Frontiers in network physiology最新文献

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Complexity synchronization analysis of neurophysiological data: Theory and methods. 神经生理数据的复杂性同步分析:理论与方法。
Frontiers in network physiology Pub Date : 2025-05-14 eCollection Date: 2025-01-01 DOI: 10.3389/fnetp.2025.1570530
Ioannis Schizas, Sabrina Sullivan, Scott Kerick, Korosh Mahmoodi, J Cortney Bradford, David L Boothe, Piotr J Franaszczuk, Paolo Grigolini, Bruce J West
{"title":"Complexity synchronization analysis of neurophysiological data: Theory and methods.","authors":"Ioannis Schizas, Sabrina Sullivan, Scott Kerick, Korosh Mahmoodi, J Cortney Bradford, David L Boothe, Piotr J Franaszczuk, Paolo Grigolini, Bruce J West","doi":"10.3389/fnetp.2025.1570530","DOIUrl":"10.3389/fnetp.2025.1570530","url":null,"abstract":"<p><strong>Introduction: </strong>We present a theoretical foundation based on the spontaneous self-organized temporal criticality (SOTC) and multifractal dimensionality <math><mrow><mi>μ</mi></mrow> </math> to model complex neurophysiological and behavioral systems to infer the optimal empirical transfer of information among them. We hypothesize that heterogeneous time series characterizing the brain, heart, and lung organ-networks (ONs) are necessarily multifractal, whose level of complexity and, therefore, their information content is measured by their multifractal dimensions.</p><p><strong>Methods: </strong>We apply modified diffusion entropy analysis (MDEA) to assess multifractal dimensions of ON time series (ONTS), and complexity synchronization (CS) analysis to infer information transfer among ONs that are part of a network-of-organ-networks (NoONs). An automated parameter selection process is proposed that relies on the Kolmogorov-Smirnov statistic to properly choose stripe sizes which are crucial in the MDEA analysis using synthetic duration times derived from the Mittag-Leffler map, shows the strength of KS-based stripe size selection to track changes in the IPL parameter <math><mrow><mi>μ</mi></mrow> </math> . The purpose of this paper is to advance the validation, standardization, and reconstruct-ability of MDEA and CS analysis of heterogeneous neurophysiological time series data.</p><p><strong>Results: </strong>Results from processing these datasets show that the complexity of brain, heart, and lung ONTS co-vary over time during cognitive task performance in 44% of subjects, while complexity of brain-heart interactions significantly co-vary in 85% of subjects.</p><p><strong>Discussion: </strong>We conclude that certain principles, guidelines, and strategies for the application of MDEA analysis need further consideration. We conclude with a summary of the MDEA's limitations and future research directions.</p>","PeriodicalId":73092,"journal":{"name":"Frontiers in network physiology","volume":"5 ","pages":"1570530"},"PeriodicalIF":0.0,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12116615/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144176000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Preictal connectivity dynamics: Exploring inflow and outflow in iEEG networks. 预测连通性动态:探索iEEG网络的流入和流出。
Frontiers in network physiology Pub Date : 2025-04-28 eCollection Date: 2025-01-01 DOI: 10.3389/fnetp.2025.1539682
Amirhossein Jahani, Camille Begin, Denahin H Toffa, Sami Obaid, Dang K Nguyen, Elie Bou Assi
{"title":"Preictal connectivity dynamics: Exploring inflow and outflow in iEEG networks.","authors":"Amirhossein Jahani, Camille Begin, Denahin H Toffa, Sami Obaid, Dang K Nguyen, Elie Bou Assi","doi":"10.3389/fnetp.2025.1539682","DOIUrl":"https://doi.org/10.3389/fnetp.2025.1539682","url":null,"abstract":"<p><strong>Introduction: </strong>Focal resective surgery can be an effective treatment option for patients with refractory epilepsy if the seizure onset zone is accurately identied through intracranial EEG recordings. The traditional concept of the epileptogenic zone has expanded to the notion of an epileptogenic network, emphasizing the role of interconnected brain regions in seizure generation. Precise delineation of this network is essential for optimizing surgical outcomes. Over the past 3 decades, several quantitative connectivity methods have been developed to study the interactions between the seizure onset zone and non-involved regions. Despite these advances, the mechanisms governing the transition from interictal to ictal periods remain poorly understood. In this study, we investigated preictal interactions between the seizure onset zone and the broader network using directed connectivity measures.</p><p><strong>Methods: </strong>We evaluated their effectiveness in identifying seizure onset zones using a multicenter intracranial EEG dataset, encompassing 243 seizures from 61 patients. Directed transfer function and partial directed coherence were used to extract connectivity matrices during 28-seconds of preictal period in patients with good surgery outcomes. Inflow and outflow metrics were computed for iEEG electrode contact annotated as seizure onset zone and the performance of each metric is assessed in disentangling these electrodes from the rest of the network.</p><p><strong>Results: </strong>We observed two distinct patterns of network connectivity preceding seizure onset. While there was an increase in inflow of information to seizure onset electrodes in one subset of patients, in the other subset, there was a prominent outflow of information from seizure onset electrodes to the rest of the network, suggesting distinct connectivity patterns associated with the seizure onset zone across patients. Further analyses showed that patients who underwent the grid/strip/depth implantation approach exhibited significantly higher area under curve (AUC) than those with electrocorticography (ECoG) or stereo-electroencephalography (sEEG) alone. Finally, examining the influence of lesional vs non-lesional neuroimaging status demonstrated that a greater proportion of high-inflow and high-outflow were lesional.</p><p><strong>Conclusion: </strong>Our findings reinforce the notion that seizure generation is driven by dynamic shifts in information flow within the brain's functional network. The preictal connectivity patterns observed --either increased inflow to the seizure onset zone or high outflow from it --underscore the network reorganization involved in epileptic transitions. These results emphasize epilepsy as a network-level phenomenon, supporting the use of network concepts for better understanding seizure dynamics and improving surgical localization strategies.</p>","PeriodicalId":73092,"journal":{"name":"Frontiers in network physiology","volume":"5 ","pages":"1539682"},"PeriodicalIF":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12067418/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144059845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Wearable multimodal sensing for quantifying the cardiovascular autonomic effects of levodopa in parkinsonism. 可穿戴式多模态传感器用于量化左旋多巴在帕金森病中的心血管自主作用。
Frontiers in network physiology Pub Date : 2025-04-24 eCollection Date: 2025-01-01 DOI: 10.3389/fnetp.2025.1543838
John A Berkebile, Omer T Inan, Paul A Beach
{"title":"Wearable multimodal sensing for quantifying the cardiovascular autonomic effects of levodopa in parkinsonism.","authors":"John A Berkebile, Omer T Inan, Paul A Beach","doi":"10.3389/fnetp.2025.1543838","DOIUrl":"https://doi.org/10.3389/fnetp.2025.1543838","url":null,"abstract":"<p><p>Levodopa is the most common therapy to reduce motor symptoms of parkinsonism. However, levodopa has potential to exacerbate cardiovascular autonomic (CVA) dysfunction that may co-occur in patients. Heart rate variability (HRV) is the most common method for assessing CVA function, but broader monitoring of CVA function and levodopa effects is typically limited to clinical settings and symptom reporting, which fail to capture its holistic nature. In this study, we evaluated the feasibility of a multimodal wearable chest patch for monitoring changes in CVA function during clinical and 24-h ambulatory (at home) conditions in 14 patients: 11 with Parkinson's disease (PD) and 3 with multiple system atrophy (MSA). In-clinic data were analyzed to examine the effects of orally administered levodopa on CVA function using a pre (OFF) and 60-min (ON) post-exposure protocol. Wearable-derived physiological markers related to the electrical and mechanical activity of the heart alongside vascular function were extracted. Pre-ejection period (PEP) and ratio of PEP to left ventricular ejection time index (LVETi) increased significantly (p <math><mrow><mo><</mo></mrow> </math> 0.05) following levodopa, indicating a decrease in cardiac contractility. We further explored dose-response relationships and how CVA responses differed between participants with orthostatic hypotension (OH) from those without OH. Heart rate variability, specifically root-mean-square-of-successive-differences (RMSSD), following levodopa decreased significantly more in participants with OH (n = 7) compared to those without (no-OH, n = 7). The results suggest that the wearable patch's measures are sensitive to CVA dynamics and provide exploratory insights into levodopa's potential role in inducing a negative inotropic effect and exacerbating CVA dysfunction. This work encourages further evaluation of these wearable-derived physiomarkers for quantifying CVA and informing individualized care of individuals with parkinsonism.</p>","PeriodicalId":73092,"journal":{"name":"Frontiers in network physiology","volume":"5 ","pages":"1543838"},"PeriodicalIF":0.0,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12058781/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144057904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Multivariate linear time-series modeling and prediction of cerebral physiologic signals: review of statistical models and implications for human signal analytics. 脑生理信号的多元线性时间序列建模和预测:统计模型的回顾及其对人类信号分析的影响。
Frontiers in network physiology Pub Date : 2025-04-16 eCollection Date: 2025-01-01 DOI: 10.3389/fnetp.2025.1551043
Nuray Vakitbilir, Amanjyot Singh Sainbhi, Abrar Islam, Alwyn Gomez, Kevin Yuwa Stein, Logan Froese, Tobias Bergmann, Davis McClarty, Rahul Raj, Frederick Adam Zeiler
{"title":"Multivariate linear time-series modeling and prediction of cerebral physiologic signals: review of statistical models and implications for human signal analytics.","authors":"Nuray Vakitbilir, Amanjyot Singh Sainbhi, Abrar Islam, Alwyn Gomez, Kevin Yuwa Stein, Logan Froese, Tobias Bergmann, Davis McClarty, Rahul Raj, Frederick Adam Zeiler","doi":"10.3389/fnetp.2025.1551043","DOIUrl":"https://doi.org/10.3389/fnetp.2025.1551043","url":null,"abstract":"<p><p>Cerebral physiological signals embody complex neural, vascular, and metabolic processes that provide valuable insight into the brain's dynamic nature. Profound comprehension and analysis of these signals are essential for unraveling cerebral intricacies, enabling precise identification of patterns and anomalies. Therefore, the advancement of computational models in cerebral physiology is pivotal for exploring the links between measurable signals and underlying physiological states. This review provides a detailed explanation of computational models, including their mathematical formulations, and discusses their relevance to the analysis of cerebral physiology dynamics. It emphasizes the importance of linear multivariate statistical models, particularly autoregressive (AR) models and the Kalman filter, in time series modeling and prediction of cerebral processes. The review focuses on the analysis and operational principles of multivariate statistical models such as AR models and the Kalman filter. These models are examined for their ability to capture intricate relationships among cerebral parameters, offering a holistic representation of brain function. The use of multivariate statistical models enables the capturing of complex relationships among cerebral physiological signals. These models provide valuable insights into the dynamic nature of the brain by representing intricate neural, vascular, and metabolic processes. The review highlights the clinical implications of using computational models to understand cerebral physiology, while also acknowledging the inherent limitations, including the need for stationary data, challenges with high dimensionality, computational complexity, and limited forecasting horizons.</p>","PeriodicalId":73092,"journal":{"name":"Frontiers in network physiology","volume":"5 ","pages":"1551043"},"PeriodicalIF":0.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12040811/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144025210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Insomnia increases the risk for specific autoimmune diseases: a large-scale retrospective cohort study. 失眠增加特定自身免疫性疾病的风险:一项大规模回顾性队列研究
Frontiers in network physiology Pub Date : 2025-04-10 eCollection Date: 2025-01-01 DOI: 10.3389/fnetp.2025.1499297
Sarah Stenger, Artem Vorobyev, Katja Bieber, Tanja Lange, Ralf J Ludwig, Jennifer E Hundt
{"title":"Insomnia increases the risk for specific autoimmune diseases: a large-scale retrospective cohort study.","authors":"Sarah Stenger, Artem Vorobyev, Katja Bieber, Tanja Lange, Ralf J Ludwig, Jennifer E Hundt","doi":"10.3389/fnetp.2025.1499297","DOIUrl":"https://doi.org/10.3389/fnetp.2025.1499297","url":null,"abstract":"<p><strong>Objective: </strong>The global rise of autoimmune diseases presents a significant medical challenge, with inadequate treatment options, high morbidity risks, and escalating healthcare costs. While the underlying mechanisms of autoimmune disease development are not fully understood, both genetic predispositions and lifestyle factors, particularly sleep, play critical roles. Insomnia and circadian rhythm sleep disorders not only impair sleep but also disrupt multi-organ interactions by dysregulating sympathetic nervous system activity, altering immune responses, and influencing neuroendocrine function. These disruptions can contribute to immune system dysregulation, increasing the risk of autoimmune disease development.</p><p><strong>Methods: </strong>To assess the impact of impaired sleep on the risk of developing autoimmune diseases, a global population-based retrospective cohort study was conducted using electronic health records from the TriNetX US Global Collaborative Network, including 351,366 subjects in each propensity score matched group. Twenty autoimmune diseases were examined, and propensity score matching was employed to reduce bias. Three sensitivity analyses were conducted to test the robustness of the results.</p><p><strong>Results: </strong>The study identified significantly increased risks for several autoimmune diseases associated with impaired sleep, likely mediated by dysregulated neuroimmune and autonomic interactions. Specifically, cutaneous lupus erythematosus [hazard ratio (HR) = 2.119; confidence interval (CI) 1.674-2.682; p < 0.0001], rheumatoid arthritis (HR = 1.404; CI 1.313-1.501; p < 0.0001), Sjögren syndrome (HR = 1.84; CI 1.64-2.066; p < 0.0001), and autoimmune thyroiditis (HR = 1.348; CI 1.246-1.458; p < 0.0001) showed significantly increased risks. No diseases demonstrated reduced risks, and 4 out of 20 tested diseases did not show significant HR increases in any analysis.</p><p><strong>Conclusion: </strong>This study highlights the integral role of sleep in maintaining immune homeostasis through multi-organ interactions involving the autonomic nervous system, immune signalling pathways, and endocrine regulation. Disruptions in these systems due to chronic sleep impairment may predispose individuals to autoimmune diseases by altering inflammatory responses and immune tolerance. These findings underscore the necessity of recognizing and treating sleep disorders not only for general wellbeing but also as a potential strategy to mitigate the long-term risk of autoimmune disease development.</p>","PeriodicalId":73092,"journal":{"name":"Frontiers in network physiology","volume":"5 ","pages":"1499297"},"PeriodicalIF":0.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12018472/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144043351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Restoring the complexity of walking in the elderly and its impact on clinical measures around the risk of falls. 恢复老年人行走的复杂性及其对跌倒风险的临床措施的影响。
Frontiers in network physiology Pub Date : 2025-04-02 eCollection Date: 2025-01-01 DOI: 10.3389/fnetp.2025.1532700
Samar Ezzina, Simon Pla, Didier Delignières
{"title":"Restoring the complexity of walking in the elderly and its impact on clinical measures around the risk of falls.","authors":"Samar Ezzina, Simon Pla, Didier Delignières","doi":"10.3389/fnetp.2025.1532700","DOIUrl":"https://doi.org/10.3389/fnetp.2025.1532700","url":null,"abstract":"<p><p><b>Introduction:</b> The hypothesis of the loss of complexity with aging and disease has received strong attention. Especially, the decrease of complexity of stride interval series in older people, during walking, was shown to correlate with falling propensity. However, recent experiments showed that a restoration of walking complexity in older people could occur through the prolonged experience of synchronized walking with a younger companion. This result was interpreted as the consequence of a complexity matching effect. <b>Experiment:</b> The aim of the present study was to analyze the link between the restoration of walking complexity in older people and clinical measures usually used in the context of rehabilitation or follow-up of older people. <b>Results:</b> We evidenced a link between restoring complexity, improving overall health and reducing fear of falling. In addition, we showed that 3 weeks of complexity matching training can have a positive effect on complexity up to 2 months post-protocol. Finally, we showed that the restoration of walking complexity obtained in the previous works is not guide-dependent.</p>","PeriodicalId":73092,"journal":{"name":"Frontiers in network physiology","volume":"5 ","pages":"1532700"},"PeriodicalIF":0.0,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11999954/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144060203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Information entropy dynamics, self-organization, and cybernetical neuroscience. 信息熵动力学、自组织与控制论神经科学。
Frontiers in network physiology Pub Date : 2025-03-21 eCollection Date: 2025-01-01 DOI: 10.3389/fnetp.2025.1539166
Alexander Fradkov
{"title":"Information entropy dynamics, self-organization, and cybernetical neuroscience.","authors":"Alexander Fradkov","doi":"10.3389/fnetp.2025.1539166","DOIUrl":"https://doi.org/10.3389/fnetp.2025.1539166","url":null,"abstract":"<p><p>A version of the speed-gradient evolution models for systems obeying the maximum information entropy principle developed by H. Haken in his book of 1988 is proposed in this article. An explicit relation specifying system dynamics for general linear constraints is established. Two versions of the human brain entropy detailed balance-breaking model are proposed. In addition, the contours of a new scientific field called <i>cybernetical neuroscience</i> dedicated to the control of neural systems have been outlined.</p>","PeriodicalId":73092,"journal":{"name":"Frontiers in network physiology","volume":"5 ","pages":"1539166"},"PeriodicalIF":0.0,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11984489/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144058642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
PINNing cerebral blood flow: analysis of perfusion MRI in infants using physics-informed neural networks. 固定脑血流:使用物理信息神经网络分析婴儿灌注MRI。
Frontiers in network physiology Pub Date : 2025-02-14 eCollection Date: 2025-01-01 DOI: 10.3389/fnetp.2025.1488349
Christoforos Galazis, Ching-En Chiu, Tomoki Arichi, Anil A Bharath, Marta Varela
{"title":"PINNing cerebral blood flow: analysis of perfusion MRI in infants using physics-informed neural networks.","authors":"Christoforos Galazis, Ching-En Chiu, Tomoki Arichi, Anil A Bharath, Marta Varela","doi":"10.3389/fnetp.2025.1488349","DOIUrl":"https://doi.org/10.3389/fnetp.2025.1488349","url":null,"abstract":"<p><p>Arterial spin labelling (ASL) magnetic resonance imaging (MRI) enables cerebral perfusion measurement, which is crucial in detecting and managing neurological issues in infants born prematurely or after perinatal complications. However, cerebral blood flow (CBF) estimation in infants using ASL remains challenging due to the complex interplay of network physiology, involving dynamic interactions between cardiac output and cerebral perfusion, as well as issues with parameter uncertainty and data noise. We propose a new spatial uncertainty-based physics-informed neural network (PINN), SUPINN, to estimate CBF and other parameters from infant ASL data. SUPINN employs a multi-branch architecture to concurrently estimate regional and global model parameters across multiple voxels. It computes regional spatial uncertainties to weigh the signal. SUPINN can reliably estimate CBF (relative error <math><mrow><mo>-</mo> <mn>0.3</mn> <mo>±</mo> <mn>71.7</mn></mrow> </math> ), bolus arrival time (AT) <math><mrow><mo>(</mo> <mrow><mn>30.5</mn> <mo>±</mo> <mn>257.8</mn></mrow> <mo>)</mo></mrow> </math> , and blood longitudinal relaxation time <math><mrow><mo>(</mo> <mrow> <msub><mrow><mi>T</mi></mrow> <mrow><mn>1</mn> <mi>b</mi></mrow> </msub> </mrow> <mo>)</mo></mrow> </math> (-4.4 <math><mrow><mo>±</mo></mrow> </math> 28.9), surpassing parameter estimates performed using least squares or standard PINNs. Furthermore, SUPINN produces physiologically plausible spatially smooth CBF and AT maps. Our study demonstrates the successful modification of PINNs for accurate multi-parameter perfusion estimation from noisy and limited ASL data in infants. Frameworks like SUPINN have the potential to advance our understanding of the complex cardio-brain network physiology, aiding in the detection and management of diseases. Source code is provided at: https://github.com/cgalaz01/supinn.</p>","PeriodicalId":73092,"journal":{"name":"Frontiers in network physiology","volume":"5 ","pages":"1488349"},"PeriodicalIF":0.0,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11868054/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143544747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Combining interictal intracranial EEG and fMRI to compute a dynamic resting-state index for surgical outcome validation. 结合间歇期颅内脑电图和功能磁共振成像计算动态静息状态指数,用于手术结果验证。
Frontiers in network physiology Pub Date : 2025-01-28 eCollection Date: 2024-01-01 DOI: 10.3389/fnetp.2024.1491967
Varina L Boerwinkle, Kristin M Gunnarsdottir, Bethany L Sussman, Sarah N Wyckoff, Emilio G Cediel, Belfin Robinson, William R Reuther, Aryan Kodali, Sridevi V Sarma
{"title":"Combining interictal intracranial EEG and fMRI to compute a dynamic resting-state index for surgical outcome validation.","authors":"Varina L Boerwinkle, Kristin M Gunnarsdottir, Bethany L Sussman, Sarah N Wyckoff, Emilio G Cediel, Belfin Robinson, William R Reuther, Aryan Kodali, Sridevi V Sarma","doi":"10.3389/fnetp.2024.1491967","DOIUrl":"10.3389/fnetp.2024.1491967","url":null,"abstract":"<p><strong>Introduction: </strong>Accurate localization of the seizure onset zone (SOZ) is critical for successful epilepsy surgery but remains challenging with current techniques. We developed a novel seizure onset network characterization tool that combines dynamic biomarkers of resting-state intracranial stereoelectroencephalography (rs-iEEG) and resting-state functional magnetic resonance imaging (rs-fMRI), vetted against surgical outcomes. This approach aims to reduce reliance on capturing seizures during invasive monitoring to pinpoint the SOZ.</p><p><strong>Methods: </strong>We computed the source-sink index (SSI) from rs-iEEG for all implanted regions and from rs-fMRI for regions identified as potential SOZs by noninvasive modalities. The SSI scores were evaluated in 17 pediatric drug-resistant epilepsy (DRE) patients (ages 3-15 years) by comparing outcomes classified as successful (Engel I or II) versus unsuccessful (Engel III or IV) at 1 year post-surgery.</p><p><strong>Results: </strong>Of 30 reviewed patients, 17 met the inclusion criteria. The combined dynamic index (im-DNM) integrating rs-iEEG and rs-fMRI significantly differentiated good (Engel I-II) from poor (Engel III-IV) surgical outcomes, outperforming the predictive accuracy of individual biomarkers from either modality alone.</p><p><strong>Conclusion: </strong>The combined dynamic network model demonstrated superior predictive performance than standalone rs-fMRI or rs-iEEG indices.</p><p><strong>Significance: </strong>By leveraging interictal data from two complementary modalities, this combined approach has the potential to improve epilepsy surgical outcomes, increase surgical candidacy, and reduce the duration of invasive monitoring.</p>","PeriodicalId":73092,"journal":{"name":"Frontiers in network physiology","volume":"4 ","pages":"1491967"},"PeriodicalIF":0.0,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11811083/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143400754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Musician's dystonia: a perspective on the strongest evidence towards new prevention and mitigation treatments. 音乐家的肌张力障碍:对新的预防和缓解治疗的最有力证据的观点。
Frontiers in network physiology Pub Date : 2025-01-22 eCollection Date: 2024-01-01 DOI: 10.3389/fnetp.2024.1508592
Joy Grifoni, Valeria Crispiatico, Anna Castagna, Rosa Maria Converti, Marina Ramella, Angelo Quartarone, Teresa L'Abbate, Karolina Armonaite, Luca Paulon, Francescaroberta Panuccio, Franca Tecchio
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