Clinatec

Bipolar disorder (BD) is a complex mood disorder characterized by recurrent depressive and manic/hypomanic episodes, accompanied by significant cognitive dysfunction and emotional dysregulation. Accurate and timely diagnosis, especially the differentiation between subtypes, remains a challenge due to overlapping symptoms, variable onset times for more specific symptoms (e.g., psychotic features), and the reliance on subjective assessments. This study examines the use of a multimodal approach combining electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to identify patterns of BD emotional dysregulation, aiming to enhance its diagnosis and subtype differentiation. The protocol employed an emotional visual task to evaluate the interference of emotional content on cognitive function. EEG data were collected using a whole-head cap, while fNIRS focused on hemodynamic changes in the frontal cortex. Furthermore, the feasibility of using a potential simplified, portable EEG-fNIRS system was explored by focusing the analysis on frontal regions. The cohort included BD patients [BP] of two main subtypes, and healthy controls [HC]. Behavioral analysis revealed significant performance differences between BP and HC groups. While EEG alone enabled groups' classification, integrating EEG and fNIRS improved accuracy by reducing misclassification rates. Although classification using only frontal EEG regions was slightly less accurate than the full-head cap, fNIRS integration ensured robust results, supporting the feasibility for a potential simplified system. These findings underscore the complementary strengths of EEG and fNIRS in capturing neural and vascular markers of emotional dysregulation in BD and support the development of multimodal diagnostic tools for BD.

We report, for the first time, the integration of ALD-grown MoS<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> into stacked channel transistors via a novel channel-last integration scheme. Our approach provides many advantages and opportunities concerning channel formation, enhanced delamination control, contact optimization, reduced contamination risks, and the ability to reuse process modules of existing Si GAA routes. To assess such a process flow, we first developped and optimized ALD MoS<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> films. We also optimized source/drain contacts using back-gated MoS<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> transistors with down to 15 nm widths. Niobium contacts delivered an I<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ON</inf>/I<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OFF</inf> ratio up to 10⁴ and outperformed titanium counterparts with up to 28× Iₒₙ enhancement. Then, we validated key modules of our novel channel-last integration, overcoming most of the technological roadblocks limiting the 2D TMD integration into advanced CMOS. Finally, TCAD simulations were performed to optimize our architecture and draw some perspectives.

Objective: We present an optical implant as an innovative tool for assessing brain tissue reflectance to monitor the progression of Parkinson’s disease (PD). Methods: We first determined the wavelength that maximized optical discrimination between tis

ABSTRACTWith the rapid expansion of data centers, there is a growing demand for high data‐rate, energy‐efficient optical links over short distances. One potential technology for this application is Gallium‐Nitride (GaN) based microlight emitting diodes (μLEDs), thanks to their compact size, high‐speed operation, and ease of manufacturability. During the development of these μLEDs, modeling plays an essential role in optimizing their performance. In this paper, we present various models to estimate both the static and dynamic performance of GaN μLEDs of various sizes. We then propose a methodology to integrate these models into a unified equivalent circuit model, enabling the simulation of the full response of the μLED. Finally, we implement this unified model in a circuit‐simulation‐compatible module and replicate the experimental setups within a simulation software to evaluate the module's ability to accurately simulate the μLED's response.

Brain-Computer Interfaces (BCIs) aim at bridging residual neural activity related to motor intents with motor commands, presenting a revolutionary tool for helping tetraplegic and paraplegic individuals to regain motor control. Electrocorticography (ECoG)-based BCIs has emerged as a good compromise between invasiveness of the recording device and quality the recorded signals, making them a promising modality for BCI applications. The WIMAGINE system developed by CEA Clinatec for ECoG recording have been implanted for several years in proof of concept clinical trials on individuals with chronic impairments after severe spinal cord injury. Nevertheless, adequate algorithms are crucial to decipher brain signals ideally in real-time on portable systems so that these solutions can be proposed to patients in their daily life. While NPLS-based ECoG decoders have been successfully trained online in closed-loop clinical trials, deep learning alternatives have primarily been evaluated in offline settings. In this study, we investigate the offline training of deep learning models on datasets that replicate the temporal dynamics of real-time data acquisition. Our approach accounts for key factors such as inter-subject variability and signal drift, ensuring a more realistic evaluation. We systematically evaluate the performance of multiple models for classifying arm, leg, and wrist movements and predicting 3D translations of the arms and wrists, using varying amounts of ECoG data recorded from tetraplegic and paraplegic patients during motor imagery tasks. Additionally, we introduce a novel deep learning model based on the transformer architecture, specifically designed to be adjustable to scenarios with low data amounts. If on the considered classification datasets the NPLS-based ECoG decoder achieved better performances, on the considered regression datasets with the lowest data amounts, our model achieved performance comparable to, or exceeding, that of an NPLS decoder, while using less than half the parameters.

Abstract The brain coordinates multiple parallel motor programs, ensuring synergy and preventing interference during movements. Yet, performance often degrades when brain–machine interfaces are used during concurrent tasks or ongoing movements. We suggest that latent neural representations may represent a strategy to solve this issue. In this study, we addressed this question using neural signals from a tetraplegic individual with partial residual motor function, implanted with a wireless epidural electrocorticography (ECoG) device. By adapting dimensionality reduction techniques, we found that motor execution and motor imagery span partially overlapping subspaces in mesoscale neural signals, shaped by specific frequency band contributions. Despite substantial shared variance, we show that identifying orthogonal, condition-specific dimensions enables successful decoding of executed and imagined movements, even when performed simultaneously. These findings show that ECoG signals can expose separable neural subspaces, allowing executed and imagined actions to be harnessed independently and in concert. This opens a promising avenue to develop brain–machine interfaces that can simultaneously control multiple external devices or operate alongside natural movements.

Alzheimer’s Disease (AD) and Parkinson’s Disease (PD) are common neurodegenerative diseases, characterized by the progressive loss of synapses and neurons, leading to cognitive and motor decline. Their pathophysiology includes cerebral lesions, oxidative stress, neuroinflammation as well as brain-gut axis microbiota dysbiosis.Preclinical investigations demonstrated that brain photobiomodulation (bPBM) reduces oxidative stress and inflammation, increases cerebral blood flow and enhance neurogenesis and synaptogenesis, which makes bPBM a promising treatment in AD and PD.This review focuses on the clinical application of bPBM in AD and PD. It aims to provide a scientific overview of the current clinical knowledge, review recent clinical studies findings, and describe future directions and upcoming clinical studies.So far, several clinical studies investigated bPBM therapy, at various parameters, both in patients with AD and related dementia, and PD. All demonstrate bPBM safety and bring valuable clinical information regarding efficacy, with particularly promising results in AD. However, their exploratory design and inconsistent quality lead to a low level of evidence, which currently does not support the widespread use of bPBM in clinical practice.Future clinical research should address two gaps: the need for robust double-blinded RCTs vs sham with a higher number of patients and a longer follow-up, and the need for research focusing on dosimetry to determine which bPBM parameters are optimal. The ongoing or unpublished clinical studies on bPBM should fill in this gap.

Phase-amplitude coupling (PAC), the modulation of high-frequency neural oscillations by the phase of slower oscillations, is increasingly recognized as a marker of goal-directed motor behavior. Despite this interest, its specific role and potential value in decoding attempted motor movements remain unclear. This study investigates whether PAC-derived features can be leveraged to classify different motor behaviors from ECoG signals within Brain-Computer Interface (BCI) systems. ECoG data were collected using the WIMAGINE implant during BCI experiments with a tetraplegic patient performing mental motor tasks. The data underwent preprocessing to extract complex neural oscillation features (amplitude, phase) through spectral decomposition techniques. These features were then used to quantify PAC by calculating different coupling indices. PAC metrics served as input features in a machine learning pipeline to evaluate their effectiveness in predicting mental tasks (idle state, right-hand movement, left-hand movement) in both offline and pseudo-online modes. The PAC features demonstrated high accuracy in distinguishing among motor tasks, with key classification features highlighting the coupling of theta/low-gamma and beta/high-gamma frequency bands. These preliminary findings hold significant potential for advancing our understanding of motor behavior and for developing optimized BCI systems.