Network Neuroscience最新文献

{"title":"Phase-dependent stimulation response is shaped by the brain's dynamic functional connectivity.","authors":"Sophie Benitez Stulz, Samy Castro, Boris Gutkin, Matthieu Gilson, Demian Battaglia","doi":"10.1162/NETN.a.548","DOIUrl":"https://doi.org/10.1162/NETN.a.548","url":null,"abstract":"<p><p>External brain stimulation is a promising tool for investigating and altering cognitive processes, with potential clinical applications to the restoration of dysfunctional neural dynamics. In line with experimental observations, we study how the effects of stimulation crucially depend on the ongoing dynamics of the brain, at the local level of the stimulated region but also of global coordinated brain activity. Specifically, we use connectome-based whole-brain computational modeling to explore how the effects of single-pulse stimulation to different regions strongly depend on both the phase of regional oscillatory activity and on the transiently occurring network of functional connectivity at the time of the applied stimulation. Importantly, we show that stimulation has not only state-dependent effects but can also induce global state switching. Lastly, predicting the effect of stimulation by using machine learning shows that functional network-aware measures (i.e., knowledge of either a discrete state of functional connectivity or of a detailed functional connectivity matrix) can increase the performance by up to 40%. Our results suggest that a fine characterization of intrinsic functional connectivity dynamics is essential for improving the reliability of exogenous stimulation.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"10 2","pages":"475-507"},"PeriodicalIF":3.1,"publicationDate":"2026-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13108500/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147786057","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}

{"title":"Cortical similarity networks in the rat brain: Postnatal development and sensitivity to early life stress.","authors":"Rachel L Smith, Stephen J Sawiak, Lena Dorfschmidt, Ethan G Dutcher, Jolyon A Jones, Joel D Hahn, Olaf Sporns, Larry W Swanson, Paul A Taylor, Daniel R Glen, Jeffrey W Dalley, Francis J McMahon, Armin Raznahan, Petra E Vértes, Edward T Bullmore","doi":"10.1162/NETN.a.546","DOIUrl":"10.1162/NETN.a.546","url":null,"abstract":"<p><p>Network models are a key tool in human neuroscience, and translation into animal models is essential for interrogating mechanistic drivers of network organization. Using magnetic resonance imaging (MRI), we present the first in vivo network representation of the individual rat brain, a key animal model in neuroscience. We measured magnetization transfer ratio (MTR) at each of 53 distinct cortical areas and estimated a cortical similarity network for each scan across two independent cohorts. We characterized normative network development in rats scanned repeatedly between postnatal days 20 (weanling) and 290 (mid-adulthood; <i>N</i> = 47) and then contrasted these findings with a cohort exposed to early life stress (repeated maternal separation [RMS]; <i>N</i> = 40). The normative rat cortical similarity network exhibited biologically meaningful organization, consistent with cytoarchitectonic and tract-tracing data, and displayed complex topological features. Developmental analyses revealed increasing interregional similarity during early postnatal and adolescent periods, followed by divergence in mid-adulthood, particularly within fronto-hippocampal systems. RMS disrupted these trajectories, especially between frontal and parahippocampal regions that were also most dynamic during development and aging. These findings introduce a new network-based methodology for studying cortical organization in a model organism, providing a translational framework to understand how environmental risk factors alter brain network development.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"10 2","pages":"418-443"},"PeriodicalIF":3.1,"publicationDate":"2026-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13108504/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147785996","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}

{"title":"The motion sensitivity and predictive utility of different estimates of interregional functional coupling in resting-state functional MRI.","authors":"Kane Pavlovich, James C Pang, Alex Fornito","doi":"10.1162/NETN.a.534","DOIUrl":"10.1162/NETN.a.534","url":null,"abstract":"<p><p>Numerous methods exist for quantifying statistical dependencies, termed \"functional coupling\" (FC), between regional brain activity recorded with resting-state functional magnetic resonance imaging (rs-fMRI). However, their efficacy in mitigating the effects of known sources of noise, such as those induced by participant head motion, and in augmenting effect sizes for brain-wide association studies (BWAS), remains unclear. Here we compared 10 different measures of FC, including correlations, partial correlations, coherence, mutual information, and partial information decomposition, and one measure of effective connectivity (EC; regression dynamic causal modeling), across two independent datasets comprising a total of 1,797 participants (867 males). Each method was evaluated for its ability to mitigate motion-related confounds in FC/EC estimates (assessed via framewise-displacement - edgewise FC correlations) and for its utility in predicting 94 behavioral measures (assessed via cross-validated kernel ridge regression). Our analyses showed that EC was most resistant to motion artifacts but had the weakest behavioral predictions. Conversely, traditional correlation-based methods showed the highest sensitivity to motion, but offered the strongest behavioral prediction across most domains and datasets. Nonetheless, relative differences in predictive accuracies were small, indicating that the use of different FC or EC metrics in rs-fMRI does not significantly impact BWAS effect sizes.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"10 1","pages":"221-243"},"PeriodicalIF":3.1,"publicationDate":"2026-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13008380/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147515726","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}

{"title":"Disrupted intrathalamic and thalamocortical structural covariance networks in posttraumatic stress disorder.","authors":"Nick Steele, Ahmed Hussain, Delin Sun, C Lexi Baird, Courtney Russell, Neda Jahanshad, Lauren E Salminen, Miranda Olff, Jessie L Frijling, Dick J Veltman, Saskia B J Koch, Laura Nawijn, Mirjam van Zuiden, Li Wang, Ye Zhu, Gen Li, Dan J Stein, Jonathan Ipser, Sheri Koopowitz, Yuval Neria, Xi Zhu, Orren Ravid, Sigal Zilcha-Mano, Amit Lazarov, Benjamin Suarez-Jimenez, Ashley A Huggins, Jennifer Stevens, Kerry Ressler, Tanja Jovanovic, Sanne J H van Rooij, Negar Fani, Emily L Dennis, David F Tate, David X Cifu, William C Walker, Elisabeth A Wilde, Ivan Rektor, Pavel Říha, Milissa L Kaufman, Lauren A M Lebois, Justin T Baker, Anthony King, Israel Liberzon, Mike Angstadt, Nicholas D Davenport, Seth G Disner, Scott R Sponheim, Thomas Straube, David Hofmann, Guang Ming Lu, Rongfeng Qi, Amit Etkin, Adi Maron-Katz, Xin Wang, Austin Kunch, Hong Xie, Yann Quidé, Wissam El-Hage, Shmuel Lissek, Hannah Berg, Steven E Bruce, Josh Cisler, Marisa Ross, Ryan Herringa, Daniel W Grupe, Jack B Nitschke, Richard J Davidson, Christine Larson, Terri A deRoon-Cassini, Carissa W Tomas, Jacklynn M Fitzgerald, Brandee Feola, Jennifer U Blackford, Bunmi O Olatunji, Geoffrey May, Steven M Nelson, Evan M Gordon, Chadi G Abdallah, Ruth Lanius, Maria Densmore, Jean Théberge, Richard W J Neufeld, Paul M Thompson, Rajendra A Morey","doi":"10.1162/NETN.a.535","DOIUrl":"10.1162/NETN.a.535","url":null,"abstract":"<p><p>The thalamus is a heterogeneous structure crucial for corticocortical communication, affective-perceptual integration, motor preparation, and memory-related functions. Posttraumatic stress disorder (PTSD) is characterized by various symptoms that likely relate to thalamic functions. Group-level and individual differential structural covariance (SC) analyses were conducted on intrathalamic, thalamocortical, and thalamosubcortical volumetric networks by segmenting structural MRI data from 2,784 subjects (PTSD <i>n</i> = 1,306; controls <i>n</i> = 1,478) into 25 thalamic nuclei per hemisphere. We found that PTSD was associated with stronger intrathalamic and thalamocortical network strength and stronger SC between the limbic thalamus and the somatomotor and auditory thalamus. PTSD severity was related to specific regional alterations in the intrathalamic network involving the lateral pulvinar. Comorbid depression severity positively correlated with global intrathalamic alterations, while avoidance symptoms positively correlated with global thalamosubcortical alterations. Hyperarousal symptoms related to altered SC in the thalamocortical network between the reuniens, central medial, paratenial, centromedian, and limitans-suprageniculate nuclei and lateral cortical regions spanning the occipital, temporal, and orbitofrontal cortices. Differential associations between avoidance, hyperarousal, and comorbid depression symptoms and thalamic SC in PTSD suggest that specific thalamic covariance patterns may be involved in unique facets of PTSD symptomatology.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"10 1","pages":"244-266"},"PeriodicalIF":3.1,"publicationDate":"2026-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13008379/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147515913","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}

{"title":"An entropic measure of diverse specialization highlights multifunctional neurons in annotated connectomes.","authors":"Sung Soo Moon, Lidia Ripoll-Sánchez, Petra Vértes, William R Schafer, Sebastian E Ahnert","doi":"10.1162/NETN.a.533","DOIUrl":"10.1162/NETN.a.533","url":null,"abstract":"<p><p>The creation and curation of synaptic-level neuronal networks, or connectomes, enables the study of the relationship between neuronal structure and function. Topological characteristics of neuronal networks have been studied extensively. Separately, there have been considerable efforts to classify the morphology, cell types, and lineages of neurons. Here, we introduce a network metric that combines topological analysis with node metadata. This entropic quantity measures the diversity of incoming or outgoing connections to a node in terms of the metadata distribution. We find that in <i>Caenorhabditis elegans</i>, the top-scoring neurons (PVR, RMGL/R, DVA, CEP, ADE, URXR, RIGL, BAG, SMBDL) have known functions that integrate and disseminate multimodal information involved in sensorimotor functions. In the nerve cord of <i>Drosophila melanogaster</i>, we find that top-scoring neurons are embryonic neurons located in the abdominal neuropil, where sensorimotor coordination is required for complex innate behavior such as mating.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"10 1","pages":"204-220"},"PeriodicalIF":3.1,"publicationDate":"2026-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13008378/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147515875","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}

{"title":"Neural compensation in persons with HIV and marijuana use: Insights from a reorganized DMN.","authors":"Mohsen Bahrami, Paul J Laurienti, Sheri L Towe, Ryan P Bell, Heather M Shappell, Christina S Meade","doi":"10.1162/NETN.a.513","DOIUrl":"https://doi.org/10.1162/NETN.a.513","url":null,"abstract":"<p><p>The interactive effects of HIV and marijuana (MJ) on the brain remain largely unknown, despite the prevalence of cognitive implications in this population. This study examined the impacts of HIV and MJ on brain networks crucial for normal cognitive function. Functional MRI data and a battery of neuropsychological tests from 237 HIV+ and HIV- adults aged 25-59 years, stratified by MJ use, were collected. The following hypotheses were then tested: (a) HIV is associated with widespread disruption of the small-world organization of the default mode network (DMN) that is exacerbated by MJ use; (b) observed differences are reflected in cognitive performance. Clustering coefficient and global efficiency were used to measure small-world organizations. We found significant differences in the DMN's clustering and efficiency between our control group (HIV-MJ-) and the other three groups (HIV+MJ-, HIV-MJ+, and HIV+MJ+). In those with HIV/MJ, the DMN reorganized toward a network explained by efficiency than clustering. Global cognitive performance was associated with this group difference. Unlike the control group, participants with HIV/MJ showed an integrated DMN across all cognitive scores. The higher integrity of the DMN in patients with HIV and MJ use (particularly when co-occurring) across cognitive scores could imply compensation to preserve cognitive function.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"10 1","pages":"118-136"},"PeriodicalIF":3.1,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12956298/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147357126","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}

{"title":"Spatiotemporal profiling of functional network overlapping modules in Alzheimer's disease.","authors":"Yue Gu, Ying Lin, Liangfang Li, Junji Ma, Sihan Wei, Zhengjia Dai","doi":"10.1162/NETN.a.516","DOIUrl":"https://doi.org/10.1162/NETN.a.516","url":null,"abstract":"<p><p>Alzheimer's disease (AD) is characterized by progressive neural network degradation. In brain functional networks, overlapping module structures provide more accurate representations of brain function than nonoverlapping structures. Since the involvement of overlapping nodes in multiple modules can vary over time, investigating dynamic functional changes in the brain may provide deeper insights into the structural characteristics of these overlapping modules. However, the spatiotemporal dynamics of overlapping modular brain organization remain unclear. We employed resting-state fMRI to explore the overlapping modular organization and dynamic multilayer modules in 64 AD (Age_mean = 74.04) and 61 healthy controls (HC, Age_mean = 74.86) from the Alzheimer's Disease Neuroimaging Initiative. Compared with HC, AD exhibited increased overlapping modules and decreased modularity, with altered nodal overlapping probability, particularly in the superior frontal cortex and hippocampus. Higher nodal overlapping probability correlated with greater flexibility and was associated with larger amyloid deposits. Lasso regression analysis further revealed strong correlations between overlapping nodal characteristics and cognitive performance. Our findings suggest that overlapping nodes are critical components in AD, demonstrating high amyloid deposition, significant functional flexibility, and strong associations to cognitive behavior. These alterations may enhance the understanding of AD pathology and contribute to the development of biomarkers for improved diagnosis and therapeutic strategies.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"10 1","pages":"185-203"},"PeriodicalIF":3.1,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12956295/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147357182","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}

{"title":"Variation in high-amplitude events across the human lifespan.","authors":"Youngheun Jo, Jacob Tanner, Caio Seguin, Joshua Faskowitz, Richard F Betzel","doi":"10.1162/NETN.a.515","DOIUrl":"https://doi.org/10.1162/NETN.a.515","url":null,"abstract":"<p><p>Edge time series decompose functional connections into their fine-scale, framewise contributions. Previous studies have demonstrated that global high-amplitude \"events\" in edge time series can be clustered into distinct patterns. However, whether events and their patterns change or persist throughout the human lifespan has not been investigated. Here, we directly address this question by clustering event frames using the Nathan Kline Institute-Rockland sample that includes subjects with ages spanning the human lifespan. We find evidence of two main clusters that appear across subjects and age groups which systematically change in magnitude and frequency with age. Our results also demonstrate that such event clusters have distinct, heterogeneous relationships with structural connectivity-derived communication measures, which change with age. Finally, event clusters were found to outperform nonevents in predicting phenotypes regarding human intelligence and achievement. Collectively, our findings fill several gaps in current knowledge about cofluctuation patterns in edge time series and human aging, setting the stage for future investigation into the causal origins of changes in functional connectivity throughout the human lifespan.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"10 1","pages":"158-184"},"PeriodicalIF":3.1,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12956297/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147357164","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}

{"title":"Inter-individual alignment of multimodal brain networks with anatomical constraints.","authors":"Yanis Aeschlimann, Anna Calissano, Theodore Papadopoulo, Samuel Deslauriers-Gauthier","doi":"10.1162/NETN.a.514","DOIUrl":"https://doi.org/10.1162/NETN.a.514","url":null,"abstract":"<p><p>When using a cortical parcellation, it is generally assumed that the regions correspond across subjects, meaning regions with the same label are expected to have the same structural or functional roles from one subject to any other. However, at a fine-grained scale, environmental and genetic factors shape both cortex and white matter differently, making it challenging to consistently label all parcels for every subject. Brain networks can be constructed with regions as nodes and their connectivity as edges. One critical step while constructing those networks is the definition of the nodes due to the spatial inter-individual variability, which can limit the reliability of group studies' results. In this work, we propose alignment criteria to register the brain nodes across subjects by allowing the permutation of tiny cortical regions. Those criteria account for multiple brain network perspectives built from different imaging modalities. Our across-subject multimodal alignment of brain networks includes constraints that restrict possible permutations to anatomically plausible ones. The identified permutations are finally applied to the brain networks not used for optimization, and also improve the alignment of these networks. This work has been validated on real magnetic resonance imaging data from the Human Connectome Project.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"10 1","pages":"137-157"},"PeriodicalIF":3.1,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12956296/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147357117","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}

{"title":"A novel framework to quantify dynamic convergence and divergence of overlapping brain states characterizing four psychiatric disorders.","authors":"Najme Soleimani, Sir-Lord Wiafe, Armin Iraji, Godfrey Pearlson, Vince D Calhoun","doi":"10.1162/NETN.a.505","DOIUrl":"10.1162/NETN.a.505","url":null,"abstract":"<p><p>Brain function is inherently dynamic, characterized by transient, overlapping functional states rather than static connectivity patterns. Current clustering-based dynamic functional network connectivity methods often fail to capture overlapping states; meanwhile, independent component analysis (ICA)-based methods typically rely on group-level analysis, limiting subject-specific accuracy. To address this gap, we introduce a novel analytical framework estimating individualized dynamic double functional independent primitives (ddFIPs)-based states. Our methodological innovation includes: (a) a two-stage ICA combining spatially constrained ICA to define group-level intrinsic connectivity networks (ICNs), followed by constrained ICA to estimate subject-specific states and timecourses; (b) calibration ensuring derived states preserve original correlation scales, enabling meaningful cross-subject and group-level comparisons; and (c) novel metrics leveraging this calibrated representation, including amplitude convergence (uniformity of simultaneous state contributions), amplitude divergence (variability of states independent of state dominance), and dynamic state density (number of concurrently active states at any given time). These methodological advances enhance our ability to characterize subtle differences in brain connectivity dynamics, offering deeper insight into healthy and disrupted connectivity patterns. Validating our framework on an extensive resting-state fMRI dataset (<i>N</i> > 5.5<i>K</i>) spanning four neuropsychiatric conditions revealed disorder-specific connectivity signatures: schizophrenia exhibited extensive variability (increased divergence), while autism displayed pronounced stability (increased convergence). In summary, our proposed method uniquely integrates subject-specific ICA estimation, unit-preserving calibration, and novel convergence-divergence metrics, providing data-driven biomarkers differentiating psychiatric disorders.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"10 1","pages":"93-117"},"PeriodicalIF":3.1,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12956294/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147357132","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}