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Introduction

Marie Bouchet, Yannicke Chupin, Agnès Edel-Roy, Julie Loison-Charles
2017 Miranda: Revue Pluridisciplinaire du Monde Anglophone  
Introduction Marie Bouchet, Yannicke Chupin, Agnès Edel-Roy and Julie Loison-Charles 1 It has been over sixty years since Lolita first appeared in its green-clad double volume in 1955 in Paris, published  ...  and the novel, especially regarding the unreliability of the narrative source, and the importance of psychoanalytical confession in both works, from a structural and thematic point of view. 8 As for Marie  ... 
doi:10.4000/miranda.11253 fatcat:mcviwzmmijhzpn7wtxc2lkrzki

Learning brain MRI quality control: a multi-factorial generalization problem [article]

Ghiles Reguig, Marie Chupin, Hugo Dary, Eric Bardinet, Stéphane Lehéricy, Romain Valabregue
2022 arXiv   pre-print
Due to the growing number of MRI data, automated quality control (QC) has become essential, especially for larger scale analysis. Several attempts have been made in order to develop reliable and scalable QC pipelines. However, the generalization of these methods on new data independent of those used for learning is a difficult problem because of the biases inherent in MRI data. This work aimed at evaluating the performances of the MRIQC pipeline on various large-scale datasets (ABIDE, N = 1102
more » ... nd CATI derived datasets, N = 9037) used for both training and evaluation purposes. We focused our analysis on the MRIQC preprocessing steps and tested the pipeline with and without them. We further analyzed the site-wise and study-wise predicted classification probability distributions of the models without preprocessing trained on ABIDE and CATI data. Our main results were that a model using features extracted from MRIQC without preprocessing yielded the best results when trained and evaluated on large multi-center datasets with a heterogeneous population (an improvement of the ROC-AUC score on unseen data of 0.10 for the model trained on a subset of the CATI dataset). We concluded that a model trained with data from a heterogeneous population, such as the CATI dataset, provides the best scores on unseen data. In spite of the performance improvement, the generalization abilities of the models remain questionable when looking at the site-wise/study-wise probability predictions and the optimal classification threshold derived from them.
arXiv:2205.15898v1 fatcat:ab4heuee5rg7xjekso4w3o7hjy

Correlation between brain atrophy in MRI and CSF markers in Alzheimer's disease (AD)

Leonardo C. De Souza, Marie Chupin, Foudil Lamari, Stéphane Lehéricy, Bruno Dubois, Olivier Colliot, Marie Sarazin
2010 Alzheimer's & Dementia  
De Souza, Marie Chupin, Foudil Lamari, Ste ´phane Lehe ´ricy, Bruno Dubois, Olivier Colliot, Marie Sarazin, Hosp. Pitie ´-Salpe ˆtrie `re, Paris, France.  ... 
doi:10.1016/j.jalz.2010.05.1668 fatcat:i6lhsm7onnhjvlizikmcu2ntva

CSF tau markers are correlated with hippocampal volume in Alzheimer's disease

Leonardo C. de Souza, Marie Chupin, Foudil Lamari, Claude Jardel, Delphine Leclercq, Olivier Colliot, Stéphane Lehéricy, Bruno Dubois, Marie Sarazin
2012 Neurobiology of Aging  
Automated hippocampal volumetry Segmentation of the hippocampus was performed using an automated method, as previously described (Chupin et al., 2009) .  ...  During the 2 last years, Dr Chupin has collaborated with the following pharmaceutical company: EISAI. Dr Lamari, Dr Jardel, Dr Leclercq, and Dr Colliot report no conflict of interest.  ... 
doi:10.1016/j.neurobiolaging.2011.02.022 pmid:21489655 fatcat:nbg3nyc46bhf7ktxwijeakchgq

Similar amyloid-β burden in posterior cortical atrophy and Alzheimer's disease

Leonardo Cruz de Souza, Fabian Corlier, Marie-Odile Habert, Olga Uspenskaya, Renaud Maroy, Foudil Lamari, Marie Chupin, Stéphane Lehéricy, Olivier Colliot, Valérie Hahn-Barma, Dalila Samri, Bruno Dubois (+2 others)
2011 Brain  
The amygdala and hippocampi were automatically segmented in each individual using the T 1 -weighted MRIs and the SACHA software (Chupin et al., 2009) .  ... 
doi:10.1093/brain/awr130 pmid:21705422 fatcat:i6g4pvgecjfk3bxxnurh2ui7ia

Similar beta-amyloid burden in posterior cortical atrophy and Alzheimer's disease

Leonardo de Souza, Fabian Corlier, Marie Odile Habert, Olga Uspenskaya, Renaud Maroy, Foudil Lamari, Marie Chupin, Stéphane Lehéricy, Olivier Colliot, Valérie Hahn-Barma, Bruno Dubois, Michel Bottlaender (+1 others)
2011 Alzheimer's & Dementia  
The amygdala and hippocampi were automatically segmented in each individual using the T 1 -weighted MRIs and the SACHA software (Chupin et al., 2009) .  ... 
doi:10.1016/j.jalz.2011.05.919 fatcat:yfaknpm4dzfvdolyzi2f3cwh4e

Distinct structural changes underpin clinical phenotypes in patients with Gilles de la Tourette syndrome

Yulia Worbe, Emilie Gerardin, Andreas Hartmann, Romain Valabrégue, Marie Chupin, Léon Tremblay, Marie Vidailhet, Olivier Colliot, Stéphane Lehéricy
2010 Brain  
doi:10.1093/brain/awq293 pmid:20959309 fatcat:n7o3ykvcuzerfkb2reqraljd64

Statistical shape analysis of large datasets based on diffeomorphic iterative centroids [article]

Claire Cury, Joan Alexis Glaunes, Roberto Toro, Marie Chupin, Gunter Shumann, Vincent Frouin, Jean baptiste Poline, Olivier Colliot
2018 bioRxiv   pre-print
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 momentum vectors which parametrize the deformations. We tested the
more » ... oach 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 1000 surfaces, and we are able to analyse this dataset in 26 hours 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 in healthy subjects.
doi:10.1101/363861 fatcat:zm5rvh2ibrf2nmsn6ws3ubrhri

Spatial and anatomical regularization of SVM for brain image analysis

Rémi Cuingnet, Marie Chupin, Habib Benali, Olivier Colliot
2010 Neural Information Processing Systems  
Support vector machines (SVM) are increasingly used in brain image analyses since they allow capturing complex multivariate relationships in the data. Moreover, when the kernel is linear, SVMs can be used to localize spatial patterns of discrimination between two groups of subjects. However, the features' spatial distribution is not taken into account. As a consequence, the optimal margin hyperplane is often scattered and lacks spatial coherence, making its anatomical interpretation difficult.
more » ... his paper introduces a framework to spatially regularize SVM for brain image analysis. We show that Laplacian regularization provides a flexible framework to integrate various types of constraints and can be applied to both cortical surfaces and 3D brain images. The proposed framework is applied to the classification of MR images based on gray matter concentration maps and cortical thickness measures from 30 patients with Alzheimer's disease and 30 elderly controls. The results demonstrate that the proposed method enables natural spatial and anatomical regularization of the classifier.
dblp:conf/nips/CuingnetCBC10 fatcat:meopw4oo6japjmisz2ae7auxji

Single nucleotide resolution RNA-seq uncovers new regulatory mechanisms in the opportunistic pathogen Streptococcus agalactiae

Isabelle Rosinski-Chupin, Elisabeth Sauvage, Odile Sismeiro, Adrien Villain, Violette Da Cunha, Marie-Elise Caliot, Marie-Agnès Dillies, Patrick Trieu-Cuot, Philippe Bouloc, Marie-Frédérique Lartigue, Philippe Glaser
2015 BMC Genomics  
Streptococcus agalactiae, or Group B Streptococcus, is a leading cause of neonatal infections and an increasing cause of infections in adults with underlying diseases. In an effort to reconstruct the transcriptional networks involved in S. agalactiae physiology and pathogenesis, we performed an extensive and robust characterization of its transcriptome through a combination of differential RNA-sequencing in eight different growth conditions or genetic backgrounds and strand-specific
more » ... ng. Results: Our study identified 1,210 transcription start sites (TSSs) and 655 transcript ends as well as 39 riboswitches and cis-regulatory regions, 39 cis-antisense non-coding RNAs and 47 small RNAs potentially acting in trans. Among these putative regulatory RNAs, ten were differentially expressed in response to an acid stress and two riboswitches sensed directly or indirectly the pH modification. Strikingly, 15% of the TSSs identified were associated with the incorporation of pseudo-templated nucleotides, showing that reiterative transcription is a pervasive process in S. agalactiae. In particular, 40% of the TSSs upstream genes involved in nucleotide metabolism show reiterative transcription potentially regulating gene expression, as exemplified for pyrG and thyA encoding the CTP synthase and the thymidylate synthase respectively. Conclusions: This comprehensive map of the transcriptome at the single nucleotide resolution led to the discovery of new regulatory mechanisms in S. agalactiae. It also provides the basis for in depth analyses of transcriptional networks in S. agalactiae and of the regulatory role of reiterative transcription following variations of intra-cellular nucleotide pools.
doi:10.1186/s12864-015-1583-4 pmid:26024923 pmcid:PMC4448216 fatcat:522hcxbcz5hoxehtxmr2lwanja

Robust imaging of hippocampal inner structure at 7T: in vivo acquisition protocol and methodological choices [article]

Linda Marrakchi-Kacem, Julien Sein, Cyril Poupon, Olivier Colliot, Marie Chupin
2016 arXiv   pre-print
OBJECTIVE:Motion-robust multi-slab imaging of hippocampal inner structure in vivo at 7T.MATERIALS AND METHODS:Motion is a crucial issue for ultra-high resolution imaging, such as can be achieved with 7T MRI. An acquisition protocol was designed for imaging hippocampal inner structure at 7T. It relies on a compromise between anatomical details visibility and robustness to motion. In order to reduce acquisition time and motion artifacts, the full slab covering the hippocampus was split into
more » ... te slabs with lower acquisition time. A robust registration approach was implemented to combine the acquired slabs within a final 3D-consistent high-resolution slab covering the whole hippocampus. Evaluation was performed on 50 subjects overall, made of three groups of subjects acquired using three acquisition settings; it focused on three issues: visibility of hippocampal inner structure, robustness to motion artifacts and registration procedure performance.RESULTS:Overall, T2-weighted acquisitions with interleaved slabs proved robust. Multi-slab registration yielded high quality datasets in 96 % of the subjects, thus compatible with further analyses of hippocampal inner structure.CONCLUSION:Multi-slab acquisition and registration setting is efficient for reducing acquisition time and consequently motion artifacts for ultra-high resolution imaging of the inner structure of the hippocampus.
arXiv:1605.02559v1 fatcat:kye56dpstjatpdarqk2nud625y

Automatic hippocampal segmentation in temporal lobe epilepsy: Impact of developmental abnormalities

Hosung Kim, Marie Chupin, Olivier Colliot, Boris C. Bernhardt, Neda Bernasconi, Andrea Bernasconi
2012 NeuroImage  
., 2010; Chupin et al., 2009b) .  ...  probabilistic priors (Chupin et al., 2009b) .  ... 
doi:10.1016/j.neuroimage.2011.11.040 pmid:22155377 fatcat:sz5tmifdcjfwriyr6ed6kqznua

Is hippocampal volume a good marker to differentiate Alzheimer's disease from frontotemporal dementia?

Leonardo de Souza, Marie Chupin, Maxime Bertoux, Stéphane Lehéricy, Bruno Dubois, Foudil Lamari, Isabelle Le Ber, Michel Bottlaender, Olivier Colliot, Marie Sarazin
2013 Alzheimer's & Dementia  
Leonardo de Souza 1 , Marie Chupin 2 , Maxime Bertoux 3 , St ephane Leh ericy 4 , Bruno Dubois 5 , Foudil Lamari 6 , Isabelle Le Ber 7 , Michel Bottlaender 8 , Olivier Colliot 2 , Marie Sarazin 2 , 1 Alzheimer  ... 
doi:10.1016/j.jalz.2013.05.743 fatcat:skcft43bincxpk56ssknl4jx5i

The Amnestic Syndrome of Hippocampal type in Alzheimer's Disease: An MRI Study

Marie Sarazin, Valérie Chauviré, Emilie Gerardin, Olivier Colliot, Serge Kinkingnéhun, Leonardo Cruz de Souza, Laurence Hugonot-Diener, Line Garnero, Stéphane Lehéricy, Marie Chupin, Bruno Dubois
2010 Journal of Alzheimer's Disease  
The Free and Cued Selective Reminding Test (FCSRT) is a verbal episodic memory test used to identify patients with mild Alzheimer's disease (AD). The present study investigates the relationships between performance on FCSRT and grey matter atrophy assessed with structural MRI in patients with AD. Three complementary MRI-based analyses (VBM analysis, ROI-based analysis, and three-dimensional hippocampal surface-based shape analysis) were performed in 35 patients with AD to analyze correlations
more » ... tween regional atrophy and their scores for episodic memory using the FCSRT. With VBM analysis, the total score on the FCSRT was correlated with left medial temporal lobe atrophy including the left hippocampus but also the thalami. In addition, using ROI-based analysis, the total recall score on the FCSRT was correlated with the left hippocampal volume. With three-dimensional hippocampal surface-based shape analysis, both free recall and total recall scores were correlated with regions corresponding approximately to the CA1 field. No correlation was found with short term memory scores using any of these methods of analysis. In AD, the FCSRT may be considered as a useful clinical marker of memory disorders due to medial temporal damage, specially the CA1 field of the hippocampus.
doi:10.3233/jad-2010-091150 pmid:20847406 fatcat:wxkyit66rvfw7h7n22ui7uxfcu

Evaluation of atlas-based segmentation of hippocampi in healthy humans

Roman Rodionov, Marie Chupin, Elaine Williams, Alexander Hammers, Chandrasekharan Kesavadas, Louis Lemieux
2009 Magnetic Resonance Imaging  
Introduction & Aim: Region of interest (ROI) based fMRI data analysis relies on extracting signals from a specific area which is presumed to be involved in the brain activity being studied. The hippocampus is of interest in many functional connectivity studies [1,2] for example in epilepsy as it plays an important role in epileptogenesis. In this context, ROI may be defined using different techniques. Our study aims at evaluating the spatial correspondence of hippocampal ROIs obtained using
more » ... e brain atlases with hippocampal ROI obtained using an automatic segmentation algorithm dedicated to the hippocampus. Material & Methods: High-resolution volumetric T1-weighted MR images of eighteen healthy volunteers (five females) were acquired on a 3T scanner. Individual ROIs for both hippocampi of each subject were segmented from the MR images using an automatic hippocampus and amygdala segmentation software called SACHA [3] providing the gold standard ROI for comparison with the atlas-derived results. For each subject, hippocampal ROIs were also obtained using three brain atlases: PickAtlas available as a commonly used software toolbox [4,5]; AAL (Automated Anatomical Labeling) atlas [6] included as a subset of ROI into PickAtlas toolbox; a frequency based brain atlas by Hammers et al [7]. The levels of agreement between the SACHA results and those obtained using the atlases were assessed based on quantitative indices measuring volume differences and spatial overlap. The comparison was performed in standard MNI space, the registration being obtained with SPM5 (http://www.fil.ion.ucl.ac.uk/spm/). Results: The mean volumetric error across all subjects was 73% for hippocampal ROIs derived from AAL atlas; 20% in case of ROIs derived from the Hammers atlas; 107% for ROIs derived from Pickatlas. The mean false positive and false negative classification rates were 60% and 10% respectively for the AAL atlas; 16% and 32% for the Hammers atlas; 6% and 72% for the PickAtlas. Conclusion: Though atlas-based ROI definition may be convenient, the resulting ROIs may be poor representations of the hippocampus in some studies critical to under-or over-sampling. Performance of the AAL atlas was inferior to that of the Hammers atlas. Hippocampal ROIs derived from PickAtlas are highly significantly smaller, and this results in the worst performance out of three atlases. It is advisable that the defined ROIs should be verified with knowledge of neuroanatomy before before using it for further data analysis. Key words: region of interest, hippocampus, segmentation, brain atlas the hippocampal ROI derived from three brain atlases: a frequency based brain atlas by Hammers et al [7] and two more widely used, single-subject atlases: AAL [6] and Brodmann areas defined in the PickAtlas toolbox [4,5]. In this study we used the extended version of the frequency atlas [7] based on manual delineations of 30 brains. The maximum probability map was obtained after co-registering all individual atlases into MNI space using the "Segment" module in SPM5 (http://www.fil.ion.ucl.ac.uk/spm/). We compare the atlas-derived ROIs with the results of the segmentation using an automatic algorithm, SACHA, implemented as part of the Brainvisa environment (http://brainvisa.info) [15]. Our interest to evaluate hippocampal ROI is explained by the importance of this structure in studies (especially ROI-based functional connectivity analisys of fMRI data) in patients with epilepsy and Alzheimer's disease [1,2,16,17,18]. Materials & Methods Data Eighteen healthy subjects (five females; mean age 34.7 years and range: 25 -56 years) were included. The criterion for inclusion into the study was absence of neurological pathology. All subjects gave written informed consent (Joint Ethics Committee of the National Hospital for Neurology and Neurosurgery and UCL Institute of Neurology). High-resolution 3D T1-weighted MR images were acquired (Fast Spoiled Gradient Recalled [FSPGR]) on a 3T General Electric Excite HD scanner using a standard head coil: TR/TE/TI -8/3.1/450ms, flip angle 20º; 156 1.1mm-thick coronal slices; matrix 256×256; 24×18 cm field of view; scan time 7 min. Data processing and analysis Individual ROIs for both hippocampi of each subject were segmented from the MR images using SACHA. The comparison of the atlas-based ROIs and individual SACHA-derived ROIs (further called as "individual ROIs") was performed in MNI space for two reasons: the fMRI analyses were to be performed in MNI space; all three atlases are available in MNI space. SACHA ROIs were evaluated by a trained observer (EW) for ensuring their consistency as a gold standard. T1-weighted images were transformed to MNI space using nonlinear warping as implemented in SPM5 [19]. The resulting transformation parameters were applied to the images of individual ROIs in order to register them to MNI space (voxel size in the template image is 2x2x2 mm). The result of the registration was checked in the area of both hippocampi by visual evaluation by an expert-neuroanatomist (CK). Using individual ROIs as the gold standard, the following volumetric and spatial correspondence measures were calculated as described in [20, 21]: RV = the relative error on volume (the optimal value is 0%); K = Dice overlap index, quantifying the proportion of properly classified voxels (the optimal value is 100%); FP and FN = the proportions (in % of total ROI volumes) of false positive and false negative voxels according to SACHA-based ROIs, respectively. In addition, the distance between the centre of the surface voxels of two ROIs is considered in three ways (indices measured in millimeters): the average symmetric distance on the whole boundary, Dm; the maximum of the symmetric distance (Hausdorff distance), DM; 95 percentile of DM, D95. The formulas for the indices can be found in the Appendix. Two tailed t-test has been performed to test difference between mean values of the calculated indices comparing performance of (1) different atlases and (2) performance of right and left ROI within each atlas. Abstract Introduction & Aim: Region of interest (ROI) based fMRI data analysis relies on extracting signals from a specific area which is presumed to be involved in the brain activity being studied. The hippocampus is of interest in many functional connectivity studies [1,2] for example in epilepsy as it plays an important role in epileptogenesis. In this context, ROI may be defined using different techniques. Our study aims at evaluating the spatial correspondence of hippocampal ROIs obtained using three brain atlases with hippocampal ROI obtained using an automatic segmentation algorithm dedicated to the hippocampus. Material & Methods: High-resolution volumetric T1-weighted MR images of eighteen healthy volunteers (five females) were acquired on a 3T scanner. Individual ROIs for both hippocampi of each subject were segmented from the MR images using an automatic hippocampus and amygdala segmentation software called SACHA [3] providing the gold standard ROI for comparison with the atlas-derived results. For each subject, hippocampal ROIs were also obtained using three brain atlases: PickAtlas available as a commonly used software toolbox [4,5]; AAL (Automated Anatomical Labeling) atlas [6] included as a subset of ROI into PickAtlas toolbox; a frequency based brain atlas by Hammers et al [7] . The levels of agreement between the SACHA results and those obtained using the atlases were assessed based on quantitative indices measuring volume differences and spatial overlap. The comparison was performed in standard MNI space, the registration being obtained with SPM5 (http://www.fil.ion.ucl.ac.uk/spm/). Results: The mean volumetric error across all subjects was 73% for hippocampal ROIs derived from AAL atlas; 20% in case of ROIs derived from the Hammers atlas; 107% for ROIs derived from Pickatlas. The mean false positive and false negative classification rates were 60% and 10% respectively for the AAL atlas; 16% and 32% for the Hammers atlas; 6% and 72% for the PickAtlas. Conclusion: Though atlas-based ROI definition may be convenient, the resulting ROIs may be poor representations of the hippocampus in some studies critical to under-or over-sampling. Performance of the AAL atlas was inferior to that of the Hammers atlas. Hippocampal ROIs derived from PickAtlas are highly significantly smaller, and this results in the worst performance out of three atlases. It is advisable that the defined ROIs should be verified with knowledge of neuroanatomy before before using it for further data analysis.
doi:10.1016/j.mri.2009.01.008 pmid:19261422 fatcat:wc67hoxtyzerhoygixtivblfna
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