There is evidence that the subthalamic nucleus (STN) and globus pallidus pars externa (GPe) involve in the development of Parkinson\'s disease, a neurodegenerative disorder characterized by motor and non-motor symptoms and loss of dopaminergic neurons in which the error index (EI) in firing patterns is widely used to address the related issues. Whether and how this interaction mechanism of STN and GPe affects EI in Parkinson\'s disease is uncertain. To account for this, we propose a kind of basal ganglia-thalamic network model associated with Parkinson\'s disease coupled with neurons, and investigate the effect of synaptic conductance of STN and GPe on EI in this network, as well as their internal relationship under EI as an index. The results show a relationship like a piecewise function between the error index and the slope of the state transition function of synaptic conductance from STN to GPe ( g snge ) and from GPe to STN ( g gesn ). And there is an approximate negative correlation between EI and g gesn . Increasing g snge and decreasing g gesn can improve the fidelity of thalamus information transmission and alleviate Parkinson\'s disease effectively. These obtained results can give some theoretical evidence that the abnormal synaptic releases of STN and GPe may be the symptoms of the development of Parkinson\'s disease, and further enrich the understanding of the pathogenesis and treatment mechanism of Parkinson\'s disease.

Alternating electric fields (AEFs) at intermediate frequencies (100-300 kHz) and low intensities (1-3 V/cm) have shown promise as an effective approach for inhibiting cancer cell proliferation. However, a noticeable research gap exists in comparing the biophysical properties of invasive and non-invasive AEFs methods, and AEFs delivery strategies require further improvement. In this study, we constructed a realistic head model to simulate the effects of non-invasive and invasive AEFs on malignant gliomas. Additionally, a novel method was proposed involving the placement of a return electrode under the scalp. We simulated the electric field and temperature distributions in the brain tissue for each method. Our results underscore the advantages of invasive AEFs, showcasing their superior tumor-targeting abilities and reduced energy requirements. The analysis of brain tissue temperature changes reveals that non-invasive AEFs primarily generate heat at the scalp level, whereas invasive methods localize heat production within the tumor itself, thereby preserving surrounding healthy brain tissue. Our proposed invasive AEFs method also shows potential for selective electric field intervention. In conclusion, invasive AEFs demonstrate potential for precise and effective tumor treatment. Its enhanced targeting capabilities and limited impact on healthy tissue make it a promising avenue for further research in the realm of cancer treatment.

Stimulus size modulation of neuronal firing activity is a fundamental property of the primary visual cortex. Numerous biological experiments have shown that stimulus size modulation is affected by multiple factors at different spatiotemporal scales, but the exact pathways and mechanisms remain incompletely understood. In this paper, we establish a large-scale neuronal network model of primary visual cortex with layer 2/3 to study how gamma oscillation properties are modulated by stimulus size and especially how long-range connections affect the modulation as realistic neuronal properties and spatial distributions of synaptic connections are considered. It is shown that long-range horizontal synaptic connections are sufficient to produce dimensional modulation of firing rates and gamma oscillations. In particular, with increasing grating stimulus size, the firing rate increases and then decreases, the peak frequency of gamma oscillations decreases and the spectral power increases. These are consistent with biological experimental observations. Furthermore, we explain in detail how the number and spatial distribution of long-range connections affect the size modulation of gamma oscillations by using the analysis of neuronal firing activity and synaptic current fluctuations. Our results provide a mechanism explanation for size modulation of gamma oscillations in the primary visual cortex and reveal the important and unique role played by long-range connections, which contributes to a deeper understanding of the cognitive function of gamma oscillations in visual cortex.

The synergistic advantage of combining tissue plasminogen activator (tPA) with pro-urokinase (proUK) for thrombolysis has been demonstrated in several in vitro experiments, and a single site proUK mutant (m-proUK) has been developed for better stability in plasma. Based on these studies, combination thrombolytic therapy with intravenous tPA and m-proUK has been suggested as a promising treatment for patients with ischemic stroke. This paper evaluates the efficacy and safety of the dual therapy by computational simulations of pharmacokinetics and pharmacodynamics coupled with a local fibrinolysis model. Seven dose regimens are simulated and compared with the standard intravenous tPA monotherapy. Our simulation results provide more insights into the complementary reaction mechanisms of tPA and m-proUK during clot lysis and demonstrate that the dual therapy can achieve a similar recanalization time (about 50 min) to tPA monotherapy, while keeping the circulating fibrinogen level within a normal range. Specifically, our results show that for all dual therapies with a 5 mg tPA bolus, the plasma concentration of fibrinogen remains stable at around 7.5 μM after a slow depletion over 50 min, whereas a rapid depletion of circulating fibrinogen (to 5 μM) is observed with the standard tPA therapy, indicating the potential advantage of dual therapy in reducing the risk of intracranial hemorrhage. Through simulations of varying dose combinations, it has been found that increasing tPA bolus can significantly affect fibrinogen level but only moderately improves recanalization time. Conversely, m-proUK doses and infusion duration exhibit a mild impact on fibrinogen level but significantly affect recanalization time. Therefore, future optimization of dose regimen should focus on limiting the tPA bolus while adjusting m-proUK dosage and infusion rate. Such adjustments could potentially maximize the therapeutic advantages of this combination therapy for ischemic stroke treatment.

Neurostimulation of the hippocampal formation has shown promising results for modulating memory but the underlying mechanisms remain unclear. In particular, the effects on hippocampal theta-nested gamma oscillations and theta phase reset, which are both crucial for memory processes, are unknown. Moreover, these effects cannot be investigated using current computational models, which consider theta oscillations with a fixed amplitude and phase velocity. Here, we developed a novel computational model that includes the medial septum, represented as a set of abstract Kuramoto oscillators producing a dynamical theta rhythm with phase reset, and the hippocampal formation, composed of biophysically realistic neurons and able to generate theta-nested gamma oscillations under theta drive. We showed that, for theta inputs just below the threshold to induce self-sustained theta-nested gamma oscillations, a single stimulation pulse could switch the network behavior from non-oscillatory to a state producing sustained oscillations. Next, we demonstrated that, for a weaker theta input, pulse train stimulation at the theta frequency could transiently restore seemingly physiological oscillations. Importantly, the presence of phase reset influenced whether these two effects depended on the phase at which stimulation onset was delivered, which has practical implications for designing neurostimulation protocols that are triggered by the phase of ongoing theta oscillations. This novel model opens new avenues for studying the effects of neurostimulation on the hippocampal formation. Furthermore, our hybrid approach that combines different levels of abstraction could be extended in future work to other neural circuits that produce dynamical brain rhythms.

For portal hypertensive patients with splenomegaly and hypersplenism, splenectomy is an effective surgery to relieve the complications. However, patients who have undergone splenectomy often suffer from portal venous system thrombosis, a sequela that requires prophylaxis and timely treatment to avoid deterioration and death. The aim of this study is to investigate the feasibility of predicting post-splenectomy thrombosis using hemodynamic metrics based on computational models. First, 15 portal hypertensive patients who had undergone splenectomy were enrolled, and their preoperative clinical data and postoperative follow-up results were collected. Next, computational models of the portal venous system were constructed based on the preoperative computed tomography angiography images and ultrasound-measured flow velocities. On this basis, splenectomy was mimicked and the postoperative area of low wall shear stress (ALWSS) was simulated for each patient-specific model. Finally, model-simulated ALWSS was statistically compared with the patient follow-up results to investigate the feasibility of predicting post-splenectomy thrombosis using hemodynamic metrics. Results showed that ALWSS could predict the occurrence of post-splenectomy thrombosis with the area under the receiver operating characteristic curve (AUC) equal to 0.75. Moreover, statistical analysis implied that the diameter of the splenic vein is positively correlated with ALWSS (r = 0.883, p < 0.0001), and the anatomical structures of the portal venous system also influence the ALWSS. These findings demonstrated that the computational model-based hemodynamic metric ALWSS, which is associated with the anatomorphological features of the portal venous system, is capable of predicting the occurrence of post-splenectomy thrombosis, promoting better prophylaxis and postoperative management for portal hypertensive patients receiving splenectomy.

Microelectrodes serve as a fundamental tool in electrophysiology research throughout the nervous system, providing a means of exploring neural function with a high resolution of neural firing information. We constructed a hybrid computational model using the finite element method and multicompartment cable models to explore factors that contribute to extracellular voltage waveforms that are produced by sensory pseudounipolar neurons, specifically smaller A-type neurons, and that are recorded by microelectrodes in dorsal root ganglia. The finite element method model included a dorsal root ganglion, surrounding tissues, and a planar microelectrode array. We built a multicompartment neuron model with multiple trajectories of the glomerular initial segment found in many A-type sensory neurons. Our model replicated both the somatic intracellular voltage profile of Aδ low-threshold mechanoreceptor neurons and the unique extracellular voltage waveform shapes that are observed in experimental settings. Results from this model indicated that tortuous glomerular initial segment geometries can introduce distinct multiphasic properties into a neuron\'s recorded waveform. Our model also demonstrated how recording location relative to specific microanatomical components of these neurons, and recording distance from these components, can contribute to additional changes in the multiphasic characteristics and peak-to-peak voltage amplitude of the waveform. This knowledge may provide context for research employing microelectrode recordings of pseudounipolar neurons in sensory ganglia, including functional mapping and closed-loop neuromodulation. Furthermore, our simulations gave insight into the neurophysiology of pseudounipolar neurons by demonstrating how the glomerular initial segment aids in increasing the resistance of the stem axon and mitigating rebounding somatic action potentials.NEW & NOTEWORTHY We built a computational model of sensory neurons in the dorsal root ganglia to investigate factors that influence the extracellular waveforms recorded by microelectrodes. Our model demonstrates how the unique structure of these neurons can lead to diverse and often multiphasic waveform profiles depending on the location of the recording contact relative to microanatomical neural components. Our model also provides insight into the neurophysiological function of axon glomeruli that are often present in these neurons.

Controlling cell-substrate interactions via the microstructural characteristics of biomaterials offers an advantageous path for modulating cell dynamics, mechanosensing, and migration, as well as for designing immune-modulating implants, all without the drawbacks of chemical-based triggers. Specifically, recent in vivo studies have suggested that a porous implant\'s microscale curvature landscape can significantly impact cell behavior and ultimately the immune response. To investigate such cell-substrate interactions, we utilized a 3D computational model incorporating the minimum necessary physics of cell migration and cell-substrate interactions needed to replicate known in vitro behaviors. This model specifically incorporates the effect of membrane tension, which was found to be necessary to replicate in vitro cell behavior on curved surfaces. Our simulated substrates represent two classes of porous materials recently used in implant studies, which have markedly different microscale curvature distributions and pore geometries. We found distinct differences between the overall migration behaviors, shapes, and actin polymerization dynamics of cells interacting with the two substrates. These differences were correlated to the shape energy of the cells as they interacted with the porous substrates, in effect interpreting substrate topography as an energetic landscape interrogated by cells. Our results demonstrate that microscale curvature directly influences cell shape and migration and, therefore, is likely to influence cell behavior. This supports further investigation of the relationship between the surface topography of implanted materials and the characteristic immune response, a complete understanding of which would broadly advance principles of biomaterial design.

OBJECTIVE: To assess whether monopolar multi-electrode transcranial direct current stimulation (tDCS) montages might selectively affect deep brain structures through computational predictions and neurophysiological assessment.
METHODS: Electric field distribution in deep brain structures (i.e., thalamus and midbrain) were estimated through computational models simulating tDCS with two monopolar and two monopolar multi-electrode montages. Monopolar multi-electrode tDCS was then applied to healthy subject, and effects on pontine and medullary circuitries was evaluated studying changes in blink reflex (BR) and masseter inhibitory reflex (MIR).
RESULTS: Computational results suggest that tDCS with monopolar multi-electrode montages might induce electric field intensities in deep brain structure comparable to those in grey matter, while neurophysiological results disclosed that BR and MIR were selectively modulated by tDCS only when cathode was placed over the right deltoid.
CONCLUSIONS: Multi-electrode tDCS (anodes over motor cortices, cathode over right deltoid) could induce significant electric fields in the thalamus and midbrain, and selectively affect brainstem neural circuits.
CONCLUSIONS: Multi-electrode tDCS (anodes over motor cortices, cathode over right deltoid) might be further explored to affect brainstem activity, also in the context of non-invasive deep brain stimulation.

Human clinical trials are important tools to advance novel systemic therapies improve treatment outcomes for cancer patients. The few durable treatment options have led to a critical need to advance new therapeutics in hepatocellular carcinoma (HCC). Recent human clinical trials have shown that new combination immunotherapeutic regimens provide unprecedented clinical response in a subset of patients. Computational methods that can simulate tumors from mathematical equations describing cellular and molecular interactions are emerging as promising tools to simulate the impact of therapy entirely in silico. To facilitate designing dosing regimen and identifying potential biomarkers, we developed a new computational model to track tumor progression at organ scale while reflecting the spatial heterogeneity in the tumor at tissue scale in HCC. This computational model is called a spatial quantitative systems pharmacology (spQSP) platform and it is also designed to simulate the effects of combination immunotherapy. We then validate the results from the spQSP system by leveraging real-world spatial multi-omics data from a neoadjuvant HCC clinical trial combining anti-PD-1 immunotherapy and a multitargeted tyrosine kinase inhibitor (TKI) cabozantinib. The model output is compared with spatial data from Imaging Mass Cytometry (IMC). Both IMC data and simulation results suggest closer proximity between CD8 T cell and macrophages among non-responders while the reverse trend was observed for responders. The analyses also imply wider dispersion of immune cells and less scattered cancer cells in responders\' samples. We also compared the model output with Visium spatial transcriptomics analyses of samples from post-treatment tumor resections in the original clinical trial. Both spatial transcriptomic data and simulation results identify the role of spatial patterns of tumor vasculature and TGFβ in tumor and immune cell interactions. To our knowledge, this is the first spatial tumor model for virtual clinical trials at a molecular scale that is grounded in high-throughput spatial multi-omics data from a human clinical trial.