Publications associées à A Whole-Body Musculoskeletal Model of the Mouse

Publications associées (14)

Revealing Brain and Spinal Cord Functional Networks by Block-Structured Graph Laplacian Mixture Models

Pascal Frossard, Dimitri Nestor Alice Van De Ville, Ilaria Ricchi, Ekansh Sareen, William Cappelletti

The Graph Laplacian Mixture Model (GLMM) allows to infer multiple underlying graph structures from multivariate time series data. Given its effectiveness in identifying brain states from brain activity measured by functional magnetic resonance imaging (fMR ...

IEEE2025

Precision non-invasive brain stimulation: an in silico pipeline for personalized control of brain dynamics

Friedhelm Christoph Hummel, Elena Beanato

Objective. Non-invasive brain stimulation (NIBS) offers therapeutic benefits for various brain disorders. Personalization may enhance these benefits by optimizing stimulation parameters for individual subjects. Approach. We present a computational pipeline ...

2025

Regional specialization of movement encoding across the primate sensorimotor cortex

Rafael Emmanuel Ornelas Kobayashi, Christopher Marc Hitz, Grégoire Courtine, Jocelyne Bloch, Tomislav Milekovic, Simon Borgognon, Ismael Seanez Gonzalez, Nicolò Macellari

The process by which the cerebral cortex generates movements to achieve different tasks remains poorly understood. Here, we leveraged the rich repertoire of well-controlled primate locomotor behaviors to study how task-specific movements are encoded across ...

Springer Science and Business Media LLC2025

Tutorial: Intelligent Neural Interfaces: Fundamentals and Future Directions

Mahsa Shoaran

In the past decade, there has been growing interest in developing AI-enabled neural interfaces for various neurological disorders and emerging brain-machine interface (BMI) applications. The focus has shifted from raw signal acquisition and data compressio ...

IEEE2025

NAT: Learning to Attack Neurons for Enhanced Adversarial Transferability

Alexandre Massoud Alahi, Krishna Kanth Nakka

The generation of transferable adversarial perturbations typically involves training a generator to maximize embedding separation between clean and adversarial images at a single mid-layer of a source model. In this work, we build on this approach and intr ...

IEEE2025

Lumbosacral spinal cord functional connectivity at rest: From feasibility to reliability

Dimitri Nestor Alice Van De Ville, Nawal Noëlle Kinany, Ilaria Ricchi

In the past decade, exploration of spontaneous blood-oxygen-level-dependent (BOLD) signal fluctuations has expanded beyond the brain to include the spinal cord. While most studies have predominantly focused on the cervical region, the lumbosacral segments ...

2024

Temporal Fluctuations of Brain Structure-Function Coupling as a Potential Marker of Drowsiness During Resting State

Dimitri Nestor Alice Van De Ville, Maria Giulia Preti

Resting-state functional magnetic resonance imaging (fMRI) is a well-established methodology to probe brain organization. Previous work has demonstrated that the probability of drowsiness and even sleep increases significantly in participants during restin ...

IEEE2024

System-level integration towards hybrid flexible implantable neuroelectronic interfaces

Yashwanth Sai Reddy Vyza

This thesis presents significant advancements in the field of brain-computer interfaces (BCIs) through the development of hybrid active implantable neuroelectronic systems. It highlights the critical need to overcome the mechanical mismatch between rigid i ...

EPFL2024

PyTorch 2 Model for the prediction of single-artery segmentations for "AngioPy segmentation: An open-source, user-guided deep learning tool for coronary artery segmentation"

Edward Carlo Giorgio Andò, Thabodhan Mahendiran

These are pytorch model weights for the angiopy-segmentation deep learning model, descriped in a paper published in 2024 in the International Journal of Cardiology: AngioPy segmentation: An open-source, user-guided deep learning tool for coronary artery se ...

Zenodo2024

Hierarchical syntax model of music predicts theta power during music listening

Martin Alois Rohrmeier, Steffen Alexander Herff, Gabriele Cecchetti

Linguistic research showed that the depth of syntactic embedding is reflected in brain theta power. Here, we test whether this also extends to non-linguistic stimuli, specifically music. We used a hierarchical model of musical syntax to continuously quanti ...

2024

Transient brain activity dynamics discriminate levels of consciousness during anesthesia

Dimitri Nestor Alice Van De Ville, Ayberk Ozkirli

The awake mammalian brain is functionally organized in terms of large-scale distributed networks that are constantly interacting. Loss of consciousness might disrupt this temporal organization leaving patients unresponsive. We hypothesize that characterizi ...

Nature Portfolio2024

Machine learning potential for the Cu-W system

William Curtin

Combining the excellent thermal and electrical properties of Cu with the high abrasion resistance and thermal stability of W, Cu-W nanoparticle-reinforced metal matrix composites and nano-multilayers are finding applications as brazing fillers and shieldin ...

2024

Optimum trajectory learning in musculoskeletal systems with model predictive control and deep reinforcement learning

Henry Markram, Marc-Oliver Gewaltig

From the computational point of view, musculoskeletal control is the problem of controlling high degrees of freedom and dynamic multi-body system that is driven by redundant muscle units. A critical challenge in the control perspective of skeletal joints w ...

2022

The predictive value of lesion and disconnectome loads for upper limb motor impairment after stroke

Chang-Hyun Park

Objective The putative effect of lesion-induced brain damage on post-stroke upper limb motor impairment can be estimated by overlaying a patient's lesion or its surrogate with key motor areas. We assessed the predictive value of imaging-based brain damage ...

SPRINGER-VERLAG ITALIA SRL2022