References of "Phillips, Christophe"
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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 (2016), 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 detailCircadian regulation of human cortical excitability
LY, Julien ULg; Gaggioni, Giulia ULg; Chellappa, Sarah et al

in Nature Communications (2016)

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See detailSleep deprivation affects brain global cortical responsiveness
Gaggioni, Giulia ULg; Chellappa; Ly et al

Poster (2016)

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See detailSleep deprivation affects brain global cortical responsiveness
Gaggioni, Giulia ULg; Ly, Julien; Chellappa, Sarah et al

Poster (2015, November 26)

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See detailSleep deprivation affects global cortical responsiveness
Gaggioni, Giulia ULg; Ly, Julien; Chellappa, Sarah et al

Conference (2015, November 02)

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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 detailSleep deprivation affects brain cortical reactivity
Gaggioni, Giulia ULg; Ly, Julien; Chellappa, Sarah et al

Poster (2015, September 04)

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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 detailCerebral responses and role of the prefrontal cortex in conditioned pain modulation: an fMRI study in healthy subjects
Bogdanov, Volodymyr; Vigano, Alessandro; Noirhomme, Quentin ULg et al

in Behavioural Brain Research (2015)

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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)

Detailed reference viewed: 54 (16 ULg)
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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