Neuroinformatics最新文献_第3页

Graph-Based White Matter Tractometry: Methods, Applications, and the Path to Validation. 基于图的白质示踪法：方法、应用和验证路径。

IF 2.9 4区医学

Neuroinformatics Pub Date : 2026-07-24 DOI: 10.1007/s12021-026-09804-2

Junhao Li, Chengzhe Zhang, Zhonghua Wan, Yu Xie, Huifeng Zhang, Xiaoming Liu, Ye Wu

{"title":"Graph-Based White Matter Tractometry: Methods, Applications, and the Path to Validation.","authors":"Junhao Li, Chengzhe Zhang, Zhonghua Wan, Yu Xie, Huifeng Zhang, Xiaoming Liu, Ye Wu","doi":"10.1007/s12021-026-09804-2","DOIUrl":"10.1007/s12021-026-09804-2","url":null,"abstract":"Graph-based white matter tractometry represents bundles as networks, preserving spatial topology that traditional along-tract profiles collapse. This enables the detection of distributed pathology patterns organized across connected regions. The field is at a critical juncture: methods are proliferating rapidly, but validation infrastructure lags. We systematically assess maturity across the analytical pipeline, from diffusion measurements through graph construction, detection models, clinical applications, and validation resources. Diffusion metrics measure water behavior rather than directly measuring tissue microstructure. Their biological interpretation relies on model assumptions that have been validated only in restricted contexts. Tractography provides spatial scaffolding with known limitations, and graph-construction choices encode implicit hypotheses about the organization of pathology but are often underspecified. Clinical studies demonstrate consistent group differences across disorders. Where controlled comparisons exist, graph methods show modest improvements over traditional approaches. However, comprehensive benchmarking against TBSS and AFQ is absent. Dedicated validation platforms for tractometry detection models do not yet exist. We document concrete barriers to systematic validation and assess emerging infrastructure addressing these gaps. Graph-based tractometry shows promise as a research tool. Realizing its clinical potential requires validation matching the maturity of traditional approaches.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148580887","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

Temporal Topology Provides an Interpretable Framework for Neuronal Morphogenesis. 时间拓扑为神经元形态发生提供了一个可解释的框架。

IF 2.9 4区医学

Neuroinformatics Pub Date : 2026-07-24 DOI: 10.1007/s12021-026-09795-0

Killian Rigaux, André Ferreira Castro, Lida Kanari

{"title":"Temporal Topology Provides an Interpretable Framework for Neuronal Morphogenesis.","authors":"Killian Rigaux, André Ferreira Castro, Lida Kanari","doi":"10.1007/s12021-026-09795-0","DOIUrl":"10.1007/s12021-026-09795-0","url":null,"abstract":"Neuronal morphogenesis arises through coordinated neurite dynamics that generate cell-type specific dendritic branching during development. Recent advances in high-throughput time-lapse imaging techniques have transformed our ability to track such growth dynamics, yielding comprehensive anatomical datasets of neuronal morphologies. However, quantifying these structural trajectories during neuronal development remains a major challenge. We introduce the Temporal Topological Morphology Descriptor (TTMD), a framework that combines persistent topology with time-resolved neuronal imaging to quantify developmental trajectories of neuronal architecture. Applying TTMD to datasets of Drosophila sensory neurons, we show that developmental stages, branching processes, and mutant-specific growth dynamics can be decoded directly from topology without manual feature selection. Topological Morphology Descriptor (TMD) accurately resolves major developmental transitions in Class I Drosophila neurons, whereas temporal topology (TTMD) is required to uncover subtle alterations in branch dynamics caused by mutations in actin regulatory pathways in Class III Drosophila neurons. TTMD further enables automated topological tracking of branch emergence and retraction across development, overcoming the limitations of labor-intensive manual annotation. These results demonstrate that temporal topology provides a unified and interpretable language for neuronal morphogenesis, enabling the automated analysis of developmental neuroanatomy, and identifying pathological alterations in neuronal structure.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13400593/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148580912","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

Analysis of a BMI System for the Assessment of Cognitive Motor Engagement for the Neurorehabilitation of Patients with Lower-Limb Impairment. 用于评估认知运动参与对下肢损伤患者神经康复的BMI系统分析。

IF 2.9 4区医学

Neuroinformatics Pub Date : 2026-07-21 DOI: 10.1007/s12021-026-09801-5

Carlo Cavaliere-Ballesta, Mario Ortiz, Vicente Quiles, Cristina Polo-Hortigüela, Isabel Sinovas-Alonso, Eduardo Iáñez, José M Azorín

{"title":"Analysis of a BMI System for the Assessment of Cognitive Motor Engagement for the Neurorehabilitation of Patients with Lower-Limb Impairment.","authors":"Carlo Cavaliere-Ballesta, Mario Ortiz, Vicente Quiles, Cristina Polo-Hortigüela, Isabel Sinovas-Alonso, Eduardo Iáñez, José M Azorín","doi":"10.1007/s12021-026-09801-5","DOIUrl":"10.1007/s12021-026-09801-5","url":null,"abstract":"Brain-Machine Interfaces (BMIs) hold significant promise for the neurorehabilitation of patients with lower-limb impairments. However, their widespread clinical adoption is hindered by high costs and system complexity. This study presents an open-loop, low-cost EEG-based BMI designed to assess the cognitive implication of users during assisted cycling therapy. The system computes two cognitive indices: a low-frequency index, associated with motor-related engagement, and a high-frequency index, related to attention during motor tasks. These indices are obtained using Filter Bank Common Spatial Patterns (FBCSP) and Linear Discriminant Analysis (LDA), enabling continuous monitoring of cognitive involvement. Leave-One-Out Cross-Validation (LOOCV) results showed a clear increase in both indices during motor engagement compared to relaxed states in healthy subjects (low-frequency: [Formula: see text] to [Formula: see text]; high-frequency: [Formula: see text] to [Formula: see text]), whereas patients exhibited smaller increments (low-frequency: [Formula: see text] to [Formula: see text]; high-frequency: [Formula: see text] to [Formula: see text]). During validation trials, index differences between relax periods and motor engagement periods reveal a mean separation of [Formula: see text] (low-frequency) and [Formula: see text] (high-frequency) in control subjects, compared to [Formula: see text] and [Formula: see text] in patients. Results suggest that the metrics obtained by the present BMI can be a useful asset to evaluate patients' performance, engagement and fatigue during neurorehabilitation.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148537610","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

NeuroFusion: A Unified Framework for Generalized Visual Stimulus Decoding from fMRI Across Datasets and Subjects. 神经融合：从跨数据集和受试者的fMRI解码广义视觉刺激的统一框架。

IF 2.9 4区医学

Neuroinformatics Pub Date : 2026-07-18 DOI: 10.1007/s12021-026-09803-3

Muhammad Kashif, Matteo Ferrante, Nicola Toschi

{"title":"NeuroFusion: A Unified Framework for Generalized Visual Stimulus Decoding from fMRI Across Datasets and Subjects.","authors":"Muhammad Kashif, Matteo Ferrante, Nicola Toschi","doi":"10.1007/s12021-026-09803-3","DOIUrl":"10.1007/s12021-026-09803-3","url":null,"abstract":"Recent advancements in neural decoding have shown promising results in reconstructing visual experiences from brain activity. However, existing approaches focus primarily on decoding within a single dataset or subject, which limits generalization across various sources of neuroimaging. In this work, we propose a novel framework for the decoding of visual stimuli between subjects and between data sets, integrating neural recordings from multiple publicly available fMRI datasets. To address inherent intersubject and interdataset variability, we introduce a contrastive learning-based alignment strategy using image embeddings from a pre-trained IP-Adapter model. Our approach learns a shared latent space by aligning subject-specific neural representations with image features, enabling generalized decoding across both subjects and datasets. In addition, we propose a simple yet effective data augmentation method using ridge regression. This method synthesizes realistic fMRI-like signals from novel images by predicting voxel activity and injecting learned noise distributions, thus enhancing training diversity and model robustness. To the best of our knowledge, while several recent studies have explored cross-subject decoding, we extend recent cross-subject decoding efforts by training a single unified framework jointly across multiple public fMRI datasets and subjects, enabling cross-dataset transfer in addition to cross-subject generalization. We distinguish this multi-dataset unified training setting, where each dataset contributes training data, from a stricter leave-one-dataset-out transfer setting in which the target dataset is excluded from source pretraining and used only for lightweight alignment-layer adaptation. Empirically, our unified model achieves strong semantic reconstruction across datasets (e.g., up to 94.8% CLIP similarity on NSD (AUG) and 0.403 SSIM on BOLD5000 after lightweight finetuning), demonstrating robust cross-subject and cross-dataset transfer.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13379418/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148473830","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

Quantifying Structural Changes in Cultured Neurons Using Fractal Analysis. 用分形分析量化培养神经元的结构变化。

IF 2.9 4区医学

Neuroinformatics Pub Date : 2026-07-13 DOI: 10.1007/s12021-026-09802-4

Angela Asir Rv, Sirish Karri, Marina Kabirova, Michael Firer, Izhak Michaelevski

{"title":"Quantifying Structural Changes in Cultured Neurons Using Fractal Analysis.","authors":"Angela Asir Rv, Sirish Karri, Marina Kabirova, Michael Firer, Izhak Michaelevski","doi":"10.1007/s12021-026-09802-4","DOIUrl":"10.1007/s12021-026-09802-4","url":null,"abstract":"Quantitative analysis of neuronal morphology in dense cultures remains technically challenging, as extensive overlap between neuritic arbors and background signal limits the applicability of traditional tracing-based approaches. Here, we applied fractal dimension (FD) and lacunarity (LAC) analysis as scale-invariant, global measures of neurite complexity and systematically compared them with classical morphometric approaches in dense primary hippocampal neuronal cultures. Neurons were treated with the Abl tyrosine kinase inhibitor imatinib to induce cytoskeletal remodeling. Fractal analysis was performed on skeletonized representations derived from standardized binarized confocal images, enabling robust quantification of neurite space-filling properties across spatial scales. Imatinib treatment induced a pronounced reduction in FD (DMSO: ~1.56 ± 0.04 vs. IMA: ~1.36 ± 0.05, p < 0.001) together with increased lacunarity, indicating reduced multiscale complexity and increased spatial heterogeneity. In contrast, classical morphometric analysis revealed a heterogeneous phenotype, with elongation of primary neurites accompanied by reduced branching. These opposing effects partially compensated for one another, obscuring overall complexity changes when assessed using individual parameters. To integrate multivariate features, we constructed a composite Neurite Complexity Index (NCI) incorporating branching, neurite length, and distribution-sensitive descriptors (R² ≈ 0.98; F(4,106) = 89.6, p < 1 × 10⁻184). FD showed strong convergence with the NCI across bootstrap analyses, and clustering and discriminant analyses confirmed its high discriminatory power. Together, these findings suggest that fractal analysis may provide a sensitive and compact framework within the present experimental context for detecting treatment-associated structural changes in dense neuronal cultures.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148431380","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

Ricci Flow-Based Approach for Early Diagnosis of Alzheimer's Disease. 基于Ricci流的阿尔茨海默病早期诊断方法

IF 2.9 4区医学

Neuroinformatics Pub Date : 2026-07-10 DOI: 10.1007/s12021-024-09695-1

Masoumeh Khodaei, Behroz Bidabad, Mohammad Ebrahim Shiri, Maral Khadem Sedaghat

{"title":"Ricci Flow-Based Approach for Early Diagnosis of Alzheimer's Disease.","authors":"Masoumeh Khodaei, Behroz Bidabad, Mohammad Ebrahim Shiri, Maral Khadem Sedaghat","doi":"10.1007/s12021-024-09695-1","DOIUrl":"10.1007/s12021-024-09695-1","url":null,"abstract":"Early diagnosis of Alzheimer's disease (AD) is increasingly important due to its rising prevalence and significant impact on individuals, families, and healthcare systems. Hippocampal atrophy is a well-established and significant biomarker for AD. Advanced techniques like MRI imaging and surface parameterization have shown considerable promise in improving the accuracy and speed of AD diagnosis. This study aims to utilize the Ricci flow method to map the 3D hippocampal surface to a 2D sphere and extract relevant features for early AD detection. The process involves several key steps: inputting an MRI scan and preprocessing to isolate the hippocampal surface, applying the Ricci flow to map this surface to a sphere, constructing a feature vector using Linear Discriminant Analysis (LDA) and Kernel LDA, and employing various classifiers to diagnose AD, with model evaluation based on the ADNI dataset. Experimental results reveal that combining Ricci flow-based feature extraction with Kernel LDA significantly improves diagnostic accuracy. The model achieves classification accuracies of 97.28% (NC/AD), 96.14% (NC/EMCI), 96.45% (NC/MCI), 94.83% (EMCI/LMCI), 95.84% (MCI/AD), and 95.37% (LMCI/AD). Additionally, it attains 93.65% and 92.30% accuracy in three-way and four-way classification tasks, respectively. These results outperform most reviewed studies and are comparable to others. This research highlights the potential of merging advanced 3D imaging techniques with mathematical models to enhance diagnostic precision, emphasizing the critical role of early detection in the effective treatment and management of Alzheimer's disease.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148425904","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

A Replicable NeuroMark Template for Whole-Brain SPECT Reveals Data-Driven Perfusion Networks and Their Alterations in Schizophrenia. 一个可复制的全脑SPECT神经标记模板揭示数据驱动的灌注网络及其在精神分裂症中的改变。

IF 2.9 4区医学

Neuroinformatics Pub Date : 2026-07-10 DOI: 10.1007/s12021-026-09798-x

Amritha Harikumar, Bradley Baker, Daniel Amen, David Keator, Vince D Calhoun

{"title":"A Replicable NeuroMark Template for Whole-Brain SPECT Reveals Data-Driven Perfusion Networks and Their Alterations in Schizophrenia.","authors":"Amritha Harikumar, Bradley Baker, Daniel Amen, David Keator, Vince D Calhoun","doi":"10.1007/s12021-026-09798-x","DOIUrl":"10.1007/s12021-026-09798-x","url":null,"abstract":"Single photon emission computed tomography (SPECT) is a highly specialized imaging modality that enables measurement of regional cerebral perfusion and, in particular, regional cerebral blood flow (rCBF). Recent technological advances have improved SPECT quantification and reliability, making it increasingly useful for studying rCBF abnormalities and perfusion-network alterations in psychiatric and neurological disorders. To characterize large-scale functional organization in SPECT data, data-driven decomposition methods such as independent component analysis (ICA) have been used to extract covarying perfusion patterns that map onto interpretable brain networks. Blind ICA provides a data-driven approach to estimate these networks without strong prior assumptions. More recently, a hybrid approach that leverages spatial priors to guide a spatially constrained ICA (sc-ICA) have been used to fully automate the ICA analysis while also providing participant-specific network estimates. While this has been reliably demonstrated in fMRI with the NeuroMark template, there is currently no comparable SPECT template. A SPECT template would enable automatic estimation of functional SPECT networks with participant-specific expressions that correspond across participants and studies. The current study introduces a new replicable NeuroMark SPECT template for estimating canonical perfusion covariance patterns (networks). We first identify replicable SPECT networks using blind ICA applied to two large sample SPECT datasets. We then demonstrate the use of the resulting template by applying sc-ICA to an independent schizophrenia dataset. In sum, this work presents and shares the first NeuroMark SPECT template and demonstrating its utility in an independent cohort, providing a scalable and robust framework for network-based analyses.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13350202/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148425929","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

Brain Graph Sparsification for fMRI-based Connectome Analysis: A Methodological Review. 基于fmri连接组分析的脑图稀疏化：方法学回顾。

IF 2.9 4区医学

Neuroinformatics Pub Date : 2026-07-10 DOI: 10.1007/s12021-026-09800-6

Peishan Dai, Li Chen, Shuyu Guo, Kaineng Huang, Shenghui Liao

{"title":"Brain Graph Sparsification for fMRI-based Connectome Analysis: A Methodological Review.","authors":"Peishan Dai, Li Chen, Shuyu Guo, Kaineng Huang, Shenghui Liao","doi":"10.1007/s12021-026-09800-6","DOIUrl":"10.1007/s12021-026-09800-6","url":null,"abstract":"Functional magnetic resonance imaging (fMRI) is widely used to characterize functional brain organization through graph-based connectome analysis. However, functional connectivity networks constructed using Pearson correlation are typically dense and noisy, which compromises the stability of network topology measures, limits interpretability, and degrades the performance of downstream tasks such as graph-based analysis. Consequently, effective brain graph sparsification has become a critical step for improving the reliability and modeling efficiency of fMRI-based network analysis. Although a growing number of sparsification methods have been proposed in recent years, existing approaches remain fragmented and a structured methodological synthesis of this area is still lacking. To address this gap, we provide a methodological review of fMRI-based brain graph sparsification techniques. According to whether edge selection is governed by intrinsic graph properties or informed by external supervision signals, we provide a taxonomy-driven methodological review of fMRI-based brain graph sparsification. We organize existing methods into topology-guided and supervision-guided paradigms, clarify their underlying principles and trade-offs, and propose an evaluation framework and reporting considerations to enhance transparency and reproducibility. We further outline key challenges and future directions toward robust and biologically meaningful connectome modeling.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148425969","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

Event Files are Common, But Semantic Event Metadata Remain Uneven in OpenNeuro BIDS Datasets. 事件文件是常见的，但语义事件元数据在OpenNeuro BIDS数据集中仍然不均衡。

IF 2.9 4区医学

Neuroinformatics Pub Date : 2026-07-08 DOI: 10.1007/s12021-026-09797-y

Yuxuan Xu

{"title":"Event Files are Common, But Semantic Event Metadata Remain Uneven in OpenNeuro BIDS Datasets.","authors":"Yuxuan Xu","doi":"10.1007/s12021-026-09797-y","DOIUrl":"10.1007/s12021-026-09797-y","url":null,"abstract":"Open neuroscience repositories support reuse by making datasets accessible, but event level reuse also depends on whether task and stimulus annotations can be interpreted by software. I audited public latest OpenNeuro BIDS snapshots using public GraphQL metadata, recursive file trees, small events.json sidecars, and bounded events.tsv header ranges. Raw neural data were left untouched. Among 1,713 public latest snapshots, 1,483 had task metadata or observed event TSV files and formed the primary event relevant denominator. Event TSV files were present in 1,175/1,483 snapshots (79.2%; Wilson 95% CI 77.1%-81.2%). Candidate event JSON sidecars were present in 604/1,483 snapshots (40.7%), but an inheritance aware path and entity check found applicable JSON sidecars for 590/1,483 (39.8%), descriptive applicable sidecars for 550/1,483 (37.1%), and experiment specific applicable sidecars for 491/1,483 (33.1%). Across the full recursive file tree, 162,034/301,681 event TSV files (53.7%) had an applicable event JSON sidecar, and 147,412/301,681 (48.9%) had an applicable experiment specific sidecar. HED was detected in event JSON for 45/1,483 snapshots (3.0%) and in sampled TSV headers for 2/1,483 (0.13%). EEG had higher sidecar coverage and HED detection than fMRI, but 21.2% of EEG candidate JSON files were bookkeeping only. These results identify a repository visible metadata gap: event timing is common, but software interpretable event meaning remains uneven.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13346197/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148406610","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

Metabolically Faithful 3D PET Restoration via Volumetric Swin Transformers. 代谢忠实的3D PET恢复通过体积旋转变压器。

IF 2.9 4区医学

Neuroinformatics Pub Date : 2026-07-01 DOI: 10.1007/s12021-026-09796-z

Ovidijus Grigas, Rytis Maskeliūnas

{"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":"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.","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}

引用次数: 0