research*, 6:1262, September 2017.

Jerome J Maller, Rodney J Anderson, Richard H Thomson, Zafiris J Daskalakis, Jeffrey V Rosenfeld, and Paul B Fitzgerald. Occipital bending in schizophrenia. *Australian & New Zealand Journal of Psychiatry*, 51(1):32–41, January 2017.
## Acknowledgments

This work was funded by the grants ANR-19-CE45-0022-01IFOPASUBA and ANR-20-CHIA-0027-01FOLDDICO.

## References

Jose Luis Ambite, Marcelo Tallis, Kathryn Alpert, David B. Keator, Margaret King, Drew Landis, George Konstantinidis, Vince D. Calhoun, Steven G. Potkin, Jessica A. Turner, and Lei Wang. SchizConnect: Virtual Data Integration in Neuroimaging. In *Data Integration in the Life Sciences*, pages 37–51, 2015.

Emre Bora, Alex Fornito, Joaquim Radua, Mark Walterfang, Marc Seal, Stephen J. Wood, Murat Yücel, Dennis Velakoulis, and Christos Pantelis. Neuroanatomical abnormalities in schizophrenia: A multimodal voxelwise meta-analysis and meta-regression analysis. *Schizophrenia Research*, 127(1):46–57, April 2011.

Léonie Borne, Denis Rivière, Martial Mancip, and Jean-François Mangin. 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.

Arnaud Cachia, Marie-Laure Paillère-Martinot, André Galinowski, Dominique Januel, Renaud de Beaurepaire, Frank Bellivier, Eric Artiges, Jamila Andoh, David Bartrés-Faz, Edouard Duchesnay, Denis Rivière, Marion Plaze, Jean-François Mangin, and Jean-Luc Martinot. Cortical folding abnormalities in schizophrenia patients with resistant auditory hallucinations. *NeuroImage*, 39(3):927–935, February 2008.

Joël Chavas, Louise Guillon, Marco Pascucci, Benoît Dufumier, Denis Rivière, and Jean-François Mangin. Unsupervised Representation Learning of Cingulate Cortical Folding Patterns. In *MICCAI 2022*, pages 77–87, 2022.

Geetha Soujanya Chilla, Ling Yun Yeow, Qian Hui Chew, Kang Sim, and K. N. Bhanu Prakash. Machine learning classification of schizophrenia patients and healthy controls using diverse neuroanatomical markers and Ensemble methods. *Scientific Reports*, 12(1): 2755, February 2022.

Benoît Dufumier. *Representation learning in neuroimaging : transferring from big healthy data to small clinical cohorts*. PhD thesis, https://www.theses.fr/2022UPASG093, université Paris-Saclay, December 2022.

Aymeric Gaudin, Louise Guillon, Clara Fischer, Arnaud Cachia, Denis Rivière, Jean-François Mangin, and Joël Chavas. Optimizing contrastive learning for cortical folding pattern detection. *SPIE 2024, in press*, 2024.

Krzysztof J. Gorgolewski, Joke Durnez, and Russell A. Poldrack. Preprocessed Consortium for Neuropsychiatric Phenomics dataset. *F1000Research*, 6:1262, September 2017.

Jerome J Maller, Rodney J Anderson, Richard H Thomson, Zafiris J Daskalakis, Jeffrey V Rosenfeld, and Paul B Fitzgerald. Occipital bending in schizophrenia. *Australian & New Zealand Journal of Psychiatry*, 51(1):32–41, January 2017.
NITRC. CANDI Neuroimaging Access Point: Tool/Resource Info, 2010. URL https://www.nitrc.org/projects/candi_share.

Michio Ono, Stefan Kubik, and Chad D Abernathey. *Atlas of the cerebral sulci*. G. Thieme Verlag; Thieme Medical Publishers, Stuttgart; New York, 1990.

Matthieu Perrot, Denis Rivière, and Jean-François Mangin. Cortical sulci recognition and spatial normalization. *Medical Image Analysis*, 15(4):529-550, August 2011.

Carol A. Tamminga, Godfrey Pearlson, Matcheri Keshavan, John Sweeney, Brett Clementz, and Gunvant Thaker. Bipolar and Schizophrenia Network for Intermediate Phenotypes: Outcomes Across the Psychosis Continuum. *Schizophrenia Bulletin*, 40(Suppl 2):S131-S137, March 2014.
NITRC. CANDI Neuroimaging Access Point: Tool/Resource Info, 2010. URL https://www.nitrc.org/projects/candi_share.

Michio Ono, Stefan Kubik, and Chad D Abernathey. *Atlas of the cerebral sulci*. G. Thieme Verlag; Thieme Medical Publishers, Stuttgart; New York, 1990.

Matthieu Perrot, Denis Rivière, and Jean-François Mangin. Cortical sulci recognition and spatial normalization. *Medical Image Analysis*, 15(4):529-550, August 2011.

Carol A. Tamminga, Godfrey Pearlson, Matcheri Keshavan, John Sweeney, Brett Clementz, and Gunvant Thaker. Bipolar and Schizophrenia Network for Intermediate Phenotypes: Outcomes Across the Psychosis Continuum. *Schizophrenia Bulletin*, 40(Suppl 2):S131-S137, March 2014.