Frontiers in Neuroinformatics最新文献

{"title":"Corrigendum: Learning the heterogeneous representation of brain's structure from serial SEM images using a masked autoencoder.","authors":"Ao Cheng, Jiahao Shi, Lirong Wang, Ruobing Zhang","doi":"10.3389/fninf.2023.1337766","DOIUrl":"https://doi.org/10.3389/fninf.2023.1337766","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.3389/fninf.2023.1118419.].</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"17 ","pages":"1337766"},"PeriodicalIF":3.5,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10712309/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138801396","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":"Tissue Oxygen Depth Explorer: an interactive database for microscopic oxygen imaging data.","authors":"Layth N Amra, Philipp Mächler, Natalie Fomin-Thunemann, Kıvılcım Kılıç, Payam Saisan, Anna Devor, Martin Thunemann","doi":"10.3389/fninf.2023.1278787","DOIUrl":"10.3389/fninf.2023.1278787","url":null,"abstract":"","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"17 ","pages":"1278787"},"PeriodicalIF":3.5,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10711099/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138801400","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":"Factorized discriminant analysis for genetic signatures of neuronal phenotypes","authors":"Mu Qiao","doi":"10.3389/fninf.2023.1265079","DOIUrl":"https://doi.org/10.3389/fninf.2023.1265079","url":null,"abstract":"<p>Navigating the complex landscape of single-cell transcriptomic data presents significant challenges. Central to this challenge is the identification of a meaningful representation of high-dimensional gene expression patterns that sheds light on the structural and functional properties of cell types. Pursuing model interpretability and computational simplicity, we often look for a linear transformation of the original data that aligns with key phenotypic features of cells. In response to this need, we introduce factorized linear discriminant analysis (FLDA), a novel method for linear dimensionality reduction. The crux of FLDA lies in identifying a linear function of gene expression levels that is highly correlated with one phenotypic feature while minimizing the influence of others. To augment this method, we integrate it with a sparsity-based regularization algorithm. This integration is crucial as it selects a subset of genes pivotal to a specific phenotypic feature or a combination thereof. To illustrate the effectiveness of FLDA, we apply it to transcriptomic datasets from neurons in the Drosophila optic lobe. We demonstrate that FLDA not only captures the inherent structural patterns aligned with phenotypic features but also uncovers key genes associated with each phenotype.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"33 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138630780","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":"Transdiagnostic clustering of self-schema from self-referential judgements identifies subtypes of healthy personality and depression","authors":"Geoffrey Chern-Yee Tan, Ziying Wang, Ethel Siew Ee Tan, Rachel Jing Min Ong, Pei En Ooi, Danan Lee, Nikita Rane, Sheryl Yu Xuan Tey, Si Ying Chua, Nicole Goh, Glynis Weibin Lam, Atlanta Chakraborty, Anthony Khye Loong Yew, Sin Kee Ong, Jin Lin Kee, Xin Ying Lim, Nawal Hashim, Sharon Huixian Lu, Michael Meany, Serenella Tolomeo, Christopher Asplund Lee, Hong Ming Tan, Jussi Keppo","doi":"10.3389/fninf.2023.1244347","DOIUrl":"https://doi.org/10.3389/fninf.2023.1244347","url":null,"abstract":"<sec><title>Introduction</title><p>The heterogeneity of depressive and anxiety disorders complicates clinical management as it may account for differences in trajectory and treatment response. Self-schemas, which can be determined by Self-Referential Judgements (SRJs), are heterogeneous yet stable. SRJs have been used to characterize personality in the general population and shown to be prognostic in depressive and anxiety disorders.</p></sec><sec><title>Methods</title><p>In this study, we used SRJs from a Self-Referential Encoding Task (SRET) to identify clusters from a clinical sample of 119 patients recruited from the Institute of Mental Health presenting with depressive or anxiety symptoms and a non-clinical sample of 115 healthy adults. The generated clusters were examined in terms of most endorsed words, cross-sample correspondence, association with depressive symptoms and the Depressive Experiences Questionnaire and diagnostic category.</p></sec><sec><title>Results</title><p>We identify a 5-cluster solution in each sample and a 7-cluster solution in the combined sample. When perturbed, metrics such as optimum cluster number, criterion value, likelihood, DBI and CHI remained stable and cluster centers appeared stable when using BIC or ICL as criteria. Top endorsed words in clusters were meaningful across theoretical frameworks from personality, psychodynamic concepts of relatedness and self-definition, and valence in self-referential processing. The clinical clusters were labeled “Neurotic” (C1), “Extraverted” (C2), “Anxious to please” (C3), “Self-critical” (C4), “Conscientious” (C5). The non-clinical clusters were labeled “Self-confident” (N1), “Low endorsement” (N2), “Non-neurotic” (N3), “Neurotic” (N4), “High endorsement” (N5). The combined clusters were labeled “Self-confident” (NC1), “Externalising” (NC2), “Neurotic” (NC3), “Secure” (NC4), “Low endorsement” (NC5), “High endorsement” (NC6), “Self-critical” (NC7). Cluster differences were observed in endorsement of positive and negative words, latency biases, recall biases, depressive symptoms, frequency of depressive disorders and self-criticism.</p></sec><sec><title>Discussion</title><p>Overall, clusters endorsing more negative words tended to endorse fewer positive words, showed more negative biases in reaction time and negative recall bias, reported more severe depressive symptoms and a higher frequency of depressive disorders and more self-criticism in the clinical population. SRJ-based clustering represents a novel transdiagnostic framework for subgrouping patients with depressive and anxiety symptoms that may support the future translation of the science of self-referential processing, personality and psychodynamic concepts of self-definition to clinical applications.</p></sec>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"1 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139420862","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":"Online interoperable resources for building hippocampal neuron models via the Hippocampus Hub","authors":"Luca Leonardo Bologna, Antonino Tocco, Roberto Smiriglia, Armando Romani, Felix Schürmann, Michele Migliore","doi":"10.3389/fninf.2023.1271059","DOIUrl":"https://doi.org/10.3389/fninf.2023.1271059","url":null,"abstract":"To build biophysically detailed models of brain cells, circuits, and regions, a data-driven approach is increasingly being adopted. This helps to obtain a simulated activity that reproduces the experimentally recorded neural dynamics as faithfully as possible, and to turn the model into a useful framework for making predictions based on the principles governing the nature of neural cells. In such a context, the access to existing neural models and data outstandingly facilitates the work of computational neuroscientists and fosters its novelty, as the scientific community grows wider and neural models progressively increase in type, size, and number. Nonetheless, even when accessibility is guaranteed, data and models are rarely reused since it is difficult to retrieve, extract and/or understand relevant information and scientists are often required to download and modify individual files, perform neural data analysis, optimize model parameters, and run simulations, on their own and with their own resources. While focusing on the construction of biophysically and morphologically accurate models of hippocampal cells, we have created an online resource, the Build section of the Hippocampus Hub -a scientific portal for research on the hippocampus- that gathers data and models from different online open repositories and allows their collection as the first step of a single cell model building workflow. Interoperability of tools and data is the key feature of the work we are presenting. Through a simple click-and-collect procedure, like filling the shopping cart of an online store, researchers can intuitively select the files of interest (i.e., electrophysiological recordings, neural morphology, and model components), and get started with the construction of a data-driven hippocampal neuron model. Such a workflow importantly includes a model optimization process, which leverages high performance computing resources transparently granted to the users, and a framework for running simulations of the optimized model, both available through the EBRAINS Hodgkin-Huxley Neuron Builder online tool.","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"8 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135271638","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 past, present and future of neuroscience data sharing: a perspective on the state of practices and infrastructure for FAIR","authors":"Maryann E. Martone","doi":"10.3389/fninf.2023.1276407","DOIUrl":"https://doi.org/10.3389/fninf.2023.1276407","url":null,"abstract":"<p>Neuroscience has made significant strides over the past decade in moving from a largely closed science characterized by anemic data sharing, to a largely open science where the amount of publicly available neuroscience data has increased dramatically. While this increase is driven in significant part by large prospective data sharing studies, we are starting to see increased sharing in the long tail of neuroscience data, driven no doubt by journal requirements and funder mandates. Concomitant with this shift to open is the increasing support of the FAIR data principles by neuroscience practices and infrastructure. FAIR is particularly critical for neuroscience with its multiplicity of data types, scales and model systems and the infrastructure that serves them. As envisioned from the early days of neuroinformatics, neuroscience is currently served by a globally distributed ecosystem of neuroscience-centric data repositories, largely specialized around data types. To make neuroscience data findable, accessible, interoperable, and reusable requires the coordination across different stakeholders, including the researchers who produce the data, data repositories who make it available, the aggregators and indexers who field search engines across the data, and community organizations who help to coordinate efforts and develop the community standards critical to FAIR. The International Neuroinformatics Coordinating Facility has led efforts to move neuroscience toward FAIR, fielding several resources to help researchers and repositories achieve FAIR. In this perspective, I provide an overview of the components and practices required to achieve FAIR in neuroscience and provide thoughts on the past, present and future of FAIR infrastructure for neuroscience, from the laboratory to the search engine.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"7 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139102310","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":"Comparing feature selection and machine learning approaches for predicting CYP2D6 methylation from genetic variation","authors":"Wei Jing Fong, Hong Ming Tan, Rishabh Garg, Ai Ling Teh, Hong Pan, Varsha Gupta, Bernadus Krishna, Zou Hui Chen, Natania Yovela Purwanto, Fabian Yap, Kok Hian Tan, Kok Yen Jerry Chan, Shiao-Yng Chan, Nicole Goh, Nikita Rane, Ethel Siew Ee Tan, Yuheng Jiang, Mei Han, Michael Meaney, Dennis Wang, Jussi Keppo, Geoffrey Chern-Yee Tan","doi":"10.3389/fninf.2023.1244336","DOIUrl":"https://doi.org/10.3389/fninf.2023.1244336","url":null,"abstract":"<sec><title>Introduction</title><p>Pharmacogenetics currently supports clinical decision-making on the basis of a limited number of variants in a few genes and may benefit paediatric prescribing where there is a need for more precise dosing. Integrating genomic information such as methylation into pharmacogenetic models holds the potential to improve their accuracy and consequently prescribing decisions. Cytochrome P450 2D6 (<italic>CYP2D6</italic>) is a highly polymorphic gene conventionally associated with the metabolism of commonly used drugs and endogenous substrates. We thus sought to predict epigenetic loci from single nucleotide polymorphisms (SNPs) related to <italic>CYP2D6</italic> in children from the GUSTO cohort.</p></sec><sec><title>Methods</title><p>Buffy coat DNA methylation was quantified using the Illumina Infinium Methylation EPIC beadchip. CpG sites associated with <italic>CYP2D6</italic> were used as outcome variables in Linear Regression, Elastic Net and XGBoost models. We compared feature selection of SNPs from GWAS mQTLs, GTEx eQTLs and SNPs within 2 MB of the <italic>CYP2D6</italic> gene and the impact of adding demographic data. The samples were split into training (75%) sets and test (25%) sets for validation. In Elastic Net model and XGBoost models, optimal hyperparameter search was done using 10-fold cross validation. Root Mean Square Error and R-squared values were obtained to investigate each models’ performance. When GWAS was performed to determine SNPs associated with CpG sites, a total of 15 SNPs were identified where several SNPs appeared to influence multiple CpG sites.</p></sec><sec><title>Results</title><p>Overall, Elastic Net models of genetic features appeared to perform marginally better than heritability estimates and substantially better than Linear Regression and XGBoost models. The addition of nongenetic features appeared to improve performance for some but not all feature sets and probes. The best feature set and Machine Learning (ML) approach differed substantially between CpG sites and a number of top variables were identified for each model.</p></sec><sec><title>Discussion</title><p>The development of SNP-based prediction models for CYP2D6 CpG methylation in Singaporean children of varying ethnicities in this study has clinical application. With further validation, they may add to the set of tools available to improve precision medicine and pharmacogenetics-based dosing.</p></sec>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"10 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139918180","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":"NeuroDecodeR: a package for neural decoding in R","authors":"Ethan M. Meyers","doi":"10.3389/fninf.2023.1275903","DOIUrl":"https://doi.org/10.3389/fninf.2023.1275903","url":null,"abstract":"<p>Neural decoding is a powerful method to analyze neural activity. However, the code needed to run a decoding analysis can be complex, which can present a barrier to using the method. In this paper we introduce a package that makes it easy to perform decoding analyses in the R programing language. We describe how the package is designed in a modular fashion which allows researchers to easily implement a range of different analyses. We also discuss how to format data to be able to use the package, and we give two examples of how to use the package to analyze real data. We believe that this package, combined with the rich data analysis ecosystem in R, will make it significantly easier for researchers to create reproducible decoding analyses, which should help increase the pace of neuroscience discoveries.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"24 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139083810","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":"Prognostic estimation for acute ischemic stroke patients undergoing mechanical thrombectomy within an extended therapeutic window using an interpretable machine learning model.","authors":"Lin Tong,&nbsp;Yun Sun,&nbsp;Yueqi Zhu,&nbsp;Hui Luo,&nbsp;Wan Wan,&nbsp;Ying Wu","doi":"10.3389/fninf.2023.1273827","DOIUrl":"https://doi.org/10.3389/fninf.2023.1273827","url":null,"abstract":"<p><strong>Background: </strong>Mechanical thrombectomy (MT) is effective for acute ischemic stroke with large vessel occlusion (AIS-LVO) within an extended therapeutic window. However, successful reperfusion does not guarantee positive prognosis, with around 40-50% of cases yielding favorable outcomes. Preoperative prediction of patient outcomes is essential to identify those who may benefit from MT. Although machine learning (ML) has shown promise in handling variables with non-linear relationships in prediction models, its \"black box\" nature and the absence of ML models for extended-window MT prognosis remain limitations.</p><p><strong>Objective: </strong>This study aimed to establish and select the optimal model for predicting extended-window MT outcomes, with the Shapley additive explanation (SHAP) approach used to enhance the interpretability of the selected model.</p><p><strong>Methods: </strong>A retrospective analysis was conducted on 260 AIS-LVO patients undergoing extended-window MT. Selected patients were allocated into training and test sets at a 3:1 ratio following inclusion and exclusion criteria. Four ML classifiers and one logistic regression (Logit) model were constructed using pre-treatment variables from the training set. The optimal model was selected through comparative validation, with key features interpreted using the SHAP approach. The effectiveness of the chosen model was further evaluated using the test set.</p><p><strong>Results: </strong>Of the 212 selected patients, 159 comprised the training and 53 the test sets. Extreme gradient boosting (XGBoost) showed the highest discrimination with an area under the curve (AUC) of 0.93 during validation, and maintained an AUC of 0.77 during testing. SHAP analysis identified ischemic core volume, baseline NHISS score, ischemic penumbra volume, ASPECTS, and patient age as the top five determinants of outcome prediction.</p><p><strong>Conclusion: </strong>XGBoost emerged as the most effective for predicting the prognosis of AIS-LVO patients undergoing MT within the extended therapeutic window. SHAP interpretation improved its clinical confidence, paving the way for ML in clinical decision-making.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"17 ","pages":"1273827"},"PeriodicalIF":3.5,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10603294/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71411450","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":"Web-based processing of physiological noise in fMRI: addition of the PhysIO toolbox to CBRAIN.","authors":"Darius Valevicius,&nbsp;Natacha Beck,&nbsp;Lars Kasper,&nbsp;Sergiy Boroday,&nbsp;Johanna Bayer,&nbsp;Pierre Rioux,&nbsp;Bryan Caron,&nbsp;Reza Adalat,&nbsp;Alan C Evans,&nbsp;Najmeh Khalili-Mahani","doi":"10.3389/fninf.2023.1251023","DOIUrl":"10.3389/fninf.2023.1251023","url":null,"abstract":"<p><p>Neuroimaging research requires sophisticated tools for analyzing complex data, but efficiently leveraging these tools can be a major challenge, especially on large datasets. CBRAIN is a web-based platform designed to simplify the use and accessibility of neuroimaging research tools for large-scale, collaborative studies. In this paper, we describe how CBRAIN's unique features and infrastructure were leveraged to integrate TAPAS PhysIO, an open-source MATLAB toolbox for physiological noise modeling in fMRI data. This case study highlights three key elements of CBRAIN's infrastructure that enable streamlined, multimodal tool integration: a user-friendly GUI, a Brain Imaging Data Structure (BIDS) data-entry schema, and convenient in-browser visualization of results. By incorporating PhysIO into CBRAIN, we achieved significant improvements in the speed, ease of use, and scalability of physiological preprocessing. Researchers now have access to a uniform and intuitive interface for analyzing data, which facilitates remote and collaborative evaluation of results. With these improvements, CBRAIN aims to become an essential open-science tool for integrative neuroimaging research, supporting FAIR principles and enabling efficient workflows for complex analysis pipelines.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"17 ","pages":"1251023"},"PeriodicalIF":3.5,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10569687/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41233929","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}