Digital twin models representing human cardiac electrophysiology have evolved from being primarily research-oriented tools to becoming integral components in clinical decision-making and patient-specific simulations. The development of such models fundamentally begins with the construction of an accurate anatomical twin, which is typically derived from high-resolution clinical imaging data. This anatomical modeling phase, while essential, is often computationally intensive and time-consuming, necessitating efficient tools to streamline the workflow. To address this need, PyMeshTool, a Python interface for MeshTool, was developed with the primary objective of simplifying and accelerating the anatomical twinning process. The C/C++ codebase of MeshTool was restructured to avoid unnecessary source code duplication and to ease the development of the Python interface PyMeshTool. Particular emphasis was placed on the design of PyMeshTool as a streamlined interface that exposes core functionalities of MeshTool in a consistent and user-friendly manner. To evaluate the effectiveness of this design, two Python scripts were implemented running the same anatomical twinning pipeline, one utilizing PyMeshTool and the other calling MeshTool as an external tool. For this purpose, a basic bi-ventricular model generation pipeline was implemented that comprises the generation of simplified universal ventricular coordinates and the assignment of rule-based fibers & sheets. Runtimes and data output of the two workflows were compared for a series of numbers of parallel OpenMP threads. In addition to the primary advantage of PyMeshTool- easy interaction with other Python modules via the Python interface and the compatibility with NumPy- the computational benefits of PyMeshTool have been demonstrated in several comparisons: (i) the PyMeshTool workflow produced less output, 4 files with 89.5MB instead of 418 files with 774.9MB (∼88% less storage usage), (ii) the Python source code was ∼63% shorter in terms of code lines, and (iii) an up to four times faster runtime. With the release of the freely available PyMeshTool module, our work aims to streamline image and mesh processing in Python to ease the development of complex pipelines.
Radiomics analyses extract quantitative biomarkers from medical images for precision modeling, yet reproducibility and scalability remain limited by heterogeneous and limited implementations. Existing tools support only partial standards and lack integration with deep learning (DL) radiomics. To address these gaps, we developed PySERA, an open-source, Python-native, standardized radiomics framework designed for automation, reproducibility, and AI integration. PySERA re-implements MATLAB-based SERA (standardized environment for radiomics analysis) in a modular, object-oriented Python architecture. It computes 557 features, including 487 features compliant with the Image Biomarker Standardization Initiative (IBSI) and 10 moment-invariant descriptors, as well as 60 additional diagnostic features, along with DL radiomics embeddings from pre-trained DL: ResNet50 (2048 features) DL radiomics features), DenseNet121 (1024), and VGG16 (512). It includes standardized preprocessing (resampling, discretization, normalization), multi-format I/O (DICOM, NIfTI, NRRD), adaptive memory handling, and a parallel multi-core engine for scalable feature extraction. PySERA integrates directly with libraries: scikit-learn/PyTorch/TensorFlow/MONAI, and others for downstream machine learning applications. PySERA demonstrated >94% IBSI reproducibility, closely matching MITK and substantially outperforming PyRadiomics against the 487 IBSI-compliant feature reference set. Across 8 public datasets, PySERA achieved accuracies of 0.43-0.84, exceeding PyRadiomics for outcome prediction tasks. Benchmarking showed efficient processing (including added higher-order features not implemented in other software): 583 seconds (305 MB) for 166 features, and 2325 seconds (491 MB) for full extraction, with deterministic outputs across platforms. By uniting standardized handcrafted/DL radiomics in a scalable, transparent, and Python-integrable framework, PySERA establishes a reproducible and extensible foundation for next-generation, AI-ready precision imaging research.
Background Clinical randomization requires more than approximate 1:1 allocation. It also requires sequence generation that is difficult to subvert, allocation concealment during enrollment, and an audit trail that can withstand retrospective review. Objective The primary objective of this proof-of-concept technical and methodological evaluation was to assess the inspectability and audit-oriented design of a lightweight Python-based two-arm allocation prototype. Secondary objectives were to characterize its short-run demonstration behavior and compare its transparency, traceability, and operational limitations with common clinical randomization workflows. Methods We performed a static review of the supplied Python source file and a retrospective review of the supplied allocation log. Log analysis included descriptive arm counts, exact binomial testing for 1:1 balance, an exploratory runs test, and lag-1 autocorrelation. The implementation was interpreted in light of the clinical-trial randomization and pseudorandom-number-generator literature. Results The current source code implements a lightweight two-arm allocation randomization prototype with features intended to support auditability and tamper-evident traceability. Each allocation advances a xorshift-inspired 64-bit generator, maps the resulting integer to arm 1 or 2, and records the underlying output and assignment arithmetic in a human-readable log. An example test log was run and contained 2,000 allocations, with 502 versus 498 assignments in the first 1,000 events and 1008 versus 992 assignments overall. The current source code also implements session seed capture and a chained SHA-256 digest intended to strengthen sequential traceability and show evidence of tampering. Conclusions The prototype's principal improvement over many ad hoc local workflows is operational transparency rather than proven statistical superiority. Its strongest contribution is a short, inspectable code path and an audit-oriented logging structure. Additional hardening would be required before use in concealment-sensitive or regulated trial settings.
Molecular maps are graphical representations of the molecular mechanisms underlying biological systems. They are a valuable tool for curating, exchanging, and understanding biological knowledge, and may serve as a backbone for data analysis and modelling. Molecular maps are supported by a rich software ecosystem. However, there are currently no tools that support advanced programmatic analysis and processing of maps, in particular the extraction of the biological concepts they represent or their comparison. We introduce momapy, a generic Python library to work with molecular maps programmatically. At its core, momapy allows users to extract and separate the data model of a map from its graphical representation, and perform a variety of base operations on them, including their manipulation and comparison. momapy currently supports the SBGN and CellDesigner formats, two of the main standards to represent molecular maps graphically, and can be easily extended to support additional formats and functionalities. momapy is implemented in Python (RRID:SCR_008394) under a GPLv3 license. The code can be downloaded freely from https://github.com/adrienrougny/momapy and is archived on Zenodo (https://doi.org/10.5281/zenodo.19088611). Complete documentation and a user manual are available at https://adrienrougny.github.io/momapy. Supplementary data are available at Bioinformatics online.
Molecular-dynamics (MD) simulations provide atom-level insight into biomolecular motion and are widely used to complement structure-guided drug discovery, ligand optimization, and mechanistic structural biology. However, reproducible MD remains technically challenging because users must coordinate structure cleanup, force-field-compatible topology generation, ligand parameterization, solvation, ionization, energy minimization, equilibration, production simulation, trajectory processing, and downstream interpretation; errors at any stage can compromise results and discourage non-expert users. Here, we present PyMACS, an open-source Python automation framework for GROMACS-based molecular dynamics using the CHARMM36 or CHARMM36/LJ-PME biomolecular force fields together with CGenFF-compatible small-molecule parameterization. PyMACS accepts common structural input formats, including PDB, CIF, and mmCIF files, and is designed to operate on a broad range of user-supplied biomolecular systems rather than being restricted to a single target class or experimentally resolved protein-ligand complexes. Compatible inputs include experimental structures, docked or model-derived complexes, computationally predicted biomolecular assemblies, apo proteins, protein-ligand systems, cofactor-containing complexes, peptide-protein assemblies, protein-protein complexes, RNA-, DNA-, and mixed nucleic-acid-protein systems for setup and simulation workflows when residue naming and CHARMM/GROMACS topology generation support the input, and PROTAC or other ternary-complex models when force-field compatibility requirements are met. The pipeline automates structure preparation, ligand and component handling, GROMACS topology generation, system assembly, solvation, ionization, equilibration, production MD, checkpoint-aware execution, and post-simulation analysis. PyMACS also converts completed trajectories into interpretable outputs including RMSD, RMSF, radius of gyration, ligand-stability metrics, residue-contact summaries, interaction-network visualizations, CSV exports, and report-ready figures. Designed for both interactive novice use and headless reproducible execution, PyMACS lowers the barrier to rigorous MD simulation while preserving configurable control over scientific parameters, computational resources, and analysis thresholds. By integrating setup, simulation, analysis, and visualization into a transparent workflow, PyMACS enables medicinal chemists and structural biology researchers to evaluate candidate binding hypotheses, compare biomolecular systems, and generate reproducible MD-derived evidence with substantially reduced manual overhead.
NEURON has been widely used as an empirically-based simulation tool, especially for multi-compartment conductance-based neuronal modeling. The network mediating feeding in Aplysia californica has been extensively studied as a model central pattern generator. Understanding the relationship between network parameter values and their effect on animal behavior is of key importance in systems such as the Aplysia feeding apparatus, where detailed biophysical models can be constructed. This study aims to develop a new Python tool called NEURONpyxl that reads parameters from a spreadsheet to construct full neural networks to make it easier to create complex models in the NEURON simulation environment, incorporating short-term forms of plasticity such as depression or facilitation. Test simulations from well-understood networks were created in NEURONpyxl, and compared to simulation results of the same network in another neural simulator, the Simulator for Neural Networks and Action Potentials (SNNAP), which has previously been used to model conductance-based networks that include complex synaptic connections and multiple forms of synaptic plasticity. NEURONpyxl was then used to conduct a parameter grid search to optimize conductances in a previously developed network model of Aplysia feeding behavior. Simulations of the test networks in NEURONpyxl and SNNAP produced numerically equivalent results, with differences remaining within the expected margin of error arising from numerical integration and implementation details. We then located parameter values that generated simulated motor patterns with durations of protraction and retraction that matched biological feeding behavior under different mechanical loads. NEURONpyxl simplifies building and simulating complex neural networks with different forms of synaptic plasticity, and locating physiologically relevant parameter values. With NEURONpyxl, future work may include the creation of ensembles of network models and the integration of biomechanics with complex conductance-based networks.
Lattice-based random sequential adsorption (RSA) simulations can be used to investigate the random packing of shapes on a surface, in particular the adsorption of molecules on a crystalline lattice in 2D. Atomic layer deposition (ALD) and by extension area-selective ALD (AS-ALD) have found applications in the manufacturing of nanoelectronics in the semiconductor industry. During deposition with these techniques, the packing of molecules on a surface─in terms of density and arrangement─largely determines the growth rate and selectivity, respectively. The packaged AdsorPy script performs RSA simulations of molecules represented by their 2D footprints and provides quantitative information about the packing properties. The script provides easy integration of other computational methods used in material science, such as density functional theory calculations. RSA simulations can for example be used to make informed decisions on the choice of inhibitor species in AS-ALD processes. Various case scenarios are demonstrated using the script in order to match the conditions of relevant experiments, such as different dosing schemes.
Background Accurate pain assessment is fundamental to effective cancer pain management. However, subjective scales such as the Numerical Rating Scale (NRS) have limitations in pediatric and elderly patients with impaired verbal communication. This study aimed to develop a real-time facial expression-based pipeline for NRS estimation and to evaluate its technical feasibility as a proof of concept (PoC). Methods A real-time analysis pipeline was implemented in Python (version 3.11; Python Software Foundation, Fredericksburg, VA, USA), integrating MediaPipe for facial detection and DeepFace for emotion estimation. Seven emotion probability scores extracted from 30 fps video streams were used as predictor variables to estimate NRS values using several regression models, including Random Forest (RF). Synthetic datasets generated for technical validation were evaluated using leave-one-out cross-validation (LOOCV). Performance was assessed using Spearman's rank correlation coefficient (ρ) and mean absolute error (MAE). Results In the pediatric dataset, the RF model achieved ρ = 0.7383 (p < 0.001) and MAE = 1.5195, demonstrating improved performance compared with the baseline model (ρ = 0.2765). In the elderly dataset, the RF model showed ρ = 0.7566 (p < 0.001) and MAE = 1.5760. Feature importance analysis indicated that "Fear" contributed prominently in both datasets, whereas "Neutral" also showed relatively higher importance in the elderly dataset. Conclusions This study demonstrated the technical feasibility of a real-time NRS estimation pipeline using artificial intelligence (AI)-based facial expression analysis. The findings suggest potential applicability as a complementary pain assessment approach for patients with limited verbal communication, pending future clinical validation.
The mcstas_gisans framework is a collection of Python scripts and modules to facilitate the simulation of grazing-incidence small-angle neutron scattering (GISANS) experiments. This approach combines McStas instrument simulation with BornAgain sample modeling capabilities. The Monte Carlo Particle Lists format for particle trajectory allows exchange between simulations that enables seamless transition from instrument modeling to sample scattering analysis. The Python-based processing utilities handle data transformation, scaling to virtual experiment times for absolute intensities, and visualization. The required software environment is managed through Conda, ensuring reproducible deployments across platforms. This integrated approach facilitates accurate simulation and analysis and enables the comparison of the GISANS capability of different neutron scattering instruments.
Understanding the quality of eye-tracking recordings, often characterized using accuracy, precision, and data loss, is crucial for the interpretation of eye tracking data. Eye-tracking data quality can furthermore place fundamental limits on what studies can be conducted with an eye tracker, and one may be required to report eye-tracking data quality when publishing a study. However, how does one determine the quality of eye-tracking data? This article provides an overview of operationalizations of accuracy, precision, and data loss and practical advice for determining eye-tracking data quality. Furthermore, the programming code for calculating various quality metrics for a segment of eye-tracking data is provided in MATLAB, Python, and R. Also provided is ETDQualitizer, a tool designed to enable anyone to easily determine the data quality of their recordings. We provide a version that is browser-based ( https://dcnieho.github.io/ETDQualitizer ) and enables determining eye-tracking data quality without installation or programming, while ensuring data privacy by running entirely locally. ETDQualitizer is further provided as a MATLAB, Python, and R library ( https://github.com/dcnieho/ETDQualitizer ) that can be integrated in one's analysis scripts. We hope that this article enables any researcher to determine, critically evaluate, and report on eye-tracking data quality, and that it spurs researchers to adopt a data quality perspective in all their future eye-tracking studies.
Turnstile rotation is a well-known polytopal rearrangement mechanism in coordination compounds, yet its computational description is often hindered by the challenges of conventional internal coordinates to represent such collective, large-amplitude motions. In this work, we present a mathematical formulation for generalized N $$ N $$ -arm turnstile rotation, a dedicated PyMOL plugin (gTA) for intuitive structure visualization and a command-line utility (gTA-cli) for structure manipulation. On this basis, we develop a practical computational workflow that combines turnstile rotation-driven relaxed scans with two downstream routes to locate the transition states. The generality of the approach is demonstrated through five chemically diverse case studies, ranging from classical rearrangements in SF 4 $$ {}_4 $$ , IF 7 $$ {}_7 $$ , and [Co(en) 3 $$ {}_3 $$ ] 3 + $$ {}^{3+} $$ to previously unexplored fluxional processes in bismuth and nickel complexes. In all cases, the method enables the identification of transition states associated with pronounced polytopal rearrangements that are difficult to access using standard coordinate schemes. These results establish generalized turnstile rotation as a fundamental molecular motion and highlight its potential as a broadly applicable strategy for studying fluxionality and dynamic stereochemistry in coordination chemistry. The PyMOL plugin gTA and the Python utility gTA-cli with the associated relaxed scan workflows are open-source and freely available on Github at https://github.com/smutao/gTA-plugin and https://github.com/smutao/gTA-workflow, respectively.
An adult's health, indicated by measurable parameters, is stable over time. With the exception of circadian rhythms, variability in these parameters typically does not exceed 20%. In this pilot study, we looked into the stability of proteome in intensive care unit (ICU) patients. This was a single-center, prospective, observational pilot study of blood plasma from adult ICU patients with statistically heterogeneous patterns of clinically observed parameters. Eight week-long batches from seven patients (one patient participated twice) were analyzed by means of bottom-up proteomics. The data were analyzed with MaxQuant software against reference proteome. The obtained intensities were further processed with in-house R and Python scripts. In total, 218 proteins were identified; however, only 68 proteins appeared in all samples from all patients. Most proteins remained stable within observation (within-patient variance was less than 30%). The random-effects model also confirmed high impact of within-patient variance on the protein levels. The effects of time on the protein level variances did not exceed 5%. Z-score-based hierarchical clustering analysis revealed that the daily data of each patient were clustered together indicating that the plasma proteome of ICU patients both bears individual traits and remains stable during short-term progression of the patients' condition. Therefore, in this pilot group of patients, the analysis over seven consecutive days fails to reveal proteome dynamics.
Accurate segmentation of acute ischemic stroke (AIS) lesions on neuroimaging is essential for diagnosis, treatment decision-making, and prognostication. Manual methods are limited by time and variability. Machine learning (ML), especially deep learning, has emerged as a powerful tool for automated lesion segmentation, yet a systematic synthesis of model performance, methodological rigor, and clinical applicability remains lacking. To systematically review and quantitatively evaluate the performance of ML-based segmentation models for AIS using a meta-analytic approach, and to identify factors associated with model accuracy and robustness across imaging modalities, architectures, and datasets. We conducted a systematic review and meta-analysis in accordance with PRISMA 2020 guidelines. Comprehensive searches were performed in PubMed, Scopus, and Web of Science databases through March 2025. Eligible studies included those reporting on machine learning (ML)-based segmentation of acute ischemic stroke (AIS) lesions on CT or MRI and providing quantitative performance metrics (e.g., Dice, sensitivity, specificity, AUC). Data were systematically extracted on study design, ML architecture, imaging modality, dataset size and composition, and segmentation performance. Random-effects meta-analyses were conducted using inverse-variance weighting, with logit transformation applied to bounded metrics to stabilize variance. Between-study heterogeneity was assessed using the I2 statistic and Cochran's Q-test. Meta-regression analyses explored the influence of covariates such as lesion volume, sample size, and stroke severity (mRS), while subgroup analyses examined performance variations by imaging modality and model type. Visualizations included forest plots, funnel plots, bubble plots, and correlation matrices, generated in Python using standardized meta-analysis and statistical libraries. Out of 4755 screened records, 101 studies met the inclusion criteria. Deep learning approaches, especially U-Net variants, dominated the field (78%). The pooled Dice coefficient was 0.84 (I2 = 0%), indicating high and consistent segmentation accuracy. Additional pooled estimates were AUC 0.91, accuracy 0.89, sensitivity 0.85, and specificity 0.93. Models using multimodal MRI inputs outperformed single-modality CT models. Meta-regressions revealed no significant association between lesion volume or sample size and Dice scores, though segmentation performance trended higher with increasing clinical severity (mRS). Specificity showed weak correlation with recall and Dice, indicating potential trade-offs in model optimization. ML-based segmentation models for AIS demonstrate high accuracy, especially when using multimodal MRI and deep learning architectures. Performance is robust across varying dataset sizes and lesion characteristics. However, heterogeneity in study designs and reporting standards underscores the need for methodological harmonization and external validation. These findings inform future model development and integration into clinical AIS workflows.
We present a 40 kHz single-axis acoustic levitator for Small-Angle X-ray Scattering (SAXS) experiments. The levitator consists of 48 ultrasonic transducers of 9.8 mm in diameter and a small opposing plane reflector. The levitation stability is evaluated by using a high-speed camera for recording the position over time of a suspended drop. To demonstrate the capability of the levitator, it was assembled in a laboratory SAXS instrument (Xenocs Xeuss 2.0) for investigating the evaporation kinetics of liquid drops containing metallic silver nanoparticles. The spontaneous evaporation of the levitated water molecules decreases the droplet volume, leading to a systematic increase in the concentration of the particles in the system. As a result, the average distance between the particles decreases, giving rise to enhanced particle-particle interactions. For the evaluation of the droplet size and selection of correct scattering data for sample and blank scattering, a Python™ package was developed, allowing automatic video analysis and accurate subtraction of the background associated with the solvent in which the particles are suspended. Although the proposed levitator was used in SAXS experiments, it can also be used in many other research fields, such as pharmacy, biology, chemistry, and material sciences.
Integrated in-incubator microscopy systems and all-in-one time-lapse culture incubators are often expensive and proprietary, which limits accessibility and customization. Open-source alternatives exist but often involve tradeoffs in imaging performance, illumination control, or adaptability. Here, we describe a do-it-yourself (DIY), low-cost, three-dimensional (3D)-printed time-lapse imaging platform designed for use inside standard cell culture incubators. The device employs a Raspberry Pi-based controller with stepper motor actuation and a programmable light-emitting diode (LED) backlight that enables both bright-field and oblique illumination modes. The system supports automated multi-well imaging, scheduled time-lapse acquisition, and real-time preview through open-source Python software. Performance validation demonstrated reliable long-term imaging of cultured cells and early embryos, with stable operation under incubator conditions. Optical resolution testing using a United States Air Force (USAF) 1951 target confirmed a minimum resolvable feature of approximately 1.55 µm. Motion evaluation showed low drift and high repeatability, with positioning accuracy closely matching commanded displacements. The system is compatible with multiple plate formats and can be adapted for specialized culture setups. Overall, this platform provides a cost-effective, reproducible, and customizable solution for long-term live-cell and embryo imaging in laboratory settings.
High-density multielectrode arrays (HD-MEAs) generate large, complex datasets that are challenging to efficiently manage and analyze with existing tools, especially in open-source environments. To address this, we developed the BYU Seizure and Analytics Tool (YSA), an open-source graphical user interface built in Python and C++ for efficient analysis and visualization of HD-MEA recordings. The YSA features raster plots, automated discharge detection and tracking, downsampling, playback, and export functions, enabling streamlined workflows for large-scale neural data. We demonstrate the utility of the tool in the context of seizure and status epilepticus-like activity, highlighting how the YSA facilitates rapid exploration of the spatiotemporal dynamics in brain networks. This platform provides an accessible and practical solution for HD-MEA data analysis, supporting a range of neuroscience applications.
The determination of quartic force fields for use in vibrational second-order perturbation (VPT2) calculations, currently available in numerous electronic structure packages, becomes very expensive as the size of the molecule increases, especially if high-level coupled-cluster theory is used. Machine-learned potentials (MLPs) for large molecules and clusters offer a viable alternative to obtaining the quartic force field (QFF). Here, we report Fortran and Python software to determine the QFF and perform VPT2 calculations of energies from the MLPs. We describe this software and then apply it to H2O and protonated oxalate as the test cases. The Fortran software is applied to 21-atom aspirin using a fast MLP reported by us. Despite the fact that there are 32,509 unique cubic force constants for aspirin, the computer time to calculate them using this MLP is trivial, i.e., around 1 min. The new software provides an efficient way to calculate quantum anharmonic energies, using the established VPT2 methodology, for machine learned potentials of large molecules.
As the largest online rosacea forum, r/Rosacea, a subreddit hosted on Reddit, provides a unique opportunity to better understand the concerns of rosacea patients. Using Python software and artificial intelligence models, a total of 1,000 posts from the r/Rosacea subreddit were analyzed for emotional tone, post category, and mentions of signs and symptoms, as well as medications. The majority of posts were classified as seeking advice (n = 631), and posts categorized as patient stories received the highest median upvotes (P<0.001). Rosacea patients were found to be most concerned with external appearance, with redness (45.2%) and pustules (24.2%) being the most discussed signs in advice-seeking posts. Posts referencing burning exhibited strong negative emotional tones of anger and disgust. Ivermectin (12.7%) and azelaic acid (10.9%) were the most discussed medications in advice-seeking posts, and ivermectin received significantly lower median upvotes (P=0.0067). The insights of this cross-sectional analysis aid in achieving a deeper understanding of the rosacea patient perspective. Additional emotional analysis of the posts highlights the need for greater focus on the psychological burden of the disease. The frequency and popularity of rosacea medications reveal potential gaps in patient education and raises concerns regarding treatment adherence to medications, including ivermectin. It is imperative to increase rosacea patient access to quality-assured educational resources and to limit the potential spread of misinformation on r/Rosacea.  .
In recent years, videogames have gathered interest in cognitive neuroscience for their potential to study cognition in dynamical and naturalistic contexts. Yet, the complexity of game environments often challenges traditional modeling approaches, and current annotation methods-typically manual or based on modified games-remain labor-intensive and limited in scope. Here, we introduce a flexible and scalable framework using the gym-retro Python library to emulate a classic action-platformer, Shinobi III: Return of the Ninja Master, and automatically annotate gameplay events directly from the game's memory states. This setup enables the identification of both player actions (e.g., jumping, hitting) and feedback events (e.g., killing an enemy, being hit), without modifying the game. Four individuals played the videogame for a combined total of 32 h (>7 h each) while undergoing functional magnetic resonance imaging (fMRI). Resulting activation maps revealed distributed engagement of visual, motor, executive, and limbic systems, consistent with the cognitive demands of gameplay. Within-participant reproducibility of brain responses across sessions was robust across event types (r ≈ .25-.55), with some consistency observed even for rarer events like HealthLoss. Between-participant correlations were notably lower, reflecting participant-specific neural signatures. Multivoxel pattern analysis showed that brain responses to different in-game events were highly discriminable, with classification accuracy typically around or above 90%, though occasionally dropping to ~40% for less frequent events. These findings demonstrate that automated emulator-based annotations enable robust, interpretable, and scalable mapping of naturalistic cognitive processes using commercial videogames.
Decision-making behavior changes over time, exhibiting temporal correlation and nonstationarity. Existing drift diffusion model (DDM) fitting methods either do not provide uncertainty quantification for parameter estimates, or rely on restrictive assumptions that decisions are independent and that parameters remain constant over time, potentially underestimating uncertainty. To address these limitations, we propose a computationally efficient method for estimating analytic uncertainties in DDM parameters that are robust to temporal dependence and unmodeled parameter variability, while explicitly modeling nonstationary variability through covariates. We apply this method to rat decision-making in a two-alternative forced-choice (2AFC) visual task, revealing dynamic decision-making states across multiple timescales. A Python implementation of the method is provided.