Network Neuroscience最新文献

{"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":"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":3.4,"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":"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":3.4,"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":3.4,"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}

{"title":"Perturbations of whole-brain model reveal critical areas related to relapse of early psychosis.","authors":"Iraïs Garcés de Marcilla Lappin, Ludovica Mana, Yasser Aleman-Gomez, Luis Alameda, Alessandra Solida, Raoul Jenni, Philipp S Baumann, Paul Klauser, Philippe Conus, Morten Kringelbach, Patric Hagmann, Gustavo Deco, Yonatan Sanz Perl","doi":"10.1162/NETN.a.502","DOIUrl":"10.1162/NETN.a.502","url":null,"abstract":"<p><p>Overcoming an initial psychotic episode does not always lead to recovery; relapses and subsequent psychotic episodes may happen afterward. Even if the characterization of psychotic disorders can be related to alterations in brain connectivity, clear identification of the brain areas for relapse is missing. Here, we leverage on whole-brain modeling linking anatomical structural information with functional activity as measured by MRI in 196 participants. Patients were classified into Stage II (first episode), IIIa (incomplete remission), IIIb (remission followed by one relapse), and IIIc (remission followed by several relapses), depending on the course of psychosis up to the time of the brain scan. From these data, a low-dimensional manifold reduction of the brain dynamics was obtained using deep learning variational autoencoders in which the different stages are represented, and a classification model can be trained to distinguish them. Then, a dimensionality analysis was performed to find the optimal dimension that allows the distinction between first episode and relapsing cases with high accuracy. Finally, perturbations were introduced in the model to reveal the brain regions associated with the absence of relapse, which could help predict which brain regions to target during therapy and assist the treatment of patients suffering from psychotic disorders.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"10 1","pages":"62-79"},"PeriodicalIF":3.1,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12798648/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145971320","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":"Benchmarking overlapping community detection methods for applications in human connectomics.","authors":"Annie G Bryant, Aditi Jha, Sumeet Agarwal, Patrick Cahill, Brandon Lam, Stuart Oldham, Aurina Arnatkevičiūtė, Alex Fornito, Ben D Fulcher","doi":"10.1162/NETN.a.39","DOIUrl":"10.1162/NETN.a.39","url":null,"abstract":"<p><p>Brain networks exhibit non-trivial modular organization, with groups of densely connected areas participating in specialized functions. Traditional community detection algorithms assign each node to one module, but this representation cannot capture integrative, multifunctional nodes that span multiple communities. Despite the increasing availability of overlapping community detection algorithms (OCDAs) to capture such integrative nodes, there is no objective procedure for selecting the most appropriate method and its parameters for a given problem. Here, we overcome this limitation by introducing a data-driven method for selecting an OCDA and its parameters from performance on a tailored ensemble of generated benchmark networks, assessing 22 unique algorithms and parameter settings. Applied to the human right-hemisphere structural connectome, we find that the \"order statistics local optimization method\" (OSLOM) best identifies ground-truth overlapping structure in the benchmark ensemble, yielding a seven-network decomposition of the right-hemisphere cortex. These modules are bridged by 15 overlapping regions that generally sit at the apex of the putative cortical hierarchy-suggesting integrative, higher order function-with network participation increasing along the cortical hierarchy, a finding not supported using a non-overlapping modular decomposition. This data-driven approach to selecting OCDAs is applicable across domains, opening new avenues to detecting and quantifying informative structures in complex real-world networks.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"10 1","pages":"25-61"},"PeriodicalIF":3.1,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12798651/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145971289","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":"On the virtues and limitations of Granger-causal brain connectivity estimate: Critical analysis using neural mass models.","authors":"Silvana Pelle, Giulia Piermaria, Elisa Magosso, Mauro Ursino","doi":"10.1162/NETN.a.38","DOIUrl":"10.1162/NETN.a.38","url":null,"abstract":"<p><p>Estimation of brain connectivity from neuroelectric data is a fundamental problem in modern neuroscience, and it is used to assess the network properties of brain function. In the present work, we critically assess the virtues and limitations of temporal Granger causality (using both conditional and unconditional formulations) for the estimation of functional brain connectivity, using a neural mass model as the ground truth. The model simulates transmission among different brain rhythms (in the <i>θ</i>, <i>α</i>, <i>β</i>, and <i>γ</i> bands) via excitatory and inhibitory synapses. The results show that Granger causality is able to detect relative changes in connectivity, but the estimated values are influenced by the operative conditions (sampling frequency, signal length, delay). Moreover, the absolute value of Granger causality depends on the particular rhythm transmitted and is affected by nonlinear phenomena, especially the activity level in the connected regions. In the case of complex connectivity networks, conditional Granger causality overwhelms the unconditional one, since the latter often discovers spurious connections. Finally, inhibitory connections can be revealed more easily by Granger causality than similar excitatory connections, a result generally neglected in brain network studies. The present results can drive the correct interpretation of Granger-causality-based connectivity networks derived from neuroelectric signals.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"10 1","pages":"1-24"},"PeriodicalIF":3.1,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12798650/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145971367","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":"Connectivity and function are coupled across cognitive domains throughout the brain.","authors":"Kelly J Hiersche, Zeynep M Saygin, David E Osher","doi":"10.1162/NETN.a.504","DOIUrl":"10.1162/NETN.a.504","url":null,"abstract":"<p><p>Decades of neuroimaging have revealed that the functional organization of the brain is roughly consistent across individuals, and at rest, it resembles group-level task-evoked networks. A fundamental assumption in the field is that the functional specialization of a brain region arises from its connections to the rest of the brain, but limitations in the amount of data that can be feasibly collected in a single individual leave open the following question: Is the association between task activation and connectivity consistent across the brain and many cognitive tasks? To answer this question, we fit ridge regression models to activation maps from 33 cognitive domains (generated with NeuroQuery) using resting-state functional connectivity data from the Human Connectome Project as the predictor. We examine how well functional connectivity fits activation and find that all regions and all cognitive domains have a very robust relationship between brain activity and connectivity. The tightest relationship exists for higher order, domain-general cognitive functions. These results support the claim that connectivity is a general organizational principle of brain function by comprehensively testing this relationship in a large sample of individuals for a broad range of cognitive domains and provide a reference for future studies engaging in individualized predictive models.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"10 1","pages":"80-92"},"PeriodicalIF":3.1,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12798649/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145971284","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":"Erratum: Structure-function coupling using fixel-based analysis and functional magnetic resonance imaging in Alzheimer's disease and mild cognitive impairment.","authors":"Charly Hugo Alexandre Billaud, Junhong Yu","doi":"10.1162/NETN.x.506","DOIUrl":"https://doi.org/10.1162/NETN.x.506","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1162/netn_a_00461.].</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 4","pages":"i-ii"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12707540/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145776036","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":"Network control theory applied to the human connectome: A study on variability and discriminability of fMRI connectomic features under normal and defective sensorineural conditions.","authors":"Simone Papallo, Alessandro Pasquale De Rosa, Sara Ponticorvo, Mario Cirillo, Mario Sansone, Francesco Di Salle, Francesco Amato, Fabrizio Esposito","doi":"10.1162/NETN.a.36","DOIUrl":"10.1162/NETN.a.36","url":null,"abstract":"<p><p>Network control theory (NCT) models human connectomes as high-dimensional input-state-output stable systems where the efficiency of neural connections can be addressed by energy cost (of state transitions) and controllability (from/to reachable states). Different options are available to extract NCT features: initial/final states, control time horizon, structural (vs. functional), and static (vs. dynamic) connectivity measure. Leveraging the minimum control paradigm, assuming the Schur stability for discrete systems, we investigate intra- and inter-individual variability of NCT features, across different settings and datasets, and assess their potential as useful connectome metrics in clinical studies. NCT was applied to structural and functional MRI (fMRI), in a cohort of 82 cognitively unimpaired elderly subjects with normal or (age-related) sensorineural condition (hearing loss), and in young adults from the Human Connectome Project database. Results demonstrated low intra-individual and moderate within-group inter-individual variability of NCT features. The energy cost was related to the time horizon of the system but did not discriminate groups. Controllability analyses revealed significant group effects and acceptable discrimination between normal and disease-affected connectomes, particularly for the default-mode network. We provide a systematic evaluation of different settings for fMRI-derived NCT features that may help guiding clinical applications toward capturing neurologically meaningful changes in the human connectome.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 4","pages":"1401-1422"},"PeriodicalIF":3.1,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12635833/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145589410","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 prior-knowledge-guided feature selection method and its application to biomarker identification of schizophrenia.","authors":"Ying Xing, Godfrey D Pearlson, Peter Kochunov, Vince D Calhoun, Yuhui Du","doi":"10.1162/NETN.a.37","DOIUrl":"10.1162/NETN.a.37","url":null,"abstract":"<p><p>Despite considerable efforts to uncover the neural basis of psychiatric disorders using neuroimaging, few methods utilize intrinsic brain-derived knowledge, leading to limited specificity and discriminability in biomarker identification. To leverage the inherent characteristics within the brain, we propose a prior-knowledge-guided feature selection method to flexibly unveil discriminative and target-oriented biomarkers of psychiatric disorders. Specifically, we construct a constrained sparse regularization allowing for the flexible integration of diverse prior knowledge to identify sparse neuroimaging features linked to specific psychopathology. Additionally, we simultaneously integrate graph-based regularization and redundancy-removal regularization to further ensure the discriminability and independence among the selected features. Different priors hold varying significance in identifying specific biomarkers. Four functional magnetic resonance imaging (fMRI) datasets from 708 healthy controls and 537 schizophrenia patients are used to evaluate our method integrated with various prior knowledge, revealing specific schizophrenia-related brain abnormalities. Compared with nine advanced feature selection methods, our method improves mean classification accuracy by 3.89% to 11.24%, particularly revealing reduced interactions within the visual domain and between subcortical and visual domains in schizophrenia patients. The proposed method offers flexible and precise biomarker identification tailored to specific targets, advancing the understanding and diagnosis of psychiatric conditions.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 4","pages":"1423-1448"},"PeriodicalIF":3.1,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12635834/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145589404","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}