-- Methamphetamine dependence poses a significant global health challenge, yet its assessment and the evaluation of treatments like repetitive transcranial magnetic stimulation (rTMS) frequently depend on subjective self-reports, which may introduce uncertainties. While objective neuroimaging modalities such as electroen-cephalography (EEG) and functional near-infrared spectroscopy (fNIRS) offer alternatives, their individual limitations and the reliance on conventional, often hand-crafted, feature extraction can compromise the reliability of derived biomarkers. To overcome these limitations, we propose NeuroCLIP, a novel deep learning framework integrating simultaneously recorded EEG and fNIRS data through a progressive learning strategy. This approach offers a robust and trustworthy biomarker for methamphetamine addiction. Validation experiments show that NeuroCLIP significantly improves discriminative capabilities among the methamphetamine-dependent individuals and healthy controls compared to models using either EEG or only fNIRS alone. Furthermore, the proposed framework facilitates objective, brain-based evaluation of rTMS treatment efficacy, demonstrating measurable shifts in neural patterns towards healthy control profiles after treatment. Critically, we establish the trustworthiness of the multimodal data-driven biomarker by showing its strong correlation with psychometrically validated craving scores. These findings suggest that biomarker derived from EEG-fNIRS data via NeuroCLIP offers enhanced robustness and reliability over single-modality approaches, providing a valuable tool for addiction neuroscience research and potentially improving clinical assessments. Ziyi Zeng is also with the School of Data Science, Xiamen University Malaysia, Selangor 43900, Malaysia. Hemmings Wu and Zhoule Zhu are with Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, China. Lihua Zhong is with the Department of Education and Correction, Zhejiang Gongchen Compulsory Isolated Detoxification Center, Hangzhou 310011, China. Weiwei Cheng is with Zhejiang Liangzhu Compulsory Isolated Detoxification Center, Hangzhou 311115, China. ETHAMPHET AMINE addiction represents a significant and growing public health concern globally.

Transcranial magnetic stimulation (TMS) is a non-invasive and safe brain stimulation procedure with growing applications in clinical treatments and neuroscience research. However, achieving precise stimulation over prolonged sessions poses significant challenges. By integrating advanced robotics with conventional TMS, robot-assisted TMS (Robo-TMS) has emerged as a promising solution to enhance efficacy and streamline procedures. Despite growing interest, a comprehensive review from an engineering perspective has been notably absent. This paper systematically examines four critical aspects of Robo-TMS: hardware and integration, calibration and registration, neuronavigation systems, and control systems. We review state-of-the-art technologies in each area, identify current limitations, and propose future research directions. Our findings suggest that broader clinical adoption of Robo-TMS is currently limited by unverified clinical applicability, high operational complexity, and substantial implementation costs. Emerging technologies, including marker-less tracking, non-rigid registration, learning-based electric field (E-field) modelling, individualised magnetic resonance imaging (MRI) generation, robot-assisted multi-locus TMS (Robo-mTMS), and automated calibration and registration, present promising pathways to address these challenges.

Transcranial magnetic stimulation (TMS) is a noninvasive medical procedure that can modulate brain activity, and it is widely used in neuroscience and neurology research. Compared to manual operators, robots may improve the outcome of TMS due to their superior accuracy and repeatability. However, there has not been a widely accepted standard protocol for performing robotic TMS using fine-segmented brain images, resulting in arbitrary planned angles with respect to the true boundaries of the modulated cortex. Given that the recent study in TMS simulation suggests a noticeable difference in outcomes when using different anatomical details, cortical shape should play a more significant role in deciding the optimal TMS coil pose. In this work, we introduce an image-guided robotic system for TMS that focuses on (1) establishing standardized planning methods and heuristics to define a reference (true zero) for the coil poses and (2) solving the issue that the manual coil placement requires expert hand-eye coordination which often leading to low repeatability of the experiments. To validate the design of our robotic system, a phantom study and a preliminary human subject study were performed. Our results show that the robotic method can half the positional error and improve the rotational accuracy by up to two orders of magnitude. The accuracy is proven to be repeatable because the standard deviation of multiple trials is lowered by an order of magnitude. The improved actuation accuracy successfully translates to the TMS application, with a higher and more stable induced voltage in magnetic field sensors.

Objective: This study shows the force/torque control strategy for the robotized TMS system whose TMS coil's floor is incurved. The strategy considered the adhesion and friction between the coil and the subject's head. Methods: Hybrid position/force control and proportional torque were used for the strategy. The force magnitude applied for the force control was scheduled by the error between the coil's current position and the target point. Results: The larger desired force for the force controller makes the error quickly. By scheduling the force magnitude applied for the force control, the low error between the coil's current and target positions is maintained with the relatively small force after the larger force is applied for around 10 seconds. The proportional torque made the adhesion better by locating the contact area between the coil and the head close to the coil. I was shown by checking the ${\tau}_c/F_c$ value from the experimental results. While the head slowly moved away from the coil during the TMS treatment, the coil still interacted with the head. Using that characteristic, the coil could locate the new target point using the force/torque strategy without any trajectory planning. Conclusion: The proposed force/torque controller enhanced the adhesion between the incurved TMS coil and the subject's head. It also reduced the error quickly by scheduling the magnitude of the force applied. Significance: This study proposes the robotized TMS system's force/torque control strategy considering the physical characteristics from the contact between the incurved TMS coil case and the subject's head.

Noninvasive brain stimulation (NIBS) encompasses transcranial stimulation techniques that can influence brain excitability. These techniques have the potential to treat conditions like depression, anxiety, and chronic pain, and to provide insights into brain function. However, a lack of standardized reporting practices limits its reproducibility and full clinical potential. This paper aims to foster interinterdisciplinarity toward adopting Computer Science Semantic reporting methods for the standardized documentation of Neuroscience NIBS studies making them explicitly Findable, Accessible, Interoperable, and Reusable (FAIR). In a large-scale systematic review of 600 repetitive transcranial magnetic stimulation (rTMS), a subarea of NIBS, dosages, we describe key properties that allow for structured descriptions and comparisons of the studies. This paper showcases the semantic publishing of NIBS in the ecosphere of knowledge-graph-based next-generation scholarly digital libraries. Specifically, the FAIR Semantic Web resource(s)-based publishing paradigm is implemented for the 600 reviewed rTMS studies in the Open Research Knowledge Graph.

Source: OpenAI's DALL·E 2 with prompt "a hyperrealistic picture of a robot reading the news on a laptop at a coffee shop" Welcome to the 4th edition of Robo-Insight, a biweekly robotics news update! In this post, we are excited to share a range of new advancements in the field and highlight robots' progress in areas like mobile applications, cleaning, underwater mining, flexibility, human well-being, depression treatments, and human interactions. In the world of system adaptions, researchers from Eindhoven University of Technology have introduced a methodology that bridges the gap between application developers and control engineers in the context of mobile robots' behavior adaptation. This approach leverages symbolic descriptions of robots' behavior, known as "behavior semantics," and translates them into control actions through a "semantic map." This innovation aims to simplify motion control programming for autonomous mobile robot applications and facilitate integration across various vendors' control software.

This paper proposes neural mechanisms of transcranial magnetic stim- ulation (TMS). TMS can stimulate the brain non-invasively through a brief magnetic pulse delivered by a coil placed on the scalp, interfering with specific cortical functions with a high temporal resolution. Due to these advantages, TMS has been a popular experimental tool in various neuroscience fields. However, the neural mechanisms underlying TMS- induced interference are still unknown; a theoretical basis for TMS has not been developed. This paper provides computational evidence that in- hibitory interactions in a neural population, not an isolated single neuron, play a critical role in yielding the neural interference induced by TMS.

The US health and medical insurance industry is a $1.1-trillion maze that is impossible to navigate. And in the bigger scenario of a massive $11-trillion-plus global healthcare industry, America is definitely not first. Americans are fed up, and a digital revolution that goes way beyond telemedicine is the only thing that will restore control. Mentioned in today's commentary includes: Sage Therapeutics, Inc. (NASDAQ: SAGE), Cassava Sciences, Inc. (NASDAQ: SAVA), COMPASS Pathways plc (NASDAQ: CMPS), Neuronetics, Inc. (NASDAQ: STIM), Acadia Healthcare Company, Inc. (NASDAQ: ACHC). Fixing it is a highly disruptive, multi-trillion-dollar opportunity.

The development of personalized human head models from medical images has become an important topic in the electromagnetic dosimetry field, including the optimization of electrostimulation, safety assessments, etc. Human head models are commonly generated via the segmentation of magnetic resonance images into different anatomical tissues. This process is time consuming and requires special experience for segmenting a relatively large number of tissues. Thus, it is challenging to accurately compute the electric field in different specific brain regions. Recently, deep learning has been applied for the segmentation of the human brain. However, most studies have focused on the segmentation of brain tissue only and little attention has been paid to other tissues, which are considerably important for electromagnetic dosimetry. In this study, we propose a new architecture for a convolutional neural network, named ForkNet, to perform the segmentation of whole human head structures, which is essential for evaluating the electrical field distribution in the brain. The proposed network can be used to generate personalized head models and applied for the evaluation of the electric field in the brain during transcranial magnetic stimulation. Our computational results indicate that the head models generated using the proposed network exhibit strong matching with those created via manual segmentation in an intra-scanner segmentation task.

Though it may sound like something from science fiction, brain zapping -- or neuro-electrostimulation, as it's known -- is already being used to treat a variety of ailments. Researchers at Boston University recently reported that zapping the brains of older people could restore their thinking powers to those of someone in their 20s. 'Neuro-electrostimulation makes sense because the brain is an organ that works by electrical impulses, and there are many different techniques that can alter the brain's electrical activity, possibly for a therapeutic effect,' says Dr Lucia Li, a neurologist and clinical lecturer at Imperial College London. The brain-zapping treatment can be given via caps, headbands, or electrodes stuck to the scalp or even implanted in the brain. In some types of treatment, such as electroconvulsive therapy, used to treat severe depression, brain zapping is so powerful, patients have to be anaesthetised in case they hurt themselves.