Journal of Neuroscience Methods最新文献

{"title":"Rapid and reliable image analysis pipeline for semi-automated quantification of CNS cell types in MATLAB","authors":"Adrian Castellanos-Molina ,&nbsp;Juliette Ferry ,&nbsp;Ana Boisvert ,&nbsp;Alexandre Chamberland ,&nbsp;Nicolas Vallières ,&nbsp;Steve Lacroix","doi":"10.1016/j.jneumeth.2025.110590","DOIUrl":"10.1016/j.jneumeth.2025.110590","url":null,"abstract":"<div><h3>Background</h3><div>Studying CNS cell responses is essential for understanding disease, injury, and developing effective therapies. While immunofluorescence and transgenic reporter models allow for specific labeling, automated quantification remains difficult due to tissue heterogeneity. Consequently, most analyses are conducted manually, introducing user bias and limiting reproducibility.</div></div><div><h3>New method</h3><div>We developed a MATLAB-based semi-automated workflow for quantifying immunofluorescence-stained CNS cells, focusing on nuclear signal detection. The pipeline uses DAPI masking and the <em>imfindcircles</em> function to detect round nuclei, requiring minimal user input.</div></div><div><h3>Results</h3><div>The pipeline enabled robust quantification of CNS-resident cells. Automated analyses of brain and spinal cord tissue sections closely resembled manual quantification, with minimal error. In a mouse model of contusion spinal cord injury, it revealed a rostro-caudal decline in myelinating oligodendrocytes from the lesion epicenter, confirming the method’s accuracy and sensitivity in detecting injury-induced cellular changes.</div></div><div><h3>Comparison with existing methods</h3><div>Unlike many commonly used quantification-based software, this novel pipeline does not perform full image segmentation. Instead, it uses nuclear morphology to detect round shapes. Moreover, the pipeline has been specifically designed and optimized for the quantification of CNS cells, whose heterogeneity and cytoarchitecture pose specific challenges to existing methods that are more generalized.</div></div><div><h3>Conclusions</h3><div>This study presents an alternative to classical segmentation models by offering a reproducible quantification of CNS-resident cells using nuclear morphology. Its simplicity, minimal input requirements, reduced time for semi-automated quantification, and specificity for CNS tissues make it a valuable tool for studying cellular responses in both healthy and pathological contexts.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"424 ","pages":"Article 110590"},"PeriodicalIF":2.3,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145182061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing transient motion-onset visual evoked potentials via stochastic resonance: Unimodal and cross-modal noise effects","authors":"Huanqing Zhang ,&nbsp;Jun Xie ,&nbsp;Hongwei Yu ,&nbsp;Fangzhao Du ,&nbsp;Zhiwei Jin ,&nbsp;Yujie Chen","doi":"10.1016/j.jneumeth.2025.110589","DOIUrl":"10.1016/j.jneumeth.2025.110589","url":null,"abstract":"<div><h3>Background</h3><div>Motion-onset visual evoked potential (mVEP) are transient brain responses triggered by sudden motion stimuli and are widely used in brain-computer interface (BCI) systems. However, the inherently weak nature of mVEP signals poses a significant challenge to achieving reliable and accurate BCI performance. Enhancing the signal quality of mVEP responses is therefore critical for improving system robustness and usability.</div></div><div><h3>New method</h3><div>This study introduces a novel approach based on stochastic resonance (SR) theory, where appropriate levels of noise can enhance the performance of nonlinear systems such as the brain. By applying auditory and visual noise of varying intensities alongside mVEP stimuli, both unimodal SR and cross-modal SR effects were investigated. The method examines the effects of these noise conditions on brain activation and classification performance in mVEP-BCI.</div></div><div><h3>Results</h3><div>The results show that moderate levels of auditory or visual noise significantly enhance the P2 component amplitude of mVEP and improve classification accuracy in BCI tasks. In contrast, excessive noise leads to suppression of neural responses, forming an inverted U-shaped relationship between noise intensity and mVEP amplitude.</div></div><div><h3>Comparison with existing methods</h3><div>Conventional mVEP enhancement techniques typically rely on signal processing methods such as spatial filtering or feature extraction. In comparison, the proposed noise modulation strategy directly enhances neural responses, offering a biologically inspired and computationally simple alternative that complements existing approaches.</div></div><div><h3>Conclusions</h3><div>Both unimodal and cross-modal SR effectively enhance mVEP responses and BCI performance. This strategy provides new insights into SR mechanisms and supports the development of more robust mVEP-BCI systems.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"424 ","pages":"Article 110589"},"PeriodicalIF":2.3,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145155537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unlocking non-invasive brain stimulations for Next-Gen Clinical Speciality: Opportunities, challenges and future.","authors":"Mohammad Shabaz, Robert E Hampson, Sara Baber Sial","doi":"10.1016/j.jneumeth.2025.110586","DOIUrl":"https://doi.org/10.1016/j.jneumeth.2025.110586","url":null,"abstract":"","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":" ","pages":"110586"},"PeriodicalIF":2.3,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145137900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and testing of an MR-compatible tactile stimulator system: Application for individuals with a brain injury","authors":"Nahid Kalantaryardebily ,&nbsp;Anna C. Feldbush ,&nbsp;Rebecca Faubion-Trejo ,&nbsp;Jonathan Lisinski ,&nbsp;Neha A. Reddy ,&nbsp;Molly G. Bright ,&nbsp;Stephen M. LaConte ,&nbsp;Netta Gurari","doi":"10.1016/j.jneumeth.2025.110583","DOIUrl":"10.1016/j.jneumeth.2025.110583","url":null,"abstract":"<div><h3>Background:</h3><div>Accurate tactile perception is essential for daily function. Abnormally perceiving tactile stimuli is associated with poorer movement recovery in individuals post brain injury. The mechanisms causing abnormal tactile perception post brain injury remain incompletely understood, partially due to insufficient examination methods. Here, we present a custom tactile stimulator system that enables examination of how abnormal tactile perception arises post brain injury.</div></div><div><h3>New Method:</h3><div>The novel tactile stimulator pneumatically inflates and deflates a membrane to stimulate the skin via a small circular contact area (diameter: <span><math><mo>∼</mo></math></span>4–5 mm). The tactile stimulator is compact (14 mm length<span><math><mo>×</mo></math></span>6.5 mm height), compatible with magnetic resonance imaging (MRI), and precise in automatically applying low intensity forces (1.1–2.5<!--> <!-->N) in the MRI.</div></div><div><h3>Results:</h3><div>Feedback on a Likert 5-point scale from 14 young adults who are neurotypical during an MRI study with the tactile stimulator identified comfort in feeling the applied force stimuli as <em>most comfortable</em> (score: 5) and <em>second most comfortable</em> (score: 4) for 11 and 3 participants, respectively. The force stimuli activated the contralateral primary somatosensory cortex and bilateral secondary somatosensory cortices.</div></div><div><h3>Comparison with Existing Methods:</h3><div>Unlike existing tools, our system combines a compact size, precise control of a range of forces, and a relatively fixed contact area.</div></div><div><h3>Conclusions:</h3><div>The novel tactile stimulator system can enable high-precision studies that lead to a better understanding of the brain processes governing tactile perceptual dysfunction in patient populations, including those living with a brain injury.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"424 ","pages":"Article 110583"},"PeriodicalIF":2.3,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145106200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EC-FST: A novel pipeline for automatically analyzing mouse forced swim test","authors":"Yang Xia ,&nbsp;Xuechun Meng ,&nbsp;Yuxing Ning ,&nbsp;Hongqi Li ,&nbsp;Yue Wu ,&nbsp;Jian Zhang ,&nbsp;Ling Liu ,&nbsp;Zhaohuan Huang ,&nbsp;Ji Liu","doi":"10.1016/j.jneumeth.2025.110585","DOIUrl":"10.1016/j.jneumeth.2025.110585","url":null,"abstract":"<div><h3>Background</h3><div>The mouse forced swim test (FST) is widely used to evaluate the efficacy of potential anti-depressant drugs. Traditional methods for analyzing forced swim test results rely on manually setting the threshold for immobility, which is time-consuming and barely reproducible.</div></div><div><h3>New method</h3><div>In the present study, we introduced a novel pipeline (EC-FST) by extracting the feature of mouse status instead of calculating immobility time. First, we utilized event camera, a powerful AI tool for dynamic object-tracking framework, to capture the mobile events from mouse forced swim test. By quantifying event numbers and their temporal distribution, we were able to determine mouse’s mobile state across time-line.</div></div><div><h3>Results</h3><div>The EC-FST results showed perfect correlation with manual scoring, suggesting that the proposed method is reliable for analyzing forced swim test. We further tested the power of the EC-FST for detecting depressive-like behavior in mouse depression models，including lipopolysaccharide (LPS) injection and chronic restraint stress (CRS). Depressive-model mice exhibited significantly fewer motion events and lower event frequency than controls, aligning with manual scoring.</div></div><div><h3>Comparison with existing methods</h3><div>Unlike traditional threshold-based approaches, EC-FST provides an automated, unbiased, and reproducible analysis of FST behavior, eliminating the subjectivity of manual scoring.</div></div><div><h3>Conclusion</h3><div>Leveraging AI-driven event cameras, we established a robust pipeline for analyzing mouse behavior in the FST, offering greater efficiency and reproducibility for depression research.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"424 ","pages":"Article 110585"},"PeriodicalIF":2.3,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145102927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wireless high-density electroencephalography in the perioperative setting","authors":"Nathan Runstadler ,&nbsp;Selena Martinez ,&nbsp;UnCheol Lee ,&nbsp;Duan Li ,&nbsp;Kourosh Maboudi ,&nbsp;George A. Mashour ,&nbsp;Phillip E. Vlisides","doi":"10.1016/j.jneumeth.2025.110584","DOIUrl":"10.1016/j.jneumeth.2025.110584","url":null,"abstract":"<div><h3>Background</h3><div>Electroencephalographic (EEG) systems used in the operating room are constrained to frontal channels, providing limited neuroanatomical insights into altered perioperative brain states. Our objective is to present pragmatic strategies for placing whole-scalp, high-density EEG systems perioperatively that enable more comprehensive analysis.</div></div><div><h3>New method</h3><div>We present the successful implementation of wireless high-density (72-channel) EEG in the perioperative setting for the ongoing Caffeine, Postoperative Delirium, and Change in Outcomes after Surgery (CAPACHINOS-2) clinical trial (NCT05574400). Placement time was calculated, impedance and data quality were assessed, and data acquisition and analysis pipelines were established. Lastly, proof-of-principle analyses using source localization were conducted.</div></div><div><h3>Results</h3><div>High-density wireless EEG data have been successfully acquired for n = 45 participants, with median (interquartile range) placement time of 34 (25 – 52) minutes. Data acquisition was supported by an established workflow, and a subsequent data processing pipeline was used to evaluate channel quality, remove artifacts, and generate proof-of-principle high-density analyses.</div></div><div><h3>Comparison with existing methods</h3><div>Compared to a low-density system used for a similar, previous clinical trial (n = 54 participants), preoperative median impedance values (kΩ) were lower with the high-density system (13 [11–16] vs. 39 [28–47] kΩ; p &lt; 0.001). Additionally, proof-of-principle analysis demonstrates a more complex connectivity matrix and broader distribution of cortical alpha rhythms after induction of general anesthesia with the high-density system, highlighting an expanded capacity for neurophysiologic analysis.</div></div><div><h3>Conclusions</h3><div>Wireless high-density EEG serves as a feasible, promising tool to advance understanding of altered perioperative brain states by providing high spatiotemporal resolution of cortical oscillations.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"424 ","pages":"Article 110584"},"PeriodicalIF":2.3,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145102947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MSAttNet: Multi-scale attention convolutional neural network for motor imagery classification","authors":"Ruiyu Zhao ,&nbsp;Ian Daly ,&nbsp;Yixin Chen ,&nbsp;Weijie Wu ,&nbsp;Lifei Liu ,&nbsp;Xingyu Wang ,&nbsp;Andrzej Cichocki ,&nbsp;Jing Jin","doi":"10.1016/j.jneumeth.2025.110578","DOIUrl":"10.1016/j.jneumeth.2025.110578","url":null,"abstract":"<div><h3>Background:</h3><div>Convolutional neural networks (CNNs) are widely employed in motor imagery (MI) classification. However, due to cumbersome data collection experiments, and limited, noisy, and non-stationary EEG signals, small MI datasets present considerable challenges to the design of these decoding algorithms.</div></div><div><h3>New method:</h3><div>To capture more feature information from inadequately sized data, we propose a new method, a multi-scale attention convolutional neural network (MSAttNet). Our method includes three main components–a multi-band segmentation module, an attention spatial convolution module, and a multi-scale temporal convolution module. First, the multi-band segmentation module adopts a filter bank with overlapping frequency bands to enhance features in the frequency domain. Then, the attention spatial convolution module is used to adaptively adjust different convolutional kernel parameters according to the input through the attention mechanism to capture the features of different datasets. The outputs of the attention spatial convolution module are grouped to perform multi-scale temporal convolution. Finally, the output of the multi-scale temporal convolution module uses the bilinear pooling layer to extract temporal features and perform noise elimination. The extracted features are then classified.</div></div><div><h3>Results:</h3><div>We use four datasets, including <em>BCI Competition IV Dataset IIa</em>, <em>BCI Competition IV Dataset IIb</em>, the <em>OpenBMI</em> dataset and the <em>ECUST-MI</em> dataset, to test our proposed method. MSAttNet achieves accuracies of 78.20%, 84.52%, 75.94% and 78.60% in cross-session experiments, respectively.</div></div><div><h3>Comparison with existing methods</h3><div>: Compared with state-of-the-art algorithms, MSAttNet enhances the decoding performance of MI tasks.</div></div><div><h3>Conclusion:</h3><div>MSAttNet effectively addresses the challenges of MI-EEG datasets, improving decoding performance by robust feature extraction.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"424 ","pages":"Article 110578"},"PeriodicalIF":2.3,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145060868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"REVS: A new open-source platform for high-resolution analysis of rodent wheel running behavior","authors":"James L. Bonanno ,&nbsp;Ciara F. O’Brien ,&nbsp;William B.J. Cafferty","doi":"10.1016/j.jneumeth.2025.110581","DOIUrl":"10.1016/j.jneumeth.2025.110581","url":null,"abstract":"<div><h3>Background</h3><div>Rodent wheel running is widely used in neuroscience and preclinical research to assess locomotor function, recovery post-trauma or disease, circadian rhythms, and exercise physiology. However, most existing wheel-running systems offer limited metrics, lack flexibility in hardware, or require costly proprietary software, reducing their usefulness for detailed behavioral phenotyping—especially in models of injury or rehabilitation.</div></div><div><h3>New method</h3><div>We developed REVS (Revolution Evaluation and Visualization Software), a low-cost, open-source hardware and software platform for analyzing and visualizing rodent wheel running behavior. REVS captures wheel revolutions using Hall effect sensors and computes 13 day-level behavioral metrics along with detailed bout-level data. Users can interactively explore high-resolution temporal features and export data in Open Data Commons (ODC)-compatible formats. REVS supports customizable wheel types, facilitating use in animals with motor and/or sensory impairments.</div></div><div><h3>Results</h3><div>We validated REVS using a mouse model of partial spinal cord injury, where fine motor control is compromised. REVS detected impairments in 10 of 13 behavioral metrics post-injury, with varied recovery trajectories across measures. Principal component analysis revealed that recovery was closely linked to bout quality and intensity, rather than timing.</div></div><div><h3>Comparison with existing methods</h3><div>Unlike commercial and open-source systems, REVS offers more detailed metrics, customizable wheel compatibility, seamless blending with common vivarium hardware, integrated data visualizations, and ODC-compatible data export. It also supports flexible analysis across individuals and groups.</div></div><div><h3>Conclusions</h3><div>REVS provides a powerful, scalable tool for granular behavioral phenotyping in rodent studies, enhancing reproducibility and revealing insights into subtle locomotor changes associated with injury, recovery, and intervention.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"424 ","pages":"Article 110581"},"PeriodicalIF":2.3,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145064877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"X-ray diffraction reveals alterations in mouse somatosensory cortex following sensory deprivation","authors":"Sasha Murokh ,&nbsp;Ezekiel Willerson ,&nbsp;Alexander Lazarev ,&nbsp;Pavel Lazarev ,&nbsp;Lev Mourokh ,&nbsp;Joshua C. Brumberg","doi":"10.1016/j.jneumeth.2025.110582","DOIUrl":"10.1016/j.jneumeth.2025.110582","url":null,"abstract":"<div><h3>Background</h3><div>Sensory experience impacts brain development. In the mouse somatosensory cortex, sensory deprivation via whisker trimming induces reductions in the perineuronal net, the size of neuronal cell bodies, the size and orientation of dendritic arbors, the density of dendritic spines, and the level of myelination, among other effects.</div></div><div><h3>New methods</h3><div>Using a custom-developed laboratory diffractometer, we measured the X-ray diffraction patterns of mouse brain tissue to establish a novel method for examining nanoscale brain structures. Two groups of mice were examined: a control group and one that underwent 30 days of whisker-trimming from birth <img> an established method of sensory deprivation that affects the mouse barrel cortex (whisker sensory processing region of the primary somatosensory cortex). Mice were perfused, and primary somatosensory cortices were isolated for immunocytochemistry and X-ray diffraction imaging.</div></div><div><h3>Results</h3><div>X-ray images were characterized using a specially developed machine-learning approach, and the clusters that correspond to the two groups are well separated in principal components space. We obtained the perfect values for sensitivity and specificity, as well as for the receiver operator curve classifier.</div></div><div><h3>Comparison with existing methods</h3><div>New machine-learning approaches allow for the first time x-ray diffraction to identify cortex that has undergone sensory deprivation without the use of stains.</div></div><div><h3>Conclusions</h3><div>We hypothesize that our results are related to the alteration of different nanoscale structural components in the brains of sensory deprived mice. The effects of these nanoscale structural formations can be reflective of changes in the micro- and macro-scale structures and assemblies with the neocortex.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"424 ","pages":"Article 110582"},"PeriodicalIF":2.3,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145054244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lost in a large EEG multiverse? Comparing sampling approaches for representative pipeline selection","authors":"Cassie Ann Short ,&nbsp;Andrea Hildebrandt ,&nbsp;Robin Bosse ,&nbsp;Stefan Debener ,&nbsp;Metin Özyağcılar ,&nbsp;Katharina Paul ,&nbsp;Jan Wacker ,&nbsp;Daniel Kristanto","doi":"10.1016/j.jneumeth.2025.110564","DOIUrl":"10.1016/j.jneumeth.2025.110564","url":null,"abstract":"<div><h3>Background</h3><div>The multiplicity of defensible pipelines for processing and analysing data has been implicated as a core contributor to low replicability, creating uncertainty about the robustness of results to defensible variations. This is exacerbated where many defensible pipelines exist, such as in processing electroencephalography (EEG) signals. In multiverse analyses, equally defensible pipelines are computed and the robustness across pipelines is reported. Computing all pipelines is often infeasible, and researchers rely on sampling approaches, assuming representativeness of the full multiverse. However, different sampling methods may yield different robustness estimates, introducing what we term <em>multiverse sampling uncertainty</em>.</div></div><div><h3>New method</h3><div>We developed an open-source tool to compare pipeline samples on their representativeness of the full multiverse. We computed a 528-pipeline use case multiverse on EEG recordings during an emotion classification task to predict extraversion scores from the Late Positive Potential. We applied three sampling methods (random, stratified, active learning) to sample 26 pipelines (5 %) and evaluated the representativeness of model fit distributions.</div></div><div><h3>Results</h3><div>Our results highlight variability in the representativeness of model fit distributions across samples, with active learning and stratified sampling most closely representing the full multiverse. Replicability of results is reported using cross-validation, and reproducibility is explored across pipeline sample sizes.</div></div><div><h3>Comparison with existing methods</h3><div>Large multiverse analyses in neuroimaging typically rely on sampling, but sampling approaches are not often systematically compared for their representation of the full multiverse.</div></div><div><h3>Conclusions</h3><div>The need for representative pipeline sampling to mitigate bias in large multiverse analyses is discussed.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"424 ","pages":"Article 110564"},"PeriodicalIF":2.3,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145040487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}