References of "Phillips, Christophe"
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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 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 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 ▲]

Detailed reference viewed: 37 (8 ULg)