Network Neuroscience最新文献_第5页

Brain rewiring during development: A comparative analysis of larval and adult Drosophila melanogaster connectomes. 发育过程中大脑重新布线：黑腹果蝇幼虫和成虫连接体的比较分析。

IF 3.1 3区医学

Network Neuroscience Pub Date : 2025-11-20 eCollection Date: 2025-01-01 DOI: 10.1162/NETN.a.26

Prateek Yadav, Pramod Shinde, Aradhana Singh

{"title":"Brain rewiring during development: A comparative analysis of larval and adult Drosophila melanogaster connectomes.","authors":"Prateek Yadav, Pramod Shinde, Aradhana Singh","doi":"10.1162/NETN.a.26","DOIUrl":"10.1162/NETN.a.26","url":null,"abstract":"The brain's ability to undergo complex rewiring during development is a fascinating aspect of neuroscience. This study conducts a detailed comparison of Drosophila melanogaster's brain networks during larval and adult stages, revealing significant changes in neuronal wiring throughout development. The larval brain network exhibits a degree distribution that fits firmly to a Weibull model. In contrast, the sparser adult brain network follows a power-law distribution, with the out-degree exponent lying in the scale-free regime and the in-degree exponent close to it. This shift toward a scale-free pattern likely reflects an adaptation to enhance robustness against failures while minimizing costs associated with reduced density during development. We also observed alterations in the structural core in relation to cell composition and topological influence. The structural core of the larva comprises neurons in the mushroom body, while neurons in the antennal lobe form the core of the adult fly brain. Furthermore, the larval network solely shows a rich club organization of which the structural core is also a part. Analysis of connectivity, rich club, and network measures reveals that the shift in the core results from a reduction in the centrality of mushroom body neurons following metamorphosis. This work stands as a step forward in understanding the rewiring of brain networks across the life stages of D. melanogaster.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 4","pages":"1299-1322"},"PeriodicalIF":3.1,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12635837/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145589401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Revealing brain network dynamics during the emotional state of suspense using TDA. 利用TDA揭示悬念情绪状态下的脑网络动态。

IF 3.1 3区医学

Network Neuroscience Pub Date : 2025-11-20 eCollection Date: 2025-01-01 DOI: 10.1162/NETN.a.34

Astrid A Olave, Jose A Perea, Francisco Gómez

{"title":"Revealing brain network dynamics during the emotional state of suspense using TDA.","authors":"Astrid A Olave, Jose A Perea, Francisco Gómez","doi":"10.1162/NETN.a.34","DOIUrl":"10.1162/NETN.a.34","url":null,"abstract":"Suspense is an affective state that is ubiquitous in human life, from art to quotidian events. However, little is known about the behavior of large-scale brain networks during suspenseful experiences. To address this question, we examined the continuous brain responses of participants watching a suspenseful movie, along with reported levels of suspense from an independent set of viewers. We employ sliding window analysis and Pearson correlation to measure functional connectivity states over time. Then, we use Mapper, a topological data analysis tool, to obtain a graphical representation that captures the dynamical transitions of the brain across states; this representation enables the anchoring of the topological characteristics of the combinatorial object with the measured suspense. Our analysis revealed changes in functional connectivity within and between the salience, fronto-parietal, and default networks associated with suspense. In particular, the functional connectivity between the salience and fronto-parietal networks increased with the level of suspense. In contrast, the connections of both networks with the default network decreased. Together, our findings reveal specific dynamical changes in functional connectivity at the network level associated with variation in suspense, and suggest topological data analysis as a potentially powerful tool for studying dynamic brain networks.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 4","pages":"1352-1376"},"PeriodicalIF":3.1,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12635835/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145589429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

A lightweight, end-to-end explainable, and generalized attention-based graph neural network model trained on high-order spatiotemporal organization of dynamic functional connectivity to classify autistics from typically developing. 一个轻量级的、端到端可解释的、基于广义注意力的图神经网络模型，在动态功能连接的高阶时空组织上进行训练，用于将自闭症与正常发育的自闭症进行分类。

IF 3.1 3区医学

Network Neuroscience Pub Date : 2025-11-20 eCollection Date: 2025-01-01 DOI: 10.1162/NETN.a.32

Km Bhavna, Niniva Ghosh, Romi Banerjee, Dipanjan Roy

{"title":"A lightweight, end-to-end explainable, and generalized attention-based graph neural network model trained on high-order spatiotemporal organization of dynamic functional connectivity to classify autistics from typically developing.","authors":"Km Bhavna, Niniva Ghosh, Romi Banerjee, Dipanjan Roy","doi":"10.1162/NETN.a.32","DOIUrl":"10.1162/NETN.a.32","url":null,"abstract":"Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social cognition, interaction, communication, restricted behaviors, and sensory abnormalities. The heterogeneity in ASD's clinical presentation complicates its diagnosis and treatment. Recent technological advancements in graph neural networks (GNNs) have been extensively used to diagnose brain disorders such as ASD, but existing machine learning models often suffer from low accuracy and explainability. In this study, we proposed a novel, explainable, and generalized node-edge connectivity-based graph attention neural network (Ex-NEGAT) model, leveraging edge-centric high-order spatiotemporal organization of dynamic functional connectivity streams between large-scale functional brain networks implicated in autism. Using the Autism Brain Imaging Data Exchange I and II datasets (total samples = 1,500), the model achieved 88% accuracy and an F1-score of 0.89. Additionally, we used meta-connectivity subtypes to identify subgroups within ASD samples using the rough fuzzy c-means algorithm. We also used connectome-based prediction modeling, which revealed critical brain networks contributing to predictions that accurately correlate with Autism Diagnostic Observation Schedule (ADOS) and full intelligent quotient (FIQ) scores. The proposed framework offers a robust approach based on previously unexplored higher order spatiotemporal correlation features of dynamic functional connectivity, which may provide critical insight into ASD heterogeneity and improve diagnostic precision.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 4","pages":"1323-1351"},"PeriodicalIF":3.1,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12635838/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145589443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Coming up short: Generative network models fail to accurately capture long-range connectivity. 不足之处：生成网络模型无法准确捕获远程连接。

IF 3.1 3区医学

Network Neuroscience Pub Date : 2025-11-20 eCollection Date: 2025-01-01 DOI: 10.1162/NETN.a.35

Stuart Oldham, Alex Fornito, Gareth Ball

{"title":"Coming up short: Generative network models fail to accurately capture long-range connectivity.","authors":"Stuart Oldham, Alex Fornito, Gareth Ball","doi":"10.1162/NETN.a.35","DOIUrl":"10.1162/NETN.a.35","url":null,"abstract":"Generative network models (GNMs) have been proposed to identify the mechanisms/constraints that shape the organization of the connectome. These models parameterize the formation of interregional connections using a trade-off between connection cost and topological complexity or biophysical similarity. Despite their simplicity, GNMs can generate synthetic networks that capture many topological properties of empirical brain networks. However, current models often fail to capture the topography (i.e., spatial embedding) of many such properties, such as the anatomical location of network hubs. In this study, we investigate a diverse array of GNM formulations and find that none can accurately capture empirical patterns of long-range connectivity. We demonstrate that the spatial embedding of longer-range connections is critical in defining hub locations and that it is precisely these connections that are poorly captured by extant models. We further show how standard measures used for model optimization and evaluation mask these and other differences between synthetic and empirical brain networks, highlighting the need for care when interpreting GNMs and metrics. Overall, our findings demonstrate common failure modes of GNMs, identify why these models do not fully capture brain network organization, and suggest ways the field can move forward to address these challenges.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 4","pages":"1377-1400"},"PeriodicalIF":3.1,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12635836/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145589461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Greater audiovisual integration with executive functions networks following a visual rhythmic reading training in children with reading difficulties: An fMRI study. 有阅读困难的儿童在视觉节奏阅读训练后，听觉视觉与执行功能网络的整合能力增强：一项功能磁共振成像研究。

IF 3.1 3区医学

Network Neuroscience Pub Date : 2025-10-30 eCollection Date: 2025-01-01 DOI: 10.1162/NETN.a.31

Tzipi Horowitz-Kraus, Tasneem Ismaeel, Marwa Badarni, Rola Farah, Keri Rosch

{"title":"Greater audiovisual integration with executive functions networks following a visual rhythmic reading training in children with reading difficulties: An fMRI study.","authors":"Tzipi Horowitz-Kraus, Tasneem Ismaeel, Marwa Badarni, Rola Farah, Keri Rosch","doi":"10.1162/NETN.a.31","DOIUrl":"10.1162/NETN.a.31","url":null,"abstract":"Reading difficulty (RD; dyslexia) is a developmental condition with neurological origins and persistent academic consequences. Children with RD often show deficits in audiovisual integration (AVI) and executive functions. Visual rhythmic reading training (RRT) has been associated with improvements in these domains, but it remains unclear whether such effects generalize to the resting-state brain activity. English-speaking children aged 8-12 years, including typical readers (TRs) and children with RD, were randomly assigned to an 8-week visual RRT or control math training group. Reading assessments and resting-state functional MRI data were collected before and after the intervention. Functional connectivity (FC) analyses examined AVI and its interaction with frontoparietal-cingulo-opercular (FP-CO) cognitive control networks during rest. Following RRT, children with RD showed significant improvements in reading fluency. The RRT group also demonstrated greater changes in AVI, which were associated with increased FC between FP-CO networks and sensory regions during the resting state. RRT improves reading performance and promotes enhanced integration between sensory and executive networks in children with RD, even in the absence of task demands. These findings support the role of RRT in fostering domain-general neuroplasticity beyond reading-specific contexts.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 4","pages":"1264-1278"},"PeriodicalIF":3.1,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12594489/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145483470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Female 3xTg-AD mice demonstrate hyperexcitability phenotype of Alzheimer's disease in structure-function and function-behavior relationships. 雌性3xTg-AD小鼠在结构-功能和功能-行为关系中表现出阿尔茨海默病的高兴奋性表型。

IF 3.1 3区医学

Network Neuroscience Pub Date : 2025-10-30 eCollection Date: 2025-01-01 DOI: 10.1162/NETN.a.28

Ziyi Wang 王子怡, Hui Li 李卉, Bowen Shi 史博文, Qikai Qin 秦琪凯, Qiong Ye 叶琼, Garth J Thompson

{"title":"Female 3xTg-AD mice demonstrate hyperexcitability phenotype of Alzheimer's disease in structure-function and function-behavior relationships.","authors":"Ziyi Wang 王子怡, Hui Li 李卉, Bowen Shi 史博文, Qikai Qin 秦琪凯, Qiong Ye 叶琼, Garth J Thompson","doi":"10.1162/NETN.a.28","DOIUrl":"10.1162/NETN.a.28","url":null,"abstract":"Alzheimer's disease (AD) causes cognitive decline with aging, hypothetically due to the accumulation of beta-amyloid (Aβ) plaques. The 3xTg-AD mouse model is increasingly used due to its initial absence of significant physical or behavioral impairments in youth and progressive Aβ plaque development with age. This mouse model thus provides an opportunity for comparison with human AD through two stages of study. Using wild-type (WT) and 3xTg-AD mice, aged 22 and 40 weeks (before and after the large increase in Aβ plaques), we measured functional connectivity (FC) and structural connectivity (SC) between brain regions. At 22 weeks, 3xTg-AD mice unexpectedly had higher SC and FC, and there was positive correlation between behavioral performance and FC density. By 40 weeks, SC and FC was lower in AD mice (similar to human AD patients), but the behavior-functional correlation was negative. Thus, our methods identified a shift in 3xTg-AD mice between two abnormal states relative to WT, moving from a hyperconnected to a hypoconnected state. Such a shift matches the hyperexcitability phenotype of AD observed in human patients, and thus suggests that 3xTg-AD mice can model the multistage etiology of AD of that phenotype.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 4","pages":"1199-1220"},"PeriodicalIF":3.1,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12594488/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145483459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Evidence for white matter intrinsic connectivity networks at rest and during a task: A large-scale study and templates. 在休息和任务期间白质内在连接网络的证据：一项大规模研究和模板。

IF 3.1 3区医学

Network Neuroscience Pub Date : 2025-10-30 eCollection Date: 2025-01-01 DOI: 10.1162/NETN.a.29

Vaibhavi S Itkyal, Armin Iraji, Kyle M Jensen, Theodore J LaGrow, Marlena Duda, Jessica A Turner, Jingyu Liu, Lei Wu, Yuhui Du, Jill Fries, Zening Fu, Peter Kochunov, Aysenil Belger, Judith M Ford, Daniel H Mathalon, Godfrey D Pearlson, Steven G Potkin, Adrian Preda, Theo G M van Erp, Kun Yang, Akira Sawa, Kent Hutchison, Elizabeth A Osuch, Jean Theberge, Christopher Abbott, Byron A Mueller, Jiayu Chen, Jing Sui, Tulay Adali, Vince D Calhoun

{"title":"Evidence for white matter intrinsic connectivity networks at rest and during a task: A large-scale study and templates.","authors":"Vaibhavi S Itkyal, Armin Iraji, Kyle M Jensen, Theodore J LaGrow, Marlena Duda, Jessica A Turner, Jingyu Liu, Lei Wu, Yuhui Du, Jill Fries, Zening Fu, Peter Kochunov, Aysenil Belger, Judith M Ford, Daniel H Mathalon, Godfrey D Pearlson, Steven G Potkin, Adrian Preda, Theo G M van Erp, Kun Yang, Akira Sawa, Kent Hutchison, Elizabeth A Osuch, Jean Theberge, Christopher Abbott, Byron A Mueller, Jiayu Chen, Jing Sui, Tulay Adali, Vince D Calhoun","doi":"10.1162/NETN.a.29","DOIUrl":"10.1162/NETN.a.29","url":null,"abstract":"Understanding white matter (WM) functional connectivity is crucial for unraveling brain function and dysfunction. In this study, we present a novel WM intrinsic connectivity network (ICN) template derived from over 100,000 fMRI scans, identifying 97 robust WM ICNs using spatially constrained independent component analysis (scICA). This WM template, combined with a previously identified gray matter (GM) ICN template from the same dataset, was applied to analyze a resting-state fMRI (rs-fMRI) dataset from the Bipolar-Schizophrenia Network on Intermediate Phenotypes 2 (BSNIP2; 590 subjects) and a task-based fMRI dataset from the MIND Clinical Imaging Consortium (MCIC; 75 subjects). Our analysis highlights distinct spatial maps for WM and GM ICNs, with WM ICNs showing higher frequency profiles. Visually modular structure within WM ICNs and interactions between WM and GM modules were identified. Task-based fMRI revealed event-related BOLD signals in WM ICNs, particularly within the corticospinal tract, lateralized to finger movement. Notable differences in static functional network connectivity (sFNC) matrices were observed between controls (HC) and schizophrenia (SZ) subjects in both WM and GM networks. This open-source WM NeuroMark template and automated pipeline offer a powerful tool for advancing WM connectivity research across diverse datasets.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 4","pages":"1221-1244"},"PeriodicalIF":3.1,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12594490/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145483474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

NeuroCarta: An automated and quantitative approach to mapping cellular networks in the mouse brain. NeuroCarta：一种自动和定量的方法来绘制老鼠大脑中的细胞网络。

IF 3.1 3区医学

Network Neuroscience Pub Date : 2025-10-30 eCollection Date: 2025-01-01 DOI: 10.1162/NETN.a.33

Tido Bergmans, Tansu Celikel

{"title":"NeuroCarta: An automated and quantitative approach to mapping cellular networks in the mouse brain.","authors":"Tido Bergmans, Tansu Celikel","doi":"10.1162/NETN.a.33","DOIUrl":"10.1162/NETN.a.33","url":null,"abstract":"Understanding the structural organization of the brain is essential for deciphering how complex functions emerge from neural circuits. The Allen Mouse Brain Connectivity Atlas (AMBCA) has revolutionized our ability to quantify anatomical connectivity at a mesoscale resolution, bridging the gap between microscopic cellular interactions and macroscopic network organization. To leverage AMBCA for automated network construction and analysis, here, we introduce NeuroCarta, an open-source MATLAB toolbox designed to extract, process, and analyze brain-wide connectivity networks. NeuroCarta generates directed and weighted connectivity graphs, computes key network metrics, and visualizes topological features of brain circuits. As an application example, using NeuroCarta on viral tracer data from the AMBCA, we demonstrate that the mouse brain exhibits a densely connected architecture, with a degree of separation of approximately four synapses, suggesting an optimized balance between local specialization and global integration. We identify attractor nodes that may serve as key convergence points in brain-wide neural computations and show that NeuroCarta facilitates comparative network analyses, revealing regional variations in projection patterns. While the toolbox is currently constrained by the resolution and coverage of the AMBCA dataset, it provides a scalable and customizable framework for investigating brain network topology, interregional communication, and anatomical constraints on mesoscale circuit organization.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 4","pages":"1279-1298"},"PeriodicalIF":3.1,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12594485/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145483473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

A multi-compartment model for pathological connectomes. 病理连接体的多室模型。

IF 3.1 3区医学

Network Neuroscience Pub Date : 2025-10-30 eCollection Date: 2025-01-01 DOI: 10.1162/NETN.a.30

Sara Bosticardo, Matteo Battocchio, Simona Schiavi, Andrew Zalesky, Cristina Granziera, Alessandro Daducci

{"title":"A multi-compartment model for pathological connectomes.","authors":"Sara Bosticardo, Matteo Battocchio, Simona Schiavi, Andrew Zalesky, Cristina Granziera, Alessandro Daducci","doi":"10.1162/NETN.a.30","DOIUrl":"10.1162/NETN.a.30","url":null,"abstract":"Brain connectivity analysis is pivotal to understanding mechanisms underpinning neurological diseases. However, current methodologies for quantitatively mapping the connectivity in vivo face challenges when focal lesions are present and can introduce strong biases in the estimates. We present a novel approach to address these challenges by introducing a multi-compartment description of the connectome, which explicitly incorporates lesion information during the estimation process. We extended the Convex Optimization Modeling for Microstructure Informed Tractography (COMMIT) framework to integrate an additional tissue compartment in voxels affected by pathology, allowing us to infer accurately the contributions of streamlines passing through lesions and to provide unbiased connectivity estimates. We evaluated the effectiveness of our approach on data from healthy subjects of the Human Connectome Project, in which we artificially introduced focal lesions to simulate pathology with varying levels of axonal damage. We also tested the performances obtained when comparing healthy subjects with patients affected by multiple sclerosis. Results demonstrate that our approach significantly enhances sensitivity to pathological changes even at low degeneracy levels compared with state-of-the-art techniques, thus representing a significant step forward to advance our understanding of neurodegenerative diseases.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 4","pages":"1245-1263"},"PeriodicalIF":3.1,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12594486/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145483491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Graph models of brain state in deep anesthesia reveal sink state dynamics of reduced spatiotemporal complexity. 深度麻醉下脑状态的图模型揭示了时空复杂性降低的下沉状态动态。

IF 3.1 3区医学

Network Neuroscience Pub Date : 2025-10-30 eCollection Date: 2025-01-01 DOI: 10.1162/NETN.a.27

James Barnard Wilsenach, Charlotte M Deane, Gesine Reinert, Katie Warnaby

{"title":"Graph models of brain state in deep anesthesia reveal sink state dynamics of reduced spatiotemporal complexity.","authors":"James Barnard Wilsenach, Charlotte M Deane, Gesine Reinert, Katie Warnaby","doi":"10.1162/NETN.a.27","DOIUrl":"10.1162/NETN.a.27","url":null,"abstract":"Anesthetisia is an important surgical and explorative tool in the study of consciousness. Much work has been done to connect the deeply anesthetized condition with decreased complexity. However, anesthesia-induced unconsciousness is also a dynamic condition in which functional activity and complexity may fluctuate, being perturbed by internal or external (e.g., noxious) stimuli. We use fMRI data from a cohort undergoing deep propofol anesthesia to investigate resting state dynamics using dynamic brain state models and spatiotemporal network analysis. We focus our analysis on group-level dynamics of brain state temporal complexity, functional activity, connectivity, and spatiotemporal modularization in deep anesthesia and wakefulness. We find that in contrast to dynamics in the wakeful condition, anesthesia dynamics are dominated by a handful of sink states that act as low-complexity attractors to which subjects repeatedly return. On a subject level, our analysis provides tentative evidence that these low-complexity attractor states appear to depend on subject-specific age and anesthesia susceptibility factors. Finally, our spatiotemporal analysis, including a novel spatiotemporal clustering of graphs representing hidden Markov models, suggests that dynamic functional organization in anesthesia can be characterized by mostly unchanging, isolated regional subnetworks that share some similarities with the brain's underlying structural connectivity, as determined from normative tractography data.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 4","pages":"1176-1198"},"PeriodicalIF":3.1,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12594487/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145483456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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