Pub Date : 2025-11-01Epub Date: 2025-08-05DOI: 10.1016/j.jneumeth.2025.110547
Juan Carlos Ramirez, Jose Vergara, Jing Lin, Jian Chen, Jeffrey M Yau
Background: Existing methods to study the effects of skin temperature on mechanical touch perception range from large hot plates, water baths, or bulky, water-controlled thermal contactors which have limited range and resolution. The limited capabilities of these methods prevent the study of thermo-tactile interactions at the finger level in a flexible and precisely controlled manner.
New method: Here, we combine small Proportional-Integral-Derivative (PID)-controlled Peltier elements with a calibrated shaker motor for a novel thermo-tactile stimulus delivery system capable of precisely controlling temperature and vibrotactile stimulation to the fingertip. This novel system enables parallel control of mechanical stimulation and thermal stimulation at congruent skin sites of the fingertip. Alternative thermoelectric elements and mechanical actuators could be used in our systems modular configuration.
Results: Our thermo-tactile delivery system can simultaneously deliver precise and stable vibrotactile and thermal stimuli over 30-250 Hz and 20-40°C, respectively, at the fingertip. We validated our system in psychophysical tests and reproduced the established finding that vibration detection thresholds vary according to temperature.
Comparison with existing method(s): Unlike our system, existing methods to study thermo-tactile interactions are restricted to testing skin regions larger than the fingertip or they use tactile probes on the fingertips that are not thermally controlled.
Conclusions: Our system represents a novel strategy for combining thermoelectric modules with mechanical actuation to study thermo-tactile interactions at mechanoreceptor-rich fingertips.
{"title":"A novel device for studying temperature and touch interactions.","authors":"Juan Carlos Ramirez, Jose Vergara, Jing Lin, Jian Chen, Jeffrey M Yau","doi":"10.1016/j.jneumeth.2025.110547","DOIUrl":"10.1016/j.jneumeth.2025.110547","url":null,"abstract":"<p><strong>Background: </strong>Existing methods to study the effects of skin temperature on mechanical touch perception range from large hot plates, water baths, or bulky, water-controlled thermal contactors which have limited range and resolution. The limited capabilities of these methods prevent the study of thermo-tactile interactions at the finger level in a flexible and precisely controlled manner.</p><p><strong>New method: </strong>Here, we combine small Proportional-Integral-Derivative (PID)-controlled Peltier elements with a calibrated shaker motor for a novel thermo-tactile stimulus delivery system capable of precisely controlling temperature and vibrotactile stimulation to the fingertip. This novel system enables parallel control of mechanical stimulation and thermal stimulation at congruent skin sites of the fingertip. Alternative thermoelectric elements and mechanical actuators could be used in our systems modular configuration.</p><p><strong>Results: </strong>Our thermo-tactile delivery system can simultaneously deliver precise and stable vibrotactile and thermal stimuli over 30-250 Hz and 20-40°C, respectively, at the fingertip. We validated our system in psychophysical tests and reproduced the established finding that vibration detection thresholds vary according to temperature.</p><p><strong>Comparison with existing method(s): </strong>Unlike our system, existing methods to study thermo-tactile interactions are restricted to testing skin regions larger than the fingertip or they use tactile probes on the fingertips that are not thermally controlled.</p><p><strong>Conclusions: </strong>Our system represents a novel strategy for combining thermoelectric modules with mechanical actuation to study thermo-tactile interactions at mechanoreceptor-rich fingertips.</p>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":" ","pages":"110547"},"PeriodicalIF":2.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12665374/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144775588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01Epub Date: 2025-07-28DOI: 10.1016/j.jneumeth.2025.110539
Bryson Gray, Andrew W Smith, Allan MacKenzie-Graham, David W Shattuck, Daniel J Tward
Background: Accurate localization of white matter pathways using diffusion MRI is critical to investigating brain connectivity, but the accuracy of current methods is not thoroughly understood. A fruitful approach to validating accuracy is to consider microscopy data that have been co-registered with MRI of post mortem samples. In this setting, structure tensor analysis is a standard approach to computing local orientations. However, structure tensor analysis itself has not been well-validated and is subject to uncertainty in its angular resolution, and selectivity to specific spatial scales.
New method: Here, we conducted a simulation study to investigate the accuracy of using structure tensors to estimate the orientations of fibers arranged in configurations with and without crossings.
Results: We examined a range of simulated conditions, with a focus on investigating the method's behavior on images with anisotropic resolution, which is particularly common in microscopy data acquisition. We also analyzed 2D and 3D optical microscopy data.
Comparison with existing methods: Our results show that parameter choice in structure tensor analysis has relatively little effect on accuracy for estimating single orientations, although accuracy decreases with increasing resolution anisotropy. On the other hand, when estimating the orientations of crossing fibers, the choice of parameters becomes critical, and poor choices result in orientation estimates that are essentially random.
Conclusions: This work provides a set of recommendations for researchers seeking to apply structure tensor analysis effectively in the study of axonal orientations in brain imaging data and quantifies the method's limitations, particularly in the case of anisotropic data.
{"title":"Validation of structure tensor analysis for orientation estimation in brain tissue microscopy.","authors":"Bryson Gray, Andrew W Smith, Allan MacKenzie-Graham, David W Shattuck, Daniel J Tward","doi":"10.1016/j.jneumeth.2025.110539","DOIUrl":"10.1016/j.jneumeth.2025.110539","url":null,"abstract":"<p><strong>Background: </strong>Accurate localization of white matter pathways using diffusion MRI is critical to investigating brain connectivity, but the accuracy of current methods is not thoroughly understood. A fruitful approach to validating accuracy is to consider microscopy data that have been co-registered with MRI of post mortem samples. In this setting, structure tensor analysis is a standard approach to computing local orientations. However, structure tensor analysis itself has not been well-validated and is subject to uncertainty in its angular resolution, and selectivity to specific spatial scales.</p><p><strong>New method: </strong>Here, we conducted a simulation study to investigate the accuracy of using structure tensors to estimate the orientations of fibers arranged in configurations with and without crossings.</p><p><strong>Results: </strong>We examined a range of simulated conditions, with a focus on investigating the method's behavior on images with anisotropic resolution, which is particularly common in microscopy data acquisition. We also analyzed 2D and 3D optical microscopy data.</p><p><strong>Comparison with existing methods: </strong>Our results show that parameter choice in structure tensor analysis has relatively little effect on accuracy for estimating single orientations, although accuracy decreases with increasing resolution anisotropy. On the other hand, when estimating the orientations of crossing fibers, the choice of parameters becomes critical, and poor choices result in orientation estimates that are essentially random.</p><p><strong>Conclusions: </strong>This work provides a set of recommendations for researchers seeking to apply structure tensor analysis effectively in the study of axonal orientations in brain imaging data and quantifies the method's limitations, particularly in the case of anisotropic data.</p>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":" ","pages":"110539"},"PeriodicalIF":2.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144753616","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}
Pub Date : 2025-10-31DOI: 10.1016/j.jneumeth.2025.110616
Xingyi Zhong , Guangye Li , Ce Xu , Ruijie Luo , Jianjun Meng , Gerwin Schalk
Background:
The ability to detect eye movements can facilitate human–computer interaction (HCI) and may complement brain–computer interfaces (BCIs). Recent studies have shown that multi-channel EEG systems can provide information about eye movements, but these systems can be bulky and/or require complex setup.
New method:
We introduce a portable, two-channel EEG platform that can be placed in seconds and detect eye blinks/movements and gaze trajectories. Forty adults performed cued blinks and horizontal/vertical gaze shifts; 21 EEG features were extracted, and machine learning models were evaluated with leave-one-subject-out validation.
Results:
Our system effectively identified eye blinks (avg. detection accuracy of 95%, 50% chance) and horizontal eye movements (avg. accuracy of 94%, 33% chance), and showed decreased performance detecting vertical eye movements (avg. accuracy of 60%, 33% chance). It was also able to predict horizontal and vertical eye movement trajectories (r = 0.79 and r = 0.14, respectively).
Comparison with existing methods: Classification accuracies for eye blinks and horizontal eye movements using our system with only two electrodes are comparable to those previously reported only for complex multi-channel EEG/EOG setups.
Conclusion:
This study provides evidence, for the first time, that a wearable EEG device can give substantial information about eye blinks and eye movements. With further refinements, this approach may enable portable solutions for real-world HCI and BCI applications.
{"title":"Detection of eye movements and eye blinks using a portable two-channel EEG platform","authors":"Xingyi Zhong , Guangye Li , Ce Xu , Ruijie Luo , Jianjun Meng , Gerwin Schalk","doi":"10.1016/j.jneumeth.2025.110616","DOIUrl":"10.1016/j.jneumeth.2025.110616","url":null,"abstract":"<div><h3>Background:</h3><div>The ability to detect eye movements can facilitate human–computer interaction (HCI) and may complement brain–computer interfaces (BCIs). Recent studies have shown that multi-channel EEG systems can provide information about eye movements, but these systems can be bulky and/or require complex setup.</div></div><div><h3>New method:</h3><div>We introduce a portable, two-channel EEG platform that can be placed in seconds and detect eye blinks/movements and gaze trajectories. Forty adults performed cued blinks and horizontal/vertical gaze shifts; 21 EEG features were extracted, and machine learning models were evaluated with leave-one-subject-out validation.</div></div><div><h3>Results:</h3><div>Our system effectively identified eye blinks (avg. detection accuracy of 95%, 50% chance) and horizontal eye movements (avg. accuracy of 94%, 33% chance), and showed decreased performance detecting vertical eye movements (avg. accuracy of 60%, 33% chance). It was also able to predict horizontal and vertical eye movement trajectories (r = 0.79 and r = 0.14, respectively).</div><div><strong>Comparison with existing methods:</strong> Classification accuracies for eye blinks and horizontal eye movements using our system with only two electrodes are comparable to those previously reported only for complex multi-channel EEG/EOG setups.</div></div><div><h3>Conclusion:</h3><div>This study provides evidence, for the first time, that a wearable EEG device can give substantial information about eye blinks and eye movements. With further refinements, this approach may enable portable solutions for real-world HCI and BCI applications.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"425 ","pages":"Article 110616"},"PeriodicalIF":2.3,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145431731","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}
Co-culturing multiple cell types within three-dimensional (3D) systems enhances the capacity to investigate intricate cell-cell and cell-microenvironment interactions, offering deeper insights into multicellular dynamics. This study comprehensively investigates the role of cellular metabolism within 3D cell culture models, offering a detailed examination of the underlying metabolic processes.
New method
In this study, a three-dimensional co-culture system was developed by encapsulating human neuroblastoma (SH-SY5Y) and human umbilical vein endothelial (HUVEC) cells within photopolymerized gelatin methacrylate (GelMA) hydrogels using photomasks for studying multiplex neural co-cultures. The structural characteristics of the hydrogel were analyzed using Fourier-transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). Cell proliferation and antioxidant enzyme activities, including glucose 6-phosphate dehydrogenase (G6PD), 6-phosphoglucanate dehydrogenase (6-PGD), glutathione reductase (GR), glutathione s-transferase (GST), and glutathione peroxidase (GPx) were measured. The levels of trace elements and minerals were also quantified.
Results
The 3D-co-culture system can be considered non-toxic based on ISO 10993–5 since cell viability did not reduce below 80 % on the 7th day compared to day 0. The 3D model did not adversely affect the indicated enzymes in the co-culture system for up to 7 days. Na, Ca, Cu, Zn, and Mg levels significantly increased in the first, 4th, and 7th days compared to day 0.
Comparison with existing methods
Although photomask-based patterning of GelMA scaffolds has been previously demonstrated, our approach is unique as it combines multiplex photomask fabrication with the co-culture of neuronal and endothelial cells. Additionally, we measure multiple metabolic pathways, including the pentose phosphate pathway (PPP) and antioxidant enzymes, as well as the dynamics of trace and mineral elements in spatially defined neurovascular co-cultures. This integration allows for the simultaneous production of numerous geometrically controlled replicates and provides the first comprehensive assessment of PPP activity alongside trace element dynamics in engineered neural co-cultures.
Conclusion
This study highlights the benefits of using GelMA-based constructs in supporting viable and metabolically active cells in a 3D environment and presents a robust framework of methodologies that can be employed in future research to elucidate the complex metabolic dynamics in 3D environments, in tissue engineering, disease modeling, and drug development.
{"title":"Development and evaluation of a 3D-engineered neural co-culture system: Impacts on oxidative stress, pentose phosphate pathway, trace element and mineral metabolisms","authors":"Duygu Aydemir , Buse Keleş , İrem Polat , Ecem Metin , Emel Sokullu , Nuriye Nuray Ulusu","doi":"10.1016/j.jneumeth.2025.110614","DOIUrl":"10.1016/j.jneumeth.2025.110614","url":null,"abstract":"<div><h3>Background</h3><div>Co-culturing multiple cell types within three-dimensional (3D) systems enhances the capacity to investigate intricate cell-cell and cell-microenvironment interactions, offering deeper insights into multicellular dynamics. This study comprehensively investigates the role of cellular metabolism within 3D cell culture models, offering a detailed examination of the underlying metabolic processes.</div></div><div><h3>New method</h3><div>In this study, a three-dimensional co-culture system was developed by encapsulating human neuroblastoma (SH-SY5Y) and human umbilical vein endothelial (HUVEC) cells within photopolymerized gelatin methacrylate (GelMA) hydrogels using photomasks for studying multiplex neural co-cultures. The structural characteristics of the hydrogel were analyzed using Fourier-transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). Cell proliferation and antioxidant enzyme activities, including glucose 6-phosphate dehydrogenase (G6PD), 6-phosphoglucanate dehydrogenase (6-PGD), glutathione reductase (GR), glutathione s-transferase (GST), and glutathione peroxidase (GPx) were measured. The levels of trace elements and minerals were also quantified.</div></div><div><h3>Results</h3><div>The 3D-co-culture system can be considered non-toxic based on ISO 10993–5 since cell viability did not reduce below 80 % on the 7th day compared to day 0. The 3D model did not adversely affect the indicated enzymes in the co-culture system for up to 7 days. Na, Ca, Cu, Zn, and Mg levels significantly increased in the first, 4<sup>th,</sup> and 7th days compared to day 0.</div></div><div><h3>Comparison with existing methods</h3><div>Although photomask-based patterning of GelMA scaffolds has been previously demonstrated, our approach is unique as it combines multiplex photomask fabrication with the co-culture of neuronal and endothelial cells. Additionally, we measure multiple metabolic pathways, including the pentose phosphate pathway (PPP) and antioxidant enzymes, as well as the dynamics of trace and mineral elements in spatially defined neurovascular co-cultures. This integration allows for the simultaneous production of numerous geometrically controlled replicates and provides the first comprehensive assessment of PPP activity alongside trace element dynamics in engineered neural co-cultures.</div></div><div><h3>Conclusion</h3><div>This study highlights the benefits of using GelMA-based constructs in supporting viable and metabolically active cells in a 3D environment and presents a robust framework of methodologies that can be employed in future research to elucidate the complex metabolic dynamics in 3D environments, in tissue engineering, disease modeling, and drug development.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"425 ","pages":"Article 110614"},"PeriodicalIF":2.3,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145426652","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}
Pub Date : 2025-10-30DOI: 10.1016/j.jneumeth.2025.110600
Addison L. Schwamb, Zongxi Yu, ShiNung Ching
Background:
Neural dynamics change over time and with physiologic state. Modeling of neural dynamics can thus be understood at two levels: (i) identifying the latent process that governs how and when states change, and (ii) identifying the generative circuit mechanisms within each state.
New method:
Here, we develop a data-driven modeling method that tackles these two levels simultaneously. We formulate a parametric network model of neural dynamics that embeds state-dependent modulation. The modulation itself is controlled by a latent switching process, modeled as a Hidden Markov Model (HMM). A key challenge is that the model itself has internal states that must be estimated from observed data. This leads to a triune optimization problem, consisting of model parameterization of the HMM and neural dynamics, alongside state estimation. Our method brings together several optimization frameworks alongside estimation-theoretic constructs to solve this problem efficiently, enabling blind identification of state transitions and neural dynamics.
Results:
We validate this methodology on ground-truth data with known parameters, and find that it accurately infers the transitions in latent state and the dynamics of each state. We demonstrate its capability of inferring changes in brain dynamics from electrophysiological data by testing it on electroencephalography recordings with labeled state transitions.
Comparison with existing methods:
While similar methods exist to infer switches and dynamics on the level of individual neurons, there is no directly comparable method available for mesoscale modeling of neural circuits.
Conclusions:
Our methodology enables blind modeling of changing neural dynamics allowing for inference of modulatory circuit mechanisms.
{"title":"Blind identification of state transitions and latent neural dynamics from electrophysiological recordings","authors":"Addison L. Schwamb, Zongxi Yu, ShiNung Ching","doi":"10.1016/j.jneumeth.2025.110600","DOIUrl":"10.1016/j.jneumeth.2025.110600","url":null,"abstract":"<div><h3>Background:</h3><div>Neural dynamics change over time and with physiologic state. Modeling of neural dynamics can thus be understood at two levels: (i) identifying the latent process that governs how and when states change, and (ii) identifying the generative circuit mechanisms within each state.</div></div><div><h3>New method:</h3><div>Here, we develop a data-driven modeling method that tackles these two levels simultaneously. We formulate a parametric network model of neural dynamics that embeds state-dependent modulation. The modulation itself is controlled by a latent switching process, modeled as a Hidden Markov Model (HMM). A key challenge is that the model itself has internal states that must be estimated from observed data. This leads to a triune optimization problem, consisting of model parameterization of the HMM and neural dynamics, alongside state estimation. Our method brings together several optimization frameworks alongside estimation-theoretic constructs to solve this problem efficiently, enabling blind identification of state transitions and neural dynamics.</div></div><div><h3>Results:</h3><div>We validate this methodology on ground-truth data with known parameters, and find that it accurately infers the transitions in latent state and the dynamics of each state. We demonstrate its capability of inferring changes in brain dynamics from electrophysiological data by testing it on electroencephalography recordings with labeled state transitions.</div></div><div><h3>Comparison with existing methods:</h3><div>While similar methods exist to infer switches and dynamics on the level of individual neurons, there is no directly comparable method available for mesoscale modeling of neural circuits.</div></div><div><h3>Conclusions:</h3><div>Our methodology enables blind modeling of changing neural dynamics allowing for inference of modulatory circuit mechanisms.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"425 ","pages":"Article 110600"},"PeriodicalIF":2.3,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145419794","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}
Pub Date : 2025-10-30DOI: 10.1016/j.jneumeth.2025.110615
Silvia Cases-Cunillera , Louise Deboeuf , Jan Pyrzowski , Juan F. Nieto-Sánchez , Kirill Smirnov , Marta Iollo , Belén Díaz-Fernández , Albert J. Becker , Michel Le Van Quyen , Elena Dossi , Gilles Huberfeld
Background
The analysis of extracellular recordings from MultiElectrode Arrays (MEAs) is central to understanding the spatio-temporal dynamics of neuronal network activity in both physiological and pathological conditions. The detection of local field potential (LFP) events (of variable amplitude and shape, embedded in noisy, overlapping signals) remains particularly challenging when relying solely on visual detection or on automated tools.
New method
We developed SpikeSpector, a standalone, Python-based graphical user interface (GUI) that enables comprehensive LFP detection, precise manual curation, spatial mapping, and multimodal integration with immunohistochemical markers.
Results
SpikeSpector tools are described using MEA data recorded from a mouse brain slice harboring a ganglioglioma tumor. SpikeSpector enables users to refine automatically detected events through interfaces, improving the reliability of field potential identification in complex datasets. The platform offers visualization of voltage traces aligned to MEA layouts, customizable spike sorting and deletion tools, and quantification of waveform metrics such as amplitude, slope, and half-width. In addition, SpikeSpector introduces novel modules for correlating spike metrics with histological staining intensities, offering a powerful framework to spatially relate electrophysiological patterns to tissue architecture.
Comparison with existing methods
Most existing tools for MEA analysis are limited by licensing costs, dependence on proprietary environments, and lack of integration with histological data, crucial for studies on brain diseases.
Conclusions
Overall, SpikeSpector represents a flexible and accessible solution for bridging electrophysiological data with histological insights, enabling a more accurate and context-rich analysis of neural dynamics.
{"title":"SpikeSpector software for MultiElectrode Arrays: Field potential analysis and spatial integration of electrophysiological and histological data","authors":"Silvia Cases-Cunillera , Louise Deboeuf , Jan Pyrzowski , Juan F. Nieto-Sánchez , Kirill Smirnov , Marta Iollo , Belén Díaz-Fernández , Albert J. Becker , Michel Le Van Quyen , Elena Dossi , Gilles Huberfeld","doi":"10.1016/j.jneumeth.2025.110615","DOIUrl":"10.1016/j.jneumeth.2025.110615","url":null,"abstract":"<div><h3>Background</h3><div>The analysis of extracellular recordings from MultiElectrode Arrays (MEAs) is central to understanding the spatio-temporal dynamics of neuronal network activity in both physiological and pathological conditions. The detection of local field potential (LFP) events (of variable amplitude and shape, embedded in noisy, overlapping signals) remains particularly challenging when relying solely on visual detection or on automated tools.</div></div><div><h3>New method</h3><div>We developed SpikeSpector, a standalone, Python-based graphical user interface (GUI) that enables comprehensive LFP detection, precise manual curation, spatial mapping, and multimodal integration with immunohistochemical markers.</div></div><div><h3>Results</h3><div>SpikeSpector tools are described using MEA data recorded from a mouse brain slice harboring a ganglioglioma tumor. SpikeSpector enables users to refine automatically detected events through interfaces, improving the reliability of field potential identification in complex datasets. The platform offers visualization of voltage traces aligned to MEA layouts, customizable spike sorting and deletion tools, and quantification of waveform metrics such as amplitude, slope, and half-width. In addition, SpikeSpector introduces novel modules for correlating spike metrics with histological staining intensities, offering a powerful framework to spatially relate electrophysiological patterns to tissue architecture.</div></div><div><h3>Comparison with existing methods</h3><div>Most existing tools for MEA analysis are limited by licensing costs, dependence on proprietary environments, and lack of integration with histological data, crucial for studies on brain diseases.</div></div><div><h3>Conclusions</h3><div>Overall, SpikeSpector represents a flexible and accessible solution for bridging electrophysiological data with histological insights, enabling a more accurate and context-rich analysis of neural dynamics.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"425 ","pages":"Article 110615"},"PeriodicalIF":2.3,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145426672","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}
Pub Date : 2025-10-29DOI: 10.1016/j.jneumeth.2025.110604
Hamid Taghipourbibalan, James Edgar McCutcheon
Background
Conventional approaches for studying feeding and reward-driven behaviours require frequent animal handling or relocation of animals to specialized chambers, inducing stress, confounding behavioural outcomes, and limiting continuous (24/7) data collection. In recent years, the Feeding Experimentation Device (FED3) has emerged as a major advance, offering programmable modes of operation, affordable costs, and flexibility for investigating a range of feeding and operant behaviours. However, certain limitations prevent researchers from fully harnessing the FED3’s capabilities in a user-friendly manner.
New method
Here, we present the Real-time and Remote FED3 (RTFED) developed for continuous and online home-cage monitoring of mice, video recording behaviours and fibre photometry recording.
Results
Validation experiments confirm RTFED integrates well with FED3 to log and transmit behavioural events in real-time. It also incorporates event-triggered video capture through USB cameras, providing additional observational depth. Moreover, RTFED handles TTL signals to the fibre photometry system allowing precise behaviour-neural synchronization.
Comparison with existing methods
A key strength of RTFED is its easily customizable architecture, enabling researchers to tailor both software and hardware configurations to meet specific experimental objectives. This flexibility, together with features such as remote data logging and email notifications that allow timely adjustments and animal welfare monitoring based on behavioural observations, substantially reduces animal disturbance and researcher intervention and labour.
Conclusions
By offering a cost-effective and modifiable alternative to proprietary commercial solutions, RTFED broadens accessibility, heightens reproducibility, and deepens investigations into feeding and reward-driven behaviours in home-cage settings, ultimately improving the quality and translational relevance of behavioural research.
{"title":"RTFED, an open-source versatile tool for home-cage monitoring of behaviour and fibre photometry recording in mice","authors":"Hamid Taghipourbibalan, James Edgar McCutcheon","doi":"10.1016/j.jneumeth.2025.110604","DOIUrl":"10.1016/j.jneumeth.2025.110604","url":null,"abstract":"<div><h3>Background</h3><div>Conventional approaches for studying feeding and reward-driven behaviours require frequent animal handling or relocation of animals to specialized chambers, inducing stress, confounding behavioural outcomes, and limiting continuous (24/7) data collection. In recent years, the Feeding Experimentation Device (FED3) has emerged as a major advance, offering programmable modes of operation, affordable costs, and flexibility for investigating a range of feeding and operant behaviours. However, certain limitations prevent researchers from fully harnessing the FED3’s capabilities in a user-friendly manner.</div></div><div><h3>New method</h3><div>Here, we present the Real-time and Remote FED3 (RTFED) developed for continuous and online home-cage monitoring of mice, video recording behaviours and fibre photometry recording.</div></div><div><h3>Results</h3><div>Validation experiments confirm RTFED integrates well with FED3 to log and transmit behavioural events in real-time. It also incorporates event-triggered video capture through USB cameras, providing additional observational depth. Moreover, RTFED handles TTL signals to the fibre photometry system allowing precise behaviour-neural synchronization.</div></div><div><h3>Comparison with existing methods</h3><div>A key strength of RTFED is its easily customizable architecture, enabling researchers to tailor both software and hardware configurations to meet specific experimental objectives. This flexibility, together with features such as remote data logging and email notifications that allow timely adjustments and animal welfare monitoring based on behavioural observations, substantially reduces animal disturbance and researcher intervention and labour.</div></div><div><h3>Conclusions</h3><div>By offering a cost-effective and modifiable alternative to proprietary commercial solutions, RTFED broadens accessibility, heightens reproducibility, and deepens investigations into feeding and reward-driven behaviours in home-cage settings, ultimately improving the quality and translational relevance of behavioural research.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"425 ","pages":"Article 110604"},"PeriodicalIF":2.3,"publicationDate":"2025-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145409297","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}
Pub Date : 2025-10-21DOI: 10.1016/j.jneumeth.2025.110603
Barbara D. Fontana , Camilla W. Pretzel , Mariana L. Müller , Kimberly Fontoura , Khadija A. Mohammed , Eduarda T. Saccol , Falco L. Gonçalves , Angela E. Uchoa , Carolina C. Jardim , Isabella P. Silva , Rossano M. Silva , Hevelyn S. Moraes , Cássio M. Resmim , Julia Canzian , Denis B. Rosemberg
Background
Accurate and scalable behavioral annotation remains a challenge in behavioral neuroscience. Manual scoring is time-consuming, variable across annotators, and may overlook transient behaviors critical for phenotyping. By learning from annotated datasets, supervised machine learning (ML) enables automated classification of behavior with high consistency and reduced bias.
New method
We benchmarked five supervised ML algorithms, Random Forest, XGBoost, Support Vector Machine, k-Nearest Neighbors, and Multilayer Perceptron (MLP), and compared data against expert human annotations of seizure-like behaviors in adult zebrafish. Twelve trained raters annotated over 43,000 video frames, enabling direct comparison of model performance with human annotation. After frame-level analysis, we also applied behavior-informed filters and then evaluated block-level temporal aggregation.
Results
Annotation variability was driven by behavioral complexity, with ambiguous behaviors showing the lowest agreement. Random Forest, XGBoost, and MLP achieved the highest accuracy and post-processing based on posture and velocity improved classification by filtering false positives. Block-level aggregation enhanced accuracy through temporal smoothing but masked short-lived behaviors critical for detecting subtle phenotypes.
Comparison with existing methods
Most zebrafish seizure studies rely on manual scoring or single-model ML applications. Direct comparisons between multiple ML algorithms and human annotations are rare. Our study uniquely integrates large-scale manual scoring with model benchmarking and temporal resolution strategies, offering insight into reproducibility and scalability in behavioral phenotyping.
Conclusions
This study advances automated behavioral analysis in zebrafish by demonstrating the strengths and limitations of machine learning compared to human annotation, and emphasizes how choices in temporal resolution and behavioral classification influence reproducibility and interpretability.
{"title":"Comparing human annotation and machine learning models for optimizing zebrafish behavioral classification in seizure analysis","authors":"Barbara D. Fontana , Camilla W. Pretzel , Mariana L. Müller , Kimberly Fontoura , Khadija A. Mohammed , Eduarda T. Saccol , Falco L. Gonçalves , Angela E. Uchoa , Carolina C. Jardim , Isabella P. Silva , Rossano M. Silva , Hevelyn S. Moraes , Cássio M. Resmim , Julia Canzian , Denis B. Rosemberg","doi":"10.1016/j.jneumeth.2025.110603","DOIUrl":"10.1016/j.jneumeth.2025.110603","url":null,"abstract":"<div><h3>Background</h3><div>Accurate and scalable behavioral annotation remains a challenge in behavioral neuroscience. Manual scoring is time-consuming, variable across annotators, and may overlook transient behaviors critical for phenotyping. By learning from annotated datasets, supervised machine learning (ML) enables automated classification of behavior with high consistency and reduced bias.</div></div><div><h3>New method</h3><div>We benchmarked five supervised ML algorithms, Random Forest, XGBoost, Support Vector Machine, k-Nearest Neighbors, and Multilayer Perceptron (MLP), and compared data against expert human annotations of seizure-like behaviors in adult zebrafish. Twelve trained raters annotated over 43,000 video frames, enabling direct comparison of model performance with human annotation. After frame-level analysis, we also applied behavior-informed filters and then evaluated block-level temporal aggregation.</div></div><div><h3>Results</h3><div>Annotation variability was driven by behavioral complexity, with ambiguous behaviors showing the lowest agreement. Random Forest, XGBoost, and MLP achieved the highest accuracy and post-processing based on posture and velocity improved classification by filtering false positives. Block-level aggregation enhanced accuracy through temporal smoothing but masked short-lived behaviors critical for detecting subtle phenotypes.</div></div><div><h3>Comparison with existing methods</h3><div>Most zebrafish seizure studies rely on manual scoring or single-model ML applications. Direct comparisons between multiple ML algorithms and human annotations are rare. Our study uniquely integrates large-scale manual scoring with model benchmarking and temporal resolution strategies, offering insight into reproducibility and scalability in behavioral phenotyping.</div></div><div><h3>Conclusions</h3><div>This study advances automated behavioral analysis in zebrafish by demonstrating the strengths and limitations of machine learning compared to human annotation, and emphasizes how choices in temporal resolution and behavioral classification influence reproducibility and interpretability.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"425 ","pages":"Article 110603"},"PeriodicalIF":2.3,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145355088","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}
Pub Date : 2025-10-20DOI: 10.1016/j.jneumeth.2025.110602
Stoyan Dimitrov , Xia Shan , Jan Born , Marion Inostroza
Background
Activity-dependent markers such as c-Fos, a rapid indicator of neuronal activation, and GAD67, an enzyme essential for GABA synthesis in inhibitory neurons, are extensively employed to elucidate neural circuit dynamics. Given that many studies span extended periods with multiple experimental groups, it is crucial to ensure long-term storage of non-frozen brain tissue does not compromise immunodetection.
New method
Here, we evaluated the impact of storage duration on the immunodetection of c-Fos and GAD67 in rat brains. Intact brains, fixed in paraformaldehyde, were stored at 4 °C in phosphate-buffered saline with sodium azide to prevent bacterial growth. Brains were assessed at two storage durations - short (1.5 months) and prolonged (10 months). Brain sections were immunostained for c-Fos and GAD67 and imaged by confocal microscopy.
Results
We observed robust c-Fos immunoreactivity across multiple regions of the medial prefrontal cortex, hippocampus, and neocortex, with no significant differences attributable to storage duration. Additionally, quantifications of GAD67-positive cells and cells co-labeled for c-Fos/ GAD67 confirmed that immunodetection of inhibitory neurons remains intact when whole brains are stored for up to 10 months. In contrast, prolonged storage of brain slices strongly reduced c-Fos, but increased GAD67 staining.
Comparison with existing method(s)
The stability of c-Fos and GAD67 in tissue stored long-term at 4 °C remains untested.
Conclusions
These findings underscore that whereas intact brains can be safely stored for prolonged periods at 4°C without compromising antigenicity, brain slices are highly susceptible to storage-induced deterioration - insights important for planning and interpreting immunohistochemical studies in neuroscience.
{"title":"Impact of tissue storage time on immunodetection of c-Fos and GAD67 in the rat brain","authors":"Stoyan Dimitrov , Xia Shan , Jan Born , Marion Inostroza","doi":"10.1016/j.jneumeth.2025.110602","DOIUrl":"10.1016/j.jneumeth.2025.110602","url":null,"abstract":"<div><h3>Background</h3><div>Activity-dependent markers such as c-Fos, a rapid indicator of neuronal activation, and GAD67, an enzyme essential for GABA synthesis in inhibitory neurons, are extensively employed to elucidate neural circuit dynamics. Given that many studies span extended periods with multiple experimental groups, it is crucial to ensure long-term storage of non-frozen brain tissue does not compromise immunodetection.</div></div><div><h3>New method</h3><div>Here, we evaluated the impact of storage duration on the immunodetection of c-Fos and GAD67 in rat brains. Intact brains, fixed in paraformaldehyde, were stored at 4 °C in phosphate-buffered saline with sodium azide to prevent bacterial growth. Brains were assessed at two storage durations - short (1.5 months) and prolonged (10 months). Brain sections were immunostained for c-Fos and GAD67 and imaged by confocal microscopy.</div></div><div><h3>Results</h3><div>We observed robust c-Fos immunoreactivity across multiple regions of the medial prefrontal cortex, hippocampus, and neocortex, with no significant differences attributable to storage duration. Additionally, quantifications of GAD67-positive cells and cells co-labeled for c-Fos/ GAD67 confirmed that immunodetection of inhibitory neurons remains intact when whole brains are stored for up to 10 months. In contrast, prolonged storage of brain slices strongly reduced c-Fos, but increased GAD67 staining.</div></div><div><h3>Comparison with existing method(s)</h3><div>The stability of c-Fos and GAD67 in tissue stored long-term at 4 °C remains untested.</div></div><div><h3>Conclusions</h3><div>These findings underscore that whereas intact brains can be safely stored for prolonged periods at 4°C without compromising antigenicity, brain slices are highly susceptible to storage-induced deterioration - insights important for planning and interpreting immunohistochemical studies in neuroscience.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"425 ","pages":"Article 110602"},"PeriodicalIF":2.3,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145344947","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}
Pub Date : 2025-10-12DOI: 10.1016/j.jneumeth.2025.110601
Sorour Nemati , Alanna Stanley , Michelle Kilcoyne , Dimitrios Zeugolis , Siobhan S. McMahon
Background
Spinal cord injury (SCI) results in a cascade of cellular and molecular events that lead to permanent tissue damage and functional impairment. A key consequence of this injury is the formation of both glial and fibrotic scars, which pose significant barriers to regeneration. The fibrotic scar that forms following SCI remains a significant therapeutic challenge. One major obstacle in developing anti-fibrotic compounds is the absence of a comprehensive in vitro screening system.
New method
In this study, we employed a macromolecular crowding (MMC) technique to accelerate ECM deposition. Leptomeningeal (LPG) cells were cultured in media supplemented with the MMC Ficoll (FC). To mimic the injury environment in vivo, the cells were exposed to either physical or chemical injury.
Results
The growth and metabolic activity of the LPG cells remained unchanged under these different injuries and treatments. Groups supplemented with the MMC FC exhibited higher deposition of ECM proteins involved in fibrotic scar formation, including fibronectin, collagen IV, collagen I, and laminin, compared to those without FC.
Comparison with existing methods
A key limitation of conventional cell culture in aqueous media is its clear difference from the naturally ‘crowded’ tissue environment, resulting in a slow rate of ECM protein deposition. Using the MMC approach, we successfully accelerated ECM protein deposition within an in vitro model of the fibrotic scar.
Conclusions
Supplementing LPG culture media with MMCs can effectively mimic the fibrotic scar environment, providing a valuable refinement in developing SCI in vitro models for drug screening and therapeutic applications.
{"title":"Development of an in vitro fibrotic scar model of spinal cord injury using macromolecular crowding","authors":"Sorour Nemati , Alanna Stanley , Michelle Kilcoyne , Dimitrios Zeugolis , Siobhan S. McMahon","doi":"10.1016/j.jneumeth.2025.110601","DOIUrl":"10.1016/j.jneumeth.2025.110601","url":null,"abstract":"<div><h3>Background</h3><div>Spinal cord injury (SCI) results in a cascade of cellular and molecular events that lead to permanent tissue damage and functional impairment. A key consequence of this injury is the formation of both glial and fibrotic scars, which pose significant barriers to regeneration. The fibrotic scar that forms following SCI remains a significant therapeutic challenge. One major obstacle in developing anti-fibrotic compounds is the absence of a comprehensive <em>in vitro</em> screening system.</div></div><div><h3>New method</h3><div>In this study, we employed a macromolecular crowding (MMC) technique to accelerate ECM deposition. Leptomeningeal (LPG) cells were cultured in media supplemented with the MMC Ficoll (FC). To mimic the injury environment <em>in vivo</em>, the cells were exposed to either physical or chemical injury.</div></div><div><h3>Results</h3><div>The growth and metabolic activity of the LPG cells remained unchanged under these different injuries and treatments. Groups supplemented with the MMC FC exhibited higher deposition of ECM proteins involved in fibrotic scar formation, including fibronectin, collagen IV, collagen I, and laminin, compared to those without FC.</div></div><div><h3>Comparison with existing methods</h3><div>A key limitation of conventional cell culture in aqueous media is its clear difference from the naturally ‘crowded’ tissue environment, resulting in a slow rate of ECM protein deposition. Using the MMC approach, we successfully accelerated ECM protein deposition within an <em>in vitro</em> model of the fibrotic scar.</div></div><div><h3>Conclusions</h3><div>Supplementing LPG culture media with MMCs can effectively mimic the fibrotic scar environment, providing a valuable refinement in developing SCI <em>in vitro</em> models for drug screening and therapeutic applications.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"424 ","pages":"Article 110601"},"PeriodicalIF":2.3,"publicationDate":"2025-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145292470","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}
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