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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 detailAge-related decline in cognitive control: the role of fluid intelligence and processing speed
Manard, Marine ULg; Carabin, Delphine; Jaspar, Mathieu ULg et al

in BMC Neuroscience (2014), 15(7),

Background Research on cognitive control suggests an age-related decline in proactive control abilities whereas reactive control seems to remain intact. However, the reason of the differential age effect ... [more ▼]

Background Research on cognitive control suggests an age-related decline in proactive control abilities whereas reactive control seems to remain intact. However, the reason of the differential age effect on cognitive control efficiency is still unclear. This study investigated the potential influence of fluid intelligence and processing speed on the selective age-related decline in proactive control. Eighty young and 80 healthy older adults were included in this study. The participants were submitted to a working memory recognition paradigm, assessing proactive and reactive cognitive control by manipulating the interference level across items. Results Repeated measures ANOVAs and hierarchical linear regressions indicated that the ability to appropriately use cognitive control processes during aging seems to be at least partially affected by the amount of available cognitive resources (assessed by fluid intelligence and processing speed abilities). Conclusions This study highlights the potential role of cognitive resources on the selective age-related decline in proactive control, suggesting the importance of a more exhaustive approach considering the confounding variables during cognitive control assessment. [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 detailDorsomedial prefrontal metabolism and unawareness of current characteristics of personality traits in Alzheimer’s disease
Jedidi, Haroun ULg; Feyers, Dorothée ULg; Collette, Fabienne ULg et al

in Social Cognitive and Affective Neuroscience (2014)

Anosognosia is a complex symptom corresponding to a lack of awareness of one’s current clinical status. Anosognosia for cognitive deficits has frequently been described in Alzheimer’s disease (AD), while ... [more ▼]

Anosognosia is a complex symptom corresponding to a lack of awareness of one’s current clinical status. Anosognosia for cognitive deficits has frequently been described in Alzheimer’s disease (AD), while unawareness of current characteristics of personality traits has rarely been considered. We used a well-established questionnaire-based method in a group of 37 AD patients and in healthy controls to probe self- and hetero-evaluation of patients’ personality and we calculated differential scores between each participant’s and his/her relative’s judgments. A brain-behavior correlation was performed using FDG-PET images. The behavioral data showed that AD patients presented with anosognosia for current characteristics of their personality and their anosognosia was primarily explained by impaired third perspective taking. The brain-behavior correlation analysis revealed a negative relationship between anosognosia for current characteristics of personality and dorsomedial prefrontal cortex (dMPFC) activity. Behavioral and neuroimaging data are consistent with the view that impairment of different functions subserved by the dMPFC (self-evaluation, inferences regarding complex enduring dispositions of self and others, confrontation of perspectives in interpersonal scripts) plays a role in anosognosia for current characteristics of personality in AD patients. [less ▲]

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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)

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See detailDifference in neural correlates of discrimination during sleep deprivation in PER3 homozygous
Shaffii-Le Bourdiec, Anahita; Muto, Vincenzo ULg; Jaspar, Mathieu ULg et al

Poster (2012, September 07)

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See detailInfluence of sleep homeostasis and circadian rhythm on executive discriminative ability during a constant routine
Jaspar, Mathieu ULg; Meyer, Christelle ULg; Muto, Vincenzo ULg et al

Poster (2012, September)

Introduction & Objectives The human brain upholds cognitive performance throughout a waking day due to putative circadian (C) arousal signal (1) which counteracts the increase in homeostatic (H) sleep ... [more ▼]

Introduction & Objectives The human brain upholds cognitive performance throughout a waking day due to putative circadian (C) arousal signal (1) which counteracts the increase in homeostatic (H) sleep pressure associated to the deterioration in brain efficiency. When wakefulness is extended into the circadian night, maintenance of cognitive performance is jeopardized (Fig.1). Some individuals are very vulnerable to the negative effects of sleep loss and circadian misalignment, whereas others are resilient (3). These individuals differences can be readily explained within the conceptual framework of the circadian and homeostatic regulation of performance (4,5) but also by individual genetic differences and notably the PERIOD3 gene polymorphism (6). In this experiment, we investigated the consequences of sleep deprivation on cognitive performance during a working memory task (3-back). Following the signal detection theory, the ability to discriminate target from non-target stimuli is estimated by d prime (d') and criterion (cr). Here we assessed whether d' and cr were modulated by the raising sleep need and the oscillatory circadian signal. We also tested whether the individual vulnerability to sleep loss predicted by the PERIOD3 gene polymorphism influences this cognitive modulation, which is also driven by the sleep/wake regulation. Materials and Methods Population: Thirty-five right-handed healthy young volunteers aged from 19 to 26 (17 females) were recruited on the basis of their PER3 polymorphism. From a sample of about 400 screened volunteers, twelve 5/5 and twenty-three 4/4 homozygotes (matched for age, gender, chronotype, IQ, and level of education at the group level) participated in this study. Study protocol: Participants wore actigraphs for three weeks before the laboratory study. The first two weeks allowed us to determine their habitual sleep/wake schedule. During the third one, a strict sleep schedule adjusted on two possible timetables (00:00-08:00 or 01:00-09:00) was imposed in order to stagger fMRI sessions. Compliance to this schedule was again checked by wrist actigraphy and sleep diaries. The laboratory study began in the evening of day 1 and ran over 5 nights (Fig. 2). During the first 2 nights (habituation and baseline), the volunteers slept according to habitual sleep/wake schedule. Participants remained awake from the morning of day 3 for 42 hours. During this period, they remained in a semi-recumbent position, under dim light conditions (5 lux, eye level), with no information on clock time, in a constant routine protocol (CR). Saliva samples was hourly collected for melatonin analysis. Every 2 hours, volunteers received calibrated isocaloric snacks, behavioral data were collected and waking EEG recorded. During CR, behavioral measures were used to assess subjective (Karolinska Sleepiness Scale, KSS) and objective alertness (psychomotor vigilance task [PVT]). Executive functioning efficiency was assessed using the 3-back (Fig. 3) and SART tasks. During fMRI, participants performed alternating blocks of 0- and 3-back task. D’ and cr (Fig. 4) were analyzed with mixed-model analysis of variance (PROC Mixed), with main factors “session” and “genotype” (PER3 4/4 & PER3 5/5). All p-values derived from r-ANOVAs were based on Huynh-Feldt's (H-F) corrected degrees of freedom (p<0.05). Exploratory analysis assessed theoretical coefficients for the homeostatic sleep pressure (derived from a quasi-linear function) and the circadian oscillation (as a 24-hour period sine wave) were utilized in a multiple regression model to predict d’ and cr performance during the CR. Before these analyses, d’ and cr have been normalized using a z-score transformation. Results. Analyses on d’ 1. MIXED MODEL : Significant effect of sessions (F(12,385) = 17.16, p < 0.0001), but no group effect (F(1,133) = 0.00, p = 0.99) or interaction (F(12,385) = 1.51, p = 0.11). 2. REGRESSION: Significant regression (R² = 0.24, F(2,440) = 69.94, p <0.0001). The two predictors are significant (homeostat: p < 0.0001 ; circadian: p < 0.0001). Analyses on cr 1. MIXED MODEL : Significant effect of sessions (F(12,385) = 4.12, p < 0.0001), but no group effect (F(1,133) = 0.00, p = 0.99) or interaction (F(12,385) = 0.75, p = 0.71). 2. REGRESSION: Significant regression (R² = 0.04, F(2,440) = 9.35 , p = 0.0001). Only one predictor was significant (homeostat: p < 0.0001 ; circadian: p = 0.96). Conclusion These preliminary results show that both sleep homeostatic pressure and circadian factors influence executive discriminative ability during sleep loss, as assessed by signal detection theory (d’). Decision criterion (cr) appears modulated only by homeostatic sleep pressure. The difference between these two parameters could be explained by the theoretical modeling of the circadian oscillation and future analyses will incorporate individual experimentally-derived homeostatic and circadian parameters. Neither discrimination ability (d’) or criterion (cr) seem sensitive measures of individual cognitive vulnerability to sleep loss predicted by PER3 polymorphism. REFERENCES (1) Aston-Jones. Sleep Med. 2005, 6(Suppl 1), S3-7. (2) Dijk & Archer. Sleep Med. Rev. 2010, 14, 151-160.(3) Van Dongen & al. Sleep. 2004, 27, 423-433. (4) Mongrain & al. J. Sleep Res. 2006, 15, 162-166. (5) Van Dongen et al. Sleep. 2007, 30, 1129-1143. (6) Groeger & al. Sleep. 2008, 31, 1159-1167. (7) Vandewalle & al. J. Neuro. 2009, 29, 7948-7956. ACKNOWLEDGEMENTS & SPONSORS Cyclotron Research Centre (CRC) ; Belgian National Funds of Scientific Research (FNRS) ; Actions de Recherches Concertées (ARC, ULg) – Fondation Médicale Reine Elisabeth (FMRE) ; Walloon Excellence in Lifesciences and Biotechnology (WELBIO) ; Wellcome Trust ; Biotechnology and Biological Sciences Research Council (BBSRC) [less ▲]

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See detailInfluence of sleep homeostasis and circadian rhythm on waking EEG oscillations during a constant routine
Muto, Vincenzo ULg; Meyer, Christelle ULg; Jaspar, Mathieu ULg et al

Poster (2012, September)

Introduction & Objectives Human sleep and wake EEG oscillations are modulated by complex non-additive interaction between homeostatic and circadian processes. Quantitative analysis of EEG data, during ... [more ▼]

Introduction & Objectives Human sleep and wake EEG oscillations are modulated by complex non-additive interaction between homeostatic and circadian processes. Quantitative analysis of EEG data, during extended wakefulness, indicate that its frequency-specificity is influenced by both factors, such that low-frequencies (<8Hz) increase with time spent awake (1), thus more homeostatically-driven, while alpha activity undergoes a clear circadian modulation (2). Interindividual differences in sleep-wake regulation in young volunteers are associated with the variable-number tandem-repeat (VNTR) polymorphism in the coding region of the circadian clock gene PERIOD3 (PER3). Individuals homozygous for the longer allele of PER3 (PER35/5) were reported to generate more slow wave activity during NREM sleep and theta activity during wakefulness, relative to individuals with the shorter allele (PER34/4). However, the phase and amplitude of circadian markers do not differ between these genotypes (3). Here we tested the hypothesis if fluctuations in the dynamics of waking EEG frequency-specificity are modulated by a polymorphism in the clock gene PER3, under 42h of sustained wakefulness. Materials and Methods Population. A total of 400 young men and women were recruited, from whom DNA samples and questionnaire data were collected. On the basis of their PER3 polymorphism, 35 healthy young volunteers (age: 19-26 y; 17 females) were recruited, out of which twelve were PER35/5 and twenty-three PER34/4 homozygotes, and matched by age, gender, level of education, chronotype and IQ at the group level. Study protocol. The laboratory part of this study began in the evening of day 1 until day 5 (Fig. 1). During the first 2 nights (habituation and baseline), volunteers followed one out of two possible sleep-wake schedules (00:00-08:00 or 01:00-09:00). Thereafter, participants underwent approximately 42 hours of sustained wakefulness under constant routine (CR) conditions (semi-recumbent position, dim light <5 lux, no time-of-day information), and a subsequent recovery sleep episode. EEG recordings. Continuous EEG measurements with 9 EEG channels (F3, Fz, F4, C3, Cz, C4, Pz, O1, O2) were performed throughout the CR. Waking EEG was recorded every 2-h, during a modified version of the Karolinska Drowsiness Test (KDT) (4). Data presented here pertain to the last 60-sec of KDT, during which subjects were instructed to relax, to fixate a dot displayed on a screen ca. 75cm and to try to suppress blinks. After re-referencing to mean mastoids, recordings were scored using Rechtschaffen criteria. The 1-min EEGs during the KDT were manually and visually scored for artifacts (eye blinks, body movements, and slow eye movements) offline by 2 independent observers. The absolute EEG power density was then calculated for artifact-free 2-s epochs in the frequency range of 0.5 to 20 Hz , overlapping by 1 second using the pwelch function in MATLAB (7.5.0). For data reduction, power density of artifact-free 2-s epochs was averaged over 20-s epochs. Statistics. Waking EEG delta (0.75-4.5Hz), theta (4.75-7.75Hz) and alpha (8-12.0Hz) power density computed on Central derivation (Cz) were analyzed with a mixed-model analysis of variance (PROC Mixed), with main factors “elapsed time awake” and “genotype” (PER34/4 and PER35/5), and the interaction of these two factors. All p-values derived from r-ANOVAs were based on Huynh-Feldt's (H-F) corrected degrees of freedom (p<0.05). Multiple comparisons were performed using Tukey-Kramer test. Theoretical coefficients for the homeostatic sleep pressure (derived from a quasi-linear function) and the circadian oscillation (24-hour period sine wave) were used in a multiple regression model to predict delta, theta and alpha activity during the CR. Prior to multiple regression analysis, data were normalized according to PROC Transreg, in order to derive the best normalization method for linear and non-linear datasets. Results. Delta activity Analysis of delta activity yielded a significant main effect of “elapsed time awake” (F=5.31; p < 0.0001), albeit no significant effects for “genotype” (F=0.01; p = 0.94) nor for the interaction of these factors (F=0.85; p = 0.65). The multiple regression model revealed a significant regression (R² = 0.0433 Adj. R² = 0.0404; F = 15.24; p <0.0001), for the homeostat (p < 0.0001 ) and circadian (p = 0.0006) coefficients. Theta activity Analysis of theta activity yielded a significant main effect of “elapsed time awake” (F= 12.2; p < 0.0001), although no significant effects for “genotype” (F= 0.1; p = 0.70) nor for the interaction of these factors (F= 0.67; p = 0.86). The multiple regression model revealed a significant regression (R²= 0.072 Adj. R² =0.069; F= 26.36; p <0.0001), for the homeostat (p < 0.0001 ) and circadian (p < 0.0001 ) coefficients. Alpha activity Analysis of alpha activity yielded a significant main effect of “elapsed time awake”(F=3.43; p < 0.0001), although no significant effects for “genotype” (F = 0.01; p = 0.92) nor for the interaction of these factors (F= 1.23; p = 0.22). The multiple regression model revealed a significant regression (R²=0.052; Adj. R²=0.05; F =18.63; p <0.0001), for the homeostat (p = 0.0012) and for the circadian (p < 0.0001) coefficients. Conclusion Our results indicate that fluctuations in the dynamics of waking EEG activity are modulated by the circadian and homeostatic processes, although the magnitude of these differences are not underlined by a polymorphism in the clock gene PER3. REFERENCES 1. Cajochen C, Brunner DP, Kräuchi K, Graw P, Wirz-Justice A. Power density in theta/alpha frequencies of the waking EEG progressively increases during sustained wakefulness. Sleep. 1995, 10:890-894. 2. Cajochen C, Wyatt JK, Czeisler CA, Dijk DJ.Separation of circadian and wake duration-dependent modulation of EEG activation during wakefulnessNeuroscience. 2002, 114:1047-60. 3. Viola AU, Archer SN, James LM, Groeger JA, Lo JC, Skene DJ, von Schantz M, Dijk DJ PER3 polymorphism predicts sleep structure and waking performance. Curr Biol 2007,17:613–618. 4. Gillberg M, Kecklund G, Akerstedt T. Relations between performance and subjective rating of sleepiness during a night awake. Sleep 1994, 17:236-241. ACKNOWLEDGEMENTS & SPONSORS Cyclotron Research Centre (CRC) ; Belgian National Funds of Scientific Research (FNRS) ; Actions de Recherche Concertées (ARC, ULg) – Fondation Médicale Reine Elisabeth (FMRE) ; Walloon Excellence in Lifesciences and Biotechnology (WELBIO) ; Wellcome Trust ; Biotechnology and Biological Sciences Research Council (BBSRC) [less ▲]

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See detailNeural Correlates of Human Sleep and Sleep-Dependent Memory Processing
Meyer, Christelle ULg; Muto, Vincenzo ULg; Jaspar, Mathieu ULg et al

in Frank, Marcos (Ed.) Sleep and Brain Activity (2012)

Wakefulness and sleep are associated with distinct patterns of neural activity and neuromodulation. In humans, functional neuroimaging was used to characterize the related changes in regional brain ... [more ▼]

Wakefulness and sleep are associated with distinct patterns of neural activity and neuromodulation. In humans, functional neuroimaging was used to characterize the related changes in regional brain metabolism and hemodynamics. Recent data combining EEG and fMRI described the transient responses associated with spindles and slow waves, as well as the changes in functional integration during NREM sleep. It was also shown that regional brain activity during sleep is influenced by the experience acquired during the preceding waking period. These data are currently interpreted in the framework of two theories. First, the use-dependent increase in slow oscillation during NREM sleep is associated with local synaptic homeostasis. Second, reactivations of memory traces during NREM sleep would reorganize declarative memories in hippocampal-neocortical networks, a systems-level memory consolidation which can be hindered by sleep deprivation. Collectively, these data reveal the dynamical changes in brain activity during sleep which support normal human cognition. [less ▲]

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See detailModulating effect of COMT genotype on the brains regions underlying inhibition
Jaspar, Mathieu ULg; Grandjean, Julien ULg; SALMON, Eric ULg et al

Conference (2012, May)

Catechol-O-methyltransferase (COMT) is an important enzyme which degrades catecholamines, such dopamine, notably in the prefrontal cortex (Männistö & Kaakkola, 1999). A large number of studies reported an ... [more ▼]

Catechol-O-methyltransferase (COMT) is an important enzyme which degrades catecholamines, such dopamine, notably in the prefrontal cortex (Männistö & Kaakkola, 1999). A large number of studies reported an effect on executive functioning of COMT genotype (Barnett & al., 2007), each genotype being associated with a different COMT enzymatic activity (Weinshilboum & al., 1999). In an event-related fMRI study, a modified form of the Stroop task was administered to 45 young adults separated in three groups according to their COMT val158met genotype : 15 homozygous val/val (VV), 15 homozygous met/met (MM) and 15 heterozygotes val/met (VM). Both behavioral and fMRI results indicated the presence of a general interference effect consistent with prior reports (Nee & al., 2007). More interestingly, group comparisons indicate that this effect is associated, for a similar behavioral performance, with increased medial frontal and precentral gyrus activity in VV and VM groups by comparison with MM group. Conversely, no supplementary brain areas were observed for the comparison of the MM to the two other groups. These observations, paralleling with the lower COMT enzymatic activity and, thus, the higher cortical dopamine level in met/met individuals, confirms our expectation of a COMT Val158Met genotype modulation of the brain regions underlying inhibition efficiency. [less ▲]

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See detailInfluence of acute sleep loss on the neural correlates of alerting, orientating and executive attention components
Muto, Vincenzo ULg; Shaffii, Anahita ULg; Matarazzo, Luca et al

in Journal of Sleep Research (2012), 21(6), 648-58

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See detailSleep, memory and the hippocampus
Foret, Ariane; Mascetti, Laura ULg; Kussé, Caroline ULg et al

in Clinical Neurobiology of the Hippocampus (2012)

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See detailModulating effect of COMT genotype on the brain regions underlying inhibition
Jaspar, Mathieu ULg; Grandjean, Julien ULg; Salmon, Eric ULg et al

Poster (2011, June 26)

Introduction Catechol-O-methytransferase (COMT) is an important enzyme which degrades catecholamines, such dopamine, notably in the prefrontal cortex (Männistö & Kaakkola, 1999). Actually, a transition of ... [more ▼]

Introduction Catechol-O-methytransferase (COMT) is an important enzyme which degrades catecholamines, such dopamine, notably in the prefrontal cortex (Männistö & Kaakkola, 1999). Actually, a transition of guanine to adenine at codon 158 of the COMT gene results in a valine to methionine substitution (Lotta & al., 1995). This phenomenon leads to different COMT genotypes, each associated with a different COMT enzymatic activity (Weinshilboum, & al., 1999). A large number of studies reported an effect of COMT on executive functioning. However, most of them used multi-determined executive tasks (e.g., Barnett & al., 2007). We are interested here to determine the effect of COMT Val158Met genotype on the activity of frontal and parietal areas (Nee & al., 2007; Laird & al., 2005) underlying the specific executive process of inhibition. Methods Procedure In an event-related fMRI experiment, a modified form of the Stroop task was administered to 44 young adults (age range: 18-30) separated into three groups according to their COMT Val158Met genotype: 15 homozygous val/val (VV), 14 homozygous met/met (MM) and 15 heterozygotes val/met (VM) carriers. The Stroop task consisted in the presentation of color words printed in various ink colors (e.g the word blue written in red). Subjects were instructed to name of ink color as fast and accurately as possible by avoiding to read the word. In this version of the Stroop task, three different contexts were created (data not showed here): (1) a congruent context (MC) with a majority of facilitator items (IC), (2) a non-congruent context (MI) with mainly interfering items (II), (3) a neutral context (MN) with mainly neutral items (IN, series of %%% written in different colors). MRI acquisition, data analysis Functional MRI time series were acquired on a 3T head-only scanner operated with the standard transmit-receive quadrature head coil. Multislice T2*-weighted functional images were acquired with a gradient-echo echo-planar imaging sequence using axial slice orientation and covering the whole brain (32 slices, FoV = 220x220 mm², voxel size 3.4x3.4x3 mm³, 30% interslice gap, matrix size 64x64x32, TR = 2130 ms, TE = 40 ms, FA = 90°). Structural images were obtained using a high resolution T1-weighted sequence (3D MDEFT [Deichmann & al., (2004)] ; TR = 7.92 ms, TE = 2.4 ms, TI = 910 ms, FA = 15°, FoV = 256 x 224 x 176 mm³, 1 mm isotropic spatial resolution). Preprocessing and statistical analyses were performed with SPM8 (p<.001 uncorrected). Results Behavioral results indicated the presence of a general interference effect (II – IN items) for reaction time (F(1,41) = 292,44 ; p < 0,001) but no significant difference in interference between the three groups (F(2,41) = 0,27; p = 0,76). FMRI results revealed that interference effect [(MI_II-MI_IN) + (MC_II-MC_IN) + (MN_II-MN_IN)] observed in our three groups is mainly associated with cerebral activity in frontal and parietal areas. Moreover, group comparisons indicates that this effect is associated with increased medial frontal and precentral gyrus activity in VV and VM groups by comparison with MM group, but also in the superior temporal gyrus and in the thalamus in the VM by comparison to MM . Conversely, no supplementary brain area was observed for the comparison of the MM to the two other groups. Conclusions The fronto-parietal brain network associated with interference resolution observed here is consistent with prior reports (Nee & al., 2007; Laird & al., 2005). Moreover, results showed activity in different brain areas according to the COMT genotype. Indeed, a similar behavioral performance is associated to the recruitment of supplementary areas in the carriers of the val allele. This observation, paralleling with the lower COMT enzymatic activity and, thus, the higher cortical dopamine level in met/met individuals, confirms our expectation of a COMT Val158Met genotype modulation of the brain regions underlying inhibition efficiency. [less ▲]

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See detailSpontaneous neural activity during human non-rapid eye movement sleep.
Mascetti, Laura ULg; Foret, Ariane ULg; Shaffii, Anahita ULg et al

in Progress in Brain Research (2011), 193

Recent neuroimaging studies characterized the neural correlates of slow waves and spindles during human non-rapid eye movement (NREM) sleep. They showed that significant activity was consistently ... [more ▼]

Recent neuroimaging studies characterized the neural correlates of slow waves and spindles during human non-rapid eye movement (NREM) sleep. They showed that significant activity was consistently associated with slow (> 140 muV) and delta waves (75-140 muV) during NREM sleep in several cortical areas including inferior frontal, medial prefrontal, precuneus, and posterior cingulate cortices. Unexpectedly, slow waves were also associated with transient responses in the pontine tegmentum and in the cerebellum. On the other hand, spindles were associated with a transient activity in the thalami, paralimbic areas (anterior cingulate and insular cortices), and superior temporal gyri. Moreover, slow spindles (11-13 Hz) were associated with increased activity in the superior frontal gyrus. In contrast, fast spindles (13-15 Hz) recruited a set of cortical regions involved in sensorimotor processing, as well as the mesial frontal cortex and hippocampus. These findings indicate that human NREM sleep is an active state during which brain activity is temporally organized by spontaneous oscillations (spindles and slow oscillation) in a regionally specific manner. The functional significance of these NREM sleep oscillations is currently interpreted in terms of synaptic homeostasis and memory consolidation. [less ▲]

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