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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 detailIncrease in cortico-thalamo-cortical connectivity during human sleep slow wave activity
Kussé, Caroline ULg; Lehembre, Rémy; Foret, Ariane et al

Poster (2012, September 05)

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See detailIncrease in cortico-thalamo-cortical connectivity during human sleep slow wave activity
Kussé, Caroline ULg; Lehembre, Rémy; Foret, Ariane et al

Poster (2012, September 04)

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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 detailDecoding spontaneous brain activity from fMRI using Gaussian Processes: tracking brain reactivation
Schrouff, Jessica ULg; Kussé, Caroline ULg; Wehenkel, Louis ULg et al

in 2012 Second International Workshop on Pattern Recognition in NeuroImaging (PRNI 2012): proceedings (2012, July 03)

While Multi-Variate Pattern Analysis techniques based on machine learning have now been regularly applied to neuroimaging data, decoding brain activity is usually performed in highly controlled ... [more ▼]

While Multi-Variate Pattern Analysis techniques based on machine learning have now been regularly applied to neuroimaging data, decoding brain activity is usually performed in highly controlled experimental paradigms. In more realistic conditions, the number, sequence and duration of mental states are unpredictably generated by the individual, resulting in complex and imbalanced fMRI data sets. Moreover, in the case of spontaneous brain activity, the mental states can not be linked to any external or internal stimulation, which makes it a highly difficult condition to decode. This study tests the classification of brain activity, acquired on 14 volunteers using fMRI, during mental imagery, a condition in which the number and duration of mental events were not externally imposed but self-generated. Application of the obtained model on rest sessions allowed classifying spontaneous brain activity linked to the task which, overall, correlated with their behavioural performance to the task. [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 detailIncrease in cortico-thalamo-cortical connectivity during human sleep slow wave activity
Kussé, Caroline ULg; Lehembre, Rémy; Foret, Ariane et al

Poster (2012, June 10)

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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 detailFunctional Neuroimaging during Human Sleep
Kussé, Caroline ULg; Maquet, Pierre ULg

in Barrett, Deirdre; McNamara, Patrick (Eds.) Encyclopedia of sleep and dreams (2 volumes): the evolution, function, nature and mysteries of slumber (2012)

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See detailNeural Correlates of Performance Variabilty during Motor Sequence Acquisition
Albouy, Geneviève ULg; Sterpenich, V.; Vandewalle, Gilles ULg et al

in NeuroImage (2012), 60(1), 324-331

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See detailDecoding Semi-Constrained Brain Activity from fMRI Using Support Vector Machines and Gaussian Processes
Schrouff, Jessica ULg; Kussé, Caroline ULg; Wehenkel, Louis ULg et al

in PLoS ONE (2012), 7(4),

Predicting a particular cognitive state from a specific pattern of fMRI voxel values is still a methodological challenge. Decoding brain activity is usually performed in highly controlled experimental ... [more ▼]

Predicting a particular cognitive state from a specific pattern of fMRI voxel values is still a methodological challenge. Decoding brain activity is usually performed in highly controlled experimental paradigms characterized by a series of distinct states induced by a temporally constrained experimental design. In more realistic conditions, the number, sequence and duration of mental states are unpredictably generated by the individual, resulting in complex and imbalanced fMRI data sets. This study tests the classification of brain activity, acquired on 16 volunteers using fMRI, during mental imagery, a condition in which the number and duration of mental events were not externally imposed but self-generated. To deal with these issues, two classification techniques were considered (Support Vector Machines, SVM, and Gaussian Processes, GP), as well as different feature extraction methods (General Linear Model, GLM and SVM). These techniques were combined in order to identify the procedures leading to the highest accuracy measures. Our results showed that 12 data sets out of 16 could be significantly modeled by either SVM or GP. Model accuracies tended to be related to the degree of imbalance between classes and to task performance of the volunteers. We also conclude that the GP technique tends to be more robust than SVM to model unbalanced data sets. [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 detailCircadian preference modulates the neural substrate of conflict processing across the day
Schmidt, Christina ULg; Peigneux, Philippe ULg; Leclercq, Yves ULg et al

in PLoS ONE (2012), 7(1), 29658

Human morning and evening chronotypes differ in their preferred timing for sleep and wakefulness, as well as in optimal daytime periods to cope with cognitive challenges. Recent evidence suggests that ... [more ▼]

Human morning and evening chronotypes differ in their preferred timing for sleep and wakefulness, as well as in optimal daytime periods to cope with cognitive challenges. Recent evidence suggests that these preferences are not a simple by-product of socio-professional timing constraints, but can be driven by inter-individual differences in the expression of circadian and homeostatic sleep-wake promoting signals. Chronotypes thus constitute a unique tool to access the interplay between those processes under normally entrained day-night conditions, and to investigate how they impinge onto higher cognitive control processes. Using functional magnetic resonance imaging (fMRI), we assessed the influence of chronotype and time-of-day on conflict processing-related cerebral activity throughout a normal waking day. Sixteen morning and 15 evening types were recorded at two individually adapted time points (1.5 versus 10.5 hours spent awake) while performing the Stroop paradigm. Results show that interference-related hemodynamic responses are maintained or even increased in evening types from the subjective morning to the subjective evening in a set of brain areas playing a pivotal role in successful inhibitory functioning, whereas they decreased in morning types under the same conditions. Furthermore, during the evening hours, activity in a posterior hypothalamic region putatively involved in sleep-wake regulation correlated in a chronotype-specific manner with slow wave activity at the beginning of the night, an index of accumulated homeostatic sleep pressure. These results shed light into the cerebral mechanisms underlying inter-individual differences of higher-order cognitive state maintenance under normally entrained day-night conditions. [less ▲]

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See detailThe fate of incoming stimuli during NREM sleep is determined by spindles and the phase of the slow oscillation
Schabus, M.; Dang Vu, Thien Thanh ULg; Heib, D. P. J. et al

in Frontiers in Neurology (2012), 3(40), 1-11

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See detailThe neural correlates of recollection and familiarity during aging
Angel, Lucie; Bastin, Christine ULg; Genon, Sarah ULg et al

in Frontiers in Human Neuroscience (2012)

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See detailThe Neural Substrates of Memory Suppression: A fMRI Exploration of Directed Forgetting
Bastin, Christine ULg; Feyers, Dorothée ULg; Majerus, Steve ULg et al

in PLoS ONE (2012), 7(1), 29905

The directed forgetting paradigm is frequently used to determine the ability to voluntarily suppress information. However, little is known about brain areas associated with information to forget. The ... [more ▼]

The directed forgetting paradigm is frequently used to determine the ability to voluntarily suppress information. However, little is known about brain areas associated with information to forget. The present study used functional magnetic resonance imaging to determine brain activity during the encoding and retrieval phases of an item-method directed forgetting recognition task with neutral verbal material in order to apprehend all processing stages that information to forget and to remember undergoes. We hypothesized that regions supporting few selective processes, namely recollection and familiarity memory processes, working memory, inhibitory and selection processes should be differentially activated during the processing of to-be-remembered and to-be-forgotten items. Successful encoding and retrieval of items to remember engaged the entorhinal cortex, the hippocampus, the anterior medial prefrontal cortex, the left inferior parietal cortex, the posterior cingulate cortex and the precuneus; this set of regions is well known to support deep and associative encoding and retrieval processes in episodic memory. For items to forget, encoding was associated with higher activation in the right middle frontal and posterior parietal cortex, regions known to intervene in attentional control. Items to forget but nevertheless correctly recognized at retrieval yielded activation in the dorsomedial thalamus, associated with familiarity-based memory processes and in the posterior intraparietal sulcus and the anterior cingulate cortex, involved in attentional processes. [less ▲]

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