Pub Date : 2025-12-18DOI: 10.1016/j.jneumeth.2025.110663
Samuel Ehrlich , Alexandra D. VandeLoo , Mohamed Badawy , Mercedes M. Gonzalez , Max Stockslager , Aimei Yang , Sapna Sinha , Shahar Bracha , Demian Park , Benjamin Magondu , Bo Yang , Edward S. Boyden , Craig R. Forest
Background:
Our ability to engineer opsins is limited by an incomplete understanding of how sequence variations influence function. The vastness of opsin sequence space makes systematic exploration difficult.
New method:
In recognition of the need for datasets linking opsin genetic sequence to function, we pursued a novel method for screening channelrhodopsins to obtain these datasets. In this method, we integrate advances in robotic intracellular electrophysiology (Patch) to measure optogenetic properties (Excite), harvest individual cells of interest (Pick) and subsequently sequence them (Sequence), thus tying sequence to function.
Results:
We used this method to sequence more than 50 cells with associated functional characterization. We further demonstrate the utility of this method with experiments on heterogeneous populations of known opsins and single point mutations of a known opsin. Of these point mutations, we found C160W ablates ChrimsonR’s response to light.
Conclusion and comparison to existing methods:
Compared to traditional manual patch clamp screening, which is labor-intensive and low-throughput, this approach enables more efficient, standardized, and scalable characterization of large opsin libraries. This method can enable opsin engineering with large datasets to increase our understanding of opsin sequence–function relationships.
{"title":"Screening channelrhodopsins using robotic intracellular electrophysiology and single cell sequencing","authors":"Samuel Ehrlich , Alexandra D. VandeLoo , Mohamed Badawy , Mercedes M. Gonzalez , Max Stockslager , Aimei Yang , Sapna Sinha , Shahar Bracha , Demian Park , Benjamin Magondu , Bo Yang , Edward S. Boyden , Craig R. Forest","doi":"10.1016/j.jneumeth.2025.110663","DOIUrl":"10.1016/j.jneumeth.2025.110663","url":null,"abstract":"<div><h3>Background:</h3><div>Our ability to engineer opsins is limited by an incomplete understanding of how sequence variations influence function. The vastness of opsin sequence space makes systematic exploration difficult.</div></div><div><h3>New method:</h3><div>In recognition of the need for datasets linking opsin genetic sequence to function, we pursued a novel method for screening channelrhodopsins to obtain these datasets. In this method, we integrate advances in robotic intracellular electrophysiology (<u>P</u>atch) to measure optogenetic properties (<u>E</u>xcite), harvest individual cells of interest (<u>P</u>ick) and subsequently sequence them (<u>S</u>equence), thus tying sequence to function.</div></div><div><h3>Results:</h3><div>We used this method to sequence more than 50 cells with associated functional characterization. We further demonstrate the utility of this method with experiments on heterogeneous populations of known opsins and single point mutations of a known opsin. Of these point mutations, we found C160W ablates ChrimsonR’s response to light.</div></div><div><h3>Conclusion and comparison to existing methods:</h3><div>Compared to traditional manual patch clamp screening, which is labor-intensive and low-throughput, this approach enables more efficient, standardized, and scalable characterization of large opsin libraries. This method can enable opsin engineering with large datasets to increase our understanding of opsin sequence–function relationships.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"428 ","pages":"Article 110663"},"PeriodicalIF":2.3,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145800610","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 : 2025-12-17DOI: 10.1016/j.jneumeth.2025.110662
Yi Zhou, Ruiwen Jiang, Jingxiang Zhang
Background
Recognizing emotion is a crucial project within the domain of brain-computer interface technology. Recently, researchers have found that deep learning have been proven to be superior to machine learning, but how to obtain more discriminative features still faces great challenges.
New method
We propose a multi-scale convolutional neural network (MSCNN) based on channel attention and spatial attention (CSA-MSCNN) for EEG emotion recognition. The channel attention enhances the feature extraction ability of critical channels by generating channel weights, while suppressing noise or interference from redundant channels. The spatial attention helps the model to more precisely locate key areas related to emotion by generating a spatial weight matrix. To extract more comprehensive features, CSA-MSCNN uses MSCNN for feature extraction, with smaller convolutional kernels capturing the local details of the signals, and larger convolutional kernels with a broader receptive field to obtain deeper signal information.
Results
CSA-MSCNN achieves average accuracies of 95.75 % and 95.39 % for three-class classification of valence and arousal on DEAP, respectively, while achieving an average three-class classification accuracy of 90.48 % on SEED.
Comparison with existing methods
The classification accuracy of CSA-MSCNN is not only significantly better than traditional machine learning models, but also shows strong competitiveness compared with mainstream deep learning models such as graph convolutional neural network (GCNN).
Conclusions
CSA-MSCNN addresses the issues of multiple EEG signal channels and complex regional information.
{"title":"A multi-scale deep CNN based on attention mechanism for EEG emotion recognition","authors":"Yi Zhou, Ruiwen Jiang, Jingxiang Zhang","doi":"10.1016/j.jneumeth.2025.110662","DOIUrl":"10.1016/j.jneumeth.2025.110662","url":null,"abstract":"<div><h3>Background</h3><div>Recognizing emotion is a crucial project within the domain of brain-computer interface technology. Recently, researchers have found that deep learning have been proven to be superior to machine learning, but how to obtain more discriminative features still faces great challenges.</div></div><div><h3>New method</h3><div>We propose a multi-scale convolutional neural network (MSCNN) based on channel attention and spatial attention (CSA-MSCNN) for EEG emotion recognition. The channel attention enhances the feature extraction ability of critical channels by generating channel weights, while suppressing noise or interference from redundant channels. The spatial attention helps the model to more precisely locate key areas related to emotion by generating a spatial weight matrix. To extract more comprehensive features, CSA-MSCNN uses MSCNN for feature extraction, with smaller convolutional kernels capturing the local details of the signals, and larger convolutional kernels with a broader receptive field to obtain deeper signal information.</div></div><div><h3>Results</h3><div>CSA-MSCNN achieves average accuracies of 95.75 % and 95.39 % for three-class classification of valence and arousal on DEAP, respectively, while achieving an average three-class classification accuracy of 90.48 % on SEED.</div></div><div><h3>Comparison with existing methods</h3><div>The classification accuracy of CSA-MSCNN is not only significantly better than traditional machine learning models, but also shows strong competitiveness compared with mainstream deep learning models such as graph convolutional neural network (GCNN).</div></div><div><h3>Conclusions</h3><div>CSA-MSCNN addresses the issues of multiple EEG signal channels and complex regional information.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"427 ","pages":"Article 110662"},"PeriodicalIF":2.3,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145794043","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 : 2025-12-17DOI: 10.1016/j.jneumeth.2025.110660
Olaf Dimigen, Ioana Badea, Iarina Simon, Mark M. Span
Background
Rapid Invisible Frequency Tagging (RIFT) enables neural frequency tagging at rates above the flicker fusion threshold, eliciting steady-state responses to flicker that is almost imperceptible. While RIFT has proven valuable for studying visuospatial attention, it has so far relied on costly projector systems, typically in combination with magnetoencephalography (MEG). The recent emergence of high-speed organic light-emitting diode (OLED) monitors for consumers suggests that RIFT may also be feasible with much more accessible hardware.
New method
Here, we provide a proof-of-concept demonstrating successful RIFT using a consumer-grade 480 Hz OLED monitor in combination with electroencephalography (EEG). We also share practical recommendations for achieving precise stimulus timing at 480 Hz with minimal frame drops.
Results
In a central fixation task, participants viewed a tapered disc stimulus flickering either centrally or peripherally. Luminance was modulated sinusoidally at 60 Hz or 64 Hz, frequencies at which the flicker was barely visible. Photodiode recordings confirmed that the monitor delivered accurate frame timing with few dropped frames. Cross-coherence analysis between occipital EEG channels and a photodiode revealed robust, frequency-specific neural tagging responses for central stimuli at both frequencies. In comparison, weaker coherence was observed for 60 Hz peripheral flicker.
Conclusions
Our findings demonstrate that RIFT can be reliably implemented using affordable stimulation hardware, a low-density EEG montage, and a minimal processing pipeline. We hope that this lowers barriers to entry, facilitating broader use of RIFT in basic research and in applied settings where cost and portability matter.
{"title":"Rapid invisible frequency tagging (RIFT) with a consumer monitor: A proof-of-concept","authors":"Olaf Dimigen, Ioana Badea, Iarina Simon, Mark M. Span","doi":"10.1016/j.jneumeth.2025.110660","DOIUrl":"10.1016/j.jneumeth.2025.110660","url":null,"abstract":"<div><h3>Background</h3><div>Rapid Invisible Frequency Tagging (RIFT) enables neural frequency tagging at rates above the flicker fusion threshold, eliciting steady-state responses to flicker that is almost imperceptible. While RIFT has proven valuable for studying visuospatial attention, it has so far relied on costly projector systems, typically in combination with magnetoencephalography (MEG). The recent emergence of high-speed organic light-emitting diode (OLED) monitors for consumers suggests that RIFT may also be feasible with much more accessible hardware.</div></div><div><h3>New method</h3><div>Here, we provide a proof-of-concept demonstrating successful RIFT using a consumer-grade 480 Hz OLED monitor in combination with electroencephalography (EEG). We also share practical recommendations for achieving precise stimulus timing at 480 Hz with minimal frame drops.</div></div><div><h3>Results</h3><div>In a central fixation task, participants viewed a tapered disc stimulus flickering either centrally or peripherally. Luminance was modulated sinusoidally at 60 Hz or 64 Hz, frequencies at which the flicker was barely visible. Photodiode recordings confirmed that the monitor delivered accurate frame timing with few dropped frames. Cross-coherence analysis between occipital EEG channels and a photodiode revealed robust, frequency-specific neural tagging responses for central stimuli at both frequencies. In comparison, weaker coherence was observed for 60 Hz peripheral flicker.</div></div><div><h3>Conclusions</h3><div>Our findings demonstrate that RIFT can be reliably implemented using affordable stimulation hardware, a low-density EEG montage, and a minimal processing pipeline. We hope that this lowers barriers to entry, facilitating broader use of RIFT in basic research and in applied settings where cost and portability matter.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"428 ","pages":"Article 110660"},"PeriodicalIF":2.3,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145794079","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 : 2025-12-15DOI: 10.1016/j.jneumeth.2025.110661
Yosuf Arab , Gabriel G. Bonassi , Marise N. Wilson , Gregory T. Macleod
Background
Over fifty million people worldwide currently live with neurodegenerative diseases, many of which are the result of pathogenic gene variants. Genetically malleable model organisms provide an avenue for research into the genetic bases of these diseases, and the large motor neurons of fruit fly larvae provide a test bed for these investigations. However, it is challenging to collect information from these neurons under physiological conditions as they terminate on rhythmically contracting muscle fibers.
New Method
As a test of in vivo neuronal performance, we expressed light-activated opsins in motor-neurons of unrestrained intact Drosophila larva and used light pulses to drive cyclical body-wall contractions that were captured on camera and analyzed offline.
Results
We describe the assembly of an apparatus to systematically activate motor-neurons in Drosophila larvae and an image acquisition system to capture the resulting body-wall contractions. To test the utility of the assay we performed a motor-neuron specific knock-down of Miro, an adaptor [protein, MIRO] for mitochondrial transport into motor-neuron terminals.
Comparison with Existing Methods
This in vivo assay allows for a test of sustained neuronal performance while sidestepping the shortcomings of electrophysiological assays of neurotransmission in situ where recordings are mechanically disrupted at endogenous firing rates. Secondly, unlike adult climbing assays and larval locomotion assays, performance is assayed independently of the organism’s motivation to perform or ability to detect stimuli.
Conclusions
Here we demonstrated an optogenetic assay for quantifying motor neuron output of intact Drosophila 3rd instar larvae. Our data established the robustness of the assay and its capacity to discriminate impaired motor neuron performance.
{"title":"An optogenetic assay of Drosophila larval motor neuron performance in vivo","authors":"Yosuf Arab , Gabriel G. Bonassi , Marise N. Wilson , Gregory T. Macleod","doi":"10.1016/j.jneumeth.2025.110661","DOIUrl":"10.1016/j.jneumeth.2025.110661","url":null,"abstract":"<div><h3>Background</h3><div>Over fifty million people worldwide currently live with neurodegenerative diseases, many of which are the result of pathogenic gene variants. Genetically malleable model organisms provide an avenue for research into the genetic bases of these diseases, and the large motor neurons of fruit fly larvae provide a test bed for these investigations. However, it is challenging to collect information from these neurons under physiological conditions as they terminate on rhythmically contracting muscle fibers.</div></div><div><h3><em>New</em> Method</h3><div>As a test of <em>in vivo</em> neuronal performance, we expressed light-activated opsins in motor-neurons of unrestrained intact <em>Drosophila</em> larva and used light pulses to drive cyclical body-wall contractions that were captured on camera and analyzed offline.</div></div><div><h3>Results</h3><div>We describe the assembly of an apparatus to systematically activate motor-neurons in <em>Drosophila</em> larvae and an image acquisition system to capture the resulting body-wall contractions. To test the utility of the assay we performed a motor-neuron specific knock-down of <em>Miro</em>, an adaptor [protein, MIRO] for mitochondrial transport into motor-neuron terminals.</div></div><div><h3><em>Comparison</em> with Existing Methods</h3><div>This <em>in vivo</em> assay allows for a test of sustained neuronal performance while sidestepping the shortcomings of electrophysiological assays of neurotransmission <em>in situ</em> where recordings are mechanically disrupted at endogenous firing rates. Secondly, unlike adult climbing assays and larval locomotion assays, performance is assayed independently of the organism’s motivation to perform or ability to detect stimuli.</div></div><div><h3>Conclusions</h3><div>Here we demonstrated an optogenetic assay for quantifying motor neuron output of intact <em>Drosophila</em> 3rd instar larvae. Our data established the robustness of the assay and its capacity to discriminate impaired motor neuron performance.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"427 ","pages":"Article 110661"},"PeriodicalIF":2.3,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145774849","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 : 2025-12-13DOI: 10.1016/j.jneumeth.2025.110659
Yutong Yao , Xinru Lan , Cheng Luo , Li Dong
{"title":"REST is a superior EEG re-referencing method: A commentary on the rCAR by Kyle Q. Lepagea et al.","authors":"Yutong Yao , Xinru Lan , Cheng Luo , Li Dong","doi":"10.1016/j.jneumeth.2025.110659","DOIUrl":"10.1016/j.jneumeth.2025.110659","url":null,"abstract":"","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"427 ","pages":"Article 110659"},"PeriodicalIF":2.3,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145762939","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}
Glioblastoma (GBM) is an aggressive primary brain cancer with a poor prognosis. Preclinical animal models are essential for studying GBM pathophysiology and therapy; however, existing models often fail to fully capture the tumor's heterogeneity and the partially immunodeficient microenvironment critical to its progression.
New method
We developed a novel GBM model using the stereotactic implantation of primary patient-derived GBM cells from various donors into the brains of rats subjected to transient, partial immune suppression.
Results
This model combines the biological heterogeneity of patient-derived cells with the anatomical advantages of a larger rodent brain, improving spatial tumor localization. The use of a mixed primary cell population better recapitulates human GBM heterogeneity. Furthermore, the model's partially preserved immune environment allows for the investigation of tumor-immune interactions.
Comparison with existing methods
Unlike fully immunodeficient models, our approach maintains a more physiologically relevant, partially intact immune system. Compared to murine models, the rat brain offers superior spatial resolution for tumor analysis and intervention.
Conclusions
This method provides a reliable and translational platform that enhances the fidelity of preclinical GBM research. It offers an improved tool for drug evaluation and the development of personalized therapeutic strategies by more accurately mimicking the complex and heterogeneous nature of human GBM.
{"title":"Stereotactic intracranial implantation of patient-derived glioblastoma cells in rats: A xenograft modeling approach","authors":"Faezeh Yaghoubi , Safieh Ebrahimi , Ali Gorji , Maryam Khaleghi Ghadiri","doi":"10.1016/j.jneumeth.2025.110658","DOIUrl":"10.1016/j.jneumeth.2025.110658","url":null,"abstract":"<div><h3>Background</h3><div>Glioblastoma (GBM) is an aggressive primary brain cancer with a poor prognosis. Preclinical animal models are essential for studying GBM pathophysiology and therapy; however, existing models often fail to fully capture the tumor's heterogeneity and the partially immunodeficient microenvironment critical to its progression.</div></div><div><h3>New method</h3><div>We developed a novel GBM model using the stereotactic implantation of primary patient-derived GBM cells from various donors into the brains of rats subjected to transient, partial immune suppression.</div></div><div><h3>Results</h3><div>This model combines the biological heterogeneity of patient-derived cells with the anatomical advantages of a larger rodent brain, improving spatial tumor localization. The use of a mixed primary cell population better recapitulates human GBM heterogeneity. Furthermore, the model's partially preserved immune environment allows for the investigation of tumor-immune interactions.</div></div><div><h3>Comparison with existing methods</h3><div>Unlike fully immunodeficient models, our approach maintains a more physiologically relevant, partially intact immune system. Compared to murine models, the rat brain offers superior spatial resolution for tumor analysis and intervention.</div></div><div><h3>Conclusions</h3><div>This method provides a reliable and translational platform that enhances the fidelity of preclinical GBM research. It offers an improved tool for drug evaluation and the development of personalized therapeutic strategies by more accurately mimicking the complex and heterogeneous nature of human GBM.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"427 ","pages":"Article 110658"},"PeriodicalIF":2.3,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145756843","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}
Intercellular interactions, particularly those between tumor cells and the surrounding vasculature, are central to the biology of the tumor microenvironment. Approaches for studying these interactions often rely on limited patient samples or time- and resource-intensive xenograft tissues in combination with histological or single-cell omics profiling. While informative, these models capture only static snapshots and limit mechanistic interrogation. Studying the mechanisms behind these interactions requires viable co-culture models for culturing different cell types together in vitro, while preserving the phenotypic integrity of each cell type.
New method
To achieve this, we developed and validated optimal in vitro culture conditions to support the co-culture of human Group 3 medulloblastoma (G3 MB) cells and microvascular brain endothelial cells (BECs) as an ideal screening model for mechanistic and interventional studies. Supported by a new optimized 1:1 mixed media formulation, this model preserves native cellular morphology and phenotypic characteristics.
Results
When cultured alone in the new optimized media, G3 MB cells retained expression of stemness markers (SOX2 & OTX2), self-renewal capacity, and undifferentiated morphology, while BECs retained tight junction formation and migratory behavior.
Comparison with existing methods
This co-culture platform permits real-time, dynamic, and mechanistic studies of tumor-endothelial cell interactions, overcoming the limitations of fixed-tissue analyses and facilitating precise experimental manipulation.
Conclusions
This well-characterized model provides a physiologically and functionally relevant platform for further dissecting the reciprocal interactions present between various brain cancer cells and vascular endothelial cells, supporting the development of targeted therapeutic strategies and advancing our understanding of brain tumor biology.
{"title":"A functionally relevant model for interrogating brain tumor-endothelial cell interactions","authors":"Akaljot Grewal , Emma Martell , Helgi Kuzmychova , Ujala Chawla , Charul Jain , Kayshana Ramnauth , Tanveer Sharif","doi":"10.1016/j.jneumeth.2025.110657","DOIUrl":"10.1016/j.jneumeth.2025.110657","url":null,"abstract":"<div><h3>Background</h3><div>Intercellular interactions, particularly those between tumor cells and the surrounding vasculature, are central to the biology of the tumor microenvironment. Approaches for studying these interactions often rely on limited patient samples or time- and resource-intensive xenograft tissues in combination with histological or single-cell omics profiling. While informative, these models capture only static snapshots and limit mechanistic interrogation. Studying the mechanisms behind these interactions requires viable co-culture models for culturing different cell types together <em>in vitro,</em> while preserving the phenotypic integrity of each cell type.</div></div><div><h3>New method</h3><div>To achieve this, we developed and validated optimal <em>in vitro</em> culture conditions to support the co-culture of human Group 3 medulloblastoma (G3 MB) cells and microvascular brain endothelial cells (BECs) as an ideal screening model for mechanistic and interventional studies. Supported by a new optimized 1:1 mixed media formulation, this model preserves native cellular morphology and phenotypic characteristics.</div></div><div><h3>Results</h3><div>When cultured alone in the new optimized media, G3 MB cells retained expression of stemness markers (SOX2 & OTX2), self-renewal capacity, and undifferentiated morphology, while BECs retained tight junction formation and migratory behavior.</div></div><div><h3>Comparison with existing methods</h3><div>This co-culture platform permits real-time, dynamic, and mechanistic studies of tumor-endothelial cell interactions, overcoming the limitations of fixed-tissue analyses and facilitating precise experimental manipulation.</div></div><div><h3>Conclusions</h3><div>This well-characterized model provides a physiologically and functionally relevant platform for further dissecting the reciprocal interactions present between various brain cancer cells and vascular endothelial cells, supporting the development of targeted therapeutic strategies and advancing our understanding of brain tumor biology.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"427 ","pages":"Article 110657"},"PeriodicalIF":2.3,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145749282","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 : 2025-12-03DOI: 10.1016/j.jneumeth.2025.110649
Noémie Frère, Lydia Chelbi, Louise Mathé, Catherine Lubetzki, Bruno Stankoff, Bernard Zalc
Background
In vitro models are crucial for exploring cellular and molecular mechanisms underlying central nervous system (CNS) development and function. While long-term neuron–glia cultures have been well established in rodents, fewer approaches exist for amphibians or invertebrates.
New method
Here, we present standardized protocols for the dissociation and culture of CNS cells from Xenopus laevis tadpoles (stages 40–46) and Drosophila melanogaster L3 larvae. We developed species-specific media enabling the survival and differentiation of mixed neuronal and glial populations for at least 14 days.
Results
Immunofluorescence analyses revealed progressive neuronal network formation and glial maturation, including oligodendrocyte process extension and early signs of myelination in Xenopus, and stable glial subtypes interacting with neurons in Drosophila.
Comparison with existing methods
Despite an initial cell loss during the first week, cultures stabilized thereafter, maintaining representative populations of neurons, astrocytes, oligodendrocytes, and microglia in Xenopus, and neurons with ensheathing, wrapping and cortical glia in Drosophila. Cell populations at D14 are similar to those found in more commonly used cultures.
Conclusion
This work provides robust platforms for investigating neuron–glia interactions in both vertebrate and invertebrate models.
{"title":"Long term cultures of Xenopus and Drosophila neurons and glial cells","authors":"Noémie Frère, Lydia Chelbi, Louise Mathé, Catherine Lubetzki, Bruno Stankoff, Bernard Zalc","doi":"10.1016/j.jneumeth.2025.110649","DOIUrl":"10.1016/j.jneumeth.2025.110649","url":null,"abstract":"<div><h3>Background</h3><div><em>In vitro</em> models are crucial for exploring cellular and molecular mechanisms underlying central nervous system (CNS) development and function. While long-term neuron–glia cultures have been well established in rodents, fewer approaches exist for amphibians or invertebrates.</div></div><div><h3>New method</h3><div>Here, we present standardized protocols for the dissociation and culture of CNS cells from <em>Xenopus laevis</em> tadpoles (stages 40–46) and <em>Drosophila melanogaster</em> L3 larvae. We developed species-specific media enabling the survival and differentiation of mixed neuronal and glial populations for at least 14 days.</div></div><div><h3>Results</h3><div>Immunofluorescence analyses revealed progressive neuronal network formation and glial maturation, including oligodendrocyte process extension and early signs of myelination in Xenopus, and stable glial subtypes interacting with neurons in Drosophila.</div></div><div><h3>Comparison with existing methods</h3><div>Despite an initial cell loss during the first week, cultures stabilized thereafter, maintaining representative populations of neurons, astrocytes, oligodendrocytes, and microglia in Xenopus, and neurons with ensheathing, wrapping and cortical glia in Drosophila. Cell populations at D14 are similar to those found in more commonly used cultures.</div></div><div><h3>Conclusion</h3><div>This work provides robust platforms for investigating neuron–glia interactions in both vertebrate and invertebrate models.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"427 ","pages":"Article 110649"},"PeriodicalIF":2.3,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145687497","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 : 2025-12-03DOI: 10.1016/j.jneumeth.2025.110647
Kyle Coutray, Christos Constantinidis
Background
Behavioral neuroscience experiments require precise stimulus control, millisecond timing, hardware integration, and robust data provenance. The growing use of 3D environments and multimodal recordings increases challenges for development, accessibility, and reproducibility. Fragmented tools often separate presentation, synchronization, and logging, creating workflow inefficiencies.
New Method
The Neuroscience Experimental Runtime by Vanderbilt (NERV) is a Unity-based C# framework that unifies experiment design, execution, and data logging. Custom Unity Editor tools automate scene and script generation, state management, and hardware-synchronized event timing via TTL pulses. A modular ExtraFunctions system enables plug-and-play modules like photodiode markers, gaze tracking, and reward control, while an automated archival process captures all code, configurations, and logs for complete provenance. The open-source framework follows a “low floor, high ceiling” design that supports both no-code use and full extensibility.
Results
Across 500 trials, Unity-to-TTL delay was 2.10 ± 1.21 ms, TTL-to-photodiode delay was 28.93 ± 0.76 ms, and Unity-to-screen delay was 31.04 ± 1.41 ms, confirming stable millisecond precision and frame-locked timing for reliable alignment of neural, behavioral, and visual events.
Comparison with existing methods
Existing frameworks involve trade-offs between timing precision, accessibility, hardware support, and 3D capability. NERV integrates millisecond precision, modular open-source design, and full provenance in a single platform, accelerating development, reducing workflow fragmentation, and enabling reproducible, scalable experiments.
Conclusion
NERV provides an accessible and extensible framework that unites rapid development, robust data provenance, and millisecond precision, establishing a scalable foundation for next-generation neuroscience research.
{"title":"NERV: A comprehensive framework for rapid, reproducible, and hardware-synchronized neuroscience experiment design and execution","authors":"Kyle Coutray, Christos Constantinidis","doi":"10.1016/j.jneumeth.2025.110647","DOIUrl":"10.1016/j.jneumeth.2025.110647","url":null,"abstract":"<div><h3>Background</h3><div>Behavioral neuroscience experiments require precise stimulus control, millisecond timing, hardware integration, and robust data provenance. The growing use of 3D environments and multimodal recordings increases challenges for development, accessibility, and reproducibility. Fragmented tools often separate presentation, synchronization, and logging, creating workflow inefficiencies.</div></div><div><h3>New Method</h3><div>The Neuroscience Experimental Runtime by Vanderbilt (NERV) is a Unity-based C# framework that unifies experiment design, execution, and data logging. Custom Unity Editor tools automate scene and script generation, state management, and hardware-synchronized event timing via TTL pulses. A modular ExtraFunctions system enables plug-and-play modules like photodiode markers, gaze tracking, and reward control, while an automated archival process captures all code, configurations, and logs for complete provenance. The open-source framework follows a “low floor, high ceiling” design that supports both no-code use and full extensibility.</div></div><div><h3>Results</h3><div>Across 500 trials, Unity-to-TTL delay was 2.10 ± 1.21 ms, TTL-to-photodiode delay was 28.93 ± 0.76 ms, and Unity-to-screen delay was 31.04 ± 1.41 ms, confirming stable millisecond precision and frame-locked timing for reliable alignment of neural, behavioral, and visual events.</div></div><div><h3>Comparison with existing methods</h3><div>Existing frameworks involve trade-offs between timing precision, accessibility, hardware support, and 3D capability. NERV integrates millisecond precision, modular open-source design, and full provenance in a single platform, accelerating development, reducing workflow fragmentation, and enabling reproducible, scalable experiments.</div></div><div><h3>Conclusion</h3><div>NERV provides an accessible and extensible framework that unites rapid development, robust data provenance, and millisecond precision, establishing a scalable foundation for next-generation neuroscience research.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"427 ","pages":"Article 110647"},"PeriodicalIF":2.3,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145687426","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 : 2025-11-30DOI: 10.1016/j.jneumeth.2025.110648
Bingyang Cai , Shize Jiang , Jiwei Li , Zhengwei Zhong , Haiqing Zhang , Zengji Zhang , Xiaolai Ye , Weiqi Bao , Jie Hu , Liang Chen , Xiaoying Liu , Jie Luo
Background
Epilepsy affects approximately 70 million people worldwide, with a third of them being drug-resistant and requiring surgical intervention. Accurate localization of the seizure onset zone (SOZ) is crucial for effective surgery but remains challenging.
New method
We proposed a method using iterative independent component analysis (ICA) to map seizure propagation of drug-resistant temporal lobe epilepsy (TLE). For each assumed seizure origin, ICA was applied to the remaining contacts to identify propagation components, with the highest correlating component being the propagating signal. Iterative removal of nearby contacts revealed spatial propagation profiles. Machine learning models were applied to the propagation profiles to distinguish the SOZ.
Results
Seizure propagation features differed significantly between SOZ and non-SOZ contacts in seizure free patients (both local cohort N = 21, and independent dataset N = 13), but not in non-seizure free patients (N = 11). Propagation-based classifiers achieved robust performance (AUC = 0.85), outperforming iEEG source imaging (AUC = 0.73). In mesial TLE, propagation maintained high accuracy (AUC = 0.84) while iEEG source imaging dropped markedly (AUC = 0.64).
Comparison with existing methods
Traditional methods for SOZ localization, such as visual inspection of SEEG and source imaging techniques, rely heavily on expert interpretation. iEEG source imaging assumes linear forward models and can be susceptible to inaccuracies due to electrode placement and noise. In contrast, our proposed iterative ICA approach is purely data-driven and adaptively identifies the dominant propagation pathways across individual seizures.
Conclusion
This work introduces a data-driven strategy to characterize seizure propagation, potentially improving SOZ localization with deep brain origins.
{"title":"Propagation mapping using iterative independent component analysis for seizure onset zone localization in temporal lobe epilepsy","authors":"Bingyang Cai , Shize Jiang , Jiwei Li , Zhengwei Zhong , Haiqing Zhang , Zengji Zhang , Xiaolai Ye , Weiqi Bao , Jie Hu , Liang Chen , Xiaoying Liu , Jie Luo","doi":"10.1016/j.jneumeth.2025.110648","DOIUrl":"10.1016/j.jneumeth.2025.110648","url":null,"abstract":"<div><h3>Background</h3><div>Epilepsy affects approximately 70 million people worldwide, with a third of them being drug-resistant and requiring surgical intervention. Accurate localization of the seizure onset zone (SOZ) is crucial for effective surgery but remains challenging.</div></div><div><h3>New method</h3><div>We proposed a method using iterative independent component analysis (ICA) to map seizure propagation of drug-resistant temporal lobe epilepsy (TLE). For each assumed seizure origin, ICA was applied to the remaining contacts to identify propagation components, with the highest correlating component being the propagating signal. Iterative removal of nearby contacts revealed spatial propagation profiles. Machine learning models were applied to the propagation profiles to distinguish the SOZ.</div></div><div><h3>Results</h3><div>Seizure propagation features differed significantly between SOZ and non-SOZ contacts in seizure free patients (both local cohort N = 21, and independent dataset N = 13), but not in non-seizure free patients (N = 11). Propagation-based classifiers achieved robust performance (AUC = 0.85), outperforming iEEG source imaging (AUC = 0.73). In mesial TLE, propagation maintained high accuracy (AUC = 0.84) while iEEG source imaging dropped markedly (AUC = 0.64).</div></div><div><h3>Comparison with existing methods</h3><div>Traditional methods for SOZ localization, such as visual inspection of SEEG and source imaging techniques, rely heavily on expert interpretation. iEEG source imaging assumes linear forward models and can be susceptible to inaccuracies due to electrode placement and noise. In contrast, our proposed iterative ICA approach is purely data-driven and adaptively identifies the dominant propagation pathways across individual seizures.</div></div><div><h3>Conclusion</h3><div>This work introduces a data-driven strategy to characterize seizure propagation, potentially improving SOZ localization with deep brain origins.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"427 ","pages":"Article 110648"},"PeriodicalIF":2.3,"publicationDate":"2025-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145658764","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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