Human Brain Mapping最新文献

{"title":"Brain Age Prediction: Deep Models Need a Hand to Generalize","authors":"Reza Rajabli,&nbsp;Mahdie Soltaninejad,&nbsp;Vladimir S. Fonov,&nbsp;Danilo Bzdok,&nbsp;D. Louis Collins","doi":"10.1002/hbm.70254","DOIUrl":"https://doi.org/10.1002/hbm.70254","url":null,"abstract":"<p>Predicting brain age from T1-weighted MRI is a promising marker for understanding brain aging and its associated conditions. While deep learning models have shown success in reducing the mean absolute error (MAE) of predicted brain age, concerns about robust and accurate generalization in new data limit their clinical applicability. The large number of trainable parameters, combined with limited medical imaging training data, contributes to this challenge, often resulting in a generalization gap where there is a significant discrepancy between model performance on training data versus unseen data. In this study, we assess a deep model, SFCN-reg, based on the VGG-16 architecture, and address the generalization gap through comprehensive preprocessing, extensive data augmentation, and model regularization. Using training data from the UK Biobank, we demonstrate substantial improvements in model performance. Specifically, our approach reduces the generalization MAE by 47% (from 5.25 to 2.79 years) in the Alzheimer's Disease Neuroimaging Initiative dataset and by 12% (from 4.35 to 3.75 years) in the Australian Imaging, Biomarker and Lifestyle dataset. Furthermore, we achieve up to 13% reduction in scan-rescan error (from 0.80 to 0.70 years) while enhancing the model's robustness to registration errors. Feature importance maps highlight anatomical regions used to predict age. These results highlight the critical role of high-quality preprocessing and robust training techniques in improving accuracy and narrowing the generalization gap, both necessary steps toward the clinical use of brain age prediction models. Our study makes valuable contributions to neuroimaging research by offering a potential pathway to improve the clinical applicability of deep learning models.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 11","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70254","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635515","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":"The Effect of APOE ε4 Allele on Dynamic Local Spontaneous Brain Activity and Functional Integration in Alzheimer's Disease","authors":"Yi Tan,&nbsp;Dan Yang,&nbsp;Zhihong Ke,&nbsp;Zheqi Hu,&nbsp;Wenting Song,&nbsp;Limoran Tang,&nbsp;Zhixin Zhou,&nbsp;Yuting Mo,&nbsp;Lili Huang,&nbsp;Yun Xu","doi":"10.1002/hbm.70269","DOIUrl":"https://doi.org/10.1002/hbm.70269","url":null,"abstract":"<p>The apolipoprotein E (<i>APOE</i>) ε4 allele is the most important genetic risk factor for sporadic Alzheimer's disease (AD), yet its mechanisms in AD pathology and cognitive decline remain unclear. Using a sliding-time window approach to directly quantify the instantaneous fluctuations of various local metrics based on continuous time series and calculate voxel-wise concordance of these metrics, we explored the impact of <i>APOE</i> ε4 on dynamic local brain activity and functional integration in AD, and its interrelations with plasma biomarkers and cognition. Results showed that <i>APOE</i> ε4 widely affected dALFF, dReHo, dGSCorr, and voxel-wise concordance. For AD patients, <i>APOE</i> ε4 carriers uniquely exhibited correlations between dALFF in the right angular gyrus/supramarginal gyrus and MoCA scores and orientation function, and between voxel-wise concordance in the right caudate nucleus (CAU) and general cognition, attention, language function, orientation function, plasma Aβ42. Critically, <i>APOE</i> ε4-related altered voxel-wise concordance in the right CAU mediated the relationship between plasma Aβ and language cognition in AD. Moreover, the combined model incorporating dynamic metrics, plasma AD biomarkers, and demographic data effectively distinguished AD from NC (AUC = 0.94, sensitivity = 87.69%, specificity = 86.84%). In conclusion, the <i>APOE</i> ε4 allele might play a pivotal role in modulating brain dynamic functional activities in AD, which may contribute to the association between Aβ pathology and cognitive decline. Our findings may provide imaging markers and targets for the diagnosis and treatment of AD.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 10","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70269","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144598267","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":"Thalamic Network Controllability Predicts Cognitive Impairment in Multiple Sclerosis","authors":"Yuping Yang,&nbsp;Anna Woollams,&nbsp;Ilona Lipp,&nbsp;Zhizheng Zhuo,&nbsp;Marta Czime Litwińczuk,&nbsp;Valentina Tomassini,&nbsp;Yaou Liu,&nbsp;Nelson J. Trujillo-Barreto,&nbsp;Nils Muhlert","doi":"10.1002/hbm.70284","DOIUrl":"https://doi.org/10.1002/hbm.70284","url":null,"abstract":"<p>Recent research suggests that individuals with multiple sclerosis (MS) and cognitive impairment exhibit more effortful and less efficient transitions in brain network activity. Previous studies further highlight the increased vulnerability of specific regions, particularly the thalamus, to disease-related damage. This study investigates whether MS affects the controllability of specific brain regions in driving network activity transitions across the brain and examines the relationship between these changes and cognitive impairment in patients. Resting-state functional MRI and neuropsychological data were collected from 102 MS and 27 healthy controls. Functional network controllability analysis was performed to quantify how specific regions influence transitions between brain activity patterns or states. Disease alterations in controllability were assessed in the main dataset and then replicated in an independent dataset of 95 MS and 45 healthy controls. Controllability metrics were then used to distinguish MS from healthy controls and predict cognitive status. MS-specific controllability changes were observed in the subcortical network, particularly the thalamus, which were further confirmed in the replication dataset. Cognitively impaired patients showed significantly greater difficulty in the thalamus steering brain transitions towards difficult-to-reach states, which are typically associated with high-energy-cost cognitive functions. Thalamic network controllability proved more effective than thalamic volume in distinguishing MS from healthy controls (AUC = 88.3%), and in predicting cognitive status in MS (AUC = 80.7%). This study builds on previous research highlighting early thalamic damage in MS, aiming to demonstrate how this damage disrupts activity transitions across the cerebrum and may predict cognitive deficits. Our findings suggest that the thalamus in MS becomes less capable of facilitating broader brain activity transitions essential for high-energy-cost cognitive functions, implying a potential pathological mechanism that links thalamic functional changes to cognitive impairment in MS.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 10","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70284","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144589628","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":"Limitations of Correlation Coefficients in Research on Functional Connectomes and Psychological Processes","authors":"Haojie Fu,&nbsp;Shuang Tang,&nbsp;Xudong Zhao","doi":"10.1002/hbm.70287","DOIUrl":"https://doi.org/10.1002/hbm.70287","url":null,"abstract":"<p>In neuroscience and psychology research, the Pearson correlation coefficient is widely used for feature selection and model performance evaluation, particularly in studies examining relationships between brain activity and psychological behavior indices. However, when predicting psychological processes using connectome models, the Pearson correlation has three main limitations: (1) it struggles to capture the complexity of brain network connections; (2) it inadequately reflects model errors, especially in the presence of systematic biases or nonlinear error; and (3) it lacks comparability across datasets, with high sensitivity to data variability and outliers, potentially distorting model evaluation results. To better assess model performance, it is crucial to combine multiple evaluation metrics, such as mean absolute error (MAE) and root mean square error (MSE), which capture different aspects of model quality. Additionally, baseline comparisons, such as using the mean value or a simple linear regression (LR) model, provide an essential reference for evaluating the added value of more complex models. This approach offers a more robust and comprehensive analysis of functional connectomes and psychological processes.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 10","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70287","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144589545","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":"Minimum-Phase Property of the Hemodynamic Response Function, and Implications for Granger Causality in fMRI","authors":"Leonardo Novelli,&nbsp;Lionel Barnett,&nbsp;Anil K. Seth,&nbsp;Adeel Razi","doi":"10.1002/hbm.70285","DOIUrl":"https://doi.org/10.1002/hbm.70285","url":null,"abstract":"<p>Granger causality (GC) is widely used in neuroimaging to estimate directed statistical dependence between brain regions using time series of brain activity. A known problem is that fMRI measures brain activity indirectly via the blood-oxygen-level-dependent (BOLD) signal, which can distort GC estimates by introducing different time-to-peak responses across brain regions. However, how these distortions affect the validity of inferred connections is not fully understood. Previous studies have shown that false positives are not introduced if the haemodynamic response function (HRF) is minimum-phase; but whether the HRF is actually minimum-phase has remained contentious. Here, we address this issue by studying the transfer functions of three realistic biophysical models. We find that the minimum-phase condition is met for a wide range of physiologically plausible parameter values. Therefore, statistical testing of GC can be viable even if the HRF varies across brain regions, with the following two limitations. First, the minimum-phase condition is violated for parameter combinations that generate an initial dip in the HRF. Second, slow sampling of the BOLD signal (seconds) compared to the timescales of neural signal propagation (milliseconds) may still introduce spurious GC inferences. Beyond GC analysis, the closed-form expressions for the transfer functions of these popular HRF models are valuable for modeling fMRI time series since they balance mathematical tractability with biological plausibility.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 10","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70285","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144589546","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":"Reduced Vestibular Function is Associated With Cortical Surface Shape Changes in the Frontal Cortex","authors":"Dominic Padova,&nbsp;J. Tilak Ratnanather,&nbsp;Andreia V. Faria,&nbsp;Yuri Agrawal","doi":"10.1002/hbm.70251","DOIUrl":"https://doi.org/10.1002/hbm.70251","url":null,"abstract":"<p>Aging-associated decline in peripheral vestibular function is linked to deficits in behaviors and cognitive abilities that are known to rely on the sensorimotor and frontal cortices, but the precise neural pathways are unknown. To fill this knowledge gap, this cross-sectional study investigates the relationship between age-related variation in vestibular function and surface shape alterations of the frontal and sensorimotor cortices, considering age, intracranial volume, and sex. Data from 117 older adults (aged 60+) from the Baltimore Longitudinal Study of Aging, who underwent end-organ-specific vestibular tests (cVEMP for the saccule, oVEMP for the utricle, and vHIT for the horizontal canal) and T1-weighted MRI scans on the same visit, were analyzed. We examined a subset of 10 frontal and sensorimotor brain structures in the broader, distributed vestibular network: the middle-superior part of the prefrontal cortex (SFG_PFC), frontal pole (SFG_pole), and posterior pars of the superior frontal gyrus (SFG), the dorsal prefrontal cortex and posterior pars of middle frontal gyrus (MFG_DPFC, MFG), the pars opercularis, pars triangularis, and pars orbitalis of the inferior frontal gyrus, as well as the precentral gyrus and postcentral gyrus (PoCG) of the sensorimotor cortex. For each region of interest (ROI), shape descriptors were estimated as local compressions and expansions of the population average ROI surface using Large Deformation Diffeomorphic Metric Mapping (LDDMM) surface registration. Shape descriptors were linearly regressed onto standardized vestibular variables, age, intracranial volume, sex, and in follow-up analyses, multisensory function (hearing, vision, proprioception). We found that lower utricular function was linked with surface compression in the left MFG and expansion in the bilateral SFG_pole and left SFG. Reduced canal function was associated with surface compression in the right SFG_PFC and SFG_pole and left SFG. Both reduced saccular and utricular function correlated with surface compression in the posterior medial part of the left MFG. Our findings illuminate the complexity of the relationship between vestibular end-organ function and the focal morphology in aging in areas of the frontal and sensorimotor cortices relevant to executive ability, motor planning, and self-motion perception. An improved understanding of these pathways could help in developing interventions to enhance the quality of life in aging and populations with cognitive impairment.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 10","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70251","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144582134","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":"Neural Mechanisms of Feedback Processing and Regulation Recalibration During Neurofeedback Training","authors":"Gustavo S. P. Pamplona,&nbsp;Jana Zweerings,&nbsp;Cindy S. Lor,&nbsp;Lindsay deErney,&nbsp;Erik Roecher,&nbsp;Arezoo Taebi,&nbsp;Lydia Hellrung,&nbsp;Kaoru Amano,&nbsp;Dustin Scheinost,&nbsp;Florian Krause,&nbsp;Monica D. Rosenberg,&nbsp;Silvio Ionta,&nbsp;Silvia Brem,&nbsp;Erno J. Hermans,&nbsp;Klaus Mathiak,&nbsp;Frank Scharnowski","doi":"10.1002/hbm.70279","DOIUrl":"https://doi.org/10.1002/hbm.70279","url":null,"abstract":"<p>The acquisition of new skills is facilitated by providing individuals with feedback that reflects their performance. This process creates a closed loop that involves feedback processing and regulation recalibration to promote effective training. Functional magnetic resonance imaging (fMRI)-based neurofeedback is unique in applying this principle by delivering direct feedback on the self-regulation of brain activity. Understanding how feedback-driven learning occurs requires examining how feedback is evaluated and how regulation adjusts in response to feedback signals. In this pre-registered mega-analysis, we re-analyzed data from eight intermittent fMRI neurofeedback studies (<i>N</i> = 153 individuals) to investigate brain regions where activity and connectivity are linked to feedback processing and regulation recalibration (i.e., regulation after feedback) during training. We harmonized feedback scores presented during training in these studies and computed their linear associations with brain activity and connectivity using parametric general linear model analyses. We observed that, during feedback processing, feedback scores were positively associated with (1) activity in the reward system, dorsal attention network, default mode network, and cerebellum; and with (2) reward system-related connectivity within the salience network. During regulation recalibration, no significant associations were observed between feedback scores and either activity or associative learning-related connectivity. Our results suggest that neurofeedback is processed in the reward system, supporting the theory that reinforcement learning shapes this form of brain training. In addition, the involvement of large-scale networks in feedback processing, continuously transitioning between evaluating external feedback and internally assessing the adopted cognitive state, suggests that higher-level processing is integral to neurofeedback learning, which usually occurs over a short time span. Our findings highlight the pivotal role of performance-related feedback as a driving force in learning, potentially extending beyond neurofeedback training to other feedback-based processes.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 10","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70279","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144573885","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":"A Method for Estimating Dynamic Functional Network Connectivity Gradients (dFNGs) From ICA Captures Smooth Inter-Network Modulation","authors":"Najme Soleimani,&nbsp;Armin Iraji,&nbsp;Theo G. M. van Erp,&nbsp;Aysenil Belger,&nbsp;Vince D. Calhoun","doi":"10.1002/hbm.70262","DOIUrl":"https://doi.org/10.1002/hbm.70262","url":null,"abstract":"&lt;p&gt;Dynamic functional network connectivity (dFNC) analysis is a widely used approach for studying brain function and offering insight into how brain networks evolve over time. Typically, dFNC studies utilize fixed spatial maps and evaluate transient changes in coupling among time courses estimated from independent component analysis (ICA). This manuscript presents a complementary approach that relaxes this assumption by spatially reordering the components dynamically at each time point to optimize for a smooth gradient in the FNC (i.e., a smooth gradient among ICA connectivity values). Several methods are presented to summarize dynamic FNC gradients (dFNGs) over time, starting with static FNC gradients (sFNGs), then exploring the reordering properties as well as the dynamics of the gradients themselves. We then apply this approach to a dataset of schizophrenia (SZ) patients and healthy controls (HCs). Functional dysconnectivity between different brain regions has been reported in SZ, yet the neural mechanisms behind it remain elusive. Using resting-state fMRI and ICA on a dataset consisting of 151 SZ patients and 160 age and gender-matched HCs, we extracted 53 intrinsic connectivity networks (ICNs) for each subject using a fully automated spatially constrained ICA approach. We develop several summaries of our functional network connectivity gradient analysis, both in a static sense, computed as the Pearson correlation coefficient between full time series, and a dynamic sense, computed using a sliding window approach followed by reordering based on the computed gradient, and evaluate group differences. Static connectivity analysis revealed significantly stronger connectivity between subcortical (SC), auditory (AUD), and visual (VIS) networks in patients, as well as hypoconnectivity in the sensorimotor (SM) network relative to controls. sFNG analysis highlighted distinctive clustering patterns in patients and HCs along cognitive control (CC)/default mode network (DMN), as well as SC/AUD/SM/cerebellar (CB) and VIS gradients. Furthermore, we observed significant differences in the sFNGs between groups in SC and CB domains. dFNG analysis suggested that SZ patients spend significantly more time in a SC/CB state based on the first gradient, while HCs favor the SM/DMN state. For the second gradient, however, patients exhibited significantly higher activity in CB domains, contrasting with HCs' DMN engagement. The gradient synchrony analysis conveyed more shifts between SM/SC networks and transmodal CC/DMN networks in patients. In addition, the dFNG coupling revealed distinct connectivity patterns between SC, SM, and CB domains in SZ patients compared to HCs. To recap, our results advance our understanding of brain network modulation by examining smooth connectivity trajectories. This provides a more complete spatiotemporal summary of the data, contributing to the growing body of current literature regarding the functional dysconnectivity in SZ patients. B","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 10","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70262","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144573884","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":"White Matter Geometry Confounds Diffusion Tensor Imaging Along Perivascular Space (DTI-ALPS) Measures","authors":"Kurt G. Schilling,&nbsp;Allen Newton,&nbsp;Chantal Tax,&nbsp;Markus Nilsson,&nbsp;Maxime Chamberland,&nbsp;Adam Anderson,&nbsp;Bennett Landman,&nbsp;Maxime Descoteaux","doi":"10.1002/hbm.70282","DOIUrl":"https://doi.org/10.1002/hbm.70282","url":null,"abstract":"<p>The perivascular space (PVS) is integral to glymphatic function, facilitating fluid exchange and waste clearance in the brain. Diffusion tensor imaging along the perivascular space (DTI-ALPS) has been proposed as a noninvasive marker of perivascular diffusion, yet its specificity remains unclear. ALPS measures assume radial symmetry in white matter (characterized by equal transverse diffusion eigenvalues, <i>λ</i>₂ = <i>λ</i>₃) and interpret deviations (i.e., radial <i>asymmetry,</i> where <i>λ</i>₂ &gt; <i>λ</i>₃) as reflecting PVS contributions. However, anatomical and microstructural confounds may influence these metrics. We systematically evaluated potential biases in ALPS-derived measures using high-resolution, multishell diffusion MRI from the Human Connectome Project (HCP) and high-field imaging. Specifically, we examined (1) the prevalence of radial asymmetry across white matter, (2) the influence of crossing fibers on ALPS indices, (3) the impact of axonal undulations and dispersion, and (4) the spatial alignment of vasculature with white matter in ALPS-associated regions. Radial asymmetry is widespread across white matter and persists even at high <i>b</i>-values, suggesting a dominant contribution from axonal geometry rather than faster PVS-specific diffusion. Crossing fibers significantly inflate ALPS indices, with greater radial asymmetry observed in regions with a greater prevalence of crossing fibers. Furthermore, anisotropic axonal dispersion and undulations introduce systematic asymmetry independent of perivascular diffusion. Finally, high-resolution vascular imaging reveals substantial heterogeneity in medullary vein orientation, challenging the assumption that PVS consistently aligns with the left–right axis in ALPS regions. ALPS indices are significantly influenced by white matter microstructure, including fiber crossings, undulations, and dispersion. These findings suggest that ALPS-derived metrics may not provide a direct measure of glymphatic function but rather reflect underlying axonal geometry. Interpretations of ALPS-derived metrics as biomarkers of glymphatic function must consider these anatomical complexities, and future studies should integrate advanced modeling approaches to disentangle perivascular contributions from white matter structure.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 10","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70282","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144573678","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":"Relational Integration Demands Are Tracked by Temporally Delayed Neural Representations in Alpha and Beta Rhythms Within Higher-Order Cortical Networks","authors":"Conor Robinson,&nbsp;Luca Cocchi,&nbsp;Takuya Ito,&nbsp;Luke Hearne","doi":"10.1002/hbm.70272","DOIUrl":"https://doi.org/10.1002/hbm.70272","url":null,"abstract":"<p>Relational reasoning is the ability to infer and understand the relations between multiple elements. In humans, this ability supports higher cognitive functions and is linked to fluid intelligence. Relational complexity (RC) is a cognitive framework that offers a generalisable method for classifying the complexity of reasoning problems. To date, increased RC has been linked to static patterns of brain activity supported by the frontoparietal system, but limited work has assessed the multivariate spatiotemporal dynamics that code for RC. To address this, we conducted representational similarity analysis in two independent neuroimaging datasets (Dataset 1 fMRI, <i>n</i> = 40; Dataset 2 EEG, <i>n</i> = 45), where brain activity was recorded while participants completed a visuospatial reasoning task that included different levels of RC (Latin Square Task). Our findings revealed that spatially, RC representations were widespread, peaking in brain networks associated with higher-order cognition (frontoparietal, dorsal-attention, and cingulo-opercular). Temporally, RC was represented in the 2.5–4.1 s post-stimuli window and emerged in the alpha and beta frequency range. Finally, multimodal fusion analysis demonstrated that shared variability within EEG-fMRI signals within higher-order cortical networks were better explained by the theorized RC model, relative to a model of cognitive effort (CE). Altogether, the results further our understanding of the neural representations supporting relational processing, highlight the spatially distributed coding of RC and CE across cortical networks, and emphasize the importance of late-stage, frequency-specific neural dynamics in resolving RC.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 10","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70272","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144573171","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}