Neuroinformatics最新文献

{"title":"Blood Flow Velocity Analysis in Cerebral Perforating Arteries on 7T 2D Phase Contrast MRI with an Open-Source Software Tool (SELMA).","authors":"S D T Pham, C Chatziantoniou, J T van Vliet, R J van Tuijl, M Bulk, M Costagli, L de Rochefort, O Kraff, M E Ladd, K Pine, I Ronen, J C W Siero, M Tosetti, A Villringer, G J Biessels, J J M Zwanenburg","doi":"10.1007/s12021-024-09703-4","DOIUrl":"10.1007/s12021-024-09703-4","url":null,"abstract":"<p><p>Blood flow velocity in the cerebral perforating arteries can be quantified in a two-dimensional plane with phase contrast magnetic imaging (2D PC-MRI). The velocity pulsatility index (PI) can inform on the stiffness of these perforating arteries, which is related to several cerebrovascular diseases. Currently, there is no open-source analysis tool for 2D PC-MRI data from these small vessels, impeding the usage of these measurements. In this study we present the Small vessEL MArker (SELMA) analysis software as a novel, user-friendly, open-source tool for velocity analysis in cerebral perforating arteries. The implementation of the analysis algorithm in SELMA was validated against previously published data with a Bland-Altman analysis. The inter-rater reliability of SELMA was assessed on PC-MRI data of sixty participants from three MRI vendors between eight different sites. The mean velocity (v_mean) and velocity PI of SELMA was very similar to the original results (v_mean: mean difference ± standard deviation: 0.1 ± 0.8 cm/s; velocity PI: mean difference ± standard deviation: 0.01 ± 0.1) despite the slightly higher number of detected vessels in SELMA (N_detected: mean difference ± standard deviation: 4 ± 9 vessels), which can be explained by the vessel selection paradigm of SELMA. The Dice Similarity Coefficient of drawn regions of interest between two operators using SELMA was 0.91 (range 0.69-0.95) and the overall intra-class coefficient for N_detected, v_mean, and velocity PI were 0.92, 0.84, and 0.85, respectively. The differences in the outcome measures was higher between sites than vendors, indicating the challenges in harmonizing the 2D PC-MRI sequence even across sites with the same vendor. We show that SELMA is a consistent and user-friendly analysis tool for small cerebral vessels.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 2","pages":"11"},"PeriodicalIF":2.7,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11754306/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143014738","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":"CDCG-UNet: Chaotic Optimization Assisted Brain Tumor Segmentation Based on Dilated Channel Gate Attention U-Net Model.","authors":"K Bhagyalaxmi, B Dwarakanath","doi":"10.1007/s12021-024-09701-6","DOIUrl":"10.1007/s12021-024-09701-6","url":null,"abstract":"<p><p>Brain tumours are one of the most deadly and noticeable types of cancer, affecting both children and adults. One of the major drawbacks in brain tumour identification is the late diagnosis and high cost of brain tumour-detecting devices. Most existing approaches use ML algorithms to address problems, but they have drawbacks such as low accuracy, high loss, and high computing cost. To address these challenges, a novel U-Net model for tumour segmentation in magnetic resonance images (MRI) is proposed. Initially, images are claimed from the dataset and pre-processed with the Probabilistic Hybrid Wiener filter (PHWF) to remove unwanted noise and improve image quality. To reduce model complexity, the pre-processed images are submitted to a feature extraction procedure known as 3D Convolutional Vision Transformer (3D-VT). To perform the segmentation approach using chaotic optimization assisted Dilated Channel Gate attention U-Net (CDCG-UNet) model to segment brain tumour regions effectively. The proposed approach segments tumour portions as whole tumour (WT), tumour Core (TC), and Enhancing Tumour (ET) positions. The optimization loss function can be performed using the Chaotic Harris Shrinking Spiral optimization algorithm (CHSOA). The proposed CDCG-UNet model is evaluated with three datasets: BRATS 2021, BRATS 2020, and BRATS 2023. For the BRATS 2021 dataset, the proposed CDCG-UNet model obtained a dice score of 0.972 for ET, 0.987 for CT, and 0.98 for WT. For the BRATS 2020 dataset, the proposed CDCG-UNet model produced a dice score of 98.87% for ET, 98.67% for CT, and 99.1% for WT. The CDCG-UNet model is further evaluated using the BRATS 2023 dataset, which yields 98.42% for ET, 98.08% for CT, and 99.3% for WT.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 2","pages":"12"},"PeriodicalIF":2.7,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143014842","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":"Complementary Strategies to Identify Differentially Expressed Genes in the Choroid Plexus of Patients with Progressive Multiple Sclerosis.","authors":"Aline Beatriz Mello Rodrigues, Fabio Passetti, Ana Carolina Ramos Guimarães","doi":"10.1007/s12021-024-09713-2","DOIUrl":"10.1007/s12021-024-09713-2","url":null,"abstract":"<p><p>Multiple sclerosis (MS) is a neurological disease causing myelin and axon damage through inflammatory and autoimmune processes. Despite affecting millions worldwide, understanding its genetic pathways remains limited. The choroid plexus (ChP) has been studied in neurodegenerative processes and diseases like MS due to its dysregulation, yet its role in MS pathophysiology remains unclear. Our work re-evaluates the ChP transcriptome in progressive MS patients and compares gene expression profiles using diverse methodological strategies. Samples from patient and healthy control RNASeq sequencing of brain tissue from post-mortem patients (GEO: GSE137619) were used. After an evaluation and quality control of these data, they had their transcripts mapped and quantified against the reference transcriptome GRCh38/hg38 of Homo sapiens using three strategies to identify differentially expressed genes in progressive MS patients. Functional analysis of genes revealed their involvement in immune processes, cell adhesion and migration, hormonal actions, amino acid transport, chemokines, metals, and signaling pathways. Our findings can offer valuable insights for progressive MS therapies, suggesting specific genes influence immune cell recruitment and potential ChP microenvironment changes. Combining complementary approaches maximizes literature coverage, facilitating a deeper understanding of the biological context in progressive MS.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 2","pages":"10"},"PeriodicalIF":2.7,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143014858","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":"Predicting Paediatric Brain Disorders from MRI Images Using Advanced Deep Learning Techniques.","authors":"Yogesh Kumar, Priya Bhardwaj, Supriya Shrivastav, Kapil Mehta","doi":"10.1007/s12021-024-09707-0","DOIUrl":"10.1007/s12021-024-09707-0","url":null,"abstract":"<p><p>The problem at hand is the significant global health challenge posed by children's diseases, where timely and accurate diagnosis is crucial for effective treatment and management. Conventional diagnosis techniques are typical, use tedious processes and generate inaccurate results since they are executed by human beings and cause delays in treatment that can be fatal. Considering these and other shortcomings there exists a need to have more efficient and accurate solutions based on artificial intelligence. Machine learning and more specifically, deep learning algorithms are of great help in analysing medical and clinical images to detect as well as classify diseases. In this paper, we propose a system for detecting various childhood diseases using a range of advanced Convolutional Neural Network models like EfficientNetB0, EfficientNetB3, Xception, InceptionV3, MobileNetV2, VGG19, DenseNet169, ResNet50V2, ResNet152V2, and the hybrid architecture InceptionResNetV2. These models are trained on MRI images of paediatric brain disorders to achieve high prediction accuracy. We use data visualization techniques such as segmentation and contour-based feature extraction to extract regions of interest before feeding the data into the models. The models are optimized using both ADAM and RMSprop optimizers. EfficientNetB0, when optimized with RMSprop, achieves an accuracy of 94.59%, a loss of 0.44, and an RMSE of 0.66. InceptionResNetV2, optimized with ADAM, achieves the highest accuracy of 97.59%, while EfficientNetB0 demonstrates the lowest loss (0.25) and RMSE (0.5). We also evaluate the models based on their precision, learning curves, recall, computational time, and F1 score, highlighting the effectiveness of AI-driven approaches for the diagnosis and management of children's diseases.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 2","pages":"9"},"PeriodicalIF":2.7,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143014872","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":"Twenty Years of Neuroinformatics: A Bibliometric Analysis.","authors":"Miguel Guillén-Pujadas, David Alaminos, Emilio Vizuete-Luciano, José M Merigó, John D Van Horn","doi":"10.1007/s12021-024-09712-3","DOIUrl":"10.1007/s12021-024-09712-3","url":null,"abstract":"<p><p>This study presents a thorough bibliometric analysis of Neuroinformatics over the past 20 years, offering insights into the journal's evolution at the intersection of neuroscience and computational science. Using advanced tools such as VOS viewer and methodologies like co-citation analysis, bibliographic coupling, and keyword co-occurrence, we examine trends in publication, citation patterns, and the journal's influence. Our analysis reveals enduring research themes like neuroimaging, data sharing, machine learning, and functional connectivity, which form the core of Neuroinformatics. These themes highlight the journal's role in addressing key challenges in neuroscience through computational methods. Emerging topics like deep learning, neuron reconstruction, and reproducibility further showcase the journal's responsiveness to technological advances. We also track the journal's rising impact, marked by a substantial growth in publications and citations, especially over the last decade. This growth underscores the relevance of computational approaches in neuroscience and the high-quality research the journal attracts. Key bibliometric indicators, such as publication counts, citation analysis, and the h-index, spotlight contributions from leading authors, papers, and institutions worldwide, particularly from the USA, China, and Europe. These metrics provide a clear view of the scientific landscape and collaboration patterns driving progress. This analysis not only celebrates Neuroinformatics's rich history but also offers strategic insights for future research, ensuring the journal remains a leader in innovation and advances both neuroscience and computational science.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 1","pages":"7"},"PeriodicalIF":2.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11735507/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142985286","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":"Patch-Wise Deep Learning Method for Intracranial Stenosis and Aneurysm Detection-the Tromsø Study.","authors":"Luca Bernecker, Ellisiv B Mathiesen, Tor Ingebrigtsen, Jørgen Isaksen, Liv-Hege Johnsen, Torgil Riise Vangberg","doi":"10.1007/s12021-024-09697-z","DOIUrl":"10.1007/s12021-024-09697-z","url":null,"abstract":"<p><p>Intracranial atherosclerotic stenosis (ICAS) and intracranial aneurysms are prevalent conditions in the cerebrovascular system. ICAS causes a narrowing of the arterial lumen, thereby restricting blood flow, while aneurysms involve the ballooning of blood vessels. Both conditions can lead to severe outcomes, such as stroke or vessel rupture, which can be fatal. Early detection is crucial for effective intervention. In this study, we introduced a method that combines classical computer vision techniques with deep learning to detect intracranial aneurysms and ICAS in time-of-flight magnetic resonance angiography images. The process began with skull-stripping, followed by an affine transformation to align the images to a common atlas space. We then focused on the region of interest, including the circle of Willis, by cropping the relevant area. A segmentation algorithm was used to isolate the arteries, after which a patch-wise residual neural network was applied across the image. A voting mechanism was then employed to identify the presence of atrophies. Our method achieved accuracies of 76.5% for aneurysms and 82.4% for ICAS. Notably, when occlusions were not considered, the accuracy for ICAS detection improved to 85.7%. While the algorithm performed well for localized pathological findings, it was less effective at detecting occlusions, which involved long-range dependencies in the MRIs. This limitation was due to the architectural design of the patch-wise deep learning approach. Regardless, this can, in the future, be mitigated in a multi-scale patch-wise algorithm.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 1","pages":"8"},"PeriodicalIF":2.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11735523/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142985285","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":"<ArticleTitle xmlns:ns0=\"http://www.w3.org/1998/Math/MathML\">Large Scale in vivo Acquisition, Segmentation and 3D Reconstruction of Cortical Vasculature using <ns0:math><ns0:mi>μ</ns0:mi></ns0:math> Doppler Ultrasound Imaging.","authors":"Anoek Strumane, Théo Lambert, Jan Aelterman, Danilo Babin, Gabriel Montaldo, Wilfried Philips, Clément Brunner, Alan Urban","doi":"10.1007/s12021-024-09706-1","DOIUrl":"10.1007/s12021-024-09706-1","url":null,"abstract":"<p><p>The brain is composed of a dense and ramified vascular network of arteries, veins and capillaries of various sizes. One way to assess the risk of cerebrovascular pathologies is to use computational models to predict the physiological effects of reduced blood supply and correlate these responses with observations of brain damage. Therefore, it is crucial to establish a detailed 3D organization of the brain vasculature, which could be used to develop more accurate in silico models. To this end, we have adapted our functional ultrasound imaging platform, previously designed for recording large scale activity, to enable rapid and reproducible acquisition, segmentation and reconstruction of the cortical vasculature. For the first time, it allows us to digitize the cortical <math><mrow><mo>∼</mo> <mn>100</mn></mrow> </math> - <math><mi>μ</mi></math> m3 spatial resolution. Unlike most available strategies, our approach can be performed in vivo within minutes. Moreover, it is easy to implement since it requires neither exogenous contrast agents nor long post-processing time. Therefore, we performed a cortex-wide reconstruction of the vasculature and its quantitative analysis, including i) classification of descending arteries versus ascending veins in more than 1500 vessels/animal and ii) rapid estimation of their length. Importantly, we confirmed the relevance of our approach in a model of cortical stroke, which allows rapid visualization of the ischemic lesion. This development contributes to extending the capabilities of ultrasound neuroimaging to better understand cerebrovascular pathologies such as stroke, vascular cognitive impairment and brain tumors, and is highly scalable for the clinic.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 1","pages":"5"},"PeriodicalIF":2.7,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11729217/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142980502","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":"Classification Prediction of Hydrocephalus After Intercerebral Haemorrhage Based on Machine Learning Approach.","authors":"Enwen Zhu, Zhuojun Zou, Jianxian Li, Jipan Chen, Ao Chen, Naifei Zhao, Qiang Yuan, Caicai Liu, Xin Tang","doi":"10.1007/s12021-024-09710-5","DOIUrl":"10.1007/s12021-024-09710-5","url":null,"abstract":"<p><p>In order to construct a clinical classification prediction model for hydrocephalus after intercerebral haemorrhage(ICH) to guide clinical treatment decisions, this paper retrospectively analyses the clinical data of 844 cases of ICH and hydrocephalus inpatients admitted to Yueyang People's Hospital from May 2019 to October 2022, of which 95 cases of hydrocephalus occurred after ICH and no hydrocephalus in 749 cases. The following indicators were compared between the two groups of patients: gender, age, Glasgow Coma Scale(GCS)score, whether the amount of bleeding was greater than 30 ml, whether it broke into the ventricle or not, modified Graeb score(MGS), modified Rankin Scale (MRS) score, whether surgery was performed or not, red blood cells, white blood cells, and platelets. After variable screening, the following six variables were selected: GCS score, MGS, MRS score, whether the bleeding volume was greater than 30 ml, whether it broke into the ventricle or not, and whether surgery was performed or not were modelled and analysed using logistic regression model and support vector machine model in machine learning. The results showed that under the same conditions, the accuracy of the support vector machine model was 0.89 and F1 was 0.838 ,the value of the AUC of the support vector machine model is 0.888; the accuracy of the logistic regression model was 0.902 and F1 was 0.89, the value of the AUC of the support vector machine model is 0.903. Compared with the group without hydrocephalus, patients in the group with hydrocephalus had bleeding volume greater than 30 ml, haemorrhage into the ventricles of the brain, and had undergone surgery in the brain, and the difference was statistically significant (P 0.001). Statistical analysis showed that GCS score ≤ 8.8, modified Graeb score (MGS) ≥ 10 and MRS score ≥ 3 were independent risk factors for the development of hydrocephalus after spontaneous ventricular haemorrhage. Therefore, patients with lower GCS score, higher modified Graeb score, higher MRS score, bleeding volume > 30 ml, haemorrhage into the ventricles of the brain, and experience of having undergone surgery in the brain should be operated on early to remove the intraventricular haematoma in order to reduce the incidence of hydrocephalus.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 1","pages":"6"},"PeriodicalIF":2.7,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142980503","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 Brain is…\": A Survey of the Brain's Many Definitions.","authors":"Taylor Bolt, Lucina Q Uddin","doi":"10.1007/s12021-024-09699-x","DOIUrl":"10.1007/s12021-024-09699-x","url":null,"abstract":"<p><p>A reader of the peer-reviewed neuroscience literature will often encounter expressions like the following: 'the brain is a dynamic system', 'the brain is a complex network', or 'the brain is a highly metabolic organ'. These expressions attempt to define the essential functions and properties of the mammalian or human brain in a simple phrase or sentence, sometimes using metaphors or analogies. We sought to survey the most common phrases of the form 'the brain is…' in the biomedical literature to provide insights into current conceptualizations of the brain. Utilizing text analytic tools applied to a large sample (> 4 million) of peer-reviewed full-text articles and abstracts, we extracted several thousand phrases of the form 'the brain is…' and identified over a dozen frequently appearing phrases. The most used phrases included metaphors (e.g., the brain as a 'information processor' or 'prediction machine') and descriptions of essential functions (e.g., 'a central organ of stress adaptation') or properties (e.g., 'a highly vascularized organ'). Comparison of these phrases with those involving other bodily organs (e.g. the heart, liver, etc.) highlighted common phrases between the brain and other organs, such as the heart as a 'complex, dynamic system'. However, the brain was unique among organs in the number and diversity of analogies ascribed to it. The results of our analysis underscore the diversity of qualities and functions attributed to the brain in the biomedical literature and suggest a range of conceptualizations that defy unification.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 1","pages":"4"},"PeriodicalIF":2.7,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11724787/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142967245","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":"Computational Generation of Long-range Axonal Morphologies.","authors":"Adrien Berchet, Remy Petkantchin, Henry Markram, Lida Kanari","doi":"10.1007/s12021-024-09696-0","DOIUrl":"10.1007/s12021-024-09696-0","url":null,"abstract":"<p><p>Long-range axons are fundamental to brain connectivity and functional organization, enabling communication between different brain regions. Recent advances in experimental techniques have yielded a substantial number of whole-brain axonal reconstructions. While previous computational generative models of neurons have predominantly focused on dendrites, generating realistic axonal morphologies is more challenging due to their distinct targeting. In this study, we present a novel algorithm for axon synthesis that combines algebraic topology with the Steiner tree algorithm, an extension of the minimum spanning tree, to generate both the local and long-range compartments of axons. We demonstrate that our computationally generated axons closely replicate experimental data in terms of their morphological properties. This approach enables the generation of biologically accurate long-range axons that span large distances and connect multiple brain regions, advancing the digital reconstruction of the brain. Ultimately, our approach opens up new possibilities for large-scale in-silico simulations, advancing research into brain function and disorders.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 1","pages":"3"},"PeriodicalIF":2.7,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11723904/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142957917","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}