Neuroinformatics最新文献

{"title":"Automated Lesion and Feature Extraction Pipeline for Brain MRIs with Interpretability.","authors":"Reza Eghbali, Pierre Nedelec, David Weiss, Radhika Bhalerao, Long Xie, Jeffrey D Rudie, Chunlei Liu, Leo P Sugrue, Andreas M Rauschecker","doi":"10.1007/s12021-024-09708-z","DOIUrl":"10.1007/s12021-024-09708-z","url":null,"abstract":"<p><p>This paper introduces the Automated Lesion and Feature Extraction (ALFE) pipeline, an open-source, Python-based pipeline that consumes MR images of the brain and produces anatomical segmentations, lesion segmentations, and human-interpretable imaging features describing the lesions in the brain. ALFE pipeline is modeled after the neuroradiology workflow and generates features that can be used by physicians for quantitative analysis of clinical brain MRIs and for machine learning applications. The pipeline uses a decoupled design which allows the user to customize the image processing, image registrations, and AI segmentation tools without the need to change the business logic of the pipeline. In this manuscript, we give an overview of ALFE, present the main aspects of ALFE pipeline design philosophy, and present case studies.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 1","pages":"2"},"PeriodicalIF":2.7,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11717894/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142957914","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":"Stimulation Effects Mapping for Optimizing Coil Placement for Transcranial Magnetic Stimulation.","authors":"Gangliang Zhong, Fang Jin, Liang Ma, Yongfeng Yang, Baogui Zhang, Dan Cao, Jin Li, Nianming Zuo, Lingzhong Fan, Zhengyi Yang, Tianzi Jiang","doi":"10.1007/s12021-024-09714-1","DOIUrl":"10.1007/s12021-024-09714-1","url":null,"abstract":"<p><p>The position and orientation of transcranial magnetic stimulation (TMS) coil, which we collectively refer to as coil placement, significantly affect both the assessment and modulation of cortical excitability. TMS electric field (E-field) simulation can be used to identify optimal coil placement. However, the present E-field simulation required a laborious segmentation and meshing procedure to determine optimal coil placement. We intended to create a framework that would enable us to offer optimal coil placement without requiring the segmentation and meshing procedure. We constructed the stimulation effects map (SEM) framework using the CASIA dataset for optimal coil placement. We used leave-one-subject-out cross-validation to evaluate the consistency of the optimal coil placement and the target regions determined by SEM for the 74 target ROIs in MRI data from the CASIA, HCP15 and HCP100 datasets. Additionally, we contrasted the E-norms determined by optimal coil placements using SEM and auxiliary dipole method (ADM) based on the DP and CASIA II datasets. We provided optimal coil placement in 'head-anatomy-based' (HAC) polar coordinates and MNI coordinates for the target region. The results also demonstrated the consistency of the SEM framework for the 74 target ROIs. The normal E-field determined by SEM was more significant than the value received by ADM. We created the SEM framework using the CASIA database to determine optimal coil placement without segmentation or meshing. We provided optimal coil placement in HAC and MNI coordinates for the target region. The validation of several target ROIs from various datasets demonstrated the consistency of the SEM approach. By streamlining the process of finding optimal coil placement, we intended to make TMS assessment and therapy more convenient.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 1","pages":"1"},"PeriodicalIF":2.7,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142957927","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":"NeuroCarto: A Toolkit for Building Custom Read-out Channel Maps for High Electrode-count Neural Probes.","authors":"Ta-Shun Su, Fabian Kloosterman","doi":"10.1007/s12021-024-09705-2","DOIUrl":"10.1007/s12021-024-09705-2","url":null,"abstract":"<p><p>Neuropixels probes contain thousands of electrodes across one or more shanks and are sufficiently small to allow chronic recording of neural activity in freely behaving small animals. However, the joint increase in the number of electrodes and miniaturization of the probe package has led to a compromise in which groups of electrodes share a single read-out channel and only a fraction of the electrodes can be read out at any given time. Experimenters then face the challenge of selecting a subset of electrodes (i.e., channel map) that both covers the brain regions of interest and adheres to the restrictions of the underlying hardware. Here, we present NeuroCarto, a Python toolkit and GUI to simplify the construction of a custom channel map for Neuropixels probes. We describe a general iterative approach to select electrodes and provide a specific implementation that allows experimenters to specify a blueprint of regions of interest along the probe shanks and the desired local electrode density. NeuroCarto assists in generating a channel map from the blueprint and visualizes potential read-out channel conflicts. We showcase the utility of NeuroCarto in an experimental workflow to simultaneously record from the dorsal and ventral hippocampus with 4-shank Neuropixels 2.0 probes in freely moving mice.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 1","pages":"16"},"PeriodicalIF":2.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11706897/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142957919","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":"Performance of Convolutional Neural Network Models in Meningioma Segmentation in Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis.","authors":"Ting-Wei Wang, Jia-Sheng Hong, Wei-Kai Lee, Yi-Hui Lin, Huai-Che Yang, Cheng-Chia Lee, Hung-Chieh Chen, Hsiu-Mei Wu, Weir Chiang You, Yu-Te Wu","doi":"10.1007/s12021-024-09704-3","DOIUrl":"10.1007/s12021-024-09704-3","url":null,"abstract":"<p><strong>Background: </strong>Meningioma, the most common primary brain tumor, presents significant challenges in MRI-based diagnosis and treatment planning due to its diverse manifestations. Convolutional Neural Networks (CNNs) have shown promise in improving the accuracy and efficiency of meningioma segmentation from MRI scans. This systematic review and meta-analysis assess the effectiveness of CNN models in segmenting meningioma using MRI.</p><p><strong>Methods: </strong>Following the PRISMA guidelines, we searched PubMed, Embase, and Web of Science from their inception to December 20, 2023, to identify studies that used CNN models for meningioma segmentation in MRI. Methodological quality of the included studies was assessed using the CLAIM and QUADAS-2 tools. The primary variable was segmentation accuracy, which was evaluated using the Sørensen-Dice coefficient. Meta-analysis, subgroup analysis, and meta-regression were performed to investigate the effects of MRI sequence, CNN architecture, and training dataset size on model performance.</p><p><strong>Results: </strong>Nine studies, comprising 4,828 patients, were included in the analysis. The pooled Dice score across all studies was 89% (95% CI: 87-90%). Internal validation studies yielded a pooled Dice score of 88% (95% CI: 85-91%), while external validation studies reported a pooled Dice score of 89% (95% CI: 88-90%). Models trained on multiple MRI sequences consistently outperformed those trained on single sequences. Meta-regression indicated that training dataset size did not significantly influence segmentation accuracy.</p><p><strong>Conclusion: </strong>CNN models are highly effective for meningioma segmentation in MRI, particularly during the use of diverse datasets from multiple MRI sequences. This finding highlights the importance of data quality and imaging sequence selection in the development of CNN models. Standardization of MRI data acquisition and preprocessing may improve the performance of CNN models, thereby facilitating their clinical adoption for the optimal diagnosis and treatment of meningioma.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 1","pages":"14"},"PeriodicalIF":2.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11706894/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142957925","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 Self-supervised Deep Learning Model for Diagonal Sulcus Detection with Limited Labeled Data.","authors":"Delfina Braggio, Hernán C Külsgaard, Mariana Vallejo-Azar, Mariana Bendersky, Paula González, Lucía Alba-Ferrara, José Ignacio Orlando, Ignacio Larrabide","doi":"10.1007/s12021-024-09700-7","DOIUrl":"10.1007/s12021-024-09700-7","url":null,"abstract":"<p><p>Sulci are a fundamental part of brain morphology, closely linked to brain function, cognition, and behavior. Tertiary sulci, characterized as the shallowest and smallest subtype, pose a challenging task for detection. The diagonal sulcus (ds), located in a crucial area in language processing, has a prevalence between 50% and 60%. Automatic detection of the ds is an unexplored field: while some sulci segmenters include the ds, their accuracy is usually low. In this work, we present a deep learning based model for ds detection using a fine-tuning approach with limited training labeled data. A convolutional autoencoder was employed to learn specific features related to brain morphology with unlabeled data through self-supervised learning. Subsequently, the pre-trained network was fine-tuned to detect the ds using a less extensive labeled dataset. We achieved a mean F1-score of 0.7176 (SD=0.0736) for the test set and a F1-score of 0.72 for a second held-out set, surpassing the results of a standard software and other alternative deep learning models. We conducted an interpretability analysis of the results using occlusion maps and observed that the models focused on adjacent sulci to the ds for prediction, consistent with the approach taken by experts in manual annotation. We also analyzed the challenges of manual labeling by conducting a thorough examination of interrater agreement on a small dataset and its relationship with our model's performance. Finally, we applied our method on a population analysis and reported the prevalence of ds in a case study.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 1","pages":"13"},"PeriodicalIF":2.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142957911","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":"Simulation Study of Envelope Wave Electrical Nerve Stimulation Based on a Real Head Model.","authors":"Yuhao Liu, Renling Zou, Liang Zhao, Linpeng Jin, Xiufang Hu, Xuezhi Yin","doi":"10.1007/s12021-024-09711-4","DOIUrl":"10.1007/s12021-024-09711-4","url":null,"abstract":"<p><p>In recent years, the modulation of brain neural activity by applied electromagnetic fields has become a hot spot in neuroscience research. Transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS) are two common non-invasive neuromodulation techniques. However, conventional tACS has limited stimulation effects in the deeper parts of the brain. In this study, a method of low and medium frequency envelope wave neurostimulation is proposed, and its effectiveness and safety are evaluated by simulation and human experiment. First, we built a real head model from head MRI image data and used the finite element method to calculate the current distribution of the envelope wave in the brain. Then, a single-compartment neuron model was constructed in NEURON software to simulate the action potential generation of neurons under different frequencies of electrical stimulation. Finally, a human experiment was conducted to investigate the threshold of human perception of envelope wave electrical stimulation. The results show that envelope wave can both increase the depth of stimulation and induce neurons to generate effective action potentials. In envelope wave electrical stimulation, the optimal modulating wave frequency was 50 Hz, and the carrier frequency was 2 kHz-3 kHz. This method is expected to play an important role in the non-invasive treatment of neurological and psychiatric disorders.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 1","pages":"15"},"PeriodicalIF":2.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142957926","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":"Interdisciplinary and Collaborative Training in Neuroscience: Insights from the Human Brain Project Education Programme.","authors":"Alice Geminiani, Judith Kathrein, Alper Yegenoglu, Franziska Vogel, Marcelo Armendariz, Ziv Ben-Zion, Petrut Antoniu Bogdan, Joana Covelo, Marissa Diaz Pier, Karin Grasenick, Vitali Karasenko, Wouter Klijn, Tina Kokan, Carmen Alina Lupascu, Anna Lührs, Tara Mahfoud, Taylan Özden, Jens Egholm Pedersen, Luca Peres, Ingrid Reiten, Nikola Simidjievski, Inga Ulnicane, Michiel van der Vlag, Lyuba Zehl, Alois Saria, Sandra Diaz-Pier, Johannes Passecker","doi":"10.1007/s12021-024-09682-6","DOIUrl":"10.1007/s12021-024-09682-6","url":null,"abstract":"<p><p>Neuroscience education is challenged by rapidly evolving technology and the development of interdisciplinary approaches for brain research. The Human Brain Project (HBP) Education Programme aimed to address the need for interdisciplinary expertise in brain research by equipping a new generation of researchers with skills across neuroscience, medicine, and information technology. Over its ten year duration, the programme engaged over 1,300 experts and attracted more than 5,500 participants from various scientific disciplines in its blended learning curriculum, specialised schools and workshops, and events fostering dialogue among early-career researchers. Key principles of the programme's approach included fostering interdisciplinarity, adaptability to the evolving research landscape and infrastructure, and a collaborative environment with a focus on empowering early-career researchers. Following the programme's conclusion, we provide here an analysis and in-depth view across a diverse range of educational formats and events. Our results show that the Education Programme achieved success in its wide geographic reach, the diversity of participants, and the establishment of transversal collaborations. Building on these experiences and achievements, we describe how leveraging digital tools and platforms provides accessible and highly specialised training, which can enhance existing education programmes for the next generation of brain researchers working in decentralised European collaborative spaces. Finally, we present the lessons learnt so that similar initiatives may improve upon our experience and incorporate our suggestions into their own programme.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"657-678"},"PeriodicalIF":2.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11579076/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142584770","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 Bayesian Multiplex Graph Classifier of Functional Brain Connectivity Across Diverse Tasks of Cognitive Control.","authors":"Sharmistha Guha, Jose Rodriguez-Acosta, Ivo D Dinov","doi":"10.1007/s12021-024-09670-w","DOIUrl":"10.1007/s12021-024-09670-w","url":null,"abstract":"<p><p>This article seeks to investigate the impact of aging on functional connectivity across different cognitive control scenarios, particularly emphasizing the identification of brain regions significantly associated with early aging. By conceptualizing functional connectivity within each cognitive control scenario as a graph, with brain regions as nodes, the statistical challenge revolves around devising a regression framework to predict a binary scalar outcome (aging or normal) using multiple graph predictors. Popular regression methods utilizing multiplex graph predictors often face limitations in effectively harnessing information within and across graph layers, leading to potentially less accurate inference and predictive accuracy, especially for smaller sample sizes. To address this challenge, we propose the Bayesian Multiplex Graph Classifier (BMGC). Accounting for multiplex graph topology, our method models edge coefficients at each graph layer using bilinear interactions between the latent effects associated with the two nodes connected by the edge. This approach also employs a variable selection framework on node-specific latent effects from all graph layers to identify influential nodes linked to observed outcomes. Crucially, the proposed framework is computationally efficient and quantifies the uncertainty in node identification, coefficient estimation, and binary outcome prediction. BMGC outperforms alternative methods in terms of the aforementioned metrics in simulation studies. An additional BMGC validation was completed using an fMRI study of brain networks in adults. The proposed BMGC technique identified that sensory motor brain network obeys certain lateral symmetries, whereas the default mode network exhibits significant brain asymmetries associated with early aging.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"457-472"},"PeriodicalIF":2.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11578796/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141301973","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":"Classifying Neuronal Cell Types Based on Shared Electrophysiological Information from Humans and Mice.","authors":"Ofek Ophir, Orit Shefi, Ofir Lindenbaum","doi":"10.1007/s12021-024-09675-5","DOIUrl":"10.1007/s12021-024-09675-5","url":null,"abstract":"<p><p>The brain is an intricate system that controls a variety of functions. It consists of a vast number of cells that exhibit diverse characteristics. To understand brain function in health and disease, it is crucial to classify neurons accurately. Recent advancements in machine learning have provided a way to classify neurons based on their electrophysiological activity. This paper presents a deep-learning framework that classifies neurons solely on this basis. The framework uses data from the Allen Cell Types database, which contains a survey of biological features derived from single-cell recordings from mice and humans. The shared information from both sources is used to classify neurons into their broad types with the help of a joint model. An accurate domain-adaptive model, integrating electrophysiological data from both mice and humans, is implemented. Furthermore, data from mouse neurons, which also includes labels of transgenic mouse lines, is further classified into subtypes using an interpretable neural network model. The framework provides state-of-the-art results in terms of accuracy and precision while also providing explanations for the predictions.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"473-486"},"PeriodicalIF":2.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11579117/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141555808","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":"Bridging the Gap: How Neuroinformatics is Preparing the Next Generation of Neuroscience Researchers.","authors":"Mathew Abrams, John Darrell Van Horn","doi":"10.1007/s12021-024-09693-3","DOIUrl":"10.1007/s12021-024-09693-3","url":null,"abstract":"<p><p>Neurotechnology and big data are two rapidly advancing fields that have the potential to transform our understanding of the brain and its functions. Advancements in neurotechnology have enabled researchers to investigate the function of the brain at unprecedented levels of granularity at the functional, molecular, and anatomical levels. Thus, resulting in the collection of not only more data, but also larger datasets. To fully harness the potential of big data and advancements in neurotechnology to improve our understanding of the nervous system, there is a need to train a new generation of neuroscientists capable of not only domain expertise, but also the computational and data science skills required to interrogate and integrate big data. Importantly, neuroinformatics is the subdiscipline of neuroscience devoted to the development of neuroscience data and knowledge bases together with computational models and analytical tools for sharing, integration and analysis of experimental data, and advancement of theories about the nervous system function. While there are only a few formal training programs in neuroinformatics, and since neuroinformatics is rarely incorporated into traditional neuroscience training programs, the neuroinformatics community has attempted to bridge the gap between the traditional neuroscience education programs and the needs of the next generation of neuroscience researchers through community initiatives and workshops. Thus, the purpose of this special collection is to highlight several such community efforts which span from in-person workshops to large-scale, global virtual training consortiums and from training students to training-the-trainers.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"619-622"},"PeriodicalIF":2.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142479143","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}