Journal of Neuroscience Methods最新文献

{"title":"Identifying epileptogenic abnormality by decomposing intracranial EEG and MEG power spectra","authors":"Csaba Kozma ,&nbsp;Gabrielle Schroeder ,&nbsp;Tom Owen ,&nbsp;Jane de Tisi ,&nbsp;Andrew W. McEvoy ,&nbsp;Anna Miserocchi ,&nbsp;John Duncan ,&nbsp;Yujiang Wang ,&nbsp;Peter N. Taylor","doi":"10.1016/j.jneumeth.2024.110180","DOIUrl":"https://doi.org/10.1016/j.jneumeth.2024.110180","url":null,"abstract":"<div><h3>Background</h3><p>Accurate identification of abnormal electroencephalographic (EEG) activity is pivotal for diagnosing and treating epilepsy. Recent studies indicate that decomposing brain activity into periodic (oscillatory) and aperiodic (trend across all frequencies) components can illuminate the drivers of spectral activity changes.</p></div><div><h3>New methods</h3><p>We analysed intracranial EEG (iEEG) data from 234 subjects, creating a normative map. This map was compared to a cohort of 63 patients with refractory focal epilepsy under consideration for neurosurgery. The normative map was computed using three approaches: (i) relative complete band power, (ii) relative band power with the aperiodic component removed, and (iii) the aperiodic exponent. Abnormalities were calculated for each approach in the patient cohort. We evaluated the spatial profiles, assessed their ability to localize abnormalities, and replicated the findings using magnetoencephalography (MEG).</p></div><div><h3>Results</h3><p>Normative maps of relative complete band power and relative periodic band power exhibited similar spatial profiles, while the aperiodic normative map revealed higher exponent values in the temporal lobe. Abnormalities estimated through complete band power effectively distinguished between good and bad outcome patients. Combining periodic and aperiodic abnormalities enhanced performance, like the complete band power approach.</p></div><div><h3>Comparison with existing methods and conclusions</h3><p>Sparing cerebral tissue with abnormalities in both periodic and aperiodic activity may result in poor surgical outcomes. Both periodic and aperiodic components do not carry sufficient information in isolation. The relative complete band power solution proved to be the most reliable method for this purpose. Future studies could investigate how cerebral location or pathology influences periodic or aperiodic abnormalities.</p></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0165027024001250/pdfft?md5=f0e39cb32daedf6874d3ccd7020516d7&pid=1-s2.0-S0165027024001250-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141095203","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":"Multi-scale self-attention approach for analysing motor imagery signals in brain-computer interfaces","authors":"Mohammed Wasim Bhatt,&nbsp;Sparsh Sharma","doi":"10.1016/j.jneumeth.2024.110182","DOIUrl":"10.1016/j.jneumeth.2024.110182","url":null,"abstract":"<div><h3>Background</h3><p>Motor imagery-based electroencephalogram (EEG) brain-computer interface (BCI) technology has seen tremendous advancements in the past several years. Deep learning has outperformed more traditional approaches, such next-gen neuro-technologies, in terms of productivity. It is still challenging to develop and train an end-to-end network that can sufficiently extract the possible characteristics from EEG data used in motor imaging. Brain-computer interface research is largely reliant on the fundamental problem of accurately classifying EEG data. There are still many challenges in the field of MI classification even after researchers have proposed a variety of methods, such as deep learning and machine learning techniques.</p></div><div><h3>Methodology</h3><p>We provide a model for four-class categorization of motor imagery EEG signals using attention mechanisms: left hand, right hand, foot, and tongue/rest. The model is built on multi-scale spatiotemporal self-attention networks. To determine the most effective channels, self-attention networks are implemented spatially to assign greater weight to channels associated with motion and lesser weight to channels unrelated to motion. To eliminate noise in the temporal domain, parallel multi-scale Temporal Convolutional Network (TCN) layers are utilized to extract temporal domain features at various scales.</p></div><div><h3>Result</h3><p>On the IV-2b dataset from the BCI Competition, the suggested model achieved an accuracy of 85.09 %; on the IV-2a and IV-2b datasets from the HGD datasets, it was 96.26 %.</p></div><div><h3>Comparison with existing methods</h3><p>In single-subject classification, this approach demonstrates superior accuracy when compared to existing methods.</p></div><div><h3>Conclusion</h3><p>The findings suggest that this approach exhibits commendable performance, resilience, and capacity for transfer learning.</p></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141138161","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":"Peaglet: A user-friendly probabilistic Kernel density estimation of intracranial cortical and subcortical stimulation sites","authors":"Andrea Bellacicca ,&nbsp;Marco Rossi ,&nbsp;Luca Viganò ,&nbsp;Luciano Simone ,&nbsp;Henrietta Howells ,&nbsp;Matteo Gambaretti ,&nbsp;Alberto Gallotti ,&nbsp;Antonella Leonetti ,&nbsp;Guglielmo Puglisi ,&nbsp;Francesca Talami ,&nbsp;Lorenzo Bello ,&nbsp;Cerri Gabriella ,&nbsp;Luca Fornia","doi":"10.1016/j.jneumeth.2024.110177","DOIUrl":"10.1016/j.jneumeth.2024.110177","url":null,"abstract":"<div><h3>Background</h3><p>Data on human brain function obtained with direct electrical stimulation (DES) in neurosurgical patients have been recently integrated and combined with modern neuroimaging techniques, allowing a connectome-based approach fed by intraoperative DES data. Within this framework is crucial to develop reliable methods for spatial localization of DES-derived information to be integrated within the neuroimaging workflow.</p></div><div><h3>New method</h3><p>To this aim, we applied the Kernel Density Estimation for modelling the distribution of DES sites from different patients into the MNI space. The algorithm has been embedded in a MATLAB-based User Interface, <em>Peaglet</em>. It allows an accurate probabilistic weighted and unweighted estimation of DES sites location both at cortical level, by using shortest path calculation along the brain 3D geometric topology, and subcortical level, by using a volume-based approach.</p></div><div><h3>Results</h3><p>We applied <em>Peaglet</em> to investigate spatial estimation of cortical and subcortical stimulation sites provided by recent brain tumour studies. The resulting NIfTI maps have been anatomically investigated with neuroimaging open-source tools.</p></div><div><h3>Comparison with existing methods</h3><p>Peaglet processes differently cortical and subcortical data following their distinguishing geometrical features, increasing anatomical specificity of DES-related results and their reliability within neuroimaging environments.</p></div><div><h3>Conclusions</h3><p><em>Peaglet</em> provides a robust probabilistic estimation of the cortical and subcortical distribution of DES sites going beyond a region of interest approach, respecting cortical and subcortical intrinsic geometrical features. Results can be easily integrated within the neuroimaging workflow to drive connectomic analysis.</p></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0165027024001225/pdfft?md5=ac61476d89b0f99cef01c77a32a87349&pid=1-s2.0-S0165027024001225-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141131446","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":"A method to find temporal structure of neuronal coactivity patterns with across-trial correlations","authors":"Duho Sihn,&nbsp;Soyoung Chae,&nbsp;Sung-Phil Kim","doi":"10.1016/j.jneumeth.2024.110172","DOIUrl":"10.1016/j.jneumeth.2024.110172","url":null,"abstract":"<div><h3>Background</h3><p>The across-trial correlation of neurons' coactivity patterns emerges to be important for information coding, but methods for finding their temporal structures remain largely unexplored.</p></div><div><h3>New method</h3><p>In the present study, we propose a method to find time clusters in which coactivity patterns of neurons are correlated across trials. We transform the multidimensional neural activity at each timing into a coactivity pattern of binary states, and predict the coactivity patterns at different timings. We devise a method suitable for these coactivity pattern predictions, call general event prediction. Cross-temporal prediction accuracy is then used to estimate across-trial correlations between coactivity patterns at two timings. We extract time clusters from the cross-temporal prediction accuracy by a modified k-means algorithm.</p></div><div><h3>Results</h3><p>The feasibility of the proposed method is verified through simulations based on ground truth. We apply the proposed method to a calcium imaging dataset recorded from the motor cortex of mice, and demonstrate time clusters of motor cortical coactivity patterns during a motor task.</p></div><div><h3>Comparison with existing methods</h3><p>While the existing cosine similarity method, which does not account for across-trial correlation, shows temporal structures only for contralateral neural responses, the proposed method reveals those for both contralateral and ipsilateral neural responses, demonstrating the effect of across-trial correlations.</p></div><div><h3>Conclusions</h3><p>This study introduces a novel method for measuring the temporal structure of neuronal ensemble activity.</p></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141087507","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":"The use of happy faces as visual stimuli improves the performance of the hybrid SSVEP+P300 brain computer interface","authors":"Deepak D. Kapgate","doi":"10.1016/j.jneumeth.2024.110170","DOIUrl":"10.1016/j.jneumeth.2024.110170","url":null,"abstract":"<div><h3>Background</h3><p>This study illustrates a hybrid brain-computer interface (BCI) in which steady-state visual evoked potentials (SSVEP) and event-related potentials (P300) are evoked simultaneously. The goal of this study was to improve the performance of the current hybrid SSVEP+P300 BCI systems by incorporating a happy face into visual stimuli.</p></div><div><h3>New method</h3><p>In this study, happy and sad faces were added to a visual stimulus to induce stronger cortical signals in a hybrid SSVEP+P300 BCI. Additionally, we developed a paradigm in which SSVEP responses were triggered by non-face stimuli, whereas P300 responses were triggered by face stimuli. We tested four paradigms: happy face paradigm (HF), sad face paradigm (SF), happy face and flicker paradigm (HFF), and sad face and flicker paradigm (SFF).</p></div><div><h3>Results and conclusions</h3><p>The results demonstrated that the HFF paradigm elicited more robust cortical responses, which resulted in enhanced system accuracy and information transfer rate (ITR). The HFF paradigm has a system communication rate of 25.9 bits per second and an average accuracy of 96.1%. Compared with other paradigms, the HFF paradigm is the best choice for BCI applications because it has the highest ITR and maximum level of comfort.</p></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141087516","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":"Comparison of electrical microstimulation artifact removal methods for high-channel-count prostheses","authors":"Feng Wang ,&nbsp;Xing Chen ,&nbsp;Pieter R. Roelfsema","doi":"10.1016/j.jneumeth.2024.110169","DOIUrl":"10.1016/j.jneumeth.2024.110169","url":null,"abstract":"<div><h3>Background</h3><p>Neuroprostheses are used to electrically stimulate the brain, modulate neural activity and restore sensory and motor function following injury or disease, such as blindness, paralysis, and other movement and psychiatric disorders. Recordings are often made simultaneously with stimulation, allowing the monitoring of neural signals and closed-loop control of devices. However, stimulation-evoked artifacts may obscure neural activity, particularly when stimulation and recording sites are nearby. Several methods have been developed to remove stimulation artifacts, but it remains challenging to validate and compare these methods because the ‘ground-truth’ of the neuronal signals may be contaminated by artifacts.</p></div><div><h3>New method</h3><p>Here, we delivered stimulation to the visual cortex via a high-channel-count prosthesis while recording neuronal activity and stimulation artifacts. We quantified the waveforms and temporal properties of stimulation artifacts from the cortical visual prosthesis (CVP) and used them to build a dataset, in which we simulated the neuronal activity and the stimulation artifacts. We illustrate how to use the simulated data to evaluate the performance of six software-based artifact removal methods (Template subtraction, Linear interpolation, Polynomial fitting, Exponential fitting, SALPA and ERAASR) in a CVP application scenario.</p></div><div><h3>Results</h3><p>We here focused on stimulation artifacts caused by electrical stimulation through a high-channel-count cortical prosthesis device. We find that the Polynomial fitting and Exponential fitting methods outperform the other methods in recovering spikes and multi-unit activity. Linear interpolation and Template subtraction recovered the local-field potentials.</p></div><div><h3>Conclusion</h3><p>Polynomial fitting and Exponential fitting provided a good trade-off between the quality of the recovery of spikes and multi-unit activity (MUA) and the computational complexity for a cortical prosthesis.</p></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0165027024001146/pdfft?md5=2cda31b969a2dc8c65f74e979d177934&pid=1-s2.0-S0165027024001146-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141087513","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":"A chinrest-based approach to measure eye movements and experimental task engagement in macaques with minimal restraint","authors":"Samy Rima ,&nbsp;Jennifer Greilsamer ,&nbsp;Marcus Haag ,&nbsp;Jaime Cadena-Valencia ,&nbsp;Morgan Sansonnens ,&nbsp;Andrea Francovich ,&nbsp;Florian Lanz ,&nbsp;Andrina Zbinden ,&nbsp;Alessandra Bergadano ,&nbsp;Michael Christopher Schmid","doi":"10.1016/j.jneumeth.2024.110173","DOIUrl":"10.1016/j.jneumeth.2024.110173","url":null,"abstract":"<div><h3>Background</h3><p>The use of Rhesus macaques in vision research is crucial due to their visual system's similarity to humans. While invasive techniques have been the norm, there has been a shift towards non-invasive methods, such as facemasks and head molds, to enhance animal welfare and address ethical concerns.</p></div><div><h3>New Method</h3><p>We present a non-invasive, 3D-printed chinrest with infrared sensors, adapted from canine research, allowing for accurate eye movement measurements and voluntary animal participation in experiments.</p></div><div><h3>Results</h3><p>The chinrest method showed a 16% and 28% increase in average trial numbers for Monkey 1 and Monkey 2, respectively, compared to the traditional headpost method. The engagement was high, with monkeys performing over 500 trials per session and initiating a new trial after an average intertrial interval of approximately 1 second. The hit rate improved by about 10% for Monkey 1 in the chinrest condition, and the fixation precision, measured by the standard deviation of gaze positions, was significantly better in the chinrest condition, with Monkey 1 showing a reduction in fixation imprecision from 0.26° to 0.17° in the X-axis.</p></div><div><h3>Comparison with Existing Methods</h3><p>The chinrest approach showed significant improvements in trial engagement and reduction in aborted trials due to fixation breaks, indicating less stress and potentially improved data quality compared to previous non-invasive methods.</p></div><div><h3>Conclusions</h3><p>The chinrest method offers a significant advancement in primate cognitive testing by allowing for precise data collection while addressing animal welfare concerns, possibly leading to better scientific outcomes and a paradigm shift in primate research methodologies.</p></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0165027024001183/pdfft?md5=f9479249950af5a07a010cc924e81e47&pid=1-s2.0-S0165027024001183-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141087505","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":"Large field-of-view metabolic profiling of murine brain tissue following morphine incubation using label-free multiphoton microscopy","authors":"Carlos A. Renteria ,&nbsp;Jaena Park ,&nbsp;Chi Zhang ,&nbsp;Janet E. Sorrells ,&nbsp;Rishyashring R. Iyer ,&nbsp;Kayvan F. Tehrani ,&nbsp;Alejandro De la Cadena ,&nbsp;Stephen A. Boppart","doi":"10.1016/j.jneumeth.2024.110171","DOIUrl":"10.1016/j.jneumeth.2024.110171","url":null,"abstract":"<div><h3>Background</h3><p>Although the effects on neural activation and glucose consumption caused by opiates such as morphine are known, the metabolic machinery underlying opioid use and misuse is not fully explored. Multiphoton microscopy (MPM) techniques have been developed for optical imaging at high spatial resolution. Despite the increased use of MPM for neural imaging, the use of intrinsic optical contrast has seen minimal use in neuroscience.</p></div><div><h3>New Method</h3><p>We present a label-free, multimodal microscopy technique for metabolic profiling of murine brain tissue following incubation with morphine sulfate (MSO₄). We evaluate two- and three-photon excited autofluorescence, and second and third harmonic generation to determine meaningful intrinsic contrast mechanisms in brain tissue using simultaneous label-free, autofluorescence multi-harmonic (SLAM) microscopy.</p></div><div><h3>Results</h3><p>Regional differences quantified in the cortex, caudate, and thalamus of the brain demonstrate region-specific changes to metabolic profiles measured from FAD intensity, along with brain-wide quantification. While the overall intensity of FAD signal significantly decreased after morphine incubation, this metabolic molecule accumulated near the nucleus accumbens.</p></div><div><h3>Comparison with existing methods</h3><p>Histopathology requires tissue fixation and staining to determine cell type and morphology, lacking information about cellular metabolism. Tools such as fMRI or PET imaging have been widely used, but lack cellular resolution. SLAM microscopy obviates the need for tissue preparation, permitting immediate use and imaging of tissue with subcellular resolution in its native environment.</p></div><div><h3>Conclusions</h3><p>This study demonstrates the utility of SLAM microscopy for label-free investigations of neural metabolism, especially the intensity changes in FAD autofluorescence and structural morphology from third-harmonic generation.</p></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S016502702400116X/pdfft?md5=2d51afba473e229958c69d7c1d4d1137&pid=1-s2.0-S016502702400116X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141081493","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":"“Counting sheep PSG”: EEGLAB-compatible open-source matlab software for signal processing, visualization, event marking and staging of polysomnographic data","authors":"L.B. Ray ,&nbsp;D. Baena ,&nbsp;S.M. Fogel","doi":"10.1016/j.jneumeth.2024.110162","DOIUrl":"10.1016/j.jneumeth.2024.110162","url":null,"abstract":"<div><h3>Background</h3><p>Progress in advancing sleep research employing polysomnography (PSG) has been negatively impacted by the limited availability of widely available, open-source sleep-specific analysis tools.</p></div><div><h3>New method</h3><p>Here, we introduce <em>Counting Sheep PSG</em>, an EEGLAB-compatible software for signal processing, visualization, event marking and manual sleep stage scoring of PSG data for MATLAB.</p></div><div><h3>Results</h3><p>Key features include: (1) signal processing tools including bad channel interpolation, down-sampling, re-referencing, filtering, independent component analysis, artifact subspace reconstruction, and power spectral analysis, (2) customizable display of polysomnographic data and hypnogram, (3) event marking mode including manual sleep stage scoring, (4) automatic event detections including movement artifact, sleep spindles, slow waves and eye movements, and (5) export of main descriptive sleep architecture statistics, event statistics and publication-ready hypnogram.</p></div><div><h3>Comparison with existing methods</h3><p><em>Counting Sheep PSG</em> was built on the foundation created by sleepSMG (<span>https://sleepsmg.sourceforge.net/</span><svg><path></path></svg>). The scope and functionalities of the current software have made significant advancements in terms of EEGLAB integration/compatibility, preprocessing, artifact correction, event detection, functionality and ease of use. By comparison, commercial software can be costly and utilize proprietary data formats and algorithms, thereby restricting the ability to distribute and share data and analysis results.</p></div><div><h3>Conclusions</h3><p>The field of sleep research remains shackled by an industry that resists standardization, prevents interoperability, builds-in planned obsolescence, maintains proprietary black-box data formats and analysis approaches. This presents a major challenge for the field of sleep research. The need for free, open-source software that can read open-format data is essential for scientific advancement to be made in the field.</p></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140916807","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":"Simultaneous invasive and non-invasive recordings in humans: A novel Rosetta stone for deciphering brain activity","authors":"Andrea Pigorini ,&nbsp;Pietro Avanzini ,&nbsp;Andrei Barborica ,&nbsp;Christian-G. Bénar ,&nbsp;Olivier David ,&nbsp;Michele Farisco ,&nbsp;Corey J. Keller ,&nbsp;Alfredo Manfridi ,&nbsp;Ezequiel Mikulan ,&nbsp;Angelique C. Paulk ,&nbsp;Nicolas Roehri ,&nbsp;Ajay Subramanian ,&nbsp;Serge Vulliémoz ,&nbsp;Rina Zelmann","doi":"10.1016/j.jneumeth.2024.110160","DOIUrl":"10.1016/j.jneumeth.2024.110160","url":null,"abstract":"<div><p>Simultaneous noninvasive and invasive electrophysiological recordings provide a unique opportunity to achieve a comprehensive understanding of human brain activity, much like a Rosetta stone for human neuroscience. In this review we focus on the increasingly-used powerful combination of intracranial electroencephalography (iEEG) with scalp electroencephalography (EEG) or magnetoencephalography (MEG). We first provide practical insight on how to achieve these technically challenging recordings. We then provide examples from clinical research on how simultaneous recordings are advancing our understanding of epilepsy. This is followed by the illustration of how human neuroscience and methodological advances could benefit from these simultaneous recordings. We conclude with a call for open data sharing and collaboration, while ensuring neuroethical approaches and argue that only with a true collaborative approach the promises of simultaneous recordings will be fulfilled.</p></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0165027024001055/pdfft?md5=5286fb4e0723b6777588ecf860c9676f&pid=1-s2.0-S0165027024001055-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140908991","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}