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See detailInfluence of COMT Genotype on Antero-Posterior Cortical Functional Connectivity Underlying Interference Resolution
Jaspar, Mathieu ULg; Manard, Marine ULg; DIDEBERG, Vinciane ULg et al

in Cerebral Cortex (in press)

Genetic variability related to the catechol-O-methyltransferase (COMT) gene (Val158Met) has received increasing attention as a possible modulator of executive functioning and its neural correlates ... [more ▼]

Genetic variability related to the catechol-O-methyltransferase (COMT) gene (Val158Met) has received increasing attention as a possible modulator of executive functioning and its neural correlates. However, this attention has generally centred on the prefrontal cortices because of the well-known direct impact of COMT enzyme on these cerebral regions. In this study, we were interested in the modulating effect of COMT genotype on anterior and posterior brain areas underlying interference resolution during a Stroop task. More specifically, we were interested in the functional connectivity between the right inferior frontal operculum (IFop), an area frequently associated with inhibitory efficiency, and posterior brain regions involved in reading/naming processes (the two main non-executive determinants of the Stroop effect). The Stroop task was administered during fMRI scanning to three groups of 15 young adults divided according to their COMT Val158Met genotype [Val/Val (VV), Val/Met (VM) and Met/Met (MM)]. Results indicate greater activity in the right IFop and the left middle temporal gyrus (MTG) in homozygous VV individuals than in Met allele carriers. In addition, the VV group exhibited stronger positive functional connectivity between these two brain regions and stronger negative connectivity between the right IFop and left lingual gyrus. These results confirm the impact of COMT genotype on frontal function. They also strongly suggest that differences in frontal activity influence posterior brain regions related to a non-executive component of the task. Especially, changes in functional connectivity between anterior and posterior brain areas might correspond to compensatory processes for performing the task efficiently when the available dopamine level is low. [less ▲]

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See detailEvolution of Treatment and Investigative Approaches in Sleep Medicine
Ly, Julien; Chellappa, Sarah Laxhmi ULg; MAQUET, Pierre ULg

in Billiard, Michael (Ed.) Sleep Medicine: a comprehensive guide (in press)

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See detailBrain metabolic dysfunction in Capgras delusion during Alzheimer’s disease: a positron emission tomography study
Jedidi, Haroun ULg; Daury, Noémy; Rémi, Capa et al

in American Journal of Alzheimer's Disease & Other Dementias (in press)

Capgras delusion is characterized by the misidentification of people and by the delusional belief that the misidentified persons have been replaced by impostors, generally perceived as persecutors. Since ... [more ▼]

Capgras delusion is characterized by the misidentification of people and by the delusional belief that the misidentified persons have been replaced by impostors, generally perceived as persecutors. Since little is known regarding the neural correlates of Capgras syndrome, the cerebral metabolic pattern of a patient with probable Alzheimer’s disease (AD) and Capgras syndrome was compared with those of 24 healthy elderly subjects and 26 AD patients without delusional syndrome. Compared to the healthy and AD groups, the patient had significant hypometabolism in frontal and posterior midline structures. In light of current neural models of face perception, our patient’s Capgras syndrome may be related to impaired recognition of a familiar face, subserved by the posterior cingulate/precuneus cortex, and impaired reflection about personally relevant knowledge related to a face, subserved by the dorsomedial prefrontal cortex. [less ▲]

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See detailSleep and Movement Disorders: Neuroimaging Aspects
Dang Vu, Thien Thanh ULg; Desseilles, Martin ULg; Ratti, Pietro-Luca et al

in Chokroverty, Sudhansu; Montagna, Pasquale; Allen, Richard (Eds.) et al Sleep and Movement Disorders (in press)

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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 detailNeuroimaging, cognition, light and circadian rhythms
Gaggioni, Giulia; Maquet, Pierre ULg; Schmidt, Christina et al

in Frontiers in Systems Neuroscience [=FNSYS] (2014)

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See detailCortical excitability depends on time awake and circadian phase
Ly, Julien; Gaggioni, Giulia; Chellappa, Sarah Laxhmi 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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See detailModulating effect of COMT genotype on the brain regions underlying proactive control process during inhibition
Jaspar, Mathieu ULg; Genon, Sarah ULg; Muto, Vincenzo ULg et al

in Cortex : A Journal Devoted to the Study of the Nervous System & Behavior (2014), 50

Introduction. Genetic variability related to the catechol-O-methyltransferase (COMT) gene (Val158Met polymorphism) has received increasing attention as a possible modulator of cognitive control functions ... [more ▼]

Introduction. Genetic variability related to the catechol-O-methyltransferase (COMT) gene (Val158Met polymorphism) has received increasing attention as a possible modulator of cognitive control functions. Methods. In an event-related fMRI study, a modified version of the Stroop task was administered to three groups of 15 young adults according to their COMT Val158Met genotype [Val/Val (VV), Val/Met (VM) and Met/Met (MM)]. Based on the theory of dual mechanisms of control (Braver, et al., 2007), the Stroop task has been built to induce proactive or reactive control processes according to the task context. Results. Behavioral results did not show any significant group differences for reaction times but Val allele carriers individuals are less accurate in the processing of incongruent items. fMRI results revealed that proactive control is specifically associated with increased activity in the anterior cingulate cortex (ACC) in carriers of the Met allele, while increased activity is observed in the middle frontal gyrus (MFG) in carriers of the Val allele. Conclusion. These observations, in keeping with a higher cortical dopamine level in MM individuals, support the hypothesis of a COMT Val158Met genotype modulation of the brain regions underlying proactive control, especially in frontal areas as suggested by Braver et al. [less ▲]

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See detailRelative Contribution of Walking Speed, Ataxia and Gait asymmetry to the Composition of Gait in Multiple Sclerosis
PHAN BA, Remy ULg; Pierard, Sébastien ULg; LOMMERS, Emilie ULg et al

Poster (2013, October)

Introduction - Objective: Walking speed measured according to the T25FW is the most widely used descriptor of gait in MS clinical research and practice but other dimensions influencing gait variance exist ... [more ▼]

Introduction - Objective: Walking speed measured according to the T25FW is the most widely used descriptor of gait in MS clinical research and practice but other dimensions influencing gait variance exist according to alternative gait analysis methods. The relative importance of these different dimensions of gait relatively to its variance is unknown. Methods: We measured the performances of persons with MS and healthy subjects on the T25FW and the Timed 20-Meter Walk (T20MW) performed in tandem with a new gait analysis system (GAIMS). We performed a factorial analysis of variance to underline the main dimensions influencing gait variance and observed their composition. Findings - Conclusion: The main factor influencing gait variance in conventional walk tests is mostly composed of features related to walking speed. Balance, gait asymmetry and variability also participate to this variance but to a lesser extent. The inverse is observed in tests performed in tandem gait. [less ▲]

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See detailDiscussion de cas de neurophysiologie clinique
WANG, François-Charles ULg; MAQUET, Pierre ULg

Scientific conference (2013, September 19)

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See detailAltered white matter architecture in BDNF Met carriers
Ziegler, Erik ULg; Foret, Ariane; Mascetti, Laura ULg et al

in PLoS ONE (2013)

Brain-derived neurotrophic factor (BDNF) modulates the pruning of synaptically-silent axonal arbors. The Met allele of the BDNF gene is associated with a reduction in the neurotrophin's activity-dependent ... [more ▼]

Brain-derived neurotrophic factor (BDNF) modulates the pruning of synaptically-silent axonal arbors. The Met allele of the BDNF gene is associated with a reduction in the neurotrophin's activity-dependent release. We used di ffusion-weighted imaging to construct structural brain networks for 36 healthy subjects with known BDNF genotypes. Through permutation testing we discovered clear di fferences in connection strength between subjects carrying the Met allele and those homozygotic for the Val allele. We trained a Gaussian process classi fier capable of identifying the subjects' allelic group with 86% accuracy and high predictive value. In Met carriers structural connectivity was greatly increased throughout the forebrain, particularly in connections corresponding to the anterior and superior corona radiata as well as corticothalamic and corticospinal projections from the sensorimotor, premotor and prefrontal portions of the internal capsule. Interhemispheric connectivity was also increased via the corpus callosum and anterior commissure, and extremely high connectivity values were found between inferior medial frontal polar regions via the anterior forceps. We propose that the decreased availability of BDNF leads to de cifits in axonal maintenance in carriers of the Met allele, and that this produces mesoscale changes in white matter architecture. [less ▲]

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See detailConnectome-based classification of BDNF Met allele carriers
Ziegler, Erik ULg; Foret, Ariane; Mascetti, Laura ULg et al

Poster (2013, June)

Secretion of brain-derived neurotrophic factor (BDNF) is essential for synaptic plasticity in the central nervous system during neurodevelopment [Huang]. A common human non-synonymous SNIP in the BDNF ... [more ▼]

Secretion of brain-derived neurotrophic factor (BDNF) is essential for synaptic plasticity in the central nervous system during neurodevelopment [Huang]. A common human non-synonymous SNIP in the BDNF gene (Val66Met, rs6265) decreases activity-dependent BDNF release in neurons transfected with the human A allele (Met-BDNF). We reasoned that the persistent differential activity-dependent BDNF release implied by this polymorphism should also be associated with differences in adult brain structure. The study population comprised 36 healthy subjects (aged 18-25): 15 (9 male) were identified as carrying the Met allele (“Met carrier” group) and 21 (9 male) were homozygotes for the Val allele (“Val/Val” group). The groups did not vary significantly in IQ, age nor scores for a battery of psychological tests. A high-resolution T1-weighted image (sMRI), 7 unweighted (b=0) and a set of diffusion-weighted (b=1000) images using 61 non-collinear directional gradients were acquired for each subject. The processing workflow relied on several pieces of software and was developed in Python and Nipype. The sMRIs were segmented using the automated labeling of Freesurfer [Desikan] and further parcellated using the Lausanne2008 atlas into 1015 regions of interest (ROIs) [Cammoun]. DWIs were corrected for image distortions (due to eddy currents) using linear coregistration functions from FSL [Smith]. Fractional anisotropy maps were generated, and a few single-fiber (high FA) voxels were used to estimate the spherical harmonic coefficients (order 8) of the response function from the DWIs [Tournier]. Then orientation distribution functions were obtained at each voxel. Probabilistic tractography was performed throughout the whole brain using seeds from subject-specific white-matter masks and a predefined number of tracts (300,000), see Fig. 1. The tracks were affine-transformed into the subject's structural space with Dipy [Garyfallidis]. Connectome mapping was performed by considering every contact point between each tract and the outlined ROIs (unlike in [Hagmann]): the connectivity matrix was incremented every time a single fiber traversed between any two ROIs. We trained a Gaussian Process Classifier [Rasmussen] (interfaced by PRoNTo [Schrouff]) on these connectivity matrices. The accuracy and generalization ability of the classification were assessed with a leave-one-subject-out cross-validation procedure. With this linear kernel method weights were also obtained indicating the contribution to the classification output (in favor of either genotypic group) of each edge in the network. The same method was employed to discriminate features related to the subjects' gender and genotype for the ADA gene. The classifier was able to discriminate between Val/Val and Met carriers with 86.1% balanced accuracy. The predictive value for the Val/Val and Met carrier groups were 94.4% (p=0.001) and 77.8% (p=0.003), respectively. In Fig. 2 the weights obtained by the classifier are visualized as edges in the brain network. For the classifier trained to identify gender or the subjects' ADA genotype, the global accuracy reached 63.9% (n.s.) and 58.3% (n.s.) respectively. Using high-resolution connectome mapping from normal young healthy human volunteers grouped based on the Met allele of the BNDF gene, we show that the BDNF genotype of an individual can be significantly identified from his structural brain wiring. These differences appear specific to this allele; no such difference could be found for the polymorphism in the ADA gene, or even for gender. We propose that the decreased availability of BDNF leads to deficits in axonal maintenance in Met carriers, and that this produces mesoscale changes in white matter architecture. Acknowledgements: the FNRS, the ULg, the Queen Elisabeth Medical Foundation, the Léon Fredericq Foundation, the Belgian Inter-University Attraction Program, the Welbio program, and the MCITN in Neurophysics (PITN-GA-2009-238593). Cammoun L. et al. (2011), ‘Mapping the human connectome at multiple scales with diffusion spectrum MRI’, J Neuroscience Methods, 203:386–397. Desikan R.S. et al. (2006), ‘An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest’, Neuroimage, 31:968-980. Hagmann P. et al. (2008), ‘Mapping the structural core of human cerebral cortex’, PLoS Biology, 6:e159 Huang E.J., Reichardt L.F. (2001), ‘Neurotrophins: roles in neuronal development and function’, Annual Review of Neuroscience, 24:677-736. Garyfallidis E. et al. (2011), ‘Dipy - a novel software library for diffusion MR and tractography’, 17th Annual Meeting of the Organization for Human Brain Mapping. http://nipy.sourceforge.net/dipy/ Rasmussen C.E. (2006), Gaussian processes for machine learning. Schrouff J. et al. (2012), ‘PRoNTo: Pattern Recognition for Neuroimaging Toolbox’, 18th Annual Meeting of the Organization for Human Brain Mapping. http://www.mlnl.cs.ucl.ac.uk/pronto Smith S.M. et al. (2004), ‘Advances in functional and structural MR image analysis and implementation as FSL’, Neuroimage, 23 Suppl 1:S208-S219. Tournier J.D., et al. (2007), ‘Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution’, Neuroimage, 35:1459-1472. [less ▲]

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See detailBrain metabolic dysfunction in Capgras syndrome during Alzheimer’s disease: a positron emission tomography study
Jedidi, Haroun ULg; Daury, Noémy; Cappa, Rémi et al

Poster (2013, June)

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See detailFunctional neuroimaging of human REM sleep
Meyer, Christelle ULg; Jedidi, Zayd ULg; Muto, Vincenzo ULg et al

in Nofzinger, Eric; Maquet, Pierre; Thorpy, Michael J. (Eds.) Neuroimaging of sleep and sleep disorders (2013)

Detailed reference viewed: 62 (27 ULg)