Neuroinformatics最新文献

Fractional-Order Spiking Bayesian Neural Model for Cognitive Computations. 认知计算的分数阶峰值贝叶斯神经模型。

IF 3.1 4区医学

Neuroinformatics Pub Date : 2026-05-07 DOI: 10.1007/s12021-026-09788-z

Vikas Arya, Daya Krishan Lobiyal

{"title":"Fractional-Order Spiking Bayesian Neural Model for Cognitive Computations.","authors":"Vikas Arya, Daya Krishan Lobiyal","doi":"10.1007/s12021-026-09788-z","DOIUrl":"https://doi.org/10.1007/s12021-026-09788-z","url":null,"abstract":"Cognition under uncertainty can be formalized through Bayesian inference, but biologically plausible neural implementations remain a challenge. This study develops a Bayesian neural model for lifespan prediction that integrates fractional-order dynamics into classical Leaky Integrate-and-Fire and Izhikevich neuron models. The inclusion of fractional derivative introduces long-term memory, and thus enhancing both biological plausibility and representational capacity of the Bayesian neural model. Experimental results demonstrate that fractional-order neuron models consistently provide closer alignment with both human predictions and optimal Bayesian predictions. The large-scale fractional-order Izhikevich model shows the most robust convergence and cortical plausibility. These findings highlight the role of fractal neural dynamics in probabilistic cognition and bridging theoretical Bayesian models with realistic spiking behavior. The study demonstrates how biologically inspired spiking neuron models can approximate Bayesian inference, suggesting pathways for computational neuroscience to design models that learn, predict, and adapt with the efficiency of cortical computations. Further, in this study, neural populations represent priors from demographic lifetables. However, a uniform likelihood and posterior that yield median lifespan predictions as probability distributions within the Neural Engineering Framework have been retained from the previous study. To investigate the influence of neural population size on biological plausibility and Bayesian optimality, experimental conditions systematically increase the neural population size and compare the predictive outcomes.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147845196","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}

引用次数: 0

Cross-atlas Identification of Narrative Hubs via Multi-embedding Graph Models in fMRI Data. fMRI数据中基于多嵌入图模型的叙事中心交叉图谱识别。

IF 3.1 4区医学

Neuroinformatics Pub Date : 2026-05-02 DOI: 10.1007/s12021-026-09787-0

Mohammad Amin Saket, Mansooreh Pakravan

{"title":"Cross-atlas Identification of Narrative Hubs via Multi-embedding Graph Models in fMRI Data.","authors":"Mohammad Amin Saket, Mansooreh Pakravan","doi":"10.1007/s12021-026-09787-0","DOIUrl":"https://doi.org/10.1007/s12021-026-09787-0","url":null,"abstract":"One of the main objectives of cognitive neuroscience is to investigate brain processes that underlie narrative comprehension. Furthermore, earlier studies that used naturalistic functional magnetic resonance imaging (fMRI) datasets, like Narratives, has advanced our knowledge of large-scale language and narrative networks, most studies have relied on correlation-based analyses or single-region importance measures, overlooking the dynamic and structural properties of brain networks. In this work, we present a new graph-based framework to identify important regions in narrative comprehension by combining a composite node importance scoring method with multiple node embedding algorithms. We first used controlled simulations with stochastic block models (SBM) with different hub nodes and community strengths to validate the framework. This made it possible to systematically assess seven embedding algorithms for node influence attribution, link prediction, and community detection. Applying the same framework to fMRI data, we analyzed two parcellation schemes, the Harvard-Oxford and Schaefer (100-parcel) atlases, to identify influential cortical regions. Our findings reveal consistent engagement of the default mode, salience, and limbic networks across stories and atlases, emphasizing their central role in narrative processing. Overall, this work offers a reliable, comprehensible method for identifying key brain regions, bridging the gap between graph representation learning and cognitive neuroscience. The framework provides a scalable basis for further research that connects naturalistic cognition, dynamic brain connectivity, and linguistic features.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147822889","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}

引用次数: 0

Diagnostic Classification of Autism Spectrum Disorder in the Frequency Domain Using Resting-State fMRI. 静息状态功能磁共振成像在频域诊断自闭症谱系障碍的分类。

IF 3.1 4区医学

Neuroinformatics Pub Date : 2026-05-01 DOI: 10.1007/s12021-026-09782-5

Hossein Haghighat

{"title":"Diagnostic Classification of Autism Spectrum Disorder in the Frequency Domain Using Resting-State fMRI.","authors":"Hossein Haghighat","doi":"10.1007/s12021-026-09782-5","DOIUrl":"https://doi.org/10.1007/s12021-026-09782-5","url":null,"abstract":"Autism spectrum disorder (ASD) is a neurodevelopmental disorder with problems in social interactions, verbal and non-verbal communication, repetitive behaviors, and limited interests in a person. Considering the challenges in diagnosing ASD based on behavioral symptoms-such as subjectivity, variability among individuals, and overlap with other developmental conditions-it seems necessary to propose computer-aided diagnosis systems (CADS) for ASD. We proposed an age-dependent CADS based on functional connectivity (FC) in the frequency domain for ASD using resting-state functional magnetic resonance imaging (rs-fMRI). Also, the features and classification accuracy obtained in the frequency and time domains were compared. First, preprocessing was performed on the rs-fMRI data. Then, group-independent component analysis (GICA) was used to obtain resting state networks (RSNs). This was followed by obtaining separate components of RSNs for each individual using dual regression. Then, coherence analysis was used to extract the features of FC in the frequency domain between RSNs. To consider the role of age in the classification process, three age groups of children, adolescents, and adults were considered, and feature selection for each age group was applied separately using an embedded approach, in which all classifiers in the Waikato Environment for Knowledge Analysis (WEKA) machine learning platform were used simultaneously. Finally, classification accuracy was obtained for each age group. The proposed CADS was able to classify 95.23% in the children group, 88.1% in the adolescent group, and 92.8% in the adult group. In addition, the frequency bands whose features obtained the most distinction in each age group were identified, highlighting their potential relevance for supporting ASD diagnosis and monitoring rehabilitation.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147822855","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}

引用次数: 0

Template-Based Label Propagation for Mouse Brain MRI Skull Stripping. 基于模板的标签传播用于小鼠脑MRI颅骨剥离。

IF 3.1 4区医学

Neuroinformatics Pub Date : 2026-05-01 DOI: 10.1007/s12021-026-09785-2

Rui Gong, Andrii Gegliuk, Daria Sharapova, Bhanu Sharma, Yoshifumi Abe, Ken Nakae, Shin Ishii, Sho Yagishita

{"title":"Template-Based Label Propagation for Mouse Brain MRI Skull Stripping.","authors":"Rui Gong, Andrii Gegliuk, Daria Sharapova, Bhanu Sharma, Yoshifumi Abe, Ken Nakae, Shin Ishii, Sho Yagishita","doi":"10.1007/s12021-026-09785-2","DOIUrl":"https://doi.org/10.1007/s12021-026-09785-2","url":null,"abstract":"Accurate skull stripping is an essential preprocessing step in mouse brain magnetic resonance imaging, particularly for reliable atlas registration and large scale population studies. Existing approaches are often labor intensive, sensitive to inter subject variability, and typically require manual brain masks for many individual subjects. We present a high throughput skull stripping pipeline that utilize template-based label propagation to efficiently generate training data for automated segmentation. A population average ex vivo T2-weighted mouse brain MRI template including the skull was constructed, and a single brain mask was manually annotated in template space. This mask was propagated to individual subjects using inverse transformations and used to train an attention-based 3D U-Net segmentation model. Compared with conventional pipelines requiring manual masks from multiple subjects, the proposed approach achieves competitive segmentation performance while substantially reducing manual annotation effort. Additional experiments comparing training with propagated labels, manual labels, and their combination showed that training on propagated labels alone provided robust performance, suggesting that label consistency may be as important as label quality. To evaluate generalizability, we conducted experiments on an independent in vivo mouse MRI dataset. Direct application of the trained model using ex vivo data to in vivo data resulted in reduced performance, indicating a domain shift between imaging conditions. However, applying the full pipeline to the in vivo dataset, including template construction and label propagation, yielded strong segmentation performance. These results indicate that, while trained models are domain specific, the proposed framework is adaptable across imaging conditions and provides a practical strategy for generating large, anatomically consistent training datasets for biomedical image segmentation.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13135007/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147822869","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}

引用次数: 0

Unveiling an ALS Blood Transcriptomic Signature: A Machine Learning Classifier Distinct from Neurodegenerative Controls. 揭示ALS血液转录组特征：不同于神经退行性控制的机器学习分类器。

IF 3.1 4区医学

Neuroinformatics Pub Date : 2026-04-29 DOI: 10.1007/s12021-026-09780-7

Elisa Gascón, Ana Cristina Calvo, Pilar Zaragoza, Rosario Osta

引用次数: 0

Cross-Platform Neurotransmitter & Alias Ambiguity for OA-AL2b1 and OA-AL2b2 Neurons in Drosophila melanogaster. 黑腹果蝇OA-AL2b1和OA-AL2b2神经元的跨平台神经递质和别名歧义。

IF 3.1 4区医学

Neuroinformatics Pub Date : 2026-04-27 DOI: 10.1007/s12021-026-09783-4

Renoy Antony Olivera

{"title":"Cross-Platform Neurotransmitter & Alias Ambiguity for OA-AL2b1 and OA-AL2b2 Neurons in Drosophila melanogaster.","authors":"Renoy Antony Olivera","doi":"10.1007/s12021-026-09783-4","DOIUrl":"https://doi.org/10.1007/s12021-026-09783-4","url":null,"abstract":"Accurate neuron identification is necessary for reproducible connectomics. While examining historically described octopaminergic neurons of the Drosophila melanogaster optic lobe, I found that most octopaminergic (OA) neurons of interest remained searchable under their original names across commonly used resources. However, two neurons did not behave the same way. OA- AL2b1, which was linked to the alias LoVCLo3, and OA-AL2b2, which was linked to the alias MeVCMe1. This created a selective nomenclature problem. OA-AL2b1 was especially notable because it remained octopaminergic in the queried resources, yet its historical OA-based name was not consistently preserved as the searchable or visible label. OA-AL2b2 showed a different pattern; in current connectomic tools, it was labeled cholinergic, whereas in other databases, it still remained under octopaminergic groupings. Importantly, Busch et al. originally noted that OA-AL2b2 was not confirmed to be octopamine immunoreactive. An additional layer of ambiguity arose because neuPrint displayed a predicted neurotransmitter (NT) field, whereas Neuroglancer displayed consensus NT, both showing Acetylcholine as its NT. Together, these observations show how small inconsistencies in nomenclature and annotation can create major practical problems in neuron retrieval, interpretation, and cross-platform reproducibility. Although this report focuses on two neurons in Drosophila, the same problem can arise more broadly whenever historical names, database aliases, and current annotation systems are not interlinked.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147787450","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}

引用次数: 0

Quantifying Correlogram Shape to Analyze Neuronal Firing Dynamics Recorded in TBI-on-a-Chip. 量化相关图形状以分析TBI-on-a-Chip记录的神经元放电动力学。

IF 3.1 4区医学

Neuroinformatics Pub Date : 2026-04-27 DOI: 10.1007/s12021-026-09770-9

Casey Erin Adam, Shatha J Mufti, Jhon Martinez, Edmond A Rogers, Martina Dalolio, Nikita Krishnan, Timothy Beauclair, Riyi Shi

引用次数: 0

Corpus Callosum Segmentation on Structural MRI Using Multi-atlas Deformable Registration. 基于多图谱形变配准的结构MRI胼胝体分割。

IF 3.1 4区医学

Neuroinformatics Pub Date : 2026-04-24 DOI: 10.1007/s12021-026-09778-1

Amir Sariaslani, Hamid Abrishami Moghaddam, Babak A Ardekani

引用次数: 0

Evaluating Homoscedastic Uncertainty Weighting and Generative Tau Imputation in a Multimodal, Multitask Deep Learning Framework for Alzheimer's Disease. 在阿尔茨海默病多模态、多任务深度学习框架中评估均方差不确定性加权和生成式Tau归算。