Guillaume Flandin

Benjamin Thyreau, Jean-Baptiste Poline, Bertrand Thirion, 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

Topological FDR for neuroimaging

Karl J. Friston, Guillaume Flandin, Keith J. Worsley, 2010, NeuroImage.

An Optical Flow Approach for Precise Visual Navigation of a Planetary Lander

Klaus Janschek, Valerij Tchernykh, Guillaume Flandin, 2006 .

A multisubject anatomo-functional parcellation of the brain

Nicholas Ayache, William D. Penny, Jean-Baptiste Poline, 2003 .

BIDS and the NeuroImaging Data Model (NIDM)

Satrajit S. Ghosh, David B. Keator, Vince D. Calhoun, 2019 .

Developing and using the data models for neuroimaging: the NIDASH Working Group

Satrajit S. Ghosh, David B. Keator, Jessica A. Turner, 2014 .

Group analysis in functional neuroimaging: selecting subjects using similarity measures

Jean-Baptiste Poline, Guillaume Flandin, Ferath Kherif, 2003, NeuroImage.

Sharing brain mapping statistical results with the neuroimaging data model

David B. Keator, Thomas E. Nichols, Mark Jenkinson, 2016 .

Functional connectivity: studying non-linear, asynchronous interactions between BOLD signals

Jean-Baptiste Poline, Guillaume Flandin, Line Garnero, 2002 .

fMRI Data Analysis Using SPM

Guillaume Flandin, Marianne J.U. Novak, G. Flandin, 2013, fMRI.

Developing and using the Neuroimaging and Data Sharing Data Model: the NIDASH Working Group

Satrajit S. Ghosh, Franck Michel, David B. Keator, 2014 .

Viewing FSL results with SPM and vice versa

Thomas E. Nichols, Guillaume Flandin, Camille Maumet, 2017 .

Blind source separation approaches to remove imaging artefacts in EEG signals recorded simultaneously with FMRI

Jean-Baptiste Poline, Guillaume Flandin, Bertrand Rivet, 2008, 2008 16th European Signal Processing Conference.

BIDS Apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods

M. Mallar Chakravarty, Anisha Keshavan, Russell A. Poldrack, 2016 .

GAIA Payload Data Handling System

Guillaume Flandin, M. Leroy, J.-F. Soucaille, 2006 .

MEG-BIDS, the brain imaging data structure extended to magnetoencephalography

Robert Oostenveld, Krzysztof J. Gorgolewski, Guillaume Flandin, 2018, Scientific Data.

EEG and MEG Data Analysis in SPM8

Karl J. Friston, William D. Penny, Robert Oostenveld, 2011, Comput. Intell. Neurosci..

Boutiques: a flexible framework to integrate command-line applications in computing platforms pdf

Tristan Glatard, Alan C. Evans, Krzysztof J. Gorgolewski, 2017, GigaScience.

Functional connectivity: studying nonlinear, delayed interactions between BOLD signals

Silke Dodel, Jean-Baptiste Poline, Guillaume Flandin, 2003, NeuroImage.

Multiple sparse priors for the M/EEG inverse problem

Karl J. Friston, Guillaume Flandin, Stefan J. Kiebel, 2008, NeuroImage.

Application of the Neuroimaging Data Model to Represent and Exchange Primary and Derived Data

Satrajit S. Ghosh, David B. Keator, Jessica A. Turner, 2015 .

Reading and displaying standardised neuroimaging results in a user-friendly environment

Thomas E. Nichols, Guillaume Flandin, Camille Maumet, 2017 .

Probing the cortical network underlying the psychological refractory period: A combined EEG–fMRI study

Guillaume Flandin, Stanislas Dehaene, Guido Hesselmann, 2011, NeuroImage.

Bayesian comparison of spatially regularised general linear models

Guillaume Flandin, Will Penny, Nelson Trujillo-Barreto, 2007, Human brain mapping.

Multimodal Integration of M/EEG and f/MRI Data in SPM12

Guillaume Flandin, Vladimir Litvak, Richard N. Henson, 2019, Front. Neurosci..

Dynamic causal modelling of mitigated epidemiological outcomes.

Adeel Razi, Karl J. Friston, Guillaume Flandin, 2020, 2011.12400.

Functional Sensitivity of 2D Simultaneous Multi-Slice Echo-Planar Imaging: Effects of Acceleration on g-factor and Physiological Noise

Steen Moeller, Nikolaus Weiskopf, Guillaume Flandin, 2017, Front. Neurosci..

Evaluation of 2D multiband EPI imaging for high-resolution, whole-brain, task-based fMRI studies at 3T: Sensitivity and slice leakage artifacts

Steen Moeller, Essa Yacoub, Nikolaus Weiskopf, 2016, NeuroImage.

MEG-BIDS: an extension to the Brain Imaging Data Structure for magnetoencephalography

Robert Oostenveld, Krzysztof J. Gorgolewski, Guillaume Flandin, 2017 .

Bayesian fMRI data analysis with sparse spatial basis function priors

William D. Penny, Guillaume Flandin, 2007, NeuroImage.

Quantitative validation of voxel-wise statistical analyses of autoradiographic rat brain volumes: Application to unilateral visual stimulation

Vincent Frouin, Guillaume Flandin, Philippe Hantraye, 2008, NeuroImage.

Functional optical signal analysis: a software tool for near-infrared spectroscopy data processing incorporating statistical parametric mapping.

Guillaume Flandin, David T Delpy, Atsushi Maki, 2007, Journal of biomedical optics.

A standardised representation for non-parametric fMRI results

Thomas E. Nichols, Bertrand Thirion, Guillaume Flandin, 2018 .

Statistical Parametric Mapping, There and Back Again.

Guillaume Flandin, Christophe Phillips, C. Phillips, 2019 .

BIDS-EEG: an extension to the Brain Imaging Data Structure (BIDS) Specification for electroencephalography

Robert Oostenveld, Guillaume Flandin, Christophe Phillips, 2018 .

The problem of low variance voxels in statistical parametric mapping; a new hat avoids a ‘haircut’

Karl J. Friston, William D. Penny, Guillaume Flandin, 2012, NeuroImage.

Brain Imaging Data Structure - a new standard for describing and organizing human neuroimaging data

Satrajit S. Ghosh, David B. Keator, Jessica A. Turner, 2015 .

Dynamic causal modelling of COVID-19.

Adeel Razi, Karl J. Friston, Guillaume Flandin, 2020, Wellcome open research.

Graph-partitioned spatial priors for functional magnetic resonance images

Karl J. Friston, William D. Penny, Nikolaus Weiskopf, 2008, NeuroImage.

How much subjects are resembling each other? Spatial and temporal similarity measures for fMRI data

Jean-Baptiste Poline, Guillaume Flandin, Ferath Kherif, 2002 .

Tracking and tracing in the UK: a dynamic causal modelling study

Adeel Razi, Karl J. Friston, Guillaume Flandin, 2020, 2005.07994.

Sharing brain mapping statistical results with the neuroimaging data model

Satrajit S. Ghosh, Thomas E. Nichols, Jean-Baptiste Poline, 2016, Scientific Data.

Utilisation d'informations géométriques pour l'analyse statistique des données d'IRM fonctionnelle. (ntroduction of geometrical features for the statistical analysis of functional MRI data)

Guillaume Flandin, G. Flandin, 2004 .

BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods

Satrajit S. Ghosh, Anisha Keshavan, Mark Jenkinson, 2016, bioRxiv.

Physiological basis of vascular autocalibration (VasA): Comparison to hypercapnia calibration methods

Nikolaus Weiskopf, Guillaume Flandin, Laurentius Huber, 2017, Magnetic resonance in medicine.

Improved Detection Sensitivity in Functional MRI Data Using a Brain Parcelling Technique

Nicholas Ayache, Grégoire Malandain, Jean-Baptiste Poline, 2002, MICCAI.

Flexible Bayesian Modelling for Nonlinear Image Registration pdf

Guillaume Flandin, John Ashburner, Mikael Brudfors, 2020, MICCAI.

Testing and tracking in the UK: A dynamic causal modelling study

Adeel Razi, Karl J. Friston, Guillaume Flandin, 2020, Wellcome Open Research.

Dealing with the shortcomings of spatial normalization: Multi‐subject parcellation of fMRI datasets

Jean-Baptiste Poline, Bertrand Thirion, Guillaume Flandin, 2006, Human brain mapping.

Should fMRI data be analyzed using a single BOLD response model across regions

Jean-Baptiste Poline, Guillaume Flandin, Ferath Kherif, 2003 .

Bayesian fMRI data analysis with sparse spatial basis function priors

Guillaume Flandin, William D. Penny, W. Penny, 2007, NeuroImage.

Convolution models for induced electromagnetic responses

Karl J. Friston, Guillaume Flandin, Vladimir Litvak, 2013, NeuroImage.

Statistical parametric mapping (SPM)

Karl J. Friston, Guillaume Flandin, G. Flandin, 2008, Scholarpedia.

Multinomial inference on distributed responses in SPM

Karl J. Friston, Guillaume Flandin, Justin R. Chumbley, 2010, NeuroImage.

Parcellation of brain images with anatomical and functional constraints for fMRI data analysis

Nicholas Ayache, Jean-Baptiste Poline, Guillaume Flandin, 2002, Proceedings IEEE International Symposium on Biomedical Imaging.

Extending NI-DM to share the results and provenance of a neuroimaging study: an example with SPM

Satrajit S. Ghosh, David B. Keator, Jessica A. Turner, 2014 .

EEG-BIDS, an extension to the brain imaging data structure for electroencephalography

Robert Oostenveld, Krzysztof J. Gorgolewski, Guillaume Flandin, 2019, Scientific Data.

Second waves, social distancing, and the spread of COVID-19 across the USA

Adeel Razi, Karl J. Friston, Guillaume Flandin, 2020, Wellcome open research.

Bayesian analysis of single-subject fMRI data : SPM 5 implementation

William D. Penny, Guillaume Flandin, Nelson Trujillo-Bareto, 2005 .

Learning Neural Representations of Human Cognition across Many fMRI Studies

Thomas E. Nichols, Guillaume Flandin, Michael Hanke, 2017 .

Deep brain stimulation has state-dependent effects on motor connectivity in Parkinson's disease.

Karl J. Friston, Guillaume Flandin, Laura Mancini, 2019, Brain : a journal of neurology.

A Parametric Empirical Bayesian framework for fMRI‐constrained MEG/EEG source reconstruction

Karl J. Friston, Guillaume Flandin, Jérémie Mattout, 2010, Human brain mapping.

Effective immunity and second waves: a dynamic causal modelling study.

Adeel Razi, Karl J. Friston, Guillaume Flandin, 2020, Wellcome open research.

Bayesian Analysis of fMRI data with Spatial Priors

Guillaume Flandin, W. Penny, G. Flandin, 2005 .

Retinotopic organization of visual mental images as revealed by functional magnetic resonance imaging.

Guillaume Flandin, Ferath Kherif, Jessica Dubois, 2004, Brain research. Cognitive brain research.