Le cerveau dans tous ses états. Des sciences cognitives au diagnostic : entretien avec Stéphane Lehéricy propos recueillis par Dominique Chouchan

Article suivi par un entretien "des sciences cognitives au diagnostic" avec Stéphane Lehéricy directeur du Centre de neuro-imagerie de recherche (CENIR) du CHU Pitié Salpêtrière et professeur dans le service de neuroradiologie de ce CHU. Propos recueillis par Dominique ChouchanNational audienceChacun de nos quelque 100 milliards de neurones peut communiquer avec des milliers d'autres : autant dire qu'à ce jour, le cerveau est pour l'essentiel terra incognita. On sait qu'il comporte des aires spécialisées (dans la vision, la marche, les émotions...) dites corticales, qui constituent la matière grise. Celles-ci s'échangent des messages, électriques notamment, au travers de fibres nerveuses, la substance blanche. La compréhension de l'anatomie du cerveau (structure spatiale) et de sa réponse à des stimuli (approche temporelle) vont donc de pair. Aujourd'hui, nous disposons de techniques de mesure et d'imagerie performantes. Mais encore faut-il interpréter les données obtenues. Un défi qui nécessite d'étroites collaborations entre mathématiciens, informaticiens, spécialistes des neurosciences et médecins

Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA

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Le cerveau dans tous ses états. Des sciences cognitives au diagnostic : entretien avec Stéphane Lehéricy propos recueillis par Dominique Chouchan

Article suivi par un entretien "des sciences cognitives au diagnostic" avec Stéphane Lehéricy directeur du Centre de neuro-imagerie de recherche (CENIR) du CHU Pitié Salpêtrière et professeur dans le service de neuroradiologie de ce CHU. Propos recueillis par Dominique ChouchanNational audienceChacun de nos quelque 100 milliards de neurones peut communiquer avec des milliers d'autres : autant dire qu'à ce jour, le cerveau est pour l'essentiel terra incognita. On sait qu'il comporte des aires spécialisées (dans la vision, la marche, les émotions...) dites corticales, qui constituent la matière grise. Celles-ci s'échangent des messages, électriques notamment, au travers de fibres nerveuses, la substance blanche. La compréhension de l'anatomie du cerveau (structure spatiale) et de sa réponse à des stimuli (approche temporelle) vont donc de pair. Aujourd'hui, nous disposons de techniques de mesure et d'imagerie performantes. Mais encore faut-il interpréter les données obtenues. Un défi qui nécessite d'étroites collaborations entre mathématiciens, informaticiens, spécialistes des neurosciences et médecins

Applicability of in vivo staging of regional amyloid burden in a cognitively normal cohort with subjective memory complaints: the INSIGHT-preAD study.

BACKGROUND:Current methods of amyloid PET interpretation based on the binary classification of global amyloid signal fail to identify early phases of amyloid deposition. A recent analysis of 18F-florbetapir PET data from the Alzheimer's disease Neuroimaging Initiative cohort suggested a hierarchical four-stage model of regional amyloid deposition that resembles neuropathologic estimates and can be used to stage an individual's amyloid burden in vivo. Here, we evaluated the validity of this in vivo amyloid staging model in an independent cohort of older people with subjective memory complaints (SMC). We further examined its potential association with subtle cognitive impairments in this population at elevated risk for Alzheimer's disease (AD). METHODS:The monocentric INSIGHT-preAD cohort includes 318 cognitively intact older individuals with SMC. All individuals underwent 18F-florbetapir PET scanning and extensive neuropsychological testing. We projected the regional amyloid uptake signal into the previously proposed hierarchical staging model of in vivo amyloid progression. We determined the adherence to this model across all cases and tested the association between increasing in vivo amyloid stage and cognitive performance using ANCOVA models. RESULTS:In total, 156 participants (49%) showed evidence of regional amyloid deposition, and all but 2 of these (99%) adhered to the hierarchical regional pattern implied by the in vivo amyloid progression model. According to a conventional binary classification based on global signal (SUVRCereb = 1.10), individuals in stages III and IV were classified as amyloid-positive (except one in stage III), but 99% of individuals in stage I and even 28% of individuals in stage II were classified as amyloid-negative. Neither in vivo amyloid stage nor conventional binary amyloid status was significantly associated with cognitive performance in this preclinical cohort. CONCLUSIONS:The proposed hierarchical staging scheme of PET-evidenced amyloid deposition generalizes well to data from an independent cohort of older people at elevated risk for AD. Future studies will determine the prognostic value of the staging approach for predicting longitudinal cognitive decline in older individuals at increased risk for AD

The brain signature of paracetamol in healthy volunteers: a double-blind randomized trial

International audienceBackground: Paracetamol’s (APAP) mechanism of action suggests the implication of supraspinal structures but no neuroimaging study has been performed in humans.Methods and results: This randomized, double-blind, crossover, placebo-controlled trial in 17 healthy volunteers (NCT01562704) aimed to evaluate how APAP modulates pain-evoked functional magnetic resonance imaging signals. We used behavioral measures and functional magnetic resonance imaging to investigate the response to experimental thermal stimuli with APAP or placebo administration. Region-of-interest analysis revealed that activity in response to noxious stimulation diminished with APAP compared to placebo in prefrontal cortices, insula, thalami, anterior cingulate cortex, and periaqueductal gray matter.Conclusion: These findings suggest an inhibitory effect of APAP on spinothalamic tracts leading to a decreased activation of higher structures, and a top-down influence on descending inhibition. Further binding and connectivity studies are needed to evaluate how APAP modulates pain, especially in the context of repeated administration to patients with pain

Comparative study of MRI biomarkers in the substantia nigra to discriminate idiopathic Parkinson disease

BACKGROUND AND PURPOSE: Several new MR imaging techniques have shown promising results in patients with Parkinson disease; however, the comparative diagnostic values of these measures at the individual level remain unclear. Our aim was to compare the diagnostic value of MR imaging biomarkers of substantia nigra damage for distinguishing patients with Parkinson disease from healthy volunteers. MATERIALS AND METHODS: Thirty-six patients and 20 healthy volunteers were prospectively included. The MR imaging protocol at 3T included 3D T2-weighted and T1-weighted neuromelanin-sensitive images, diffusion tensor images, and R2* mapping. T2* high-resolution images were also acquired at 7T to evaluate the dorsal nigral hyperintensity sign. Quantitative analysis was performed using ROIs in the substantia nigra drawn manually around the area of high signal intensity on neuromelanin-sensitive images and T2-weighted images. Visual analysis of the substantia nigra neuromelanin-sensitive signal intensity and the dorsolateral nigral hyperintensity on T2* images was performed. RESULTS: There was a significant decrease in the neuromelanin-sensitive volume and signal intensity in patients with Parkinson disease. There was also a significant decrease in fractional anisotropy and an increase in mean, axial, and radial diffusivity in the neuromelanin-sensitive substantia nigra at 3T and a decrease in substantia nigra volume on T2* images. The combination of substantia nigra volume, signal intensity, and fractional anisotropy in the neuromelanin-sensitive substantia nigra allowed excellent diagnostic accuracy (0.93). Visual assessment of both substantia nigra dorsolateral hyperintensity and neuromelanin-sensitive images had good diagnostic accuracy (0.91 and 0.86, respectively). CONCLUSIONS: The combination of neuromelanin signal and volume changes with fractional anisotropy measurements in the substantia nigra showed excellent diagnostic accuracy. Moreover, the high diagnostic accuracy of visual assessment of substantia nigra changes using dorsolateral hyperintensity analysis or neuromelanin-sensitive signal changes indicates that these techniques are promising for clinical practice

Machine learning based estimation of axonal permeability: validation on cuprizone treated in-vivo mouse model of axonal demyelination

Estimating axonal permeability reliably is extremely important, however not yet achieved because mathematical models that express its relationship to the MR signal accurately are intractable. Recently introduced machine learning based computational model showed to outperforms previous approximate mathematical models. Here we apply and validate this novel method experimentally on a highly controlled in-vivo mouse model of axonal demyelination, and demonstrate for the first time in practice the power of machine learning as a mechanism to construct complex biophysical models for quantitative MRI

Deep neural network based framework for in-vivo axonal permeability estimation

This study introduces a novel framework for estimating permeability from diffusion-weighted MRI data using deep learning. Recent work introduced a random forest (RF) regressor model that outperforms approximate mathematical models (Kärger model). Motivated by recent developments in machine learning, we propose a deep neural network (NN) approach to estimate the permeability associated with the water residence time. We show in simulations and in in-vivo mouse brain data that the NN outperforms the RF method. We further show that the performance of either ML method is unaffected by the choice of training data, i.e. raw diffusion signals or signal-derived features yield the same results

Longitudinal comparison of subjects with and without Sleep Disorders in Parkinson's Disease

International audiencePatients with Parkinson’s Disease (PD) may have very different patterns of progression, corresponding to distinct disease subtypes. Here, we describe quantitatively the overall pattern of progression in subgroups of PD by using a Bayesian non-linear mixed effect model that describes the continuous progression of biomarkers at both population and individual level. This approach allows to model variability in progression patterns and disease stage between patients. We analyzed two subgroups of patients, with (RBD+) and without sleep disorders (RBD-), that are known to present different patterns of progression [1]. We compared the two groups by extracting the ordering of abnormalities that occurred over the disease course, and by studying their disease onset and speed of progression