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André Luxen

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23 papers
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23

YNIMG Journal 2023 Journal Article

Association between sleep slow-wave activity and in-vivo estimates of myelin in healthy young men

  • Michele Deantoni
  • Marion Baillet
  • Gregory Hammad
  • Christian Berthomier
  • Mathilde Reyt
  • Mathieu Jaspar
  • Christelle Meyer
  • Maxime Van Egroo

Sleep has been suggested to contribute to myelinogenesis and associated structural changes in the brain. As a principal hallmark of sleep, slow-wave activity (SWA) is homeostatically regulated but also differs between individuals. Besides its homeostatic function, SWA topography is suggested to reflect processes of brain maturation. Here, we assessed whether interindividual differences in sleep SWA and its homeostatic response to sleep manipulations are associated with in-vivo myelin estimates in a sample of healthy young men. Two hundred twenty-six participants (18-31 y.) underwent an in-lab protocol in which SWA was assessed at baseline (BAS), after sleep deprivation (high homeostatic sleep pressure, HSP) and after sleep saturation (low homeostatic sleep pressure, LSP). Early-night frontal SWA, the frontal-occipital SWA ratio, as well as the overnight exponential SWA decay were computed over sleep conditions. Semi-quantitative magnetization transfer saturation maps (MTsat), providing markers for myelin content, were acquired during a separate laboratory visit. Early-night frontal SWA was negatively associated with regional myelin estimates in the temporal portion of the inferior longitudinal fasciculus. By contrast, neither the responsiveness of SWA to sleep saturation or deprivation, its overnight dynamics, nor the frontal/occipital SWA ratio were associated with brain structural indices. Our results indicate that frontal SWA generation tracks inter-individual differences in continued structural brain re-organization during early adulthood. This stage of life is not only characterized by ongoing region-specific changes in myelin content, but also by a sharp decrease and a shift towards frontal predominance in SWA generation.

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YNIMG Journal 2018 Journal Article

Human fronto-parietal response scattering subserves vigilance at night

  • Giulia Gaggioni
  • Julien Q.M. Ly
  • Sarah L. Chellappa
  • Dorothée Coppieters ‘t Wallant
  • Mario Rosanova
  • Simone Sarasso
  • André Luxen
  • Eric Salmon

Lack of sleep has a considerable impact on vigilance: we perform worse, we make more errors, particularly at night, when we should be sleeping. Measures of brain functional connectivity suggest that decrease in vigilance during sleep loss is associated with an impaired cross-talk within the fronto-parietal cortex. However, fronto-parietal effective connectivity, which is more closely related to the causal cross-talk between brain regions, remains unexplored during prolonged wakefulness. In addition, no study has simultaneously investigated brain effective connectivity and wake-related changes in vigilance, preventing the concurrent incorporation of the two aspects. Here, we used electroencephalography (EEG) to record responses evoked by Transcranial Magnetic Stimulation (TMS) applied over the frontal lobe in 23 healthy young men (18–30 yr.), while they simultaneously performed a vigilance task, during 8 sessions spread over 29 h of sustained wakefulness. We assessed Response Scattering (ReSc), an estimate of effective connectivity, as the propagation of TMS-evoked EEG responses over the fronto-parietal cortex. Results disclose a significant change in fronto-parietal ReSc with time spent awake. When focusing on the night-time period, when one should be sleeping, participants with lower fronto-parietal ReSc performed worse on the vigilance task. Conversely, no association was detected during the well-rested, daytime period. Night-time fronto-parietal ReSc also correlated with objective EEG measures of sleepiness and alertness. These changes were not accompanied by variations in fronto-parietal response complexity. These results suggest that decreased brain response propagation within the fronto-parietal cortex is associated to increased vigilance failure during night-time prolonged wakefulness. This study reveals a novel facet of the detrimental effect on brain function of extended night-time waking hours, which is increasingly common in our societies.

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YNICL Journal 2014 Journal Article

Biased binomial assessment of cross-validated estimation of classification accuracies illustrated in diagnosis predictions

  • Quentin Noirhomme
  • Damien Lesenfants
  • Francisco Gomez
  • Andrea Soddu
  • Jessica Schrouff
  • Gaëtan Garraux
  • André Luxen
  • Christophe Phillips

Multivariate classification is used in neuroimaging studies to infer brain activation or in medical applications to infer diagnosis. Their results are often assessed through either a binomial or a permutation test. Here, we simulated classification results of generated random data to assess the influence of the cross-validation scheme on the significance of results. Distributions built from classification of random data with cross-validation did not follow the binomial distribution. The binomial test is therefore not adapted. On the contrary, the permutation test was unaffected by the cross-validation scheme. The influence of the cross-validation was further illustrated on real-data from a brain-computer interface experiment in patients with disorders of consciousness and from an fMRI study on patients with Parkinson disease. Three out of 16 patients with disorders of consciousness had significant accuracy on binomial testing, but only one showed significant accuracy using permutation testing. In the fMRI experiment, the mental imagery of gait could discriminate significantly between idiopathic Parkinson's disease patients and healthy subjects according to the permutation test but not according to the binomial test. Hence, binomial testing could lead to biased estimation of significance and false positive or negative results. In our view, permutation testing is thus recommended for clinical application of classification with cross-validation.

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YNICL Journal 2013 Journal Article

Multiclass classification of FDG PET scans for the distinction between Parkinson's disease and atypical parkinsonian syndromes

  • Gaëtan Garraux
  • Christophe Phillips
  • Jessica Schrouff
  • Alexandre Kreisler
  • Christian Lemaire
  • Christian Degueldre
  • Christian Delcour
  • Roland Hustinx

Most available pattern recognition methods in neuroimaging address binary classification problems. Here, we used relevance vector machine (RVM) in combination with booststrap resampling ('bagging') for non-hierarchical multiclass classification. The method was tested on 120 cerebral (18)fluorodeoxyglucose (FDG) positron emission tomography (PET) scans performed in patients who exhibited parkinsonian clinical features for 3.5 years on average but that were outside the prevailing perception for Parkinson's disease (PD). A radiological diagnosis of PD was suggested for 30 patients at the time of PET imaging. However, at follow-up several years after PET imaging, 42 of them finally received a clinical diagnosis of PD. The remaining 78 APS patients were diagnosed with multiple system atrophy (MSA, N = 31), progressive supranuclear palsy (PSP, N = 26) and corticobasal syndrome (CBS, N = 21), respectively. With respect to this standard of truth, classification sensitivity, specificity, positive and negative predictive values for PD were 93% 83% 75% and 96%, respectively using binary RVM (PD vs. APS) and 90%, 87%, 79% and 94%, respectively, using multiclass RVM (PD vs. MSA vs. PSP vs. CBS). Multiclass RVM achieved 45%, 55% and 62% classification accuracy for, MSA, PSP and CBS, respectively. Finally, a majority confidence ratio was computed for each scan on the basis of class pairs that were the most frequently assigned by RVM. Altogether, the results suggest that automatic multiclass RVM classification of FDG PET scans achieves adequate performance for the early differentiation between PD and APS on the basis of cerebral FDG uptake patterns when the clinical diagnosis is felt uncertain. This approach cannot be recommended yet as an aid for distinction between the three APS classes under consideration.

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YNIMG Journal 2012 Journal Article

Neural correlates of performance variability during motor sequence acquisition

  • Geneviève Albouy
  • Virginie Sterpenich
  • Gilles Vandewalle
  • Annabelle Darsaud
  • Steffen Gais
  • Géraldine Rauchs
  • Martin Desseilles
  • Mélanie Boly

During the initial training of a motor sequence, performance becomes progressively faster but also increasingly reproducible and consistent. However, performance temporarily becomes more variable at mid-training, reflecting a change in the motor representation and the eventual selection of the optimal performance mode (Adi-Japha et al. , 2008). At the cerebral level, whereas performance speed is known to be related to the activity in cerebello-cortical and striato-cortical networks, the neural correlates of performance variability remain unknown. We characterized the latter using functional magnetic resonance imaging (fMRI) during the initial training to the Finger Tapping Task (FTT), during which participants produced a 5-element finger sequence on a keyboard with their left non-dominant hand. Our results show that responses in the precuneus decrease whereas responses in the caudate nucleus increase as performance becomes more consistent. In addition, a variable performance is associated with enhanced interaction between the hippocampus and fronto-parietal areas and between the striatum and frontal areas. Our results suggest that these dynamic large-scale interactions represent a cornerstone in the implementation of consistent motor behavior in humans.

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YNIMG Journal 2012 Journal Article

PET radiotracers for molecular imaging in the brain: Past, present and future

  • Luc Zimmer
  • André Luxen

Neuroimaging of brain receptors began in the early 1980s. Now, some thirty-five years later, PET imaging is still an expanding field of preclinical and clinical investigations. In addition to improvements in PET cameras and image analysis, the availability of suitable radiotracers is a crucial factor leading this expansion. Radiotracers have been developed to visualize and quantify a growing numbers of brain receptors, transporters, enzymes and other molecular targets. The development of adequate PET radiotracers represents an exciting challenge, given the large number of targets and neurochemical functions that have yet to be explored. In this article, we review the main evolutions led by preclinical radiotracers and clinical radiopharmaceuticals. The current main contributions of PET radiotracers are described in terms of imaging of neuronal metabolism, receptor and transporter quantification and neurodegenerative, neuroinflammatory and neurooncologic process imaging. In the last part, we highlight some applications presenting a potential for novel functional explorations of the brain.

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YNIMG Journal 2011 Journal Article

“Relevance vector machine” consciousness classifier applied to cerebral metabolism of vegetative and locked-in patients

  • Christophe L. Phillips
  • Marie-Aurelie Bruno
  • Pierre Maquet
  • Mélanie Boly
  • Quentin Noirhomme
  • Caroline Schnakers
  • Audrey Vanhaudenhuyse
  • Maxime Bonjean

The vegetative state is a devastating condition where patients awaken from their coma (i. e. , open their eyes) but fail to show any behavioural sign of conscious awareness. Locked-in syndrome patients also awaken from their coma and are unable to show any motor response to command (except for small eye movements or blinks) but recover full conscious awareness of self and environment. Bedside evaluation of residual cognitive function in coma survivors often is difficult because motor responses may be very limited or inconsistent. We here aimed to disentangle vegetative from “locked-in” patients by an automatic procedure based on machine learning using fluorodeoxyglucose PET data obtained in 37 healthy controls and in 13 patients in a vegetative state. Next, the trained machine was tested on brain scans obtained in 8 patients with locked-in syndrome. We used a sparse probabilistic Bayesian learning framework called “relevance vector machine” (RVM) to classify the scans. The trained RVM classifier, applied on an input scan, returns a probability value (p-value) of being in one class or the other, here being “conscious” or not. Training on the control and vegetative state groups was assessed with a leave-one-out cross-validation procedure, leading to 100% classification accuracy. When applied on the locked-in patients, all scans were classified as “conscious” with a mean p-value of. 95 (min. 85). In conclusion, even with this relatively limited data set, we could train a classifier distinguishing between normal consciousness (i. e. , wakeful conscious awareness) and the vegetative state (i. e. , wakeful unawareness). Cross-validation also indicated that the clinical classification and the one predicted by the automatic RVM classifier were in accordance. Moreover, when applied on a third group of “locked-in” consciously aware patients, they all had a strong probability of being similar to the normal controls, as expected. Therefore, RVM classification of cerebral metabolic images obtained in coma survivors could become a useful tool for the automated PET-based diagnosis of altered states of consciousness.

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