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Christophe Phillips

Publications and source records attributed to Christophe Phillips.

14 recordsLinked to original sources
PubMed2006

Daytime light exposure dynamically enhances brain responses.

In humans, light enhances both alertness and performance during nighttime and daytime [1-4] and influences regional brain function [5]. These effects do not correspond to classical visual responses but involve a non-image forming (NIF) system, which elicits greater endocrine, physiological, neurophysiological, and behavioral responses to shorter light wavelengths than to wavelengths geared toward the visual system [6-11]. During daytime, the neural changes induced by light exposure, and their time courses, are largely unknown. With functional magnetic resonance imaging (fMRI), we characterized the neural correlates of the alerting effect of daytime light by assessing the responses to an auditory oddball task [12-15], before and after a short exposure to a bright white light. Light-induced improvement in subjective alertness was linearly related to responses in the posterior thalamus. In addition, light enhanced responses in a set of cortical areas supporting attentional oddball effects, and it prevented decreases of activity otherwise observed during continuous darkness. Responses to light were remarkably dynamic. They declined within minutes after the end of the light stimulus, following various region-specific time courses. These findings suggest that light can modulate activity of subcortical structures involved in alertness, thereby dynamically promoting cortical activity in networks involved in ongoing nonvisual cognitive processes.

Acoustic Stimulation↗
PubMed2006

A prominent role for amygdaloid complexes in the Variability in Heart Rate (VHR) during Rapid Eye Movement (REM) sleep relative to wakefulness.

Rapid eye movement sleep (REMS) is associated with intense neuronal activity, rapid eye movements, muscular atonia and dreaming. Another important feature in REMS is the instability in autonomic, especially in cardiovascular regulation. The neural mechanisms underpinning the variability in heart rate (VHR) during REMS are not known in detail, especially in humans. During wakefulness, the right insula has frequently been reported as involved in cardiovascular regulation but this might not be the case during REMS. We aimed at characterizing the neural correlates of VHR during REMS as compared to wakefulness and to slow wave sleep (SWS), the other main component of human sleep, in normal young adults, based on the statistical analysis of a set of H(2)(15)O positron emission tomography (PET) sleep data acquired during SWS, REMS and wakefulness. The results showed that VHR correlated more tightly during REMS than during wakefulness with the rCBF in the right amygdaloid complex. Moreover, we assessed whether functional relationships between amygdala and any brain area changed depending the state of vigilance. Only the activity within in the insula was found to covary with the amygdala, significantly more tightly during wakefulness than during REMS in relation to the VHR. The functional connectivity between the amygdala and the insular cortex, two brain areas involved in cardiovascular regulation, differs significantly in REMS as compared to wakefulness. This suggests a functional reorganization of central cardiovascular regulation during REMS.

Adult↗
PubMed2006

Implicit oculomotor sequence learning in humans: Time course of offline processing.

Studies of manual and digital sequence learning indicate that motor memories continue to be processed after training has ended, following a succession of identifiable steps. However, it is not known whether this offline memory processing constitutes a basic feature of motor learning and generalizes to the implicit learning of a sequence of eye movements. To assess this hypothesis, we have created the serial oculomotor reaction time task (SORT). Participants were trained to the SORT then tested after either 30 min, 5 h or 24 h. During training, ocular reaction times decreased monotonically over practice of a repeated sequence, then increased when a different sequence was displayed, demonstrating oculomotor learning of the trained sequence. When tested 30 min after training, a significant gain in oculomotor performance was observed irrespective of the sequence learning. This gain was no longer present after 5 h. Remarkably, a gain in performance specific to the learned sequence emerged only 24 h after training. After testing, a generation task confirmed that most subjects learned implicitly the regularities of the sequence. Our results show that, as for manual or digital sequences, oculomotor sequences can be implicitly learned. The offline processing of oculomotor memories follows distinct stages in a way similar to those observed after manual or digital sequence learning.

Adult↗
PubMed2006

Bayesian estimation of evoked and induced responses.

We describe an extension of our empirical Bayes approach to magnetoencephalography/electroencephalography (MEG/EEG) source reconstruction that covers both evoked and induced responses. The estimation scheme is based on classical covariance component estimation using restricted maximum likelihood (ReML). We have focused previously on the estimation of spatial covariance components under simple assumptions about the temporal correlations. Here we extend the scheme, using temporal basis functions to place constraints on the temporal form of the responses. We show how the same scheme can estimate evoked responses that are phase-locked to the stimulus and induced responses that are not. For a single trial the model is exactly the same. In the context of multiple trials, however, the inherent distinction between evoked and induced responses calls for different treatments of the underlying hierarchical multitrial model. We derive the respective models and show how they can be estimated efficiently using ReML. This enables the Bayesian estimation of evoked and induced changes in power or, more generally, the energy of wavelet coefficients.

Algorithms↗
PubMed2005

MEG source localization under multiple constraints: an extended Bayesian framework.

To use Electroencephalography (EEG) and Magnetoencephalography (MEG) as functional brain 3D imaging techniques, identifiable distributed source models are required. The reconstruction of EEG/MEG sources rests on inverting these models and is ill-posed because the solution does not depend continuously on the data and there is no unique solution in the absence of prior information or constraints. We have described a general framework that can account for several priors in a common inverse solution. An empirical Bayesian framework based on hierarchical linear models was proposed for the analysis of functional neuroimaging data [Friston, K., Penny, W., Phillips, C., Kiebel, S., Hinton, G., Ashburner, J., 2002. Classical and Bayesian inference in neuroimaging: theory. NeuroImage 16, 465-483] and was evaluated recently in the context of EEG [Phillips, C., Mattout, J., Rugg, M.D., Maquet, P., Friston, K., 2005. An empirical Bayesian solution to the source reconstruction problem in EEG. NeuroImage 24, 997-1011]. The approach consists of estimating the expected source distribution and its conditional variance that is constrained by an empirically determined mixture of prior variance components. Estimation uses Expectation-Maximization (EM) to give the Restricted Maximum Likelihood (ReML) estimate of the variance components (in terms of hyperparameters) and the Maximum A Posteriori (MAP) estimate of the source parameters. In this paper, we extend the framework to compare different combinations of priors, using a second level of inference based on Bayesian model selection. Using Monte-Carlo simulations, ReML is first compared to a classic Weighted Minimum Norm (WMN) solution under a single constraint. Then, the ReML estimates are evaluated using various combinations of priors. Both standard criterion and ROC-based measures were used to assess localization and detection performance. The empirical Bayes approach proved useful as: (1) ReML was significantly better than WMN for single priors; (2) valid location priors improved ReML source localization; (3) invalid location priors did not significantly impair performance. Finally, we show how model selection, using the log-evidence, can be used to select the best combination of priors. This enables a global strategy for multiple prior-based regularization of the MEG/EEG source reconstruction.

Bayes Theorem↗
PubMed2005

Cerebral correlates of delta waves during non-REM sleep revisited.

We aimed at characterizing the neural correlates of delta activity during Non Rapid Eye Movement (NREM) sleep in non-sleep-deprived normal young adults, based on the statistical analysis of a positron emission tomography (PET) sleep data set. One hundred fifteen PET scans were obtained using H(2)(15)O under continuous polygraphic monitoring during stages 2-4 of NREM sleep. Correlations between regional cerebral blood flow (rCBF) and delta power (1.5-4 Hz) spectral density were analyzed using statistical parametric mapping (SPM2). Delta power values obtained at central scalp locations negatively correlated during NREM sleep with rCBF in the ventromedial prefrontal cortex, the basal forebrain, the striatum, the anterior insula, and the precuneus. These regions embrace the set of brain areas in which rCBF decreases during slow wave sleep (SWS) as compared to Rapid Eye Movement (REM) sleep and wakefulness (Maquet, P., Degueldre, C., Delfiore, G., Aerts, J., Peters, J.M., Luxen, A., Franck, G., 1997. Functional neuroanatomy of human slow wave sleep. J. Neurosci. 17, 2807-S2812), supporting the notion that delta activity is a valuable prominent feature of NREM sleep. A strong association was observed between rCBF in the ventromedial prefrontal regions and delta power, in agreement with electrophysiological studies. In contrast to the results of a previous PET study investigating the brain correlates of delta activity (Hofle, N., Paus, T., Reutens, D., Fiset, P., Gotman, J., Evans, A.C., Jones, B.E., 1997. Regional cerebral blood flow changes as a function of delta and spindle activity during slow wave sleep in humans. J. Neurosci. 17, 4800-4808), in which waking scans were mixed with NREM sleep scans, no correlation was found with thalamus activity. This latter result stresses the importance of an extra-thalamic delta rhythm among the synchronous NREM sleep oscillations. Consequently, this rCBF distribution might preferentially reflect a particular modulation of the cellular processes involved in the generation of cortical delta waves during NREM sleep.

Adult↗
PubMed2005

An empirical Bayesian solution to the source reconstruction problem in EEG.

Distributed linear solutions of the EEG source localisation problem are used routinely. In contrast to discrete dipole equivalent models, distributed linear solutions do not assume a fixed number of active sources and rest on a discretised fully 3D representation of the electrical activity of the brain. The ensuing inverse problem is underdetermined and constraints or priors are required to ensure the uniqueness of the solution. In a Bayesian framework, the conditional expectation of the source distribution, given the data, is attained by carefully balancing the minimisation of the residuals induced by noise and the improbability of the estimates as determined by their priors. This balance is specified by hyperparameters that control the relative importance of fitting and conforming to various constraints. Here we formulate the conventional "Weighted Minimum Norm" (WMN) solution in terms of hierarchical linear models. An "Expectation-Maximisation" (EM) algorithm is used to obtain a "Restricted Maximum Likelihood" (ReML) estimate of the hyperparameters, before estimating the "Maximum a Posteriori" solution itself. This procedure can be considered a generalisation of previous work that encompasses multiple constraints. Our approach was compared with the "classic" WMN and Maximum Smoothness solutions, using a simplified 2D source model with synthetic noisy data. The ReML solution was assessed with four types of source location priors: no priors, accurate priors, inaccurate priors, and both accurate and inaccurate priors. The ReML approach proved useful as: (1) The regularisation (or influence of the a priori source covariance) increased as the noise level increased. (2) The localisation error (LE) was negligible when accurate location priors were used. (3) When accurate and inaccurate location priors were used simultaneously, the solution was not influenced by the inaccurate priors. The ReML solution was then applied to real somatosensory-evoked responses to illustrate the application in an empirical setting.

Algorithms↗
PubMed2004

Are spatial memories strengthened in the human hippocampus during slow wave sleep?

In rats, the firing sequences observed in hippocampal ensembles during spatial learning are replayed during subsequent sleep, suggesting a role for posttraining sleep periods in the offline processing of spatial memories. Here, using regional cerebral blood flow measurements, we show that, in humans, hippocampal areas that are activated during route learning in a virtual town are likewise activated during subsequent slow wave sleep. Most importantly, we found that the amount of hippocampal activity expressed during slow wave sleep positively correlates with the improvement of performance in route retrieval on the next day. These findings suggest that learning-dependent modulation in hippocampal activity during human sleep reflects the offline processing of recent episodic and spatial memory traces, which eventually leads to the plastic changes underlying the subsequent improvement in performance.

Adult↗
PubMed2003

Increased cerebral functional connectivity underlying the antinociceptive effects of hypnosis.

The neural mechanisms underlying the antinociceptive effects of hypnosis are not well understood. Using positron emission tomography (PET), we recently showed that the activity in the anterior cingulate cortex (midcingulate area 24a') covaries with the hypnosis-induced reduction of affective and sensory responses to noxious thermal stimulation [Faymonville et al., Anesthesiology 92 (2000) 1257-1267]. In the present study, we assessed changes in cerebral functional connectivity related to the hypnotic state, compared to simple distraction and the resting state. Nineteen highly hypnotizable right-handed volunteers were studied using H2(15)O-PET. The experimental conditions were hot noxious or warm non-noxious stimulation of the right hand during resting state, mental imagery and hypnotic state. Using a psychophysiological interaction analysis, we identified brain areas that would respond to noxious stimulations under the modulatory action of the midcingulate cortex in, and only in, the hypnotic state. Hypnosis, compared to the resting state, reduced pain perception by 50%. Pain perception during rest and mental imagery was not significantly different. Analysis of PET data showed that the hypnotic state, compared to normal alertness (i.e., rest and mental imagery), significantly enhanced the functional modulation between midcingulate cortex and a large neural network encompassing bilateral insula, pregenual anterior cingulate cortex, pre-supplementary motor area, right prefrontal cortex and striatum, thalamus and brainstem. These findings point to a critical role for the midcingulate cortex in the modulation of a large cortical and subcortical network underlying its influence on sensory, affective, cognitive and behavioral aspects of nociception, in the specific context of hypnosis.

Adult↗
PubMed2003

Learned material content and acquisition level modulate cerebral reactivation during posttraining rapid-eye-movements sleep.

We have previously shown that several brain areas are activated both during sequence learning at wake and during subsequent rapid-eye-movements (REM) sleep (Nat. Neurosci. 3 (2000) 831-836), suggesting that REM sleep participates in the reprocessing of recent memory traces in humans. However, the nature of the reprocessed information remains open. Here, we show that regional cerebral reactivation during posttraining REM sleep is not merely related to the acquisition of basic visuomotor skills during prior practice of the serial reaction time task, but rather to the implicit acquisition of the probabilistic rules that defined stimulus sequences. Moreover, functional connections between the reactivated cuneus and the striatum--the latter being critical for implicit sequence learning--are reinforced during REM sleep after practice on a probabilistic rather than on a random sequence of stimuli. Our results therefore support the hypothesis that REM sleep is deeply involved in the reprocessing and optimization of the high-order information contained in the material to be learned. In addition, we show that the level of acquisition of probabilistic rules attained prior to sleep is correlated to the increase in regional cerebral blood flow during subsequent REM sleep. This suggests that posttraining cerebral reactivation is modulated by the strength of the memory traces developed during the learning episode. Our data provide the first experimental evidence for a link between behavioral performance and cerebral reactivation during REM sleep.

Adult↗
PubMed2002

Anatomically informed basis functions for EEG source localization: combining functional and anatomical constraints.

Distributed linear solutions have frequently been used to solve the source localization problem in EEG. Here we introduce an approach based on the weighted minimum norm (WMN) method that imposes constraints using anatomical and physiological information derived from other imaging modalities. The anatomical constraints are used to reduce the solution space a priori by modeling the spatial source distribution with a set of basis functions. These spatial basis functions are chosen in a principled way using information theory. The reduced problem is then solved with a classical WMN method. Further (functional) constraints can be introduced in the weighting of the solution using fMRI brain responses to augment spatial priors. We used simulated data to explore the behavior of the approach over a range of the model's hyperparameters. To assess the construct validity of our method we compared it with two established approaches to the source localization problem, a simple weighted minimum norm and a maximum smoothness (Loreta-like) solution. This involved simulations, using single and multiple sources that were analyzed under different levels of confidence in the priors.

Brain↗
PubMed2002

Systematic regularization of linear inverse solutions of the EEG source localization problem.

Distributed linear solutions of the EEG source localization problem are used routinely. Here we describe an approach based on the weighted minimum norm method that imposes constraints using anatomical and physiological information derived from other imaging modalities to regularize the solution. In this approach the hyperparameters controlling the degree of regularization are estimated using restricted maximum likelihood (ReML). EEG data are always contaminated by noise, e.g., exogenous noise and background brain activity. The conditional expectation of the source distribution, given the data, is attained by carefully balancing the minimization of the residuals induced by noise and the improbability of the estimates as determined by their priors. This balance is specified by hyperparameters that control the relative importance of fitting and conforming to prior constraints. Here we introduce a systematic approach to this regularization problem, in the context of a linear observation model we have described previously. In this model, basis functions are extracted to reduce the solution space a priori in the spatial and temporal domains. The basis sets are motivated by knowledge of the evoked EEG response and information theory. In this paper we focus on an iterative "expectation-maximization" procedure to jointly estimate the conditional expectation of the source distribution and the ReML hyperparameters on which this solution rests. We used simulated data mixed with real EEG noise to explore the behavior of the approach with various source locations, priors, and noise levels. The results enabled us to conclude: (i) Solutions in the space of informed basis functions have a high face and construct validity, in relation to conventional analyses. (ii) The hyperparameters controlling the degree of regularization vary largely with source geometry and noise. The second conclusion speaks to the usefulness of using adaptative ReML hyperparameter estimates.

Algorithms↗
PubMed2002

Brain function in the vegetative state.

Positron emission tomography (PET) techniques represent a useful tool to better understand the residual brain function in vegetative state patients. It has been shown that overall cerebral metabolic rates for glucose are massively reduced in this condition. However, the recovery of consciousness from vegetative state is not always associated with substantial changes in global metabolism. This finding led us to hypothesize that some vegetative patients are unconscious not just because of a global loss of neuronal function, but rather due to an altered activity in some critical brain regions and to the abolished functional connections between them. We used voxel-based Statistical Parametric Mapping (SPM) approaches to characterize the functional neuroanatomy of the vegetative state. The most dysfunctional brain regions were bilateral frontal and parieto-temporal associative cortices. Despite the metabolic impairment, external stimulation still induced a significant neuronal activation (i.e., change in blood flow) in vegetative patients as shown by both auditory click stimuli and noxious somatosensory stimuli. However, this activation was limited to primary cortices and dissociated from higher-order associative cortices, thought to be necessary for conscious perception. Finally, we demonstrated that vegetative patients have impaired functional connections between distant cortical areas and between the thalami and the cortex and, more importantly, that recovery of consciousness is paralleled by a restoration of this cortico-thalamo-cortical interaction.

Cerebral Cortex↗