Quantized valley Hall response from local bulk density variations

The application of a mechanical strain to a 2D material can create pseudo-magnetic fields and lead to a quantized valley Hall effect. However, measuring valley-resolved effects remains a challenging task due to their inherent fragility and dependence on the sample’s proper design. Additionally, non-local transport probes based on multiterminal devices have often proven to be inadequate in yielding conclusive evidence of the valley Hall signal. Here, we introduce an alternative way of detecting the quantized valley Hall effect, which entirely relies on local density measurements, performed deep in the bulk of the sample. The resulting quantized signal is a genuine Fermi sea response, independent of the edge physics, and reflects the underlying valley Hall effect through the Widom-Středa formula. Specifically, our approach is based on measuring the variation of the particle density, locally in the bulk, upon varying the strength of the applied strain. This approach to the quantized valley Hall effect is particularly well suited for experiments based on synthetic lattices, where the particle density (or integrated density of states) can be spatially resolved

Quantized valley Hall response from local bulk density variations

The application of a mechanical strain to a 2D material can create pseudo-magnetic fields and lead to a quantized valley Hall effect. However, measuring valley-resolved effects remains a challenging task due to their inherent fragility and dependence on the sample's proper design. Additionally, non-local transport probes based on multiterminal devices have often proven to be inadequate in yielding conclusive evidence of the valley Hall signal. Here, we introduce an alternative way of detecting the quantized valley Hall effect, which entirely relies on local density measurements, performed deep in the bulk of the sample. The resulting quantized signal is a genuine Fermi sea response, independent of the edge physics, and reflects the underlying valley Hall effect through the Widom-St\v{r}eda formula. Specifically, our approach is based on measuring the variation of the particle density, locally in the bulk, upon varying the strength of the applied strain. This approach to the quantized valley Hall effect is particularly well suited for experiments based on synthetic lattices, where the particle density (or integrated density of states) can be spatially resolved.Comment: 17 pages, 12 figure

Stimulation Cérébrale Profonde, Maladie de Parkinson, Apprentissage Machine, Système d’Aide à la Décision Clinique

Deep Brain Stimulation (DBS) is a successful and encouraging way of treating abnormal movement diseases, such as Parkinson’s Disease (PD). The success of the surgical procedure depends on many variables, most of which are derivative from a great number of modalities. Various problems gravitate throughout the care of the pa-tient, from its screening, to the procedure itself and the stimulation follow-up, creating an urging need to develop computer assisting tools. In this thesis, we used data-driven methods to design two systems in order to address two concrete clinical applications. Firstly, we propose a tool to assist clinicians in decision making for selecting patients and stimulation targets. It consists in a data-driven method which is able to predict the clinical outcomes (motor, neuropsychologic, cognitive etc.) of the surgery, from pre-operative multimodal biomarkers. Secondarily, we propose to greatly fasten the surgical procedure by automatizing the location of the target nucleus via a real time treatment of the electrophysiological signal arising from the patient’s brain, from micro-electrode recordings(MER). Our method is able, in one second, to accurately analyse the MER and predict whether the electrode lead is inside the STN or not, and does not require any parameter tuning nor calibration to work on a new data source.La Stimulation Cérébrale Profonde (SCP) est une thérapie efficace pour traiter les maladies des mouvements anormaux, telle que la Maladie de Parkinson (MP). Le succès de la SCP dépend de nombreuses variables issues d’un grand nombre de modalités de données. Divers problèmes sont rencontrés tout au long de la prise en charge du patient, de sa sélection à la procédure elle-même et au suivi post-opératoire, dénotant un besoin urgent de développer des outils d’assistance informatique. Dans cette thèse, nous proposons deux systèmes, basés sur l’apprentissage machine, afin de résoudre deux problèmes cliniques concrets. Premièrement, nous proposons un outil capable d’aider les cliniciens dans le choix de sélection des patients et des cibles de stimulation. Notre méthode est capable de prédire les résultats cliniques (moteurs, neuropsychologiques, cognitifs, etc.) de laSCP à partir de biomarqueurs multimodaux préopératoires. Deuxièmement, nous proposons un outil permettant d’accélérer grandement la chirurgie en assistant la localisation du noyau cible via un traitement en temps réel du signal électrophysiologique provenant du cerveau du patient, à partir d’enregistrements par micro-électrodes d’une seconde seulement

PassFlow: a multimodal workflow for predicting deep brain stimulation outcomes

International audiencePurpose: Deep Brain Stimulation (DBS) is a proven therapy for Parkinson's Disease (PD), frequently resulting in an enhancement of motor function. Nonetheless, several undesirable side effects can occur after DBS, which can worsen the quality of life of the patient. Thus, the clinical team has to carefully select patients on whom to perform DBS. Over the past decade, there have been some attempts to relate pre-operative data and DBS clinical outcomes, with most focused on the motor symptomatology. In this paper, we propose a machine learning based method able to predict a large number of DBS clinical outcomes for PD.Methods: We propose a multimodal pipeline, referred to as PassFlow, which predicts 84 clinical post-operative clinical scores. PassFlow is composed of an artificial neural network to compress clinical information, an image processing method from the state-of-the-art to extract morphological biomarkers our of T1 imaging, and an SVM to perform the regressions. We validated PassFlow on 196 PD patients who undergone a DBS.Results: PassFlow showed correlation coefficients as high as 0.71 and were able to significantly predict 63 out of the 84 scores, outperforming a comparative linear method. The number of metrics that are predicted with this pre-operative information was also found to be correlated with the number of patients with this information available, indicating that the PassFlow method is still actively learning.Conclusion: We presented a novel, machine learning based pipeline to predict a variety of post-operative clinical outcomes of DBS for PD patients. PassFlow took into account various bio-markers, arising from different data modalities, showing high correlation coefficients for some scores from pre-operative data only. It indicates that many clinical outcomes of DBS can be predicted agnostic to the specific simulation parameters, as PassFlow has been validated without such stimulation-related information

Data Imputation and Compression For Parkinson's Disease Clinical Questionnaires

International audienceMedical questionnaires are a valuable source of information but are often difficult to analyse due to both their size and the high possibility of having missing values. This is a problematic issue in biomedical data science as it may complicate how individual questionnaire data is represented for statistical or machine learning analysis. In this paper, we propose a deeply-learnt residual autoencoder to simultaneously perform non-linear data imputation and dimensionality reduction. We present an extensive analysis of the dynamics of the performances of this autoencoder regarding the compression rate and the proportion of missing values. This method is evaluated on motor and non-motor clinical questionnaires of the Parkinson's Progression Markers Initiative (PPMI) database and consistently outperforms linear coupled imputation and reduction approaches

Striatal Shape Alteration as a Staging Biomarker for Parkinson's Disease

International audienceParkinson's Disease provokes alterations of subcortical deep gray matter, leading to subtle changes in the shape of several subcortical structures even before the manifestation of motor and non-motor clinical symptoms. We used an automated registration and segmentation pipeline to measure this structural alteration in one early and one advanced Parkinson's Disease (PD) cohorts, one prodromal stage cohort and one healthy control cohort. These structural alterations are then passed to a machine learning pipeline to classify these populations. Our workflow is able to distinguish different stages of PD based solely on shape analysis of the bilateral caudate nucleus and putamen, with balanced accuracies in the range of 59% to 85%. Furthermore, we compared the significance of each of these subcortical structure, compared the performances of different classifiers on this task, thus quantifying the informativeness of striatal shape alteration as a staging bio-marker for PD

Quantized valley Hall response from local bulk density variations

Abstract The application of a mechanical strain to a 2D material can create pseudo-magnetic fields and lead to a quantized valley Hall effect. However, measuring valley-resolved effects remains a challenging task due to their inherent fragility and dependence on the sample’s proper design. Additionally, non-local transport probes based on multiterminal devices have often proven to be inadequate in yielding conclusive evidence of the valley Hall signal. Here, we introduce an alternative way of detecting the quantized valley Hall effect, which entirely relies on local density measurements, performed deep in the bulk of the sample. The resulting quantized signal is a genuine Fermi sea response, independent of the edge physics, and reflects the underlying valley Hall effect through the Widom-Středa formula. Specifically, our approach is based on measuring the variation of the particle density, locally in the bulk, upon varying the strength of the applied strain. This approach to the quantized valley Hall effect is particularly well suited for experiments based on synthetic lattices, where the particle density (or integrated density of states) can be spatially resolved.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Quantized valley Hall response from local bulk density variations

info:eu-repo/semantics/publishe