Human Brain Mapping最新文献

{"title":"Microstructural Characterization of Short Association Fibers Related to Long-Range White Matter Tracts in Normative Development","authors":"Chloe Cho,&nbsp;Maxime Chamberland,&nbsp;Francois Rheault,&nbsp;Daniel Moyer,&nbsp;Bennett A. Landman,&nbsp;Kurt G. Schilling","doi":"10.1002/hbm.70255","DOIUrl":"https://doi.org/10.1002/hbm.70255","url":null,"abstract":"<p>Short association fibers (SAFs) in the superficial white matter play a key role in mediating local cortical connections but have not been well-studied as innovations in whole-brain diffusion tractography have only recently been developed to study superficial white matter. Characterizing SAFs and their relationship to long-range white matter tracts is crucial to advance our understanding of neurodevelopment during the period from childhood to young adulthood. This study aims to (1) map SAFs in relation to long-range white matter tracts, (2) characterize typical neurodevelopmental changes across these white matter pathways, and (3) investigate the relationship between microstructural changes in SAFs and long-range white matter tracts. Leveraging a cohort of 616 participants ranging in age from 5.6 to 21.9 years old, we performed quantitative diffusion tractography and advanced diffusion modeling with diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI). Robust linear regression models were applied to analyze microstructural features, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), intracellular volume fraction (ICVF), isotropic volume fraction (ISOVF), and orientation dispersion index (ODI). Our results reveal that both SAFs and long-range tracts exhibit similar overall developmental patterns, characterized by negative associations of MD, AD, and RD with age and positive associations of FA, ICVF, ISOVF, and ODI with age. Notably, FA, AD, and ODI exhibit significant differences between SAFs and long-range tracts, suggesting distinct neurodevelopmental trajectories between superficial and deep white matter. In addition, significant differences were found in MD, RD, and ICVF between males and females, highlighting variations in neurodevelopment. This normative study provides insights into typical microstructural changes of SAFs and long-range white matter tracts during development, laying a foundation for future research to investigate atypical development and dysfunction in disease pathology.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 8","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70255","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144244639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neurophysiological Basis of Emotional Face Perception and Working Memory Load in a Dual-Task MEG Study","authors":"Katharina Lingelbach,&nbsp;Jochem W. Rieger","doi":"10.1002/hbm.70242","DOIUrl":"https://doi.org/10.1002/hbm.70242","url":null,"abstract":"<p>Research on the neurophysiological effects of emotional face processing, working memory (WM) load, and their interaction in dual-tasks remains scarce. Therefore, we conducted a combined magnetoencephalography eye-tracking study with 47 participants. The dual-task temporally interleaved a facial emotion discrimination task with a visuo-spatial n-back task. Source-space cluster analyzes of event-related magnetic fields (ERFs) and oscillations revealed significant main effects of emotional expression and WM load. During emotion discrimination, enhanced ERFs for negative facial expressions located across the insula, ACC, and face-specific occipital regions suggest amplified emotion processing but also the recruitment of attentional control mechanisms. During the n-back phase, emotional faces did not affect evoked responses when they were task-irrelevant. Interaction trends in pupil dilation indicated that emotion-specific processing is diminished under high WM load. During the n-back phase, increased WM load reduced alpha and low beta oscillations in temporo- and parieto-occipital areas. In addition, reduced target fixations in the presence of negative facial distractors indicated a tendency toward emotion-specific interference. Furthermore, sustained increased WM load affected perceived valence, pupil size, and reaction time in both subtasks. A convergence of neurophysiological, physiological, and behavioural findings points to specific processing modes with greater resource depletion for negative expressions and high WM load in the dual-task. In conclusion, the study advanced our understanding of (a) circumstances under which emotional faces modulate ERFs in a dual-task, (b) mechanisms underlying emotion discrimination, (c) interaction effects of emotional expression and WM load in gaze behavior, as well as (d) how WM-related oscillatory alpha and beta power is affected by increasing load.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 8","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70242","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144244638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Do Transformers and CNNs Learn Different Concepts of Brain Age?","authors":"Nys Tjade Siegel,&nbsp;Dagmar Kainmueller,&nbsp;Fatma Deniz,&nbsp;Kerstin Ritter,&nbsp;Marc-Andre Schulz","doi":"10.1002/hbm.70243","DOIUrl":"https://doi.org/10.1002/hbm.70243","url":null,"abstract":"<p>“Predicted brain age” refers to a biomarker of structural brain health derived from machine learning analysis of T1-weighted brain magnetic resonance (MR) images. A range of machine learning methods have been used to predict brain age, with convolutional neural networks (CNNs) currently yielding state-of-the-art accuracies. Recent advances in deep learning have introduced transformers, which are conceptually distinct from CNNs, and appear to set new benchmarks in various domains of computer vision. Given that transformers are not yet established in brain age prediction, we present three key contributions to this field: First, we examine whether transformers outperform CNNs in predicting brain age. Second, we identify that different deep learning model architectures potentially capture different (sub-)sets of brain aging effects, reflecting divergent “concepts of brain age”. Third, we analyze whether such differences manifest in practice. To investigate these questions, we adapted a Simple Vision Transformer (sViT) and a shifted window transformer (SwinT) to predict brain age, and compared both models with a ResNet50 on 46,381 T1-weighted structural MR images from the UK Biobank. We found that SwinT and ResNet performed on par, though SwinT is likely to surpass ResNet in prediction accuracy with additional training data. Furthermore, to assess whether sViT, SwinT, and ResNet capture different concepts of brain age, we systematically analyzed variations in their predictions and clinical utility for indicating deviations in neurological and psychiatric disorders. Reassuringly, we observed no substantial differences in the structure of brain age predictions across the model architectures. Our findings suggest that the choice of deep learning model architecture does not appear to have a confounding effect on brain age studies.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 8","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70243","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144244694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brain Aging in Patients With Cardiovascular Disease From the UK Biobank","authors":"Elizabeth Mcavoy,&nbsp;Emma A. M. Stanley,&nbsp;Anthony J. Winder,&nbsp;Matthias Wilms,&nbsp;Nils D. Forkert","doi":"10.1002/hbm.70252","DOIUrl":"https://doi.org/10.1002/hbm.70252","url":null,"abstract":"<p>The brain undergoes complex but normal structural changes during the aging process in healthy adults, whereas deviations from the normal aging patterns of the brain can be indicative of various conditions as well as an increased risk for the development of diseases. The brain age gap (BAG), which is defined as the difference between the chronological age and the machine learning-predicted biological age of an individual, is a promising biomarker for determining whether an individual deviates from normal brain aging patterns. While the BAG has shown promise for various neurological diseases and cardiovascular risk factors, its utility to quantify brain changes associated with diagnosed cardiovascular diseases has not been investigated to date, which is the aim of this study. T1-weighted MRI scans from healthy participants in the UK Biobank were used to train a convolutional neural network (CNN) model for biological brain age prediction. The trained model was then used to quantify and compare the BAGs for all participants in the UK Biobank with known cardiovascular diseases, as well as healthy controls and patients with known neurological diseases for benchmark comparisons. Saliency maps were computed for each individual to investigate whether brain regions used for biological brain age prediction by the CNN differ between groups. The analyses revealed significant differences in BAG distributions for 10 of the 42 sex-specific cardiovascular disease groups investigated compared to healthy participants, indicating disease-specific variations in brain aging. However, no significant differences were found regarding the brain regions used for brain age prediction as determined by saliency maps, indicating that the model mostly relied on healthy brain aging patterns, even in the presence of cardiovascular diseases. Overall, the findings of this work demonstrate that the BAG is a sensitive imaging biomarker to detect differences in brain aging associated with specific cardiovascular diseases. This further supports the theory of the heart–brain axis by exemplifying that many cardiovascular diseases are associated with atypical brain aging.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 8","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70252","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144244702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neuroanatomical Changes in the Stopping Network Across the Adult Lifespan Assessed With Quantitative and Diffusion MRI","authors":"Sarah A. Kemp,&nbsp;Pierre-Louis Bazin,&nbsp;Steven Miletić,&nbsp;Russell J. Boag,&nbsp;Max C. Keuken,&nbsp;Mark R. Hinder,&nbsp;Birte U. Forstmann","doi":"10.1002/hbm.70240","DOIUrl":"https://doi.org/10.1002/hbm.70240","url":null,"abstract":"<p>Response inhibition, the cancellation of planned movement, is essential for everyday motor control. Extensive fMRI and brain stimulation research provides evidence for the crucial role of a number of cortical and subcortical regions in response inhibition, including the subthalamic nucleus (STN), presupplementary motor area (preSMA) and the inferior frontal gyrus (IFG). Current models assume that these regions operate as a network, with action cancellation originating in the cortical areas and then executed rapidly via the subcortex. Response inhibition slows in older age, a change that has been attributed to deterioration or changes in the connectivity and integrity of this network. However, previous research has mainly used whole-brain approaches when investigating changes in structural connectivity across the lifespan or has used simpler measures to investigate structural ageing. Here, we used high-resolution quantitative and diffusion MRI to extensively examine the anatomical changes that occur in this network across the lifespan. We found age-related changes in iron concentration in these tracts, increases in the apparent diffusion coefficient and some evidence for a decrease in myelin content. Conversely, we found very little evidence for age-related anatomical changes in the regions themselves. We propose that some of the functional changes observed in these regions in older adult populations (e.g., increased BOLD recruitment) are a reflection of alterations to the connectivity between the regions rather than localised regional change.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 8","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70240","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Network Occlusion Sensitivity Analysis Identifies Regional Contributions to Brain Age Prediction","authors":"Lingfei He,&nbsp;Siyu Wang,&nbsp;Cheng Chen,&nbsp;Yaping Wang,&nbsp;Qingcheng Fan,&nbsp;Congying Chu,&nbsp;Lingzhong Fan,&nbsp;Junhai Xu","doi":"10.1002/hbm.70239","DOIUrl":"https://doi.org/10.1002/hbm.70239","url":null,"abstract":"<p>Deep learning frameworks utilizing convolutional neural networks (CNNs) have frequently been used for brain age prediction and have achieved outstanding performance. Nevertheless, deep learning remains a black box as it is hard to interpret which brain parts contribute significantly to the predictions. To tackle this challenge, we first trained a lightweight, fully CNN model for brain age estimation on a large sample data set (<i>N</i> = 3054, age range = [8,80 years]) and tested it on an independent data set (<i>N</i> = 555, age range = [8,80 years]). We then developed an interpretable scheme combining network occlusion sensitivity analysis (NOSA) with a fine-grained human brain atlas to uncover the learned invariance of the model. Our findings show that the dorsolateral, dorsomedial frontal cortex, anterior cingulate cortex, and thalamus had the highest contributions to age prediction across the lifespan. More interestingly, we observed that different regions showed divergent patterns in their predictions for specific age groups and that the bilateral hemispheres contributed differently to the predictions. Regions in the frontal lobe were essential predictors in both the developmental and aging stages, with the thalamus remaining relatively stable and saliently correlated with other regional changes throughout the lifespan. The lateral and medial temporal brain regions gradually became involved during the aging phase. At the network level, the frontoparietal and the default mode networks show an inverted U-shape contribution from the developmental to the aging stages. The framework could identify regional contributions to the brain age prediction model, which could help increase the model interpretability when serving as an aging biomarker.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 8","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70239","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Elucidating Development Trajectories of Brain Functional Abnormalities in Major Depressive Disorder Utilizing a Data-Driven Disease Progression Model","authors":"Yuhong Zheng,&nbsp;Peng Wang,&nbsp;Chi Yao,&nbsp;Jinghua Wang,&nbsp;Jinhui Wang,&nbsp;Shao-Wei Xue","doi":"10.1002/hbm.70249","DOIUrl":"https://doi.org/10.1002/hbm.70249","url":null,"abstract":"<p>Concerns have arisen regarding the heterogeneity of patients with major depressive disorder (MDD), particularly when the varying disease progression trajectories among individuals are overlooked. Recognizing these distinct trajectories is crucial for personalized assessments and accurate disease progression predictions in MDD, posing a significant challenge in clinical practice. We utilized a data-driven subtype and stage inference (SuStaIn) model to infer trajectories based on cross-sectional amplitude of low-frequency fluctuations (ALFF) derived from resting-state functional magnetic resonance imaging data of 833 patients with MDD and 834 healthy controls. Based on distinct trajectories, two subtypes of MDD were identified: Subtype 1 showed declining ALFF from paracentral lobule (PCL) to thalamus to medial orbitofrontal cortex (OFCmed), with higher core depression scores and gray matter atrophy, whereas Subtype 2 had an opposing trajectory, with initial OFCmed ALFF decrease gradually extending to PCL. Our findings contribute to a better understanding of MDD heterogeneity and facilitate precise disease progression predictions.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 8","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70249","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping Caudolenticular Gray Matter Bridges in the Human Brain Striatum Through Diffusion Magnetic Resonance Imaging and Tractography","authors":"Graham Little,&nbsp;Charles Poirier,&nbsp;Arnaud Bore,&nbsp;Martin Parent,&nbsp;Laurent Petit,&nbsp;Maxime Descoteaux","doi":"10.1002/hbm.70245","DOIUrl":"https://doi.org/10.1002/hbm.70245","url":null,"abstract":"<p>In primates, the putamen and the caudate nucleus are connected by ~1 mm-thick caudolenticular gray matter bridges (CLGBs) interspersed between the white matter bundles of the internal capsule. Little is understood about the functional or microstructural properties of the CLGBs. In studies proposing high resolution diffusion magnetic resonance imaging (dMRI) techniques, CLGBs have been qualitatively identified as an example of superior imaging quality; however, the microstructural properties of these structures have yet to be examined. In this study, it is demonstrated for the first time that dMRI is sensitive to an organized anisotropic signal oriented in the direction parallel to the CLGBs, suggesting that dMRI could be a useful imaging method for probing the microstructure of the CLGBs. To demonstrate the anisotropic diffusion signal is coherently organized along the extent of the CLGBs and to enable a subsequent CLGB microstructural measurement, a customized tractography seeding and filtering method is proposed that utilizes the shape of the human striatum (putamen + caudate nucleus) to reconstruct the CLGBs in 3D. The proposed seeding strategy seeds tractography streamlines outward and normal to the surface of a 3D model of the striatum such that reconstructed streamlines are more likely to follow the diffusion signal peaks aligned parallel to the CLGBs. The method is applied to three different diffusion datasets, namely a high resolution 760 μm isotropic diffusion dataset acquired on a single subject, the test–retest cohort included as part of the human connectome project (<i>N</i> = 44) with diffusion data acquired at 1.25 mm isotropic, and a locally acquired “clinical” test–retest dataset acquired at 2.0 mm isotropic (<i>N</i> = 24). Reconstructed CLGBs directly overlap expected gray matter regions in the human brain for all three datasets. In addition, the method is shown to accurately reconstruct CLGBs repeatedly across multiple test–retest cohorts. The tractography CLGB reconstructions are then used to extract a quantitative measurement of microstructure from a local model of the diffusion signal along the CLGBs themselves. This is the first work to comprehensively study the CLGBs in vivo using dMRI and presents techniques suitable for future human neuroscience studies targeting these structures.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 8","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70245","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144197030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatio-Temporal Signatures of Cognitive Function After Pediatric Arterial Ischemic Stroke—A Pilot Study","authors":"Karl-Heinz Nenning,&nbsp;Florian Ph. S. Fischmeister,&nbsp;Astrid Novak,&nbsp;Rainer Seidl,&nbsp;Smadar Ovadia-Caro,&nbsp;Ting Xu,&nbsp;Gregor Kasprian,&nbsp;Lisa Bartha-Doering,&nbsp;Kathrin Kollndorfer","doi":"10.1002/hbm.70248","DOIUrl":"https://doi.org/10.1002/hbm.70248","url":null,"abstract":"<p>Childhood arterial ischemic stroke is a severe disorder that can cause lasting cognitive impairments, particularly in executive functions. Although early research assumed an improved outcome in childhood stroke patients compared to adults, more recent studies indicate similar rates of disabilities and cognitive impairment, with widespread brain network disruptions underlying these deficits. Here, we used resting-state fMRI to study alterations in functional brain dynamics and their association with cognitive outcome in children and adolescents after childhood stroke. We used co-activation pattern analysis to characterize five recurring brain states and their temporal properties in a cohort of 16 patients and 17 age-matched controls. We found that in pediatric stroke patients, a specific brain state characterizing the frontoparietal network was more prevalent and more frequently involved in state transitions. This was paralleled by lower occurrence rates of a brain state related to default mode network deactivation. Moreover, our analysis showed that these dynamics relate more to the extent to which the lesion impacts functional networks than to lesion size alone. Taken together, our findings suggest that disrupted brain dynamics following childhood stroke relate to cognitive performance and that the location of a focal lesion can have wide-ranging implications on brain state dynamics.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 8","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70248","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144197031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Event-Marked Windowed Communication: Inferring Activity Propagation from Neural Time Series","authors":"Varun Madan Mohan,&nbsp;Thomas F. Varley,&nbsp;Robin F. H. Cash,&nbsp;Caio Seguin,&nbsp;Andrew Zalesky","doi":"10.1002/hbm.70223","DOIUrl":"https://doi.org/10.1002/hbm.70223","url":null,"abstract":"<p>Tracking how activity or signal perturbations propagate in nervous systems is crucial to understanding interareal communication in the brain. Current analytical methodologies are not well suited to systematically infer interareal activity propagation from neural time series recordings. Here, we propose Event-marked Windowed Communication (EWC), a framework to infer activity propagation between neural elements by tracking the statistical consequence of spontaneous, endogenous regional perturbations. EWC tracks the downstream effect of these perturbations by subsampling the neural time series and quantifying statistical dependences using established functional connectivity measures. We test EWC on simulations of neural dynamics and demonstrate the retrieval of ground truth motifs of directional signaling, over a range of model configurations. We also show that EWC can capture activity propagation in a computationally efficient manner by benchmarking it against more advanced FC estimation methods such as transfer entropy. Lastly, we showcase the utility of EWC to infer whole-brain activity propagation maps from magnetoencephalography (MEG) recordings. Networks computed using EWC were compared to those inferred using transfer entropy and were found to be highly correlated (median <i>r</i> = 0.81 across subjects). Importantly, our framework is flexible and can be applied to activity time series captured by diverse functional neuroimaging modalities, opening new avenues for the study of neural communication.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 8","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70223","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}