搜索结果：Brain stimulation

Non-invasive stimulation of small, variably shaped brain sub-regions is crucial for advancing our understanding of brain functions. Current ultrasound neuromodulation faces two significant trade-offs when targeting brain sub-regions: miniaturization versus volumetric control and spatial resolution versus transcranial capability. Here, we present an optically-generated Bessel beam ultrasound (OBUS) device designed to overcome these limitations. This 2.33 mm-diameter miniaturized device delivers a column-shaped field achieving a lateral resolution of 152 um and an axial resolution of 1.93 mm, targeting brain sub-regions with an elongated volume of tissue activation. Immunofluorescence imaging of mouse brain slices confirms its ability to stimulate cells at a depth of 2.2 mm. Additionally, OBUS outperforms conventional Gaussian ultrasound in transcranial transmission efficiency and beam shape preservation. Electrophysiological recordings and functional MRI captured rodent brain responses evoked by OBUS, demonstrating OBUS's ability to non-invasively activate neural circuits in intact brains. This technology offers new possibilities for studying brain functions with precision and volum

Adaptive brain stimulation can treat neurological conditions such as Parkinson's disease and post-stroke motor deficits by influencing abnormal neural activity. Because of patient heterogeneity, each patient requires a unique stimulation policy to achieve optimal neural responses. Model-free reinforcement learning (MFRL) holds promise in learning effective policies for a variety of similar control tasks, but is limited in domains like brain stimulation by a need for numerous costly environment interactions. In this work we introduce Coprocessor Actor Critic, a novel, model-based reinforcement learning (MBRL) approach for learning neural coprocessor policies for brain stimulation. Our key insight is that coprocessor policy learning is a combination of learning how to act optimally in the world and learning how to induce optimal actions in the world through stimulation of an injured brain. We show that our approach overcomes the limitations of traditional MFRL methods in terms of sample efficiency and task success and outperforms baseline MBRL approaches in a neurologically realistic model of an injured brain.

Deep Brain Stimulation (DBS) is an effective treatment for Parkinson's disease, but conventional fixed-parameter stimulation can reduce battery life and cause side effects while failing to adapt to changing neural dynamics. Recent reinforcement learning approaches improve adaptability, yet most rely on deep neural networks that require offline training and are computationally too expensive for implantable hardware. This paper presents a resource-conscious adaptive DBS framework based on a Time- and Threshold-Triggered Pruned Multi-Armed Bandit (T3P MAB) algorithm. The proposed method jointly tunes stimulation frequency and amplitude, avoids prior training, and remains transparent enough to support clinician-guided adjustment. Using a computational basal ganglia-thalamic model, we show that T3P converges faster than competing MAB methods and outperforms deep-RL baselines in suppressing pathological beta-band activity while reducing stimulation power. We implemented it on different microcontrollers and report detailed energy measurements, showing convergence in under two minutes and suitability for resource-constrained implantable systems. These results support lightweight bandit-bas

Deep Brain Stimulation (DBS) is a highly effective treatment for Parkinson's Disease (PD). Recent research uses reinforcement learning (RL) for DBS, with RL agents modulating the stimulation frequency and amplitude. But, these models rely on biomarkers that are not measurable in patients and are only present in brain-on-chip (BoC) simulations. In this work, we present an RL-based DBS approach that adapts these stimulation parameters according to brain activity measurable in vivo. Using a TD3 based RL agent trained on a model of the basal ganglia region of the brain, we see a greater suppression of biomarkers correlated with PD severity compared to modern clinical DBS implementations. Our agent outperforms the standard clinical approaches in suppressing PD biomarkers while relying on information that can be measured in a real world environment, thereby opening up the possibility of training personalized RL agents specific to individual patient needs.

Deep Brain Stimulation (DBS) is a well-established neurosurgical treatment aiming at symptom alleviation in a range of neurological and psychiatric diseases. Computational models of DBS are widely used to investigate the effects of stimulation on neural tissue, to explore stimulation targets and sweetspots, and ultimately, to aid clinicians in the DBS programming by calculating the stimulation parameters. Commonly, DBS is performed bilaterally, i.e. with one lead in each brain hemisphere, where computational models are solved independently for one lead at a time. This paper treats scenarios where multiple DBS leads are implanted in close proximity to one another, resulting in interacting electrical fields and, therefore, potentially overlapping stimulation spreads. In particular, a global dual-lead model is compared to approximations derived from single-lead approaches in a cohort of twelve multiple sclerosis (MS) tremor patients. It is concluded that simple superposition of volumes of tissue activated (VTAs) underestimates activation, while superposition of electric fields or activating functions leads to overestimation. It is concluded that given close proximity of DBS leads, the

Deep Brain Stimulation (DBS) is an effective treatment for neurological disorders but requires invasive surgery. This work presents a method for non-invasive DBS, based on microwave focusing of amplitude-modulated electric fields using an external antenna array of magnetic point dipoles. The proposed method combines iterative time reversal (iTR) and temporal interference (TI) optimization to jointly address electromagnetic field focusing and physiologically relevant neural stimulation. Antenna element positions, orientations, frequencies, amplitudes, and phases are optimized to localize stimulation within a target region. The method is evaluated in an anatomically realistic voxel head model with heterogeneous and lossy tissue properties. Systematic numerical studies, including perturbation analysis and statistical evaluation, demonstrate consistent spatial localization and robustness across all reported configurations. Safety is quantified using specific absorption rate (SAR), ensuring compliance with exposure limits. The study further provides insight into the influence of key parameters on field behavior and the associated trade-offs between focality, penetration, and safety in p

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 towar

Network control theory (NCT) has recently been utilized in neuroscience to facilitate our understanding of brain stimulation effects. A particularly useful branch of NCT is optimal control, which focuses on applying theoretical and computational principles of control theory to design optimal strategies to achieve specific goals in neural processes. However, most existing research focuses on optimally controlling brain network dynamics from the original state to a target state at a specific time point. In this paper, we present the first investigation of introducing optimal stochastic tracking control strategy to synchronize the dynamics of the brain network to a target dynamics rather than to a target state at a specific time point. We utilized fMRI data from healthy groups, and cases of stroke and post-stroke aphasia. For all participants, we utilized a gradient descent optimization method to estimate the parameters for the brain network dynamic system. We then utilized optimal stochastic tracking control techniques to drive original unhealthy dynamics by controlling a certain number of nodes to synchronize with target healthy dynamics. Results show that the energy associated with

Deep Brain Stimulation (DBS) is a therapy widely used for treating the symptoms of neurological disorders. Electrical pulses are chronically delivered in DBS to a disease-specific brain target via a surgically implanted electrode. The stimulating contact configuration, stimulation polarity, as well as amplitude, frequency, and pulse width of the DBS pulse sequence are utilized to optimize the therapeutic effect. In this paper, the utility of therapy individualization by means of patient-specific mathematical modeling is investigated with respect to a specific case of a patient diagnosed with Essential Tremor (ET). Two computational models are compared in their ability to elucidate the impact of DBS stimulation on the dentato-rubrothalamic tract: (i) a conventional model of Volume of Tissue Activated (VTA) and (ii) a well-established neural fiber activation modeling framework known as OSS-DBS. The simulation results are compared with tremor measured in the patient under different DBS settings using a smartphone application. The findings of the study highlight that temporally static VTA models do not adequately describe the differences in the outcomes of bipolar stimulation settings

Deep Brain Stimulation (DBS) is an established and powerful treatment method in various neurological disorders. It involves chronically delivering electrical pulses to a certain stimulation target in the brain in order to alleviate the symptoms of a disease. Traditionally, the effect of DBS on neural tissue has been modeled based on the geometrical intersection of the static Volume of Tissue Activated (VTA) and the stimulation target. Recent studies suggest that the Dentato-Rubro-Thalamic Tract (DRTT) may serve as a potential common underlying stimulation target for tremor control in Essential Tremor (ET). However, clinical observations highlight that the therapeutic effect of DBS, especially in ET, is strongly influenced by the dynamic DBS parameters such as pulse width and frequency, as well as stimulation polarity. This study introduces a computational model to elucidate the effect of the stimulation signal shape on the DRTT under neural input. The simulation results suggest that achieving a specific pulse amplitude threshold is necessary before eliciting the therapeutic effect through adjustments in pulse widths and frequencies becomes feasible. Longer pulse widths proved more

Conventional deep brain stimulation (DBS) of basal ganglia uses high-frequency regular electrical pulses to treat Parkinsonian motor symptoms and has a series of limitations. Relatively new and not yet clinically tested optogenetic stimulation is an effective experimental stimulation technique to affect pathological network dynamics. We compared the effects of electrical and optogenetic stimulation of the basal ganglia on the pathological parkinsonian rhythmic neural activity. We studied the network response to electrical stimulation and excitatory and inhibitory optogenetic stimulations. Different stimulations exhibit different interactions with pathological activity in the network. We studied these interactions for different network and stimulation parameter values. Optogenetic stimulation was found to be more efficient than electrical stimulation in suppressing pathological rhythmicity. Our findings indicate that optogenetic control of neural synchrony may be more efficacious than electrical control because of the different ways of how stimulations interact with network dynamics.

Brain stimulation is a core method in neuroscience. Numerous non-invasive brain stimulation (NIBS) techniques are currently in use in basic and clinical research, and recent advances promise the ability to non-invasively access deep brain structures. While encouraging, there is a surprising gap in our understanding of precisely how NIBS perturbs neural activity throughout an interconnected network, and how such perturbed neural activity ultimately links to behaviour. In this review, we will consider why non-human primate (NHP) models of NIBS are ideally situated to address this gap in knowledge, and will consider why the oculomotor network that moves our line of sight offers a particularly valuable platform in which to empirically test hypothesis regarding NIBS-induced changes in brain and behaviour. NHP models of NIBS will enable investigation of the complex, dynamic effects of brain stimulation across multiple hierarchically interconnected brain areas, networks, and effectors. By establishing such links between brain and behavioural output, work in NHPs can help optimize experimental and therapeutic approaches, improve NIBS efficacy, and reduce side-effects of NIBS.

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.