A5085263913- Advanced Neuroimaging Techniques and Applications
- Neural dynamics and brain function
- Functional Brain Connectivity Studies
- Medical Imaging and Analysis
- Vestibular and auditory disorders
- Hearing, Cochlea, Tinnitus, Genetics
- Mental Health Research Topics
- Advanced MRI Techniques and Applications
- Musculoskeletal pain and rehabilitation
- Cervical and Thoracic Myelopathy
- Stroke Rehabilitation and Recovery
- Brain Tumor Detection and Classification
- Spine and Intervertebral Disc Pathology
- Radiomics and Machine Learning in Medical Imaging
- Spinal Cord Injury Research
University of Geneva
2021-2025
École Polytechnique Fédérale de Lausanne
2021-2025
Harvard University
2021-2023
École Polytechnique
2021
Abstract The lumbosacral spinal cord contains neural circuits crucial for locomotion, organized into rostrocaudal levels with distinct somatosensory and motor neuron pools that project to from the muscles of lower limbs. However, specific innervate each muscle locations vary significantly between individuals, presenting challenges targeted therapies neurosurgical interventions aimed at restoring locomotion. Non-invasive approaches functionally map segmental distribution innervation – or...
Understanding the organizational principles of human brain activity at systems level remains a major challenge in network neuroscience. Here, we introduce fully data-driven approach based on graph learning to extract meaningful repeating patterns from regionally-averaged timecourses. We use Graph Laplacian Mixture Model (GLMM), generative model that treats functional data as collection signals expressed multiple underlying graphs. By exploiting covariance between regions, these graphs can be...
In recent years, while the exploration of spontaneous brain activity has shifted from static methods (i.e., examining average connectivity patterns over entire run) towards dynamic approaches accounting for non-stationarity resting-state fluctuations), non-stationary nature intrinsic in spinal cord seldom been studied. Here, we propose to probe time-varying functional using a sliding-window correlation approach, as commonly employed brain. Our results suggest potential this approach unravel...
Summary The cerebellum is thought to detect and correct errors between intended executed commands 1–3 critical for social behaviors, cognition emotion 4–6 . Computations motor control must be performed quickly in real time should sensitive small differences patterns fine error correction while being resilient noise 7 Influential theories of cerebellar information processing have largely assumed random network connectivity, which increases the encoding capacity network’s first layer 8–13...
Abstract In the past decade, exploration of spontaneous blood-oxygen-level-dependent (BOLD) signal fluctuations has expanded beyond brain to include spinal cord. While most studies have predominantly focused on cervical region, lumbosacral segments play a crucial role in motor control and sensory processing lower limbs. Addressing this gap, aims current study were twofold: first, confirming presence nature organized BOLD signals human cord; second, systematically assessing impact various...
Motivation: Spinal cord cross-sectional area (CSA) is an important biomarker for neurodegenerative and traumatic diseases. However, CSA measurements vary across MRI contrasts imaging protocols, limiting its use in multi-center studies. Goal(s): The goal to evaluate variability using a novel contrast-agnostic segmentation method. Approach: We compared this method the Cord Toolbox's DeepSeg, analyzing different sites, vendors. Additionally, we segmentations diverse datasets pathologies....
Abstract In the past decade, exploration of spontaneous blood-oxygen-level-dependent (BOLD) signal fluctuations has expanded beyond brain to include spinal cord. While most studies have predominantly focused on cervical region, lumbosacral segments play a crucial role in motor control and sensory processing lower limbs. Addressing this gap, aims current study were two-fold: first, confirming presence nature organized BOLD signals human cord; second, systematically assessing impact various...
Abstract Understanding the organizational principles of human brain activity at systems level remains a major challenge in network neuroscience. Here, we introduce fully data-driven approach based on graph learning to extract meaningful repeating patterns from regionally-averaged time-courses. We use Graph Laplacian Mixture Model (GLMM), generative model that treats functional data as collection signals expressed multiple underlying graphs. By exploiting covariance between regions, these...