Neuroinformatics最新文献

{"title":"Metabolically Faithful 3D PET Restoration via Volumetric Swin Transformers.","authors":"Ovidijus Grigas, Rytis Maskeliūnas","doi":"10.1007/s12021-026-09796-z","DOIUrl":"10.1007/s12021-026-09796-z","url":null,"abstract":"<p><p>Positron Emission Tomography (PET) diagnostic precision is often compromised by low spatial resolution. Deep learning restoration models tend to sacrifice quantitative accuracy for visual sharpness, and most are trained on a single fixed degradation profile, limiting generalization across scanners. This paper presents a metabolically faithful 3D restoration framework pairing a volumetric extension of SwinFIR with two innovations: (1) a composite metabolic-aware loss enforcing structural, distributional, and frequency-domain agreement with the ground truth, and (2) a stochastic degradation augmentation strategy that randomizes point spread function parameters, voxel sampling, and counting noise during training, exposing the model to a distribution of simulated scanner-like degradations rather than a single fixed simulation. Evaluated on NeuroEXPLORER data, the proposed method outperforms baselines with a Structural Similarity Index Measure (SSIM) of 0.843, Peak Signal to Noise Ratio (PSNR) of 27.08 dB, and Normalized Root Mean Squared Error (NRMSE) of 0.117, while maintaining metabolic fidelity (Concordance Correlation Coefficient (CCC) 0.948, Wasserstein distance 0.018). Ablation experiments confirm that stochastic degradation augmentation improves robustness over fixed-profile training. The framework recovers anatomical detail with only small but measurable regional SUVR biases (≤3.3%) in selected cortical regions, which are symmetric across diagnostic groups.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148363472","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":"CytoCLIP: Learning Cytoarchitectural Characteristics in Developing Human Brain Using Contrastive Language Image Pre-Training.","authors":"Pralaypati Ta, Sriram Venkatesaperumal, Keerthi Ram, Mohanasankar Sivaprakasam","doi":"10.1007/s12021-026-09793-2","DOIUrl":"10.1007/s12021-026-09793-2","url":null,"abstract":"<p><p>The functions of different regions of the human brain are closely linked to their distinct cytoarchitecture, which is defined by the spatial arrangement and morphology of the cells. Identifying brain regions by their cytoarchitecture enables various scientific analyses of the brain. However, delineating these areas manually in brain histological sections is time-consuming and requires specialized knowledge. An automated approach is necessary to minimize the effort needed from human experts. To address this, we propose CytoCLIP, a suite of vision-language models derived from pre-trained Contrastive Language-Image Pre-Training (CLIP) frameworks to learn joint visual-text representations of brain cytoarchitecture. CytoCLIP comprises two model variants: one is trained using low-resolution whole-region images to understand the overall cytoarchitectural pattern of an area, and the other is trained on high-resolution image tiles for detailed cellular-level representation. The training dataset is created from NISSL-stained histological sections of developing fetal brains of different gestational weeks. It includes 86 distinct regions for low-resolution images and 379 brain regions for high-resolution tiles. We evaluate the model's understanding of the cytoarchitecture and generalization ability using region classification and cross-modal retrieval tasks. Multiple experiments are performed under various data setups, including data from samples of different ages and sectioning planes. Experimental results demonstrate that CytoCLIP outperforms existing methods. It achieves a weighted F1 score of 0.87 for whole-region classification and 0.91 for high-resolution image tile classification.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148340915","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":"Increasing the Reliability of Functional Connectivity by Predicting Long-Scan Functional Connectivity based on Short-Scan Functional Connectivity: Model Exploration, Explanation, Validation, and Application.","authors":"Bo Hu, Juan Liu","doi":"10.1007/s12021-026-09794-1","DOIUrl":"10.1007/s12021-026-09794-1","url":null,"abstract":"<p><p>Functional connectivity (FC) is a widely used metric in functional magnetic resonance imaging (fMRI) research. However, its reliability has long been a notable concern, especially in studies with small sample sizes. Previous research has demonstrated that FC derived from longer fMRI scans exhibits higher reliability, making the prediction of long-scan FC from existing short-scan FC data a feasible and promising approach. First, we constructed three general linear models (GLMs) to predict long-scan FC from short-scan FC data using the Human Connectome Project (HCP) dataset. Next, we interpreted the models by visualizing their weight distributions. Subsequently, we validated our findings across multiple independent datasets and with different machine learning models. Finally, we applied the models to enhance both the test-retest reliability of FC and the performance of connectome-based predictive modeling (CPM). Our results showed that GLMs based on individual short-scan FC successfully predicted individual long-scan FC values. Moreover, the differences between the three GLMs could be explained by the distinct distribution characteristics of the FC matrices they predicted. Our findings were validated using data from the Consortium for Reliability and Reproducibility (CoRR) project and an in-house local dataset. Additionally, our models outperformed conventional machine learning approaches. Critically, these models effectively improved both the test-retest reliability of FC and the predictive performance of CPM. In conclusion, GLMs built on individual short-scan FC can robustly predict individual long-scan FC values. These models show strong generalizability across different datasets, and can be widely applied to improve the test-retest reliability of FC and the performance of CPM in neuroimaging studies.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148297153","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":"Multimodal Branched Transport Infers Anatomically Aligned Brain Reaction Maps.","authors":"Cristian Mendico","doi":"10.1007/s12021-026-09791-4","DOIUrl":"10.1007/s12021-026-09791-4","url":null,"abstract":"<p><p>How external stimulation is transformed into distributed reaction patterns remains unresolved at the level of propagation architecture. Existing large-scale control models quantify transition costs on prescribed networks but do not infer the routing map itself from source and target activity. Here we combine task-related blood-oxygen-level-dependent responses, source-reconstructed electrophysiology and tractography-derived anisotropy to estimate stimulation and reaction measures, define an anatomical transport cost, and infer a branched propagation architecture by variational optimisation. Unlike standard transport formulations, branched transport favours aggregation of signal into shared neural highways before redistribution. We further equip the inferred map with a stochastic graph-induced dynamical model to the inferred map and quantify the trade-off between geometric efficiency and dynamical controllability. We show that multimodal data generate anatomically aligned brain reaction maps, that anisotropic costs qualitatively reshape routing backbones relative to isotropic baselines, and that hybrid geometric-dynamical optimisation reveals non-trivial rank reversals across branching regimes.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 2","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13253786/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148220598","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":"HESREN: A Derivative-Informed Reservoir Framework for Detecting Transient Neural Events and Windowless Estimation of Dynamic Functional Connectivity.","authors":"Ugur Kadak","doi":"10.1007/s12021-026-09792-3","DOIUrl":"10.1007/s12021-026-09792-3","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Dynamic functional connectivity (dFC) analysis in functional magnetic resonance imaging (fMRI) faces a fundamental challenge: conventional sliding-window methods must trade temporal resolution against statistical reliability, while rare transient neural events risk becoming undetectable when included in training data. We introduce HESREN (Hermite-Enhanced Software Reservoir Network), a novel framework integrating echo state networks with derivative-informed Hermite-type neural operators to enable windowless dFC estimation and leakage-free transient detection. HESREN employs a leaky-integrator reservoir that projects multivariate fMRI time series into high-dimensional state spaces, augmented with Gaussian-smoothed temporal derivatives to form enhanced feature vectors encoding value, velocity, and acceleration. Strict temporal partitioning trains all components exclusively on baseline segments while evaluating on complete time series, preserving transient events as out-of-distribution signals. Teacher-student distillation transfers the temporal precision of micro-window connectivity estimates into stable windowless operators via ridge-regularised linear readout; all hyperparameters and initialisation procedures are fully specified to ensure reproducibility. Validation on the NEBULA101 resting-state fMRI dataset across [Formula: see text] participants demonstrates consistent and substantial improvements over conventional methods. Transient event detection achieves AUC[Formula: see text] and average precision AP[Formula: see text], compared to AUC[Formula: see text] for raw-derivative baselines (Wilcoxon [Formula: see text], [Formula: see text], Cohen's [Formula: see text]), with phase-randomised surrogate testing confirming statistical robustness in all participants ([Formula: see text], [Formula: see text] surrogates). Comparison against mainstream dFC alternatives shows that HESREN statistically significant performance gains Gaussian Hidden Markov Models (AUC[Formula: see text]), temporal convolutional networks (AUC[Formula: see text]), LSTM autoregressive predictors (AUC[Formula: see text]), and conventional sliding-window correlation (AUC[Formula: see text]), with all advantages statistically significant ([Formula: see text]). Windowless dFC trajectories attain lag-corrected correlation [Formula: see text] with micro-window teachers while providing 3-[Formula: see text] finer temporal resolution than 25-TR sliding windows. Network-level analysis reveals that HESREN detects transient events an average of 4.5 TR (9 s) earlier than sliding-window methods, selectively amplifies within-language-network coupling by [Formula: see text] and default-mode-network coupling by [Formula: see text] during detected events, and is the only evaluated method to yield a positive network segregation index ([Formula: see text]), consistent with the known modular organisation of resting-state brain networks. HESREN overcomes fundamental limitations of sliding-w","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 2","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13253793/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148220574","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 Morphometry of Peripheral Nerves: A Pipeline Perspective on Reproducibility and Generalization.","authors":"Antonina Spalińska, Michał Kopka, Karolina Kopka, Wiktor Pascal, Krzysztof Spaliński, Magdalena Jasionowska-Skop, Artur Przelaskowski","doi":"10.1007/s12021-026-09777-2","DOIUrl":"10.1007/s12021-026-09777-2","url":null,"abstract":"<p><p>Computational morphometry has transformed the quantitative analysis of peripheral nerve structure, enabling large-scale, computational, and longitudinal studies that were previously impractical using manual methods. However, this review argues that the reliability and interpretability of morphometric outputs are fundamentally pipeline-conditional, shaped by assumptions introduced across sample acquisition, preparation, imaging, annotation, segmentation, and metric extraction rather than by segmentation accuracy alone. By examining the full morphometry pipeline, we show how protocol variability, limited model generalization, and ambiguity in expert-defined ground truth propagate downstream and constrain reproducibility, particularly in pathological tissue. Using peripheral nerve morphometry as a tractable model system, we highlight issues that are representative of broader challenges in medical image analysis and quantitative neuroanatomy. We conclude that progress in computational morphometry will depend less on incremental algorithmic improvements and more on shared datasets, uncertainty-aware validation, and closer alignment between structural metrics and functional relevance in both experimental and clinical contexts.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 2","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148220562","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":"Model Validation Pipeline Against Longitudinal Alzheimer's Biomarker Data.","authors":"Rabha W Ibrahim, Mona Hmoud AlSheikh","doi":"10.1007/s12021-026-09790-5","DOIUrl":"10.1007/s12021-026-09790-5","url":null,"abstract":"<p><p>This study develops a fractional-order model of Alzheimer's disease using a [Formula: see text]-generalized Atangana-Baleanu-Caputo (ABC) operator to capture the spatiotemporal dynamics of amyloid-beta and tau protein spread, coupled with a neuron regeneration mechanism. The fractional parameters α, [Formula: see text], and τ control memory depth, deformation of the kernel, and temporal scaling, respectively. Numerical simulations demonstrate that: (i) intermediate fractional orders [Formula: see text] produce biologically realistic propagation delays, (ii) lower [Formula: see text] values enhance nonlocal interactions and accelerate tau diffusion across the connectome, and (iii) increasing the scaling parameter τ slows accumulation, mimicking effective clearance or treatment response. Incorporating a treatment term with drug diffusion and decay reveals that sustained low decay rates ([Formula: see text]) markedly reduce tau concentrations and protect neuron populations. These findings show that the [Formula: see text]-ABC framework not only captures the hereditary and memory effects of Alzheimer's progression but also provides a flexible platform for simulating therapeutic interventions and predicting disease trajectories using real brain connectome data.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 2","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148007280","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":"When Random Variation Results in Functional Segregation.","authors":"Jacob Barfield, Patrick Kells, Shree Gautam, Woodrow Shew","doi":"10.1007/s12021-026-09779-0","DOIUrl":"10.1007/s12021-026-09779-0","url":null,"abstract":"<p><p>Many functional properties vary dramatically across neurons in cerebral cortex. Two fundamental goals of systems neuroscience are to determine which neurons execute which functions and how the different functional properties of a neuron are related. Here we focus on functional segregation - when two separate subpopulation of neurons encodes two distinct functions. Often, it is assumed that if two functional properties are uncorrelated across the population, then there is no functional segregation. Here we show that this assumption can lead to wrong conclusions; functional segregation can emerge, by chance, due to random variation when that variation is distributed according to skewed, heavy-tailed distributions. We reexamine the results we previously reported (Nature communications, 10(1), 1575-1575 2019), which showed that neurons in primary motor cortex tend to be functionally segregated, with neurons that are either strongly coupled to body movements or to ongoing cortical population activity. Here we show that this is a prime example of functional segregation due to random variation.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 2","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13186786/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147976604","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":"Instance-Aware Pseudo-Labeling and Class-Focused Contrastive Learning for Weakly Supervised Domain Adaptive Segmentation of Electron Microscopy.","authors":"Shan Xiong, Jiabao Chen, Ye Wang, Jialin Peng","doi":"10.1007/s12021-026-09786-1","DOIUrl":"10.1007/s12021-026-09786-1","url":null,"abstract":"<p><p>Annotation-efficient segmentation of the numerous mitochondria instances from various electron microscopy (EM) images is highly valuable for biological and neuroscience research. Although unsupervised domain adaptation (UDA) methods can help mitigate domain shifts and reduce the high costs of annotating each domain, they typically have relatively low performance in practical applications. Thus, we investigate weakly supervised domain adaptation (WDA) that utilizes additional sparse point labels on the target domain, which require minimal annotation effort and minimal expert knowledge. To take full use of the incomplete and imprecise point annotations, we introduce a multitask learning framework that jointly conducts segmentation and center detection with a novel cross-teaching mechanism and class-focused cross-domain contrastive learning. While leveraging unlabeled image regions is essential, we introduce segmentation self-training with a novel instance-aware pseudo-label (IPL) selection strategy. Unlike existing methods that typically rely on pixel-wise pseudo-label filtering, the IPL semantically selects reliable and diverse pseudo-labels with the help of the detection task. Comprehensive validations and comparisons on challenging datasets demonstrate that our method outperforms existing UDA and WDA methods, significantly narrowing the performance gap with the supervised upper bound. Furthermore, under the UDA setting, our method also achieves substantial improvements over other UDA techniques.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 2","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147870094","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":"Fractional-Order Spiking Bayesian Neural Model for Cognitive Computations.","authors":"Vikas Arya, Daya Krishan Lobiyal","doi":"10.1007/s12021-026-09788-z","DOIUrl":"10.1007/s12021-026-09788-z","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 2","pages":""},"PeriodicalIF":2.9,"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}