References of "Phillips, Christophe"
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See detailDynamics of human cortical ensembles are set by circadian system and sleep homeostasis
Chellappa, Sarah Laxhmi ULg; Ly, Julien; Gaggioni, Giulia et al

Conference (2014)

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See detailCortical excitability dynamics of during sleep deprivation set PVT performance
Borsu, Chloé; Gaggioni, Giulia ULg; Ly, Julien et al

Conference (2014)

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See detailPrior light history impacts on cognitive brain function
Chellappa, Sarah Laxhmi ULg; Ly, Julien; Meyer, Christelle ULg et al

Conference (2014)

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See detailPrior light history impacts on higher order cognitive brain function
Chellappa, Sarah Laxhmi ULg; Ly, Julien; Meyer, Christelle ULg et al

Conference (2014)

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See detailMemory Reactivation During Rapid Eye Movement (REM) Sleep Promotes Its Generalization and Integration in Cortical Stores
Sterpenich, Virginie; Schmidt, Christina ULg; Albouy, Genevièvre et al

in Sleep (2014), 37(6), 1061-1075

Memory reactivation appears to be a fundamental process in memory consolidation. Here, we tested the influence of memory reactivation during REM sleep on memory performance and brain responses at ... [more ▼]

Memory reactivation appears to be a fundamental process in memory consolidation. Here, we tested the influence of memory reactivation during REM sleep on memory performance and brain responses at retrieval in healthy human participants. Auditory cues were associated with pictures of faces during their encoding. These memory cues delivered during REM sleep enhanced subsequent accurate recollections but also false recognitions. These results suggest that reactivated memories interacted with semantically-related representations, and induced new creative associations, which subsequently reduced the distinction between new and previously encoded exemplars. Cues had no effect if presented during stage 2 sleep, or if they were not associated with faces during encoding. Functional MRI revealed that following exposure to conditioned cues during REM sleep, responses to faces during retrieval were enhanced both in a visual area and in a cortical region of multisensory (auditory-visual) convergence. These results show that reactivating memories during REM sleep enhances cortical responses during retrieval, suggesting the integration of recent memories within cortical circuits, favoring the generalization and schematization of the information. [less ▲]

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See detailBrains creating stories of selves: the neural basis of autobiographical reasoning.
D'Argembeau, Arnaud ULg; Cassol, Helena; Phillips, Christophe ULg et al

in Social Cognitive and Affective Neuroscience (2014), 9

Personal identity critically depends on the creation of stories about the self and one's life. The present study investigates the neural substrates of autobiographical reasoning, a process central to the ... [more ▼]

Personal identity critically depends on the creation of stories about the self and one's life. The present study investigates the neural substrates of autobiographical reasoning, a process central to the construction of such narratives. During fMRI scanning, participants approached a set of personally significant memories in two different ways: on some trials, they remembered the concrete content of the events (autobiographical remembering), whereas on other trials they reflected on the broader meaning and implications of their memories (autobiographical reasoning). Relative to remembering, autobiographical reasoning recruited a left-lateralized network involved in conceptual processing (including the dorsal medial prefrontal cortex (MPFC), inferior frontal gyrus, middle temporal gyrus, and angular gyrus). The ventral MPFC-an area that may function to generate personal/affective meaning-was not consistently engaged during autobiographical reasoning across participants but, interestingly, the activity of this region was modulated by individual differences in interest and willingness to engage in self-reflection. These findings support the notion that autobiographical reasoning and the construction of personal narratives go beyond mere remembering in that they require deriving meaning and value from past experiences. [less ▲]

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See detailPhotic memory for executive brain responses
Chellappa*, Sarah Laxhmi ULg; Ly*, Julien ULg; Meyer, Christelle ULg et al

in Proceedings of the National Academy of Sciences of the United States of America (2014), Epub ahead of print

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See detailOn the statistical assessment of small sample classification
Noirhomme, Quentin ULg; Lesenfants, Damien ULg; Gomez, Francisco et al

Conference (2013, December)

Classifiers start to be used in medical application to infer diagnosis. Their results are assessed through either a binomial or a permutation test. Distributions built from classification of random data ... [more ▼]

Classifiers start to be used in medical application to infer diagnosis. Their results are assessed through either a binomial or a permutation test. Distributions built from classification of random data with cross-validation, did not follow the theoretical binomial distribution, showing that binomial test was not conservative enough. A permutation test is thus recommended. [less ▲]

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See detailAnisotropy preserving DTI processing
Collard, Anne ULg; Bonnabel, Silvère; Phillips, Christophe ULg et al

in International Journal of Computer Vision (2013)

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See detailChanges in Effective Connectivity by Propofol Sedation
Gomez Jaramillo, Francisco Albeiro ULg; Phillips, Christophe ULg; Soddu, Andrea ULg et al

in PLoS ONE (2013), 8(8), 71370

Mechanisms of propofol-induced loss of consciousness remain poorly understood. Recent fMRI studies have shown decreases in functional connectivity during unconsciousness induced by this anesthetic agent ... [more ▼]

Mechanisms of propofol-induced loss of consciousness remain poorly understood. Recent fMRI studies have shown decreases in functional connectivity during unconsciousness induced by this anesthetic agent. Functional connectivity does not provide information of directional changes in the dynamics observed during unconsciousness. The aim of the present study was to investigate, in healthy humans during an auditory task, the changes in effective connectivity resulting from propofol induced loss of consciousness. We used Dynamic Causal Modeling for fMRI (fMRI-DCM) to assess how causal connectivity is influenced by the anesthetic agent in the auditory system. Our results suggest that the dynamic observed in the auditory system during unconsciousness induced by propofol, can result in a mixture of two effects: a local inhibitory connectivity increase and a decrease in the effective connectivity in sensory cortices. [less ▲]

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See detailAltered white matter architecture in BDNF Met carriers
Ziegler, Erik ULg; Foret, Ariane; Mascetti, Laura ULg et al

in PLoS ONE (2013)

Brain-derived neurotrophic factor (BDNF) modulates the pruning of synaptically-silent axonal arbors. The Met allele of the BDNF gene is associated with a reduction in the neurotrophin's activity-dependent ... [more ▼]

Brain-derived neurotrophic factor (BDNF) modulates the pruning of synaptically-silent axonal arbors. The Met allele of the BDNF gene is associated with a reduction in the neurotrophin's activity-dependent release. We used di ffusion-weighted imaging to construct structural brain networks for 36 healthy subjects with known BDNF genotypes. Through permutation testing we discovered clear di fferences in connection strength between subjects carrying the Met allele and those homozygotic for the Val allele. We trained a Gaussian process classi fier capable of identifying the subjects' allelic group with 86% accuracy and high predictive value. In Met carriers structural connectivity was greatly increased throughout the forebrain, particularly in connections corresponding to the anterior and superior corona radiata as well as corticothalamic and corticospinal projections from the sensorimotor, premotor and prefrontal portions of the internal capsule. Interhemispheric connectivity was also increased via the corpus callosum and anterior commissure, and extremely high connectivity values were found between inferior medial frontal polar regions via the anterior forceps. We propose that the decreased availability of BDNF leads to de cifits in axonal maintenance in carriers of the Met allele, and that this produces mesoscale changes in white matter architecture. [less ▲]

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See detailMultivariate pattern interpretation using PRoNTo
Schrouff, Jessica ULg; Rosa, Maria; Rondina, Jane et al

Poster (2013, June)

Recently, machine learning models have been applied to neuroimaging data, allowing to make predictions about a variable of interest based on the pattern of activation or anatomy over a set of voxels. In ... [more ▼]

Recently, machine learning models have been applied to neuroimaging data, allowing to make predictions about a variable of interest based on the pattern of activation or anatomy over a set of voxels. In addition, they might lead to an increased sensitivity to detect the presence of a particular mental representation compared to univariate methods such as the General Linear Model (GLM). Application of these methods made it possible to decode the category of a seen object or the orientation of a striped pattern seen by the subject. They also allowed classification of patients and healthy controls and could therefore be used as a diagnostic tool due to their ability to predict the class of an unseen sample. The main disadvantage of multivariate machine learning models is that local inference with respect to the brain neuroanatomy is complex: although linear models generate weights for each voxel, the model predictions are based on the whole pattern and therefore one cannot threshold the weights to make regional statistical inferences as in univariate analysis. In the present work, we developed a methodology based on a labelled anatomical template (e.g. AAL or Brodmann) to display a smoothed version of the model weights and compute a ranking of the regions according their contribution to the predictive model. This work is distributed in PRoNTo (Pattern Recognition for Neuroimaging Toolbox), a user-friendly toolbox, making machine learning models available to every neuroscientist. [less ▲]

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See detailConnectome-based classification of BDNF Met allele carriers
Ziegler, Erik ULg; Foret, Ariane; Mascetti, Laura ULg et al

Poster (2013, June)

Secretion of brain-derived neurotrophic factor (BDNF) is essential for synaptic plasticity in the central nervous system during neurodevelopment [Huang]. A common human non-synonymous SNIP in the BDNF ... [more ▼]

Secretion of brain-derived neurotrophic factor (BDNF) is essential for synaptic plasticity in the central nervous system during neurodevelopment [Huang]. A common human non-synonymous SNIP in the BDNF gene (Val66Met, rs6265) decreases activity-dependent BDNF release in neurons transfected with the human A allele (Met-BDNF). We reasoned that the persistent differential activity-dependent BDNF release implied by this polymorphism should also be associated with differences in adult brain structure. The study population comprised 36 healthy subjects (aged 18-25): 15 (9 male) were identified as carrying the Met allele (“Met carrier” group) and 21 (9 male) were homozygotes for the Val allele (“Val/Val” group). The groups did not vary significantly in IQ, age nor scores for a battery of psychological tests. A high-resolution T1-weighted image (sMRI), 7 unweighted (b=0) and a set of diffusion-weighted (b=1000) images using 61 non-collinear directional gradients were acquired for each subject. The processing workflow relied on several pieces of software and was developed in Python and Nipype. The sMRIs were segmented using the automated labeling of Freesurfer [Desikan] and further parcellated using the Lausanne2008 atlas into 1015 regions of interest (ROIs) [Cammoun]. DWIs were corrected for image distortions (due to eddy currents) using linear coregistration functions from FSL [Smith]. Fractional anisotropy maps were generated, and a few single-fiber (high FA) voxels were used to estimate the spherical harmonic coefficients (order 8) of the response function from the DWIs [Tournier]. Then orientation distribution functions were obtained at each voxel. Probabilistic tractography was performed throughout the whole brain using seeds from subject-specific white-matter masks and a predefined number of tracts (300,000), see Fig. 1. The tracks were affine-transformed into the subject's structural space with Dipy [Garyfallidis]. Connectome mapping was performed by considering every contact point between each tract and the outlined ROIs (unlike in [Hagmann]): the connectivity matrix was incremented every time a single fiber traversed between any two ROIs. We trained a Gaussian Process Classifier [Rasmussen] (interfaced by PRoNTo [Schrouff]) on these connectivity matrices. The accuracy and generalization ability of the classification were assessed with a leave-one-subject-out cross-validation procedure. With this linear kernel method weights were also obtained indicating the contribution to the classification output (in favor of either genotypic group) of each edge in the network. The same method was employed to discriminate features related to the subjects' gender and genotype for the ADA gene. The classifier was able to discriminate between Val/Val and Met carriers with 86.1% balanced accuracy. The predictive value for the Val/Val and Met carrier groups were 94.4% (p=0.001) and 77.8% (p=0.003), respectively. In Fig. 2 the weights obtained by the classifier are visualized as edges in the brain network. For the classifier trained to identify gender or the subjects' ADA genotype, the global accuracy reached 63.9% (n.s.) and 58.3% (n.s.) respectively. Using high-resolution connectome mapping from normal young healthy human volunteers grouped based on the Met allele of the BNDF gene, we show that the BDNF genotype of an individual can be significantly identified from his structural brain wiring. These differences appear specific to this allele; no such difference could be found for the polymorphism in the ADA gene, or even for gender. We propose that the decreased availability of BDNF leads to deficits in axonal maintenance in Met carriers, and that this produces mesoscale changes in white matter architecture. Acknowledgements: the FNRS, the ULg, the Queen Elisabeth Medical Foundation, the Léon Fredericq Foundation, the Belgian Inter-University Attraction Program, the Welbio program, and the MCITN in Neurophysics (PITN-GA-2009-238593). Cammoun L. et al. (2011), ‘Mapping the human connectome at multiple scales with diffusion spectrum MRI’, J Neuroscience Methods, 203:386–397. Desikan R.S. et al. (2006), ‘An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest’, Neuroimage, 31:968-980. Hagmann P. et al. (2008), ‘Mapping the structural core of human cerebral cortex’, PLoS Biology, 6:e159 Huang E.J., Reichardt L.F. (2001), ‘Neurotrophins: roles in neuronal development and function’, Annual Review of Neuroscience, 24:677-736. Garyfallidis E. et al. (2011), ‘Dipy - a novel software library for diffusion MR and tractography’, 17th Annual Meeting of the Organization for Human Brain Mapping. http://nipy.sourceforge.net/dipy/ Rasmussen C.E. (2006), Gaussian processes for machine learning. Schrouff J. et al. (2012), ‘PRoNTo: Pattern Recognition for Neuroimaging Toolbox’, 18th Annual Meeting of the Organization for Human Brain Mapping. http://www.mlnl.cs.ucl.ac.uk/pronto Smith S.M. et al. (2004), ‘Advances in functional and structural MR image analysis and implementation as FSL’, Neuroimage, 23 Suppl 1:S208-S219. Tournier J.D., et al. (2007), ‘Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution’, Neuroimage, 35:1459-1472. [less ▲]

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See detailPattern Recognition for Neuroimaging
Phillips, Christophe ULg

Scientific conference (2013, April 24)

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See detailPRoNTo: Pattern Recognition for Neuroimaging Toolbox
Schrouff, Jessica ULg; Rosa, Maria Joao; Rondina, Jane et al

in Neuroinformatics (2013)

In the past years, mass univariate statistical analyses of neuroimaging data have been complemented by the use of multivariate pattern analyses, especially based on machine learning models. While these ... [more ▼]

In the past years, mass univariate statistical analyses of neuroimaging data have been complemented by the use of multivariate pattern analyses, especially based on machine learning models. While these allow an increased sensitivity for the detection of spatially distributed effects compared to univariate techniques, they lack an established and accessible software framework. The goal of this work was to build a toolbox comprising all the necessary functionalities formultivariate analyses of neuroimaging data, based on machine learning models. The “Pattern Recognition for Neuroimaging Toolbox” (PRoNTo) is open-source, cross-platform, MATLAB-based and SPM compatible, therefore being suitable for both cognitive and clinical neuroscience research. In addition, it is designed to facilitate novel contributions from developers, aiming to improve the interaction between the neuroimaging and machine learning communities. Here, we introduce PRoNTo by presenting examples of possible research questions that can be addressed with the machine learning framework implemented in PRoNTo, and cannot be easily investigated with mass univariate statistical analysis. [less ▲]

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See detailClassification of positron emission tomography images using multiple kernel learning
Segovia-Román, Fermín ULg; Bastin, Christine ULg; Salmon, Eric ULg et al

in Proceeding of 3rd NIPS 2013 Workshop on Machine Learning and Interpretation in NeuroImaging (2013)

Over the last years, several approaches to analyze nuclear medicine imaging using computer systems have been proposed with the aim of assisting the diagnosis of neurodegenerative disorders. Probably one ... [more ▼]

Over the last years, several approaches to analyze nuclear medicine imaging using computer systems have been proposed with the aim of assisting the diagnosis of neurodegenerative disorders. Probably one of the most complex challenges facing these approaches is to deal with the huge amount of data provided by brain images. In this work, we propose an original approach based on multiple kernel learning. First the images were parcellated (according to the structure of the brain) by means of the automatic anatomical labeling atlas. Then, the importance of each region for the assisted diagnosis was estimated using a classifi- cation procedure. Finally, all the regions were combined in a multiple kernel method in which one kernel per region was computed and all the kernels were weighted according to the importance of the region they represented. For testing purposes, a database with 46 PET images from stable mild cognitive impairment subjects and early Alzheimer’s disease converter patients was used. An accuracy rate of 73.91% was achieved when differentiating between both groups. [less ▲]

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See detailSNR dependence of mean kurtosis and how to correct it
André, Elodie ULg; Phillips, Christophe ULg; Farrher, Ezequiel et al

in Proceedings of the International Society for Magnetic Resonance in Medicine ... Scientific Meeting and Exhibition. International Society For Magnetic Resonance in Medicine. Scientific Meeting and Exhibition (2013), 21

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