Biology subjects

Hashemi, M.

Publications and source records attributed to Hashemi, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Structural integrity moderates the cognitive bearing of dopamine clearance in aging

Age-related cognitive decline in humans is well established, yet how neurobiological variations influence brain dynamics, and subsequently behavioral performance, remains insufficiently explored. Microscale accounts of cognitive aging revolve around attenuations in dopamine (DA) receptors and transporters (DAT). In contrast, macroscale accounts associate individual declines in cognitive performance with functional brain networks' reorganization and white-matter tracts' deterioration. We endeavor to bridge this gap with an amortized dopaminergic aging model (ADAM) which assembles aging brain twins by coupling dopaminergic modulation with structural connectomes to constrain emergent whole-brain dynamics. ADAM thereby augments established DAT decline with personalized estimates, through a reusable and generalizable estimator capable of disentangling neuromodulatory from structural contributions to age-related decline within each individual. To that end, we embed structural connectomes into a low-dimensional space exhibiting high reconstruction fidelity and -pseudonymized- individual identifiability. The spectrum of structural embeddings is subsequently updated with fMRI observations to estimate DA clearance timescales in 3,650 adults aged 36 to 95 years across five independent aging cohorts. Our analysis reveals an age-related elevation of DA clearance timescales. Critically, low performers exhibit elevated clearance in adults with marked structural degradation, and only within sliding age windows centred between 56 and 76 years. This supports a contribution of dopamine to aging, and reveals a structure- and age-conditional association between its clearance and cognitive performance. Our findings suggest that structural connectivity gates the macroscale reach of dopaminergic modulation, thus rendering the gain-control framework of neuromodulation mechanistically explicit across individuals and cohorts. ADAM therefore provides a biologically plausible framework that anchors interindividual variability in cognitive aging to perturbations in the dopaminergic system by learning cohort-scale mappings which can be shared across sites, establishing a transferable testbed for precision neuromodulation and future in-silico pharmacological trials.

neuroscience↗

bioRxiv2026

Ahead of the membrane curve: in silico insights into amyloid-β aggregation

Membrane surfaces can accelerate amyloid $\beta$ (A$\beta$) aggregation, yet the role of membrane curvature in this process remains poorly understood. Here, we used multi-million atom all-atom molecular dynamics simulations to compare the adsorption, conformational dynamics, and oligomerization of four A$\beta$42 peptides at a planar neuronal membrane and a highly curved lipid vesicle. For both systems, all peptides adsorbed within the first 2 $\mu$s, but their subsequent behavior differed substantially. The curved membrane exhibited a larger area per lipid and more extensive hydrophobic packing defects, allowing A$\beta$42 to penetrate more deeply and form strong contacts with lipid tails through its central hydrophobic core and C-terminal region. These interactions disrupted a solution-formed dimer and limited peptide-peptide association during the simulated interval. Additionally, vesicle-bound peptides adopted more extended conformations with increased $\beta$-structure and $\beta$-hairpin formation compared with peptides at the planar membrane. A$\beta$42 adsorption was also corelated to lipid reorganization in the vesicle. In contrast, the planar membrane supported weaker adsorption and stable dimer-to-trimer growth but showed little large-scale lipid segregation. These findings reveal that curvature reshapes the early A$\beta$42 aggregation landscape by strengthening peptide-lipid interactions, altering aggregation-prone conformations, and reorganizing membrane domains. Membrane geometry should therefore be considered alongside lipid composition in mechanistic models of A$\beta$42 oligomerization and membrane-associated toxicity.

biophysics↗

bioRxiv2026

A PDMS-based microfluidic platform enabling dual biochemical and electric-field stimulation for modeling sensory neuron-intervertebral disc crosstalk

Inflammation-driven increases in nociception are prominent in pain pathologies associated with intervertebral disc (IVD) degeneration yet are difficult to model in vitro. Since neurons are exposed to multi-modal stimuli in vivo, it is critical for these exposures to be conserved in an in vitro test system. We developed a polydimethylsiloxane (PDMS)-based microfluidic platform to interrogate peripheral sensory neurons (SNs) in the presence of conditioned media from nucleus pulposus cells from the degenerated IVD, to model a potential impact of IVD cells secretome on pain sensing. Our platform enables controlled perfusion of cell-derived biochemical cues alongside a defined homogeneous electric field (EF) and supports real-time optical analysis. Computational modeling, fluid perfusion experiments, and conductivity measurements confirmed stable fluid transport and tunable homogeneous EF generation within the device. As proof of concept for neuronal stimulation, neuroblastoma (N2a) cells loaded with a fluorescent Ca2+ indicator exhibited a 56% increase in Ca2+ transient activity when exposed to media from degenerated IVDs, concomitant with increased IVD-derived IL-1{beta} production. Importantly, EF-stimulated Ca2+ transients increased in SNs derived from human induced pluripotent stem cells when exposed to conditioned media from primary human IVD cells, demonstrating the translation of this model system to human cells. Together, these results establish a versatile platform that enables controlled and simultaneous exposure to biochemical and electrical stimuli to quantify inflammation-driven peripheral neuronal hyperexcitability in tissue-neuron crosstalk.

bioengineering↗

bioRxiv2026

Xeno-Free Peptide-Functionalized Hydrogels Support hiPSC Encapsulation and In Situ Differentiation into Structurally Mature Cardiomyocytes

While defined synthetic substrates can replace Matrigel for human induced pluripotent stem cell (hiPSC) culture and hiPSC-derived cardiomyocyte (hiPSC-CM) production, existing approaches culture cells on two-dimensional surfaces and yield structurally immature cardiomyocytes, limiting their use in disease modeling and regenerative medicine. Here, we developed a xeno-free, fully-defined cyclic RGD (cRGD)-functionalized alginate platform in which we encapsulated hiPSCs to support their expansion and in situ cardiac differentiation. cRGD functionalization was essential for hiPSC survival and pluripotency, with maximal support achieved at a low ligand density (25 {micro}M). In the presence of cRGD, hiPSC encapsulation into softer gels made from lower molecular weight alginates led to enhanced hiPSC expansion and improved cardiogenesis. Strikingly, differentiation in situ with 3D gels led to hiPSC-CM with higher structural maturity, including a markedly increased proportion of Desmin-positive cardiomyocytes. Finally, after enzymatic retrieval from hydrogels, cardiomyocytes derived from softer gels formed tissue-engineered myocardium with superior contractile force compared to tissue fashioned from hiPSC-CM derived from more rigid gels. Together, these results demonstrate the promise of this defined, tunable platform for biomanufacturing of structurally mature cardiomyocytes from hiPSC.

bioengineering↗

bioRxiv2026

Normative Modeling of Static and Dynamic Functional Connectivity

The transition toward individual-level modeling in functional neuroimaging is severely bottlenecked by methodological heterogeneity, which confounds subject-specific effects with study-dependent variance. Normative modeling leverages a multi-level statistical model to parameterize and account for such variability. We examine whether this framework can harmonize fragmented legacy datasets from seven large cohorts (N = 4705), bypassing the need for intensive reprocessing under an arbitrary unified pipeline. We evaluate the construction of a lifespan normative chart by harmonizing open-access functional MRI data extracted with highly heterogeneous processing pipelines, incorporating study-level random effects within a generalized additive model. From this well-calibrated model we drive normative trajectories of both static and dynamic functional connectivity and quantify their invariance to the choice of metric used to compute the interactions between regions. We observe a key decoupling between static and dynamic connectivity. While static connectivity exhibits a monotonic age-related decline, dynamic connectivity follows a more complex trajectory: decreasing during pediatric stabilization, peaking in mid-adulthood metastability, and ultimately declining into senescent rigidity. Overall, this framework establishes a scalable statistical blueprint for modeling functional brain organization to circumvent massive data homogenization, while preserving sensitivity to both between-subject variability and within-subject temporal dynamics.

neuroscience↗

bioRxiv2026

SK2/3 CHANNELS COUPLE WITH T-TYPE CA2+ CHANNELS TO GATE SPINAL LOCOMOTOR RHYTHM GENERATION

Initiating locomotion requires central pattern generator (CPG) interneurons to transition from tonic firing to persistent sodium current (INaP)-dependent bursting. While INaP provides the rhythmogenic drive, the conductances gating this transition remain unclear. Here, we show that functional coupling between small-conductance calcium-activated potassium (SK2/3) channels and low-threshold T-type Ca2+ channels, notably Cav3.2, gates locomotor rhythm generation. Pharmacological or genetic disruption of this SK-T-type axis triggers intrinsic bursting in Hb9 interneurons, a genetically identified rhythmogenic population of the locomotor CPG, and initiates fictive locomotion, whereas SK activation silences ongoing rhythmic output. Immunohistochemical co-expression of SK2, SK3 and Cav3.2 in Hb9 interneurons provides an anatomical basis for this functional coupling. Simulation-based inference further shows that, beyond this gating mechanism, burst diversity is primarily determined by the balance between INaP and M-type potassium conductances. Together, these findings identify SK-T-type coupling as a tunable brake on CPG activation, defining a biophysical module that controls the initiation and termination of locomotor rhythmic activity, with potential relevance for rhythmogenic circuits beyond locomotion.

neuroscience↗

bioRxiv2026

Beyond Co-Existence: α-Synuclein Sequesters Amyloid-β42 into Distinct Hybrid Assemblies

Amyloid-{beta} (A{beta}) deposits often appear in Parkinson's disease and -synuclein (-syn) pathology in Alzheimer's disease. As soluble oligomers contribute importantly to cytotoxicity, their early crosstalk is critical to understand. Using all-atom molecular dynamics, we investigated how the initial oligomeric configuration of A{beta}42 shapes its early association with -syn, simulating -syn with (i) one A{beta}42 monomer, (ii) two separated monomers, and (iii) a pre-formed dimer plus a free monomer, across 15 independent 3 s trajectories. Association patterns depended strongly on the initial configuration. With monomeric or nascent dimeric A{beta}42, -syn frequently contacted the metal-binding, central hydrophobic core (CHC), and C-terminal regions, and these contacts were temporally coordinated, most reproducibly the -syn NAC with the A{beta}42 CHC and polar regions. {beta}-hairpin sampling was reduced specifically when -syn engaged the CHC and C-terminus, an interface-dependent rather than a general effect that did not track contact duration, and coincided with more expanded conformations. With a pre-formed dimer, -syn associated mainly peripherally, while the dimer's {beta}-structured interface remained comparatively stable. First-order dihedral entropy differed only modestly between the free and associated states, indicating that association redistributed conformational populations without globally reducing local backbone diversity. Overall, -syn's early structural effect on A{beta}42 is configuration-dependent: it engages and perturbs lower-order species while associating only peripherally with the sampled dimer.

biochemistry↗

bioRxiv2026

Bigger Isn't Always Better: Comparing System Size, Hydration, and Software for Lipid Membrane Analysis

The structural and dynamic properties of membranes are known to vary with bilayer size and hydration. While Molecular Dynamic (MD) simulations are a powerful tool for studying cellular membrane systems, the results can be sensitive to the analysis work-flow and software. In this study, all-atom MD simulations (500 ns) were conducted on systems of 256, 512, and 1024 POPC lipids at 40, 80, and 160 waters per lipid. With these simulations, a two-fold study was performed: (1) to assess the convergence of structural and dynamic properties of POPC bilayers as a function of membrane size and hydration level using CPPTRAJ (CPP), including area per lipid (APL), bilayer thickness, order parameter, headgroup orientation, and lateral diffusion, and (2) to compare the analysis output and performance of four software packages: CPP, GROMACS (GRO), MDAnalysis (MDA), and LiPyphilic (LiP). For the first objective, our results show that the average values of the bilayer thickness, order parameter, and headgroup orientation are largely independent of the size and hydration levels studied. In contrast, lateral diffusion coefficient was sensitive to both size and hydration. We found that increasing the system size primarily decreased the statistical variance of the APL and thickness. For the second objective, all four packages produced consistent results for APL and thickness, with the most significant discrepancy being a known artifact from the gmx order tool when applied to unsaturated carbons. Performance bench-marks identified CPP as the fastest serial tool for all properties, whereas parallelization benefited MDA and LiP in some metrics. These findings provide a practical roadmap, demonstrating that moderately sized systems (e.g., 256L), combined with an optimized tool such as CPP, offer an efficient workflow for membrane structural property analysis.

biophysics↗

bioRxiv2025

A deformable attractor manifold organizes human resting-state brain dynamics

Intrinsic brain activity is often described as wandering within a continuous multivariate space, yet the organizing principles that constrain these dynamics remain unclear. Here, we show that spontaneous human brain activity during rest is structured by a deformable attractor manifold. Using large-scale fMRI datasets and a latent dynamical model, we find that cortical activity occupies two reproducible regimes: a low-coherence state with a unimodal latent distribution and a high-coherence state that exhibits bimodality, consistent with transient bistability across association networks. A compact two-parameter energy landscape explains these dynamics, revealing that transitions arise not from switching between discrete states, but from continuous deformation of the manifold that reshapes attractor geometry. Excursions into the bistable regime occur as rapid "jumps", whereas returns follow slow drifts along the manifold, reflecting network-specific timescales. Individuals with greater expression of the bistable regime show higher cognitive fluidity, and manifold parameters differentiate mild cognitive impairment from matched controls. These findings identify an organizing geometric and dynamical principle of resting activity, linking large-scale cortical coordination, cognitive variability, and vulnerability to pathology.

neuroscience↗

bioRxiv2025

Degeneracy and bifurcation diversity at the single neuron level

Degeneracy--the capacity of structurally distinct systems to achieve similar functions--is a fundamental property of living organisms, enabling adaptability and resilience. Neurons can maintain stable activity patterns despite wide variability in ion channel expression, highlighting how distinct internal configurations can yield equivalent electrophysiological behaviors. However, it remains unclear how degeneracy manifests in the context of bifurcations, the critical transitions between activity regimes that underlie phenomena such as different firing patterns, seizures and depolarization block. Here, we investigate how different biophysical parameter sets can lead to equivalent bifurcation sequences. Using a minimal neuron model with three variable conductances, we systematically explore how changes in extracellular potassium--mimicking physiological and pathological conditions--affect neuronal dynamics. Our results reveal that neurons with distinct intrinsic properties can traverse the same bifurcation pathways, entering regimes of bursting, seizure-like activity, and depolarization block. Yet, the specific parameter set determines the sensitivity and thresholds for these transitions. This work clarifies how degeneracy extends to the dynamical landscape of neurons, with implications for understanding resilience and vulnerability in neural circuits. 1 Author SummaryBiophysical models expressed through differential equations can reproduce the dynamics of biological systems with varying levels of detail. By changing model parameters, simulations can capture the natural variability that allows biological systems to achieve the same behavior through different mechanisms. This property, called degeneracy, underlies the robustness of living systems to internal and external perturbations. In this work, we identify dis-tinct behaviors in single-neuron models and link them to electrophysiological patterns observed under elevated extracellular potassium during epileptic events. These patterns correspond to specific classes of spontaneous bursting activity within the framework of nonlinear dynamics. We show that different combinations of conductance parameters, at a fixed extracellular potassium level, can generate the same class of patterns, defining degeneracy groups. Finally, we assess the robustness of these groups by analyzing how they respond to changes in extracellular potassium. Our findings provide a basis for studying variability and resilience in the dynamics of more complex neuronal systems.

neuroscience↗

bioRxiv2025

Virtual Brain Inference (VBI): A flexible and integrative toolkit for efficient probabilistic inference on virtual brain models

Network neuroscience has proven essential for understanding the principles and mechanisms underlying complex brain (dys)function and cognition. In this context, whole-brain network modeling-also known as virtual brain modeling-combines computational models of brain dynamics (placed at each network node) with individual brain imaging data (to coordinate and connect the nodes), advancing our understanding of the complex dynamics of the brain and its neurobiological underpinnings. However, there remains a critical need for automated model inversion tools to estimate control (bifurcation) parameters at large scales associated with neuroimaging modalities, given their varying spatio-temporal resolutions. This study aims to address this gap by introducing a flexible and integrative toolkit for efficient Bayesian inference on virtual brain models, called Virtual Brain Inference (VBI). This open-source toolkit provides fast simulations, taxonomy of feature extraction, efficient data storage and loading, and probabilistic machine learning algorithms, enabling biophysically interpretable inference from non-invasive and invasive recordings. Through in-silico testing, we demonstrate the accuracy and reliability of inference for commonly used whole-brain network models and their associated neuroimaging data. VBI shows potential to improve hypothesis evaluation in network neuroscience through uncertainty quantification, and contribute to advances in precision medicine by enhancing the predictive power of virtual brain models.

neuroscience↗

bioRxiv2024

Dynamic Causal Modeling in Probabilistic Programming Languages

Understanding the intricate dynamics of brain activities necessitates models that incorporate causality and nonlinearity. Dynamic Causal Modelling (DCM) presents a statistical framework that embraces causal relationships among brain regions and their responses to experimental manipulations, such as stimulation. In this study, we perform Bayesian inference on a neurobiologically plausible generative model that simulates event-related potentials observed in magne-to/encephalography data. This translates into probabilistic inference of latent and observed states of a system driven by input stimuli, described by a set of nonlinear ordinary differential equations (ODEs) and potentially correlated parameters. We provide a guideline for reliable inference in the presence of multimodality, which arises from parameter degeneracy, ultimately enhancing the predictive accuracy of neural dynamics. Solutions include optimizing the hyperparameters, leveraging initialization with prior information, and employing weighted stacking based on predictive accuracy. Moreover, we implement the inference and conduct comprehensive model comparison in several probabilistic programming languages to streamline the process and benchmark their efficiency. Our investigation shows that model inversion in DCM extends beyond variational approximation frameworks, demonstrating the effectiveness of gradient-based Markov Chain Monte Carlo methods. We illustrate the accuracy and efficiency of posterior estimation using a self-tuning variant of Hamiltonian Monte Carlo and the automatic Laplace approximation, effectively addressing parameter degeneracy challenges. This technical endeavor holds the potential to advance the inversion of state-space ODE models, and contribute to neuro-science research and applications in neuroimaging through automatic DCM.

neuroscience↗

bioRxiv2024

Principles and Operation of Virtual Brain Twins

Current clinical methods often overlook individual variability by relying on population-wide trials, while mechanism-based trials remain underutilized in neuroscience due to the brains complexity. A Virtual Brain Twin (VBT) is a personalized digital replica of an individuals brain, integrating structural and functional brain data into advanced computational models and inference algorithms. By bridging the gap between molecular mechanisms, whole-brain dynamics, and imaging data, VBTs enhance the understanding of (patho)physiological mechanisms, advancing insights into both healthy and disordered brain function. Central to VBT is the network modeling that couples mesoscopic representation of neuronal activity through white matter connectivity, enabling the simulation of brain dynamics at a network level. This transformative approach provides interpretable predictive capabilities, supporting clinicians in personalizing treatments and optimizing interventions. This manuscript outlines the key components of VBT development, covering the conceptual, mathematical, technical, and clinical aspects. We describe the stages of VBT construction-from anatomical coupling and modeling to simulation and Bayesian inference-and demonstrate their applications in resting-state, healthy aging, epilepsy, and multiple sclerosis. Finally, we discuss potential extensions to other neurological disorders, such as Parkinsons disease, and explore future applications in consciousness research and brain-computer interfaces, paving the way for advancements in personalized medicine and brain-machine integration. "The brain is conceived as a self-organizing system operating close to instabilities where its activities are governed by collective variables, the order parameters, that enslave the individual parts, i.e., the neurons." Professor Hermann Haken (1927-2024).

neuroscience↗

bioRxiv2024

Probing other's Presence: Probabilistic Inference Across Brain Scales Reveals Enhanced Excitatory Synaptic Efficacy

The presence of conspecifics is a fundamental and arguably invariant prerequisite of social cognition across numerous animal species. While the influence of social presence on behavior has been among the focal points of investigation in social psychology for over a century, its underlying neural mechanisms remain largely unexplored. Here, we attempt to bridge this gap by investigating how the presence of conspecifics changes synaptic efficacy from measurements across spatiotemporal brain scales, and how such changes could lead to modulations of task performance in monkeys and humans. In monkeys performing an association learning task, social presence increased excitatory synaptic efficacy in attention-oriented regions dorsolateral prefrontal cortex and anterior cingulate cortex. In humans performing a visuomotor task, the presence of conspecifics facilitated performance in one of the subject groups, and this facilitation was linked to enhanced excitatory synaptic efficacy within the dorsal and ventral attention networks. We propose that presence-induced improvements in task performance arise from attentional modulation mediated by changes in excitatory synaptic efficacy across three spatiotemporal brain scales, namely, micro-scale (single neurons), meso-scale (cortical columns) and macro-scale (whole-brain). Our findings from Bayesian learning converge to establish a novel, multi-scale framework for understanding the neural underpinnings of social presence effects. This probabilistic framework offers a fresh perspective on social presence research, and lays the groundwork for future investigations into the complex interplay between social presence, neural dynamics, and behavior.

neuroscience↗

bioRxiv2024

Nanoscale interaction of endonuclease APE-1 with DNA characterized by atomic force microscopy

Apurinic/apyrimidinic endonuclease 1 (APE1) is involved in DNA replication, repair, and transcriptional regulation mechanisms. This multifunctional activity of APE1 should be supported by specific structural properties of APE1 that have not yet been elucidated. Here we applied atomic force microscopy (AFM) to characterize the interactions of APE1 with DNA. Complexes of APE1 with DNA containing G-rich segments were visualized, and analysis of the complexes revealed the affinity of APE1 to G-rich DNA sequences. Furthermore, loops in the DNA-APE1 complexes were visualized, and their yield was as high as 53 %. However, the loops were non-specific, with quantitative analysis revealing the yield of loops bridging two G-rich DNA segments to be 41%. Analysis of protein size in various complexes was performed, and these data showed that loops are formed by APE1 monomer, suggesting that APE1 has two DNA binding sites. The data lead us to a model for the interaction of APE1 with DNA that describes its molecular site search mechanism. The new properties of APE1 in organizing DNA, by bringing two distant sites together, may be important for facilitating the scanning for damage and coordinating repair and transcription.

biophysics↗

bioRxiv2024

Antivirals for Monkeypox Virus: Proposing an Effective Machine/Deep Learning Framework

Monkeypox is one of the infectious viruses which caused morbidity and mortality problems in these years. Despite its danger to public health, there is no approved drug to stand and handle Monkeypox. On the other hand, drug repurposing is a promising screening method for the low-cost introduction of approved drugs for emerging diseases and viruses which utilizes computational methods. Therefore, drug repurposing is a promising approach to suggesting approved drugs for the monkeypox virus. This paper proposes a computational framework for monkeypox antiviral prediction. To do this, we have geenrated a new virus-antiviral dataset. Moreover, we applied several machine learning and one deep learning method for virus-antiviral prediction. The suggested drugs by the learning methods have been investigated using docking studies. To the best of our knowledge, this work is the first work to study deep learning methods for the prediction of monkeypox antivirals. The screening results confirm that Tilorone, Valacyclovir, Ribavirin, Favipiravir, and Baloxavir marboxil are effective drugs for monkeypox treatment.

bioinformatics↗

bioRxiv2024

Efficient Inference on a Network of Spiking Neurons using Deep Learning

The process of making inference on networks of spiking neurons is crucial to decipher the underlying mechanisms of neural computation. Mean-field theory simplifies the interactions between neurons to produce macroscopic network behavior, facilitating the study of information processing and computation within the brain. In this study, we perform inference on a mean-field model of spiking neurons to gain insight into likely parameter values, uniqueness and degeneracies, and also to explore how well the statistical relationship between parameters is maintained by traversing across scales. We benchmark against state-of-the-art optimization and Bayesian estimation algorithms to identify their strengths and weaknesses in our analysis. We show that when confronted with dynamical noise or in the case of missing data in the presence of bistability, generating probability distributions using deep neural density estimators outperforms other algorithms, such as adaptive Monte Carlo sampling. However, this class of deep generative models may result in an overestimation of uncertainty and correlation between parameters. Nevertheless, this issue can be improved by incorporating time-delay embedding. Moreover, we show that training deep Neural ODEs on spiking neurons enables the inference of system dynamics from microscopic states. In summary, this work demonstrates the enhanced accuracy and efficiency of inference on networks of spiking neurons when deep learning is harnessed to solve inverse problems in neural computation.

neuroscience↗

bioRxiv2023

Parameter estimation in a whole-brain network model of epilepsy: comparison of parallel global optimization solvers

The Virtual Epileptic Patient (VEP) refers to a computer-based representation of a patient with epilepsy that combines personalized anatomical data with dynamical models of abnormal brain activities. It is capable of generating spatio-temporal seizure patterns that resemble those recorded with invasive methods such as stereoelectro EEG data, allowing for the evaluation of clinical hypotheses before planning surgery. This study highlights the effectiveness of calibrating VEP models using a global optimization approach. The approach utilizes SaCeSS, a cooperative metaheuristic capable of parallel computation, to yield high-quality solutions without requiring excessive computational time. Through extensive benchmarking, our proposal successfully solved a set of different configurations of VEP models, demonstrating better scalability and superior performance against other parallel solvers. These results were further enhanced using a Bayesian optimization framework for hyperparameter tuning, with significant gains in terms of both accuracy and computational cost. Additionally, we added an scalable uncertainty quantification phase after model calibration, and used it to assess the variability in estimated parameters across different problems. Overall, this study has the potential to improve the estimation of pathological brain areas in drug-resistant epilepsy, thereby to inform the clinical decision-making process. Author summaryMotivated by the problem of parameter estimation in a set of whole-brain network models of epilepsy (of increasing complexity), this study addresses the question of choosing a robust global optimization solver that can be accelerated by exploiting parallelism in different infrastructures, from desktop workstations to supercomputers. By leveraging data-driven techniques with robust cooperative global optimization methods, we aim to achieve accurate parameter estimation with reduced reliance on prior information. This is due to the dependency of Bayesian inference on the level of information in the prior, while this approach allows us to quantify uncertainty in the absence of any prior knowledge effectively. In this work, we construct an efficient and accurate method to perform parameter estimation and uncertainty quantification for the VEP model, and we use it to infer the brain regional epileptogenicity from source and sensor level whole-brain data. Of specific interest is the ability of our method to produce inference for high-dimensional state-space models governed by deterministic, stochastic, well-behaved, and stiff differential equations, using only partial observations and sparse encoding from system states to the observation.

neuroscience↗