TAKERKART Sylvain

Nom : TAKERKART
Prénom : Sylvain
Téléphone : 04 91 32 40 07
Fonction : Ingénieur Traitement d'Images - Responsable CRISE
Grade : IR
Bureau : 1.04

Présentation

I have been a CNRS Research Engineer since 2006. At present, I am the technical coordinator of the NIT (Neuroinfomatics and Information Technology) core facility at INT. I’m also a member of two research teams at INT : MECA and BANCO

My research focuses on developing and applying machine learning tools for new problems encountered in neuroscience. For this, I collaborate with the Qarma team of the Laboratoire d’Informatique Fondamentale de Marseille. At the moment, my main interest is to understand, model and exploit inter-individual differences measured through multi-modal MRI to better understand brain functions, using a generic framework called inter-subject learning. For this, I am promoting the development of open source code and the sharing of open data.

Recent highlights :
- 2019 : Qi Wang’s paper has been accepted to NeuroImage : Inter-subject pattern analysis : A straightforward and powerful scheme for group-level MVPA. You can get the pdf here.
- 2019 : our project SharElec has been funded as part as the Open Science program of the ANR : it aims at disseminating the data structuration tools developed between Julich’s research center and the INT
- 2019 : I co-organized (and hosted at INT) the INCF-sponsored GEANT workshop, dedicated to data management practices in neuroscience
- 2018 : our multi-modal MRI data set InterTVA is online on the OpenNeuro portal ; it is aimed at studying inter-individual differences in voice perception and benchmarking multi-view machine learning algorithms
- 2018 : Qi Wang’s paper accepted to PRNI workshop : Population Averaging of Neuroimaging Data Using Lp Distance-based Optimal Transport
- Two papers that include new applications of inter-subject learning : the first one (2017) to quantify the power of EEG features in newborns to predict language acquisition (Enhanced Neonatal Brain Responses To Sung Streams Predict Vocabulary Outcomes By Age 18 Months) and the second one (2018) to demonstrate structure-function relationships (Anatomo-functional correspondence in the superior temporal sulcus)
- 2017 : yet another demonstration of the usefuleness of our graph-kernel approach to design predictive models, this time for diffusion-MRI : Learning from Diffusion-Weighted Magnetic Resonance Images using graph kernels
- 2016 : a new paper using our graph kernel has been accepted to Medical Image Analysis : Structural graph-based morphometry : A multiscale searchlight framework based on sulcal pits
- 2015 : I have, finally, defended my PhD thesis in machine learning applied to neuroimaging : A multi-source perspective on inter-subject learning. Contributions to neuroimaging.
- 2015 : our ANR project LIVES (Learning with Interacting ViEwS) has been funded ; I am the coordinator of the INT team and we will design and apply multi-modal machine learning methods on MRI data (anatomical, structural, resting-state, task-functional)
- 2014 : our graph kernel paper has been accepted in Plos ONE : Graph-based inter-subject pattern analysis of fMRI data ; the source code and dataset are available here
- 2013 : release of Vobi One, the first open source software suite dedicated to the processing of functional optical imaging ; the paper is available here
- 2013 : co-PI of the PEPS BMI CAMPA (CAractérisation Multimodale des déficits sensori-moteurs Post-AVC) project ;
- 2011 : organizer and chair of the MLNI workshop, which gathered leading researchers in machine learning and neuroimaging ;
- 2010-2012 : PI of the Neuro-IC GRABBR (GRAph-Based Brain Reading) project ;
- 2009 : publication of our PNAS paper on ICA and fMRI, which includes some the work I did during my six years at Princeton University.

Publications



  • Aglieri V., Chaminade T., Takerkart S., et Belin P. (2018). Functional connectivity within the voice perception network and its behavioural relevance. NeuroImage, 183: 356-365.


  • Chemla S., Muller L., Reynaud A., Takerkart S., Destexhe A., et Chavane F. (2017). Improving voltage-sensitive dye imaging: with a little help from computational approaches. Neurophotonics, 4: 031215.


  • Demolliens M., Isbaine F., Takerkart S., Huguet P., et Boussaoud D. (2017). Social and asocial prefrontal cortex neurons: a new look at social facilitation and the social brain. Social Cognitive and Affective Neuroscience.


  • Deneux T., Takerkart S., Grinvald A., Masson G.S., et Vanzetta I. (2012). A processing work-flow for measuring erythrocytes velocity in extended vascular networks from wide field high-resolution optical imaging data. NeuroImage, 59: 2569-2588.

  • François C., Jaillet F., Takerkart S., et Schön D. (2014). Faster sound stream segmentation in musicians than in nonmusicians. PloS One, 9.


  • François C., Teixidó M., Takerkart S., Agut T., Bosch L., et Rodriguez-Fornells A. (2017). Enhanced Neonatal Brain Responses To Sung Streams Predict Vocabulary Outcomes By Age 18 Months. Scientific Reports, 7.

  • Kilavik B.E., Ponce-Alvarez A., Trachel R., Confais J., Takerkart S., et Riehle A. (2012). Context-related frequency modulations of macaque motor cortical LFP beta oscillations. Cerebral cortex (New York, N.Y.: 1991), 22: 2148-2159.

  • Mahmoudi A., Takerkart S., Regragui F., Boussaoud D., et Brovelli A. (2012). Multivoxel pattern analysis for FMRI data: a review. Computational and mathematical methods in medicine, 2012.


  • Monfardini E., Brovelli A., Boussaoud D., Takerkart S., et Wicker B. (2008). I learned from what you did: Retrieving visuomotor associations learned by observation. NeuroImage, 42: 1207-1213.


  • Paquette S., Takerkart S., Saget S., Peretz I., et Belin P. (2018). Cross-classification of musical and vocal emotions in the auditory cortex: Cross-classification of musical and vocal emotions. Annals of the New York Academy of Sciences, 1423: 329-337.


  • Reynaud A., Takerkart S., Masson G.S., et Chavane F. (2011). Linear model decomposition for voltage-sensitive dye imaging signals: Application in awake behaving monkey. NeuroImage, 54: 1196-1210.


  • Roux S., Matonti F., Dupont F., Hoffart L., Takerkart S., Picaud S., Pham P., et Chavane F. (2016). Probing the functional impact of sub-retinal prosthesis. eLife, 5.


  • Takerkart S., Auzias G., Brun L., et Coulon O. (2017). Structural graph-based morphometry: A multiscale searchlight framework based on sulcal pits. Medical Image Analysis, 35: 32-45.


  • Takerkart S., Auzias G., Brun L., et Coulon O. (2015). Mapping cortical shape differences using a searchlight approach based on classification of sulcal pit graphs. IEEE, 1514-1517.


  • Takerkart S., Auzias G., Thirion B., et Ralaivola L. (2014). Graph-Based Inter-Subject Pattern Analysis of fMRI Data. PLoS ONE, 9.

  • Takerkart S., Auzias G., Thirion B., Schön D., et Ralaivola L. (2012). Graph-Based Inter-subject Classification of Local fMRI Patterns. Machine Learning in Medical Imaging, 184-192.


  • Takerkart S., Katz P., Garcia F., Roux S., Reynaud A., et Chavane F. (2014). Vobi One: a data processing software package for functional optical imaging. Frontiers in Neuroscience, 8.


  • Takerkart S. et Ralaivola L. (2011). MKPM: A multiclass extension to the kernel projection machine. IEEE, 2785-2791.


  • Wang Q., Cagna B., Chaminade T., et Takerkart S. (2020). Inter-subject pattern analysis: A straightforward and powerful scheme for group-level MVPA. NeuroImage, 204: 116205.
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