Frontiers in Neuroinformatics最新文献

{"title":"PyDapsys: an open-source library for accessing electrophysiology data recorded with DAPSYS.","authors":"Peter Konradi,&nbsp;Alina Troglio,&nbsp;Ariadna Pérez Garriga,&nbsp;Aarón Pérez Martín,&nbsp;Rainer Röhrig,&nbsp;Barbara Namer,&nbsp;Ekaterina Kutafina","doi":"10.3389/fninf.2023.1250260","DOIUrl":"https://doi.org/10.3389/fninf.2023.1250260","url":null,"abstract":"<p><p>In the field of neuroscience, a considerable number of commercial data acquisition and processing solutions rely on proprietary formats for data storage. This often leads to data being locked up in formats that are only accessible by using the original software, which may lead to interoperability problems. In fact, even the loss of data access is possible if the software becomes unsupported, changed, or otherwise unavailable. To ensure FAIR data management, strategies should be established to enable long-term, independent, and unified access to data in proprietary formats. In this work, we demonstrate PyDapsys, a solution to gain open access to data that was acquired using the proprietary recording system DAPSYS. PyDapsys enables us to open the recorded files directly in Python and saves them as NIX files, commonly used for open research in the electrophysiology domain. Thus, PyDapsys secures efficient and open access to existing and prospective data. The manuscript demonstrates the complete process of reverse engineering a proprietary electrophysiological format on the example of microneurography data collected for studies on pain and itch signaling in peripheral neural fibers.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"17 ","pages":"1250260"},"PeriodicalIF":3.5,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10539619/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41171758","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":"NeuroBridge: a prototype platform for discovery of the long-tail neuroimaging data.","authors":"Lei Wang,&nbsp;José Luis Ambite,&nbsp;Abhishek Appaji,&nbsp;Janine Bijsterbosch,&nbsp;Jerome Dockes,&nbsp;Rick Herrick,&nbsp;Alex Kogan,&nbsp;Howard Lander,&nbsp;Daniel Marcus,&nbsp;Stephen M Moore,&nbsp;Jean-Baptiste Poline,&nbsp;Arcot Rajasekar,&nbsp;Satya S Sahoo,&nbsp;Matthew D Turner,&nbsp;Xiaochen Wang,&nbsp;Yue Wang,&nbsp;Jessica A Turner","doi":"10.3389/fninf.2023.1215261","DOIUrl":"10.3389/fninf.2023.1215261","url":null,"abstract":"<p><strong>Introduction: </strong>Open science initiatives have enabled sharing of large amounts of already collected data. However, significant gaps remain regarding how to find appropriate data, including underutilized data that exist in the long tail of science. We demonstrate the NeuroBridge prototype and its ability to search PubMed Central full-text papers for information relevant to neuroimaging data collected from schizophrenia and addiction studies.</p><p><strong>Methods: </strong>The NeuroBridge architecture contained the following components: (1) Extensible ontology for modeling study metadata: subject population, imaging techniques, and relevant behavioral, cognitive, or clinical data. Details are described in the companion paper in this special issue; (2) A natural-language based document processor that leveraged pre-trained deep-learning models on a small-sample document corpus to establish efficient representations for each article as a collection of machine-recognized ontological terms; (3) Integrated search using ontology-driven similarity to query PubMed Central and NeuroQuery, which provides fMRI activation maps along with PubMed source articles.</p><p><strong>Results: </strong>The NeuroBridge prototype contains a corpus of 356 papers from 2018 to 2021 describing schizophrenia and addiction neuroimaging studies, of which 186 were annotated with the NeuroBridge ontology. The search portal on the NeuroBridge website https://neurobridges.org/ provides an interactive Query Builder, where the user builds queries by selecting NeuroBridge ontology terms to preserve the ontology tree structure. For each return entry, links to the PubMed abstract as well as to the PMC full-text article, if available, are presented. For each of the returned articles, we provide a list of clinical assessments described in the Section \"Methods\" of the article. Articles returned from NeuroQuery based on the same search are also presented.</p><p><strong>Conclusion: </strong>The NeuroBridge prototype combines ontology-based search with natural-language text-mining approaches to demonstrate that papers relevant to a user's research question can be identified. The NeuroBridge prototype takes a first step toward identifying potential neuroimaging data described in full-text papers. Toward the overall goal of discovering \"enough data of the right kind,\" ongoing work includes validating the document processor with a larger corpus, extending the ontology to include detailed imaging data, and extracting information regarding data availability from the returned publications and incorporating XNAT-based neuroimaging databases to enhance data accessibility.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"17 ","pages":"1215261"},"PeriodicalIF":3.5,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10500076/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10291328","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":"Time to consider animal data governance: perspectives from neuroscience.","authors":"Damian Eke, George Ogoh, William Knight, Bernd Stahl","doi":"10.3389/fninf.2023.1233121","DOIUrl":"10.3389/fninf.2023.1233121","url":null,"abstract":"<p><strong>Introduction: </strong>Scientific research relies mainly on multimodal, multidimensional big data generated from both animal and human organisms as well as technical data. However, unlike human data that is increasingly regulated at national, regional and international levels, regulatory frameworks that can govern the sharing and reuse of non-human animal data are yet to be established. Whereas the legal and ethical principles that shape animal data generation in many countries and regions differ, the generated data are shared beyond boundaries without any governance mechanism. This paper, through perspectives from neuroscience, shows conceptually and empirically that there is a need for animal data governance that is informed by ethical concerns. There is a plurality of ethical views on the use of animals in scientific research that data governance mechanisms need to consider.</p><p><strong>Methods: </strong>Semi-structured interviews were used for data collection. Overall, 13 interviews with 12 participants (10 males and 2 females) were conducted. The interviews were transcribed and stored in NviVo 12 where they were thematically analyzed.</p><p><strong>Results: </strong>The participants shared the view that it is time to consider animal data governance due to factors such as differences in regulations, differences in ethical principles, values and beliefs and data quality concerns. They also provided insights on possible approaches to governance.</p><p><strong>Discussion: </strong>We therefore conclude that a procedural approach to data governance is needed: an approach that does not prescribe a particular ethical position but allows for a quick understanding of ethical concerns and debate about how different positions differ to facilitate cross-cultural and international collaboration.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"17 ","pages":"1233121"},"PeriodicalIF":2.5,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10497762/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10260661","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":"Effect of subthalamic coordinated reset deep brain stimulation on Parkinsonian gait.","authors":"Kai M Bosley, Ziling Luo, Sana Amoozegar, Kit Acedillo, Kanon Nakajima, Luke A Johnson, Jerrold L Vitek, Jing Wang","doi":"10.3389/fninf.2023.1185723","DOIUrl":"10.3389/fninf.2023.1185723","url":null,"abstract":"<p><strong>Introduction: </strong>Coordinated Reset Deep Brain Stimulation (CR DBS) is a novel DBS approach for treating Parkinson's disease (PD) that uses lower levels of burst stimulation through multiple contacts of the DBS lead. Though CR DBS has been demonstrated to have sustained therapeutic effects on rigidity, tremor, bradykinesia, and akinesia following cessation of stimulation, i.e., carryover effect, its effect on Parkinsonian gait has not been well studied. Impaired gait is a disabling symptom of PD, often associated with a higher risk of falling and a reduced quality of life. The goal of this study was to explore the carryover effect of subthalamic CR DBS on Parkinsonian gait.</p><p><strong>Methods: </strong>Three non-human primates (NHPs) were rendered Parkinsonian and implanted with a DBS lead in the subthalamic nucleus (STN). For each animal, STN CR DBS was delivered for several hours per day across five consecutive days. A clinical rating scale modified for NHP use (mUPDRS) was administered every morning to monitor the carryover effect of CR DBS on rigidity, tremor, akinesia, and bradykinesia. Gait was assessed quantitatively before and after STN CR DBS. The stride length and swing speed were calculated and compared to the baseline, pre-stimulation condition.</p><p><strong>Results: </strong>In all three animals, carryover improvements in rigidity, bradykinesia, and akinesia were observed after CR DBS. Increased swing speed was observed in all the animals; however, improvement in stride length was only observed in NHP B2. In addition, STN CR DBS using two different burst frequencies was evaluated in NHP B2, and differential effects on the mUPDRS score and gait were observed.</p><p><strong>Discussion: </strong>Although preliminary, our results indicate that STN CR DBS can improve Parkinsonian gait together with other motor signs when stimulation parameters are properly selected. This study further supports the continued development of CR DBS as a novel therapy for PD and highlights the importance of parameter selection in its clinical application.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"17 ","pages":"1185723"},"PeriodicalIF":3.5,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10483836/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10570300","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":"versaFlow: a versatile pipeline for resolution adapted diffusion MRI processing and its application to studying the variability of the PRIME-DE database.","authors":"Alex Valcourt Caron,&nbsp;Amir Shmuel,&nbsp;Ziqi Hao,&nbsp;Maxime Descoteaux","doi":"10.3389/fninf.2023.1191200","DOIUrl":"10.3389/fninf.2023.1191200","url":null,"abstract":"<p><p>The lack of \"gold standards\" in Diffusion Weighted Imaging (DWI) makes validation cumbersome. To tackle this task, studies use translational analysis where results in humans are benchmarked against findings in other species. Non-Human Primates (NHP) are particularly interesting for this, as their cytoarchitecture is closely related to humans. However, tools used for processing and analysis must be adapted and finely tuned to work well on NHP images. Here, we propose versaFlow, a modular pipeline implemented in Nextflow, designed for robustness and scalability. The pipeline is tailored to <i>in vivo</i> NHP DWI at any spatial resolution; it allows for maintainability and customization. Processes and workflows are implemented using cutting-edge and state-of-the-art Magnetic Resonance Imaging (MRI) processing technologies and diffusion modeling algorithms, namely Diffusion Tensor Imaging (DTI), Constrained Spherical Deconvolution (CSD), and DIstribution of Anisotropic MicrOstructural eNvironments in Diffusion-compartment imaging (DIAMOND). Using versaFlow, we provide an in-depth study of the variability of diffusion metrics computed on 32 subjects from 3 sites of the Primate Data Exchange (PRIME-DE), which contains anatomical T1-weighted (T1w) and T2-weighted (T2w) images, functional MRI (fMRI), and DWI of NHP brains. This dataset includes images acquired over a range of resolutions, using single and multi-shell gradient samplings, on multiple scanner vendors. We perform a reproducibility study of the processing of versaFlow using the Aix-Marseilles site's data, to ensure that our implementation has minimal impact on the variability observed in subsequent analyses. We report very high reproducibility for the majority of metrics; only gamma distribution parameters of DIAMOND display less reproducible behaviors, due to the absence of a mechanism to enforce a random number seed in the software we used. This should be taken into consideration when future applications are performed. We show that the PRIME-DE diffusion data exhibits a great level of variability, similar or greater than results obtained in human studies. Its usage should be done carefully to prevent instilling uncertainty in statistical analyses. This hints at a need for sufficient harmonization in acquisition protocols and for the development of robust algorithms capable of managing the variability induced in imaging due to differences in scanner models and/or vendors.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"17 ","pages":"1191200"},"PeriodicalIF":3.5,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10449583/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10109297","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":"Microglial morphometric analysis: so many options, so little consistency.","authors":"Jack Reddaway, Peter Eulalio Richardson, Ryan J Bevan, Jessica Stoneman, Marco Palombo","doi":"10.3389/fninf.2023.1211188","DOIUrl":"10.3389/fninf.2023.1211188","url":null,"abstract":"<p><p>Quantification of microglial activation through morphometric analysis has long been a staple of the neuroimmunologist's toolkit. Microglial morphological phenomics can be conducted through either manual classification or constructing a digital skeleton and extracting morphometric data from it. Multiple open-access and paid software packages are available to generate these skeletons via semi-automated and/or fully automated methods with varying degrees of accuracy. Despite advancements in methods to generate morphometrics (quantitative measures of cellular morphology), there has been limited development of tools to analyze the datasets they generate, in particular those containing parameters from tens of thousands of cells analyzed by fully automated pipelines. In this review, we compare and critique the approaches using cluster analysis and machine learning driven predictive algorithms that have been developed to tackle these large datasets, and propose improvements for these methods. In particular, we highlight the need for a commitment to <i>open science</i> from groups developing these classifiers. Furthermore, we call attention to a need for communication between those with a strong software engineering/computer science background and neuroimmunologists to produce effective analytical tools with simplified operability if we are to see their wide-spread adoption by the glia biology community.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"17 ","pages":"1211188"},"PeriodicalIF":3.5,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10448193/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10109299","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":"NeuroBridge ontology: computable provenance metadata to give the long tail of neuroimaging data a FAIR chance for secondary use.","authors":"Satya S Sahoo, Matthew D Turner, Lei Wang, Jose Luis Ambite, Abhishek Appaji, Arcot Rajasekar, Howard M Lander, Yue Wang, Jessica A Turner","doi":"10.3389/fninf.2023.1216443","DOIUrl":"10.3389/fninf.2023.1216443","url":null,"abstract":"<p><strong>Background: </strong>Despite the efforts of the neuroscience community, there are many published neuroimaging studies with data that are still not <i>findable</i> or <i>accessible</i>. Users face significant challenges in <i>reusing</i> neuroimaging data due to the lack of provenance metadata, such as experimental protocols, study instruments, and details about the study participants, which is also required for <i>interoperability.</i> To implement the FAIR guidelines for neuroimaging data, we have developed an iterative ontology engineering process and used it to create the NeuroBridge ontology. The NeuroBridge ontology is a computable model of provenance terms to implement FAIR principles and together with an international effort to annotate full text articles with ontology terms, the ontology enables users to locate relevant neuroimaging datasets.</p><p><strong>Methods: </strong>Building on our previous work in metadata modeling, and in concert with an initial annotation of a representative corpus, we modeled diagnosis terms (e.g., schizophrenia, alcohol usage disorder), magnetic resonance imaging (MRI) scan types (T1-weighted, task-based, etc.), clinical symptom assessments (PANSS, AUDIT), and a variety of other assessments. We used the feedback of the annotation team to identify missing metadata terms, which were added to the NeuroBridge ontology, and we restructured the ontology to support both the final annotation of the corpus of neuroimaging articles by a second, independent set of annotators, as well as the functionalities of the NeuroBridge search portal for neuroimaging datasets.</p><p><strong>Results: </strong>The NeuroBridge ontology consists of 660 classes with 49 properties with 3,200 axioms. The ontology includes mappings to existing ontologies, enabling the NeuroBridge ontology to be interoperable with other domain specific terminological systems. Using the ontology, we annotated 186 neuroimaging full-text articles describing the participant types, scanning, clinical and cognitive assessments.</p><p><strong>Conclusion: </strong>The NeuroBridge ontology is the first computable metadata model that represents the types of data available in recent neuroimaging studies in schizophrenia and substance use disorders research; it can be extended to include more granular terms as needed. This metadata ontology is expected to form the computational foundation to help both investigators to make their data FAIR compliant and support users to conduct reproducible neuroimaging research.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"17 ","pages":"1216443"},"PeriodicalIF":2.5,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406126/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9956234","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":"NIDM-Terms: community-based terminology management for improved neuroimaging dataset descriptions and query.","authors":"Nazek Queder,&nbsp;Vivian B Tien,&nbsp;Sanu Ann Abraham,&nbsp;Sebastian Georg Wenzel Urchs,&nbsp;Karl G Helmer,&nbsp;Derek Chaplin,&nbsp;Theo G M van Erp,&nbsp;David N Kennedy,&nbsp;Jean-Baptiste Poline,&nbsp;Jeffrey S Grethe,&nbsp;Satrajit S Ghosh,&nbsp;David B Keator","doi":"10.3389/fninf.2023.1174156","DOIUrl":"10.3389/fninf.2023.1174156","url":null,"abstract":"<p><p>The biomedical research community is motivated to share and reuse data from studies and projects by funding agencies and publishers. Effectively combining and reusing neuroimaging data from publicly available datasets, requires the capability to query across datasets in order to identify cohorts that match both neuroimaging and clinical/behavioral data criteria. Critical barriers to operationalizing such queries include, in part, the broad use of undefined study variables with limited or no annotations that make it difficult to understand the data available without significant interaction with the original authors. Using the Brain Imaging Data Structure (BIDS) to organize neuroimaging data has made querying across studies for specific image types possible at scale. However, in BIDS, beyond file naming and tightly controlled imaging directory structures, there are very few constraints on ancillary variable naming/meaning or experiment-specific metadata. In this work, we present NIDM-Terms, a set of user-friendly terminology management tools and associated software to better manage individual lab terminologies and help with annotating BIDS datasets. Using these tools to annotate BIDS data with a Neuroimaging Data Model (NIDM) semantic web representation, enables queries across datasets to identify cohorts with specific neuroimaging and clinical/behavioral measurements. This manuscript describes the overall informatics structures and demonstrates the use of tools to annotate BIDS datasets to perform integrated cross-cohort queries.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"17 ","pages":"1174156"},"PeriodicalIF":3.5,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10392125/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9987559","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 scalable implementation of the recursive least-squares algorithm for training spiking neural networks.","authors":"Benjamin J Arthur, Christopher M Kim, Susu Chen, Stephan Preibisch, Ran Darshan","doi":"10.3389/fninf.2023.1099510","DOIUrl":"10.3389/fninf.2023.1099510","url":null,"abstract":"<p><p>Training spiking recurrent neural networks on neuronal recordings or behavioral tasks has become a popular way to study computations performed by the nervous system. As the size and complexity of neural recordings increase, there is a need for efficient algorithms that can train models in a short period of time using minimal resources. We present optimized CPU and GPU implementations of the recursive least-squares algorithm in spiking neural networks. The GPU implementation can train networks of one million neurons, with 100 million plastic synapses and a billion static synapses, about 1,000 times faster than an unoptimized reference CPU implementation. We demonstrate the code's utility by training a network, in less than an hour, to reproduce the activity of > 66, 000 recorded neurons of a mouse performing a decision-making task. The fast implementation enables a more interactive <i>in-silico</i> study of the dynamics and connectivity underlying multi-area computations. It also admits the possibility to train models as <i>in-vivo</i> experiments are being conducted, thus closing the loop between modeling and experiments.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"17 ","pages":"1099510"},"PeriodicalIF":2.5,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10333503/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9871920","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":"Alternative patterns of deep brain stimulation in neurologic and neuropsychiatric disorders.","authors":"Ricardo A Najera, Anil K Mahavadi, Anas U Khan, Ujwal Boddeti, Victor A Del Bene, Harrison C Walker, J Nicole Bentley","doi":"10.3389/fninf.2023.1156818","DOIUrl":"10.3389/fninf.2023.1156818","url":null,"abstract":"<p><p>Deep brain stimulation (DBS) is a widely used clinical therapy that modulates neuronal firing in subcortical structures, eliciting downstream network effects. Its effectiveness is determined by electrode geometry and location as well as adjustable stimulation parameters including pulse width, interstimulus interval, frequency, and amplitude. These parameters are often determined empirically during clinical or intraoperative programming and can be altered to an almost unlimited number of combinations. Conventional high-frequency stimulation uses a continuous high-frequency square-wave pulse (typically 130-160 Hz), but other stimulation patterns may prove efficacious, such as continuous or bursting theta-frequencies, variable frequencies, and coordinated reset stimulation. Here we summarize the current landscape and potential clinical applications for novel stimulation patterns.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"17 ","pages":"1156818"},"PeriodicalIF":2.5,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10320008/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10182397","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}