RESUMEN

Deep brain stimulation (DBS) is a neurosurgical procedure that involves implanting electrodes into specific areas of the brain to treat a variety of medical conditions, including Parkinson's disease. Doubts and questions from patients prior to or following surgery should be addressed in line with the most recent scientific and clinical practice. ChatGPT emerges as an example of how artificial intelligence can be used, with its ability to comprehend and answer medical questions in an understandable way, accessible to everyone. However, the risks of these resources still need to be fully understood.ChatGPT models 3.5 and 4 responses to 40 questions in English and Portuguese were independently graded by two experienced specialists in functional neurosurgery and neurological movement disorders and resolved by a third reviewer. ChatGPT 3.5 and 4 demonstrated a good level of accuracy in responding to 80 questions in both English and Portuguese, related to DBS surgery for Parkinson's disease. The proportion of responses graded as correct was 57.5% and 83.8% for GPT 3.5 and GPT 4, respectively. GPT 3.5 provided potentially harmful answers for 6.3% (5/80) of its responses. No responses from GPT 4 were graded as harmful. In general, ChatGPT 3.5 and 4 demonstrated good performance in terms of quality and reliability across two different languages. Nonetheless, harmful responses should not be scorned, and it's crucial to consider this aspect when addressing patients using these resources. Considering the current safety concerns, it's not advisable for patients to use such models for DBS surgery guidance. Performance of ChatGPT 3.5 and 4 as a tool for patient support before and after DBS surgery for Parkinson's disease.

RESUMEN

Introduction: In-silico simulations are a powerful tool in modern neuroscience for enhancing our understanding of complex brain systems at various physiological levels. To model biologically realistic and detailed systems, an ideal simulation platform must possess: (1) high performance and performance scalability, (2) flexibility, and (3) ease of use for non-technical users. However, most existing platforms and libraries do not meet all three criteria, particularly for complex models such as the Hodgkin-Huxley (HH) model or for complex neuron-connectivity modeling such as gap junctions. Methods: This work introduces ExaFlexHH, an exascale-ready, flexible library for simulating HH models on multi-FPGA platforms. Utilizing FPGA-based Data-Flow Engines (DFEs) and the dataflow programming paradigm, ExaFlexHH addresses all three requirements. The library is also parameterizable and compliant with NeuroML, a prominent brain-description language in computational neuroscience. We demonstrate the performance scalability of the platform by implementing a highly demanding extended-Hodgkin-Huxley (eHH) model of the Inferior Olive using ExaFlexHH. Results: Model simulation results show linear scalability for unconnected networks and near-linear scalability for networks with complex synaptic plasticity, with a 1.99 × performance increase using two FPGAs compared to a single FPGA simulation, and 7.96 × when using eight FPGAs in a scalable ring topology. Notably, our results also reveal consistent performance efficiency in GFLOPS per watt, further facilitating exascale-ready computing speeds and pushing the boundaries of future brain-simulation platforms. Discussion: The ExaFlexHH library shows superior resource efficiency, quantified in FLOPS per hardware resources, benchmarked against other competitive FPGA-based brain simulation implementations.

RESUMEN

Elucidating the intricate neural mechanisms underlying brain functions requires integrative brain dynamics modeling. To facilitate this process, it is crucial to develop a general-purpose programming framework that allows users to freely define neural models across multiple scales, efficiently simulate, train, and analyze model dynamics, and conveniently incorporate new modeling approaches. In response to this need, we present BrainPy. BrainPy leverages the advanced just-in-time (JIT) compilation capabilities of JAX and XLA to provide a powerful infrastructure tailored for brain dynamics programming. It offers an integrated platform for building, simulating, training, and analyzing brain dynamics models. Models defined in BrainPy can be JIT compiled into binary instructions for various devices, including Central Processing Unit, Graphics Processing Unit, and Tensor Processing Unit, which ensures high-running performance comparable to native C or CUDA. Additionally, BrainPy features an extensible architecture that allows for easy expansion of new infrastructure, utilities, and machine-learning approaches. This flexibility enables researchers to incorporate cutting-edge techniques and adapt the framework to their specific needs.

Asunto(s)

RESUMEN

The analysis of the topography of brain neuromodulation following transcranial alternating current (AC) stimulation is relevant for defining strategies directed to specific nuclei stimulation in patients. Among the different procedures of AC stimulation, temporal interference (tTIS) is a novel method for non-invasive neuromodulation of specific deep brain targets. However, little information is currently available about its tissue effects and its activation topography in in vivo animal models. After a single session (30 min, 0.12 mA) of transcranial alternate current (2,000 Hz; ES/AC group) or tTIS (2,000/2,010 Hz; Es/tTIS group) stimulation, rat brains were explored by whole-brain mapping analysis of c-Fos immunostained serial sections. For this analysis, we used two mapping methods, namely density-to-color processed channels (independent component analysis (ICA) and graphical representation (MATLAB) of morphometrical and densitometrical values obtained by density threshold segmentation. In addition, to assess tissue effects, alternate serial sections were stained for glial fibrillary acidic protein (GFAP), ionized calcium-binding adapter molecule 1 (Iba1), and Nissl. AC stimulation induced a mild superficial increase in c-Fos immunoreactivity. However, tTIS stimulation globally decreased the number of c-Fos-positive neurons and increased blood brain barrier cell immunoreactivity. tTIS also had a stronger effect around the electrode placement area and preserved neuronal activation better in restricted areas of the deep brain (directional stimulation). The enhanced activation of intramural blood vessels' cells and perivascular astrocytes suggests that low-frequency interference (10 Hz) may also have a trophic effect.

RESUMEN

Simulated whole-brain connectomes demonstrate enhanced inter-individual variability depending on the data processing and modelling approach. By considering the human brain connectome as an individualized attribute, we investigate how empirical and simulated whole-brain connectome-derived features can be utilized to classify patients with Parkinson's disease against healthy controls in light of varying data processing and model validation. To this end, we applied simulated blood oxygenation level-dependent signals derived by a whole-brain dynamical model simulating electrical signals of neuronal populations to reveal differences between patients and controls. In addition to the widely used model validation via fitting the dynamical model to empirical neuroimaging data, we invented a model validation against behavioural data, such as subject classes, which we refer to as behavioural model fitting and show that it can be beneficial for Parkinsonian patient classification. Furthermore, the results of machine learning reported in this study also demonstrated that the performance of the patient classification can be improved when the empirical data are complemented by the simulation results. We also showed that the temporal filtering of blood oxygenation level-dependent signals influences the prediction results, where filtering in the low-frequency band is advisable for Parkinsonian patient classification. In addition, composing the feature space of empirical and simulated data from multiple brain parcellation schemes provided complementary features that improved prediction performance. Based on our findings, we suggest that combining the simulation results with empirical data is effective for inter-individual research and its clinical application.

RESUMEN

Simulating the brain-body-environment trinity in closed loop is an attractive proposal to investigate how perception, motor activity and interactions with the environment shape brain activity, and vice versa. The relevance of this embodied approach, however, hinges entirely on the modeled complexity of the various simulated phenomena. In this article, we introduce a software framework that is capable of simulating large-scale, biologically realistic networks of spiking neurons embodied in a biomechanically accurate musculoskeletal system that interacts with a physically realistic virtual environment. We deploy this framework on the high performance computing resources of the EBRAINS research infrastructure and we investigate the scaling performance by distributing computation across an increasing number of interconnected compute nodes. Our architecture is based on requested compute nodes as well as persistent virtual machines; this provides a high-performance simulation environment that is accessible to multi-domain users without expert knowledge, with a view to enable users to instantiate and control simulations at custom scale via a web-based graphical user interface. Our simulation environment, entirely open source, is based on the Neurorobotics Platform developed in the context of the Human Brain Project, and the NEST simulator. We characterize the capabilities of our parallelized architecture for large-scale embodied brain simulations through two benchmark experiments, by investigating the effects of scaling compute resources on performance defined in terms of experiment runtime, brain instantiation and simulation time. The first benchmark is based on a large-scale balanced network, while the second one is a multi-region embodied brain simulation consisting of more than a million neurons and a billion synapses. Both benchmarks clearly show how scaling compute resources improves the aforementioned performance metrics in a near-linear fashion. The second benchmark in particular is indicative of both the potential and limitations of a highly distributed simulation in terms of a trade-off between computation speed and resource cost. Our simulation architecture is being prepared to be accessible for everyone as an EBRAINS service, thereby offering a community-wide tool with a unique workflow that should provide momentum to the investigation of closed-loop embodiment within the computational neuroscience community.

RESUMEN

Introduction: Computational brain network modeling using The Virtual Brain (TVB) simulation platform acts synergistically with machine learning (ML) and multi-modal neuroimaging to reveal mechanisms and improve diagnostics in Alzheimer's disease (AD). Methods: We enhance large-scale whole-brain simulation in TVB with a cause-and-effect model linking local amyloid beta (Aß) positron emission tomography (PET) with altered excitability. We use PET and magnetic resonance imaging (MRI) data from 33 participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI3) combined with frequency compositions of TVB-simulated local field potentials (LFP) for ML classification. Results: The combination of empirical neuroimaging features and simulated LFPs significantly outperformed the classification accuracy of empirical data alone by about 10% (weighted F1-score empirical 64.34% vs. combined 74.28%). Informative features showed high biological plausibility regarding the AD-typical spatial distribution. Discussion: The cause-and-effect implementation of local hyperexcitation caused by Aß can improve the ML-driven classification of AD and demonstrates TVB's ability to decode information in empirical data using connectivity-based brain simulation.

RESUMEN

Recent evidence suggests that glial cells take an active role in a number of brain functions that were previously attributed solely to neurons. For example, astrocytes, one type of glial cells, have been shown to promote coordinated activation of neuronal networks, modulate sensory-evoked neuronal network activity, and influence brain state transitions during development. This reinforces the idea that astrocytes not only provide the "housekeeping" for the neurons, but that they also play a vital role in supporting and expanding the functions of brain circuits and networks. Despite this accumulated knowledge, the field of computational neuroscience has mostly focused on modeling neuronal functions, ignoring the glial cells and the interactions they have with the neurons. In this chapter, we introduce the biology of neuron-glia interactions, summarize the existing computational models and tools, and emphasize the glial properties that may be important in modeling brain functions in the future.

Asunto(s)

RESUMEN

Despite the acceleration of knowledge and data accumulation in neuroscience over the last years, the highly prevalent neurodegenerative disease of AD remains a growing problem. Alzheimer's Disease (AD) is the most common cause of dementia and represents the most prevalent neurodegenerative disease. For AD, disease-modifying treatments are presently lacking, and the understanding of disease mechanisms continues to be incomplete. In the present review, we discuss candidate contributing factors leading to AD, and evaluate novel computational brain simulation methods to further disentangle their potential roles. We first present an overview of existing computational models for AD that aim to provide a mechanistic understanding of the disease. Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain (www.thevirtualbrain.org), an open-source, multiscale, whole-brain simulation neuroinformatics platform. Finally, we discuss how this methodological approach may contribute to the understanding, improved diagnostics, and treatment optimization of AD.

RESUMEN

Electroencephalogram (EEG) power reductions in the aging brain have been described by numerous previous studies. However, the underlying mechanism for the observed brain signal power reduction remains unclear. One possible cause for reduced EEG signals in elderly subjects might be the increased distance from the primary neural electrical currents on the cortex to the scalp electrodes as the result of cortical atrophies. While brain shrinkage itself reflects age-related neurological changes, the effects of changes in the distribution of electrical conductivity are often not distinguished from altered neural activity when interpreting EEG power reductions. To address this ambiguity, we employed EEG forward models to investigate whether brain shrinkage is a major factor for the signal attenuation in the aging brain. We simulated brain shrinkage in spherical and realistic brain models and found that changes in the conductor geometry cannot fully account for the EEG power reductions even when the brain was shrunk to unrealistic sizes. Our results quantify the extent of power reductions from brain shrinkage and pave the way for more accurate inferences about deficient neural activity and circuit integrity based on EEG power reductions in the aging population.

RESUMEN

Performance of supercomputers has been steadily and exponentially increasing for the past 20 years, and is expected to increase further. This unprecedented computational power enables us to build and simulate large-scale neural network models composed of tens of billions of neurons and tens of trillions of synapses with detailed anatomical connections and realistic physiological parameters. Such "human-scale" brain simulation could be considered a milestone in computational neuroscience and even in general neuroscience. Towards this milestone, it is mandatory to introduce modern high-performance computing technology into neuroscience research. In this article, we provide an introductory landscape about large-scale brain simulation on supercomputers from the viewpoints of computational neuroscience and modern high-performance computing technology for specialists in experimental as well as computational neurosciences. This introduction to modeling and simulation methods is followed by a review of various representative large-scale simulation studies conducted to date. Then, we direct our attention to the cerebellum, with a review of more simulation studies specific to that region. Furthermore, we present recent simulation results of a human-scale cerebellar network model composed of 86 billion neurons on the Japanese flagship supercomputer K (now retired). Finally, we discuss the necessity and importance of human-scale brain simulation, and suggest future directions of such large-scale brain simulation research.

Asunto(s)

RESUMEN

Efficient uncertainty quantification algorithms are key to understand the propagation of uncertainty-from uncertain input parameters to uncertain output quantities-in high resolution mathematical models of brain physiology. Advanced Monte Carlo methods such as quasi Monte Carlo (QMC) and multilevel Monte Carlo (MLMC) have the potential to dramatically improve upon standard Monte Carlo (MC) methods, but their applicability and performance in biomedical applications is underexplored. In this paper, we design and apply QMC and MLMC methods to quantify uncertainty in a convection-diffusion model of tracer transport within the brain. We show that QMC outperforms standard MC simulations when the number of random inputs is small. MLMC considerably outperforms both QMC and standard MC methods and should therefore be preferred for brain transport models.

Asunto(s)

RESUMEN

Neuregulin-1 (NRG1) represents an important factor for multiple processes including neurodevelopment, brain functioning or cognitive functions. Evidence from animal research suggests an effect of NRG1 on the excitation-inhibition (E/I) balance in cortical circuits. However, direct evidence for the importance of NRG1 in E/I balance in humans is still lacking. In this work, we demonstrate the application of computational, biophysical network models to advance our understanding of the interaction between cortical activity observed in neuroimaging and the underlying neurobiology. We employed a biophysical neuronal model to simulate large-scale brain dynamics and to investigate the role of polymorphisms in the NRG1 gene (rs35753505, rs3924999) in n = 96 healthy adults. Our results show that G/G-carriers (rs3924999) exhibit a significant difference in global coupling (P = 0.048) and multiple parameters determining E/I-balance such as excitatory synaptic coupling (P = 0.047), local excitatory recurrence (P = 0.032) and inhibitory synaptic coupling (P = 0.028). This indicates that NRG1 may be related to excitatory recurrence or excitatory synaptic coupling potentially resulting in altered E/I-balance. Moreover, we suggest that computational modeling is a suitable tool to investigate specific biological mechanisms in health and disease.

Asunto(s)

RESUMEN

Accurate simulations of brain structures is a major problem in neuroscience. Many works are dedicated to design better models or to develop more efficient simulation schemes. In this paper, we propose a hybrid simulation scheme that combines time-stepping second-order integration of Hodgkin-Huxley (HH) type neurons with event-driven updating of the synaptic currents. As the HH model is a continuous model, there is no explicit spike events. Thus, in order to preserve the accuracy of the integration method, a spike detection algorithm is developed that accurately determines spike times. This approach allows us to regenerate the outgoing connections at each event, thereby avoiding the storage of the connectivity. Consequently, memory consumption is significantly reduced while preserving execution time and accuracy of the simulations, especially the spike times of detailed point neuron models. The efficiency of the method, implemented in the SiReNe software, is demonstrated by the simulation of a striatum model which consists of more than 106 neurons and 108 synapses (each neuron has a fan-out of 504 post-synaptic neurons), under normal and Parkinson's conditions.

RESUMEN

Background: Subthalamic nucleus deep brain stimulation (STN-DBS) is a valuable alternative to pharmacotherapy alone in an advanced Parkinson's disease (PD). Given the growing number of patients with STN-DBS, its impact on the comorbidities should be considered. Aim: The aim of this study was to evaluate the influence of bilateral STN-DBS on the lipid profile in patients with PD. Methods: Three groups of parkinsonian patients were included: 20 treated pharmacologically-PHT group, 20 newly qualified for STN-DBS-DBS group, and 14 postoperative patients (median 30 months after surgery)-POP group. Plasma concentrations of the total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), and body weight were measured thrice in 9 ± 2 month intervals. Results: A significant increase in the LDL-C concentration is observed early after surgery in the DBS group (11.4 mg/dl, P < 0.01) followed by adverse changes in the HDL-C (-7.7 mg/dl, P = 0.01) and TG (14.1 mg/dl, P = 0.05) plasma levels. In the POP group, the average level of TC at the first visit was significantly higher (P < 0.01) than in the other groups and the TG level was higher than in the PHT group during the follow-up (P < 0.01). A strong positive correlation with body weight alteration after surgery was observed only for long-term changes in the TG levels. Conclusions: Our data indicate that STN-DBS may negatively affect the cardiometabolic profile of patients. Similarly to body weight gain, an increase in the LDL-C concentration occurred early after surgery while adverse changes in the HDL-C and TG plasma levels were more gradual.

RESUMEN

Simulations of neural networks can be used to study the direct effect of internal or external changes on brain dynamics. However, some changes are not immediate but occur on the timescale of weeks, months, or years. Examples include effects of strokes, surgical tissue removal, or traumatic brain injury but also gradual changes during brain development. Simulating network activity over a long time, even for a small number of nodes, is a computational challenge. Here, we model a coupled network of human brain regions with a modified Wilson-Cowan model representing dynamics for each region and with synaptic plasticity adjusting connection weights within and between regions. Using strategies ranging from different models for plasticity, vectorization and a different differential equation solver setup, we achieved one second runtime for one second biological time.

RESUMEN

BACKGROUND: Some patients suffer from persistent and severely disabling Obsessive-Compulsive Disorder (OCD) symptoms that cannot be alleviated by conventional treatments or neuroablative interventions targeting cortico-striatal loop circuits. Currently, it is unclear how to manage the clinical symptoms of these unique patients. We reasoned that deep brain stimulation (DBS) of the habenula (HB) could be a valuable subsequent treatment option for these otherwise medically intractable cases of severe OCD. The HB is an epithalamic structure critically involved in the encoding and responding to aversive stimulus events, cognitive and brain processes known to be impaired in many patients with OCD. Similarly, HB DBS can alleviate depression and anxiety, which often co-occur with OCD. Here, we explore the clinical benefits and risks of HB DBS treatment in a patient with severe and refractory OCD. CASE PRESENTATION: A 30-year-old male patient presented with persistent and severely disabling OCD symptoms that were refractory to previous psychological and pharmacological treatments as well as to neuroablative surgical interventions involving both capsulotomy and cingulotomy. After HB DBS, however, the severity of the patient's OCD symptoms was markedly reduced at 1-month follow-up, which was sustained until the final (at 12-month) follow-up. The patient also reported enduring improvements in depression, anxiety, and health-related quality of life after several months of HB DBS treatment. CONCLUSIONS: This case report provides the first clinical evidence suggesting that HB DBS could serve as a safe and effective alternative neurosurgical intervention for severe and refractory OCD. The present findings are promising and warrant further research into the role of the HB in pathophysiology and treatment of OCD.

RESUMEN

One of the most important steps in the near-future space age will be a crew mission returning to the Moon and even a manned mission to Mars. Unfortunately, such a mission will expose astronauts to unavoidable cosmic radiation in deep space and on the Martian or lunar surface. Thus, a better understanding of the radiation environment for such a mission and the consequent biological impacts on humans, in particular the human brains, is critical. The need for this better understanding is strongly suggested by investigations on animal models and on human patients who were undergoing irradiation for cancer therapy in the head. These have revealed unexpected alterations in the central nervous system behavior and sensitivity of mature neurons in the brain to charged particles. However, such experiments shall not be carried out realistically in space using humans. Therefore, to investigate the impact of cosmic radiation on human brains and the potential influence on the brain functions, we model and study the cosmic particle-induced radiation dose in a realistic head structure. Specifically speaking, 134 slices of computed tomography (CT) images of an actual human head have been used as a 3D phantom in Geant4 (GEometry ANd Tracking), which is a Monte Carlo tool for simulating energetic particles impinging into different parts of the brain and deliver radiation dose therein. As a first step, we compare the influence of different brain structures (e.g., with or without bones, with or without soft tissues) to the resulting dose therein to demonstrate the necessity of using a realistic brain structure for our investigation. Afterward, we calculate energy-dependent functions of dose distribution, for the most important (some of the most abundant and most biologically-relevant) particle types encountered during a deep space mission inside a spacecraft or habitat such as protons, helium ions, neutrons and some major heavier ions like carbon, nitrogen, and iron particles. Furthermore, two different scenarios have been modeled as a comparison: a human head without shielding protection and a human head with an aluminum shielding shell around (of varying thickness). These functions can then be used to fold with energetic cosmic-ray particle spectra of the ambient environment for obtaining the dose rate distribution at different lobes of the human brain. Our calculation of these functions can serve as a ready tool and a baseline for further evaluations of the radiation in the brain encountered during a space mission with different radiation fields, such as on the surface of the Moon or Mars.

Asunto(s)

RESUMEN

A key element of the European Union's Human Brain Project (HBP) and other large-scale brain research projects is the simulation of large-scale model networks of neurons. Here, we argue why such simulations will likely be indispensable for bridging the scales between the neuron and system levels in the brain, and why a set of brain simulators based on neuron models at different levels of biological detail should therefore be developed. To allow for systematic refinement of candidate network models by comparison with experiments, the simulations should be multimodal in the sense that they should predict not only action potentials, but also electric, magnetic, and optical signals measured at the population and system levels.

Asunto(s)

RESUMEN

Connectivity and biophysical processes determine the functionality of neuronal networks. We, therefore, developed a real-time framework, called Neural Interactome,, to simultaneously visualize and interact with the structure and dynamics of such networks. Neural Interactome is a cross-platform framework, which combines graph visualization with the simulation of neural dynamics, or experimentally recorded multi neural time series, to allow application of stimuli to neurons to examine network responses. In addition, Neural Interactome supports structural changes, such as disconnection of neurons from the network (ablation feature). Neural dynamics can be explored on a single neuron level (using a zoom feature), back in time (using a review feature), and recorded (using presets feature). The development of the Neural Interactome was guided by generic concepts to be applicable to neuronal networks with different neural connectivity and dynamics. We implement the framework using a model of the nervous system of Caenorhabditis elegans (C. elegans) nematode, a model organism with resolved connectome and neural dynamics. We show that Neural Interactome assists in studying neural response patterns associated with locomotion and other stimuli. In particular, we demonstrate how stimulation and ablation help in identifying neurons that shape particular dynamics. We examine scenarios that were experimentally studied, such as touch response circuit, and explore new scenarios that did not undergo elaborate experimental studies.