Journal of Neuroscience Methods最新文献

{"title":"A new spatial contrast coding approach for SSVEP-based BCIs","authors":"Hui Zhong ,&nbsp;Gege Ming ,&nbsp;Weihua Pei ,&nbsp;Xiaorong Gao ,&nbsp;Yijun Wang","doi":"10.1016/j.jneumeth.2024.110357","DOIUrl":"10.1016/j.jneumeth.2024.110357","url":null,"abstract":"<div><h3>Background</h3><div>Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems mainly adopt the frequency, phase, and hybrid coding approaches in previous studies. This study proposes a new encoding approach based on spatial contrast, which is one of the spatial properties of visual stimuli.</div></div><div><h3>New method</h3><div>First, this study designed checkerboard-like stimuli with 11 kinds of background contrast to explore the effect of background contrast on stimulus-response characteristics of SSVEPs. Based on the spatial contrast related modulation of responses, this study conducted offline simulations to evaluate the feasibility of a multi-target contrast coding approach. Finally, this study designed a four-target SSVEP-BCI system to demonstrate the contrast coding approach.</div></div><div><h3>Results</h3><div>Checkerboard-like stimuli with the same frequency and initial phase but different background contrasts have different SSVEP responses in terms of amplitude, topography, and phase. Taking advantage of the characteristics, both offline simulations and online verifications indicated that the proposed BCI system achieved good classification performance. Online BCI experiments found that the four-target SSVEP-BCI system achieved averaged information transfer rates of 59.58 ± 0.42 bits/min at the 15 Hz condition and 52.54 ± 2.32 bits/min at the 30 Hz condition, respectively.</div></div><div><h3>Comparison with existing method</h3><div>Different from previous frequency, phase, and spatial coding approaches, this study adopts a background contrast-based coding approach to achieve a four-target BCI system.</div></div><div><h3>Conclusion</h3><div>This study proposes a new spatial contrast coding approach, which will enrich the encoding approach of the SSVEP-BCI systems and promote the applications of the SSVEP-BCI systems in more scenarios.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"415 ","pages":"Article 110357"},"PeriodicalIF":2.7,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimation of fiber orientation distributions in superficial white matter using an asymmetric constrained spherical deconvolution method","authors":"Jingxin Meng,&nbsp;Jianglin He,&nbsp;Yuanjun Wang","doi":"10.1016/j.jneumeth.2024.110353","DOIUrl":"10.1016/j.jneumeth.2024.110353","url":null,"abstract":"<div><h3>Background</h3><div>Superficial white matter is an important component of white matter. Estimation of fiber orientation distributions based on diffusion magnetic resonance imaging is a critical step in white matter tractography imaging. However, due to the complex structure of superficial white matter, existing models for estimating fiber orientation distributions are ineffective in reconstructing superficial white matter and even reconstruct incorrect orientation distributions.</div></div><div><h3>New method</h3><div>In this paper, we improve the traditional constrained spherical deconvolution method and propose a novel asymmetric constrained spherical deconvolution method. The method takes into account that the displacement profile of the water molecules in brain tissue are non-Gaussian diffusion and the core parameter kurtosis might characterize tissue structure better than diffusivity coefficients. So diffusion kurtosis imaging model is used to estimate the white matter response function. The proposed method applies the diffusion kurtosis imaging model response function to the asymmetric fiber orientation distributions, and this is the first attempt to obtain more accurate fiber orientation distributions. Furthermore, the Gaussian-Distribution distance weight and Watson-Distribution angle weight are used for asymmetric regularization.</div></div><div><h3>Results</h3><div>We evaluate the method using FiberCup phantom, ISMRM 2015 data and in vivo data provided CHCP dataset. The results show that our proposed method can more accurately reconstruct the complex fiber structure of superficial white matter with more accurate fiber orientation, fewer pseudo-peaks, and mitigate gyral bias.</div></div><div><h3>Comparison with existing methods</h3><div>Our proposed method has higher accuracy in estimating the fiber orientation distributions and can reconstruct highly curved fiber voxels.</div></div><div><h3>Conclusion</h3><div>This proposed method provides new insights into the estimation of the orientation distribution of superficial white matter fibers.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"415 ","pages":"Article 110353"},"PeriodicalIF":2.7,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Periodicity-based multi-dimensional interaction convolution network with multi-scale feature fusion for motor imagery EEG classification","authors":"Yunshuo Dai,&nbsp;Xiao Deng,&nbsp;Xiuli Fu,&nbsp;Yixin Zhao","doi":"10.1016/j.jneumeth.2024.110356","DOIUrl":"10.1016/j.jneumeth.2024.110356","url":null,"abstract":"<div><h3>Background</h3><div>The Motor Imagery (MI)-based Brain-Computer Interface (BCI) has vast potential in fields such as medical rehabilitation and control engineering. In recent years, MI decoding methods based on deep learning have gained extensive attention. However, capturing the complex dynamic changes in EEG signals remains a challenge, and the decoding performance still needs further improvement.</div></div><div><h3>New methods</h3><div>The paper proposes a novel method, Periodicity-based Multi-Dimensional Interaction Convolution Network with Multi-Scale Feature Fusion (PMD-MSNet), for MI-EEG signal classification. It converts 1D EEG signals into multi-period 2D tensors to capture intra-period and inter-period variations and enables cross-dimensional interaction based on periodic features. Subsequently, parallel multi-scale convolution is utilized to adaptively extract temporal, frequency, and time-frequency features.</div></div><div><h3>Results</h3><div>Experimental results on the BCI IV-2a dataset demonstrate that the PMD-MSNet model achieves a classification accuracy of 82.25 % on average and a kappa value of 0.763, which significantly outperforms seven other deep learning-based EEG decoding models. The model attained the highest classification accuracy and kappa value among the seven subjects, showcasing its superior performance and robustness.</div></div><div><h3>Conclusions</h3><div>The PMD-MSNet model incorporates periodic features, multi-dimensional interaction mechanisms, multi-scale convolutions to achieve efficient feature extraction and classification of EEG signals, significantly enhancing the performance of MI classification tasks.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"415 ","pages":"Article 110356"},"PeriodicalIF":2.7,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143176075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human pluripotent stem cell-derived microglia shape neuronal morphology and enhance network activity in vitro","authors":"L.M.L. Kok ,&nbsp;K. Helwegen ,&nbsp;N.F. Coveña ,&nbsp;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}

{"title":"IDyOMpy: A new Python-based model for the statistical analysis of musical expectations","authors":"Guilhem Marion ,&nbsp;Fei Gao ,&nbsp;Benjamin P. Gold ,&nbsp;Giovanni M. Di Liberto ,&nbsp;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}

{"title":"SSSort 2.0: A semi-automated spike detection and sorting system for single sensillum recordings","authors":"Lydia Ellison ,&nbsp;Georg Raiser ,&nbsp;Alicia Garrido-Peña ,&nbsp;György Kemenes ,&nbsp;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}

{"title":"Generation and validation of a D1 dopamine receptor Flpo knock-in mouse","authors":"Alexis M. Oppman ,&nbsp;William J. Paradee ,&nbsp;Nandakumar S. Narayanan ,&nbsp;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}

{"title":"Exploring persistence in animal models: The sinking platform test","authors":"Caterina Branca ,&nbsp;Giulia Braccagni ,&nbsp;Dario Finardi ,&nbsp;Eleonora Corridori ,&nbsp;Sara Salviati ,&nbsp;Simona Scheggi ,&nbsp;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}

{"title":"Detecting fast-ripples on both micro- and macro-electrodes in epilepsy: A wavelet-based CNN detector","authors":"Ludovic Gardy ,&nbsp;Jonathan Curot ,&nbsp;Luc Valton ,&nbsp;Louis Berthier ,&nbsp;Emmanuel J. Barbeau ,&nbsp;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 &gt; 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}

{"title":"Using a mixed-reality headset to elicit and track clinically relevant movement in the clinic","authors":"Dylan Calame ,&nbsp;Evan Lester ,&nbsp;Phil Chiu ,&nbsp;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}