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NeuroSpin

 

 

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Published on 28 June 2018

​​NeuroSpin, directed by Stanislas Dehaene, is a research centre for the innovation of brain imaging. Activities carried out includes biomedical imaging and diagnostic and therapeutic innovation.

At NeuroSpin Department, physicists, mathematicians and neuroscientists join forces to jointly develop tools and models that will enable a better understanding normal and pathological brain function, before or after treatment. Focused on neuroimaging, research concerns several topics :

  • ​Technological and methodological development (acquisition and processing of data),
  • Cognitive neuroscience,
  • Preclinical and clinical neuroscience.

NeuroSpin includes 5 Research Entities : MRI and Spectroscopy Unit (UNIRS) ; Analysis and Information Processing Unit (UNATI), Cognitive Neuroimaging Unit (UNICOG), a Mixed Research Unity (U992), belonging to CEA, Paris-Sud University and Inserm ; Translational and Applicative Neuroimaging Research Unit (UNIACT), belonging to UMR 1129 ; and Neurofunctional Imaging Group (GIN) in Bordeaux who is integrated in the Institute of Neurodegenerative Diseases (UMR 5296, belonging to CNRS and Bordeaux University).

(click on image to enlarge)

To carry out their activities, researchers of NeuroSpin Department have access to platforms, certified IBiSA and part of the National Infrastructures in Biology and Health France Life Imaging (FLI) et NeurATRIS :

  • Clinical MRI 3T, 7T and soon 11,7T,
  • Preclinical MRI for small animals 7T, 11,7T et 17T,
  • EEG et MEG,
  • 3-photon imaging (soon).

These instruments are open to the national or international scientific community, to academics or industrials.

Do you want to submit your project to NeuroSpin ?


 
 

 

  
Early asymmetric inter-hemispheric transfer in the auditory network: insights from infants with corpus callosum agenesis
Adibpour P., Dubois J., Moutard M. L. and Dehaene-Lambertz G.
Ventromedial Prefrontal Volume in Adolescence Predicts Hyperactive/Inattentive Symptoms in Adulthood
Albaugh M. D., Ivanova M., Chaarani B., Orr C., Allgaier N., Althoff R. R., N D' Alberto, Hudson K., Mackey S., Spechler P. A., Banaschewski T., Bruhl R., Bokde A. L. W., Bromberg U., Buchel C., Cattrell A., Conrod P. J., Desrivieres S., Flor H., Frouin V., Gallinat J., Goodman R., Gowland P., Grimmer Y., Heinz A., Kappel V., Martinot J. L., Martinot M. P., Nees F., Papadopoulos Orfanos D., Penttila J., Poustka L., Paus T., Smolka M. N., Struve M., Walter H., Whelan R., Schumann G., Garavan H. and Potter A. S.
Computing the Social Brain Connectome Across Systems and States
Alcala-Lopez D., Smallwood J., Jefferies E., Van Overwalle F., Vogeley K., Mars R. B., Turetsky B. I., Laird A. R., Fox P. T., Eickhoff S. B. and Bzdok D.
Building blocks of social cognition: Mirror, mentalize, share?
Alcala-Lopez D., Vogeley K., Binkofski F. and Bzdok D.
On the role of visual experience in mathematical development: Evidence from blind mathematicians
Amalric M., Denghien I. and Dehaene S.
Modulation of orbitofrontal-striatal reward activity by dopaminergic functional polymorphisms contributes to a predisposition to alcohol misuse in early adolescence
Baker T. E., Castellanos-Ryan N., Schumann G., Cattrell A., Flor H., Nees F., Banaschewski T., Bokde A., Whelan R., Buechel C., Bromberg U., Papadopoulos Orfanos D., Gallinat J., Garavan H., Heinz A., Walter H., Bruhl R., Gowland P., Paus T., Poustka L., Martinot J. L., Lemaitre H., Artiges E., Paillere Martinot M. L., Smolka M. N. and Conrod P.
A validation dataset for Macaque brain MRI segmentation
Balbastre Y., Riviere D., Souedet N., Fischer C., Herard A. S., Williams S., Vandenberghe M. E., Flament J., Aron-Badin R., Hantraye P., Mangin J. F. and Delzescaux T.
A lateral-to-mesial organization of human ventral visual cortex at birth
Barttfeld P., Abboud S., Lagercrantz H., Aden U., Padilla N., Edwards A. D., Cohen L., Sigman M., Dehaene S. and Dehaene-Lambertz G.
Distinct brain structure and behavior related to ADHD and conduct disorder traits
Bayard F., Nymberg Thunell C., Abe C., Almeida R., Banaschewski T., Barker G., Bokde A. L. W., Bromberg U., Buchel C., Quinlan E. B., Desrivieres S., Flor H., Frouin V., Garavan H., Gowland P., Heinz A., Ittermann B., Martinot J. L., Martinot M. P., Nees F., Orfanos D. P., Paus T., Poustka L., Conrod P., Stringaris A., Struve M., Penttila J., Kappel V., Grimmer Y., Fadai T., van Noort B., Smolka M. N., Vetter N. C., Walter H., Whelan R., Schumann G. and Petrovic P.
Post-mortem inference of the human hippocampal connectivity and microstructure using ultra-high field diffusion MRI at 11.7 T
Beaujoin J., Palomero-Gallagher N., Boumezbeur F., Axer M., Bernard J., Poupon F., Schmitz D., Mangin J. F. and Poupon C.
Impaired conscious access and abnormal attentional amplification in schizophrenia
Berkovitch L., Del Cul A., Maheu M. and Dehaene S.
Processing number and length in the parietal cortex: Sharing resources, not a common code
Borghesani V., de Hevia M. D., Viarouge A., Pinheiro-Chagas P., Eger E. and Piazza M.
Three-Dimensional Probabilistic Maps of Mesial Temporal Lobe Structures in Children and Adolescents' Brains
Bouyeure A., Germanaud D., Bekha D., Delattre V., Lefevre J., Pinabiaux C., Mangin J. F., Riviere D., Fischer C., Chiron C., Hertz-Pannier L. and Noulhiane M.
Extending the Construct Network of Trait Disinhibition to the Neuroimaging Domain: Validation of a Bridging Scale for Use in the European IMAGEN Project
Brislin S. J., Patrick C. J., Flor H., Nees F., Heinrich A., Drislane L. E., Yancey J. R., Banaschewski T., Bokde A. L. W., Bromberg U., Buchel C., Quinlan E. B., Desrivieres S., Frouin V., Garavan H., Gowland P., Heinz A., Ittermann B., Martinot J. L., Martinot M. P., Papadopoulos Orfanos D., Poustka L., Frohner J. H., Smolka M. N., Walter H., Whelan R., Conrod P., Stringaris A., Struve M., van Noort B., Grimmer Y., Fadai T., Schumann G. and Foell J.
POINTS OF SIGNIFICANCE Statistics versus machine learning
Bzdok D., Altman N. and Krzywinski M.
Statistics versus machine learning
Bzdok D., Altman N. and Krzywinski M.
Machine learning: supervised methods
Bzdok D., Krzywinski M. and Altman N.
Machine Learning for Precision Psychiatry: Opportunities and Challenges
Bzdok D. and Meyer-Lindenberg A.
How interindividual differences in brain anatomy shape reading accuracy
Cachia A., Roell M., Mangin J. F., Sun Z. Y., Jobert A., Braga L., Houde O., Dehaene S. and Borst G.
Mapping adolescent reward anticipation, receipt, and prediction error during the monetary incentive delay task
Cao Z., Bennett M., Orr C., Icke I., Banaschewski T., Barker G. J., Bokde A. L. W., Bromberg U., Buchel C., Quinlan E. B., Desrivieres S., Flor H., Frouin V., Garavan H., Gowland P., Heinz A., Ittermann B., Martinot J. L., Nees F., Orfanos D. P., Paus T., Poustka L., Hohmann S., Frohner J. H., Smolka M. N., Walter H., Schumann G. and Whelan R.
Asymmetrical interference between number and item size perception provides evidence for a domain specific impairment in dyscalculia
Castaldi E., Mirassou A., Dehaene S., Piazza M. and Eger E.
Large-Scale Cortical Networks for Hierarchical Prediction and Prediction Error in the Primate Brain
Chao Z. C., Takaura K., Wang L., Fujii N. and Dehaene S.
A population-based atlas of the human pyramidal tract in 410 healthy participants
Chenot Q., Tzourio-Mazoyer N., Rheault F., Descoteaux M., Crivello F., Zago L., Mellet E., Jobard G., Joliot M., Mazoyer B. and Petit L.
Whole-Body Diffusion-weighted MR Imaging of Iron Deposits in Hodgkin, Follicular, and Diffuse Large B-Cell Lymphoma
Cottereau A. S., Mule S., Lin C., Belhadj K., Vignaud A., Copie-Bergman C., Boyez A., Zerbib P., Tacher V., Scherman E., Haioun C., Luciani A., Itti E. and Rahmouni A.
Statistical Shape Analysis of Large Datasets Based on Diffeomorphic Iterative Centroids
Cury C., Glaunes J. A., Toro R., Chupin M., Schumann G., Frouin V., Poline J. B. and Colliot O.
Individual differences in stop-related activity are inflated by the adaptive algorithm in the stop signal task
D'Alberto N., Chaarani B., Orr C. A., Spechler P. A., Albaugh M. D., Allgaier N., Wonnell A., Banaschewski T., Bokde A. L. W., Bromberg U., Buchel C., Quinlan E. B., Conrod P. J., Desrivieres S., Flor H., Frohner J. H., Frouin V., Gowland P., Heinz A., Itterman B., Martinot J. L., Paillere Martinot M. L., Artiges E., Nees F., Papadopoulos Orfanos D., Poustka L., Robbins T. W., Smolka M. N., Walter H., Whelan R., Schumann G., Potter A. S. and Garavan H.
Local structural connectivity is associated with social cognition in autism spectrum disorder
d'Albis M. A., Guevara P., Guevara M., Laidi C., Boisgontier J., Sarrazin S., Duclap D., Delorme R., Bolognani F., Czech C., Bouquet C., Ly-Le Moal M., Holiga S., Amestoy A., Scheid I., Gaman A., Leboyer M., Poupon C., Mangin J. F. and Houenou J.
Prediction of activation patterns preceding hallucinations in patients with schizophrenia using machine learning with structured sparsity
de Pierrefeu A., Fovet T., Hadj-Selem F., Lofstedt T., Ciuciu P., Lefebvre S., Thomas P., Lopes R., Jardri R. and Duchesnay E.
Structured Sparse Principal Components Analysis With the TV-Elastic Net Penalty
de Pierrefeu A., Lofstedt T., Hadj-Selem F., Dubois M., Jardri R., Fovet T., Ciuciu P., Frouin V. and Duchesnay E.
Identifying a neuroanatomical signature of schizophrenia, reproducible across sites and stages, using machine learning with structured sparsity
de Pierrefeu A., Lofstedt T., Laidi C., Hadj-Selem F., Bourgin J., Hajek T., Spaniel F., Kolenic M., Ciuciu P., Hamdani N., Leboyer M., Fovet T., Jardri R., Houenou J. and Duchesnay E.
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