Frontiers in Computational Neuroscience最新文献

{"title":"Multi-atlas ensemble graph neural network model for major depressive disorder detection using functional MRI data.","authors":"Nojod M Alotaibi, Areej M Alhothali, Manar S Ali","doi":"10.3389/fncom.2025.1537284","DOIUrl":"10.3389/fncom.2025.1537284","url":null,"abstract":"<p><p>Major depressive disorder (MDD) is one of the most common mental disorders, with significant impacts on many daily activities and quality of life. It stands as one of the most common mental disorders globally and ranks as the second leading cause of disability. The current diagnostic approach for MDD primarily relies on clinical observations and patient-reported symptoms, overlooking the diverse underlying causes and pathophysiological factors contributing to depression. Therefore, scientific researchers and clinicians must gain a deeper understanding of the pathophysiological mechanisms involved in MDD. There is growing evidence in neuroscience that depression is a brain network disorder, and the use of neuroimaging, such as magnetic resonance imaging (MRI), plays a significant role in identifying and treating MDD. Rest-state functional MRI (rs-fMRI) is among the most popular neuroimaging techniques used to study MDD. Deep learning techniques have been widely applied to neuroimaging data to help with early mental health disorder detection. Recent years have seen a rise in interest in graph neural networks (GNNs), which are deep neural architectures specifically designed to handle graph-structured data like rs-fMRI. This research aimed to develop an ensemble-based GNN model capable of detecting discriminative features from rs-fMRI images for the purpose of diagnosing MDD. Specifically, we constructed an ensemble model by combining functional connectivity features from multiple brain region segmentation atlases to capture brain complexity and detect distinct features more accurately than single atlas-based models. Further, the effectiveness of our model is demonstrated by assessing its performance on a large multi-site MDD dataset. We applied the synthetic minority over-sampling technique (SMOTE) to handle class imbalance across sites. Using stratified 10-fold cross-validation, the best performing model achieved an accuracy of 75.80%, a sensitivity of 88.89%, a specificity of 61.84%, a precision of 71.29%, and an F1-score of 79.12%. The results indicate that the proposed multi-atlas ensemble GNN model provides a reliable and generalizable solution for accurately detecting MDD.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1537284"},"PeriodicalIF":2.1,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12183270/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144474463","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":"Enhancing medical image privacy in IoT with bit-plane level encryption using chaotic map.","authors":"Fatima Asiri, Wajdan Al Malwi, Tamara Zhukabayeva, Ibtehal Nafea, Abdullah Aziz, Nadhmi A Gazem, Abdullah Qayyum","doi":"10.3389/fncom.2025.1591972","DOIUrl":"10.3389/fncom.2025.1591972","url":null,"abstract":"<p><strong>Introduction: </strong>Preserving privacy is a critical concern in medical imaging, especially in resource limited settings like smart devices connected to the IoT. To address this, a novel encryption method for medical images that operates at the bit plane level, tailored for IoT environments, is developed.</p><p><strong>Methods: </strong>The approach initializes by processing the original image through the Secure Hash Algorithm (SHA) to derive the initial conditions for the Chen chaotic map. Using the Chen chaotic system, three random number vectors are generated. The first two vectors are employed to shuffle each bit plane of the plaintext image, rearranging rows and columns. The third vector is used to create a random matrix, which further diffuses the permuted bit planes. Finally, the bit planes are combined to produce the ciphertext image. For further security enhancement, this ciphertext is embedded into a carrier image, resulting in a visually secured output.</p><p><strong>Results: </strong>To evaluate the effectiveness of our algorithm, various tests are conducted, including correlation coefficient analysis (<i>C</i>.<i>C</i> < or negative), histogram analysis, key space [(10⁹⁰)⁸] and sensitivity assessments, entropy evaluation [<i>E</i>(<i>S</i>) > 7.98], and occlusion analysis.</p><p><strong>Conclusion: </strong>Extensive evaluations have proven that the designed scheme exhibits a high degree of resilience to attacks, making it particularly suitable for small IoT devices with limited processing power and memory.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1591972"},"PeriodicalIF":2.1,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12179213/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144474434","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":"Constraint-based modeling of bioenergetic differences between synaptic and non-synaptic components of dopaminergic neurons in Parkinson's disease.","authors":"Xi Luo, Diana C El Assal, Yanjun Liu, Samira Ranjbar, Ronan M T Fleming","doi":"10.3389/fncom.2025.1594330","DOIUrl":"10.3389/fncom.2025.1594330","url":null,"abstract":"<p><strong>Introduction: </strong>Emerging evidence suggests that different metabolic characteristics, particularly bioenergetic differences, between the synaptic terminal and soma may contribute to the selective vulnerability of dopaminergic neurons in patients with Parkinson's disease (PD).</p><p><strong>Method: </strong>To investigate the metabolic differences, we generated four thermodynamically flux-consistent metabolic models representing the synaptic and non-synaptic (somatic) components under both control and PD conditions. Differences in bioenergetic features and metabolite exchanges were analyzed between these models to explore potential mechanisms underlying the selective vulnerability of dopaminergic neurons. Bioenergetic rescue analyses were performed to identify potential therapeutic targets for mitigating observed energy failure and metabolic dysfunction in PD models.</p><p><strong>Results: </strong>All models predicted that oxidative phosphorylation plays a significant role under lower energy demand, while glycolysis predominates when energy demand exceeds mitochondrial constraints. The synaptic PD model predicted a lower mitochondrial energy contribution and higher sensitivity to Complex I inhibition compared to the non-synaptic PD model. Both PD models predicted reduced uptake of lysine and lactate, indicating coordinated metabolic processes between these components. In contrast, decreased methionine and urea uptake was exclusively predicted in the synaptic PD model, while decreased histidine and glyceric acid uptake was exclusive to the non-synaptic PD model. Furthermore, increased flux of the mitochondrial ornithine transaminase reaction (ORNTArm), which converts oxoglutaric acid and ornithine into glutamate-5-semialdehyde and glutamate, was predicted to rescue bioenergetic failure and improve metabolite exchanges for both the synaptic and non-synaptic PD models.</p><p><strong>Discussion: </strong>The predicted differences in ATP contribution between models highlight the bioenergetic differences between these neuronal components, thereby contributing to the selective vulnerability observed in PD. The observed differences in metabolite exchanges reflect distinct metabolic patterns between these neuronal components. Additionally, mitochondrial ornithine transaminase was predicted to be the potential bioenergetic rescue target for both the synaptic and non-synaptic PD models. Further research is needed to validate these dysfunction mechanisms across different components of dopaminergic neurons and to explore targeted therapeutic strategies for PD patients.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1594330"},"PeriodicalIF":2.1,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12176876/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144332687","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 oscillation in low-rank SNNs: bridging network dynamics and cognitive function.","authors":"Bin Li, Tianyi Zheng, Reo Otsuki, Masato Sugino, Kenta Shimba, Kiyoshi Kotani","doi":"10.3389/fncom.2025.1598138","DOIUrl":"10.3389/fncom.2025.1598138","url":null,"abstract":"<p><p>Neural oscillation, particularly gamma oscillation, are fundamental to cognitive processes such as attention, perception, and decision-making. Experimental studies have shown that the phase of gamma oscillation modulates neuronal response selectivity, suggesting a direct link between oscillatory dynamics and cognition. However, there remains a lack of computational models that can systematically simulate and investigate this effect. To address this, we construct a low-rank spiking neural network (low-rank SNN) based on the voltage-dependent theta model to explore how structured connectivity shapes oscillatory dynamics and cognitive function. Using macroscopic model analysis, we identify different network states, ranging from stationary firing to gamma oscillation. Our model successfully reproduces phase-dependent response modulation in a Go-Nogo task, consistent with <i>in vivo</i> findings, providing an explanation for how neural oscillation influences task performance. Besides phase dependency, our findings suggest that gamma oscillation can enhance and prolong signal response. Compared to prior studies that applied low-rank connectivity to SNNs but remained limited to stationary or weak oscillatory regimes, our work extends to population-level synchronous activity while maintaining biological plausibility under Dale's principle. Our study offers a theoretical framework for understanding how neural oscillations emerge in structured spiking networks and provides a foundation for future experimental and computational investigations into oscillatory modulation of cognition.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1598138"},"PeriodicalIF":2.1,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12174079/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144324946","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 new method for community-based intelligent screening of early Alzheimer's disease populations based on digital biomarkers of the writing process.","authors":"Shuwu Li, Kai Li, Jiakang Liu, Shouqiang Huang, Chen Wang, Yuting Tu, Bo Wang, Pengpeng Zhang, Yuntian Luo, Yanli Zhang, Tong Chen","doi":"10.3389/fncom.2025.1564932","DOIUrl":"10.3389/fncom.2025.1564932","url":null,"abstract":"<p><strong>Background: </strong>In response to the shortcomings of the current Alzheimer's disease (AD) early populations assessment, which is based on neuropsychological scales with high subjectivity, low accuracy of repeated measurements, tedious process and dependence on physicians, it was found that digital biomarkers based on the writing process can effectively characterize the cognitive deficits of patients with mild cognitive impairment (MCI) due to AD.</p><p><strong>Methods: </strong>This study designed a digital writing assessment paradigm, extracted dynamic handwriting and image data during the paradigm assessment process, and analyzed digital biomarkers of the writing process to assess subjects' cognitive functions. A total of 72 subjects, including 34 health controls (HC) and 38 MCI due to AD, were enrolled in this study.</p><p><strong>Results: </strong>Their combined screening efficacy of digital biomarkers based on the MCI writing process due to AD populations having an area under curve (AUC) of 0.918, and a confidence interval (CI) of 0.854-0.982, was higher than the Montreal Cognitive Assessment Scale (AUC = 0.859, CI = 0.772-0.947) and the Mini-mental State Examination Scale (AUC = 0.783, CI = 0.678-0.888).</p><p><strong>Conclusion: </strong>Therefore, digital biomarkers based on the writing process can characterize and quantify the cognitive function of MCI due to AD populations at a fine-grained level, which is expected to be a new method for intelligent screening and early warning of early AD populations in a community-based physician-free setting.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1564932"},"PeriodicalIF":2.1,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12174119/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144324945","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":"Corrigendum: An enhanced pattern detection and segmentation of brain tumors in MRI images using deep learning technique.","authors":"Lubna Kiran, Asim Zeb, Qazi Nida Ur Rehman, Taj Rahman, Muhammad Shehzad Khan, Shafiq Ahmad, Muhammad Irfan, Muhammad Naeem, Shamsul Huda, Haitham Mahmoud","doi":"10.3389/fncom.2025.1570979","DOIUrl":"https://doi.org/10.3389/fncom.2025.1570979","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.3389/fncom.2024.1418280.].</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1570979"},"PeriodicalIF":2.1,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12170620/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144316304","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":"iSeizdiag: toward the framework development of epileptic seizure detection for healthcare.","authors":"Ashish Sharma, Akshat Saxena, Mradul Agrawal, Kunal Kishor, Deepti Kaushik, Prateek Jain, Arvind R Yadav, Manob Jyoti Saikia","doi":"10.3389/fncom.2025.1545425","DOIUrl":"10.3389/fncom.2025.1545425","url":null,"abstract":"<p><strong>Introduction: </strong>The seizure episodes result from abnormal and excessive electrical discharges by a group of brain cells. EEG framework-based signal acquisition is the real-time module that records the electrical discharges produced by the brain cells. The electrical discharges are amplified and appear as a graph on electroencephalogram systems. Different neurological disorders are represented as different waves on EEG records.</p><p><strong>Method: </strong>This paper involves the detection of Epilepsy which appears as rapid spiking on electroencephalogram signals, using feature extraction and machine learning techniques. Various models, such as the Support Vector Machine, K Nearest Neighbor, and random forest, have been trained, and accuracy has been analyzed to predict the seizure.</p><p><strong>Result: </strong>An average accuracy of 95% has been claimed using the optimized model for epileptic seizure detection during training and validation. During the analysis of multiple models, the 97% accuracy is claimed after testing. Some statistical parameters are calculated to justify the optimized framework.</p><p><strong>Discussion: </strong>The proposed approach represents a satisfactory contribution in precise detection for smart healthcare.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1545425"},"PeriodicalIF":2.1,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12149152/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144265826","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":"Neuromorphic energy economics: toward biologically inspired and sustainable power market design.","authors":"Aoshuang Ye, Dong Xu, Yichao Li, Jiawei Du, Zhiwei Wu, Junjie Tang","doi":"10.3389/fncom.2025.1597038","DOIUrl":"10.3389/fncom.2025.1597038","url":null,"abstract":"","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1597038"},"PeriodicalIF":2.1,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12149131/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144265827","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":"Regulation of sharp wave-ripples by cholecystokinin-expressing interneurons and parvalbumin-expressing basket cells in the hippocampal CA3 region.","authors":"Yuchen Yang, Xiaojuan Sun","doi":"10.3389/fncom.2025.1591003","DOIUrl":"10.3389/fncom.2025.1591003","url":null,"abstract":"<p><p>To explore the individual and interactive effects of the interneurons cholecystokinin-expressing interneurons (CCKs) and parvalbumin-expressing basket cells (BCs) on sharp wave-ripples (SWR) and the underlying mechanisms, we constructed a mathematical model of the hippocampal CA3 network. By modulating the activity of CCKs and BCs, it was verified that CCKs inhibit the generation of SWR, while the activity of BCs affects the occurrence of SWR. Additionally, it was postulated that CCKs exert an influence on SWR through a direct mechanism, wherein CCKs directly modulate pyramidal cells (PCs). It was also discovered that BCs control SWR mainly through mutual inhibition among BCs. Furthermore, by adjusting the strength of the interaction between BCs and CCKs at various levels, it was identified that the interaction between these two types of interneurons has a relatively symmetrical effect on the regulation of SWR, functioning through a mutual inhibition mechanism. Our findings not only offer a deeper understanding of how CCKs and BCs independently regulate the generation of SWR but also provide novel insights into how changes in the strength of their interaction affect network oscillations. The results emphasize the crucial role of inhibitory interneurons in maintaining normal hippocampal oscillations, which are essential for proper brain function, particularly in the domains of memory consolidation and cognitive processes.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1591003"},"PeriodicalIF":2.1,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12146282/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144257730","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":"Reinforced liquid state machines-new training strategies for spiking neural networks based on reinforcements.","authors":"Dominik Krenzer, Martin Bogdan","doi":"10.3389/fncom.2025.1569374","DOIUrl":"10.3389/fncom.2025.1569374","url":null,"abstract":"<p><strong>Introduction: </strong>Feedback and reinforcement signals in the brain act as natures sophisticated teaching tools, guiding neural circuits to self-organization, adaptation, and the encoding of complex patterns. This study investigates the impact of two feedback mechanisms within a deep liquid state machine architecture designed for spiking neural networks.</p><p><strong>Methods: </strong>The Reinforced Liquid State Machine architecture integrates liquid layers, a winner-takes-all mechanism, a linear readout layer, and a novel reward-based reinforcement system to enhance learning efficacy. While traditional Liquid State Machines often employ unsupervised approaches, we introduce strict feedback to improve network performance by not only reinforcing correct predictions but also penalizing wrong ones.</p><p><strong>Results: </strong>Strict feedback is compared to another strategy known as forgiving feedback, excluding punishment, using evaluations on the Spiking Heidelberg data. Experimental results demonstrate that both feedback mechanisms significantly outperform the baseline unsupervised approach, achieving superior accuracy and adaptability in response to dynamic input patterns.</p><p><strong>Discussion: </strong>This comparative analysis highlights the potential of feedback integration in deepened Liquid State Machines, offering insights into optimizing spiking neural networks through reinforcement-driven architectures.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1569374"},"PeriodicalIF":2.1,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12141346/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144247258","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}