Network Neuroscience最新文献_第6页

Reproducibility of resting-state functional connectivity in healthy aging and brain injury: A mini-multiverse analysis. 健康衰老和脑损伤中静息状态功能连接的可重复性：一个微型多元宇宙分析。

IF 3.4 3区医学

Network Neuroscience Pub Date : 2025-09-22 eCollection Date: 2025-01-01 DOI: 10.1162/netn_a_00459

Hollie A Mullin, Catherine M Carpenter, Andrew P Cwiek, Gloria Lan, Spencer O Chase, Emily E Carter, Samantha M Vervoordt, Amanda Rabinowitz, Umesh Venkatesan, Frank G Hillary

{"title":"Reproducibility of resting-state functional connectivity in healthy aging and brain injury: A mini-multiverse analysis.","authors":"Hollie A Mullin, Catherine M Carpenter, Andrew P Cwiek, Gloria Lan, Spencer O Chase, Emily E Carter, Samantha M Vervoordt, Amanda Rabinowitz, Umesh Venkatesan, Frank G Hillary","doi":"10.1162/netn_a_00459","DOIUrl":"10.1162/netn_a_00459","url":null,"abstract":"Resting-state functional connectivity (RSFC) methods are the most widely applied tools in the network neurosciences, but their reliability remains an active area of study. We use back-to-back 10-min resting-state scans in a healthy aging (n = 41) and traumatic brain injury (TBI) sample (n = 45) composed of older adults to assess the replicability of RSFC using a \"mini\" multiverse approach. The goal was to evaluate the reproducibility of commonly used graph metrics and determine if aging and moderate-severe TBI influences RSFC reliability using intraclass correlation coefficients (ICCs). There is clear evidence for reliable results in aging and TBI. Global network metrics such as within-network connectivity and segregation were most reliable whereas other whole-brain connectivity estimates (e.g., clustering coefficient, eigenvector centrality) were least reliable. Analysis of canonical networks revealed the default mode and salience networks as most reliable. There was a notable influence of motion scrubbing on ICCs, with diminished reliability proportional to the number of volumes removed. Choice of brain atlas had a modest effect on findings. Overall, RSFC reproducibility is preserved in older adults and after significant neurological compromise. We also identify a subset of graph metrics and canonical networks with promising reliability.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 3","pages":"1154-1175"},"PeriodicalIF":3.4,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12548667/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145379420","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

Characterizing dynamic functional connectivity subnetwork contributions in narrative classification with Shapley values. 用Shapley值表征动态功能连通性子网络在叙事分类中的贡献。

IF 3.1 3区医学

Network Neuroscience Pub Date : 2025-09-19 eCollection Date: 2025-01-01 DOI: 10.1162/NETN.a.25

Aurora Rossi, Yanis Aeschlimann, Emanuele Natale, Samuel Deslauriers-Gauthier, Peter Ford Dominey

{"title":"Characterizing dynamic functional connectivity subnetwork contributions in narrative classification with Shapley values.","authors":"Aurora Rossi, Yanis Aeschlimann, Emanuele Natale, Samuel Deslauriers-Gauthier, Peter Ford Dominey","doi":"10.1162/NETN.a.25","DOIUrl":"10.1162/NETN.a.25","url":null,"abstract":"Functional connectivity derived from functional magnetic resonance imaging (fMRI) data has been increasingly used to study brain activity. In this study, we model brain dynamic functional connectivity during narrative tasks as a temporal brain network and employ a machine learning model to classify in a supervised setting the modality (audio, movie), the content (airport, restaurant situations) of narratives, and both combined. Leveraging Shapley values, we analyze subnetwork contributions within Yeo parcellations (7- and 17-subnetworks) to explore their involvement in narrative modality and comprehension. This work represents the first application of this approach to functional aspects of the brain, validated by existing literature, and provides novel insights at the whole-brain level. Our findings suggest that schematic representations in narratives may not depend solely on preexisting knowledge of the top-down process to guide perception and understanding, but may also emerge from a bottom-up process driven by the temporal parietal subnetwork.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 3","pages":"1138-1153"},"PeriodicalIF":3.1,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12548664/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145379410","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

Dynamic fluctuations of intrinsic brain activity are associated with consistent topological patterns in puberty and are biomarkers of neural maturation. 大脑内在活动的动态波动与青春期一致的拓扑模式有关，是神经成熟的生物标志物。

IF 3.1 3区医学

Network Neuroscience Pub Date : 2025-09-19 eCollection Date: 2025-01-01 DOI: 10.1162/netn_a_00452

Jethro Lim, Kaitlynn Cooper, Catherine Stamoulis

{"title":"Dynamic fluctuations of intrinsic brain activity are associated with consistent topological patterns in puberty and are biomarkers of neural maturation.","authors":"Jethro Lim, Kaitlynn Cooper, Catherine Stamoulis","doi":"10.1162/netn_a_00452","DOIUrl":"10.1162/netn_a_00452","url":null,"abstract":"Intrinsic brain dynamics play a fundamental role in cognitive function, but their development is incompletely understood. We investigated pubertal changes in temporal fluctuations of intrinsic network topologies (focusing on the strongest connections and coordination patterns) and signals, in an early longitudinal sample from the Adolescent Brain Cognitive Development (ABCD) study, with resting-state fMRI (n = 4,099 at baseline; n = 3,376 at follow-up [median age = 10.0 (1.1) and 12.0 (1.1) years; n = 2,116 with both assessments]). Reproducible, inverse associations between low-frequency signal and topological fluctuations were estimated (p < 0.05, β = -0.20 to -0.02, 95% confidence interval (CI) = [-0.23, -0.001]). Signal (but not topological) fluctuations increased in somatomotor and prefrontal areas with pubertal stage (p < 0.03, β = 0.06-0.07, 95% CI = [0.03, 0.11]), but decreased in orbitofrontal, insular, and cingulate cortices, as well as cerebellum, hippocampus, amygdala, and thalamus (p < 0.05, β = -0.09 to -0.03, 95% CI = [-0.15, -0.001]). Higher temporal signal and topological variability in spatially distributed regions were estimated in girls. In racial/ethnic minorities, several associations between signal and topological fluctuations were in the opposite direction of those in the entire sample, suggesting potential racial differences. Our findings indicate that during puberty, intrinsic signal dynamics change significantly in developed and developing brain regions, but their strongest coordination patterns may already be sufficiently developed and remain temporally consistent.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 3","pages":"1039-1064"},"PeriodicalIF":3.1,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12548668/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145379459","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

Task-guided generative adversarial networks for synthesizing and augmenting structural connectivity matrices for connectivity-based prediction. 用于合成和增强结构连接矩阵的任务导向生成对抗网络，用于基于连接的预测。

IF 3.1 3区医学

Network Neuroscience Pub Date : 2025-09-19 eCollection Date: 2025-01-01 DOI: 10.1162/NETN.a.24

Tatsuya Yamamoto, Tomoki Sugiura, Tomoyuki Hiroyasu, Satoru Hiwa

{"title":"Task-guided generative adversarial networks for synthesizing and augmenting structural connectivity matrices for connectivity-based prediction.","authors":"Tatsuya Yamamoto, Tomoki Sugiura, Tomoyuki Hiroyasu, Satoru Hiwa","doi":"10.1162/NETN.a.24","DOIUrl":"10.1162/NETN.a.24","url":null,"abstract":"Recent machine learning techniques have improved connectome-based predictions by modeling complex dependencies between brain connectivity and cognitive traits. However, they typically require large datasets that are costly and time-consuming to collect. To address this, we propose Task-guided generative adversarial network (GAN) II, a novel data augmentation method that uses GANs to expand sample sizes in connectome-based prediction tasks. Our method incorporates a task-guided branch within the Wasserstein GAN framework, specifically designed to synthesize structural connectivity matrices and improve prediction accuracy by capturing task-relevant features. We evaluated Task-guided GAN II on the prediction of fluid intelligence using the NIMH Health Research Volunteer Dataset. Results showed that data augmentation improved prediction accuracy. To further assess whether augmentation can substitute for increasing actual collected sample sizes, we conducted additional validation using the Human Connectome Project WU-Minn S1200 dataset. Task-guided GAN II improved prediction performance with limited real data, with gains of up to twofold augmentation observed. However, excessive augmentation did not result in further improvements, suggesting that augmentation complements, but does not fully replace, real data augmentation. These results suggest that Task-guided GAN II is a promising tool for harnessing small datasets in human connectomics research, improving predictive modeling where large-scale data collection is impractical.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 3","pages":"1110-1137"},"PeriodicalIF":3.1,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12548665/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145379430","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

Stable brain PET metabolic networks using a multiple sampling scheme. 使用多重采样方案的稳定脑PET代谢网络。

IF 3.1 3区医学

Network Neuroscience Pub Date : 2025-09-19 eCollection Date: 2025-01-01 DOI: 10.1162/NETN.a.23

Guilherme Schu, Christian Limberger, Wagner S Brum, Marco Antônio De Bastiani, Yuri Elias Rodrigues, Julio Cesar de Azeredo, Tharick A Pascoal, Andrea L Benedet, Sulantha Mathotaarachchi, Pedro Rosa-Neto, Jorge Almeida, Daniele de Paula Faria, Fábio Luiz de Souza Duran, Carlos Alberto Buchpiguel, Artur Martins Coutinho, Geraldo F Busatto, Eduardo R Zimmer

{"title":"Stable brain PET metabolic networks using a multiple sampling scheme.","authors":"Guilherme Schu, Christian Limberger, Wagner S Brum, Marco Antônio De Bastiani, Yuri Elias Rodrigues, Julio Cesar de Azeredo, Tharick A Pascoal, Andrea L Benedet, Sulantha Mathotaarachchi, Pedro Rosa-Neto, Jorge Almeida, Daniele de Paula Faria, Fábio Luiz de Souza Duran, Carlos Alberto Buchpiguel, Artur Martins Coutinho, Geraldo F Busatto, Eduardo R Zimmer","doi":"10.1162/NETN.a.23","DOIUrl":"10.1162/NETN.a.23","url":null,"abstract":"Interregional communication within the human brain is essential for maintaining functional integrity. A promising approach for investigating how brain regions communicate relies on the assumption that the brain operates as a complex network. In this context, positron emission tomography (PET) images have been suggested as a valuable source for understanding brain networks. However, such networks are typically assembled through direct computation without accounting for outliers, impacting the reliability of group representative networks. In this study, we used brain [18F]fluoro-2-deoxyglucose PET data from 1,227 individuals in the Alzheimer's disease (AD) continuum from the Alzheimer's Disease Neuroimaging Initiative cohort to develop a novel method for constructing stable metabolic brain networks that are resilient to spurious data points. Our multiple sampling scheme generates brain networks with greater stability compared with conventional approaches. The proposed method is robust to imbalanced datasets and requires 50% fewer subjects to achieve stability than the conventional method. We further validated the approach in an independent AD cohort (n = 114) from São Paulo, Brazil (Faculdade de Medicina da Universidade de São Paulo). This innovative method is flexible and improves the robustness of metabolic brain network analyses, supporting better insights into brain connectivity and resilience to data variability across multiple radiotracers for both health and disease.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 3","pages":"1087-1109"},"PeriodicalIF":3.1,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12548669/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145379400","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

Partial correlation as a tool for mapping functional-structural correspondence in human brain connectivity. 部分相关作为绘制人类大脑连接中功能-结构对应关系的工具。

IF 3.1 3区医学

Network Neuroscience Pub Date : 2025-09-19 eCollection Date: 2025-01-01 DOI: 10.1162/NETN.a.22

Francesca Santucci, Antonio Jimenez-Marin, Andrea Gabrielli, Paolo Bonifazi, Miguel Ibáñez-Berganza, Tommaso Gili, Jesus M Cortes

{"title":"Partial correlation as a tool for mapping functional-structural correspondence in human brain connectivity.","authors":"Francesca Santucci, Antonio Jimenez-Marin, Andrea Gabrielli, Paolo Bonifazi, Miguel Ibáñez-Berganza, Tommaso Gili, Jesus M Cortes","doi":"10.1162/NETN.a.22","DOIUrl":"10.1162/NETN.a.22","url":null,"abstract":"Brain structure-function coupling has been studied in health and disease by many different researchers in recent years. Most of the studies have estimated functional connectivity matrices as correlation coefficients between different brain areas, despite well-known disadvantages compared with partial correlation connectivity matrices. Indeed, partial correlation represents a more sensible model for structural connectivity since, under a Gaussian approximation, it accounts only for direct dependencies between brain areas. Motivated by this and following previous results by different authors, we investigate structure-function coupling using partial correlation matrices of functional magnetic resonance imaging brain activity time series under various regularization (also known as noise-cleaning) algorithms. We find that, across different algorithms and conditions, partial correlation provides a higher match with structural connectivity retrieved from density-weighted imaging data than standard correlation, and this occurs at both subject and population levels. Importantly, we also show that regularization and thresholding are crucial for this match to emerge. Finally, we assess neurogenetic associations in relation to structure-function coupling, which presents promising opportunities to further advance research in the field of network neuroscience, particularly concerning brain disorders.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 3","pages":"1065-1086"},"PeriodicalIF":3.1,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12548666/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145373234","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

Metastable dynamics emerge from local excitatory-inhibitory homeostasis in the cortex at rest. 静止状态下皮层局部兴奋抑制性稳态产生亚稳态动力学。

IF 3.1 3区医学

Network Neuroscience Pub Date : 2025-07-29 eCollection Date: 2025-01-01 DOI: 10.1162/netn_a_00460

Francisco Páscoa Dos Santos, Paul F M J Verschure

{"title":"Metastable dynamics emerge from local excitatory-inhibitory homeostasis in the cortex at rest.","authors":"Francisco Páscoa Dos Santos, Paul F M J Verschure","doi":"10.1162/netn_a_00460","DOIUrl":"10.1162/netn_a_00460","url":null,"abstract":"The dynamics of the human cortex are highly metastable, driving the spontaneous exploration of network states. This metastability depends on circuit-level edge-of-bifurcation dynamics, which emerge from firing-rate control through multiple mechanisms of excitatory-inhibitory (E-I) homeostasis. However, it is unclear how these contribute to the metastability of cortical networks. We propose that individual mechanisms of the E-I homeostasis contribute uniquely to the emergence of resting-state dynamics and test this hypothesis in a large-scale model of the human cortex. We show that empirical connectivity and dynamics can only be reproduced when accounting for multiple mechanisms of the E-I homeostasis. More specifically, while the homeostasis of excitation and inhibition enhances metastability, the regulation of intrinsic excitability ensures moderate synchrony, maximizing functional complexity. Furthermore, the modulation bifurcation modulation by the homeostasis of excitation and intrinsic excitability compensates for strong input fluctuations in connector hubs. Importantly, this only occurs in models accounting for local gamma oscillations, suggesting a relationship between E-I balance, gamma rhythms, and metastable dynamics. Altogether, our results show that cortical networks self-organize toward maximal metastability through the multifactor homeostasis of E-I balance. Therefore, the benefits of combining multiple homeostatic mechanisms transcend the circuit level, supporting the metastable dynamics of large-scale cortical networks.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 3","pages":"938-968"},"PeriodicalIF":3.1,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12543307/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145356562","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

Alterations in topology, cost, and dynamics of gamma-band EEG functional networks in a preclinical model of traumatic brain injury. 外伤性脑损伤临床前模型中伽马波段脑电图功能网络的拓扑、成本和动态变化。

IF 3.1 3区医学

Network Neuroscience Pub Date : 2025-07-29 eCollection Date: 2025-01-01 DOI: 10.1162/netn.a.21

Konstantinos Tsikonofilos, Michael Bruyns-Haylett, Hazel G May, Cornelius K Donat, Andriy S Kozlov

{"title":"Alterations in topology, cost, and dynamics of gamma-band EEG functional networks in a preclinical model of traumatic brain injury.","authors":"Konstantinos Tsikonofilos, Michael Bruyns-Haylett, Hazel G May, Cornelius K Donat, Andriy S Kozlov","doi":"10.1162/netn.a.21","DOIUrl":"10.1162/netn.a.21","url":null,"abstract":"Traumatic brain injury (TBI) is a major cause of disability leading to multiple sequelae in cognitive, sensory, and physical domains, including posttraumatic epilepsy. Despite extensive research, our understanding of its impact on macroscopic brain circuitry remains incomplete. We analyzed electrophysiological functional connectomes in the gamma band from an animal model of blast-induced TBI over multiple time points after injury. We revealed differences in small-world propensity and rich-club structure compared with age-matched controls, indicating functional reorganization following injury. We further investigated cost-efficiency trade-offs, propose a computationally efficient normalization procedure for quantifying the cost of spatially embedded networks that controls for connectivity strength differences, and observed dynamic changes across the injury timeline. To explore potential links between altered network topology and epileptic activity, we employed a brain-wide computational model of seizure dynamics and attribute brain reorganization to a homeostatic mechanism of activity regulation with the potential unintended consequence of driving generalized seizures. Finally, we demonstrated post-injury hyperexcitability that manifests as an increase in sound-evoked response amplitudes at the cortical level. Our work characterizes, for the first time, gamma-band functional network reorganization in a model of brain injury and proposes potential causes of these changes, thus identifying targets for future therapeutic interventions.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 3","pages":"1013-1038"},"PeriodicalIF":3.1,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12543302/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145356591","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

The exponential distance rule-based network model predicts topology and reveals functionally relevant properties of the Drosophila projectome. 基于指数距离规则的网络模型预测拓扑结构并揭示果蝇项目组的功能相关属性。

IF 3.4 3区医学

Network Neuroscience Pub Date : 2025-07-29 eCollection Date: 2025-01-01 DOI: 10.1162/netn_a_00455

Balázs Péntek, Mária Ercsey-Ravasz

{"title":"The exponential distance rule-based network model predicts topology and reveals functionally relevant properties of the Drosophila projectome.","authors":"Balázs Péntek, Mária Ercsey-Ravasz","doi":"10.1162/netn_a_00455","DOIUrl":"10.1162/netn_a_00455","url":null,"abstract":"Studying structural brain networks has witnessed significant advancement in recent decades. Findings revealed a geometric principle, the exponential distance rule (EDR) showing that the number of neurons decreases exponentially with the length of their axons. This neuron-level information was used to build a region-level EDR network model that was able to explain various characteristics of interareal cortical networks in macaques, mice, and rats. The complete connectome of the Drosophila has recently been mapped providing information also about the network of neuropils (projectome). A recent study demonstrated the presence of the EDR in the Drosophila. In our study, we first revisit the EDR itself and precisely measure the characteristic decay rate. Next, we demonstrate that the EDR model effectively accounts for numerous binary and weighted properties of the projectome. Our study illustrates that the EDR model is a suitable null model for analyzing networks of brain regions, as it captures properties of region-level networks in very different species. The importance of the null model lies in its ability to facilitate the identification of functionally significant features not caused by inevitable geometric constraints, as we illustrate with the pronounced asymmetry of connection weights important for functional hierarchy.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 3","pages":"869-895"},"PeriodicalIF":3.4,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12543305/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145356524","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 connectome from neuronal morphology. 来自神经元形态学的脑连接组。

IF 3.1 3区医学

Network Neuroscience Pub Date : 2025-07-29 eCollection Date: 2025-01-01 DOI: 10.1162/netn_a_00458

Suhui Jin, Junle Li, Jinhui Wang

{"title":"Brain connectome from neuronal morphology.","authors":"Suhui Jin, Junle Li, Jinhui Wang","doi":"10.1162/netn_a_00458","DOIUrl":"10.1162/netn_a_00458","url":null,"abstract":"Single-subject morphological brain networks derived from cross-feature correlation of macroscopic MRI-derived morphological measures provide an important means for studying the brain connectome. However, the validity of this approach remains to be confirmed at the microscopic level. Here, we constructed morphological brain networks at the single-cell level by extending features from macroscopic morphological measures to microscopic descriptions of neuronal morphology. We demonstrated the feasibility and generalizability of the method using neurons in the somatosensory cortex of a rat, neurons over the whole brain of a mouse, and neurons in the middle temporal gyrus (MTG) of a human. We found that interneuron morphological similarity was higher for intra- than interclass connections, depended on cytoarchitectonic, chemoarchitectonic, and laminar classification of neurons (rat), differed between regions with different evolutionary timelines (mouse), and correlated with neuronal axonal projections (mouse). Furthermore, highly connected hub neurons were disproportionately from superficial layers (rat), inhibitory neurons (rat), and subcortical regions (mouse), and exhibited unique morphology. Finally, we demonstrated a more segregated, less integrated, and economic network architecture with worse resistance to targeted attacks for neurons in human MTG than neurons in a mouse's primary visual cortex. Overall, our method provides an alternative avenue to study neuronal wiring diagrams in brains.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 3","pages":"913-937"},"PeriodicalIF":3.1,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12543300/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145356580","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