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

Poster (2012, October 27)

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

in Clinical Neurobiology of the Hippocampus (2012)

Detailed reference viewed: 34 (11 ULg)
See detailDecoding semi-constrained brain activity from fMRI using SVM and GP
Schrouff, Jessica ULg; Kussé, Caroline ULg; Wehenkel, Louis ULg et al

Scientific conference (2011, November 22)

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 detailDecoding Directed Brain Activity in fMRI using Support Vector Machines and Gaussian Processes
Schrouff, Jessica ULg; Kussé, Caroline ULg; Wehenkel, Louis ULg et al

Poster (2011, June 26)

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 ▲]

Detailed reference viewed: 77 (13 ULg)
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See detailExperience-dependent induction of hypnagogic images during daytime naps: a combined behavioral and EEG study.
Kussé, Caroline ULg; Shaffii-Le Bourdiec, Anahita; Schrouff, Jessica ULg et al

in Association for the Scientific Study of Consciousness 15, Kyoto, Japan, 9-12 June 2011 (2011, June 09)

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See detailFunctional neuroimaging of the reciprocal influences between sleep and wakefulness.
JEDIDI, Zayd ULg; Rikir, Estelle ULg; Muto, Vincenzo ULg et al

in Pflugers Archiv : European journal of physiology (2011), 463(1), 103-9

The activity patterns adopted by brain neuronal populations differ dramatically between wakefulness and sleep. However, these vigilance states are not independent and they reciprocally interact. Here, we ... [more ▼]

The activity patterns adopted by brain neuronal populations differ dramatically between wakefulness and sleep. However, these vigilance states are not independent and they reciprocally interact. Here, we provide evidence that in humans, regional brain activity during wakefulness is influenced by sleep regulation, namely by the interaction between sleep homeostasis and circadian signals. We also show that, by contrast, regional brain activity during sleep is influenced by the experience acquired during the preceding waking period. These data reveal the dynamic interactions by which the succession of vigilance states support normal brain function and human cognition. [less ▲]

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See detailReciprocal interactions between wakefulness and sleep influence global and regional brain activity
Muto, Vincenzo ULg; Mascetti, Laura ULg; Matarazzo, Luca et al

in Current Topics in Medicinal Chemistry (2011), 11(19), 2403-13

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See detailNeural Correlates of Human NREM Sleep Oscillations
Foret, Ariane ULg; Shaffii, Anahita ULg; Muto, Vincenzo ULg et al

in Hutt, Axel (Ed.) Sleep and Anesthesia (2011)

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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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See detailExperience-dependent induction of hypnagogic images during daytime naps: a combined behavioural and EEG study.
Kussé, Caroline ULg; Shaffii, Anahita ULg; Schrouff, Jessica ULg et al

in Journal of Sleep Research (2011)

This study characterizes hypnagogic hallucinations reported during a polygraphically recorded 90-min daytime nap following or preceding practice of the computer game Tetris. In the experimental group (N ... [more ▼]

This study characterizes hypnagogic hallucinations reported during a polygraphically recorded 90-min daytime nap following or preceding practice of the computer game Tetris. In the experimental group (N = 16), participants played Tetris in the morning for 2 h during three consecutive days, while in a first control group (N = 13, controlling the effect of experience) participants did not play any game, and in a second control group (N = 14, controlling the effect of anticipation) participants played Tetris after the nap. During afternoon naps, participants were repetitively awakened 15, 45, 75, 120 or 180 s after the onset of S1, and were asked to report their mental content. Reports content was scored by three judges (inter-rater reliability 85%). In the experimental group, 48 out of 485 (10%) sleep-onset reports were Tetris-related. They mostly consisted of images and sounds with very little emotional content. They exactly reproduced Tetris elements or mixed them with other mnemonic components. By contrast, in the first control group, only one report out of 107 was scored as Tetris-related (1%), and in the second control group only three reports out of 112 were scored as Tetris-related (3%; between-groups comparison; P = 0.006). Hypnagogic hallucinations were more consistently induced by experience than by anticipation (P = 0.039), and they were predominantly observed during the transition of wakefulness to sleep. The observed attributes of experience-related hypnagogic hallucinations are consistent with the particular organization of regional brain activity at sleep onset, characterized by high activity in sensory cortices and in the default-mode network. [less ▲]

Detailed reference viewed: 53 (15 ULg)