Search Results for « Brain Stimulation »

The characterisation of CMB polarisation is one of the next challenge in observationnal cosmology. This is especially true for the so-called B-modes that are at least 3 order of magnitude lower than CMB temperature fluctuations. A precise measurement of the angular power spectrum of these B-modes will give important constraints on inflation parameters. In this talk, I will describe two complementary experiments, BRAIN and CLOVER, dedicated to CMB polarisation measurement. These experiments are proposed to be installed in Dome-C, Antarctica, to take advantage of the extreme dryness of the atmosphere and to allow long integration time.

Transcranial ultrasound stimulation (TUS) offers non-invasive deep-brain neuromodulation with high spatial precision, but reliably generating complex multi-target acoustic fields through the skull remains challenging. Here, we introduce a physics-aware hologram technique that directly generates fabrication-ready holographic implementations while preserving consistency between numerical field synthesis and physical acoustic realization. The method enables single-, dual-, and tri-focal transcranial stimulation patterns and was validated through in silico simulations, ex vivo skull measurements, and in vivo experiments. Compared with representative state-of-the-art methods, the proposed approach improved focal reconstruction and energy confinement at intended targets while reducing off-target acoustic leakage. In a neuropathic-pain mouse model, simultaneous bilateral stimulation of thalamic nuclei reduced c-Fos expression and showed preliminary improvements in pain-related behavioral responses. These findings support the use of fabrication-consistent holographic design for spatially localized and reproducible multi-target transcranial neuromodulation.

3D medical image processing with deep learning greatly suffers from a lack of data. Thus, studies carried out in this field are limited compared to works related to 2D natural image analysis, where very large datasets exist. As a result, powerful and efficient 2D convolutional neural networks have been developed and trained. In this paper, we investigate a way to transfer the performance of a two-dimensional classiffication network for the purpose of three-dimensional semantic segmentation of brain tumors. We propose an asymmetric U-Net network by incorporating the EfficientNet model as part of the encoding branch. As the input data is in 3D, the first layers of the encoder are devoted to the reduction of the third dimension in order to fit the input of the EfficientNet network. Experimental results on validation and test data from the BraTS 2020 challenge demonstrate that the proposed method achieve promising performance.

Artificial intelligence (AI) is increasingly explored across deep brain stimulation (DBS) for movement disorders, yet whether current systems are approaching deployment remains unclear. To characterise their scope, validation maturity, and translational readiness, we systematically evaluated 239 peer-reviewed studies published between 2000 and 2025, assessing AI methods, validation practices, and barriers constraining clinical translation. Research was dominated by Parkinson's disease and subthalamic nucleus targeting, with limited coverage of other disorders and targets. Most studies reported encouraging internal performance; however, external validation was rare, evaluations remained predominantly retrospective and single-centre, and more than one-quarter involved small-sample, high-dimensional datasets with elevated overfitting risk. Technology readiness assessment revealed that most systems remain at early-to-intermediate translational stages, constrained more by limited validation than by algorithmic inadequacy, compounded by the biological heterogeneity and dynamic complexity inherent to DBS. Nevertheless, emerging external and prospective studies suggest a field moving toward clinical maturity, with promising applications in targeting, programming, outcome prediction, and adaptive therapy delivery.

Availability of large, diverse, and multi-national datasets is crucial for the development of effective and clinically applicable AI systems in the medical imaging domain. However, forming a global model by bringing these datasets together at a central location, comes along with various data privacy and ownership problems. To alleviate these problems, several recent studies focus on the federated learning paradigm, a distributed learning approach for decentralized data. Federated learning leverages all the available data without any need for sharing collaborators' data with each other or collecting them on a central server. Studies show that federated learning can provide competitive performance with conventional central training, while having a good generalization capability. In this work, we have investigated several federated learning approaches on the brain tumor segmentation problem. We explore different strategies for faster convergence and better performance which can also work on strong Non-IID cases.