Network Neuroscience最新文献_第6页

Generative dynamical models for classification of rsfMRI data. rsfMRI数据分类的生成动力学模型。

IF 3.6 3区医学

Network Neuroscience Pub Date : 2024-12-10 eCollection Date: 2024-01-01 DOI: 10.1162/netn_a_00412

Grace Huckins, Russell A Poldrack

{"title":"Generative dynamical models for classification of rsfMRI data.","authors":"Grace Huckins, Russell A Poldrack","doi":"10.1162/netn_a_00412","DOIUrl":"10.1162/netn_a_00412","url":null,"abstract":"The growing availability of large-scale neuroimaging datasets and user-friendly machine learning tools has led to a recent surge in studies that use fMRI data to predict psychological or behavioral variables. Many such studies classify fMRI data on the basis of static features, but fewer try to leverage brain dynamics for classification. Here, we pilot a generative, dynamical approach for classifying resting-state fMRI (rsfMRI) data. By fitting separate hidden Markov models to the classes in our training data and assigning class labels to test data based on their likelihood under those models, we are able to take advantage of dynamical patterns in the data without confronting the statistical limitations of some other dynamical approaches. Moreover, we demonstrate that hidden Markov models are able to successfully perform within-subject classification on the MyConnectome dataset solely on the basis of transition probabilities among their hidden states. On the other hand, individual Human Connectome Project subjects cannot be identified on the basis of hidden state transition probabilities alone-although a vector autoregressive model does achieve high performance. These results demonstrate a dynamical classification approach for rsfMRI data that shows promising performance, particularly for within-subject classification, and has the potential to afford greater interpretability than other approaches.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"8 4","pages":"1613-1633"},"PeriodicalIF":3.6,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11675094/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142903859","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

Discovering prominent differences in structural and functional connectomes using a multinomial stochastic block model. 使用多项随机块模型发现结构和功能连接体的显著差异。

IF 3.6 3区医学

Network Neuroscience Pub Date : 2024-12-10 eCollection Date: 2024-01-01 DOI: 10.1162/netn_a_00399

Nina Braad Iskov, Anders Stevnhoved Olsen, Kristoffer Hougaard Madsen, Morten Mørup

{"title":"Discovering prominent differences in structural and functional connectomes using a multinomial stochastic block model.","authors":"Nina Braad Iskov, Anders Stevnhoved Olsen, Kristoffer Hougaard Madsen, Morten Mørup","doi":"10.1162/netn_a_00399","DOIUrl":"10.1162/netn_a_00399","url":null,"abstract":"Understanding the differences between functional and structural human brain connectivity has been a focus of an extensive amount of neuroscience research. We employ a novel approach using the multinomial stochastic block model (MSBM) to explicitly extract components that characterize prominent differences across graphs. We analyze structural and functional connectomes derived from high-resolution diffusion-weighted MRI and fMRI scans of 250 Human Connectome Project subjects, analyzed at group connectivity level across 50 subjects. The inferred brain partitions revealed consistent, spatially homogeneous clustering patterns across inferred resolutions demonstrating the MSBM's reliability in identifying brain areas with prominent structure-function differences. Prominent differences in low-resolution brain maps (K = {3, 4} clusters) were attributed to weak functional connectivity in the bilateral anterior temporal lobes, while higher resolution results (K ≥ 25) revealed stronger interhemispheric functional than structural connectivity. Our findings emphasize significant differences in high-resolution functional and structural connectomes, revealing challenges in extracting meaningful connectivity measurements from both modalities, including tracking fibers through the corpus callosum and attenuated functional connectivity in anterior temporal lobe fMRI data, which we attribute to increased noise levels. The MSBM emerges as a valuable tool for understanding differences across graphs, with potential future applications and avenues beyond the current focus on characterizing modality-specific distinctions in connectomics data.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"8 4","pages":"1243-1264"},"PeriodicalIF":3.6,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11674489/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142903658","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

Hemodynamic cortical ripples through cyclicity analysis. 通过周期性分析研究皮层血液动力学涟漪。

IF 3.6 3区医学

Network Neuroscience Pub Date : 2024-12-10 eCollection Date: 2024-01-01 DOI: 10.1162/netn_a_00392

Ivan Abraham, Somayeh Shahsavarani, Benjamin Zimmerman, Fatima T Husain, Yuliy Baryshnikov

{"title":"Hemodynamic cortical ripples through cyclicity analysis.","authors":"Ivan Abraham, Somayeh Shahsavarani, Benjamin Zimmerman, Fatima T Husain, Yuliy Baryshnikov","doi":"10.1162/netn_a_00392","DOIUrl":"10.1162/netn_a_00392","url":null,"abstract":"A fine-grained understanding of dynamics in cortical networks is crucial to unpacking brain function. Resting-state functional magnetic resonance imaging (fMRI) gives rise to time series recordings of the activity of different brain regions, which are aperiodic and lack a base frequency. Cyclicity analysis, a novel technique robust under time reparametrizations, is effective in recovering the temporal ordering of such time series, collectively considered components of a multidimensional trajectory. Here, we extend this analytical method for characterizing the dynamic interaction between distant brain regions and apply it to the data from the Human Connectome Project. Our analysis detected cortical traveling waves of activity propagating along a spatial axis, resembling cortical hierarchical organization with consistent lead-lag relationships between specific brain regions in resting-state scans. In fMRI scans involving tasks, we observed short bursts of task-modulated strong temporal ordering that dominate overall lead-lag relationships between pairs of regions in the brain that align temporally with stimuli from the tasks. Our results suggest a possible role played by waves of excitation sweeping through brain regions that underlie emergent cognitive functions.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"8 4","pages":"1105-1128"},"PeriodicalIF":3.6,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11674492/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142903861","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

Segregation-to-integration transformation model of memory evolution. 记忆演化的分离-集成转换模型。

IF 3.6 3区医学

Network Neuroscience Pub Date : 2024-12-10 eCollection Date: 2024-01-01 DOI: 10.1162/netn_a_00415

Luz Bavassi, Lluís Fuentemilla

{"title":"Segregation-to-integration transformation model of memory evolution.","authors":"Luz Bavassi, Lluís Fuentemilla","doi":"10.1162/netn_a_00415","DOIUrl":"10.1162/netn_a_00415","url":null,"abstract":"Memories are thought to use coding schemes that dynamically adjust their representational structure to maximize both persistence and efficiency. However, the nature of these coding scheme adjustments and their impact on the temporal evolution of memory after initial encoding is unclear. Here, we introduce the Segregation-to-Integration Transformation (SIT) model, a network formalization that offers a unified account of how the representational structure of a memory is transformed over time. The SIT model asserts that memories initially adopt a highly modular or segregated network structure, functioning as an optimal storage buffer by balancing protection from disruptions and accommodating substantial information. Over time, a repeated combination of neural network reactivations involving activation spreading and synaptic plasticity transforms the initial modular structure into an integrated memory form, facilitating intercommunity spreading and fostering generalization. The SIT model identifies a nonlinear or inverted U-shaped function in memory evolution where memories are most susceptible to changing their representation. This time window, located early during the transformation, is a consequence of the memory's structural configuration, where the activation diffusion across the network is maximized.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"8 4","pages":"1529-1544"},"PeriodicalIF":3.6,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11675164/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142903815","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

Patterns of the left thalamus embedding into the connectome associated with reading skills in children with reading disabilities. 左丘脑嵌入连接体的模式与阅读障碍儿童的阅读技能有关。

IF 3.6 3区医学

Network Neuroscience Pub Date : 2024-12-10 eCollection Date: 2024-01-01 DOI: 10.1162/netn_a_00414

Chenglin Lou, Alexandra M Cross, Lien Peters, Daniel Ansari, Marc F Joanisse

{"title":"Patterns of the left thalamus embedding into the connectome associated with reading skills in children with reading disabilities.","authors":"Chenglin Lou, Alexandra M Cross, Lien Peters, Daniel Ansari, Marc F Joanisse","doi":"10.1162/netn_a_00414","DOIUrl":"10.1162/netn_a_00414","url":null,"abstract":"We examined how thalamocortical connectivity structure reflects children's reading performance. Diffusion-weighted MRI at 3 T and a series of reading measures were collected from 64 children (33 girls) ages 8-14 years with and without dyslexia. The topological properties of the left and right thalamus were computed based on the whole-brain white matter network and a hub-attached reading network, and were correlated with scores on several tests of children's reading and reading-related abilities. Significant correlations between topological metrics of the left thalamus and reading scores were observed only in the hub-attached reading network. Local efficiency was negatively correlated with rapid automatized naming. Transmission cost was positively correlated with phonemic decoding, and this correlation was independent of network efficiency scores; follow-up analyses further demonstrated that this effect was specific to the pulvinar and mediodorsal nuclei of the left thalamus. We validated these results using an independent dataset and demonstrated that that the relationship between thalamic connectivity and phonemic decoding was specifically robust. Overall, the results highlight the role of the left thalamus and thalamocortical network in understanding the neurocognitive bases of skilled reading and dyslexia in children.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"8 4","pages":"1507-1528"},"PeriodicalIF":3.6,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11675173/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142903866","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 serotonergic psychedelic N,N-dipropyltryptamine alters information-processing dynamics in in vitro cortical neural circuits. 5-羟色胺能迷幻剂 N,N-二丙基色胺改变了体外皮层神经回路的信息处理动态。

IF 3.6 3区医学

Network Neuroscience Pub Date : 2024-12-10 eCollection Date: 2024-01-01 DOI: 10.1162/netn_a_00408

Thomas F Varley, Daniel Havert, Leandro Fosque, Abolfazl Alipour, Naruepon Weerawongphrom, Hiroki Naganobori, Lily O'Shea, Maria Pope, John Beggs

{"title":"The serotonergic psychedelic N,N-dipropyltryptamine alters information-processing dynamics in in vitro cortical neural circuits.","authors":"Thomas F Varley, Daniel Havert, Leandro Fosque, Abolfazl Alipour, Naruepon Weerawongphrom, Hiroki Naganobori, Lily O'Shea, Maria Pope, John Beggs","doi":"10.1162/netn_a_00408","DOIUrl":"10.1162/netn_a_00408","url":null,"abstract":"Most of the recent work in psychedelic neuroscience has been done using noninvasive neuroimaging, with data recorded from the brains of adult volunteers under the influence of a variety of drugs. While these data provide holistic insights into the effects of psychedelics on whole-brain dynamics, the effects of psychedelics on the mesoscale dynamics of neuronal circuits remain much less explored. Here, we report the effects of the serotonergic psychedelic N,N-diproptyltryptamine (DPT) on information-processing dynamics in a sample of in vitro organotypic cultures of cortical tissue from postnatal rats. Three hours of spontaneous activity were recorded: an hour of predrug control, an hour of exposure to 10-μM DPT solution, and a final hour of washout, once again under control conditions. We found that DPT reversibly alters information dynamics in multiple ways: First, the DPT condition was associated with a higher entropy of spontaneous firing activity and reduced the amount of time information was stored in individual neurons. Second, DPT also reduced the reversibility of neural activity, increasing the entropy produced and suggesting a drive away from equilibrium. Third, DPT altered the structure of neuronal circuits, decreasing the overall information flow coming into each neuron, but increasing the number of weak connections, creating a dynamic that combines elements of integration and disintegration. Finally, DPT decreased the higher order statistical synergy present in sets of three neurons. Collectively, these results paint a complex picture of how psychedelics regulate information processing in mesoscale neuronal networks in cortical tissue. Implications for existing hypotheses of psychedelic action, such as the entropic brain hypothesis, are discussed.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"8 4","pages":"1421-1438"},"PeriodicalIF":3.6,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11674936/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142903901","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 signaling becomes less integrated and more segregated with age. 随着年龄的增长，大脑信号变得越来越不整合，越来越分离。

IF 3.6 3区医学

Network Neuroscience Pub Date : 2024-12-10 eCollection Date: 2024-01-01 DOI: 10.1162/netn_a_00389

Rostam M Razban, Botond B Antal, Ken A Dill, Lilianne R Mujica-Parodi

引用次数: 0

The impact of functional correlations on task information coding. 功能关联对任务信息编码的影响。

IF 3.6 3区医学

Network Neuroscience Pub Date : 2024-12-10 eCollection Date: 2024-01-01 DOI: 10.1162/netn_a_00402

Takuya Ito, John D Murray

{"title":"The impact of functional correlations on task information coding.","authors":"Takuya Ito, John D Murray","doi":"10.1162/netn_a_00402","DOIUrl":"10.1162/netn_a_00402","url":null,"abstract":"State-dependent neural correlations can be understood from a neural coding framework. Noise correlations-trial-to-trial or moment-to-moment covariability-can be interpreted only if the underlying signal correlation-similarity of task selectivity between pairs of neural units-is known. Despite many investigations in local spiking circuits, it remains unclear how this coding framework applies to large-scale brain networks. Here, we investigated relationships between large-scale noise correlations and signal correlations in a multitask human fMRI dataset. We found that task-state noise correlation changes (e.g., functional connectivity) did not typically change in the same direction as their underlying signal correlation (e.g., tuning similarity of two regions). Crucially, noise correlations that changed in the opposite direction as their signal correlation (i.e., anti-aligned correlations) improved information coding of these brain regions. In contrast, noise correlations that changed in the same direction (aligned noise correlations) as their signal correlation did not. Interestingly, these aligned noise correlations were primarily correlation increases, suggesting that most functional correlation increases across fMRI networks actually degrade information coding. These findings illustrate that state-dependent noise correlations shape information coding of functional brain networks, with interpretation of correlation changes requiring knowledge of underlying signal correlations.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"8 4","pages":"1331-1354"},"PeriodicalIF":3.6,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11675092/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142903822","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 spatially constrained independent component analysis jointly informed by structural and functional network connectivity. 基于结构和功能网络连通性的空间约束独立分量分析。

IF 3.6 3区医学

Network Neuroscience Pub Date : 2024-12-10 eCollection Date: 2024-01-01 DOI: 10.1162/netn_a_00398

Mahshid Fouladivanda, Armin Iraji, Lei Wu, Theo G M van Erp, Aysenil Belger, Faris Hawamdeh, Godfrey D Pearlson, Vince D Calhoun

{"title":"A spatially constrained independent component analysis jointly informed by structural and functional network connectivity.","authors":"Mahshid Fouladivanda, Armin Iraji, Lei Wu, Theo G M van Erp, Aysenil Belger, Faris Hawamdeh, Godfrey D Pearlson, Vince D Calhoun","doi":"10.1162/netn_a_00398","DOIUrl":"10.1162/netn_a_00398","url":null,"abstract":"There are a growing number of neuroimaging studies motivating joint structural and functional brain connectivity. The brain connectivity of different modalities provides an insight into brain functional organization by leveraging complementary information, especially for brain disorders such as schizophrenia. In this paper, we propose a multimodal independent component analysis (ICA) model that utilizes information from both structural and functional brain connectivity guided by spatial maps to estimate intrinsic connectivity networks (ICNs). Structural connectivity is estimated through whole-brain tractography on diffusion-weighted MRI (dMRI), while functional connectivity is derived from resting-state functional MRI (rs-fMRI). The proposed structural-functional connectivity and spatially constrained ICA (sfCICA) model estimates ICNs at the subject level using a multiobjective optimization framework. We evaluated our model using synthetic and real datasets (including dMRI and rs-fMRI from 149 schizophrenia patients and 162 controls). Multimodal ICNs revealed enhanced functional coupling between ICNs with higher structural connectivity, improved modularity, and network distinction, particularly in schizophrenia. Statistical analysis of group differences showed more significant differences in the proposed model compared with the unimodal model. In summary, the sfCICA model showed benefits from being jointly informed by structural and functional connectivity. These findings suggest advantages in simultaneously learning effectively and enhancing connectivity estimates using structural connectivity.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"8 4","pages":"1212-1242"},"PeriodicalIF":3.6,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11674407/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142903902","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 generative model of the connectome with dynamic axon growth. 具有动态轴突生长的连接体生成模型。

IF 3.6 3区医学

Network Neuroscience Pub Date : 2024-12-10 eCollection Date: 2024-01-01 DOI: 10.1162/netn_a_00397

Yuanzhe Liu, Caio Seguin, Richard F Betzel, Daniel Han, Danyal Akarca, Maria A Di Biase, Andrew Zalesky

{"title":"A generative model of the connectome with dynamic axon growth.","authors":"Yuanzhe Liu, Caio Seguin, Richard F Betzel, Daniel Han, Danyal Akarca, Maria A Di Biase, Andrew Zalesky","doi":"10.1162/netn_a_00397","DOIUrl":"10.1162/netn_a_00397","url":null,"abstract":"Connectome generative models, otherwise known as generative network models, provide insight into the wiring principles underpinning brain network organization. While these models can approximate numerous statistical properties of empirical networks, they typically fail to explicitly characterize an important contributor to brain organization-axonal growth. Emulating the chemoaffinity-guided axonal growth, we provide a novel generative model in which axons dynamically steer the direction of propagation based on distance-dependent chemoattractive forces acting on their growth cones. This simple dynamic growth mechanism, despite being solely geometry-dependent, is shown to generate axonal fiber bundles with brain-like geometry and features of complex network architecture consistent with the human brain, including lognormally distributed connectivity weights, scale-free nodal degrees, small-worldness, and modularity. We demonstrate that our model parameters can be fitted to individual connectomes, enabling connectome dimensionality reduction and comparison of parameters between groups. Our work offers an opportunity to bridge studies of axon guidance and connectome development, providing new avenues for understanding neural development from a computational perspective.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"8 4","pages":"1192-1211"},"PeriodicalIF":3.6,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11674315/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142903966","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