Frontiers in Neuroinformatics

One of the prominent methods in neuroimaging data processing is SSM-PCA, which is based on principal component analysis and allows for the identification of diagnostically significant patterns in the form of statistical maps. We developed software, PIE Toolbox, employs SSM-PCA and classification based on the obtained diagnostic patterns revealed from functional and structural tomographic brain imaging. The program supports the entire analysis pipeline including preprocessing of brain images, diagnostic patterns extraction, building classification models, and prediction based on them. The resulting diagnostic patterns are weighted principal components obtained through SSM-PCA, or their linear combinations. PIE Toolbox allows selection of relevant structural and functional brain patterns, computation of their expression values in regions of interest, classification using support vector machines, and evaluation of model performance via cross-validation. This approach enables the use of patterns as features of intergroup differences for individual diagnosis. The software has been validated on both simulated and ADNI datasets.

Convolutional neural networks (CNNs) are a class of artificial neural networks (ANNs). Since the early 2010s, they have been widely adopted as models of primate vision and classifiers of neuroimaging data, becoming relevant for a wealth of neuroscientific fields. However, the majority of neuroscience researchers come from soft-science backgrounds (like medicine, biology, or psychology) and do not have enough quantitative skills to understand the inner workings of A/CNNs. To avoid undesirable black boxes, neuroscientists should acquire some rudiments of computational neuroscience and machine learning. However, most researchers do not have the time nor the resources to make big learning investments, and self-study materials are hardly tailored to people with little mathematical background. This paper aims to fill this gap by providing a concise but accurate introduction to CNNs and their use in neuroscience -- using the minimum required mathematics, neuroscientific analogies, and Python code examples. A companion Jupyter Notebook guides readers through code examples, translating theory into practice and providing visual outputs. The paper is organised in three sections: The Concepts, The Implementation, and The Biological Plausibility of A/CNNs. The three sections are largely independent, so readers can either go through the entire paper or select a section of interest.

The primate retina dissects visual scenes into multiple retinocortical streams. The most numerous retinal ganglion cell (GC) types, midget and parasol cells, are further divided into ON and OFF subtypes. These four GC populations have anatomical and physiological asymmetries, which are reflected in the spike trains received by downstream circuits. Computational models of the visual cortex, however, rarely take GC signal processing into account. We have built a macaque retina simulator with the aim of providing biologically plausible spike trains for downstream visual cortex simulations. The simulator is based on realistic sampling density and receptive field size as a function of eccentricity, as well as on two distinct spatial and three temporal receptive field models. Starting from data from literature and earlier receptive field measurements, we synthetize distributions for receptive field parameters, from which the synthetic units are sampled. The models are restricted for monocular and monochromatic stimuli and follow data from the temporal hemiretina which is more isotropic. We show that the model patches conform to anatomical data not used in the reconstruction process and characterize the responses with respect to spatial and temporal contrast sensitivity functions. This simulator allows starting from a stimulus video and provides biologically plausible spike trains for the distinct unit types. This supports development of thalamocortical primate model systems of vision. In addition, it can provide a reference for more biophysical retina models. The independent parameters are housed in text files supporting reparameterization for particular macaque data or other primate species. Author summaryVisual environment provides a rich source of information, and the visual system structure and function has been studied for decades in many species, including humans. The most complex data in mammalian species are processed in the cerebral cortex, but to date we are still missing a functioning model of cortical computations. While the earlier anatomical and physiological data describe many details of the visual system, to understand the functional logic we need to numerically simulate the complex interactions within this system. To pave the way for simulating visual cortex computations, we have developed a functioning model for macaque retina. The neuroinformatics comprises a review and re-digitized existing retina data from literature, as well as statistics of earlier macaque receptive field data. Finally, we provide software which brings the collected neuroinformatics to life and allows researchers to convert visual input into biologically feasible spike trains for simulation experiments of visual cortex.

Lick microstructure is a term used in behavioural neuroscience to describe the information that can be obtained from a detailed examination of rodent drinking behaviour. Rather than simply recording total intake (volume consumed), lick microstructure examines how licks are grouped, and the spacing of these groups of licks. This type of analysis can provide important insights into why an animal is drinking, for example, whether it is influenced by taste or affected by consequences of consumption (e.g., feeling "full"). Here we present a software package, lickcalc, that allows detailed microstructural analysis of licking patterns. The software is browser-based and is hosted at https://lickcalc.uit.no or the repository can be downloaded and installed locally. Lick timestamps can be loaded from a variety of formats and different analysis and plotting options allow quality control of data and determining critical parameters for microstructural analysis number and size of lick bursts. Data can be divided into epochs for detailed examination of changes across session. Batch processing and custom configurations are supported. In this manuscript, we demonstrate use of the functions exposed by lickcalc by analysing data comparing lick patterns between mice on a protein-restricted and control (non-restricted diet). We show that lickcalc allows quality control of the data and uncovering of subtle differences in lick behaviour that are not apparent when just considering the total number of licks. This software makes microstructural analysis accessible to any researchers who wish to employ it while providing sophisticated analyses with high scientific value.

Despite growing understanding of the benefits of having Findable, Accessible, Interoperable, and Reusable (FAIR) data, many datasets still cannot be shared. Federated analysis methods can enable multisite studies that do not require the sharing of participant-level information. However, there are many practical hurdles that prevent the large-scale adoption of federated methods. We discuss challenges related to cross-site data preparation for federated learning, present solutions offered by recent neuroinformatics projects, and showcase an example of tool integration applied to neurodegenerative disease data.

Anatomically and biophysically detailed models of neurons have been widely used to study information processing in these cells. Most studies focused on understanding specific phenomena, while more general models that aim to capture various cellular processes simultaneously remain rare even though such models are required to predict neuronal behavior under more complex, natural conditions. In this study, we aimed to develop a detailed, data-driven, general-purpose biophysical model of hippocampal CA1 pyramidal neurons. We leveraged extensive morphological, biophysical and physiological data available for this cell type, and established a systematic workflow for model construction and validation that relies on our recently developed software tools. The model is based on a high-quality morphological reconstruction and includes a diverse curated set of ion channel models. After incorporating the available constraints on the distribution of ion channels, the remaining free parameters were optimized using the Neuroptimus tool to fit a variety of electrophysiological features extracted from somatic whole-cell recordings. Validation using HippoUnit confirmed the models ability to replicate key electrophysiological features, including somatic voltage responses to current input, the attenuation of synaptic potentials and backpropagating action potentials, and nonlinear synaptic integration in oblique dendrites. Our model also included active dendritic spines, modeled either explicitly or by merging their biophysical mechanisms into those of the parent dendrite. We found that many aspects of neuronal behavior were unaffected by the level of detail in modeling spines, but modeling nonlinear synaptic integration accurately required the explicit modeling of spines. Our data-driven model of CA1 pyramidal cells matching diverse experimental constraints is a general tool for the investigation of the activity and plasticity of these cells and can also be a reliable component of detailed models of the hippocampal network. Our systematic approach to building and validating general-purpose models should apply to other cell types as well. Author SummaryThe brain processes information through the activity of billions of individual neurons. To understand how these cells work, scientists build detailed computer models that reproduce their electrical behavior. These models make it possible to explore situations that are difficult or impossible to test experimentally. However, many existing neuron models were designed to explain only a few specific phenomena, which limits their usefulness in more complex settings. In this study, we developed a comprehensive computer model of a hippocampal CA1 pyramidal neuron, a cell type that plays a central role in learning and memory. We built the model using extensive experimental data and applied automated methods to ensure that it reproduces a broad range of observed neuronal behaviors. We also examined how small structures called dendritic spines--tiny protrusions where most synaptic communication occurs--affect how neurons combine incoming signals. We found that even simplified models without individual spines can capture many aspects of neuronal activity, but understanding more complex forms of signal integration requires modeling spines explicitly. Our work also supports the development of more realistic simulations of brain circuits.

Three-dimensional (3D) whole-organ imaging and analysis at cellular resolution (termed 3D histology) provide profound insights into the organization and interactions of cells throughout organs. However, the quantitative analysis of these massive datasets remains a significant bottleneck due to the lack of integrated, user-friendly tools. Here, we present 3DBrainOne, an end-to-end ImageJ plugin that streamlines the essential 3D histological analysis of the mouse brain--from raw image preprocessing to region-wise quantification--within a single platform. 3DBrainOne features a robust whole-brain cell-counting module that uses a Difference-of-Gaussians (DoG) blob detection algorithm followed by a ResNet18-based deep learning classifier, enabling high-fidelity automatic whole-brain cell counting with a graphical user interface (GUI) for visual inspection and manual curation of analysis results. 3DBrainOne also supports multi-channel colocalization analysis. Furthermore, this platform includes modules for atlas alignment and brain-region-wise volumetric quantification, enabling brain region-resolved cell counting and structural analyses. As an ImageJ plugin, 3DBrainOne is compatible with a range of operating systems and hardware. In summary, 3DBrainOne is an integrated, versatile, and easy-to-use platform that will facilitate 3D histological analyses in experimental neuroscience.

Preprocessing and analysis of neuroimaging data are technically demanding, often requiring a combination of multiple software tools, modality-specific pipelines, and extensive parameter tuning to match dataset characteristics. These complexities make it difficult to document workflows in sufficient detail to ensure complete transparency and reproducibility. To address these challenges, we introduce QuNex recipes, a framework for defining and executing complete neuroimaging workflows - encompassing data onboarding, preprocessing, and analysis - in a transparent, machine- and human-readable format. Recipes are implemented as an integrated feature of the Quantitative Neuroimaging Environment & Toolbox (QuNex), a containerized, open-source platform for end-to-end multimodal and multi-species neuroimaging processing. The recipes framework enables seamless integration of QuNex commands with custom scripts and external tools, capturing every processing step and parameter setting. A fully reproducible study can thus be shared and replicated by providing only (a) the QuNex version used, (b) the recipe file, and (c) the data. This approach standardizes workflow specification, enhances transparency, and enables one-command replication of complex neuroimaging analyses. By providing a standardized way to describe and share workflows, recipes facilitate open exchange of best practices and reproducible methods within the neuroimaging community.

A persistent bottleneck in post-Independent Component Analysis (ICA) Electroencephalogram (EEG) preprocessing is the manual identification of artifact components for removal. In practice, this step can be slow, subjective, and difficult to standardize, particularly for cardiac contamination and trigger-related leakage, where artifact structure may be distributed across multiple components or appear outside the highest-variance Independent Components (ICs). We developed the SENSI-EEG-Preproc-ICA-EKG-Trigger Module to make this stage faster and more reproducible without removing the user from the decision process. The Module is a semi-automated MATLAB framework for post-ICA screening of cardiac and trigger-related artifact components using spectral characteristics. EKG candidates are prioritized by detecting harmonic structure around a physiologically plausible heart-rate fundamental, whereas trigger-related candidates are prioritized by measuring harmonic concentration at frequencies determined by the known repetition period of the trigger sequence. The resulting candidates are then reviewed in dedicated interfaces that present scalp topography, time-domain activity, and frequency-domain structure together, allowing the final classification to be confirmed or corrected by the user. In this way, the Module narrows the search space while preserving interpretability and explicit human control over the final keep/remove decision. The release includes a public codebase, a user manual, example workflows, and an accompanying example dataset. This paper presents the Module as a practical methods-and-software contribution for post-ICA EEG cleaning.

Currently, there is no consensus about how investigators should format their NIBS data for sharing. This presents a barrier to the advancement of big data analyses because it requires time-consuming operations to generate consistent formats across different shared datasets. Recently, we launched Big non-invasive brain stimulation data (Big NIBS data), an open-access platform and repository for NIBS data (https://www.bignibsdata.com/), providing a structured mechanism for researchers to share NIBS data. However, the reusability and interoperability of data uploaded to Big NIBS data is restricted by the absence of a common data structure. The current paper addresses this problem by creating the NIBS data analysis structure (NIBS-DAS), a template pipeline for the layout, management, and analysis of collated NIBS outcome data. While its primary purpose is to provide a template layout for uploading collated data to the Big NIBS data repository, NIBS-DAS also offers guidelines for the management and analysis of collated NIBS data, thereby forming a data analysis pipeline that can be freely used by the NIBS field in general. We anticipate that NIBS-DAS will serve to facilitate data sharing on the Big NIBS data platform and promote greater standardisation of data management and analytical practices in the NIBS field.

We present a free and open-source, semi-automated, topologically robust pipeline for fitting cable models to 3D surface mesh morphology data of neuronal membranes, particularly suited to structures with complex shapes and topological holes. The motivation for this work is the discovery of morphologically complex neural spines on the auditory space-specific neurons of the barn owl (Tyto alba, Tyto furcata), dubbed "toric spines", notable for their high curvature, branching density, and holes/loops. Multicompartmental simulation software requires morphology to be represented as cable models (e.g., SWC format), yet existing software tools for fitting cable models to complex 3D surface meshes have not produced satisfactory results for toric spines, and loops are generally unsupported. We present the Mesh and Skeleton Cable Fitting (MASCAF) pipeline and software, which fits a cable model (e.g., SWC format) to a surface mesh using mean-curvature flow skeletonization. In this paper, we demonstrate how MASCAF is applied to fit cable models, how loops can be reconstructed in simulations with the Arbor and NEURON simulation software, and how the results can be validated using geometry and simulator-based methods. While non-tree morphologies such as toric spines are neuroanatomically special, our software pipeline provides a cable-model fitting approach for surface mesh data that is topologically robust, deterministic, open-source, and applicable to general morphologies, thereby closing a crucial gap between neuronal imaging and high-resolution simulation.

BackgroundDeveloping generalizable neuroimaging models is often hindered by limited labeled data which has led to an increased interest in unsupervised inverse learning. Existing approaches often neglect geometric principles and struggle with diverse pathologies. We propose a symmetry-informed inverse learning foundation model to address these shortcomings for robust and efficient anomaly detection in brain MRI. MethodsOur framework employs a reconstruction-to-embedding pipeline, trained exclusively on healthy brain MRI slices. A 2D U-Net uses a novel, symmetry-aware masking strategy to reconstruct a disorder-free slice. Difference maps are embedded into a 1024-dimensional latent space via a Beta-VAE. Anomaly scoring is performed using Mahalanobis distance. We evaluated generalization by fine-tuning on external lesion datasets, BraTS Africa (SSA), and the ADNI-derived Alzheimers disease cohort (Alz). ResultsOn the source metastasis (Mets) dataset, the framework achieved high performance (AB1+MSE: 99.28% accuracy, 99.79% sensitivity). Generalization to the external lesion dataset (SSA) was robust, with the Symmetry ROC configuration achieving 91.93% accuracy. Transfer to the Alzheimers dataset (Alz) was more challenging, achieving a peak accuracy of 70.54% with a high false-positive rate, suggesting difficulty in separating subtle, diffuse changes. ConclusionThe symmetry-informed inverse learning framework establishes a robust foundation model for neuroimaging, showing strong performance for focal lesions and successful generalization under domain shift. Limitations in diffuse neurodegeneration underscore the necessity for richer representations and multimodal integration to improve future foundation models. Summary StatementA symmetry-informed inverse learning framework trained on normal brain MRI achieved high accuracy for detecting focal lesions and demonstrated strong generalization across external datasets under domain shift. Key Points[bullet] A symmetry-informed disorder-free reconstruction framework trained only on normal brain MRI achieved 99.28% accuracy and 99.79% sensitivity for metastasis detection on the BrainMetShare dataset, demonstrating non-inferior performance compared with all but one strategy while offering improved computational efficiency. [bullet]The model generalized effectively to an external tumor dataset (BraTS SSA), achieving up to 91.93% accuracy using receiver operating characteristic-optimized thresholding with minimal fine-tuning. [bullet]Embedding-based anomaly detection using Mahalanobis distance enabled consistent separation between normal and abnormal slices, supporting robust and interpretable anomaly detection across datasets.

Octopus vulgaris and other cephalopods are of increasing interest as neurobiological model organisms. This protocol describes a method to record calcium activity from individual cells in acute brain slices from Octopus vulgaris hatchlings during exogenous application of neurotransmitters. Using this protocol, we characterized single-cell responses to specific neurotransmitters in the optic lobes, which process visual information. The approach is readily adaptable to other cephalopods and small invertebrate species. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=146 HEIGHT=200 SRC="FIGDIR/small/711860v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@1564eaeorg.highwire.dtl.DTLVardef@147b682org.highwire.dtl.DTLVardef@11f3b85org.highwire.dtl.DTLVardef@17c9d70_HPS_FORMAT_FIGEXP M_FIG C_FIG

Background: The rapid expansion of large-scale neuroscience datasets has increased the need for automated, accurate, and standardized quality control (QC). Manual proofreading of 3-dimensional neural morphology (SWC files) remains labor-intensive, error-prone, and non-scalable. We developed and evaluated a fully automated, machine-learning driven QC pipeline to standardize neural reconstructions, detect and correct structural anomalies, and rectify dendritic labeling in pyramidal neurons. Methods: We developed an end-to-end, cloud-deployed pipeline for automated QC, correction, and standardization of SWC-formatted neural morphologies. The framework integrates deterministic structural normalization, topology repair, geometric correction, quantitative morphometric analysis, and graph-based dendritic relabeling within a containerized React/Flask architecture deployed on Amazon Web Services. Rule-based algorithms systematically detect, classify, and correct structural irregularities including overlapping nodes, spurious side branches, non-positive radii, disconnected components, and anomalously long parent-child connections. A graph convolutional network, trained on Sholl-derived features from 20,500 pyramidal neurons, performs dendritic relabeling. Model training employed an 80/10/10 train-validation-test split with adaptive learning-rate scheduling and distributed execution across ten runs to evaluate stability and reproducibility. The pipeline generates images of the final product and computes quantitative morphometrics using L-Measure. Results: All neuronal reconstructions were processed without manual intervention. Automated normalization and topology repair restored structurally coherent and biologically accurate morphologies suitable for quantitative analysis and visualization without data loss. Dendritic relabeling achieved a mean accuracy of 99.51%, consistent between validation and test sets, with class-weighted precision of 0.978, recall of 0.977, and F1-score of 0.977. Enforcing a single apical dendritic tree per neuron improved anatomical consistency without reducing classification performance. Distributed training completed all runs in approximately 25 hours, demonstrating scalability and reproducibility for large datasets. Conclusions: We present a fully automated and cloud-scalable open-source pipeline for standardizing neural reconstructions and performing biologically consistent dendritic classification with near-perfect accuracy. The automated correction and relabeling procedures do not alter or compromise the size or unaffected morphological detail of the original SWC files, ensuring geometric fidelity and compatibility with downstream analysis tools. This open-access framework provides a robust foundation for high-throughput neural morphology curation and large-scale neuroanatomical analysis.

Neural information flow describes the movement of activity between neurons or brain areas. Advances in experimental methods have allowed production of large amounts of observational data related to neuronal activity from the single-neuron to population level. Most current methods for analysing these data are based on pairwise comparison of activity, and fall short of reliably extracting neural information flow network structure. Dynamic Bayesian networks may overcome some of these limitations. Here we evaluate the performance of a range of Bayesian network scoring metrics against the performance of multivariate Granger causality and LASSO regression for their ability to learn the connectivity underlying simulated single-neuron and neuronal population data. We find that discrete dynamic Bayesian networks are the best performing method for single-neuron data, and perform consistently for neural-population data. Continuous dynamic Bayesian networks have a tenancy to learn overly dense structures for both data types, but may have utility in scoping studies on single-neuron data. Multivariate Granger causality is the most robust method for learning structure of neural information flow between neural-populations, but performs poorly on single-neuron data. Significance testing within multivariate Granger causality produces variable results between data types. Overall, this work highlights how the analysis of neural information flow can vary depending on they type and structure of underlying data, and promotes discrete dynamic Bayesian networks as a useful and consistent tool for neural information flow analysis.

Online processing and visualization of large-scale neural data is critical for neuroscientific discovery and advancements in neural engineering. However, with the development of technologies like Neuropixels (NP) probes, which enable simultaneous streaming from hundreds of recording electrodes, handling such data in real-time has become an ongoing challenge. Moreover, keeping pace with recording hardware has required most existing software, such as SpikeGLX for NP probes, to prioritize acquisition stability, leaving data processing and visualization to primarily be performed offline. Thus, we created OP-GLX, a MATLAB-based toolbox designed to operate in tandem with SpikeGLX to enhance the fetching, processing, and visualization of incoming neural data. The OP-GLX toolbox features several processing capabilities, including spike detection, computing time-binned firing rates, plotting spike waveforms, and conducting principal component analysis (PCA). The processed neural data is displayed on a native graphical user interface (GUI) for intuitive and customizable interaction with the experiment. The performance testing of OP-GLX showed that it supports real-time operation, confirmed by the absence of SpikeGLX stream buffer fetch errors across multiple acquisition settings. By complementing current neural data acquisition methods and providing stable online functionality, we envision that OP-GLX will enable researchers to visualize and interpret their data more effectively during ongoing neuroscience experiments.

Scientists have for decades attempted to automate the manual sleep staging problem not only for human polysomnography data but also for rodent data. No model has, however, succeeded in fully replacing the manual procedure across clinics and laboratories. We hypothesize that this is due to the models limited ability to generalize to data from unseen laboratories. Our findings show that despite the high performance of four state-of-the-art models reported in initial publications, the published models struggle to generalize to other laboratories. We further show a significant improvement in model performance across labs by re-training them on a diverse dataset from five different sites. To assess the contribution of variability in manual scoring, ten experts from five laboratories all labelled the same nine mouse sleep recordings. The result revealed substantial scoring variability, particularly for rapid eye movement (REM) sleep, both within and between labs. In conclusion our study demonstrates that key challenges in the generalizability of state-of-the-art sleep scoring models are signal variability and label noise. Our study highlights the need for a standardized set of mouse sleep scoring guidelines to enable consistency and collaboration across the field. Until such a consensus is reached, we present four sufficiently robust models trained on diverse datasets that can serve as standardized tools across labs.

Electroencephalography (EEG) preprocessing is a critical yet time-consuming step that often relies on expert-driven, semi-automatic pipelines, limiting scalability and reproducibility across large datasets. In this work, we present sEEGnal, a fully automated and modular pipeline for EEG preprocessing designed to produce outputs comparable to expert-driven analyses while ensuring consistency and computational efficiency. The pipeline integrates three main modules: data standardization following the EEG extension of the Brain Imaging Data Structure (BIDS), bad channel detection, and artifact identification, combining physiologically grounded criteria with independent component analysis and ICLabel-based classification. Performance was evaluated against manual preprocessing performed by EEG experts at two complementary levels: preprocessing metadata (bad channels, artifact duration, and rejected components) and EEG-derived measures. In addition, test-retest analyses were conducted to assess the stability of the pipeline across repeated recordings. Results show that sEEGnal achieves performance comparable to expert-driven preprocessing while preserving key neurophysiological features. Furthermore, the pipeline demonstrates reduced variability and increased consistency compared to human experts. These findings support sEEGnal as a robust and scalable solution for automated EEG preprocessing in both research and large-scale applications. HighlightsFully automated and modular EEG preprocessing pipeline. Benchmarked against expert-driven preprocessing. Comparable performance in metadata and EEG-derived measures. Demonstrates stable performance in test-retest recordings. BIDS-based framework for reproducible EEG data handling.

AO_SCPLOWBSTRACTC_SCPLOWOne of the most compelling ideas for bridging neuroscience and artificial neural networks is the establishment of a framework based on three main components: network architecture, optimization mechanism, and loss (or objective) function to be minimized. While the first two components have been extensively explored, the definition of a loss or objective function in neuroscience has been addressed less thoroughly, often from perspectives such as predictive coding. In this work, we propose an elementary loss function grounded in the comparison of neuronal responses to two signals: an external one, used for learning, and an internal one, reflecting the acquired knowledge. The loss function is thus simply the basic difference between the two, which, in terms of logical signals, corresponds to a well-known non-linearly separable function: the XOR function. We illustrate with a computational example how a binarized image recognition algorithm can be straightforwardly implemented in an autoencoder, and we show how a neuronal motif organized around an inhibitory neuron could implement such XOR operation and provide a feedback signal that makes optimization possible.

BackgroundMotor threshold (MT) estimation is fundamental to transcranial magnetic stimulation (TMS), guiding individualized stimulation intensity in research and therapy. Conventional methods such as the 5-out-of-10 rule require many stimuli, while adaptive approaches like Parameter Estimation by Sequential Testing (PEST) improve efficiency but can exhibit poor convergence under certain conditions. ObjectiveThis study introduces the Bayesian Uncertainty Dynamic Algorithm for Parameter Estimation by Sequential Testing (BUDAPEST), a Bayesian adaptive method for fast, accurate MT estimation with user-controlled uncertainty. The aims were to validate its accuracy in simulations and human data, promote usability through a MATLAB-based graphical interface, and evaluate experimental utility through resting and active MT comparisons and session-to-session reliability. MethodsBUDAPEST infers MT from binary MEP responses using sequential Bayesian updating and terminates when a user-defined uncertainty threshold is reached. Performance was evaluated in 10,000 virtual simulations and in human rMT and aMT measurements across two sessions per subject, including 3x5 cortical motor mapping to assess physiological spatial patterns. ResultsIn simulations, BUDAPEST achieved a mean absolute error of 1.9% MSO within ~10 pulses using a 2% uncertainty criterion while avoiding PEST misestimations. In human data, MT estimates were accurate within {+/-}4% MSO and robust to initialization; rMT showed strong session-to-session reliability (r = 0.78), whereas aMT exhibited greater variability. Motor mapping revealed coherent excitability gradients centered on the hotspot. ConclusionBUDAPEST enables rapid, reliable, and uncertainty-controlled MT estimation while reducing procedure time and participant burden. The accompanying GUI facilitates immediate adoption in research and clinical TMS environments. HighlightsO_LIIntroduces BUDAPEST, a Bayesian uncertainty-aware algorithm for rapid and reliable TMS motor threshold estimation. C_LIO_LIAchieves accurate MT estimates ({approx}2% MSO error) in ~10 pulses with user-controlled trade-offs between precision and procedure duration. C_LIO_LIDemonstrates robust performance in simulations and human data, with strong resting MT reliability and an open-source GUI enabling immediate adoption. C_LI