Network Neuroscience最新文献

Firing rate distributions in plastic networks of spiking neurons.

IF 3.6 3区医学

Network Neuroscience Pub Date : 2025-03-20 eCollection Date: 2025-01-01 DOI: 10.1162/netn_a_00442

Marina Vegué, Antoine Allard, Patrick Desrosiers

{"title":"Firing rate distributions in plastic networks of spiking neurons.","authors":"Marina Vegué, Antoine Allard, Patrick Desrosiers","doi":"10.1162/netn_a_00442","DOIUrl":"10.1162/netn_a_00442","url":null,"abstract":"In recurrent networks of leaky integrate-and-fire neurons, the mean-field theory has been instrumental in capturing the statistical properties of neuronal activity, like firing rate distributions. This theory has been applied to networks with either homogeneous synaptic weights and heterogeneous connections per neuron or vice versa. Our work expands mean-field models to include networks with both types of structural heterogeneity simultaneously, particularly focusing on those with synapses that undergo plastic changes. The model introduces a spike trace for each neuron, a variable that rises with neuron spikes and decays without activity, influenced by a degradation rate r p and the neuron's firing rate ν. When the ratio α = ν/r p is significantly high, this trace effectively estimates the neuron's firing rate, allowing synaptic weights at equilibrium to be determined by the firing rates of connected neurons. This relationship is incorporated into our mean-field formalism, providing exact solutions for firing rate and synaptic weight distributions at equilibrium in the high α regime. However, the model remains accurate within a practical range of degradation rates, as demonstrated through simulations with networks of excitatory and inhibitory neurons. This approach sheds light on how plasticity modulates both activity and structure within neuronal networks, offering insights into their complex behavior.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 1","pages":"447-474"},"PeriodicalIF":3.6,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11949577/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143753745","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

Translational network neuroscience: Nine roadblocks and possible solutions.

IF 3.6 3区医学

Network Neuroscience Pub Date : 2025-03-20 eCollection Date: 2025-01-01 DOI: 10.1162/netn_a_00435

Lucius S Fekonja, Stephanie J Forkel, Dogu Baran Aydogan, Pantelis Lioumis, Alberto Cacciola, Carolin Weiß Lucas, Jacques-Donald Tournier, Francesco Vergani, Petra Ritter, Robert Schenk, Boshra Shams, Melina Julia Engelhardt, Thomas Picht

{"title":"Translational network neuroscience: Nine roadblocks and possible solutions.","authors":"Lucius S Fekonja, Stephanie J Forkel, Dogu Baran Aydogan, Pantelis Lioumis, Alberto Cacciola, Carolin Weiß Lucas, Jacques-Donald Tournier, Francesco Vergani, Petra Ritter, Robert Schenk, Boshra Shams, Melina Julia Engelhardt, Thomas Picht","doi":"10.1162/netn_a_00435","DOIUrl":"10.1162/netn_a_00435","url":null,"abstract":"Translational network neuroscience aims to integrate advanced neuroimaging and data analysis techniques into clinical practice to better understand and treat neurological disorders. Despite the promise of technologies such as functional MRI and diffusion MRI combined with network analysis tools, the field faces several challenges that hinder its swift clinical translation. We have identified nine key roadblocks that impede this process: (a) theoretical and basic science foundations; (b) network construction, data interpretation, and validation; (c) MRI access, data variability, and protocol standardization; (d) data sharing; (e) computational resources and expertise; (f) interdisciplinary collaboration; (g) industry collaboration and commercialization; (h) operational efficiency, integration, and training; and (i) ethical and legal considerations. To address these challenges, we propose several possible solution strategies. By aligning scientific goals with clinical realities and establishing a sound ethical framework, translational network neuroscience can achieve meaningful advances in personalized medicine and ultimately improve patient care. We advocate for an interdisciplinary commitment to overcoming translational hurdles in network neuroscience and integrating advanced technologies into routine clinical practice.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 1","pages":"352-370"},"PeriodicalIF":3.6,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11949582/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143755249","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

Test-retest reliability of dynamic functional connectivity parameters for a two-state model.

IF 3.6 3区医学

Network Neuroscience Pub Date : 2025-03-20 eCollection Date: 2025-01-01 DOI: 10.1162/netn_a_00437

Xiaojing Fang, Michael Marxen

{"title":"Test-retest reliability of dynamic functional connectivity parameters for a two-state model.","authors":"Xiaojing Fang, Michael Marxen","doi":"10.1162/netn_a_00437","DOIUrl":"10.1162/netn_a_00437","url":null,"abstract":"Reliability of imaging parameters is of pivotal importance for further correlation analyses. Here, we investigated the test-retest reliability of two dynamic functional connectivity (dFC) brain states and related parameters for different scan length, atlases with 116 versus 442 regions, and data centering in 23 participants and reproduced the findings in 501 subjects of the Human Connectome Project. Results showed an integrated and a segregated brain state with high intraclass correlation coefficient (ICC) values of the states between sessions (0.67 ≥ ICC ≥ 0.99). The most reliable dFC parameter was state prevalence with an ICC ≈ 0.5 for ∼15 min of uncentered data, while other parameters, such as mean dwell time, were much less reliable. While shorter scans and within-subject data centering further reduce reliability, the atlas choice had no effects. Spearman's correlations among dFC parameters strongly depend on data centering. The effect of global signal regression and a higher number of states is discussed. In conclusion, we recommend formulating hypotheses on cross-sectional differences and correlations between dFC measures of brain integration and other subject-specific measures in terms of state prevalence, especially in small-scale studies.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 1","pages":"371-391"},"PeriodicalIF":3.6,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11949578/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143755110","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

Functional brain networks predicting sustained attention are not specific to perceptual modality.

IF 3.6 3区医学

Network Neuroscience Pub Date : 2025-03-20 eCollection Date: 2025-01-01 DOI: 10.1162/netn_a_00430

Anna Corriveau, Jin Ke, Hiroki Terashima, Hirohito M Kondo, Monica D Rosenberg

{"title":"Functional brain networks predicting sustained attention are not specific to perceptual modality.","authors":"Anna Corriveau, Jin Ke, Hiroki Terashima, Hirohito M Kondo, Monica D Rosenberg","doi":"10.1162/netn_a_00430","DOIUrl":"10.1162/netn_a_00430","url":null,"abstract":"Sustained attention is essential for daily life and can be directed to information from different perceptual modalities, including audition and vision. Recently, cognitive neuroscience has aimed to identify neural predictors of behavior that generalize across datasets. Prior work has shown strong generalization of models trained to predict individual differences in sustained attention performance from patterns of fMRI functional connectivity. However, it is an open question whether predictions of sustained attention are specific to the perceptual modality in which they are trained. In the current study, we test whether connectome-based models predict performance on attention tasks performed in different modalities. We show first that a predefined network trained to predict adults' visual sustained attention performance generalizes to predict auditory sustained attention performance in three independent datasets (N 1 = 29, N 2 = 60, N 3 = 17). Next, we train new network models to predict performance on visual and auditory attention tasks separately. We find that functional networks are largely modality general, with both model-unique and shared model features predicting sustained attention performance in independent datasets regardless of task modality. Results support the supposition that visual and auditory sustained attention rely on shared neural mechanisms and demonstrate robust generalizability of whole-brain functional network models of sustained attention.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 1","pages":"303-325"},"PeriodicalIF":3.6,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11949588/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143753790","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

Functional connectotomy of a whole-brain model reveals tumor-induced alterations to neuronal dynamics in glioma patients.

IF 3.6 3区医学

Network Neuroscience Pub Date : 2025-03-20 eCollection Date: 2025-01-01 DOI: 10.1162/netn_a_00426

Christoffer G Alexandersen, Linda Douw, Mona L M Zimmermann, Christian Bick, Alain Goriely

引用次数: 0

Whole-brain causal discovery using fMRI.

IF 3.6 3区医学

Network Neuroscience Pub Date : 2025-03-20 eCollection Date: 2025-01-01 DOI: 10.1162/netn_a_00438

Fahimeh Arab, AmirEmad Ghassami, Hamidreza Jamalabadi, Megan A K Peters, Erfan Nozari

{"title":"Whole-brain causal discovery using fMRI.","authors":"Fahimeh Arab, AmirEmad Ghassami, Hamidreza Jamalabadi, Megan A K Peters, Erfan Nozari","doi":"10.1162/netn_a_00438","DOIUrl":"10.1162/netn_a_00438","url":null,"abstract":"Despite significant research, discovering causal relationships from fMRI remains a challenge. Popular methods such as Granger causality and dynamic causal modeling fall short in handling contemporaneous effects and latent common causes. Methods from causal structure learning literature can address these limitations but often scale poorly with network size and need acyclicity. In this study, we first provide a taxonomy of existing methods and compare their accuracy and efficiency on simulated fMRI from simple topologies. This analysis demonstrates a pressing need for more accurate and scalable methods, motivating the design of Causal discovery for Large-scale Low-resolution Time-series with Feedback (CaLLTiF). CaLLTiF is a constraint-based method that uses conditional independence between contemporaneous and lagged variables to extract causal relationships. On simulated fMRI from the macaque connectome, CaLLTiF achieves significantly higher accuracy and scalability than all tested alternatives. From resting-state human fMRI, CaLLTiF learns causal connectomes that are highly consistent across individuals, show clear top-down flow of causal effect from attention and default mode to sensorimotor networks, exhibit Euclidean distance dependence in causal interactions, and are highly dominated by contemporaneous effects. Overall, this work takes a major step in enhancing causal discovery from whole-brain fMRI and defines a new standard for future investigations.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 1","pages":"392-420"},"PeriodicalIF":3.6,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11949584/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143755251","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

Multimodal MRI accurately identifies amyloid status in unbalanced cohorts in Alzheimer's disease continuum.

IF 3.6 3区医学

Network Neuroscience Pub Date : 2025-03-20 eCollection Date: 2025-01-01 DOI: 10.1162/netn_a_00423

Giorgio Dolci, Charles A Ellis, Federica Cruciani, Lorenza Brusini, Anees Abrol, Ilaria Boscolo Galazzo, Gloria Menegaz, Vince D Calhoun

{"title":"Multimodal MRI accurately identifies amyloid status in unbalanced cohorts in Alzheimer's disease continuum.","authors":"Giorgio Dolci, Charles A Ellis, Federica Cruciani, Lorenza Brusini, Anees Abrol, Ilaria Boscolo Galazzo, Gloria Menegaz, Vince D Calhoun","doi":"10.1162/netn_a_00423","DOIUrl":"10.1162/netn_a_00423","url":null,"abstract":"Amyloid-β (Aβ) plaques in conjunction with hyperphosphorylated tau proteins in the form of neurofibrillary tangles are the two neuropathological hallmarks of Alzheimer's disease. It is well-known that the identification of individuals with Aβ positivity could enable early diagnosis. In this work, we aim at capturing the Aβ positivity status in an unbalanced cohort enclosing subjects at different disease stages, exploiting the underlying structural and connectivity disease-induced modulations as revealed by structural, functional, and diffusion MRI. Of note, due to the unbalanced cohort, the outcomes may be guided by those factors rather than amyloid accumulation. The partial views provided by each modality are integrated in the model, allowing to take full advantage of their complementarity in encoding the effects of the Aβ accumulation, leading to an accuracy of 0.762 ± 0.04. The specificity of the information brought by each modality is assessed by post hoc explainability analysis (guided backpropagation), highlighting the underlying structural and functional changes. Noteworthy, well-established biomarker key regions related to Aβ deposition could be identified by all modalities, including the hippocampus, thalamus, precuneus, and cingulate gyrus, witnessing in favor of the reliability of the method as well as its potential in shedding light on modality-specific possibly unknown Aβ deposition signatures.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 1","pages":"259-279"},"PeriodicalIF":3.6,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11949592/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143755089","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

Individualized mouse brain network models produce asymmetric patterns of functional connectivity after simulated traumatic injury.

IF 3.6 3区医学

Network Neuroscience Pub Date : 2025-03-20 eCollection Date: 2025-01-01 DOI: 10.1162/netn_a_00431

Adam C Rayfield, Taotao Wu, Jared A Rifkin, David F Meaney

{"title":"Individualized mouse brain network models produce asymmetric patterns of functional connectivity after simulated traumatic injury.","authors":"Adam C Rayfield, Taotao Wu, Jared A Rifkin, David F Meaney","doi":"10.1162/netn_a_00431","DOIUrl":"10.1162/netn_a_00431","url":null,"abstract":"The functional and cognitive effects of traumatic brain injury (TBI) are poorly understood, as even mild injuries (concussion) can lead to long-lasting, untreatable symptoms. Simplified brain dynamics models may help researchers better understand the relationship between brain injury patterns and functional outcomes. Properly developed, these computational models provide an approach to investigate the effects of both computational and in vivo injury on simulated dynamics and cognitive function, respectively, for model organisms. In this study, we apply the Kuramoto model and an existing mesoscale mouse brain structural network to develop a simplified computational model of mouse brain dynamics. We explore how to optimize our initial model to predict existing mouse brain functional connectivity collected from mice under various anesthetic protocols. Finally, to determine how strongly the changes in our optimized models' dynamics can predict the extent of a brain injury, we investigate how our simulations respond to varying levels of structural network damage. Results predict a mixture of hypo- and hyperconnectivity after experimental TBI, similar to results in TBI survivors, and also suggest a compensatory remodeling of connections that may have an impact on functional outcomes after TBI.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 1","pages":"326-351"},"PeriodicalIF":3.6,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11949614/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754907","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

Validating MEG estimated resting-state connectome with intracranial EEG.

IF 3.6 3区医学

Network Neuroscience Pub Date : 2025-03-20 eCollection Date: 2025-01-01 DOI: 10.1162/netn_a_00441

Jawata Afnan, Zhengchen Cai, Jean-Marc Lina, Chifaou Abdallah, Giovanni Pellegrino, Giorgio Arcara, Hassan Khajehpour, Birgit Frauscher, Jean Gotman, Christophe Grova

{"title":"Validating MEG estimated resting-state connectome with intracranial EEG.","authors":"Jawata Afnan, Zhengchen Cai, Jean-Marc Lina, Chifaou Abdallah, Giovanni Pellegrino, Giorgio Arcara, Hassan Khajehpour, Birgit Frauscher, Jean Gotman, Christophe Grova","doi":"10.1162/netn_a_00441","DOIUrl":"10.1162/netn_a_00441","url":null,"abstract":"Magnetoencephalography (MEG) is widely used for studying resting-state brain connectivity. However, MEG source imaging is ill posed and has limited spatial resolution. This introduces source-leakage issues, making it challenging to interpret MEG-derived connectivity in resting states. To address this, we validated MEG-derived connectivity from 45 healthy participants using a normative intracranial EEG (iEEG) atlas. The MEG inverse problem was solved using the wavelet-maximum entropy on the mean method. We computed four connectivity metrics: amplitude envelope correlation (AEC), orthogonalized AEC (OAEC), phase locking value (PLV), and weighted-phase lag index (wPLI). We compared spatial correlation between MEG and iEEG connectomes across standard canonical frequency bands. We found moderate spatial correlations between MEG and iEEG connectomes for AEC and PLV. However, when considering metrics that correct/remove zero-lag connectivity (OAEC/wPLI), the spatial correlation between MEG and iEEG connectomes decreased. MEG exhibited higher zero-lag connectivity compared with iEEG. The correlations between MEG and iEEG connectomes suggest that relevant connectivity patterns can be recovered from MEG. However, since these correlations are moderate/low, MEG connectivity results should be interpreted with caution. Metrics that correct for zero-lag connectivity show decreased correlations, highlighting a trade-off; while MEG may capture more connectivity due to source-leakage, removing zero-lag connectivity can eliminate true connections.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 1","pages":"421-446"},"PeriodicalIF":3.6,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11949576/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143755250","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 connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations.

IF 3.6 3区医学

Network Neuroscience Pub Date : 2025-03-05 eCollection Date: 2025-01-01 DOI: 10.1162/netn_a_00429

Christoph Pokorny, Omar Awile, James B Isbister, Kerem Kurban, Matthias Wolf, Michael W Reimann

{"title":"A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations.","authors":"Christoph Pokorny, Omar Awile, James B Isbister, Kerem Kurban, Matthias Wolf, Michael W Reimann","doi":"10.1162/netn_a_00429","DOIUrl":"10.1162/netn_a_00429","url":null,"abstract":"Synaptic connectivity at the neuronal level is characterized by highly nonrandom features. Hypotheses about their role can be developed by correlating structural metrics to functional features. But, to prove causation, manipulations of connectivity would have to be studied. However, the fine-grained scale at which nonrandom trends are expressed makes this approach challenging to pursue experimentally. Simulations of neuronal networks provide an alternative route to study arbitrarily complex manipulations in morphologically and biophysically detailed models. Here, we present Connectome-Manipulator, a Python framework for rapid connectome manipulations of large-scale network models in Scalable Open Network Architecture TemplAte (SONATA) format. In addition to creating or manipulating the connectome of a model, it provides tools to fit parameters of stochastic connectivity models against existing connectomes. This enables rapid replacement of any existing connectome with equivalent connectomes at different levels of complexity, or transplantation of connectivity features from one connectome to another, for systematic study. We employed the framework in the detailed model of the rat somatosensory cortex in two exemplary use cases: transplanting interneuron connectivity trends from electron microscopy data and creating simplified connectomes of excitatory connectivity. We ran a series of network simulations and found diverse shifts in the activity of individual neuron populations causally linked to these manipulations.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 1","pages":"207-236"},"PeriodicalIF":3.6,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11949583/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143755241","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