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

Scientific conference (2014, October 04)

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

in NeuroImage (2014), 99

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 detailAutomatic artifact detection for whole-night polysomnographic sleep recordings
Coppieters't Wallant, Dorothée ULg; Chellappa, Sarah Laxhmi ULg; Gaggioni, Giulia ULg et al

Poster (2014, September 17)

Detecting of bad channels and artifacts for whole-night polysomnographic recordings is very time consuming and tedious. We therefore developed an automatic procedure to automatize this job.

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See detailHuman cortical excitability depends on time 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 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 detailAutomatic biorythms description from actigraphic data
González y Viagas, Miguel ULg; Ly, Julien ULg; Gaggioni, Giulia ULg et al

Poster (2014, September)

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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 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 (2014)

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 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 ULg et al

Conference (2014, June 16)

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See detailDecoding memory processing from electro-corticography in human posteromedial cortex
Schrouff, Jessica ULg; Foster, Brett L.; Rangarajan, Vinitha et al

in International Workshop on Pattern Recognition in Neuroimaging (2014, June)

Recently machine learning models have been applied to neuroimaging data, which allow predictions about a variable of interest based on the pattern of activation or anatomy over a set of voxels. These ... [more ▼]

Recently machine learning models have been applied to neuroimaging data, which allow 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 clear benefits over classical (univariate) techniques, by providing predictions for unseen data, as well as the weights of each feature in the model. Machine learning methods have been applied to a range of data, from MRI to EEG. However, these multivariate techniques have scarcely been applied to electrocorticography (ECoG) data to investigate cognitive neuroscience questions. In this work, we used previously published ECoG data from 8 subjects to show that machine learning techniques can complement univariate techniques and be more sensitive to certain effects. [less ▲]

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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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