NeuroImage最新文献

{"title":"Abnormal dynamic functional networks during pain-free periods: Resting-state co-activation pattern analysis in primary dysmenorrhea","authors":"Huiping Liu ,&nbsp;Xing Su ,&nbsp;Meiling Shang ,&nbsp;Ling Ma ,&nbsp;Weixian Bai ,&nbsp;Hui Wang ,&nbsp;Lu Quan ,&nbsp;Youjun Li ,&nbsp;Zigang Huang ,&nbsp;Jiaxi He ,&nbsp;Wanghuan Dun ,&nbsp;Yuchen Zhang","doi":"10.1016/j.neuroimage.2025.121009","DOIUrl":"10.1016/j.neuroimage.2025.121009","url":null,"abstract":"<div><div>Chronic pain alters the configuration of brain functional networks. Primary dysmenorrhea (PDM) is a form of chronic visceral pain, which has been identified spatial alterations in brain functional networks using static functional connectivity analysis methods. However, the dynamics alterations of brain functional networks during pain-free periovulation phase remain unclear. Using the co-activation pattern (CAP) method, we investigated the dynamic network characteristics of brain functional networks and their relationship with pain-related emotions in a sample of 59 women with PDM and 57 demographically matched healthy controls (HCs) during the pain-free periovulation phase. We observed that patients with PDM showed significant alterations in brain dynamics compared to HCs in the slow-4 (0.027–0.073 Hz) frequency band during the pain-free periovulation phase. Additionally, the fraction of time for CAP state 2 was positively correlated with the Pain Catastrophizing Scale-helplessness score, while the persistence time for CAP state 1 was positively correlated with the McGill Pain Questionnaire score. Our results provide new insights, suggesting that the atypical brain functional network dynamics may serve as a potential biological marker of patients with PDM during the pain-free periovulation phase.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"306 ","pages":"Article 121009"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142966106","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":"Continuous theta-burst stimulation demonstrates language-network-specific causal effects on syntactic processing","authors":"Chenyang Gao ,&nbsp;Junjie Wu ,&nbsp;Yao Cheng ,&nbsp;Yuming Ke ,&nbsp;Xingfang Qu ,&nbsp;Mingchuan Yang ,&nbsp;Gesa Hartwigsen ,&nbsp;Luyao Chen","doi":"10.1016/j.neuroimage.2025.121014","DOIUrl":"10.1016/j.neuroimage.2025.121014","url":null,"abstract":"<div><div>Hierarchical syntactic structure processing is proposed to be at the core of the human language faculty. Syntactic processing is supported by the left fronto-temporal language network, including a core area in the inferior frontal gyrus as well as its interaction with the posterior temporal lobe (i.e., “IFG + pTL”). Moreover, during complex syntactic processes, left IFG also interacts with executive control regions, such as the superior parietal lobule (SPL). However, the functional relevance of these network interactions is largely unclear. In particular, it remains to be demonstrated whether the language network plays a specific causal role in comparatively challenging syntactic processes, separable from the interaction between IFG and other general cognitive regions (i.e., “IFG + SPL” in the present study). The present study was designed to address this question. Thirty healthy adult Chinese native speakers underwent four continuous theta-burst stimulation (cTBS) sessions: stimulation over IFG, stimulation over IFG + pTL, stimulation over IFG + SPL, and sham stimulation over IFG + irrelevant region in a pseudo-randomized order. In each session, participants were required to label the syntactic categories of jabberwocky sequences retaining real Chinese function words (e.g., “ム了ウ” is labeled as a verb phrase (VP): “[_VP [V了]N]”, similar to “ziff-ed a wug”, where “ziff” and “wug” are nonsense pseudowords, and the whole phrase is a VP). Contrasted with sham cTBS, change percentage of accuracy rates (ΔACCR%), reaction times (ΔRT%), and coefficient of variation (ΔCV%) were calculated and compared across conditions. First-order behavioral results showed a significantly higher ΔCV% after stimulating IFG + pTL compared to stimulating the IFG + SPL, indicating that syntactic processing became more unstable. Second-order representational similarity analysis (RSA) results revealed that cTBS effects on IFG + pTL selectively depended on the hierarchical embedding depth, a key measure of syntactic hierarchical complexity, whereas the effects on IFG + SPL were sensitive to the dependency length, a crucial index reflecting the working memory load. Collectively, these findings reveal the specific causal relevance of the language areas for hierarchical syntactic processing, separable from other general cognitive (such as working memory) capacities. These results shed light on the uniqueness and the specific causal role of the language network for the human language faculty, further supporting the causally separable view of the functional dissociation between the language network and the domain-general/multiple-demand network.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"306 ","pages":"Article 121014"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142966194","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":"Development and routine implementation of deep learning algorithm for automatic brain metastases segmentation on MRI for RANO-BM criteria follow-up","authors":"Loïse Dessoude ,&nbsp;Raphaëlle Lemaire ,&nbsp;Romain Andres ,&nbsp;Thomas Leleu ,&nbsp;Alexandre G. Leclercq ,&nbsp;Alexis Desmonts ,&nbsp;Typhaine Corroller ,&nbsp;Amirath Fara Orou-Guidou ,&nbsp;Luca Laduree ,&nbsp;Loic Le Henaff ,&nbsp;Joëlle Lacroix ,&nbsp;Alexis Lechervy ,&nbsp;Dinu Stefan ,&nbsp;Aurélien Corroyer-Dulmont","doi":"10.1016/j.neuroimage.2025.121002","DOIUrl":"10.1016/j.neuroimage.2025.121002","url":null,"abstract":"<div><h3>Rationale and objectives</h3><div>The RANO-BM criteria, which employ a one-dimensional measurement of the largest diameter, are imperfect due to the fact that the lesion volume is neither isotropic nor homogeneous. Furthermore, this approach is inherently time-consuming. Consequently, in clinical practice, monitoring patients in clinical trials in compliance with the RANO-BM criteria is rarely achieved. The objective of this study was to develop and validate an AI solution capable of delineating brain metastases (BM) on MRI to easily obtain, using an in-house solution, RANO-BM criteria as well as BM volume in a routine clinical setting.</div></div><div><h3>Materials (patients) and methods</h3><div>A total of 27,456 post-Gadolinium-T1 MRI from 132 patients with BM were employed in this study. A deep learning (DL) model was constructed using the PyTorch and PyTorch Lightning frameworks, and the UNETR transfer learning method was employed to segment BM from MRI.</div></div><div><h3>Results</h3><div>A visual analysis of the AI model results demonstrates confident delineation of the BM lesions. The model shows 100 % accuracy in predicting RANO-BM criteria in comparison to that of an expert medical doctor. There was a high degree of overlap between the AI and the doctor's segmentation, with a mean DICE score of 0.77. The diameter and volume of the BM lesions were found to be concordant between the AI and the reference segmentation. The user interface developed in this study can readily provide RANO-BM criteria following AI BM segmentation.</div></div><div><h3>Conclusion</h3><div>The in-house deep learning solution is accessible to everyone without expertise in AI and offers effective BM segmentation and substantial time savings.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"306 ","pages":"Article 121002"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142971711","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":"Characterizing the microstructural transition at the gray matter-white matter interface: Implementation and demonstration of age-associated differences","authors":"Joan Y Song ,&nbsp;Roman Fleysher ,&nbsp;Kenny Ye ,&nbsp;Mimi Kim ,&nbsp;Molly E Zimmerman ,&nbsp;Richard B Lipton ,&nbsp;Michael L Lipton","doi":"10.1016/j.neuroimage.2025.121019","DOIUrl":"10.1016/j.neuroimage.2025.121019","url":null,"abstract":"<div><h3>Background</h3><div>The cortical gray matter-white matter interface (GWI) is a natural transition zone where the composition of brain tissue abruptly changes and is a location for pathologic change in brain disorders. While diffusion magnetic resonance imaging (dMRI) is a reliable and well-established technique to characterize brain microstructure, the GWI is difficult to assess with dMRI due to partial volume effects and is normally excluded from such studies.</div></div><div><h3>Methods</h3><div>In this study, we introduce an approach to characterize the dMRI microstructural profile across the GWI and to assess the sharpness of the microstructural transition from cortical gray matter (GM) to white matter (WM). This analysis includes cross-sectional data from a total of 146 participants (18–91 years; mean age: 52.4 (SD 21.4); 83 (57 %) female) enrolled in two normative lifespan cohorts at Albert Einstein College of Medicine from 2019 to 2023. We compute the aggregate GWI slope for each parameter, across each of 6 brain regions (cingulate, frontal, occipital, orbitofrontal, parietal, and temporal) for each participant. The association of GWI slope in each region with age was assessed using a linear model, with biological sex as a covariate.</div></div><div><h3>Results</h3><div>We demonstrate this method captures an inherent change in fractional anisotropy (FA), axial diffusivity (AD), orientation dispersion index (ODI) and intracellular volume fraction (ICVF) across the GWI that is characterized by small variance. We identified statistically significant associations of FA slope with age in all regions (<em>p</em> &lt; 0.002 for all analyses), with FA slope magnitude inversely associated with higher age. Similar statistically significant age-related associations were found for AD slope in cingulate, occipital, and temporal regions, for ODI slope in parietal and occipital regions, and for ICVF slope in frontal, orbitofrontal, parietal, and temporal regions.</div></div><div><h3>Conclusion</h3><div>The inverse association of slope magnitude with age indicates loss of the sharp GWI transition in aging, which is consistent with processes such as dendritic pruning, axonal degeneration, and inflammation. This method overcomes techniques issues related to interrogating the GWI. Beyond characterizing normal aging, it could be applied to explore pathological effects at this crucial, yet under-researched region.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"306 ","pages":"Article 121019"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142984310","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":"Fast EEG/MEG BEM-based forward problem solution for high-resolution head models","authors":"William A. Wartman ,&nbsp;Guillermo Nuñez Ponasso ,&nbsp;Zhen Qi ,&nbsp;Jens Haueisen ,&nbsp;Burkhard Maess ,&nbsp;Thomas R. Knösche ,&nbsp;Konstantin Weise ,&nbsp;Gregory M. Noetscher ,&nbsp;Tommi Raij ,&nbsp;Sergey N. Makaroff","doi":"10.1016/j.neuroimage.2024.120998","DOIUrl":"10.1016/j.neuroimage.2024.120998","url":null,"abstract":"<div><div>A fast BEM (boundary element method) based approach is developed to solve an EEG/MEG forward problem for a modern high-resolution head model. The method utilizes a charge-based BEM accelerated by the fast multipole method (BEM-FMM) with an adaptive mesh pre-refinement method (called <em>b</em>-refinement) close to the singular dipole source(s). No costly matrix-filling or direct solution steps typical for the standard BEM are required; the method generates on-skin voltages as well as MEG magnetic fields for high-resolution head models within 90 s after initial model assembly using a regular workstation. The forward method is validated by comparison against an analytical solution on a spherical shell model as well as comparison against a full <em>h</em>-refinement method on realistic 1M facet human head models, both of which yield agreement to within 5 % for the EEG skin potential and MEG magnetic fields. The method is further applied to an EEG source localization (inverse) problem for real human data, and a reasonable source dipole distribution is found.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"306 ","pages":"Article 120998"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142927525","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":"Cardiorespiratory dynamics in the brain: Review on the significance of cardiovascular and respiratory correlates in functional MRI signal","authors":"Mahathi Kandimalla ,&nbsp;Seokbeen Lim ,&nbsp;Jay Thakkar ,&nbsp;Sannidhi Dewan ,&nbsp;Daehun Kang ,&nbsp;Myung-Ho In ,&nbsp;Hang Joon Jo ,&nbsp;Dong Pyo Jang ,&nbsp;Zuzana Nedelska ,&nbsp;Maria I. Lapid ,&nbsp;Yunhong Shu ,&nbsp;Cheon-Pyung ,&nbsp;Petrice M. Cogswell ,&nbsp;Val J. Lowe ,&nbsp;Jeyeon Lee ,&nbsp;Hoon-Ki Min","doi":"10.1016/j.neuroimage.2024.121000","DOIUrl":"10.1016/j.neuroimage.2024.121000","url":null,"abstract":"<div><div>Cardiorespiratory signals have long been treated as \"noise\" in functional magnetic resonance imaging (fMRI) research, with the goal of minimizing their impact to isolate neural activity. However, there is a growing recognition that these signals, once seen as confounding variables, provide valuable insights into brain function and overall health. This shift reflects the dynamic interaction between the cardiovascular, respiratory, and neural systems, which together support brain activity. In this review, we explore the role of cardiorespiratory dynamics—such as heart rate variability (HRV), respiratory sinus arrhythmia (RSA), and changes in blood flow, oxygenation, and carbon dioxide levels—embedded within fMRI signals. These physiological signals reflect critical aspects of neurovascular coupling and are influenced by factors such as physiological stress, breathing patterns, and age-related changes. We also discuss the complexities of distinguishing these signals from neuronal activity in fMRI data, given their significant contribution to signal variability and interactions with cerebrospinal fluid (CSF). Recognizing the influence of these cardiorespiratory dynamics is crucial for improving the interpretation of fMRI data, shedding light on heart-brain and respiratory-brain connections, and enhancing our understanding of circulation, oxygen delivery, and waste elimination within the brain.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"306 ","pages":"Article 121000"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142927518","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":"Task-specific temporal prediction mechanisms revealed by motor and electroencephalographic indicators","authors":"Alana Arrouet ,&nbsp;José Eduardo Marques-Carneiro ,&nbsp;Pierre Marquet ,&nbsp;Anne Giersch","doi":"10.1016/j.neuroimage.2024.120982","DOIUrl":"10.1016/j.neuroimage.2024.120982","url":null,"abstract":"<div><div>Time prediction is pervasive, and it is unclear whether it is supra-modal or task-specific. This study aimed to investigate the role of motor temporal prediction in preparing to stop a movement following a sensory stimulus. Participants performed a straight-line movement with their finger until a target signal, which occurred after a short or long foreperiod. In one task, participants changed movement direction between trials (multidirectional task), while in the other, they always moved in the same direction (unidirectional task). The motor trajectory and EEG signals were continuously recorded. During the foreperiod, participants slowed down their movement, reflecting preparation to stop. To assess the influence of motor temporal prediction we examined how a given trial influences performance on the subsequent trial (sequential effect) when the movement changes or stays the same (multi- vs. unidirectional). In the unidirectional task, but not in the multidirectional task we found sequential effects on several behavioural parameters. In contrast, sequential effects were observed in both tasks on EEG results. This study revealed a temporal prediction related to motor movement (behavioural indicators), and a temporal preparation while waiting for the target (EEG indicator). These findings highlight the importance of considering various temporal prediction mechanisms.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"306 ","pages":"Article 120982"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142872670","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 correlates of olfactory working memory in the human brain","authors":"Zhuofeng Li ,&nbsp;Shu-bin Li ,&nbsp;Shaozhen Tan ,&nbsp;Lu-lu Liu ,&nbsp;Chao Yan ,&nbsp;Lai-quan Zou","doi":"10.1016/j.neuroimage.2025.121005","DOIUrl":"10.1016/j.neuroimage.2025.121005","url":null,"abstract":"<div><div>Previous research has revealed that the insula, pallidum, thalamus, hippocampus, middle frontal gyrus, and supplementary motor area are activated during odor memory and that the performance of olfactory working memory is affected by the verbalization of odors. However, the neural mechanisms underlying olfactory working memory and the role of verbalization in olfactory working memory are not fully understood. Twenty-nine participants were enrolled in a study to complete olfactory and visual n-back tasks using high- and low-verbalizability stimuli while undergoing fMRI imaging. The behavioral results showed that the participants achieved greater accuracy in the visual rather than olfactory n-back task. We observed increased activation in the precentral gyrus, superior frontal gyrus, middle frontal gyrus, supplementary motor area, and inferior parietal gyrus during olfactory working memory. Interestingly, decreased activation was observed in the olfactory 2-back task versus the 0-back task. Moreover, the left angular gyrus and inferior parietal gyrus were more strongly activated during processing of olfactory working memory using high-verbalizability odors. In conclusion, olfactory working memory engages cross-modal regions to facilitate responses, is involved in the monitoring and manipulation of information during working memory, and boasts a unique activation pattern that is different from that of visual working memory. Semantic information supports the representation of odor information in the working memory system.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"306 ","pages":"Article 121005"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142952273","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":"Repairbads: An automatic and adaptive method to repair bad channels and segments for OPM-MEG","authors":"Fulong Wang ,&nbsp;Yujie Ma ,&nbsp;Tianyu Gao ,&nbsp;Yue Tao ,&nbsp;Ruonan Wang ,&nbsp;Ruochen Zhao ,&nbsp;Fuzhi Cao ,&nbsp;Yang Gao ,&nbsp;Xiaolin Ning","doi":"10.1016/j.neuroimage.2024.120996","DOIUrl":"10.1016/j.neuroimage.2024.120996","url":null,"abstract":"<div><div>The optically pumped magnetometer (OPM) based magnetoencephalography (MEG) system offers advantages such as flexible layout and wearability. However, the position instability or jitter of OPM sensors can result in bad channels and segments, which significantly impede subsequent preprocessing and analysis. Most common methods directly reject or interpolate to repair these bad channels and segments. Direct rejection leads to data loss, and when the number of sensors is limited, interpolation using neighboring sensors can cause significant signal distortion and cannot repair bad segments present in all channels. Therefore, most existing methods are unsuitable for OPM-MEG systems with fewer channels. We introduce an automatic bad segments and bad channels repair method for OPM-MEG, called Repairbads. This method aims to repair all bad data and reduce signal distortion, especially capable of automatically repairing bad segments present in all channels simultaneously. Repairbads employs Riemannian Potato combined with joint decorrelation to project out artifact components, achieving automatic bad segment repair. Then, an adaptive algorithm is used to segment the signal into relatively stable noise data chunks, and the source-estimate-utilizing noise-discarding algorithm is applied to each chunk to achieve automatic bad channel repair. We compared the performance of Repairbads with the Autoreject method on both simulated and real auditory evoked data, using five evaluation metrics for quantitative assessment. The results demonstrate that Repairbads consistently outperforms across all five metrics. In both simulated and real OPM-MEG data, Repairbads shows better performance than current state-of-the-art methods, reliably repairing bad data with minimal distortion. The automation of this method significantly reduces the burden of manual inspection, promoting the automated processing and clinical application of OPM-MEG.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"306 ","pages":"Article 120996"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142952306","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":"Functional connectome gradient of prefrontal cortex as biomarkers of high risk for internet gaming disorder","authors":"Xinwen Wen ,&nbsp;Lirong Yue ,&nbsp;Zhe Du ,&nbsp;Jiahao Zhao ,&nbsp;Mengjiao Ge ,&nbsp;Cunfeng Yuan ,&nbsp;Hongmei Wang ,&nbsp;Qinghua He ,&nbsp;Kai Yuan","doi":"10.1016/j.neuroimage.2025.121010","DOIUrl":"10.1016/j.neuroimage.2025.121010","url":null,"abstract":"<div><div>Adolescents and young adults are considered a high-risk group for internet gaming disorder (IGD). Early screening for high-risk individuals with IGD and exploring the underlying neural mechanisms is an effective strategy to reduce the harm of IGD. We recruited 219 non-internet gaming addicted college students and evaluated them with magnetic resonance imaging, followed by a two-year longitudinal follow-up. We used functional connectome gradient (FCG) to capture the macroscopic hierarchical organization of human brain. Canonical correlation analysis was employed to identify components mapping relationships between FCG and behavioral scores. Consequently, K-means clustering was used to define distinct subtypes. The risk of developing IGD and FCG patterns were compared among the subtypes. Three subtypes were identified and subtype 3 exhibited the highest risk for developing IGD according to the occurrence rates of IGD two years later: (1) subtype 1 (5.3 %, 4 participants), (2) subtype 2 (10.8 %, 9 participants), (3) subtype 3 (20 %, 12 participants). The abnormal FCG in the inferior frontal gyrus and posterior cingulate cortex at baseline were observed in subtype 3, which were correlated with impulsivity. These findings advanced understanding of the biological and behavioral heterogeneity associated with developing of IGD, and represented a promising step toward the prediction of high-risk individuals.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"306 ","pages":"Article 121010"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142971720","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}