References of "Phillips, Christophe"
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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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See detailDifferential effects of aging on the neural correlates of recollection and familiarity
Angel, Lucie; Bastin, Christine ULg; Genon, Sarah ULg et al

in Cortex : A Journal Devoted to the Study of the Nervous System & Behavior (2013), 49

The present experiment aimed to investigate age differences in the neural correlates of familiarity and recollection, while keeping performance similar across age groups by varying task difficulty. Twenty ... [more ▼]

The present experiment aimed to investigate age differences in the neural correlates of familiarity and recollection, while keeping performance similar across age groups by varying task difficulty. Twenty young and twenty older adults performed an episodic memory task in an event-related fMRI design. At encoding, participants were presented with pictures, either once or twice. Then, they performed a recognition task, with a Remember/Know paradigm. A similar performance was observed for the two groups in the Easy condition for recollection and in the Hard condition for familiarity. Imaging data revealed the classic recollection-related and familiarity-related networks, common to young and older groups. In addition, we observed that some activity related to recollection (left frontal, left temporal, left parietal cortices and left parahippocampus) and familiarity (bilateral anterior cingulate, right frontal gyrus and left superior temporal gyrus) was reduced in older compared to young adults. However, for recollection processes only, older adults additionally recruited the right precuneus, possibly to successfully compensate for their difficulties, as suggested by a positive correlation between recollection and precuneus activity. [less ▲]

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See detailStatistical tests for group comparison of manifold-valued data
Collard, Anne ULg; Phillips, Christophe ULg; Sepulchre, Rodolphe

in Proceedings of the 52nd IEEE Conference on Decision and Control (2013)

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See detailMulticlass classification of FDG PET scans for the distinction between Parkinson's disease and atypical parkinsonian syndromes
Garraux, Gaëtan ULg; Phillips, Christophe ULg; Schrouff, Jessica ULg et al

in NeuroImage: Clinical (2013), 2

Most available pattern recognition methods in neuroimaging address binary classification problems. Here, we used relevance vector machine (RVM) in combination with booststrap resampling (‘bagging’) for ... [more ▼]

Most available pattern recognition methods in neuroimaging address binary classification problems. Here, we used relevance vector machine (RVM) in combination with booststrap resampling (‘bagging’) for non-hierarchical multiclass classification. The method was tested on 120 cerebral 18fluorodeoxyglucose (FDG) positron emission tomography (PET) scans performed in patients who exhibited parkinsonian clinical features for 3.5 years on average but that were outside the prevailing perception for Parkinson's disease (PD). A radiological diagnosis of PD was suggested for 30 patients at the time of PET imaging. However, at follow-up several years after PET imaging, 42 of them finally received a clinical diagnosis of PD. The remaining 78 APS patients were diagnosed with multiple system atrophy (MSA, N = 31), progressive supranuclear palsy (PSP, N = 26) and corticobasal syndrome (CBS, N = 21), respectively. With respect to this standard of truth, classification sensitivity, specificity, positive and negative predictive values for PD were 93% 83% 75% and 96%, respectively using binary RVM (PD vs. APS) and 90%, 87%, 79% and 94%, respectively, using multiclass RVM (PD vs. MSA vs. PSP vs. CBS). Multiclass RVM achieved 45%, 55% and 62% classification accuracy for, MSA, PSP and CBS, respectively. Finally, a majority confidence ratio was computed for each scan on the basis of class pairs that were the most frequently assigned by RVM. Altogether, the results suggest that automatic multiclass RVM classification of FDG PET scans achieves adequate performance for the early differentiation between PD and APS on the basis of cerebral FDG uptake patterns when the clinical diagnosis is felt uncertain. This approach cannot be recommended yet as an aid for distinction between the three APS classes under consideration. [less ▲]

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See detailConcurrent Synaptic and Systems Memory Consolidation during Sleep
Mascetti, Laura; Foret, Ariane; Schrouff, Jessica ULg et al

in Journal of Neuroscience (2013), 33(24), 10182-10190

Memories are consolidated during sleep by two apparently antagonistic processes: (1) reinforcement of memory-specific cortical interactions and (2) homeostatic reduction in synaptic efficiency. Using fMRI ... [more ▼]

Memories are consolidated during sleep by two apparently antagonistic processes: (1) reinforcement of memory-specific cortical interactions and (2) homeostatic reduction in synaptic efficiency. Using fMRI, we assessed whether episodic memories are processed during sleep by either or both mechanisms, by comparing recollection before and after sleep. We probed whether LTP influences these processes by contrasting two groups of individuals prospectively recruited based on BDNF rs6265 (Val66Met) polymorphism. Between immediate retrieval and delayed testing scheduled after sleep, responses to recollection increased significantly more in Val/Val individuals than in Met carriers in parietal and occipital areas not previously engaged in retrieval, consistent with “systems-level consolidation.” Responses also increased differentially between allelic groups in regions already activated before sleep but only in proportion to slow oscillation power, in keeping with “synaptic downscaling.” Episodic memories seem processed at both synaptic and systemic levels during sleep by mechanisms involving LTP. [less ▲]

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See detailInteraction between hippocampal and striatal systems predicts subsequent consolidation of motor sequence memory.
Albouy, Geneviève; Sterpenich, Virginie; Vandewalle, Gilles ULg et al

in PLoS ONE (2013), 8(3), 59490

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See detailBlue Light Stimulates Cognitive Brain Activity in Visually Blind Individuals
Vandewalle, Gilles ULg; Collignon, Olivier; Hull, Joseph et al

in Journal of Cognitive Neuroscience (2013)

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See detailLocalizing and comparing weight maps generated from linear kernel machine learning models
Schrouff, Jessica ULg; CREMERS, Julien ULg; GARRAUX, Gaëtan ULg et al

in 2013 Third International Workshop on Pattern Recognition in NeuroImaging (PRNI 2013): proceedings (2013)

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 ... [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. These pattern recognition based methods present undeniable assets over classical (univariate) techniques, by providing predictions for unseen data, as well as the weights of each voxel in the model. However, the obtained weight map cannot be thresholded to perform regionally specific inference, leading to a difficult localization of the variable of interest. In this work, we provide local averages of the weights according to regions defined by anatomical or functional atlases (e.g. Brodmann atlas). These averages can then be ranked, thereby providing a sorted list of regions that can be (to a certain extent) compared with univariate results. Furthermore, we defined a “ranking distance”, allowing for the quantitative comparison between localized patterns. These concepts are illustrated with two datasets. [less ▲]

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See detailPattern Recognition for Neuroimaging Toolbox
Schrouff, Jessica ULg; Rosa, Maria; Rondina, Jane et al

in Suykens, J.A.K.; Argyriou, A.; De Brabanter, K. (Eds.) et al International workshop on advances in Regularization, Optimization, Kernel Methods and Support Vector Machines: theory and applications (ROKS 2013), Book of Abstracts (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 e ffects compared to univariate techniques, they lack an established and accessible software framework. Here we introduce the \Pattern Recognition for Neuroimaging Toolbox" (PRoNTo), an open-source, cross-platform and MATLAB-based software comprising many necessary functionalities for machine learning modelling of neuroimaging data. [less ▲]

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See detailClassification of MCI and AD patients combining PET data and psychological scores
Segovia-Román, Fermín ULg; Bastin, Christine ULg; Salmon, Eric ULg et al

in International Workshop on Advances in Regularization, Optimization, Kernel Methods and Support Vector Machines: theory and applications (2013)

This study’s aim was to measure the advantages of using psychological test data in the automatic classification of functional brain images in order to assist the diagnosis of neurodegenerative disorders ... [more ▼]

This study’s aim was to measure the advantages of using psychological test data in the automatic classification of functional brain images in order to assist the diagnosis of neurodegenerative disorders such as Alzheimer’s disease (AD). Several computer-aided diagnosis systems for AD based on PET images were developed. Some of them used psychological scores beside the image data in the classification step and others did not. The results show the ones that take into account the psychological scores achieve higher accuracy rates. [less ▲]

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See detailAutomatic differentiation between Alzheimer’s Disease and Mild Cognitive Impairment combining PET data and psychological scores
Segovia-Román, Fermín ULg; Bastin, Christine ULg; Salmon, Eric ULg et al

in 3rd International Workshop on Pattern Recognition in Neuroimaging (2013)

In recent years, several approaches to develop computer aided diagnosis systems for dementia have been pro- posed. The purpose of this work is to measure the advantages of using not only brain images as ... [more ▼]

In recent years, several approaches to develop computer aided diagnosis systems for dementia have been pro- posed. The purpose of this work is to measure the advantages of using not only brain images as data source for those systems but also some psychological scores. To this aim, we compared the accuracy rates achieved by systems that use psychological scores beside the image data in the classification step and systems that use only the image data. The experiments show that the formers achieve higher accuracy rates regardless of the procedure carried out to analyze the image data. [less ▲]

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See detailSleep stabilizes visuomotor adaptation memory: a functional magnetic resonance imaging study
Albouy, Geneviève ULg; Vandewalle, Gilles ULg; Sterpenich, Virginie et al

in Journal of Sleep Research (2013), 22(2), 144-54

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See detailItem familiarity and controlled associative retrieval in Alzheimer's disease: An fMRI study
Genon, Sarah ULg; Collette, Fabienne ULg; Feyers, Dorothée ULg et al

in Cortex : A Journal Devoted to the Study of the Nervous System & Behavior (2013), 49

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See detailBenevolent sexism alters executive brain responses
Dardenne, Benoît ULg; Dumont, Murielle; Sarlet, Marie et al

in Neuroreport (2013), 24(10), 572-577

Benevolence is widespread in our societies. It is defined as considering a subordinate group nicely but condescendingly, that is, with charity. Deleterious consequences for the target have been reported ... [more ▼]

Benevolence is widespread in our societies. It is defined as considering a subordinate group nicely but condescendingly, that is, with charity. Deleterious consequences for the target have been reported in the literature. In this experiment, we used functional MRI (fMRI) to identify whether being the target of (sexist) benevolence induces changes in brain activity associated with a working memory task. Participants were confronted by benevolent, hostile, or neutral comments before and while performing a reading span test in an fMRI environment. fMRI data showed that brain regions associated previously with intrusive thought suppression (bilateral, dorsolateral,prefrontal, and anterior cingulate cortex) reacted specifically to benevolent sexism compared with hostile sexism and neutral conditions during the performance of the task. These findings indicate that, despite being subjectively positive, benevolence modifies task-related brain networks by recruiting supplementary areas likely to impede optimal cognitive performance. [less ▲]

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