Frontiers in Computational Neuroscience最新文献

{"title":"Network-level restoration of impaired memory function in early Alzheimer's disease via GluN2B modulation: insights from a hippocampal CA1-CA3 model.","authors":"Fabio Librizzi, Saana Seppälä, Marja-Leena Linne, Michele Migliore, Ausra Saudargiene","doi":"10.3389/fncom.2026.1953352","DOIUrl":"10.3389/fncom.2026.1953352","url":null,"abstract":"<p><p>Memory storage and recall are essential tasks for the healthy function of the hippocampus, adversely affected by neurodegenerative diseases, such as Alzheimer's Disease (AD). Pathological levels of amyloid-β peptide (Aβ), found already in early-stage AD, can disrupt the NMDAR-dependent plasticity at hippocampal networks. Here we investigate these effects in a biophysically detailed computational model of a hippocampal CA1-CA3 network and examine potential mechanisms of functional recovery. We show that, at a network level, long-term potentiation (LTP) impairments caused by Aβ accumulation can be substantially restored by partially blocking the GluN2B-NMDA receptors-a mechanism previously observed and modeled only at a single cell level. Experimental access to network-level processes of memory storage and recall in pathological conditions remains highly challenging. Therefore, computational modeling may help to bridge the gap between cellular impairments and system-level cognitive dysfunction. Our work transfers the established GluN2B-dependent rescue mechanisms from single cells to a network-level system of hippocampal pyramidal neurons and interneurons. The results demonstrate that also at a higher cognition level, the partial inhibition of the activity of GluN2B-NMDA receptors can be an effective strategy to counteract early AD impairments on memory storage and recall.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1953352"},"PeriodicalIF":3.3,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13668818/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149215434","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":"An innovative framework based on GMV-ALFF multi-modal MRI and Gaussian data augmentation for psychiatric disorder classification.","authors":"Wenjing Zhu, Xiaoying Jiang","doi":"10.3389/fncom.2026.1835559","DOIUrl":"https://doi.org/10.3389/fncom.2026.1835559","url":null,"abstract":"<p><strong>Objective: </strong>Schizophrenia (SZ) and bipolar disorder (BD) exhibit significant overlap in clinical symptoms, posing substantial challenges to accurate diagnosis. This study proposes an innovative multi-modal fusion framework based on convolutional neural networks (CNNs), integrating structural and functional MRI biomarkers-gray matter volume (GMV) and amplitude of low-frequency fluctuation (ALFF).</p><p><strong>Methods: </strong>Total 31 patients with schizophrenia, 48 patients with bipolar disorder, and 38 healthy controls (HC) were enrolled and underwent resting-state fMRI scans. The framework comprises three core components: Gaussian data augmentation to mitigate the limitation of small sample sizes; a dedicated CNN architecture for processing GMV and ALFF features to generate class probability outputs for SZ, BD, and HC; and DempsterShafer (D-S) evidence theory for robust multi-modal decision fusion.</p><p><strong>Results: </strong>Extensive 5-fold cross-validation confirms the framework's efficacy: it achieves superior performance metrics (Accuracy: 0.9656 ± 0.0324, Precision: 0.9698 ± 0.0319, Recall: 0.9656 ± 0.0324, F1-Score: 0.9661 ± 0.0324) and outperforms traditional machine learning methods (SVM, Random Forest, XGBoost, KNN).</p><p><strong>Conclusion: </strong>The findings demonstrate that the proposed multi-modal fusion approach enhances the ability to differentiate among SZ, BD, and HC compared to unimodal classification methods, providing a reliable technical support for the auxiliary diagnosis of psychiatric disorders.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1835559"},"PeriodicalIF":3.3,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13661931/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149192321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A computational roadmap to activity-dependent myelination.","authors":"Thomas Nguyen, Jeremie Lefebvre","doi":"10.3389/fncom.2026.1895171","DOIUrl":"https://doi.org/10.3389/fncom.2026.1895171","url":null,"abstract":"<p><p>There is growing evidence to support myelin plasticity as a complementary mechanism to synaptic plasticity in the brain. Activity-dependent myelination (ADM) enables oligodendrocytes to modify myelin structure in response to neural activity, modulating axonal conduction velocity and delays to maintain precise signal timing. This introduces a timing-based plasticity mechanism that operates in parallel to changes in signal strength. Despite its significance, ADM lacks a strong theoretical framework due to the interaction of multiple mechanisms operating on widely different time scales. Computational models are beginning to address this gap by providing powerful tools to interpret and guide experimental studies. This perspective explores the growing number of computational ADM models, comparing how they link neural activity to myelin adaptation, and their assumptions, strengths, and limitations. We also discuss the challenges of modeling ADM and open questions, positioning ADM as a key component of neural plasticity.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1895171"},"PeriodicalIF":3.3,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13650009/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149167853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A high-precision synchronization and centroid-based framework for single-trial decoding of perceived natural speech.","authors":"Cristian Y Olivares, Norelli Schettini","doi":"10.3389/fncom.2026.1947069","DOIUrl":"10.3389/fncom.2026.1947069","url":null,"abstract":"<p><p>Decoding natural speech from EEG is constrained by low signal-to-noise ratios, commonly addressed through event-related potential (ERP) grand averaging. However, this approach acts as a low-pass filter, obscuring trial-to-trial variability and high-frequency neural dynamics. To overcome these limitations, we propose an open-box, FPGA-synchronized framework for single-trial EEG analysis of Spanish vowels (/a/, /i/, /u/). The proposed architecture enforces sub-millisecond hardware determinism, eliminating software-induced temporal jitter and enabling robust extraction of high-frequency oscillatory features, including the Gamma band, without relying on averaged ERPs. This synchronization framework facilitates the separation of early exogenous sensory responses from late endogenous cognitive processing, while comprehensive ablation studies were performed to evaluate the effect of software-induced jitter on decoding performance. Results showed that introducing simulated jitter degraded classification performance to theoretical chance levels (33.20%), confirming hardware synchronization as a strict prerequisite for reliable single-trial decoding. The framework was further validated using a Centroid-PCA-SVM pipeline operating on a 16-channel spatial subset. Using the full proposed architecture, a grand mean classification accuracy of 67.70% was achieved, substantially exceeding the theoretical chance level of 33.33%. The across-participant mean of the maximum fold accuracies reached 83.00%, with individual validation folds achieving up to 90.00%. In contrast, zero-calibration leave-one-subject-out (LOSO) generalization remained at chance level (33.08%; κ_pooled = -0.0036), indicating limited cross-participant transferability and highlighting the current need for participant-specific calibration or alignment. Overall, these findings demonstrate that hardware-deterministic EEG acquisition enables the recovery of robust trial-by-trial brain dynamics that are typically obscured by conventional averaging approaches. By combining precise synchronization with low-latency algorithmic inference, the proposed framework establishes a foundation for real-time neural decoding and represents a stepping stone for practical brain-computer interfaces and advanced clinical monitoring applications.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1947069"},"PeriodicalIF":3.3,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13642302/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149126962","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":"How to train your neuron: developing a detailed, up-to-date, multipurpose model of hippocampal CA1 pyramidal cells.","authors":"Luca Tar, Sára Sáray, Máté Mohácsi, Tamás F Freund, Szabolcs Káli","doi":"10.3389/fncom.2026.1897214","DOIUrl":"10.3389/fncom.2026.1897214","url":null,"abstract":"<p><p>Anatomically and biophysically detailed models of neurons have been widely used to study information processing in these cells. Most studies focused on understanding specific phenomena, while more general models that aim to capture various cellular processes simultaneously remain rare even though such models are required to predict neuronal behavior under more complex, natural conditions. In this study, we aimed to develop a detailed, data-driven, general-purpose biophysical model of hippocampal CA1 pyramidal neurons. We leveraged extensive morphological, biophysical and physiological data available for this cell type, and established a systematic workflow for model construction and validation that relies on our recently developed software tools. The model is based on a high-quality morphological reconstruction and includes a diverse curated set of ion channel models. After incorporating the available constraints on the distribution of ion channels, the remaining free parameters were optimized using the Neuroptimus tool to fit a variety of electrophysiological features extracted from somatic whole-cell recordings. Validation using HippoUnit confirmed the model's ability to replicate key electrophysiological features, including somatic voltage responses to current input, the attenuation of synaptic potentials and backpropagating action potentials, and nonlinear synaptic integration in oblique dendrites. Our model also included active dendritic spines, modeled either explicitly or by merging their biophysical mechanisms into those of the parent dendrite. We found that many aspects of neuronal behavior were unaffected by the level of detail in modeling spines, but modeling nonlinear synaptic integration accurately required the explicit modeling of spines. Our data-driven model of CA1 pyramidal cells matching diverse experimental constraints is a general tool for the investigation of the activity and plasticity of these cells and can also be a reliable component of detailed models of the hippocampal network. Our systematic approach to building and validating multi-purpose models should apply to other cell types as well.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1897214"},"PeriodicalIF":3.3,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13638379/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149106224","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":"Locality and weight sharing shape Hebbian principal-component learning in biologically constrained visual models.","authors":"Patrick Inoue, Florian Röhrbein, Andreas Knoblauch","doi":"10.3389/fncom.2026.1943064","DOIUrl":"https://doi.org/10.3389/fncom.2026.1943064","url":null,"abstract":"<p><strong>Introduction: </strong>Biologically motivated artificial visual learning requires local plasticity rules to be evaluated within architectures whose connectivity assumptions remain explicit. Convolutional Hebbian networks demonstrate that competitive local learning can support visual classification, but their performance is coupled to exact weight sharing, a prior that is not anatomically literal. This study asks whether Hebbian principal component analysis (HPCA) can support representations when spatial locality is preserved but exact translational weight sharing is removed.</p><p><strong>Methods: </strong>HPCA was evaluated on CIFAR-10 as the primary benchmark, with MNIST and STL-10 used as controls. We compared fully connected HPCA, position-specific locally connected HPCA with fixed receptive fields, non-shared synaptic weights, and population-wise divisive response normalization, and HPCA substitutions in two shared-kernel convolutional Hebbian benchmarks. Representations were learned without supervised feature-layer targets or BP-based feature-layer fine-tuning, frozen, and evaluated through supervised readouts.</p><p><strong>Results: </strong>Position-specific locally connected HPCA outperformed the fully connected reference. On CIFAR-10, the best locally connected model reached 61.42 ± 0.36%. Shared-kernel HPCA reached 74.51 ± 0.38% in the modular benchmark with common HPCA preprocessing and 76.57 ± 0.55% in the SoftHebb architecture with reference preprocessing.</p><p><strong>Discussion: </strong>The results show that HPCA remains effective across position-specific locally connected and shared-kernel convolutional architectures, but that performance depends strongly on the surrounding architecture and benchmark protocol. The tested shared-kernel configurations achieved higher accuracy, yet useful frozen visual representations still emerged when exact parameter sharing was removed. Locally connected HPCA therefore provides a controlled model for examining how local plasticity interacts with receptive-field structure and convolutional inductive bias in biologically constrained visual learning.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1943064"},"PeriodicalIF":3.3,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13635126/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149106322","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":"Editorial: Theoretical and computational insights into brain-based cognition.","authors":"José Roberto Castilho Piqueira, Antonio C Roque, Renato Teodoro Ramos","doi":"10.3389/fncom.2026.1986598","DOIUrl":"https://doi.org/10.3389/fncom.2026.1986598","url":null,"abstract":"","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1986598"},"PeriodicalIF":3.3,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13635378/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149106259","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":"Design principles for cost functions in optimal sensory coding.","authors":"Veronika Koren, Stefano Panzeri","doi":"10.3389/fncom.2026.1924396","DOIUrl":"https://doi.org/10.3389/fncom.2026.1924396","url":null,"abstract":"<p><p>Studies of neural codes for sensory variables are rooted in the long-established principle that neural codes are organized to encode as much information as possible about sensory stimuli. This principle has been successful in explaining many features of neural activity, suggesting that a key component of a cost function determining sensory neural coding from first principles is the stimulus encoding error. However, recent work has shown that neural cost functions may have other terms reflecting costs or limitations of the biological hardware. One key piece is the metabolic cost of neural activity. Including metabolic cost in the cost function leads to optimized spiking models that exhibit spike-triggered adaptation. Another possible component of the neural cost function is reliable downstream transmission of information. Neurons have short integration time constants, which make it difficult for information to be transmitted reliably unless inputs to neurons are correlated. This suggests that cost functions accounting for constraints on downstream readout promote codes with redundancy-inducing correlations. We review and discuss evidence that neural codes in the brain have features consistent with the predictions expected if cost functions have additional terms beyond the encoding error. Adaptation currents are indeed present in biological neurons with the time scales predicted by metabolically efficient codes, and neural population codes have information-reducing correlations that support downstream information transmission. Finally, we discuss future directions important for fully establishing the different contributions to neural cost functions.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1924396"},"PeriodicalIF":3.3,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13631812/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149106310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A subject-specific mechanistic model of affect links electroencephalography, cardiorespiratory feedback, and monoaminergic dynamics.","authors":"Masami Iwamoto","doi":"10.3389/fncom.2026.1875657","DOIUrl":"https://doi.org/10.3389/fncom.2026.1875657","url":null,"abstract":"<p><strong>Introduction: </strong>Affective experience emerges from interactions among cortical activity, bodily physiology, and contextual interpretation, yet the mechanisms linking these processes remain incompletely understood. Although EEG-based studies have advanced affect decoding, many data-driven approaches provide limited mechanistic insight into inter-individual variability. We developed a subject-specific, hypothesis-generating computational framework linking EEG-derived cortical activity and cardiorespiratory dynamics to subjective affect through putative neuromodulatory processes.</p><p><strong>Methods: </strong>The framework integrates a respiratory-circulatory subsystem with spiking-neuron models representing the locus coeruleus, ventral tegmental area, raphe nucleus, and basolateral amygdala. EEG band-power features were used as hypothesis-driven, indirect inputs to these modeled processes rather than as direct measures of deep-brain activity, while cardiorespiratory variables provided physiological feedback to the neural circuit. Model-derived activity was converted into putative norepinephrine-, dopamine-, and serotonin-related signals. Together with two fitted serotonin-related modulation coefficients, these signals constituted five latent parameters used to represent valence-arousal states. The modulation coefficients were estimated for each participant-stimulus observation by nonlinear optimization so that model-derived arousal and valence matched the corresponding subjective ratings. Model calibration and predictive validation were evaluated separately. Predictive validity was assessed using leave-one-stimulus-out cross-validation, in which each stimulus was held out in turn and predicted from regression equations fitted only to the remaining 15 stimuli.</p><p><strong>Results: </strong>The framework was evaluated using the AMIGOS dataset (nine participants, 16 video stimuli; 144 participant-stimulus observations). Under the calibration procedure, the five latent parameters reproduced participant-specific valence-arousal ratings. In cross-validation across all participants, the mean root mean square error was 0.380 ± 0.288 for arousal and 1.283 ± 1.512 for valence, indicating more consistent prediction of unseen stimuli for arousal and substantial inter-individual variability in valence prediction. Cross-participant regression showed weaker generalization, further supporting the importance of subject-specific modeling.</p><p><strong>Discussion: </strong>The proposed framework does not directly measure deep-brain or monoaminergic activity. Rather, it provides a physiologically informed and interpretable framework for generating hypotheses about how cortical activity, cardiorespiratory feedback, and putative neuromodulatory processes may jointly contribute to individual affective experience. The findings also highlight the need for validation in larger independent datasets using direct neurophysiological measurements.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1875657"},"PeriodicalIF":3.3,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13631044/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149106298","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":"Neural state reduction: a quantum-inspired framework for how latent conscious thought becomes reportable.","authors":"A K Pradeep","doi":"10.3389/fncom.2026.1865388","DOIUrl":"https://doi.org/10.3389/fncom.2026.1865388","url":null,"abstract":"<p><p>Human conscious thought often appears as a diffuse, unstable, and only partly verbalizable field until attention, questioning, decision, or speech stabilizes it into a reportable form. This manuscript develops a quantum-inspired neural framework for that transition. The proposal does not claim that consciousness literally depends on microscopic quantum computation, that the brain implements a physical quantum computer, or that neuronal tissue maintains macroscopic quantum coherence. Instead, it uses the mathematical architecture of quantum theory-state vectors, density matrices, projection operators, non-commuting measurements, interference, decoherence-like contextual stabilization, and state reduction-as a formal language for modeling the transformation from latent conscious potential to reportable thought. In the proposed framework, pre-report consciousness is represented as a high-dimensional latent neural-cognitive state over candidate thoughts. This state evolves under a neural-cognitive Hamiltonian shaped by memory, salience, affect, attention, language, task demands, and spontaneous associative dynamics. A question such as \"What are you thinking about?\" is modeled as a measurement context that selects a basis and projects the latent state into a reportable cognitive eigenstate. The framework, therefore, treats an introspective report not as a passive readout of a pre-existing mental object but as an active state-reducing operation. Illustrative simulations demonstrate three formal consequences: (1) Non-commuting introspective questions generate order effects; (2) selective reporting reduces candidate-state entropy and suppresses off-diagonal latent coherence; and (3) overlapping candidate thoughts can produce interference-like probability structures. The model generates testable predictions for experience sampling, electroencephalography (EEG)/magnetoencephalography (MEG)/functional magnetic resonance imaging (fMRI) representational decoding, intracranial recordings, and perturbation studies: Introspective reports should depend on question order; spoken reports should reduce latent-state entropy more strongly than silent attention; ambiguous thought states should exhibit interference-like probability signatures; and neural activity should transition from high-dimensional latent dynamics toward lower-dimensional, report-stabilized states. Neural state reduction provides a computational bridge among quantum cognition, consciousness science, metacognition, global access, and the neuroscience of reportability.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1865388"},"PeriodicalIF":3.3,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13627331/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149106495","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}