References of "Phillips, Christophe"
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See detailMapping track density changes in nigrostriatal and extranigral pathways in Parkinson's disease
Ziegler, Erik ULg; Rouillard, Maud; André, Elodie et al

in NeuroImage (in press)

Highlights First whole-brain probabilistic tractography study in Parkinson's disease High quality diffusion-weighted images (120 gradient directions, b = 2500 s/mm2) Voxel-based group analysis comparing ... [more ▼]

Highlights First whole-brain probabilistic tractography study in Parkinson's disease High quality diffusion-weighted images (120 gradient directions, b = 2500 s/mm2) Voxel-based group analysis comparing early-stage patients and controls Abnormal reconstructed track density in the nigrostriatal pathway and brainstem Track density also increased in limbic and cognitive circuits. [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 (in press)

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 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 (in press)

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 detailInfluence of noise correction on intra- and inter-subject variability of quantitative metrics in diffusion kurtosis imaging
André, Elodie ULg; Grinberg, Farida; Farrher, Ezequiel et al

in PLoS ONE (in press)

Diffusion kurtosis imaging (DKI) is a promising extension of diffusion tensor imaging, giving new insights into the white matter microstructure and providing new biomarkers. Given the rapidly increasing ... [more ▼]

Diffusion kurtosis imaging (DKI) is a promising extension of diffusion tensor imaging, giving new insights into the white matter microstructure and providing new biomarkers. Given the rapidly increasing number of studies, DKI has a potential to establish itself as a valuable tool in brain diagnostics. However, to become a routine procedure, DKI still needs to be improved in terms of robustness, reliability, and reproducibility. As it requires acquisitions at higher diffusion31 weightings, results are more affected by noise than in diffusion tensor imaging. The lack of standard procedures for post-processing, especially for noise correction, might become a significant obstacle for the use of DKI in clinical routine limiting its application. We considered two noise correction schemes accounting for the noise properties of multichannel phased-array coils, in order to improve the data quality at signal-to-noise ratio (SNR) typical for DKI. The SNR dependence of estimated DKI metrics such as mean kurtosis (MK), mean diffusivity (MD) and fractional anisotropy (FA) is investigated for these noise correction approaches in Monte Carlo simulations and in in vivo human studies. The intra-subject reproducibility is investigated in a single subject study by varying the SNR level and SNR spatial distribution. Then the impact of the noise correction on inter-subject variability is evaluated in a homogeneous sample of 25 healthy volunteers. Results show a strong impact of noise correction on the MK estimate, while the estimation of FA and MD was affected to a lesser extent. Both intra- and inter-subject SNR related variability of the MK estimate is considerably reduced after correction for the noise bias, providing more accurate and reproducible measures. In this work, we have proposed a straightforward method that improves accuracy of DKI metrics. This should contribute to standardization of DKI applications in clinical studies and making valuable inferences in group analysis and longitudinal studies. [less ▲]

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See detailHuman cortical excitability depends on time spent awake and circadian phase
Ly, Julien ULg; Chellappa, Sarah Laxhmi ULg; Gaggioni, Giulia ULg et al

Conference (2014, September 17)

At any point in time, human performance results from the interaction of two main factors: a circadian signal varying with the time of the day and the sleep need accrued throughout the preceding waking ... [more ▼]

At any point in time, human performance results from the interaction of two main factors: a circadian signal varying with the time of the day and the sleep need accrued throughout the preceding waking period. But what’s happen at the cortical cerebral level? We used a novel technique coupling transcranial magnetic stimulation with electroencephalography (TMS/EEG) to assess the influence of time spent awake and circadian phasis on human cortical excitability. Twenty-two healthy young men underwent 8 TMS/EEG sessions during a 28 hour sleep deprivation protocole. We found that cortical excitability depends on both time spent awake and circadian phasis. [less ▲]

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

Poster (2014, September)

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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, June 17)

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See detailThe circadian system sets the temporal organization of basic human neuronal function
Chellappa, Sarah Laxhmi ULg; Ly, Julien; Gaggioni, Giulia et al

Conference (2014, June 16)

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See detailIdentifying endophenotypes of autism: a multivariate approach
Segovia-Román, Fermín ULg; Holt, Rosemary; Spencer, Michael et al

in Frontiers in Computational Neuroscience (2014), 8

The existence of an endophenotype of autism spectrum condition (ASC) has been recently suggested by several commentators. It can be estimated by finding differences between controls and people with ASC ... [more ▼]

The existence of an endophenotype of autism spectrum condition (ASC) has been recently suggested by several commentators. It can be estimated by finding differences between controls and people with ASC that are also present when comparing controls and the unaffected siblings of ASC individuals. In this work, we used a multivariate methodology applied on magnetic resonance images to look for such differences. The proposed procedure consists of combining a searchlight approach and a support vector machine classifier to identify the differences between three groups of participants in pairwise comparisons: controls, people with ASC and their unaffected siblings. Then we compared those differences selecting spatially collocated as candidate endophenotypes of ASC. [less ▲]

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See detailCan we interpret linear kernel machine learning models using anatomically labelled regions?
Schrouff, Jessica ULg; Monteiro, Joao; Joao Rosa, Maria et al

Poster (2014, June)

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See detailBiased binomial assessment of cross-validated estimation of classification accuracies illustrated in diagnosis predictions
Noirhomme, Quentin ULg; Lesenfants, Damien ULg; Gomez, Francisco et al

in NeuroImage: Clinical (2014), 4

Multivariate classification is used in neuroimaging studies to infer brain activation or in medical applications to infer diagnosis. Their results are often assessed through either a binomial or a ... [more ▼]

Multivariate classification is used in neuroimaging studies to infer brain activation or in medical applications to infer diagnosis. Their results are often assessed through either a binomial or a permutation test. Here, we simulated classification results of generated random data to assess the influence of the cross-validation scheme on the significance of results. Distributions built from classification of random data with crossvalidation did not follow the binomial distribution. The binomial test is therefore not adapted. On the contrary, the permutation test was unaffected by the cross-validation scheme. The influence of the crossvalidation was further illustrated on real-data from a brain–computer interface experiment in patients with disorders of consciousness and from an fMRI study on patients with Parkinson disease. Three out of 16 patients with disorders of consciousness had significant accuracy on binomial testing, but only one showed significant accuracy using permutation testing. In the fMRI experiment, the mental imagery of gait could discriminate significantly between idiopathic Parkinson’s disease patients and healthy subjects according to the permutation test but not according to the binomial test. Hence, binomial testing could lead to biased estimation of significance and false positive or negative results. In our view, permutation testing is thus recommended for clinical application of classification with cross-validation. [less ▲]

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See detailCombining PET images and neuropsychological test data for automatic diagnosis of Alzheimer’s disease
Segovia-Román, Fermín ULg; Bastin, Christine ULg; Salmon, Eric ULg et al

in PLoS ONE (2014), 9(2),

In recent years, several approaches to develop computer aided diagnosis (CAD) systems for dementia have been proposed. Some of these systems analyze neurological brain images by means of machine learning ... [more ▼]

In recent years, several approaches to develop computer aided diagnosis (CAD) systems for dementia have been proposed. Some of these systems analyze neurological brain images by means of machine learning algorithms in order to find the patterns that characterize the disorder, and a few combine several imaging modalities to improve the diagnostic accuracy. However, they usually do not use neuropsychological testing data in that analysis. The purpose of this work is to measure the advantages of using not only neuroimages as data source in CAD systems for dementia but also neuropsychological scores. To this aim, we compared the accuracy rates achieved by systems that use neuropsychological scores beside the imaging data in the classification step and systems that use only one of these data sources. In order to address the small sample size problem and facilitate the data combination, a dimensionality reduction step (implemented using three different algorithms) was also applied on the imaging data. After each image is summarized in a reduced set of image features, the data sources were combined and classified using three different data combination approaches and a Support Vector Machine classifier. That way, by testing different dimensionality reduction methods and several data combination approaches, we aim not only highlighting the advantages of using neuropsychological scores in the classification, but also implementing the most accurate computer system for early dementia detention. The accuracy of the CAD systems were estimated using a database with records from 46 subjects, diagnosed with MCI or AD. A peak accuracy rate of 89% was obtained. In all cases the accuracy achieved using both, neuropsychological scores and imaging data, was substantially higher than the one obtained using only the imaging data. [less ▲]

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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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