References of "Phillips, Christophe"
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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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See detailImpact of blindness onset on the functional organization and the connectivity of the occipital cortex
Collignon, Olivier; Dormal, Giulia; Albouy, Geneviève et al

in Brain : A Journal of Neurology (2013)

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See detailExploration of the mechanisms underlying the ISPC effect: Evidence from behavioral and neuroimaging data
Grandjean, Julien; D'Ostilio, Kevin ULg; Fias, Wim et al

in Neuropsychologia (2013), 51

The item-specific proportion congruent (ISPC) effect in a Stroop task – the observation of reduced interference for color words mostly presented in an incongruent color – has attracted growing interest ... [more ▼]

The item-specific proportion congruent (ISPC) effect in a Stroop task – the observation of reduced interference for color words mostly presented in an incongruent color – has attracted growing interest since the original study by Jacoby (2003). Two mechanisms have been proposed to explain the effect: associative learning of contingencies and item-specific control through word reading modulation. Both interpretations have received empirical support from behavioral data. Therefore, the aim of this study was to investigate the responsible mechanisms of the ISPC effect with the classic two-item sets design using fMRI. Results showed that the ISPC effect is associated with increased activity in the anterior cingulate (ACC), dorsolateral prefrontal (DLPFC), and inferior and superior parietal cortex. Importantly, behavioral and fMRI analyses specifically addressing the respective contribution of associative learning and item-specific control mechanisms brought support for the contingency learning account of the ISPC effect. Results are discussed in reference to task and procedure characteristics that may influence the extent to which item-specific control and/or contingency learning contribute to the ISPC effect. [less ▲]

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See detailThe impact of visual perceptual learning on sleep and local slow wave initiation
Mascetti, Laura ULg; Muto, Vincenzo ULg; Matarazzo, Luca et al

in Journal of Neuroscience (2013)

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See detailDiscriminant BOLD Activation Patterns during Mental Imagery in Parkinson’s Disease
Schrouff, Jessica ULg; Cremers, Julien ULg; D'Ostilio, Kevin ULg et al

in Proceedings of MLINI 2012 (2012, December 07)

Using machine learning based models in clinical applications has become current practice and can prove useful to provide information at the subject’s level, such as predicting an (early) diagnosis or ... [more ▼]

Using machine learning based models in clinical applications has become current practice and can prove useful to provide information at the subject’s level, such as predicting an (early) diagnosis or monitoring the evolution of a disease. However, the performance of these models depends on the choice of a biomarker to detect the presence or absence of a disease. Choosing a biomarker is not straightforward, especially in the case of Parkinson’s disease when compared to healthy subjects. In the present work, we investigated the mental imagery of gait as a biomarker of Parkinson’s disease and showed that the signal in the mesencephalic locomotor region during the mental imagery of gait at a comfortable pace can discriminate significantly between idiopathic Parkinson’s disease patients and healthy subjects. Although there is room for improvement, the results of this preliminary study are promising. [less ▲]

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