Effects of Anesthetic Agents on Brain Blood Oxygenation Level Revealed with Ultra-High Field MRI

During general anesthesia it is crucial to control systemic hemodynamics and oxygenation levels. However, anesthetic agents can affect cerebral hemodynamics and metabolism in a drug-dependent manner, while systemic hemodynamics is stable. Brain-wide monitoring of this effect remains highly challenging. Because T2*-weighted imaging at ultra-high magnetic field strengths benefits from a dramatic increase in contrast to noise ratio, we hypothesized that it could monitor anesthesia effects on brain blood oxygenation. We scanned rat brains at 7T and 17.2T under general anesthesia using different anesthetics (isoflurane, ketamine-xylazine, medetomidine). We showed that the brain/vessels contrast in T2*-weighted images at 17.2T varied directly according to the applied pharmacological anesthetic agent, a phenomenon that was visible, but to a much smaller extent at 7T. This variation is in agreement with the mechanism of action of these agents. These data demonstrate that preclinical ultra-high field MRI can monitor the effects of a given drug on brain blood oxygenation level in the absence of systemic blood oxygenation changes and of any neural stimulation

Stimulation thalamique et tremblement essentiel

PARIS6-Bibl.Pitié-Salpêtrie (751132101) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Production de dopamine dans le striatum par transfert lentiviral des gènes de la TH, AADC et CH1 (étude dans des modèles primates de la maladie de Parkinson)

La déplétion dopaminergique du striatum entraîne une akinésie et une difficulté à initier les programmes moteurs qui caractérisent la maladie de Parkinson. Cependant, le traitement dopaminergique oral discontinu et chronique induit des fluctuations motrices et des mouvements anormaux involontaires appelés dyskinésies. Ici nous rapportons que le transfert lentiviral des gènes codant pour la biosynthèse de la dopamine (TH, AADC, CH1) aux cellules du striatum a induit une modification du fonctionnement cellulaire avec une sécrétion locale de dopamine, une restauration importante du comportement moteur, une prévention complète des complications motrices induites par le traitement dopaminergique systémique, une résistance à l induction des complications motrices après administration de médicaments dopaminergiques dans des modèles primates de la maladie de Parkinson avancée. La production locale et continue de dopamine par transfert lentiviral de gènes a permis également de restaurer la circuiterie des ganglions de la base en normalisant l activité neuronale électrique dans le globus pallidus interne ainsi que le métabolisme du noyau subthalamique. Nos résultats démontrent que la thérapie génique de la dopamine a un potentiel pour restaurer la fonction dopaminergique perdue au cours de la maladie de Parkinson.Reduced dopamine innervation of the striatum in Parkinson s disease results in akinesia and difficulty in initiating different motor programs. Conversely, enhanced striatal dopamine activity after months of pulsatile dopaminergic treatment will instead give rise to motor fluctuations and abnormal involuntary movements called dyskinesias. A concept postulates that a continuous delivery of a dopaminergic molecule will prevent motor complications by restoring dopaminergic tonus in the striatum. Whereas current systemic pharmacological strategies fail to restore local dopamine levels, grafting with fetal dopaminergic neurons in the striatum have been associated with off-phase dyskinesias. Here we report that striatal delivery of genes necessary for dopamine synthesis, i.e. TH, AADC, and CH1, in a single lentiviral vector restored the dopaminergic tonus, corrected motor deficits, prevented and reversed drug induced motor complications in primate models of advanced Parkinson s disease. Lentiviral dopamine production in the striatum normalized internal globus pallidus neuronal activity and restored subthalamic nucleus metabolism. Our results demonstrate that dopamine gene therapy has a potential to solve the clinically relevant issue of parkinsonism correction in patients.PARIS-BIUSJ-Thèses (751052125) / SudocPARIS-BIUSJ-Physique recherche (751052113) / SudocSudocFranceF

Revisiting the standard for modeling functional brain network activity: application to consciousness

Consciousness can be characterized by studying spontaneous fluctuations in brain activity, commonly measured with resting-state functional Magnetic Resonance Imaging (rs-fMRI). Previous rs-fMRI studies in monkeys and humans have shown that different levels of consciousness are defined by the relative prevalence of different dynamical functional connectivity patterns, also called brain patterns. These patterns closely match the underlying structural connectivity when consciousness is lost. The results suggest that changes in the state of consciousness lead to changes in connectivity patterns, not only at the level of co-activation strength between regions, but also at the level of entire networks.Here, we use a linear latent variable model that provides interpretable brain networks to reveal a new signature of consciousness and its chemically induced loss during anesthesia. To identify interpretable spatial signatures of consciousness, we apply a four-step framework by i) generating a list of atlases, ii) filtering and extracting the time series associated with the brain Regions of Interest (ROIs) of each atlas, iii) decomposing the signals into tailored brain networks with associated Brain Network Activities (BNAs), and iv) performing statistical inference and multivariate analysis of the BNAs. The novelty of the framework lies in the adoption of a constrained linear latent variable model that provides BNAs based on identifiable and disjoint ROIs, called brain networks, and the ability to offer a sound basis for atlas selection given the underlying clinical question.The model yields a set of tailored brain networks and associated BNAs that characterize states of consciousness. Our results suggest that a network composed of fronto-parietal and cingular cortices strongly influences the shift of consciousness state, especially between anesthesia and wakefulness. Interestingly, this observation is consistent with the global neural workspace theory of consciousness. We also decipher the level of anesthesia from rs-fMRI-derived BNAs. We identify neurobiologically relevant brain networks that provide novel interpretable signatures of consciousness and its loss during anesthesia. These findings pave the way for translational applications such as the diagnosis of consciousness disorders

Quantification of iron in the non-human primate brain with diffusion-weighted magnetic resonance imaging

AbstractPathological iron deposits in the brain, especially within basal ganglia, are linked to severe neurodegenerative disorders like Parkinson's disease. As iron induces local changes in magnetic susceptibility, its presence can be visualized with magnetic resonance imaging (MRI). The usual approach, based on iron induced changes in magnetic relaxation (T2/T2′), is often prone, however, to confounding artifacts and lacks specificity. Here, we propose a new method to quantify and map iron deposits using water diffusion MRI. This method is based on the differential sensitivity of two image acquisition schemes to the local magnetic field gradients induced by iron deposits and their cross-term with gradient pulses used for diffusion encoding. Iron concentration could be imaged and estimated with high accuracy in the brain cortex, the thalamus, the substantia nigra and the globus pallidus of macaques, showing iron distributions in agreement with literature. Additionally, iron maps could clearly show a dramatic increase in iron content upon injection of an UltraSmall Particle Iron Oxide (USPIO) contrast agent, notably in the cortex and the thalamus, reflecting regional differences in blood volume. The method will benefit clinical investigations on the effect of iron deposits in the brain or other organs, as iron deposits are increasingly seen as a biomarker for a wide range of diseases, notably, neurodegenerative diseases in the pre-symptomatic stage. It also has the potential for quantifying variations in blood volume induced by brain activation in fMRI studies using USPIOs

Deep learning models reveal the link between dynamic brain connectivity patterns and states of consciousness

Decoding states of consciousness from brain activity is a central challenge in neuroscience. Dynamic functional connectivity (dFC) allows the study of short-term temporal changes in functional connectivity (FC) between distributed brain areas. By clustering dFC matrices from resting-state fMRI, we previously described "brain patterns" that underlie different functional configurations of the brain at rest. The networks associated with these patterns have been extensively analyzed. However, the overall dynamic organization and how it relates to consciousness remains unclear. We hypothesized that deep learning networks would help to model this relationship. Using low-dimensional variational autoencoders (VAE), recent studies have attempted to learn meaningful representations that can help explain consciousness. Here, we investigated the complexity ofselecting such a generative model to study brain dynamics, and extended the available methods for latent space characterization and modeling. Therefore, our contributions are threefold. First, in comparison with probabilistic principal component analysis and sparse VAE, we showed that the selected low-dimensional VAE exhibits balanced performance in reconstructing dFCs and classifying brain patterns. The organization of the obtained low-dimensional dFC latent representations was then explored. We showed how these representations stratify the dynamic organization of the brain patterns as well as the experimental conditions. Finally, we proposed to delve into the proposed brain computational model. A receptive field analysis was first applied to identify preferred directions in the latent space to move from one brain pattern to another. Then, an ablation studywas achieved where specific brain areas were virtually inactivated. We demonstrated the efficiency of the model in summarizing consciousness-specific information that is encoded in key inter-areal connections, as described in the global neural workspace theory of consciousness. The proposed framework advocates the possibility to develop an interpretable computational brain model of interest for disorders of consciousness, paving the way for a dynamic diagnostic support tool

Codes associated with Deep learning models reveal the link between dynamic brain connectivity patterns and states of consciousness

<p>This record contains the material of the paper "<strong>Deep learning models reveal the link between dynamic brain connectivity patterns and states of consciousness</strong>". In this paper, our contributions can be summarized in three aspects. First, we demonstrated that the selected low-dimensional Variational Autoencoder (VAE) performs well in reconstructing dynamic functional connectivity (dFCs) and classifying brain patterns, compared to other linear and nonlinear models. Second, we explored the organization of the obtained dFC latent representations, revealing their ability to stratify the dynamic organization of brain patterns and experimental conditions. Finally, we proposed two simulation paradigms, including a receptive field analysis to identify preferred directions in the latent space to move from one brain pattern to another, and an ablation study in which specific brain areas were virtually inactivated to study the transition from wakefulness to unconsciousness.</p&gt

Using the Accelerometers Integrated in Smartphones to Evaluate Essential Tremor

&lt;b&gt;&lt;i&gt;Background/Aims:&lt;/i&gt;&lt;/b&gt; Evaluation of tremor constitutes a crucial step from the diagnosis to the initial treatment and follow-up of patients with essential tremor. The severity of tremor can be evaluated using clinical rating scales, accelerometry, or electrophysiology. Clinical scores are subjectively given, may be affected by intra- and interevaluator variations due to different experience, delays between consultations, and subtle changes in tremor severity. Existing medical devices are not routinely used: they are expensive, time-consuming, not easily accessible. We aimed at showing that a smartphone application using the accelerometers embedded in smartphones is effective for quantifying the tremor of patients presenting with essential tremor. &lt;b&gt;&lt;i&gt;Methods:&lt;/i&gt;&lt;/b&gt; We developed a free iPhone/iPod application, Itremor, and evaluated different parameters on 8 patients receiving deep brain stimulation of the ventral intermediate nucleus of the thalamus: average and maximum accelerations, time above 1 &lt;i&gt;g&lt;/i&gt; of acceleration, peak frequency, typical magnitude of tremor, for postural and action tremors, on and off stimulation. &lt;b&gt;&lt;i&gt;Results:&lt;/i&gt;&lt;/b&gt; We demonstrated good correlations between the parameters measured with Itremor and clinical score in all conditions. Itremor evaluation enabled higher discriminatory power and degree of reproducibility than clinical scores. &lt;b&gt;&lt;i&gt;Conclusion:&lt;/i&gt;&lt;/b&gt; Itremor can be used for routine objective evaluation of essential tremor, and may facilitate adjustment of the treatment.</jats:p

Revisiting the standard for modeling functional brain network activity: Application to consciousness.

Functional connectivity (FC) of resting-state fMRI time series can be estimated using methods that differ in their temporal sensitivity (static vs. dynamic) and the number of regions included in the connectivity estimation (derived from a prior atlas). This paper presents a novel framework for identifying and quantifying resting-state networks using resting-state fMRI recordings. The study employs a linear latent variable model to generate spatially distinct brain networks and their associated activities. It specifically addresses the atlas selection problem, and the statistical inference and multivariate analysis of the obtained brain network activities. The approach is demonstrated on a dataset of resting-state fMRI recordings from monkeys under different anesthetics using static FC. Our results suggest that two networks, one fronto-parietal and cingular and another temporo-parieto-occipital (posterior brain) strongly influences shifts in consciousness, especially between anesthesia and wakefulness. Interestingly, this observation aligns with the two prominent theories of consciousness: the global neural workspace and integrated information theories of consciousness. The proposed method is also able to decipher the level of anesthesia from the brain network activities. Overall, we provide a framework that can be effectively applied to other datasets and may be particularly useful for the study of disorders of consciousness