NeuroImage最新文献

{"title":"Right inferior frontal cortex and preSMA in response inhibition: An investigation based on PTC model","authors":"Lili Wu ,&nbsp;Mengjie Jiang ,&nbsp;Min Zhao ,&nbsp;Xin Hu ,&nbsp;Jing Wang ,&nbsp;Kaihua Zhang ,&nbsp;Ke Jia ,&nbsp;Fuxin Ren ,&nbsp;Fei Gao","doi":"10.1016/j.neuroimage.2025.121004","DOIUrl":"10.1016/j.neuroimage.2025.121004","url":null,"abstract":"<div><div>Response inhibition is an essential component of cognitive function. A large body of literature has used neuroimaging data to uncover the neural architecture that regulates inhibitory control in general and movement cancelation. The presupplementary motor area (preSMA) and the right inferior frontal cortex (rIFC) are the key nodes in the inhibitory control network. However, how these two regions contribute to response inhibition remains controversial. Based on the Pause-then-Cancel Model (PTC), this study employed functional magnetic resonance imaging (fMRI) to investigate the functional specificity of two regions in the stopping process. The Go/No-Go task (GNGT) and the Stop Signal Task (SST) were administered to the same group of participants. We used the GNGT to dissociate the pause process and both the GNGT and the SST to investigate the inhibition mechanism. Imaging data revealed that response inhibition produced by both tasks activated the preSMA and rIFC. Furthermore, an across-participants analysis showed that increased activation in the rIFC was associated with a delay in the go response in the GNGT. In contrast, increased activation in the preSMA was associated with good inhibition efficiency via the striatum in both GNGT and SST. These behavioral and imaging findings support the PTC model of the role of rIFC and preSMA, that the former is involved in a pause process to delay motor responses, whereas the preSMA is involved in the stopping of motor responses.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"306 ","pages":"Article 121004"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142971729","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":"qPRF: A system to accelerate population receptive field modeling","authors":"Sebastian Waz ,&nbsp;Yalin Wang ,&nbsp;Zhong-Lin Lu","doi":"10.1016/j.neuroimage.2024.120994","DOIUrl":"10.1016/j.neuroimage.2024.120994","url":null,"abstract":"<div><div>BOLD response can be fitted using the population receptive field (PRF) model to reveal how visual input is represented on the cortex (Dumoulin and Wandell, 2008). Fitting the PRF model costs considerable time, often requiring days to analyze BOLD signals for a small cohort of subjects. We introduce the qPRF (“quick PRF”), a system for accelerated PRF modeling that reduced the computation time by a factor <span><math><mrow><mo>&gt;</mo><mn>1</mn><mo>,</mo><mn>000</mn></mrow></math></span> without losing goodness-of-fit when compared to another widely available PRF modeling package (Kay et al., 2013) on a benchmark of data from the Human Connectome Project (HCP; Van Essen et al. (2013). The system achieves this level of acceleration by pre-computing a tree-like data structure, which it rapidly searches during the fitting step for an optimal parameter combination. We tested the method on a constrained four-parameter version of the PRF model (Strategy 1 herein) and an unconstrained five-parameter PRF model, which the qPRF fitted at comparable speed (Strategy 2). We show how an additional search step can guarantee optimality of qPRF solutions with little additional time cost (Strategy 3). To assess the quality of qPRF solutions, we compared our Strategy 1 solutions to those provided by Benson et al. (2018) who performed a similar four-parameter fit. Both hemispheres of the 181 subjects in the HCP dataset (a total of 10,753,572 vertices, each with a unique BOLD time series of 1800 frames) were analyzed by qPRF in 12.82 h on an ordinary CPU. The absolute difference in <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> achieved by the qPRF compared to Benson et al. (2018) was negligible, with a median of 0.025% (<span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> units being between 0% and 100%). In general, the qPRF yielded a slightly better fitting solution, achieving a greater <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> on 70.2% of vertices. We also assess the qPRF method’s model-recovery ability using a simulated dataset. The qPRF may facilitate the development and use of more elaborate models based on the PRF framework and may pave the way for novel clinical applications.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"306 ","pages":"Article 120994"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962214","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 impact of channel density, inverse solutions, connectivity metrics and calibration errors on OPM-MEG connectivity analysis: A simulation study","authors":"Shengjie Qi ,&nbsp;Xinda Song ,&nbsp;Le Jia ,&nbsp;Hongyu Cui ,&nbsp;Yuchen Suo ,&nbsp;Tengyue Long ,&nbsp;Zhendong Wu ,&nbsp;Xiaolin Ning","doi":"10.1016/j.neuroimage.2025.121056","DOIUrl":"10.1016/j.neuroimage.2025.121056","url":null,"abstract":"<div><div>Magnetoencephalography (MEG) systems based on optically pumped magnetometers (OPMs) have rapidly developed in the fields of brain function, health, and disease. Functional connectivity analysis related to the resting-state has gained popularity as a field of research in recent years. Several studies have attempted to use OPM-based MEG (OPM-MEG) for brain network estimation research; however, the choice of source connectivity analysis pipeline may lead to outcome variability. Several methods and related parameters must be selected carefully at each step of the analysis. Therefore, this study assessed the effect of such analytical variability on the OPM-MEG connectivity analysis by conducting simulations. Synthetic MEG data corresponding to two default mode networks (DMN) with six or ten DMN regions were generated using the Gaussian Graphical Spectral (GGS) model. Six intersensor spacings were constructed, and six inverse algorithms and six functional connectivity measures were selected to assess their impact on the network reconstruction accuracy. Three potential sources of error – errors in the sensor gain, crosstalk, and angular errors of the sensitive axis of the OPM – were also assessed. Analytical variability with regard to the tested intersensor spacings, inverse solutions, and functional connectivity measures led to high result variability. Crosstalk exerted a significant impact on the accuracy, which may lead to network reconstruction failure. The accuracy improvement caused by an increase in the sensor density may be reduced by gain and angular errors. The minimum norm estimate (MNE) and weighted minimum norm estimate (wMNE) exhibited low robustness to sensor noise and calibration errors. Hence, a calibration workflow for accurate sensor parameters, such as the gain and direction of the sensitive axis, before commencing OPM-MEG measurement and a careful choice of different method combinations play crucial roles in ensuring that OPMs yield optimal results for functional connectivity analysis. A thorough framework for analyzing brain connectivity networks was provided herein.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"308 ","pages":"Article 121056"},"PeriodicalIF":4.7,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143080716","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 connection strength leads to enhancement of working memory capacity in cognitive training","authors":"Guiyang Lv ,&nbsp;Tianyong Xu ,&nbsp;Jinhang Li ,&nbsp;Ping Zhu ,&nbsp;Feiyan Chen ,&nbsp;Dongping Yang ,&nbsp;Guoguang He","doi":"10.1016/j.neuroimage.2025.121055","DOIUrl":"10.1016/j.neuroimage.2025.121055","url":null,"abstract":"<div><div>It has been widely observed that cognitive training can enhance the working memory capacity (WMC) of participants, yet the underlying mechanisms remain unexplained. Previous research has confirmed that abacus-based mental calculation (AMC) training can enhance the WMC of subjects and suggested its possible association with changes in functional connectivity. With fMRI data, we construct whole brain resting state connectivity of subjects who underwent long-term AMC training and other subjects from a control group. Their working memory capacity is simulated based on their whole brain resting state connectivity and reservoir computing. It is found that the AMC group has higher WMC than the control group, and especially the WMC involved in the frontoparietal network (FPN), visual network (VIS) and sensorimotor network (SMN) associated with the AMC training is even higher in the AMC group. However, the advantage of the AMC group disappears if the connection strengths between brain regions are neglected. The effects on WMC from the connection strength differences between the AMC and control groups are evaluated. The results show that the WMC of the control group is enhanced and achieved consistency with or even better than that the AMC group if the connection strength of the control group are weakened. And the advantage of FPN, VIS and SMN is reproduced too. In conclusion, our work reveals a correlation between reduction in functional connection strength and enhancements in the WMC of subjects undergoing cognitive training.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"308 ","pages":"Article 121055"},"PeriodicalIF":4.7,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143075268","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":"High resolution multi-delay arterial spin labeling with self-supervised deep learning denoising for pediatric choroid plexus perfusion MRI","authors":"Qinyang Shou ,&nbsp;Chenyang Zhao ,&nbsp;Xingfeng Shao ,&nbsp;Megan M Herting ,&nbsp;Danny JJ Wang","doi":"10.1016/j.neuroimage.2025.121070","DOIUrl":"10.1016/j.neuroimage.2025.121070","url":null,"abstract":"<div><div>Choroid plexus (CP) is an important brain structure that produces cerebrospinal fluid (CSF). CP perfusion has been studied using multi-delay arterial spin labeling (MD-ASL) in adults but not in pediatric populations due to the challenge of small CP size in children. Here we present a high resolution (iso2 mm) MDASL protocol with 10-minute scan time and performed test-retest scans on 21 typically developing children aged 8 to 17 years. We further proposed a Transformer-based deep learning (DL) model with k-space weighted image average (KWIA) denoised images as reference for training the model. The performance of the model was evaluated by the SNR, bias and repeatability of the fitted perfusion parameters of the CP and gray matter. The proposed method was compared to several benchmark methods including KWIA, joint denoising and reconstruction with total generalized variation (TGV) regularization, as well as another self-supervised method termed Noise2Void. The results show that the proposed Transformer model with KWIA reference can effectively denoise multi-delay ASL images, not only improving the SNR for perfusion images of each delay, but also improving the SNR for the fitted perfusion maps for visualizing and quantifying CP perfusion in children. This may facilitate the use of MDASL in neurodevelopmental studies to characterize the development of CP and glymphatic system.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"308 ","pages":"Article 121070"},"PeriodicalIF":4.7,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143075213","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":"Different cortex activation between young and middle-aged people during different type problem-solving: An EEG&fNIRS study","authors":"Mevhibe Saricaoglu ,&nbsp;Meryem Ayşe Yücel ,&nbsp;Miray Budak ,&nbsp;Ahmet Omurtag ,&nbsp;Lutfu Hanoglu","doi":"10.1016/j.neuroimage.2025.121062","DOIUrl":"10.1016/j.neuroimage.2025.121062","url":null,"abstract":"<div><div>Problem-solving strategies vary depending on the type of problem and aging. This study investigated the hemodynamic response measured by the changes in the oxyhemoglobin concentration (HbO), alpha frequency power, and their interrelation during problem-solving in healthy young and middle-aged individuals, employing combined electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) recordings. The study included 39 young and 30 middle-aged subjects. The brain activation that occurred while answering different questions was recorded using combined EEG and fNIRS. During the EEG &amp; fNIRS recording, four questions (arithmetic, general knowledge, insight, and basic operation) were used for problem-solving. Alpha power (8–13 Hz) and HbO changes were analyzed. The behavioral results indicated significant differences between age groups in various question types. While the middle-aged group performed better on the general knowledge questions, the older group performed better on the insight and four-process questions. The fNIRS results reveal significant differences in brain activation during problem-solving tasks, particularly in regions like DLPFC/TA, STG, pSSC/Wernicke, and STG/angular gyrus Wernicke's area. The young group with the highest HbO was recorded during arithmetic questions, general knowledge questions, and basic operation questions. In contrast, there was no significant highest HbO during insight questions. Similar findings were observed in the middle-aged group, with the highest HbO recorded during general knowledge questions. However, there was no significant HbO in other channels during the solving of other question types in this group. The alpha power varied across different electrodes for various question types in both young and middle-aged groups. The highest alpha frequency band power for different electrodes was recorded while solving general knowledge questions in the young group and insight questions in the middle-aged group. Finally, the EEG and fNIRS correlation results showed positive correlations between HbO and alpha frequency band power in specific brain regions while solving general knowledge questions, particularly in the middle-aged group.</div><div>The study reveals age-related differences in behavioral performance, brain activation patterns, and neural correlates during various cognitive tasks, showcasing distinct strengths between middle-aged and young individuals in specific question types.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"308 ","pages":"Article 121062"},"PeriodicalIF":4.7,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143075129","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":"UK Biobank MRI data can power the development of generalizable brain clocks: A study of standard ML/DL methodologies and performance analysis on external databases","authors":"Marco Capó ,&nbsp;Silvia Vitali ,&nbsp;Georgios Athanasiou ,&nbsp;Nicole Cusimano ,&nbsp;Daniel García ,&nbsp;Garth Cruickshank ,&nbsp;Bipin Patel ,&nbsp;Alzheimer's Disease Neuroimaging Initiative","doi":"10.1016/j.neuroimage.2025.121064","DOIUrl":"10.1016/j.neuroimage.2025.121064","url":null,"abstract":"<div><div>In this study, we present a comprehensive pipeline to train and compare a broad spectrum of machine learning and deep learning brain clocks, integrating diverse preprocessing strategies and correction terms. Our analysis also includes established methodologies which have shown success in prior UK Biobank-related studies. For our analysis we used T1-weighted MRI scans and processed de novo all images via FastSurfer, transforming them into a conformed space for deep learning and extracting image-derived phenotypes for our machine learning approaches. We rigorously evaluated these approaches both as robust age predictors for healthy individuals and as potential biomarkers for various neurodegenerative conditions, leveraging data from the UK Biobank, ADNI, and NACC datasets. To this end we designed a statistical framework to assess age prediction performance, the robustness of the prediction across cohort variability (database, machine type and ethnicity) and its potential as a biomarker for neurodegenerative conditions. Results demonstrate that highly accurate brain age models, typically utilising penalised linear machine learning models adjusted with Zhang's methodology, with mean absolute errors under 1 year in external validation, can be achieved while maintaining consistent prediction performance across different age brackets and subgroups (e.g., ethnicity and MRI machine/manufacturer). Additionally, these models show strong potential as biomarkers for neurodegenerative conditions, such as dementia, where brain age prediction achieved an AUROC of up to 0.90 in distinguishing healthy individuals from those with dementia.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"308 ","pages":"Article 121064"},"PeriodicalIF":4.7,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143075270","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":"Structural damage-driven brain compensation among near-centenarians and centenarians without dementia","authors":"Hui Tang ,&nbsp;Haichao Zhao ,&nbsp;Hao Liu ,&nbsp;Jiyang Jiang ,&nbsp;Nicole Kochan ,&nbsp;Jing Jing ,&nbsp;Henry Brodaty ,&nbsp;Wei Wen ,&nbsp;Perminder S. Sachdev ,&nbsp;Tao Liu","doi":"10.1016/j.neuroimage.2025.121065","DOIUrl":"10.1016/j.neuroimage.2025.121065","url":null,"abstract":"<div><div>Compensation has been proposed as a mechanism to explain how individuals in very old age remain able to maintain normal cognitive functioning. Previous studies have provided evidence on the role of increasing functional connectivity as a compensatory mechanism for age-related white matter damage. However, we lack direct investigation into how these mechanisms contribute to the preservation of cognition in the very old population. We examined a cohort of near-centenarians and centenarians without dementia (aged 95-103 years, n=44). We constructed a structural disconnection matrix based on the disruption of white matter pathways caused by white matter hyperintensities (WMHs), aiming to explore the relationship between functional connections, cognitive preservation and white matter damage. Our results revealed that structural damage can reliably explain the variations of functional connections or cognitive maintenance. Notably, we found significant correlations between the weights in the functional connectivity model and the weights in the cognition model. We observed positive correlations between models for brain disconnections and cognitive function in near-centenarians and centenarians. The strongest effects were found between attention and somatomotor network (SMN) (r=0.397, p&lt;0.001), memory and SMN (r=0.333 p&lt;0.001), fluency and visual network (VIS) - control network (CN) (r=0.406, p&lt;0.001), language and VIS (r=0.309, p&lt;0.001), visuospatial ability and VIS-default mode network (DMN) (r=0.464, p&lt;0.001), as well as global cognition and VIS-DMN (r=0.335, p&lt;0.001). These findings suggest that enhancement of functional connectivity may serve as a compensatory mechanism, such that it mitigates the effects of white matter damage and contributes to preserved cognitive performance in very old age.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"308 ","pages":"Article 121065"},"PeriodicalIF":4.7,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143075271","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 Test-Retest Study of Single- and Multi-Delay pCASL for Choroid Plexus Perfusion Imaging in Healthy Subjects Aged 19 to 87 Years","authors":"Zixuan Liu ,&nbsp;Qinyang Shou ,&nbsp;Kay Jann ,&nbsp;Chenyang Zhao ,&nbsp;Danny JJ Wang ,&nbsp;Xingfeng Shao","doi":"10.1016/j.neuroimage.2025.121048","DOIUrl":"10.1016/j.neuroimage.2025.121048","url":null,"abstract":"<div><div>There is a growing interest in the choroid plexus (ChP) due to its critical role in cerebrospinal fluid (CSF) production and its involvement in neurodegenerative and cerebrovascular diseases. However, comprehensive studies comparing the accuracy and reliability of single- and multi-PLD (post-labeling delay) arterial spin labeling (ASL) techniques, specifically in relation to the ChP, remain limited. This study systematically evaluated the test-retest reliability and quantification accuracy of cerebral blood flow (CBF) measurements, focusing on the ChP, using single-delay and multi-delay 3D gradient-and-spin echo (GRASE) pseudo-continuous ASL (pCASL) on 28 subjects (aged 19 to 87 years, 14 males/14 females) at 3.0 tesla. Both single-delay (2 s) and 5-PLD (0.5 – 2.5 s) pCASL scans were repeated approximately one week apart with a spatial resolution of 2.5 × 2.5 × 3 mm³. Voxel-wise and regional CBF and arterial transit time (ATT) measurements were compared to assess test-retest reliability, with a particular focus on ChP perfusion changes with age. In this study, 12.15 % of ChP voxels exhibited ATTs longer than 2 s, potentially leading to a significant underestimation of CBF using single-delay ASL. Multi-delay ASL showed improved accuracy in estimating CBF values for the ChP compared to single-delay ASL when ATT &gt; PLD. Additionally, ChP volume (mean ± std = 1.72± 0.85 ml) increased (p &lt; 0.01) and ChP perfusion (43.07±14.18 mL/100 g/min) decreased (<em>p</em> = 0.04) with age. These findings underscore the robustness of multi-delay ASL with model-fitting quantification in assessing ChP perfusion, making it the preferred method for accurate CBF and ATT estimation, particularly in regions with prolonged transit time such as ChP.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"308 ","pages":"Article 121048"},"PeriodicalIF":4.7,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143075177","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 novel method for functional brain networks based on static cerebral blood flow","authors":"Changwen Wu ,&nbsp;Yu He ,&nbsp;Junle Li ,&nbsp;Xiaofan Qiu ,&nbsp;Qihong Zou ,&nbsp;Jinhui Wang","doi":"10.1016/j.neuroimage.2025.121069","DOIUrl":"10.1016/j.neuroimage.2025.121069","url":null,"abstract":"<div><div>Cerebral blood flow (CBF) offers a quantitative and reliable measurement for brain activity and is increasingly used to study functional networks. However, current methods evaluate inter-regional relations mainly based on CBF temporal dynamics, which suffers from low signal-to-noise ratio and poor temporal resolution. Here we proposed a method to construct functional brain networks by estimating shape similarity (index by Jensen–Shannon divergence) in probability distributions of regional static CBF measured by arterial spin labeling perfusion imaging over a scanning period. Based on CBF data of 30 healthy participants from 10 visits, we found that the CBF networks exhibited non-trivial topological features (e.g., small-world organization, modular architecture, and hubs) and showed low-to-fair test-retest reliability and high between-subject consistency. We further found that interregional CBF similarities were depended on anatomical distance and differed between high- and lower-order subnetworks. Moreover, interregional CBF similarities within high-order subnetworks showed significantly lower reliability than those within low-order subnetworks. Finally, we showed that nodal degree of the CBF networks were related to regional sizes and CBF levels and spatially aligned with maps of the dopamine transporter and metabolic glutamate receptor 5 intensities, expression levels of genes primarily enriched in cholesterol-related pathways and endothelial cells, and meta-analytic activations related to memory, language, and executive function. Altogether, our proposed method provide a novel, relatively reliable, and neurobiologically meaningful means to study functional network organization of the human brain.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"308 ","pages":"Article 121069"},"PeriodicalIF":4.7,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143075170","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}