Claire Cury

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

Scopus Publications

Medial positioning of the hippocampus and hippocampal fissure volume in developmental topographical disorientation
Agustina Fragueiro, Claire Cury, Federica Santacroce, Ford Burles, Giuseppe Iaria, and Giorgia Committeri
Wiley
AbstractDevelopmental 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, Maximilien Riche, Lisa Hemforth, Alexandre Martin, Tobias Banaschewski, Arun L. W. Bokde,et al.
Springer Science and Business Media LLC



Incomplete Hippocampal Inversion and Hippocampal Subfield Volumes: Implementation and Inter-Reliability of Automatic Segmentation
Agustina Fragueiro, Giorgia Committeri, and Claire Cury
IEEE
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, Hugh Garavan, Rüdiger Brühl, Jean-Luc Martinot, Marie-Laure Paillère Martinot, Eric Artiges,et al.
SPIE
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, and Julie Coloigner
Springer Nature Switzerland



Brainhack: Developing a culture of open, inclusive, community-driven neuroscience
Rémi Gau, Stephanie Noble, Katja Heuer, Katherine L. Bottenhorn, Isil P. Bilgin, Yu-Fang Yang, Julia M. Huntenburg, Johanna M.M. Bayer, Richard A.I. Bethlehem, Shawn A. Rhoads,et al.
Elsevier BV
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, Linda Marrakchi-Kacem, Johanne Germain, Claire Boutet, Claire Cury, Lucie Hertz-Pannier,et al.
Institute of Electrical and Electronics Engineers (IEEE)
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, Anatole Lécuyer, and Christian Barillot
Springer Science and Business Media LLC
AbstractCombining 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, Catherine Oppenheim, François Medjkane, Pierre Thomas, and Renaud Jardri
Springer Science and Business Media LLC
AbstractAuditory 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, Jane Tisi, Jane L. Burdett, Meneka K. Sidhu, Lorenzo Caciagli, Gavin P. Winston,et al.
Wiley
Cognitive problems, especially disturbances in episodic memory, and hippocampal sclerosis are common in temporal lobe epilepsy (TLE), but little is known about the relationship of hippocampal morphology with memory. We aimed to relate hippocampal surface‐shape patterns to verbal and visual learning.


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, and Christian Barillot
IEEE
Bi-modal EEG-fMRI neurofeedback (NF) is a new technique of great interest. First, it can improve the quality of NF training by combining different real-time information (haemody-namic and electrophysiological) from the participant's brain activity; Second, it has potential to better understand the link and the synergy between the two modalities (EEG-fMRI). However there are different ways to show to the participant his NF scores during bi-modal NF sessions. To improve data fusion methodologies, we investigate the impact of a 1D or 2D representation when a visual feedback is given during motor imagery task. Results show a better synergy between EEG and fMRI when a 2D display is used. Subjects have better fMRI scores when 1D is used for bi-modal EEG-fMRI NF sessions; on the other hand, they regulate EEG more specifically when the 2D metaphor is used.


A Sparse EEG-Informed fMRI Model for Hybrid EEG-fMRI Neurofeedback Prediction
Claire Cury, Pierre Maurel, Rémi Gribonval, and Christian Barillot
Frontiers Media SA
Measures of brain activity through functional magnetic resonance imaging (fMRI) or electroencephalography (EEG), two complementary modalities, are ground solutions in the context of neurofeedback (NF) mechanisms for brain rehabilitation protocols. While NF-EEG (in which real-time neurofeedback scores are computed from EEG signals) has been explored for a very long time, NF-fMRI (in which real-time neurofeedback scores are computed from fMRI signals) appeared more recently and provides more robust results and more specific brain training. Using fMRI and EEG simultaneously for bi-modal neurofeedback sessions (NF-EEG-fMRI, in which real-time neurofeedback scores are computed from fMRI and EEG) is very promising for the design of brain rehabilitation protocols. However, fMRI is cumbersome and more exhausting for patients. The original contribution of this paper concerns the prediction of bi-modal NF scores from EEG recordings only, using a training phase where EEG signals as well as the NF-EEG and NF-fMRI scores are available. We propose a sparse regression model able to exploit EEG only to predict NF-fMRI or NF-EEG-fMRI in motor imagery tasks. We compared different NF-predictors stemming from the proposed model. We showed that predicting NF-fMRI scores from EEG signals adds information to NF-EEG scores and significantly improves the correlation with bi-modal NF sessions compared to classical NF-EEG scores.


Genome wide association study of incomplete hippocampal inversion in adolescents
Claire Cury, Marzia Antonella Scelsi, Roberto Toro, Vincent Frouin, Eric Artiges, Antoine Grigis, Andreas Heinz, Hervé Lemaître, Jean-Luc Martinot, Jean-Baptiste Poline,et al.
Public Library of Science (PLoS)
Incomplete hippocampal inversion (IHI), also called hippocampal malrotation, is an atypical presentation of the hippocampus present in about 20% of healthy individuals. Here we conducted the first genome-wide association study (GWAS) in IHI to elucidate the genetic underpinnings that may contribute to the incomplete inversion during brain development. A total of 1381 subjects contributed to the discovery cohort obtained from the IMAGEN database. The incidence rate of IHI was 26.1%. Loci with P<1e-5 were followed up in a validation cohort comprising 161 subjects from the PING study. Summary statistics from the discovery cohort were used to compute IHI heritability as well as genetic correlations with other traits. A locus on 18q11.2 (rs9952569; OR = 1.999; Z = 5.502; P = 3.755e-8) showed a significant association with the presence of IHI. A functional annotation of the locus implicated genes AQP4 and KCTD1. However, neither this locus nor the other 16 suggestive loci reached a significant p-value in the validation cohort. The h2 estimate was 0.54 (sd: 0.30) and was significant (Z = 1.8; P = 0.036). The top three genetic correlations of IHI were with traits representing either intelligence or education attainment and reached nominal P< = 0.013.


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, Martina Bocchetta, John C. van Swieten, Barbara Borroni, Daniela Galimberti, Mario Masellis,et al.
Elsevier BV
ABSTRACT Brain atrophy as measured from structural MR images, is one of the primary imaging biomarkers used to track neurodegenerative disease progression. In diseases such as frontotemporal dementia or Alzheimer's disease, atrophy can be observed in key brain structures years before any clinical symptoms are present. Atrophy is most commonly captured as volume change of key structures and the shape changes of these structures are typically not analysed despite being potentially more sensitive than summary volume statistics over the entire structure. In this paper we propose a spatiotemporal analysis pipeline based on Large Diffeomorphic Deformation Metric Mapping (LDDMM) to detect shape changes from volumetric MRI scans. We applied our framework to a cohort of individuals with genetic variants of frontotemporal dementia and healthy controls from the Genetic FTD Initiative (GENFI) study. Our method, take full advantage of the LDDMM framework, and relies on the creation of a population specific average spatiotemporal trajectory of a relevant brain structure of interest, the thalamus in our case. The residuals from each patient data to the average spatiotemporal trajectory are then clustered and studied to assess when presymptomatic mutation carriers differ from healthy control subjects. We found statistical differences in shape in the anterior region of the thalamus at least five years before the mutation carrier subjects develop any clinical symptoms. This region of the thalamus has been shown to be predominantly connected to the frontal lobe, consistent with the pattern of cortical atrophy seen in the disease. HIGHLIGHTSClustering shape parametrisation allows local shape analysis.Thalamic shape changes appear 5 years before onset of fronto temporal dementia.Shape changes seem to occur before volume changes.Pre‐symptomatic shape changes in thalamus are dorsofrontal, where connecting to temporal lobes.


Statistical shape analysis of large datasets based on diffeomorphic iterative centroids
Claire Cury, Joan A. Glaunès, Roberto Toro, Marie Chupin, Gunter Schumann, Vincent Frouin, Jean-Baptiste Poline, Olivier Colliot, and
Frontiers Media SA
In this paper, we propose an approach for template-based shape analysis of large datasets, using diffeomorphic centroids as atlas shapes. Diffeomorphic centroid methods fit in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework and use kernel metrics on currents to quantify surface dissimilarities. The statistical analysis is based on a Kernel Principal Component Analysis (Kernel PCA) performed on the set of initial momentum vectors which parametrize the deformations. We tested the approach on different datasets of hippocampal shapes extracted from brain magnetic resonance imaging (MRI), compared three different centroid methods and a variational template estimation. The largest dataset is composed of 1,000 surfaces, and we are able to analyse this dataset in 26 h using a diffeomorphic centroid. Our experiments demonstrate that computing diffeomorphic centroids in place of standard variational templates leads to similar shape analysis results and saves around 70% of computation time. Furthermore, the approach is able to adequately capture the variability of hippocampal shapes with a reasonable number of dimensions, and to predict anatomical features of the hippocampus, only present in 17% of the population, in healthy subjects.


Analysis of anatomical variability using diffeomorphic iterative centroid in patients with Alzheimer's disease
Claire Cury, Joan Glaunès, Marie Chupin, and Olivier Colliot
Informa UK Limited
This article presents a new approach for template-based analysis of anatomical variability in populations, in the framework of Large Deformation Diffeomorphic Metric Mappings and mathematical currents. We propose a fast approach in which the template is computed using a diffeomorphic iterative centroid method. Statistical analysis is then performed on the initial momenta that define the deformations between the centroid and each individual subject. We applied the approach to study the variability of the hippocampus in 134 patients with Alzheimer's disease (AD) and 160 elderly control subjects. We show that this approach can describe the main modes of variability of the two populations and can predict the performance to a memory test in AD patients.


Hippocampal volume predicts antidepressant efficacy in depressed patients without incomplete hippocampal inversion
Romain Colle, Claire Cury, Marie Chupin, Eric Deflesselle, Patrick Hardy, Ghaidaa Nasser, Bruno Falissard, Denis Ducreux, Olivier Colliot, and Emmanuelle Corruble
Elsevier BV
Background Incomplete hippocampal inversion (IHI), also called malrotation, is a frequent atypical anatomical pattern of the hippocampus. Because of the crucial implication of the hippocampus in Major Depressive Disorder (MDD) and the neurodevelopmental hypothesis of MDD, we aimed to assess the prevalence of IHI in patients with MDD, the link of IHI with hippocampal volume (HV) and the impact of IHI on the predictive value of HV for response and remission after antidepressant treatment. Methods IHI (right and left, partial and total and IHI scores) and HV were assessed in 60 patients with a current Major Depressive Episode (MDE) in a context of MDD and 60 matched controls. Patients were prospectively assessed at baseline and after one, three and six months of antidepressant treatment for response and remission. Results The prevalence of IHI did not significantly differ between MDD patients (right = 23.3%; left = 38.3%) and controls (right = 16.7%; left = 33.3%). IHI was not significantly associated with MDD clinical characteristics. IHI alone did not predict response and remission after antidepressant treatment. However, an interaction between left HV and left IHI predicted six-month response (p = 0.04), HDRS score decrease (p = 0.02) and both three-month (p = 0.04) and six-month (p = 0.03) remission. A case-control design in 30 matched patients with or without left IHI confirmed that interaction. In patients without left IHI, left HV at baseline were smaller in six-month non-remitters as compared to remitters (2.2(± 0.43) cm3 vs 2.97(± 0.5) cm3 p = 0.02), and in six-month non-responders as compared to responders (2.18(± 0.42) cm3 vs 2.86(± 0.54) cm3, p = 0.03). In patients with left IHI, no association was found between left HV at baseline and antidepressant response and remission. Conclusion IHI is not more frequent in MDD patients than in controls, is not associated with HV, but is a confounder that decreases the predictive value of hippocampal volume to predict response or remission after antidepressant treatment. IHI should be systematically assessed in future research studies assessing hippocampal volume in MDD.


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, Jonathan Rohrer, Sebastien Ourselin, Stanley Durrleman, and Marc Modat
Springer International Publishing
The detection of pathological changes in neurodegenerative diseases that occur before clinical onset would be of great value for identifying suitable subjects and assessing drug ecacy in trials aimed at preventing or slowing onset. Using MRI derived volumetric information, researchers have been able to detect significant di↵erences between patients in the presymptomatic phase of neurodegenerative diseases and healthy controls. However, volumetric studies provide only a summary representation of complex morphological changes. Shape analysis has already been successfully applied to model pathological features in neu-rodegeneration and represents a valuable instrument to model presymp-tomatic anatomical changes occurring in specific brain regions. In this study we propose a computational framework to model group-wise spatio-temporal shape di↵erences, and to statistically evaluate the e↵ects of time and pathological components on the modeled variability. The proposed approach leverages the geodesic regression framework based on varifolds, and models the spatio-temporal shape variability via dimensionality reduction of the subject-specific " residual " transformations normalised in a common reference frame through parallel transport. The proposed approach is applied to patients with genetic variants of fronto-temporal dementia, and shows that shape di↵erences in the posterior part of the thalamus can be observed several years before the appearance of clinical symptoms.


Incomplete hippocampal inversion: A comprehensive MRI study of over 2000 subjects
Claire Cury, Roberto Toro, Fanny Cohen, Clara Fischer, Amel Mhaya, Jorge Samper-González, Dominique Hasboun, Jean-François Mangin, Tobias Banaschewski, Arun L. W. Bokde,et al.
Frontiers Media SA
The incomplete-hippocampal-inversion (IHI), also known as malrotation, is an atypical anatomical pattern of the hippocampus, which has been reported in healthy subjects in different studies. However, extensive characterization of IHI in a large sample has not yet been performed. Furthermore, it is unclear whether IHI are restricted to the medial-temporal lobe or are associated with more extensive anatomical changes. Here, we studied the characteristics of IHI in a community-based sample of 2008 subjects of the IMAGEN database and their association with extra-hippocampal anatomical variations. The presence of IHI was assessed on T1-weighted anatomical magnetic resonance imaging (MRI) using visual criteria. We assessed the association of IHI with other anatomical changes throughout the brain using automatic morphometry of cortical sulci. We found that IHI were much more frequent in the left hippocampus (left: 17%, right: 6%, χ2−test, p < 10−28). Compared to subjects without IHI, subjects with IHI displayed morphological changes in several sulci located mainly in the limbic lobe. Our results demonstrate that IHI are a common left-sided phenomenon in normal subjects and that they are associated with morphological changes outside the medial temporal lobe.


Depressed suicide attempters have smaller hippocampus than depressed patients without suicide attempts
Romain Colle, Marie Chupin, Claire Cury, Christophe Vandendrie, Florence Gressier, Patrick Hardy, Bruno Falissard, Olivier Colliot, Denis Ducreux, and Emmanuelle Corruble
Elsevier BV
BACKGROUND Despite known relationship between hippocampal volumes and major depressive episodes (MDE) and the increased suicidality in MDE, the links between hippocampal volumes and suicidality remain unclear in major depressive disorders (MDD). If the hippocampus could be a biomarker of suicide attempts in depression, it could be useful for prevention matters. This study assessed the association between hippocampal volumes and suicide attempts in MDD. METHODS Hippocampal volumes assessed with automatic segmentation were compared in 63 patients with MDD, with (n = 24) or without (n = 39) suicide attempts. Acute (one month) suicide attempts were studied. RESULTS Although not different in terms of socio-demographic, MDD and MDE clinical features, suicide attempters had lower total hippocampus volumes than non-attempters (4.61 (± 1.15) cm(3) vs 5.22 (± 0.99) cm(3); w = 625.5; p = 0.03), especially for acute suicide attempts (4.19 (± 0.81) cm(3) vs 5.22 (± 0.99) cm(3); w = 334; p = 0.005), even after adjustment on brain volumes, sex, age, Hamilton Depression Rating Scale (HDRS) scores and MDD duration. A ROC analysis showed that a total hippocampal volume threshold of 5.00 cm(3) had a 98.2% negative predictive value for acute suicide attempts. CONCLUSION Depressed suicide attempters have smaller hippocampus than depressed patients without suicide attempts, independently from socio-demographics and MDD characteristics. This difference is related to acute suicide attempts but neither to past suicide attempts nor to duration since the first suicide attempt, suggesting that hippocampal volume could be a suicidal state marker in MDE. Further studies are required to better understand this association.


Template estimation for large database: A diffeomorphic iterative centroid method using currents
Claire Cury, Joan A. Glaunès, and Olivier Colliot
Springer Berlin Heidelberg
Computing a template in the Large Deformation Diffeomorphic Metric Mapping framework is a key step for the shape analysis of anatomical structures, but can lead to very computationally expensive algorithms in the case of large databases. We present an iterative method which quickly provides a centroid of the population in shape space. This centroid can be used as a rough template estimate or as initialization for template estimation methods.

RECENT SCHOLAR PUBLICATIONS

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 2024



Automatic rating of incomplete hippocampal inversions evaluated across multiple cohorts
L Hemforth, B Couvy-Duchesne, K De Matos, C Brianceau, M Joulot, ...
ArXiv, arXiv: 2408.02496 v1 2024



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



Genetic algorithm applied to hyperparameter selection for fMRI neurofeedback scores prediction from EEG signals
C Pinte, C Cury, P Maurel
IABM 2024-Colloque Franais d'Intelligence Artificielle en Imagerie Biomdicale 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



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



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



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



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



Interpretable automatic detection of incomplete hippocampal inversions using anatomical criteria
L Hemforth, C Cury, V Frouin, S Desrivires, A Grigis, H Garavan, R Brhl, ...
Medical Imaging 2023: Image Processing 12464, 145-151 2023



Amliorer la portabilit du neurofeedback bimodal: prdire par apprentissage automatique les scores NF-IRMf partir des signaux EEG
C Pinte, C Cury, P Maurel
IABM 2023-Colloque Franais d'Intelligence Artificielle en Imagerie Biomdicale 2023



Pre-post change in mental health and brain structure in pediatric mild traumatic brain injury
F Dgeilh, T von Soest, C Cury, L Ferschmann, CK Tamnes
IBIA 2023-14th Biennial World Congress on Brain Injury 37 (8), 758-1040 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



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



L'imagerie crbrale au service de la rducation
C Cury, I Bonan, A Lcuyer, G Lioi
Le corps en images. Les nouvelles imageries pour la sant, 141-152 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



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



Hippocampal shape is associated with memory deficits in temporal lobe epilepsy
TS Postma, C Cury, S Baxendale, PJ Thompson, I Cano‐Lpez, J de Tisi, ...
Annals of neurology 88 (1), 170-182 2020



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 Lcuyer, C Barillot
Scientific data 7 (1), 173 2020

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
Citations: 103



Incomplete hippocampal inversion: a comprehensive MRI study of over 2000 subjects
C Cury, R Toro, F Cohen, C Fischer, A Mhaya, J Samper-Gonzlez, ...
Frontiers in neuroanatomy 9, 160 2015
Citations: 62



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 Lcuyer, C Barillot
Scientific data 7 (1), 173 2020
Citations: 48



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
Citations: 42



Hippocampal shape is associated with memory deficits in temporal lobe epilepsy
TS Postma, C Cury, S Baxendale, PJ Thompson, I Cano‐Lpez, J de Tisi, ...
Annals of neurology 88 (1), 170-182 2020
Citations: 36



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
Citations: 33



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
Citations: 26



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
Citations: 24



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
Citations: 22



Learning 2-in-1: towards integrated EEG-fMRI-neurofeedback
L Perronnet, A Lcuyer, M Mano, M Fleury, G Lioi, C Cury, M Clerc, ...
BioRxiv, 397729 2018
Citations: 21



Diffeomorphic iterative centroid methods for template estimation on large datasets
C Cury, JA Glauns, O Colliot
Geometric Theory of Information, 273-299 2014
Citations: 14



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
Citations: 14



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, ...
Spectral and Shape Analysis in Medical Imaging: First International Workshop 2016
Citations: 13



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
Citations: 11



Statistical shape analysis of large datasets based on diffeomorphic iterative centroids
C Cury, JA Glauns, R Toro, M Chupin, G Schumann, V Frouin, JB Poline, ...
Frontiers in Neuroscience 12, 803 2018
Citations: 11



Analysis of anatomical variability using diffeomorphic iterative centroid in patients with Alzheimer's disease
C Cury, J Glauns, M Chupin, O Colliot
Computer Methods in Biomechanics and Biomedical Engineering: Imaging 2017
Citations: 8



Statistical shape analysis of the anatomical variability of the human hippocampus in large populations.
C Cury
Paris-Sorbonne University 2015
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
Citations: 5



Fast Template-based Shape Analysis using Diffeomorphic Iterative Centroid
C Cury, JA Glauns, M Chupin, O Colliot
MIUA 2014 - Medical Image Understanding and Analysis 2014 2014
Citations: 4



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
Citations: 3