Claire Cury

@inria.fr

Empenn Lab
Research Institute of Computer Science and Random Systems (IRISA) - Inria Rennes

Claire Cury


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https://researchid.co/ccury
since Nov 2020 : Inria Research scientist at IRISA / Inria Rennes in the Empenn team.

2017 - 2020 : Postdoctoral fellow at IRISA / Inria Rennes.

2015 - 2017 : Research associate at the Centre for Medical Imaging Computing (CMIC), University College London, UK

EDUCATION

Feb 2015 : PhD in Computational Neuroscience ”Statistical shape analysis of the anatomical variability of the human hippocampus in large under the supervision of Dr O.Colliot and Dr. J. A. Glaunès, at the Paris Brain Institute (ICM), Paris Sorbonne University, Paris, France.


2011: Master of science in Image and Signal processing. Paris Sorbonne University and Telecom Paristech, Paris, France.

RESEARCH INTERESTS

Computational Neuroscience, Shape analysis, EEG-fMRI neurofeedback
22

Scopus Publications

643

Scholar Citations

14

Scholar h-index

15

Scholar i10-index

Scopus Publications

  • Eye-Tracking and Skin Conductance to Monitor Cognitive Task Engagement
    Agustina Fragueiro, Julie Fournier, René‐Paul Debroize, Claire Cury
    Psychophysiology, 2026
    Neurofeedback is a promising technique for brain rehabilitation and cognitive enhancement; however, it suffers from an inefficacy problem, as more than a third of participants do not learn to regulate their own brain activity. Lack of task engagement, probably related to an inadequate difficulty level, has been proposed among the possible factors that can affect the learning process. Here, we explored the possibility of monitoring cognitive engagement using physiological signals through eye tracking and electrodermal activity. We recorded these signals while participants completed different tasks designed to stimulate cognitive load and attentional focus. From the assumption that high performance in any task requires an optimal level of engagement, we finally trained a linear model to predict participants' performance during different cognitive tasks by using their physiological signals. Results showed that pupil diameter strongly discriminates internal focus of attention from mind‐wandering states, and that pupil diameter and skin conductance response are sensitive to differentiate between task and rest conditions. In addition, both features are sensitive to habituation and cognitive load effects. The model was able to predict performance when trained on a specific task, as well as when combining different tasks together. Our results provide new insights regarding the physiological bases of cognitive engagement and propose a model with the potential to monitor it, while predicting performance. The capacity of our model to be generalized across different tasks encourage further research to test its potential in other domains, for example, in the context of individualized neurofeedback protocols.
  • Medial positioning of the hippocampus and hippocampal fissure volume in developmental topographical disorientation
    Agustina Fragueiro, Claire Cury, Federica Santacroce, Ford Burles, Giuseppe Iaria, et al.
    Hippocampus, 2024
    Developmental topographical disorientation (DTD) refers to the lifelong inability to orient by means of cognitive maps in familiar surroundings despite otherwise well‐preserved general cognitive functions, and the absence of any acquired brain injury or neurological condition. While reduced functional connectivity between the hippocampus and other brain regions has been reported in DTD individuals, no structural differences in gray matter tissue for the whole brain neither for the hippocampus were detected. Considering that the human hippocampus is the main structure associated with cognitive map‐based navigation, here, we investigated differences in morphological and morphometric hippocampal features between individuals affected by DTD (N = 20) and healthy controls (N = 238). Specifically, we focused on a developmental anomaly of the hippocampus that is characterized by the incomplete infolding of hippocampal subfields during fetal development, giving the hippocampus a more round or pyramidal shape, called incomplete hippocampal inversion (IHI). We rated IHI according to standard criteria and extracted hippocampal subfield volumes after FreeSurfer's automatic segmentation. We observed similar IHI prevalence in the group of individuals with DTD with respect to the control population. Neither differences in whole hippocampal nor major hippocampal subfield volumes have been observed between groups. However, when assessing the IHI independent criteria, we observed that the hippocampus in the DTD group is more medially positioned comparing to the control group. In addition, we observed bigger hippocampal fissure volume for the DTD comparing to the control group. Both of these findings were stronger for the right hippocampus comparing to the left. Our results provide new insights regarding the hippocampal morphology of individuals affected by DTD, highlighting the role of structural anomalies during early prenatal development in line with the developmental nature of the spatial disorientation deficit.
  • Temporo-basal sulcal connections: a manual annotation protocol and an investigation of sexual dimorphism and heritability
    Kevin de Matos, Claire Cury, Lydia Chougar, Lachlan T. Strike, Thibault Rolland, et al.
    Brain Structure and Function, 2023
  • Incomplete Hippocampal Inversion and Hippocampal Subfield Volumes: Implementation and Inter-Reliability of Automatic Segmentation
    Agustina Fragueiro, Giorgia Committeri, Claire Cury
    Proceedings International Symposium on Biomedical Imaging, 2023
    The incomplete hippocampal inversion (IHI) is an atypical anatomical pattern of the hippocampus. However, the hippocampus is not a homogeneous structure, as it consists of segregated subfields with specific characteristics. While IHI is not related to whole hippocampal volume, higher IHI scores have been associated to smaller CA1 in aging. Although the segmentation of hippocampal subfields is challenging due to their small size, there are algorithms allowing their automatic segmentation. By using a Human Connectome Project dataset of healthy young adults, we first tested the inter-reliability of two methods for automatic segmentation of hippocampal subfields, and secondly, we explored the relationship between IHI and subfield volumes. Results evidenced strong correlations between volumes obtained thorough both segmentation methods. Furthermore, higher IHI scores were associated to bigger subiculum and smaller CA1 volumes. Here, we provide new insights regarding IHI subfields volumetry, and we offer support for automatic segmentation inter-method reliability.
  • Interpretable Automatic Detection of Incomplete Hippocampal Inversions Using Anatomical Criteria
    Lisa Hemforth, Claire Cury, Vincent Frouin, Sylvane Desrivières, Antoine Grigis, et al.
    Progress in Biomedical Optics and Imaging Proceedings of SPIE, 2023
    Incomplete Hippocampal Inversion (IHI) is an atypical anatomical pattern of the hippocampus that has been associated with several brain disorders (epilepsy, schizophrenia). IHI can be visually detected on coronal T1 weighted MRI images. IHI can be absent, partial or complete (no IHI, partial IHI, IHI). However, visual evaluation can be long and tedious, justifying the need for an automatic method. In this paper, we propose, to the best of our knowledge, the first automatic IHI detection method from T1-weighted MRI. The originality of our approach is that, instead of directly detecting IHI, we propose to predict several anatomical criteria, which each characterize a particular anatomical feature of IHI, and that can ultimately be combined for IHI detection. Such individual criteria have the advantage of providing interpretable anatomical information regarding the morphological aspect of a given hippocampus. We relied on a large population of 2,008 participants from the IMAGEN study. The approach is general and can be used with different machine learning models. In this paper, we explored two different backbone models for the prediction: a linear method (ridge regression) and a deep convolutional neural network. We demonstrated that the interpretable, anatomical based prediction was at least as good as when predicting directly the presence of IHI, while providing interpretable information to the clinician or neuroscientist. This approach may be applied to other diagnostic tasks which can be characterized radiologically by several anatomical features.
  • Shape-Based Features of White Matter Fiber-Tracts Associated with Outcome in Major Depression Disorder
    Claire Cury, Jean-Marie Batail, Julie Coloigner
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2022
  • Brainhack: Developing a culture of open, inclusive, community-driven neuroscience
    Rémi Gau, Stephanie Noble, Katja Heuer, Katherine L. Bottenhorn, Isil P. Bilgin, et al.
    Neuron, 2021
    Brainhack is an innovative meeting format that promotes scientific collaboration and education in an open, inclusive environment. This NeuroView describes the myriad benefits for participants and the research community and how Brainhacks complement conventional formats to augment scientific progress.
  • A Diffeomorphic Vector Field Approach to Analyze the Thickness of the Hippocampus from 7 T MRI
    Alexis Guyot, Ana B. Graciano Fouquier, Emilie Gerardin, Marie Chupin, Joan A. Glaunes, et al.
    IEEE Transactions on Biomedical Engineering, 2021
    Objective: 7-Tesla MRI of the hippocampus enhances the visualization of its internal substructures. Among these substructures, the cornu Ammonis and subiculum form a contiguous folded ribbon of gray matter. Here, we propose a method to analyze local thickness measurements of this ribbon. Methods: We introduce an original approach based upon the estimation of a diffeomorphic vector field that traverses the ribbon. The method is designed to handle specificities of the hippocampus and corresponding 7-Tesla acquisitions: highly convoluted surface, non-closed ribbon, incompletely defined inner/outer boundaries, anisotropic acquisitions. We furthermore propose to conduct group comparisons using a population template built from the central surfaces of individual subjects. Results: We first assessed the robustness of our approach to anisotropy, as well as to inter-rater variability, on a post-mortem scan and on in vivo acquisitions respectively. We then conducted a group study on a dataset of in vivo MRI from temporal lobe epilepsy (TLE) patients and healthy controls. The method detected local thinning patterns in patients, predominantly ipsilaterally to the seizure focus, which is consistent with medical knowledge. Conclusion: This new technique allows measuring the thickness of the hippocampus from 7-Tesla MRI. It shows good robustness with respect to anisotropy and inter-rater variability and has the potential to detect local atrophy in patients. Significance: As 7-Tesla MRI is increasingly available, this new method may become a useful tool to study local alterations of the hippocampus in brain disorders. It is made freely available to the community (code: https://github.com/aramis-lab/hiplay7-thickness, postmortem segmentation: https://doi.org/10.5281/zenodo.3533264).
  • Simultaneous EEG-fMRI during a neurofeedback task, a brain imaging dataset for multimodal data integration
    Giulia Lioi, Claire Cury, Lorraine Perronnet, Marsel Mano, Elise Bannier, et al.
    Scientific Data, 2020
    Combining EEG and fMRI allows for integration of fine spatial and accurate temporal resolution yet presents numerous challenges, noticeably if performed in real-time to implement a Neurofeedback (NF) loop. Here we describe a multimodal dataset of EEG and fMRI acquired simultaneously during a motor imagery NF task, supplemented with MRI structural data. The study involved 30 healthy volunteers undergoing five training sessions. We showed the potential and merit of simultaneous EEG-fMRI NF in previous work. Here we illustrate the type of information that can be extracted from this dataset and show its potential use. This represents one of the first simultaneous recording of EEG and fMRI for NF and here we present the first open access bi-modal NF dataset integrating EEG and fMRI. We believe that it will be a valuable tool to (1) advance and test methodologies for multi-modal data integration, (2) improve the quality of NF provided, (3) improve methodologies for de-noising EEG acquired under MRI and (4) investigate the neuromarkers of motor-imagery using multi-modal information.
  • Deviations in early hippocampus development contribute to visual hallucinations in schizophrenia
    Arnaud Cachia, Claire Cury, Jérôme Brunelin, Marion Plaze, Christine Delmaire, et al.
    Translational Psychiatry, 2020
    Auditory hallucinations (AHs) are certainly the most emblematic experiences in schizophrenia, but visual hallucinations (VHs) are also commonly observed in this developmental psychiatric disorder. Notably, several studies have suggested a possible relationship between the clinical variability in hallucinations′ phenomenology and differences in brain development/maturation. In schizophrenia, impairments of the hippocampus, a medial temporal structure involved in mnesic and neuroplastic processes, have been repeatedly associated with hallucinations, particularly in the visual modality. However, the possible neurodevelopmental origin of hippocampal impairments in VHs has never been directly investigated. A classic marker of early atypical hippocampal development is incomplete hippocampal inversion (IHI). In this study, we compared IHI patterns in healthy volunteers, and two subgroups of carefully selected schizophrenia patients experiencing frequent hallucinations: (a) those with pure AHs and (b) those with audio–visual hallucinations (A+VH). We found that VHs were associated with a specific IHI pattern. Schizophrenia patients with A+VH exhibited flatter left hippocampi than patients with pure AHs or healthy controls. This result first confirms that the greater clinical impairment observed in A+VH patients may relate to an increased neurodevelopmental weight in this subpopulation. More importantly, these findings bring crucial hints to better specify the sensitivity period of A+VH-related IHI during early brain development.
  • Hippocampal Shape Is Associated with Memory Deficits in Temporal Lobe Epilepsy
    Tjardo S. Postma, Claire Cury, Sallie Baxendale, Pamela J. Thompson, Irene Cano‐López, et al.
    Annals of Neurology, 2020
  • Impact of 1D and 2D Visualisation on EEG-fMRI Neurofeedback Training during a Motor Imagery Task
    Claire Cury, Giulia Lioi, Lorraine Perronnet, Anatole Lecuyer, Pierre Maurel, et al.
    Proceedings International Symposium on Biomedical Imaging, 2020
  • A Sparse EEG-Informed fMRI Model for Hybrid EEG-fMRI Neurofeedback Prediction
    Claire Cury, Pierre Maurel, Rémi Gribonval, Christian Barillot
    Frontiers in Neuroscience, 2020
  • Genome wide association study of incomplete hippocampal inversion in adolescents
    Claire Cury, Marzia Antonella Scelsi, Roberto Toro, Vincent Frouin, Eric Artiges, et al.
    Plos One, 2020
  • Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases: Initial application to the GENFI cohort
    Claire Cury, Stanley Durrleman, David M. Cash, Marco Lorenzi, Jennifer M. Nicholas, et al.
    Neuroimage, 2019
  • Statistical shape analysis of large datasets based on diffeomorphic iterative centroids
    Claire Cury, Joan A. Glaunès, Roberto Toro, Marie Chupin, Gunter Schumann, et al.
    Frontiers in Neuroscience, 2018
  • Analysis of anatomical variability using diffeomorphic iterative centroid in patients with Alzheimer's disease
    Claire Cury, Joan Glaunès, Marie Chupin, Olivier Colliot
    Computer Methods in Biomechanics and Biomedical Engineering Imaging and Visualization, 2017
  • Hippocampal volume predicts antidepressant efficacy in depressed patients without incomplete hippocampal inversion
    Romain Colle, Claire Cury, Marie Chupin, Eric Deflesselle, Patrick Hardy, et al.
    Neuroimage Clinical, 2016
  • Spatio-temporal shape analysis of cross-sectional data for detection of early changes in neurodegenerative disease
    Claire Cury, Marco Lorenzi, David Cash, Jennifer M. Nicholas, Alexandre Routier, et al.
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2016
  • Incomplete hippocampal inversion: A comprehensive MRI study of over 2000 subjects
    Claire Cury, Roberto Toro, Fanny Cohen, Clara Fischer, Amel Mhaya, et al.
    Frontiers in Neuroanatomy, 2015
  • Depressed suicide attempters have smaller hippocampus than depressed patients without suicide attempts
    Romain Colle, Marie Chupin, Claire Cury, Christophe Vandendrie, Florence Gressier, et al.
    Journal of Psychiatric Research, 2015
  • Template estimation for large database: A diffeomorphic iterative centroid method using currents
    Claire Cury, Joan A. Glaunès, Olivier Colliot
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2013

RECENT SCHOLAR PUBLICATIONS

  • Automatic detection of EEG electrodes on T1-weighted MR Images
    M Georgeais, K Le Mouël, P Maurel, C Cury
    2026
  • Eye‐Tracking and Skin Conductance to Monitor Cognitive Task Engagement
    A Fragueiro, J Fournier, RP Debroize, C Cury
    Psychophysiology 63 (5), e70291 , 2026
    2026
  • Genome wide association study of incomplete hippocampal inversion on 40,000 subjects of the UK Biobank
    L Hemforth, C Cury, K de Matos, N Fournier, A Yazdan-Panah, O Colliot, ...
    2026
  • Eye-tracking and skin conductance to monitor task engagement during neurofeedback sessions
    A Fragueiro, RP Debroize, E Bannier, C Cury
    BCI 2024-9th Graz Brain-Computer Interface Conference, 1-6 , 2024
    2024
    Citations: 4
  • Automatic rating of incomplete hippocampal inversions evaluated across multiple cohorts
    L Hemforth, B Couvy-Duchesne, K De Matos, C Brianceau, M Joulot, ...
    MELBA, https://doi.org/10.59275/j.melba.2024-3d , 2024
    2024
    Citations: 3
  • Medial positioning of the hippocampus and hippocampal fissure volume in developmental topographical disorientation
    A Fragueiro, C Cury, F Santacroce, F Burles, G Iaria, G Committeri
    Hippocampus 34 (4), 204-216 , 2024
    2024
    Citations: 2
  • Genetic algorithm applied to hyperparameter selection for fMRI neurofeedback scores prediction from EEG signals
    C Pinte, C Cury, P Maurel
    IABM 2024-Colloque Français d'Intelligence Artificielle en Imagerie … , 2024
    2024
  • Investigating fMRI neurofeedback score prediction from EEG signals: genetic algorithm applied to hyperparameter selection
    C Pinte, C Cury, P Maurel
    2024
  • Shift in hippocampal medial position and increased fissure volumes in individuals affected by Developmental Topographical Disorientation
    A Fragueiro, F Santacroce, F Burles, C Cury, G Laria, G Committeri
    FESN HNPS 2023-8th Scientific Meeting of the Federation of European … , 2023
    2023
  • Improving portability of bimodal neurofeedback: predicting NF-fMRI scores from EEG signals
    C Pinte, C Cury, P Maurel
    OHBM 2023-Organization for Human Brain Mapping, 1-1 , 2023
    2023
  • Temporo-basal sulcal connections: a manual annotation protocol and an investigation of sexual dimorphism and heritability
    K de Matos, C Cury, L Chougar, LT Strike, T Rolland, M Riche, L Hemforth, ...
    Brain Structure and Function 228 (6), 1459-1478 , 2023
    2023
    Citations: 1
  • Pilot Study: eye-tracking and skin conductance to monitor task engagement during bimodal neurofeedback
    A Fragueiro, RP Debroize, A Coutrot, E Bannier, C Cury
    ISBI 2023 , 2023
    2023
  • Incomplete Hippocampal Inversion and hippocampal subfield volumes: Implementation and inter-reliability of automatic segmentation
    A Fragueiro, G Committeri, C Cury
    2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), 1-5 , 2023
    2023
    Citations: 5
  • Interpretable automatic detection of incomplete hippocampal inversions using anatomical criteria
    L Hemforth, C Cury, V Frouin, S Desrivières, A Grigis, H Garavan, R Brühl, ...
    Medical Imaging 2023: Image Processing 12464, 145-151 , 2023
    2023
    Citations: 2
  • Améliorer la portabilité du neurofeedback bimodal: prédire par apprentissage automatique les scores NF-IRMf à partir des signaux EEG
    C Pinte, C Cury, P Maurel
    IABM 2023-Colloque Français d'Intelligence Artificielle en Imagerie Biomédicale , 2023
    2023
  • Pre-post change in mental health and brain structure in pediatric mild traumatic brain injury
    F Dégeilh, T von Soest, C Cury, L Ferschmann, CK Tamnes
    IBIA 2023-14th Biennial World Congress on Brain Injury 37 (8), 758-1040 , 2023
    2023
  • RNN-LSTM neural network for predicting fMRI neurofeedback scores from EEG signals
    C Pinte, C Cury, P Maurel
    rtFIN 2022-Real-time Functional Imaging and Neurofeedback , 2022
    2022
  • Shape-based features of white matter fiber-tracts associated with outcome in Major Depression Disorder
    C Cury, JM Batail, J Coloigner
    International Conference on Medical Image Computing and Computer-assisted … , 2022
    2022
    Citations: 6
  • L'imagerie cérébrale au service de la rééducation
    C Cury, I Bonan, A Lécuyer, G Lioi
    Le corps en images. Les nouvelles imageries pour la santé, 141-152 , 2022
    2022
  • A graph-based similarity approach to classify recurrent complex motifs from their context in RNA structures
    C Gianfrotta, V Reinharz, D Barth, A Denise
    19th Symposium on Experimental Algorithms , 2021
    2021
    Citations: 6

MOST CITED SCHOLAR PUBLICATIONS

  • Depressed suicide attempters have smaller hippocampus than depressed patients without suicide attempts
    R Colle, M Chupin, C Cury, C Vandendrie, F Gressier, P Hardy, ...
    Journal of psychiatric research 61, 13-18 , 2015
    2015
    Citations: 117
  • Incomplete hippocampal inversion: a comprehensive MRI study of over 2000 subjects
    C Cury, R Toro, F Cohen, C Fischer, A Mhaya, J Samper-González, ...
    Frontiers in neuroanatomy 9, 160 , 2015
    2015
    Citations: 73
  • Simultaneous EEG-fMRI during a neurofeedback task, a brain imaging dataset for multimodal data integration
    G Lioi, C Cury, L Perronnet, M Mano, E Bannier, A Lécuyer, C Barillot
    Scientific data 7 (1), 173 , 2020
    2020
    Citations: 65
  • Brainhack: Developing a culture of open, inclusive, community-driven neuroscience
    R Gau, S Noble, K Heuer, KL Bottenhorn, IP Bilgin, YF Yang, ...
    Neuron 109 (11), 1769-1775 , 2021
    2021
    Citations: 51
  • Hippocampal shape is associated with memory deficits in temporal lobe epilepsy
    TS Postma, C Cury, S Baxendale, PJ Thompson, I Cano‐López, J de Tisi, ...
    Annals of neurology 88 (1), 170-182 , 2020
    2020
    Citations: 48
  • A sparse EEG-informed fMRI model for hybrid EEG-fMRI neurofeedback prediction
    C Cury, P Maurel, R Gribonval, C Barillot
    Frontiers in Neuroscience 13 , 2020
    2020
    Citations: 46
  • Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases: Initial application to the GENFI cohort
    C Cury, S Durrleman, DM Cash, M Lorenzi, JM Nicholas, M Bocchetta, ...
    NeuroImage 188, 282-290 , 2019
    2019
    Citations: 32
  • Deviations in early hippocampus development contribute to visual hallucinations in schizophrenia
    A Cachia, C Cury, J Brunelin, M Plaze, C Delmaire, C Oppenheim, ...
    Translational Psychiatry , 2020
    2020
    Citations: 29
  • Hippocampal volume predicts antidepressant efficacy in depressed patients without incomplete hippocampal inversion
    R Colle, C Cury, M Chupin, E Deflesselle, P Hardy, G Nasser, B Falissard, ...
    NeuroImage: Clinical 12, 949-955 , 2016
    2016
    Citations: 24
  • Learning 2-in-1: towards integrated EEG-fMRI-neurofeedback
    L Perronnet, A Lécuyer, M Mano, M Fleury, G Lioi, C Cury, M Clerc, ...
    BioRxiv, 397729 , 2018
    2018
    Citations: 23
  • Template estimation for large database: a diffeomorphic iterative centroid method using currents
    C Cury, JA Glaunes, O Colliot
    International Conference on Geometric Science of Information, 103-111 , 2013
    2013
    Citations: 16
  • Spatio-temporal shape analysis of cross-sectional data for detection of early changes in neurodegenerative disease
    C Cury, M Lorenzi, D Cash, JM Nicholas, A Routier, J Rohrer, S Ourselin, ...
    International Workshop on Spectral and Shape Analysis in Medical Imaging, 63-75 , 2016
    2016
    Citations: 15
  • Statistical shape analysis of large datasets based on diffeomorphic iterative centroids
    C Cury, JA Glaunès, R Toro, M Chupin, G Schumann, V Frouin, JB Poline, ...
    Frontiers in Neuroscience 12, 803 , 2018
    2018
    Citations: 14
  • Diffeomorphic iterative centroid methods for template estimation on large datasets
    C Cury, JA Glaunès, O Colliot
    Geometric Theory of Information, 273-299 , 2014
    2014
    Citations: 14
  • Genome wide association study of incomplete hippocampal inversion in adolescents
    C Cury, M Scelsi, R Toro, V Frouin, E Artiges, A Heinz, H Lemaitre, ...
    PLoS ONE 15 (1) , 2020
    2020
    Citations: 13
  • Analysis of anatomical variability using diffeomorphic iterative centroid in patients with Alzheimer's disease
    C Cury, J Glaunès, M Chupin, O Colliot
    Computer Methods in Biomechanics and Biomedical Engineering: Imaging … , 2017
    2017
    Citations: 8
  • Shape-based features of white matter fiber-tracts associated with outcome in Major Depression Disorder
    C Cury, JM Batail, J Coloigner
    International Conference on Medical Image Computing and Computer-assisted … , 2022
    2022
    Citations: 6
  • A graph-based similarity approach to classify recurrent complex motifs from their context in RNA structures
    C Gianfrotta, V Reinharz, D Barth, A Denise
    19th Symposium on Experimental Algorithms , 2021
    2021
    Citations: 6
  • A multi-modal human neuroimaging dataset for data integration: simultaneous EEG and MRI acquisition during a motor imagery neurofeedback task: XP2
    G Lioi, C Cury, L Perronnet, M Mano, E Bannier, A Lecuyer, C Barillot
    OpenNeuro https://doi. org/10.18112/openneuro. ds002336. v2. 0.0 , 2019
    2019
    Citations: 6
  • Statistical shape analysis of the anatomical variability of the human hippocampus in large populations.
    C Cury
    Paris-Sorbonne University , 2015
    2015
    Citations: 6