Network Neuroscience最新文献

Developmental differences in canonical cortical networks: Insights from microstructure-informed tractography. 典型皮质网络的发育差异：微观结构信息牵引成像的启示

IF 3.6 3区医学

Network Neuroscience Pub Date : 2024-10-01 eCollection Date: 2024-01-01 DOI: 10.1162/netn_a_00378

Sila Genc, Simona Schiavi, Maxime Chamberland, Chantal M W Tax, Erika P Raven, Alessandro Daducci, Derek K Jones

{"title":"Developmental differences in canonical cortical networks: Insights from microstructure-informed tractography.","authors":"Sila Genc, Simona Schiavi, Maxime Chamberland, Chantal M W Tax, Erika P Raven, Alessandro Daducci, Derek K Jones","doi":"10.1162/netn_a_00378","DOIUrl":"10.1162/netn_a_00378","url":null,"abstract":"In response to a growing interest in refining brain connectivity assessments, this study focuses on integrating white matter fiber-specific microstructural properties into structural connectomes. Spanning ages 8-19 years in a developmental sample, it explores age-related patterns of microstructure-informed network properties at both local and global scales. First, the diffusion-weighted signal fraction associated with each tractography-reconstructed streamline was constructed. Subsequently, the convex optimization modeling for microstructure-informed tractography (COMMIT) approach was employed to generate microstructure-informed connectomes from diffusion MRI data. To complete the investigation, network characteristics within eight functionally defined networks (visual, somatomotor, dorsal attention, ventral attention, limbic, fronto-parietal, default mode, and subcortical networks) were evaluated. The findings underscore a consistent increase in global efficiency across child and adolescent development within the visual, somatomotor, and default mode networks (p < 0.005). Additionally, mean strength exhibits an upward trend in the somatomotor and visual networks (p < 0.001). Notably, nodes within the dorsal and ventral visual pathways manifest substantial age-dependent changes in local efficiency, aligning with existing evidence of extended maturation in these pathways. The outcomes strongly support the notion of a prolonged developmental trajectory for visual association cortices. This study contributes valuable insights into the nuanced dynamics of microstructure-informed brain connectivity throughout different developmental stages.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"8 3","pages":"946-964"},"PeriodicalIF":3.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11424039/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142362331","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

Frequency modulation increases the specificity of time-resolved connectivity: A resting-state fMRI study. 频率调制提高了时间分辨连通性的特异性：静息态 fMRI 研究。

IF 3.6 3区医学

Network Neuroscience Pub Date : 2024-10-01 eCollection Date: 2024-01-01 DOI: 10.1162/netn_a_00372

Ashkan Faghiri, Kun Yang, Andreia Faria, Koko Ishizuka, Akira Sawa, Tülay Adali, Vince Calhoun

{"title":"Frequency modulation increases the specificity of time-resolved connectivity: A resting-state fMRI study.","authors":"Ashkan Faghiri, Kun Yang, Andreia Faria, Koko Ishizuka, Akira Sawa, Tülay Adali, Vince Calhoun","doi":"10.1162/netn_a_00372","DOIUrl":"10.1162/netn_a_00372","url":null,"abstract":"Representing data using time-resolved networks is valuable for analyzing functional data of the human brain. One commonly used method for constructing time-resolved networks from data is sliding window Pearson correlation (SWPC). One major limitation of SWPC is that it applies a high-pass filter to the activity time series. Therefore, if we select a short window (desirable to estimate rapid changes in connectivity), we will remove important low-frequency information. Here, we propose an approach based on single sideband modulation (SSB) in communication theory. This allows us to select shorter windows to capture rapid changes in the time-resolved functional network connectivity (trFNC). We use simulation and real resting-state functional magnetic resonance imaging (fMRI) data to demonstrate the superior performance of SSB+SWPC compared to SWPC. We also compare the recurring trFNC patterns between individuals with the first episode of psychosis (FEP) and typical controls (TC) and show that FEPs stay more in states that show weaker connectivity across the whole brain. A result exclusive to SSB+SWPC is that TCs stay more in a state with negative connectivity between subcortical and cortical regions. Based on all the results, we argue that SSB+SWPC is more sensitive for capturing temporal variation in trFNC.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"8 3","pages":"734-761"},"PeriodicalIF":3.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11349031/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142362332","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

Network-level enrichment provides a framework for biological interpretation of machine learning results. 网络级富集为机器学习结果的生物学解释提供了一个框架。

IF 3.6 3区医学

Network Neuroscience Pub Date : 2024-10-01 eCollection Date: 2024-01-01 DOI: 10.1162/netn_a_00383

Jiaqi Li, Ari Segel, Xinyang Feng, Jiaxin Cindy Tu, Andy Eck, Kelsey T King, Babatunde Adeyemo, Nicole R Karcher, Likai Chen, Adam T Eggebrecht, Muriah D Wheelock

{"title":"Network-level enrichment provides a framework for biological interpretation of machine learning results.","authors":"Jiaqi Li, Ari Segel, Xinyang Feng, Jiaxin Cindy Tu, Andy Eck, Kelsey T King, Babatunde Adeyemo, Nicole R Karcher, Likai Chen, Adam T Eggebrecht, Muriah D Wheelock","doi":"10.1162/netn_a_00383","DOIUrl":"10.1162/netn_a_00383","url":null,"abstract":"Machine learning algorithms are increasingly being utilized to identify brain connectivity biomarkers linked to behavioral and clinical outcomes. However, research often prioritizes prediction accuracy at the expense of biological interpretability, and inconsistent implementation of ML methods may hinder model accuracy. To address this, our paper introduces a network-level enrichment approach, which integrates brain system organization in the context of connectome-wide statistical analysis to reveal network-level links between brain connectivity and behavior. To demonstrate the efficacy of this approach, we used linear support vector regression (LSVR) models to examine the relationship between resting-state functional connectivity networks and chronological age. We compared network-level associations based on raw LSVR weights to those produced from the forward and inverse models. Results indicated that not accounting for shared family variance inflated prediction performance, the k-best feature selection via Pearson correlation reduced accuracy and reliability, and raw LSVR model weights produced network-level associations that deviated from the significant brain systems identified by forward and inverse models. Our findings offer crucial insights for applying machine learning to neuroimaging data, emphasizing the value of network enrichment for biological interpretation.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"8 3","pages":"762-790"},"PeriodicalIF":3.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11349033/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142362333","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

Individual variability in neural representations of mind-wandering. 思维游走神经表征的个体差异性

IF 3.6 3区医学

Network Neuroscience Pub Date : 2024-10-01 eCollection Date: 2024-01-01 DOI: 10.1162/netn_a_00387

Aaron Kucyi, Nathan Anderson, Tiara Bounyarith, David Braun, Lotus Shareef-Trudeau, Isaac Treves, Rodrigo M Braga, Po-Jang Hsieh, Shao-Min Hung

{"title":"Individual variability in neural representations of mind-wandering.","authors":"Aaron Kucyi, Nathan Anderson, Tiara Bounyarith, David Braun, Lotus Shareef-Trudeau, Isaac Treves, Rodrigo M Braga, Po-Jang Hsieh, Shao-Min Hung","doi":"10.1162/netn_a_00387","DOIUrl":"10.1162/netn_a_00387","url":null,"abstract":"Mind-wandering is a frequent, daily mental activity, experienced in unique ways in each person. Yet neuroimaging evidence relating mind-wandering to brain activity, for example in the default mode network (DMN), has relied on population- rather than individual-based inferences owing to limited within-person sampling. Here, three densely sampled individuals each reported hundreds of mind-wandering episodes while undergoing multi-session functional magnetic resonance imaging. We found reliable associations between mind-wandering and DMN activation when estimating brain networks within individuals using precision functional mapping. However, the timing of spontaneous DMN activity relative to subjective reports, and the networks beyond DMN that were activated and deactivated during mind-wandering, were distinct across individuals. Connectome-based predictive modeling further revealed idiosyncratic, whole-brain functional connectivity patterns that consistently predicted mind-wandering within individuals but did not fully generalize across individuals. Predictive models of mind-wandering and attention that were derived from larger-scale neuroimaging datasets largely failed when applied to densely sampled individuals, further highlighting the need for personalized models. Our work offers novel evidence for both conserved and variable neural representations of self-reported mind-wandering in different individuals. The previously unrecognized interindividual variations reported here underscore the broader scientific value and potential clinical utility of idiographic approaches to brain-experience associations.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"8 3","pages":"808-836"},"PeriodicalIF":3.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11349032/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142362706","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

Analyzing asymmetry in brain hierarchies with a linear state-space model of resting-state fMRI data. 利用静息态 fMRI 数据的线性状态空间模型分析大脑层次结构的不对称性。

IF 3.6 3区医学

Network Neuroscience Pub Date : 2024-10-01 eCollection Date: 2024-01-01 DOI: 10.1162/netn_a_00381

Danilo Benozzo, Giacomo Baggio, Giorgia Baron, Alessandro Chiuso, Sandro Zampieri, Alessandra Bertoldo

{"title":"Analyzing asymmetry in brain hierarchies with a linear state-space model of resting-state fMRI data.","authors":"Danilo Benozzo, Giacomo Baggio, Giorgia Baron, Alessandro Chiuso, Sandro Zampieri, Alessandra Bertoldo","doi":"10.1162/netn_a_00381","DOIUrl":"10.1162/netn_a_00381","url":null,"abstract":"This study challenges the traditional focus on zero-lag statistics in resting-state functional magnetic resonance imaging (rsfMRI) research. Instead, it advocates for considering time-lag interactions to unveil the directionality and asymmetries of the brain hierarchy. Effective connectivity (EC), the state matrix in dynamical causal modeling (DCM), is a commonly used metric for studying dynamical properties and causal interactions within a linear state-space system description. Here, we focused on how time-lag statistics are incorporated within the framework of DCM resulting in an asymmetric EC matrix. Our approach involves decomposing the EC matrix, revealing a steady-state differential cross-covariance matrix that is responsible for modeling information flow and introducing time-irreversibility. Specifically, the system's dynamics, influenced by the off-diagonal part of the differential covariance, exhibit a curl steady-state flow component that breaks detailed balance and diverges the dynamics from equilibrium. Our empirical findings indicate that the EC matrix's outgoing strengths correlate with the flow described by the differential cross covariance, while incoming strengths are primarily driven by zero-lag covariance, emphasizing conditional independence over directionality.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"8 3","pages":"965-988"},"PeriodicalIF":3.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11424037/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142362329","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

Propagation of transient explosive synchronization in a mesoscale mouse brain network model of epilepsy. 癫痫中尺度小鼠大脑网络模型中的瞬时爆发性同步传播。

IF 3.6 3区医学

Network Neuroscience Pub Date : 2024-10-01 eCollection Date: 2024-01-01 DOI: 10.1162/netn_a_00379

Avinash Ranjan, Saurabh R Gandhi

{"title":"Propagation of transient explosive synchronization in a mesoscale mouse brain network model of epilepsy.","authors":"Avinash Ranjan, Saurabh R Gandhi","doi":"10.1162/netn_a_00379","DOIUrl":"10.1162/netn_a_00379","url":null,"abstract":"Generalized epileptic attacks, which exhibit widespread disruption of brain activity, are characterized by recurrent, spontaneous, and synchronized bursts of neural activity that self-initiate and self-terminate through critical transitions. Here we utilize the general framework of explosive synchronization (ES) from complex systems science to study the role of network structure and resource dynamics in the generation and propagation of seizures. We show that a combination of resource constraint and adaptive coupling in a Kuramoto network oscillator model can reliably generate seizure-like synchronization activity across different network topologies, including a biologically derived mesoscale mouse brain network. The model, coupled with a novel algorithm for tracking seizure propagation, provides mechanistic insight into the dynamics of transition to the synchronized state and its dependence on resources; and identifies key brain areas that may be involved in the initiation and spatial propagation of the seizure. The model, though minimal, efficiently recapitulates several experimental and theoretical predictions from more complex models and makes novel experimentally testable predictions.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"8 3","pages":"883-901"},"PeriodicalIF":3.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11398721/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142362334","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

Reorganization of structural connectivity in the brain supports preservation of cognitive ability in healthy aging. 大脑结构连接的重组有助于在健康老龄化过程中保持认知能力。

IF 3.6 3区医学

Network Neuroscience Pub Date : 2024-10-01 eCollection Date: 2024-01-01 DOI: 10.1162/netn_a_00377

Josh Neudorf, Kelly Shen, Anthony R McIntosh

{"title":"Reorganization of structural connectivity in the brain supports preservation of cognitive ability in healthy aging.","authors":"Josh Neudorf, Kelly Shen, Anthony R McIntosh","doi":"10.1162/netn_a_00377","DOIUrl":"10.1162/netn_a_00377","url":null,"abstract":"The global population is aging rapidly, and a research question of critical importance is why some older adults suffer tremendous cognitive decline while others are mostly spared. Past aging research has shown that older adults with spared cognitive ability have better local short-range information processing while global long-range processing is less efficient. We took this research a step further to investigate whether the underlying structural connections, measured in vivo using diffusion magnetic resonance imaging (dMRI), show a similar shift to support cognitive ability. We analyzed the structural connectivity streamline probability (representing the probability of connection between regions) and nodal efficiency and local efficiency regional graph theory metrics to determine whether age and cognitive ability are related to structural network differences. We found that the relationship between structural connectivity and cognitive ability with age was nuanced, with some differences with age that were associated with poorer cognitive outcomes, but other reorganizations that were associated with spared cognitive ability. These positive changes included strengthened local intrahemispheric connectivity and increased nodal efficiency of the ventral occipital-temporal stream, nucleus accumbens, and hippocampus for older adults, and widespread local efficiency primarily for middle-aged individuals.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"8 3","pages":"837-859"},"PeriodicalIF":3.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11398719/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142362335","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 Bayesian incorporated linear non-Gaussian acyclic model for multiple directed graph estimation to study brain emotion circuit development in adolescence. 贝叶斯纳入线性非高斯非循环模型的多重有向图估算，用于研究青春期大脑情感回路的发展。

IF 3.6 3区医学

Network Neuroscience Pub Date : 2024-10-01 eCollection Date: 2024-01-01 DOI: 10.1162/netn_a_00384

Aiying Zhang, Gemeng Zhang, Biao Cai, Tony W Wilson, Julia M Stephen, Vince D Calhoun, Yu-Ping Wang

{"title":"A Bayesian incorporated linear non-Gaussian acyclic model for multiple directed graph estimation to study brain emotion circuit development in adolescence.","authors":"Aiying Zhang, Gemeng Zhang, Biao Cai, Tony W Wilson, Julia M Stephen, Vince D Calhoun, Yu-Ping Wang","doi":"10.1162/netn_a_00384","DOIUrl":"10.1162/netn_a_00384","url":null,"abstract":"Emotion perception is essential to affective and cognitive development which involves distributed brain circuits. Emotion identification skills emerge in infancy and continue to develop throughout childhood and adolescence. Understanding the development of the brain's emotion circuitry may help us explain the emotional changes during adolescence. In this work, we aim to deepen our understanding of emotion-related functional connectivity (FC) from association to causation. We proposed a Bayesian incorporated linear non-Gaussian acyclic model (BiLiNGAM), which incorporated association model into the estimation pipeline. Simulation results indicated stable and accurate performance over various settings, especially when the sample size was small. We used fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC) to validate the approach. It included 855 individuals aged 8-22 years who were divided into five different adolescent stages. Our network analysis revealed the development of emotion-related intra- and intermodular connectivity and pinpointed several emotion-related hubs. We further categorized the hubs into two types: in-hubs and out-hubs, as the center of receiving and distributing information, respectively. In addition, several unique developmental hub structures and group-specific patterns were discovered. Our findings help provide a directed FC template of brain network organization underlying emotion processing during adolescence.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"8 3","pages":"791-807"},"PeriodicalIF":3.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11349030/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142362328","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

Brain sodium MRI-derived priors support the estimation of epileptogenic zones using personalized model-based methods in epilepsy. 脑钠磁共振成像衍生先验支持使用基于个性化模型的癫痫方法估计致痫区。

IF 3.6 3区医学

Network Neuroscience Pub Date : 2024-10-01 eCollection Date: 2024-01-01 DOI: 10.1162/netn_a_00371

Mikhael Azilinon, Huifang E Wang, Julia Makhalova, Wafaa Zaaraoui, Jean-Philippe Ranjeva, Fabrice Bartolomei, Maxime Guye, Viktor Jirsa

{"title":"Brain sodium MRI-derived priors support the estimation of epileptogenic zones using personalized model-based methods in epilepsy.","authors":"Mikhael Azilinon, Huifang E Wang, Julia Makhalova, Wafaa Zaaraoui, Jean-Philippe Ranjeva, Fabrice Bartolomei, Maxime Guye, Viktor Jirsa","doi":"10.1162/netn_a_00371","DOIUrl":"10.1162/netn_a_00371","url":null,"abstract":"Patients presenting with drug-resistant epilepsy are eligible for surgery aiming to remove the regions involved in the production of seizure activities, the so-called epileptogenic zone network (EZN). Thus the accurate estimation of the EZN is crucial. Data-driven, personalized virtual brain models derived from patient-specific anatomical and functional data are used in Virtual Epileptic Patient (VEP) to estimate the EZN via optimization methods from Bayesian inference. The Bayesian inference approach used in previous VEP integrates priors, based on the features of stereotactic-electroencephalography (SEEG) seizures' recordings. Here, we propose new priors, based on quantitative 23Na-MRI. The 23Na-MRI data were acquired at 7T and provided several features characterizing the sodium signal decay. The hypothesis is that the sodium features are biomarkers of neuronal excitability related to the EZN and will add additional information to VEP estimation. In this paper, we first proposed the mapping from 23Na-MRI features to predict the EZN via a machine learning approach. Then, we exploited these predictions as priors in the VEP pipeline. The statistical results demonstrated that compared with the results from current VEP, the result from VEP based on 23Na-MRI prior has better balanced accuracy, and the similar weighted harmonic mean of the precision and recall.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"8 3","pages":"673-696"},"PeriodicalIF":3.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11340996/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142362330","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

Retinal waves in adaptive rewiring networks orchestrate convergence and divergence in the visual system. 自适应再布线网络中的视网膜波协调了视觉系统中的聚合和发散。

IF 3.6 3区医学

Network Neuroscience Pub Date : 2024-10-01 eCollection Date: 2024-01-01 DOI: 10.1162/netn_a_00370

Raúl Luna, Jia Li, Roman Bauer, Cees van Leeuwen

{"title":"Retinal waves in adaptive rewiring networks orchestrate convergence and divergence in the visual system.","authors":"Raúl Luna, Jia Li, Roman Bauer, Cees van Leeuwen","doi":"10.1162/netn_a_00370","DOIUrl":"10.1162/netn_a_00370","url":null,"abstract":"Spontaneous retinal wave activity shaping the visual system is a complex neurodevelopmental phenomenon. Retinal ganglion cells are the hubs through which activity diverges throughout the visual system. We consider how these divergent hubs emerge, using an adaptively rewiring neural network model. Adaptive rewiring models show in a principled way how brains could achieve their complex topologies. Modular small-world structures with rich-club effects and circuits of convergent-divergent units emerge as networks evolve, driven by their own spontaneous activity. Arbitrary nodes of an initially random model network were designated as retinal ganglion cells. They were intermittently exposed to the retinal waveform, as the network evolved through adaptive rewiring. A significant proportion of these nodes developed into divergent hubs within the characteristic complex network architecture. The proportion depends parametrically on the wave incidence rate. Higher rates increase the likelihood of hub formation, while increasing the potential of ganglion cell death. In addition, direct neighbors of designated ganglion cells differentiate like amacrine cells. The divergence observed in ganglion cells resulted in enhanced convergence downstream, suggesting that retinal waves control the formation of convergence in the lateral geniculate nuclei. We conclude that retinal waves stochastically control the distribution of converging and diverging activity in evolving complex networks.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"8 3","pages":"653-672"},"PeriodicalIF":3.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11340993/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142362336","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