Microglia, the resident immune cells of the central nervous system, play a critical role in maintaining neuronal health, but are often overlooked in traditional neuron-focused in vitro models.
New method
In this study, we developed a novel co-culture system of human pluripotent stem cell (hPSC)-derived microglia and neurons to investigate how hPSC-derived microglia influence neuronal morphology and network activity. Using high-content morphological analysis and multi-electrode arrays (MEA), we demonstrate that these microglia successfully incorporate into neuronal networks and modulate key aspects of neuronal function.
Results
hPSC-derived microglia significantly reduced cellular debris and altered neuronal morphology by decreasing axonal and dendritic segments and reducing synapse density. Interestingly, despite the decrease in synapse density, neuronal network activity increased.
Conclusion
Our findings underscore the importance of including hPSC-derived microglia in in vitro models to better simulate in vivo neuroglial interactions and provide a platform for investigating neuron-glia dynamics in health and disease.
{"title":"Human pluripotent stem cell-derived microglia shape neuronal morphology and enhance network activity in vitro","authors":"L.M.L. Kok , K. Helwegen , N.F. Coveña , V.M. Heine","doi":"10.1016/j.jneumeth.2024.110354","DOIUrl":"10.1016/j.jneumeth.2024.110354","url":null,"abstract":"<div><h3>Background</h3><div>Microglia, the resident immune cells of the central nervous system, play a critical role in maintaining neuronal health, but are often overlooked in traditional neuron-focused <em>in vitro</em> models.</div></div><div><h3>New method</h3><div>In this study, we developed a novel co-culture system of human pluripotent stem cell (hPSC)-derived microglia and neurons to investigate how hPSC-derived microglia influence neuronal morphology and network activity. Using high-content morphological analysis and multi-electrode arrays (MEA), we demonstrate that these microglia successfully incorporate into neuronal networks and modulate key aspects of neuronal function.</div></div><div><h3>Results</h3><div>hPSC-derived microglia significantly reduced cellular debris and altered neuronal morphology by decreasing axonal and dendritic segments and reducing synapse density. Interestingly, despite the decrease in synapse density, neuronal network activity increased.</div></div><div><h3>Conclusion</h3><div>Our findings underscore the importance of including hPSC-derived microglia in <em>in vitro</em> models to better simulate <em>in vivo</em> neuroglial interactions and provide a platform for investigating neuron-glia dynamics in health and disease.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"415 ","pages":"Article 110354"},"PeriodicalIF":2.7,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142895532","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 : 2024-12-19DOI: 10.1016/j.jneumeth.2024.110347
Guilhem Marion , Fei Gao , Benjamin P. Gold , Giovanni M. Di Liberto , Shihab Shamma
Background
: IDyOM (Information Dynamics of Music) is the statistical model of music the most used in the community of neuroscience of music. It has been shown to allow for significant correlations with EEG (Marion, 2021), ECoG (Di Liberto, 2020) and fMRI (Cheung, 2019) recordings of human music listening. The language used for IDyOM -Lisp- is not very familiar to the neuroscience community and makes this model hard to use and more importantly to modify.
New method
: IDyOMpy is a new Python re-implementation and extension of IDyOM. This new model allows for computing the information content and entropy for each melody note after training on a corpus of melodies. In addition to those features, two new features are presented: probability estimation of silences and enculturation modeling.
Results
: We first describe the mathematical details of the implementation. We extensively compare the two models and show that they generate very similar outputs. We also support the validity of IDyOMpy by using its output to replicate previous EEG and behavioral results that relied on the original Lisp version (Gold, 2019; Di Liberto, 2020; Marion, 2021). Finally, it reproduced the computation of cultural distances between two different datasets as described in previous studies (Pearce, 2018).
Comparison with existing methods and Conclusions
: Our model replicates the previous behaviors of IDyOM in a modern and easy-to-use language -Python. In addition, more features are presented. We deeply think this new version will be of great use to the community of neuroscience of music.
背景：IDyOM （Information Dynamics of Music）是音乐神经科学领域使用最多的音乐统计模型。研究表明，它与脑电图（Marion, 2021）、脑电图（Di Liberto, 2020）和功能磁共振成像（b张，2019）记录的人类音乐听力存在显著相关性。用于IDyOM的语言——lisp——对于神经科学社区来说不是很熟悉，这使得这个模型很难使用，更重要的是难以修改。新方法：IDyOMpy是对IDyOM的一个新的Python重新实现和扩展。这个新模型允许在旋律语料库上训练后计算每个旋律音符的信息内容和熵。在此基础上，提出了两个新的特征：沉默概率估计和适应建模。结果：我们首先描述了实现的数学细节。我们广泛地比较了这两个模型，并表明它们产生非常相似的输出。我们还通过使用IDyOMpy的输出来复制依赖于原始Lisp版本的先前EEG和行为结果来支持IDyOMpy的有效性(Gold, 2019；《自由》，2020；马里昂,2021)。最后，它再现了先前研究中描述的两个不同数据集之间文化距离的计算（即Pearce， 2018）。与现有方法和结论的比较：我们的模型在现代和易于使用的语言-Python中复制了IDyOM的先前行为。此外，还提出了更多的特性。我们深信这个新版本将对音乐的神经科学社区有很大的用处。
{"title":"IDyOMpy: A new Python-based model for the statistical analysis of musical expectations","authors":"Guilhem Marion , Fei Gao , Benjamin P. Gold , Giovanni M. Di Liberto , Shihab Shamma","doi":"10.1016/j.jneumeth.2024.110347","DOIUrl":"10.1016/j.jneumeth.2024.110347","url":null,"abstract":"<div><h3>Background</h3><div>: IDyOM (Information Dynamics of Music) is the statistical model of music the most used in the community of neuroscience of music. It has been shown to allow for significant correlations with EEG (Marion, 2021), ECoG (Di Liberto, 2020) and fMRI (Cheung, 2019) recordings of human music listening. The language used for IDyOM -Lisp- is not very familiar to the neuroscience community and makes this model hard to use and more importantly to modify.</div></div><div><h3>New method</h3><div>: IDyOMpy is a new Python re-implementation and extension of IDyOM. This new model allows for computing the information content and entropy for each melody note after training on a corpus of melodies. In addition to those features, two new features are presented: probability estimation of silences and enculturation modeling.</div></div><div><h3>Results</h3><div>: We first describe the mathematical details of the implementation. We extensively compare the two models and show that they generate very similar outputs. We also support the validity of IDyOMpy by using its output to replicate previous EEG and behavioral results that relied on the original Lisp version (Gold, 2019; Di Liberto, 2020; Marion, 2021). Finally, it reproduced the computation of cultural distances between two different datasets as described in previous studies (Pearce, 2018).</div></div><div><h3>Comparison with existing methods and Conclusions</h3><div>: Our model replicates the previous behaviors of IDyOM in a modern and easy-to-use language -Python. In addition, more features are presented. We deeply think this new version will be of great use to the community of neuroscience of music.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"415 ","pages":"Article 110347"},"PeriodicalIF":2.7,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142872035","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 : 2024-12-19DOI: 10.1016/j.jneumeth.2024.110351
Lydia Ellison , Georg Raiser , Alicia Garrido-Peña , György Kemenes , Thomas Nowotny
Background:
Single-sensillum recordings are a valuable tool for sensory research which, by their nature, access extra-cellular signals typically reflecting the combined activity of several co-housed sensory neurons. However, isolating the contribution of an individual neuron through spike-sorting has remained a major challenge due to firing rate-dependent changes in spike shape and the overlap of co-occurring spikes from several neurons. These challenges have so far made it close to impossible to investigate the responses to more complex, mixed odour stimuli.
New Method:
Here we present SSSort 2.0, a method and software addressing both problems through automated and semi-automated signal processing. We have also developed a method for more objective validation of spike sorting methods based on generating surrogate ground truth data and we have tested the practical effectiveness of our software in a user study.
Results:
We find that SSSort 2.0 typically matches or exceeds the performance of expert manual spike sorting. We further demonstrate that, for novices, accuracy is much better with SSSort 2.0 under most conditions.
Conclusion:
Overall, we have demonstrated that spike-sorting with SSSort 2.0 software can automate data processing of SSRs with accuracy levels comparable to, or above, expert manual performance.
{"title":"SSSort 2.0: A semi-automated spike detection and sorting system for single sensillum recordings","authors":"Lydia Ellison , Georg Raiser , Alicia Garrido-Peña , György Kemenes , Thomas Nowotny","doi":"10.1016/j.jneumeth.2024.110351","DOIUrl":"10.1016/j.jneumeth.2024.110351","url":null,"abstract":"<div><h3>Background:</h3><div>Single-sensillum recordings are a valuable tool for sensory research which, by their nature, access extra-cellular signals typically reflecting the combined activity of several co-housed sensory neurons. However, isolating the contribution of an individual neuron through spike-sorting has remained a major challenge due to firing rate-dependent changes in spike shape and the overlap of co-occurring spikes from several neurons. These challenges have so far made it close to impossible to investigate the responses to more complex, mixed odour stimuli.</div></div><div><h3>New Method:</h3><div>Here we present SSSort 2.0, a method and software addressing both problems through automated and semi-automated signal processing. We have also developed a method for more objective validation of spike sorting methods based on generating surrogate ground truth data and we have tested the practical effectiveness of our software in a user study.</div></div><div><h3>Results:</h3><div>We find that SSSort 2.0 typically matches or exceeds the performance of expert manual spike sorting. We further demonstrate that, for novices, accuracy is much better with SSSort 2.0 under most conditions.</div></div><div><h3>Conclusion:</h3><div>Overall, we have demonstrated that spike-sorting with SSSort 2.0 software can automate data processing of SSRs with accuracy levels comparable to, or above, expert manual performance.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"415 ","pages":"Article 110351"},"PeriodicalIF":2.7,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142872199","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 : 2024-12-17DOI: 10.1016/j.jneumeth.2024.110345
Alexis M. Oppman , William J. Paradee , Nandakumar S. Narayanan , Young-cho Kim
Background
Dopamine is a powerful neuromodulator of diverse brain functions, including movement, motivation, reward, and cognition. D1-type dopamine receptors (D1DRs) are the most prevalently expressed dopamine receptors in the brain. Neurons expressing D1DRs are heterogeneous and involve several subpopulations. Although these neurons can be studied with BAC-transgenic rodents, these models have some limitations especially when considering their integration with conditional or intersectional genetic tools.
New Method
We developed a novel Drd1-P2A-Flpo (Drd1-Flpo) mouse line in which the Flpo gene was knocked in immediately after the Drd1 gene using CRISPR-Cas9. We validated the Drd1-Flpo line by confirming Flp expression and functionality specific to D1DR+ neurons with immunohistochemistry and in situ hybridization.
Comparison with Existing Methods
The Drd1-Flpo line is a useful resource for studying subpopulations of D1DR+ neurons with intersectional genetic tools.
Conclusions
We demonstrated brain-wide GFP expression driven by Drd1-Flpo, suggesting that this mouse line may be useful for comprehensive anatomical and functional studies in many brain regions. The Drd1-Flpo model will advance the study of dopaminergic signaling by providing a new tool for investigating the diverse roles of D1DR+ neurons and their subpopulations in brain disease.
{"title":"Generation and validation of a D1 dopamine receptor Flpo knock-in mouse","authors":"Alexis M. Oppman , William J. Paradee , Nandakumar S. Narayanan , Young-cho Kim","doi":"10.1016/j.jneumeth.2024.110345","DOIUrl":"10.1016/j.jneumeth.2024.110345","url":null,"abstract":"<div><h3>Background</h3><div>Dopamine is a powerful neuromodulator of diverse brain functions, including movement, motivation, reward, and cognition. D1-type dopamine receptors (D1DRs) are the most prevalently expressed dopamine receptors in the brain. Neurons expressing D1DRs are heterogeneous and involve several subpopulations. Although these neurons can be studied with BAC-transgenic rodents, these models have some limitations especially when considering their integration with conditional or intersectional genetic tools.</div></div><div><h3>New Method</h3><div>We developed a novel Drd1-P2A-Flpo (Drd1-Flpo) mouse line in which the Flpo gene was knocked in immediately after the Drd1 gene using CRISPR-Cas9. We validated the Drd1-Flpo line by confirming Flp expression and functionality specific to D1DR+ neurons with immunohistochemistry and in situ hybridization.</div></div><div><h3>Comparison with Existing Methods</h3><div>The Drd1-Flpo line is a useful resource for studying subpopulations of D1DR+ neurons with intersectional genetic tools.</div></div><div><h3>Conclusions</h3><div>We demonstrated brain-wide GFP expression driven by Drd1-Flpo, suggesting that this mouse line may be useful for comprehensive anatomical and functional studies in many brain regions. The Drd1-Flpo model will advance the study of dopaminergic signaling by providing a new tool for investigating the diverse roles of D1DR+ neurons and their subpopulations in brain disease.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"415 ","pages":"Article 110345"},"PeriodicalIF":2.7,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142864540","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 : 2024-12-15DOI: 10.1016/j.jneumeth.2024.110352
Caterina Branca , Giulia Braccagni , Dario Finardi , Eleonora Corridori , Sara Salviati , Simona Scheggi , Marco Bortolato
Persistence is the capacity to sustain goal-oriented behavior despite recurring obstacles and setbacks. Recent studies have underscored the importance of this attribute as an integral facet of resilience and a protective factor against depression. In animal models, persistence is commonly examined through operant paradigms, wherein it is operationalized as resistance to the extinction of reward-directed actions. However, these methods are labor-intensive and resource-demanding, prompting questions about their efficiency in exploring the biological underpinnings of persistence and evaluating pharmacological interventions. To address these challenges, our team developed the Sinking Platform Test (SPT), a high-throughput animal task designed to assess persistence under stressful conditions. In the SPT, mice are trained to escape from a water-filled tank by climbing onto a platform above the water. Training also encompasses occasional "failure trials", where the platform is submerged after being climbed, compelling the mice to locate and ascend a new platform. The final test consists of a 5-minute session exclusively comprising failure trials, and persistence is measured as the number of climbed platforms. Our research revealed that chronic stress diminishes performance in the SPT, an effect reversed by chronic antidepressant treatment or voluntary exercise. These findings highlight the potential of SPT for investigating persistence and exploring its role in resilience and depression. Ongoing efforts within our laboratory focus on refining the SPT to minimize stress while enhancing methodological rigor and reproducibility, notably through automation. Future research endeavors will aim to improve SPT's translational relevance by adapting the paradigm for human application, potentially leveraging virtual-reality technologies.
{"title":"Exploring persistence in animal models: The sinking platform test","authors":"Caterina Branca , Giulia Braccagni , Dario Finardi , Eleonora Corridori , Sara Salviati , Simona Scheggi , Marco Bortolato","doi":"10.1016/j.jneumeth.2024.110352","DOIUrl":"10.1016/j.jneumeth.2024.110352","url":null,"abstract":"<div><div>Persistence is the capacity to sustain goal-oriented behavior despite recurring obstacles and setbacks. Recent studies have underscored the importance of this attribute as an integral facet of resilience and a protective factor against depression. In animal models, persistence is commonly examined through operant paradigms, wherein it is operationalized as resistance to the extinction of reward-directed actions. However, these methods are labor-intensive and resource-demanding, prompting questions about their efficiency in exploring the biological underpinnings of persistence and evaluating pharmacological interventions. To address these challenges, our team developed the Sinking Platform Test (SPT), a high-throughput animal task designed to assess persistence under stressful conditions. In the SPT, mice are trained to escape from a water-filled tank by climbing onto a platform above the water. Training also encompasses occasional \"failure trials\", where the platform is submerged after being climbed, compelling the mice to locate and ascend a new platform. The final test consists of a 5-minute session exclusively comprising failure trials, and persistence is measured as the number of climbed platforms. Our research revealed that chronic stress diminishes performance in the SPT, an effect reversed by chronic antidepressant treatment or voluntary exercise. These findings highlight the potential of SPT for investigating persistence and exploring its role in resilience and depression. Ongoing efforts within our laboratory focus on refining the SPT to minimize stress while enhancing methodological rigor and reproducibility, notably through automation. Future research endeavors will aim to improve SPT's translational relevance by adapting the paradigm for human application, potentially leveraging virtual-reality technologies.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"415 ","pages":"Article 110352"},"PeriodicalIF":2.7,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142836948","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 : 2024-12-14DOI: 10.1016/j.jneumeth.2024.110350
Ludovic Gardy , Jonathan Curot , Luc Valton , Louis Berthier , Emmanuel J. Barbeau , Christophe Hurter
Background
Fast-ripples (FR) are short (∼10 ms) high-frequency oscillations (HFO) between 200 and 600 Hz that are helpful in epilepsy to identify the epileptogenic zone. Our aim is to propose a new method to detect FR that had to be efficient for intracerebral EEG (iEEG) recorded from both usual clinical macro-contacts (millimeter scale) and microwires (micrometer scale).
New Method
Step 1 of the detection method is based on a convolutional neural network (CNN) trained using a large database of > 11,000 FR recorded from the iEEG of 38 patients with epilepsy from both macro-contacts and microwires. The FR and non-FR events were fed to the CNN as normalized time-frequency maps. Step 2 is based on feature-based control techniques in order to reject false positives. In step 3, the human is reinstated in the decision-making process for final validation using a graphical user interface.
Results
WALFRID achieved high performance on the realistically simulated data with sensitivity up to 99.95 % and precision up to 96.51 %. The detector was able to adapt to both macro and micro-EEG recordings. The real data was used without any pre-processing step such as artefact rejection. The precision of the automatic detection was of 57.5. Step 3 helped eliminating remaining false positives in a few minutes per subject.
Comparison with Existing Methods
WALFRID performed as well or better than 6 other existing methods.
Conclusion
Since WALFRID was created to mimic the work-up of the neurologist, clinicians can easily use, understand, interpret and, if necessary, correct the output.
{"title":"Detecting fast-ripples on both micro- and macro-electrodes in epilepsy: A wavelet-based CNN detector","authors":"Ludovic Gardy , Jonathan Curot , Luc Valton , Louis Berthier , Emmanuel J. Barbeau , Christophe Hurter","doi":"10.1016/j.jneumeth.2024.110350","DOIUrl":"10.1016/j.jneumeth.2024.110350","url":null,"abstract":"<div><h3>Background</h3><div>Fast-ripples (FR) are short (∼10 ms) high-frequency oscillations (HFO) between 200 and 600 Hz that are helpful in epilepsy to identify the epileptogenic zone. Our aim is to propose a new method to detect FR that had to be efficient for intracerebral EEG (iEEG) recorded from both usual clinical macro-contacts (millimeter scale) and microwires (micrometer scale).</div></div><div><h3>New Method</h3><div>Step 1 of the detection method is based on a convolutional neural network (CNN) trained using a large database of > 11,000 FR recorded from the iEEG of 38 patients with epilepsy from both macro-contacts and microwires. The FR and non-FR events were fed to the CNN as normalized time-frequency maps. Step 2 is based on feature-based control techniques in order to reject false positives. In step 3, the human is reinstated in the decision-making process for final validation using a graphical user interface.</div></div><div><h3>Results</h3><div>WALFRID achieved high performance on the realistically simulated data with sensitivity up to 99.95 % and precision up to 96.51 %. The detector was able to adapt to both macro and micro-EEG recordings. The real data was used without any pre-processing step such as artefact rejection. The precision of the automatic detection was of 57.5. Step 3 helped eliminating remaining false positives in a few minutes per subject.</div></div><div><h3>Comparison with Existing Methods</h3><div>WALFRID performed as well or better than 6 other existing methods.</div></div><div><h3>Conclusion</h3><div>Since WALFRID was created to mimic the work-up of the neurologist, clinicians can easily use, understand, interpret and, if necessary, correct the output.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"415 ","pages":"Article 110350"},"PeriodicalIF":2.7,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142829006","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}
21st century neurology will require scalable and quantitative tools that can improve neurologic evaluations over telehealth and expand access to care. Commercially available mixed-reality headsets allow for simultaneous presentation of stimuli via holograms projected into the real world and objective and quantitative measurement of hand movement, eye movement, and phonation.
New method
We created 6 tasks designed to mimic standard neurologic assessments and administered them to a single participant via the Microsoft HoloLens 2 mixed-reality headset. The tasks assessed postural hand tremor, finger tapping, pronation and supination of hands, hand and eye tracking of a center-out task, hand and eye tracking of a random motion task, and vocal assessment.
Results
We show the utility of the HoloLens for commonly used neurological exams. First, we demonstrate that headset-derived holograms can project hand movements and objects in 3D space, providing a method to accurately and reproducibly present test stimuli to reduce test-test variability. Second, we found that participant hand movements closely matched holographic stimuli using a variety of metrics calculated on recorded movement data. Third, we showed that the HoloLens can record and playback exam tasks for visual inspection, sharing with other medical providers, and future analysis. Fourth, we showed that vocal recordings and analysis could be used to profile vocal characteristics over time. Together, this demonstrates the versatility of mixed reality headsets and possible applications for neurological assessment.
Conclusions
Administering components of the neurologic exam via a self-contained and commercially available mixed-reality headset has numerous benefits including detailed kinematic quantification, reproducible stimuli presentation from test to test, and can be self-administered expanding access to neurological care and saving hospital time and money.
{"title":"Using a mixed-reality headset to elicit and track clinically relevant movement in the clinic","authors":"Dylan Calame , Evan Lester , Phil Chiu , Lauren Seeberger","doi":"10.1016/j.jneumeth.2024.110349","DOIUrl":"10.1016/j.jneumeth.2024.110349","url":null,"abstract":"<div><h3>Background</h3><div>21st century neurology will require scalable and quantitative tools that can improve neurologic evaluations over telehealth and expand access to care. Commercially available mixed-reality headsets allow for simultaneous presentation of stimuli via holograms projected into the real world and objective and quantitative measurement of hand movement, eye movement, and phonation.</div></div><div><h3>New method</h3><div>We created 6 tasks designed to mimic standard neurologic assessments and administered them to a single participant via the Microsoft HoloLens 2 mixed-reality headset. The tasks assessed postural hand tremor, finger tapping, pronation and supination of hands, hand and eye tracking of a center-out task, hand and eye tracking of a random motion task, and vocal assessment.</div></div><div><h3>Results</h3><div>We show the utility of the HoloLens for commonly used neurological exams. First, we demonstrate that headset-derived holograms can project hand movements and objects in 3D space, providing a method to accurately and reproducibly present test stimuli to reduce test-test variability. Second, we found that participant hand movements closely matched holographic stimuli using a variety of metrics calculated on recorded movement data. Third, we showed that the HoloLens can record and playback exam tasks for visual inspection, sharing with other medical providers, and future analysis. Fourth, we showed that vocal recordings and analysis could be used to profile vocal characteristics over time. Together, this demonstrates the versatility of mixed reality headsets and possible applications for neurological assessment.</div></div><div><h3>Conclusions</h3><div>Administering components of the neurologic exam via a self-contained and commercially available mixed-reality headset has numerous benefits including detailed kinematic quantification, reproducible stimuli presentation from test to test, and can be self-administered expanding access to neurological care and saving hospital time and money.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"415 ","pages":"Article 110349"},"PeriodicalIF":2.7,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142829008","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 : 2024-12-10DOI: 10.1016/j.jneumeth.2024.110346
Alon Amir , Drew B. Headley , Mohammad M. Herzallah , Asriya Karki , Ian T. Kim , Denis Paré
Background
The neural bases of decision-making and contextual sensory discriminations have traditionally been studied in primates, highlighting the role of the prefrontal cortex in cognitive control and flexibility. With the advent of molecular tools to manipulate and monitor neuronal activity, these processes have increasingly been studied in rodents. However, rodent tasks typically consist of two-alternative forced choice paradigms that usually feature coarse sensory discriminations and no contextual dependence, limiting prefrontal involvement in task performance.
New method
To circumvent these limitations, we developed a novel contextual visual discrimination task that lends itself to rigorous psychophysical analyses. In this task, rats learn to detect left-right differences in one dimension (e.g. luminance or speed) depending on context while ignoring another (e.g. speed or luminance, respectively). Depending on trials, speed and luminance can be greater on the same side (congruent trials) or on opposite sides (incongruent trials).
Results
Rats learned the task in four phases: nose-poking and lever-pressing (∼7 days), discriminating left-right differences in one dimension (∼20 days), discriminating left-right differences in a second dimension (∼10 days), and discriminating left-right differences in one of the two dimensions depending on context (∼2.5 months). A 20:80 ratio of congruent to incongruent trials is used to prevent rats from adopting alternative strategies.
Comparison with existing methods
This task is comparable to contextual sensory discrimination tasks used in monkeys. Few equivalent tasks exist in rodents.
Conclusions
This task will allow investigators to use the full neuroscientific armamentarium to study contextual neural coding in the rat prefrontal cortex.
{"title":"Studying decision making in rats using a contextual visual discrimination task: Detection and prevention of alternative behavioral strategies","authors":"Alon Amir , Drew B. Headley , Mohammad M. Herzallah , Asriya Karki , Ian T. Kim , Denis Paré","doi":"10.1016/j.jneumeth.2024.110346","DOIUrl":"10.1016/j.jneumeth.2024.110346","url":null,"abstract":"<div><h3>Background</h3><div>The neural bases of decision-making and contextual sensory discriminations have traditionally been studied in primates, highlighting the role of the prefrontal cortex in cognitive control and flexibility. With the advent of molecular tools to manipulate and monitor neuronal activity, these processes have increasingly been studied in rodents. However, rodent tasks typically consist of two-alternative forced choice paradigms that usually feature coarse sensory discriminations and no contextual dependence, limiting prefrontal involvement in task performance.</div></div><div><h3>New method</h3><div>To circumvent these limitations, we developed a novel contextual visual discrimination task that lends itself to rigorous psychophysical analyses. In this task, rats learn to detect left-right differences in one dimension (e.g. luminance or speed) depending on context while ignoring another (e.g. speed or luminance, respectively). Depending on trials, speed and luminance can be greater on the same side (congruent trials) or on opposite sides (incongruent trials).</div></div><div><h3>Results</h3><div>Rats learned the task in four phases: nose-poking and lever-pressing (∼7 days), discriminating left-right differences in one dimension (∼20 days), discriminating left-right differences in a second dimension (∼10 days), and discriminating left-right differences in one of the two dimensions depending on context (∼2.5 months). A 20:80 ratio of congruent to incongruent trials is used to prevent rats from adopting alternative strategies.</div></div><div><h3>Comparison with existing methods</h3><div>This task is comparable to contextual sensory discrimination tasks used in monkeys. Few equivalent tasks exist in rodents.</div></div><div><h3>Conclusions</h3><div>This task will allow investigators to use the full neuroscientific armamentarium to study contextual neural coding in the rat prefrontal cortex.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"415 ","pages":"Article 110346"},"PeriodicalIF":2.7,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142818322","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 : 2024-12-07DOI: 10.1016/j.jneumeth.2024.110336
J. Pascual-Guerra , M. Torres-Rico , C.L. Paíno , J.A. Rodríguez-Navarro , A.G. García
Background
Oligodendroglial development is accompanied by increased cell complexity. A simple and cost-effective evaluation of the pro-myelinating activity of different drugs and/or treatments would be of great interest. In cultured oligodendroglia, an evaluation of the pro-myelinating activity of different drugs and/or treatments can be achieved through fractal analysis, which allows measuring cell complexity.
New Method
Fractal dimension was assessed in two O4+ cell types (neural stem cell-derived and lineage-converted adipose tissue mesenchymal cells) under proliferating or differentiating conditions.
Comparison with Existing Methods
This analysis, which was originally developed to analyze microglia, assigns a quantitative value (fractal dimension) to cellular profiles, obtaining higher coefficients as cells increase in size and arborizations instead of mRNA or protein quantification of mature oligodendroglial markers, such as MBP, MAG, O1 or PLP1/DM20.
Results
This article describes a methodology to perform fractal analysis in immunofluorescent images of O4-positive (O4+) oligodendroglia using the FracLac plugin of ImageJ software. Pro-myelinating drug Benztropine-treated O4+ cells exhibit higher fractal dimension than control group.
Conclusions
The results demonstrated the effectiveness and sensitivity of the fractal dimension coefficient provided by FracLac software to assess the effects of treatments on oligodendroglial differentiation
{"title":"Fractal analysis to assess the differentiation state of oligodendroglia in culture","authors":"J. Pascual-Guerra , M. Torres-Rico , C.L. Paíno , J.A. Rodríguez-Navarro , A.G. García","doi":"10.1016/j.jneumeth.2024.110336","DOIUrl":"10.1016/j.jneumeth.2024.110336","url":null,"abstract":"<div><h3>Background</h3><div>Oligodendroglial development is accompanied by increased cell complexity. A simple and cost-effective evaluation of the pro-myelinating activity of different drugs and/or treatments would be of great interest. In cultured oligodendroglia, an evaluation of the pro-myelinating activity of different drugs and/or treatments can be achieved through fractal analysis, which allows measuring cell complexity.</div></div><div><h3>New Method</h3><div>Fractal dimension was assessed in two O4<sup>+</sup> cell types (neural stem cell-derived and lineage-converted adipose tissue mesenchymal cells) under proliferating or differentiating conditions.</div></div><div><h3>Comparison with Existing Methods</h3><div>This analysis, which was originally developed to analyze microglia, assigns a quantitative value (fractal dimension) to cellular profiles, obtaining higher coefficients as cells increase in size and arborizations instead of mRNA or protein quantification of mature oligodendroglial markers, such as MBP, MAG, O1 or PLP1/DM20.</div></div><div><h3>Results</h3><div>This article describes a methodology to perform fractal analysis in immunofluorescent images of O4-positive (O4<sup>+</sup>) oligodendroglia using the FracLac plugin of ImageJ software. Pro-myelinating drug Benztropine-treated O4<sup>+</sup> cells exhibit higher fractal dimension than control group.</div></div><div><h3>Conclusions</h3><div>The results demonstrated the effectiveness and sensitivity of the fractal dimension coefficient provided by FracLac software to assess the effects of treatments on oligodendroglial differentiation</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"415 ","pages":"Article 110336"},"PeriodicalIF":2.7,"publicationDate":"2024-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142785993","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 : 2024-12-05DOI: 10.1016/j.jneumeth.2024.110324
A. Marasco , C.A. Lupascu , C. Tribuzi
Background:
In computational neuroscience, performance measures are essential for quantitatively assessing the predictive power of neuron models, while similarity measures are used to estimate the level of synchrony between two or more spike trains. Most of the measures proposed in the literature require setting an appropriate time-scale and often neglect silent periods.
New method:
Four time-scale adaptive performance and similarity measures are proposed and implemented in the STSimM (Spike Trains Similarity Measures) Python tool. These measures are designed to accurately capture both the precise timing of individual spikes and shared periods of inactivity among spike trains.
Results:
The proposed ST-measures demonstrate enhanced sensitivity over Spike-contrast and SPIKE-distance in detecting spike train similarity, aligning closely with SPIKE-synchronization. Correlations among all similarity measures were observed in Poisson datasets, whereas in vivo-like synaptic stimulations showed correlations only between ST-measures and SPIKE-synchronization.
Comparison of existing method:
The STSimM measures are compared with SPIKE-distance, SPIKE-synchronization and Spike-contrast using four spike train datasets with varying similarity levels.
Conclusion:
ST-measures appear more suitable for detecting both the precise timing of single spikes and shared periods of inactivity among spike trains compared to those considered in this work. Their flexibility originates from two primary factors: firstly, the inclusion of four key measures — ST-Accuracy, ST-Precision, ST-Recall, ST-Fscore — capable of discerning similarity levels across neuronal activity, whether interleaved with silent periods or solely focusing on spike timing accuracy; secondly, the integration of three model parameters that govern both precise spike detection and the weighting of silent periods.
{"title":"STSimM: A new tool for evaluating neuron model performance and detecting spike trains similarity","authors":"A. Marasco , C.A. Lupascu , C. Tribuzi","doi":"10.1016/j.jneumeth.2024.110324","DOIUrl":"10.1016/j.jneumeth.2024.110324","url":null,"abstract":"<div><h3>Background:</h3><div>In computational neuroscience, performance measures are essential for quantitatively assessing the predictive power of neuron models, while similarity measures are used to estimate the level of synchrony between two or more spike trains. Most of the measures proposed in the literature require setting an appropriate time-scale and often neglect silent periods.</div></div><div><h3>New method:</h3><div>Four time-scale adaptive performance and similarity measures are proposed and implemented in the <em>STSimM</em> (Spike Trains Similarity Measures) Python tool. These measures are designed to accurately capture both the precise timing of individual spikes and shared periods of inactivity among spike trains.</div></div><div><h3>Results:</h3><div>The proposed ST-measures demonstrate enhanced sensitivity over <em>Spike-contrast</em> and <em>SPIKE-distance</em> in detecting spike train similarity, aligning closely with <em>SPIKE-synchronization</em>. Correlations among all similarity measures were observed in Poisson datasets, whereas in vivo-like synaptic stimulations showed correlations only between ST-measures and SPIKE-synchronization.</div></div><div><h3>Comparison of existing method:</h3><div>The <em>STSimM</em> measures are compared with <em>SPIKE-distance</em>, SPIKE-synchronization and <em>Spike-contrast</em> using four spike train datasets with varying similarity levels.</div></div><div><h3>Conclusion:</h3><div>ST-measures appear more suitable for detecting both the precise timing of single spikes and shared periods of inactivity among spike trains compared to those considered in this work. Their flexibility originates from two primary factors: firstly, the inclusion of four key measures — ST-Accuracy, ST-Precision, ST-Recall, ST-Fscore — capable of discerning similarity levels across neuronal activity, whether interleaved with silent periods or solely focusing on spike timing accuracy; secondly, the integration of three model parameters that govern both precise spike detection and the weighting of silent periods.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"415 ","pages":"Article 110324"},"PeriodicalIF":2.7,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142791828","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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