References of "Phillips, Christophe"
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See detailInter-individual differences in night-time cerebral responses to high and low sleep pressure conditions
Maire, Micheline; Reichert, Carolin; Phillips, Christophe ULg et al

Poster (2014)

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See detailMemory reactivation during rapid eye movement sleep promotes its generalization and integration in cortical stores.
Sterpenich, Virginie; Schmidt, Christina ULg; Albouy, Genevieve et al

in Sleep (2014), 37(6), 1061-751075-1075

STUDY OBJECTIVES: Memory reactivation appears to be a fundamental process in memory consolidation. In this study we tested the influence of memory reactivation during rapid eye movement (REM) sleep on ... [more ▼]

STUDY OBJECTIVES: Memory reactivation appears to be a fundamental process in memory consolidation. In this study we tested the influence of memory reactivation during rapid eye movement (REM) sleep on memory performance and brain responses at retrieval in healthy human participants. PARTICIPANTS: Fifty-six healthy subjects (28 women and 28 men, age [mean +/- standard deviation]: 21.6 +/- 2.2 y) participated in this functional magnetic resonance imaging (fMRI) study. METHODS AND RESULTS: 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 magnetic resonance imaging 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. CONCLUSIONS: 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 detailAssessing dynamical correlations between functional and structural brain connectivity
Liegeois, Raphaël ULg; Ziegler, Erik; Phillips, Christophe ULg et al

Poster (2014)

The link between resting­‐state functional connectivity (FC), measured by the correlations of the fMRI BOLD time courses, and structural connectivity (SC) has been repeatedly investigated recently ... [more ▼]

The link between resting­‐state functional connectivity (FC), measured by the correlations of the fMRI BOLD time courses, and structural connectivity (SC) has been repeatedly investigated recently. Meanwhile, the importance of considering the dynamics of neuronal processes has also been highlighted. In this work we show how the classical static (i.e. considered as constant) relationship between SC and FC could be enriched when the FC dynamics are taken into account. We use a sliding window approach to explore these dynamics and show that the window width should be chosen in a particular range in order to unveil statistically significant (i.e. not due to noise) fluctuations of the FC-­‐SC correlation. [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 (2014), 107

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See detailPETRA: Multivariate analyses for neuroimaging data
Segovia-Román, Fermín ULg; Álvarez Illán, Ignacio; Salas-Gonzalez, Diego et al

in Proceeding of 2nd International Work-Conference on Bioinformatics and Biomedical Engineering (2014)

In last years, many research efforts in neurosciences have focused in multivariate approaches based on machine learning as an al- ternative to the use of Statistical Parametric Mapping and the univariate ... [more ▼]

In last years, many research efforts in neurosciences have focused in multivariate approaches based on machine learning as an al- ternative to the use of Statistical Parametric Mapping and the univariate analyses that it provides. However, this relatively new field still lacks of a software framework that completely meets the needs of the scientific community. In this work we present a toolbox designed to facilitate the access to the recent advances in neuroimaging data analysis based on multivariate approaches. The toolbox, written on Matlab, is freely avail- able and implements a Graphical User Interface that allows managing neuroimaging data in an easy way. [less ▲]

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See detailPET imaging analysis using a parcelation approach and multiple kernel classification
Segovia-Román, Fermín ULg; Phillips, Christophe ULg

in International Workshop on Pattern Recognition in Neuroimaging, Tübingen 4-6 June 2014 (2014)

Positron Emission Tomography (PET) is a non-invasive medical imaging modality that provides information about physiological processes. Due to its ability to measure the brain metabolism, it is widely used ... [more ▼]

Positron Emission Tomography (PET) is a non-invasive medical imaging modality that provides information about physiological processes. Due to its ability to measure the brain metabolism, it is widely used to assist the diagnosis of neurodegenerative disorders such as Alzheimer’s disease (AD) of Parkinsonism. In order to avoid the subjectivity inherent to the visual exploration of the images, several computer systems to analyze PET data were developed during the last years. However, dealing with the huge amount of information provided by PET imaging is still a challenge. In this work we present a novel methodology to analyze PET data that improves the automatic differentiation between controls and AD patients. First the images are divided into small regions or parcels, defined either anatomically, geometrically or randomly. Secondly, the accuray of each single region is estimated using a Support Vector Machine (SVM) classifier and a cross-validation approach. Finally, all the regions are assessed together using multiple kernel SVM with a kernel per region. The classifier is built so that the most discriminative regions have more weight in the final decision. This method was evaluated using a PET dataset that contained images from healthy controls and AD patients. The classification results obtained with the proposed approach outperformed two recently reported computer systems based on Principal Component Analysis and Independent Component Analysis. [less ▲]

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See detailSeasonal variation in human executive brain responses
Meyer, Christelle ULg; Jaspar, Mathieu ULg; Muto, Vincenzo ULg et al

Poster (2014)

It is well established that cognition shows daily fluctuations with changes in circadian phase and sleep pressure. The physiological impact of season changes, which is well characterized in animals ... [more ▼]

It is well established that cognition shows daily fluctuations with changes in circadian phase and sleep pressure. The physiological impact of season changes, which is well characterized in animals, remains largely unexplored in human. Here we investigated the impact of seasonal variation on human cognitive brain function. This cross-sectional study,conducted in Liège (Belgium),spanned from May 2010 to October 2011. Following 8h in-lab baseline night of sleep, 30 volunteers (age 20.9+1.5; 15F)spent 42h awake under constant routine conditions(<5lux, semi-recumbent position, no time-cues). After12h recovery night, they underwent15minfMRI recording while performing a working memory 3-back task (3b) and a letter detection 0-back task (0b). Thus, fMRI data were acquired when volunteers had been in isolation under controlled conditionsfor 63h. Executive brain responses were isolated by subtracting 0b activity from 3b responses (3b>0b).Analysis tested seasonal influence on executive brain responses at the random effects level, using a phasoranalysis across the year.Inferences were conducted at p<0.05, after correction for multiple comparisons over a priori small volume of interest. Significanteffects of season on executive responses were detected inmiddle frontal and frontopolarregions, insula, and thalamus, with a maximum response at the end of summer and a minimum response at the end of winter.These brain areas are key regions for executive control and alertness. These results constitute the first demonstration that seasonality directly impacts on human cognitive brain functions. [less ▲]

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See detailDecoding neural correlates of verbal working memory by attention-based visual working memory.
Majerus, Steve ULg; Cowan, N.; Péters, N. et al

Scientific 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 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 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 detailConnectome-based classification of BDNF Met allele carriers
Phillips, Christophe ULg; Foret, Ariane; Mascetti, Laura et al

Conference (2013, June 19)

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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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