References of "Phillips, Christophe"
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See detailDecoding intracranial EEG data with multiple kernel learning method
Schrouff, Jessica ULg; Mourao-Miranda, Janaina; Phillips, Christophe ULg et al

in Journal of Neuroscience Methods (2016), 261

Machine learning models have been successfully applied to neuroimaging data to make predictions about behavioral and cognitive states of interest. While these multivariate methods have greatly advanced ... [more ▼]

Machine learning models have been successfully applied to neuroimaging data to make predictions about behavioral and cognitive states of interest. While these multivariate methods have greatly advanced the field of neuroimaging, their application to electrophysiological data has been less common especially in the analysis of human intracranial electroencephalography (iEEG, also known as electrocorticography or ECoG) data, which contains a rich spectrum of signals recorded from a relatively high number of recording sites. In the present work, we introduce a novel approach to determine the contribution of different bandwidths of EEG signal in different recording sites across different experimental conditions using the Multiple Kernel Learning (MKL) method. To validate and compare the usefulness of our approach, we applied this method to an ECoG dataset that was previously analysed and published with univariate methods. Our findings proved the usefulness of the MKL method in detecting changes in the power of various frequency bands during a given task and selecting automatically the most contributory signal in the most contributory site(s) of recording. With a single computation, the contribution of each frequency band in each recording site in the estimated multivariate model can be highlighted, which then allows formulation of hypotheses that can be tested a posteriori with univariate methods if needed. [less ▲]

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See detailCross-Modal Decoding of Neural Patterns Associated with Working Memory: Evidence for Attention-Based Accounts of Working Memory
Majerus, Steve ULg; Cowan, Nelson; Peters, Frédéric ULg et al

in Cerebral Cortex (2016), 26

Recent studies suggest common neural substrates involved in verbal and visual working memory (WM), interpreted as reflecting shared attention-based, short-term retention mechanisms. We used a machine ... [more ▼]

Recent studies suggest common neural substrates involved in verbal and visual working memory (WM), interpreted as reflecting shared attention-based, short-term retention mechanisms. We used a machine-learning approach to determine more directly the extent to which common neural patterns characterize retention in verbal WM and visual WM. Verbal WM was assessed via a standard delayed probe recognition task for letter sequences of variable length. Visual WM was assessed via a visual array WM task involving the maintenance of variable amounts of visual information in the focus of attention. We trained a classifier to distinguish neural activation patterns associated with high- and low-visual WM load and tested the ability of this classifier to predict verbal WM load (high–low) from their associated neural activation patterns, and vice versa. We observed significant between-task prediction of load effects during WM maintenance, in posterior parietal and superior frontal regions of the dorsal attention network; in contrast, between-task prediction in sensory processing cortices was restricted to the encoding stage. Furthermore, between-task prediction of load effects was strongest in those participants presenting the highest capacity for the visual WM task. This study provides novel evidence for common, attention-based neural patterns supporting verbal and visual WM. [less ▲]

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See detailAutomatic artifacts and arousals detection in whole-night sleep EEG recordings
Coppieters't Wallant, Dorothée ULg; Muto, Vincenzo ULg; Gaggioni, Giulia ULg et al

in Journal of Neuroscience Methods (2016), 258

In sleep electroencephalographic (EEG) signals, artifacts and arousals marking are usually part of the processing. This visual inspection by a human expert has two main drawbacks: it is very time ... [more ▼]

In sleep electroencephalographic (EEG) signals, artifacts and arousals marking are usually part of the processing. This visual inspection by a human expert has two main drawbacks: it is very time consuming and subjective. To detect artifacts and arousals in a reliable, systematic and reproducible automatic way, we developed an automatic detection based on time and frequency analysis with adapted thresholds derived from data themselves. The automatic detection performance is assessed using 5 statistic parameters, on 60 whole night sleep recordings coming from 35 healthy volunteers (male and female) aged between 19 and 26. The proposed approach proves its robustness against inter- and intra-, subjects and raters’ scorings, variability. The agreement with human raters is rated overall from substantial to excellent and provides a significantly more reliable method than between human raters. Existing methods detect only specific artifacts or only arousals, and/or these methods are validated on short episodes of sleep recordings, making it difficult to compare with our whole night results. The method works on a whole night recording and is fully automatic, reproducible, and reliable. Furthermore the implementation of the method will be made available online as open source code. [less ▲]

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See detailCombining feature extraction methods to assist the diagnosis of Alzheimer’s disease
Segovia, Fermin; Gorriz, J. M.; Ramirez, J. et al

in Current Alzheimer Research (2016)

Neuroimaging data as 18F-FDG PET is widely used to assist the diagnosis of Alzheimer’s disease (AD). Looking for regions with hypoperfusion/ hypometabolism, clinicians may predict or corroborate the ... [more ▼]

Neuroimaging data as 18F-FDG PET is widely used to assist the diagnosis of Alzheimer’s disease (AD). Looking for regions with hypoperfusion/ hypometabolism, clinicians may predict or corroborate the diagnosis of the patients. Modern computer aided diagnosis (CAD) systems based on the statistical analysis of whole neuroimages are more accurate than classical systems based on quantifying the uptake of some predefined regions of interests (ROIs). In addition, these new systems allow determining new ROIs and take advantage of the huge amount of information comprised in neuroimaging data. A major branch of modern CAD systems for AD is based on multivariate techniques, which analyse a neuroimage as a whole, considering not only the voxel intensities but also the relations among them. In order to deal with the vast dimensionality of the data, a number of feature extraction methods have been successfully applied. In this work, we propose a CAD system based on the combination of several feature extraction techniques. First, some commonly used feature extraction methods based on the analysis of the variance (as principal component analysis), on the factorization of the data (as non-negative matrix factorization) and on classical magnitudes (as Haralick features) were simultaneously applied to the original data. These feature sets were then combined by means of two different combination approaches: i) using a single classifier and a multiple kernel learning approach and ii) using an ensemble of classifier and selecting the final decision by majority voting. The proposed approach was evaluated using a labelled neuroimaging database along with a cross validation scheme. As conclusion, the proposed CAD system performed better than approaches using only one feature extraction technique. We also provide a fair comparison (using the same database). - See more at: http://www.eurekaselect.com/136992/article#sthash.PPiyE35K.dpuf [less ▲]

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See detailEyes Open on Sleep and Wake: In Vivo to In Silico Neural Networks
Vanvinckenroye, Amaury ULg; Vandewalle, Gilles ULg; Phillips, Christophe ULg et al

in Neural Plasticity (2015)

Functional and effective connectivity of cortical areas are essential for normal brain function under different behavioral states. Appropriate cortical activity during sleep and wakefulness is ensured by ... [more ▼]

Functional and effective connectivity of cortical areas are essential for normal brain function under different behavioral states. Appropriate cortical activity during sleep and wakefulness is ensured by the balanced activity of excitatory and inhibitory circuits. Ultimately, fast, millisecond cortical rhythmic oscillations shape cortical function in time and space. On a much longer time scale, brain function also depends on prior sleep-wake history and circadian processes. However,much remains to be established on how the brain operates at the neuronal level in humans during sleep and wakefulness. A key limitation of human neuroscience is the difficulty in isolating neuronal excitation/inhibition drive in vivo. Therefore, computational models are noninvasive approaches of choice to indirectly access hidden neuronal states. In this review, we present a physiologically driven in silico approach, Dynamic Causal Modelling (DCM), as a means to comprehend brain function under different experimental paradigms. Importantly, DCM has allowed for the understanding of how brain dynamics underscore brain plasticity, cognition, and different states of consciousness. In a broader perspective, noninvasive computational approaches, such as DCM, may help to puzzle out the spatial and temporal dynamics of human brain function at different behavioural states. [less ▲]

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See detailMonte Carlo simulations of the dose from imaging with GE eXplore 120 micro-CT using gate.
Bretin, Florian; Bahri, Mohamed Ali ULg; Luxen, André ULg et al

in Medical Physics (2015), 42(10), 5711-5719

Purpose: Small animals are increasingly used as translational models in preclinical imaging studies, during which the subjects can be exposed to large amounts of radiation. While the radiation levels are ... [more ▼]

Purpose: Small animals are increasingly used as translational models in preclinical imaging studies, during which the subjects can be exposed to large amounts of radiation. While the radiation levels are generally sublethal, studies have shown that low-level radiation can change physiological parameters in mice. In order to rule out any influence of radiation on the outcome of such experiments, or resulting deterministic effects in the subjects, the levels of radiation involved need to be addressed. The aim of this study was to investigate the radiation dose delivered by the GE eXplore 120 microCT non-invasively using Monte Carlo simulations in GATE and to compare results to previously obtained experimental values. Methods: Tungsten X-ray spectra were simulated at 70, 80, and 97 kVp using an analytical tool and their half-value layers were simulated for spectra validation against experimentally measured values of the physical X-ray tube. A Monte Carlo model of the microCT system was set up and four protocols that are regularly applied to live animal scanning were implemented. The computed tomography dose index (CTDI) inside a PMMA phantom was derived and multiple field of view acquisitions were simulated using the PMMA phantom, a representative mouse and rat. Results: Simulated half-value layers agreed with experimentally obtained results within a 7% error window. The CTDI ranged from 20 to 56 mGy and closely matched experimental values. Derived organ doses in mice reached 459 mGy in bones and up to 200 mGy in soft tissue organs using the highest energy protocol. Dose levels in rats were lower due to the increased mass of the animal compared to mice. The uncertainty of all dose simulations was below 14%. Conclusions: Monte Carlo simulations proved a valuable tool to investigate the 3D dose distribution in animals from microCT. Small animals, especially mice (due to their small volume), receive large amounts of radiation from the GE eXplore 120 microCT, which might alter physiological parameters in a longitudinal study setup. [less ▲]

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See detailSeasonality in human cognitive brain responses.
Meyer, Christelle ULg; Muto, Vincenzo ULg; Jaspar, Mathieu ULg et al

Poster (2015, September 04)

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See detailCircadian and homeostatic modulation of cerebral correlates of vigilance under high and low sleep pressure
Maire, Micheline; Reichert, Carolin; Phillips, Christophe ULg et al

Scientific conference (2015, September)

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See detailCerebral functional connectivity periodically (de)synchronizes with anatomical constraints
Liegeois, Raphaël ULg; Ziegler, Erik; Bahri, Mohamed Ali ULg et al

in Brain Structure and Function (2015)

This paper studies the link between resting-state functional connectivity (FC), measured by the correlations of the fMRI BOLD time courses, and structural connectivity (SC), estimated through fiber ... [more ▼]

This paper studies the link between resting-state functional connectivity (FC), measured by the correlations of the fMRI BOLD time courses, and structural connectivity (SC), estimated through fiber tractography. Instead of a static analysis based on the correlation between SC and the FC averaged over the entire fMRI time series, we propose a dynamic analysis, based on the time evolution of the correlation between SC and a suitably windowed FC. Assessing the statistical significance of the time series against random phase permutations, our data show a pronounced peak of significance for time window widths around 20-30 TR (40-60 sec). Using the appropriate window width, we show that FC patterns oscillate between phases of high modularity, primarily shaped by anatomy, and phases of low modularity, primarily shaped by inter-network connectivity. Building upon recent results in dynamic FC, this emphasizes the potential role of SC as a transitory architecture between different highly connected resting state FC patterns. Finally, we show that networks implied in consciousness-related processes, such as the default mode network (DMN), contribute more to these brain-level fluctuations compared to other networks, such as the motor or somatosensory networks. This suggests that the fluctuations between FC and SC are capturing mind-wandering effects. [less ▲]

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

Poster (2015, January 27)

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See detailComparison of brain functional connectivity methods for the diagnosis of Parkinson’s disease using resting state fMRI
Baquero Duarte, Katherine Andrea ULg; Rouillard, Maud; Depierreux, Frédérique ULg et al

Scientific conference (2015, January 27)

Background In the absence of validated biomarkers, the early diagnosis of Parkinson’s disease (PD), the second most common neurodegenerative disorder worldwide [1], is challenging and is prone to low ... [more ▼]

Background In the absence of validated biomarkers, the early diagnosis of Parkinson’s disease (PD), the second most common neurodegenerative disorder worldwide [1], is challenging and is prone to low accuracy [2]. Recent evidence suggests that the average pattern of functional connectivity (FC) between the basal ganglia and cerebral cortex assessed in the resting state using functional magnetic resonance imaging (rs-fMRI) might discriminate between mild PD and healthy controls with 85% overall accuracy [3]. Goal We will test if this finding can be replicated in our population. We will also compare the diagnosis accuracy of this approach, which depicts an average pattern of connectivity during the whole scanning period, with that of dynamic FC that investigates the spontaneous fluctuations of the pattern of connectivity over the scanning period [4]. Methods We are currently processing and analyzing rs-fMRI data prospectively acquired on a 3T MRI in 39 patients with PD (mean disease duration 5.4 years; mean Hoehn and Yahr stage 1.5) and 39 healthy controls matched for age, gender and levels of education. For dynamic FC we will compare two different methods [4], one that use slice-time windows to capture brain dynamics with another that captures spatial co-activation patters (CAPs) at specific time points. Conclusion The selected methods will be further validated in a new cohort of de novo drug-naïve PD patients. [1] Tessitore, A., et al. Sensorimotor connectivity in Parkinson’s disease: the role of functional neuroimaging. Frontiers in neurology 5 (2014). [2] Adler et al. Low clinical diagnostic accuracy of early vs advanced Parkinson disease. Clinicopathologic study. Neurology 2014;83:406–412 [3] Szewczyk-Krolikowski, K., et al. Functional connectivity in the basal ganglia network differentiates PD patients from controls. Neurology 83.3 (2014): 208-214. [4] Hutchison, M., et al. Dynamic functional connectivity: promise, issues, and interpretations. Neuroimage 80 (2013): 360-378. [less ▲]

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See detailCombining feature extraction methods to assist the diagnosis of Alzheimer's disease
Segovia, Fermin; Górriz, J. M.; Ramírez, J. et al

in Current Alzheimer Research (2015), 13

Neuroimaging data as 18F-FDG PET is widely used to assist the diagnosis of Alzheimer’s disease (AD). Looking for regions with hypoperfusion/ hypometabolism, clinicians may predict or corroborate the ... [more ▼]

Neuroimaging data as 18F-FDG PET is widely used to assist the diagnosis of Alzheimer’s disease (AD). Looking for regions with hypoperfusion/ hypometabolism, clinicians may predict or corroborate the diagnosis of the patients. Modern computer aided diagnosis (CAD) systems based on the statistical analysis of whole neuroimages are more accurate than classical systems based on quantifying the uptake of some predefined regions of interests (ROIs). In addition, these new systems allow determining new ROIs and take advantage of the huge amount of information comprised in neuroimaging data. A major branch of modern CAD systems for AD is based on multivariate techniques, which analyse a neuroimage as a whole, considering not only the voxel intensities but also the relations among them. In order to deal with the vast dimensionality of the data, a number of feature extraction methods have been successfully applied. In this work, we propose a CAD system based on the combination of several feature extraction techniques. First, some commonly used feature extraction methods based on the analysis of the variance (as principal component analysis), on the factorization of the data (as non-negative matrix factorization) and on classical magnitudes (as Haralick features) were simultaneously applied to the original data. These feature sets were then combined by means of two different combination approaches: i) using a single classifier and a multiple kernel learning approach and ii) using an ensemble of classifier and selecting the final decision by majority voting. The proposed approach was evaluated using a labelled neuroimaging database along with a cross validation scheme. As conclusion, the proposed CAD system performed better than approaches using only one feature extraction technique. We also provide a fair comparison (using the same database). [less ▲]

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See detailFighting Sleep at Night: Brain Correlates and Vulnerability to Sleep Loss.
Maire, Micheline; Reichert, Carolin Franziska; Gabel, Virginie et al

in Annals of neurology (2015), 78(2), 235-47

OBJECTIVE: Even though wakefulness at night leads to profound performance deterioration and is regularly experienced by shift workers, its cerebral correlates remain virtually unexplored. METHODS: We ... [more ▼]

OBJECTIVE: Even though wakefulness at night leads to profound performance deterioration and is regularly experienced by shift workers, its cerebral correlates remain virtually unexplored. METHODS: We assessed brain activity in young healthy adults during a vigilant attention task under high and low sleep pressure during night-time, coinciding with strongest circadian sleep drive. We examined sleep-loss-related attentional vulnerability by considering a PERIOD3 polymorphism presumably impacting on sleep homeostasis. RESULTS: Our results link higher sleep-loss-related attentional vulnerability to cortical and subcortical deactivation patterns during slow reaction times (i.e., suboptimal vigilant attention). Concomitantly, thalamic regions were progressively less recruited with time-on-task and functionally less connected to task-related and arousal-promoting brain regions in those volunteers showing higher attentional instability in their behavior. The data further suggest that the latter is linked to shifts into a task-inactive default-mode network in between task-relevant stimulus occurrence. INTERPRETATION: We provide a multifaceted view on cerebral correlates of sleep loss at night and propose that genetic predisposition entails differential cerebral coping mechanisms, potentially compromising adequate performance during night work. [less ▲]

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See detailTotal connectivity: a marker of dynamical functional connectivity applied to consciousness
Liegeois, Raphaël ULg; Phillips, Christophe ULg; Bahri, Mohamed Ali ULg et al

Poster (2015)

In the last years functional connectivity (FC) has become one of the most popular tools to explore and characterize information contained in fMRI =me series. The classical hypothesis on FC consists of ... [more ▼]

In the last years functional connectivity (FC) has become one of the most popular tools to explore and characterize information contained in fMRI =me series. The classical hypothesis on FC consists of considering it as constant (or static) over the whole fMRI time series. However, it has been emphasized recently that FC should be treated as a dynamical quantity, for example by using sliding windows of the fMRI time courses in order to compute a dynamical FC. We propose a comprehensive marker of FC based on an auto-regressive (AR) model of fMRI time series capturing its static and dynamic properties. We call it total connectivity and we illustrate the benefits of our approach on data of patients undergoing four different states of consciousness. [less ▲]

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See detailA finite-element reciprocity solution for EEG forward modeling with realistic individual head models
Ziegler, Erik ULg; Chellappa, Sarah Laxhmi ULg; Gaggioni, Giulia ULg et al

in NeuroImage (2014), 103

Highlights • Creates EEG forward models suitable for high-resolution source localization. • Automatic T1-based whole-head finite element meshing and leadfield computation. • Pipelines can incorporate ... [more ▼]

Highlights • Creates EEG forward models suitable for high-resolution source localization. • Automatic T1-based whole-head finite element meshing and leadfield computation. • Pipelines can incorporate conductivity tensors from diffusion-weighted images. • Open-source toolbox shared under a permissive software license. • Accuracy comparable to SimBio FEM and superior to OpenMEEG BEM solutions. [less ▲]

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