NeuroImage最新文献

{"title":"Electroencephalography-guided transcranial direct current stimulation improves picture-naming performance.","authors":"Tomoya Gyoda, Ryuichiro Hashimoto, Satoru Inagaki, Nobuhiro Tsushi, Takashi Kitao, Ludovico Minati, Natsue Yoshimura","doi":"10.1016/j.neuroimage.2024.120997","DOIUrl":"https://doi.org/10.1016/j.neuroimage.2024.120997","url":null,"abstract":"<p><p>Transcranial direct current stimulation (tDCS) is a potential method for improving verbal function by stimulating Broca's area. Previous studies have shown the effectiveness of using functional magnetic resonance imaging (fMRI) to optimize the stimulation site, but it is unclear whether similar optimization can be achieved using scalp electroencephalography (EEG). Here, we investigated whether tDCS targeting a brain area identified by EEG can improve verbalization performance during a picture-naming task. In Experiment 1, EEG and fMRI data were acquired during a naming task with 21 participants. Comparison of EEG and fMRI data showed overlap in the highest areas of activation for 80% of the participants. In Experiment 2, tDCS was administered to 15 participants using a crossover design, with stimulation targeting the EEG-guided area, Broca's area, and sham conditions. Our findings indicated that tDCS targeting the EEG-guided area significantly improved lexical retrieval speed compared with stimulation over Broca's area and sham conditions. These results support the validity of EEG-based area identification and its use in optimizing the effects of tDCS on improving language function.</p>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":" ","pages":"120997"},"PeriodicalIF":4.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142952344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","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, Yujie Ma, Tianyu Gao, Yue Tao, Ruonan Wang, Ruochen Zhao, Fuzhi Cao, Yang Gao, Xiaolin Ning","doi":"10.1016/j.neuroimage.2024.120996","DOIUrl":"https://doi.org/10.1016/j.neuroimage.2024.120996","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"306 ","pages":"120996"},"PeriodicalIF":4.7,"publicationDate":"2025-01-06","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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Glucocorticoid receptor gene (NR3C1) methylation, childhood maltreatment, multilevel reward responsiveness and depressive and anxiety symptoms: A neuroimaging epigenetic study.","authors":"Yajing Xu, Shan Yang, Cong Cao","doi":"10.1016/j.neuroimage.2025.121003","DOIUrl":"10.1016/j.neuroimage.2025.121003","url":null,"abstract":"<p><strong>Background: </strong>Although epigenomic and environment interactions (Epigenome × Environment; Epi × E) might constitute a novel mechanism underlying reward processing, direct evidence is still scarce. We conducted the first longitudinal study to investigate the extent to which DNA methylation of a stress-related gene-NR3C1-interacts with childhood maltreatment in association with young adult reward responsiveness (RR) and the downstream risk of depressive (anhedonia dimension in particular) and anxiety symptoms.</p><p><strong>Method: </strong>A total of 192 Chinese university students aged 18∼25 (M_age = 21.08 ± 1.91 years; 59.4% females) were followed in two waves. Reward positivity (RewP) and its time‒frequency components were elicited via a classic monetary reward task. Cytosine methylation in the promoter exon 1F of NR3C1 (NR3C1-1F) was sequenced via buccal cells. Childhood maltreatment, self-reported RR and depressive and anxiety symptoms were assessed via questionnaires.</p><p><strong>Results: </strong>NR3C1-1F methylation significantly interacted with childhood maltreatment on RewP but not the delta and theta components or self-reported RR. The severity and exposure number of childhood maltreatment were negatively associated with RewP among individuals with heightened NR3C1-1F methylation but positively associated with RewP among individuals with blunted NR3C1-1F methylation, demonstrating a \"goodness-of-fit\" interaction. This interaction was specifically linked with anhedonia dimension but not with total scores of depressive or anxiety symptoms.</p><p><strong>Conclusions: </strong>The current findings provide preliminary evidence for an Epi × E interaction underlying reward processing, highlight cross-level analyses of electrophysiological signals and advance knowledge of the biological foundation of stress-induced reward function and relevant symptoms. However, caution should be paid to the generalizability of these findings in high-risk clinical samples given the high-functioning characteristic of the present sample.</p>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":" ","pages":"121003"},"PeriodicalIF":4.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142952348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","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, Yalin Wang, Zhong-Lin Lu","doi":"10.1016/j.neuroimage.2024.120994","DOIUrl":"10.1016/j.neuroimage.2024.120994","url":null,"abstract":"<p><p>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 >1,000 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 R² achieved by the qPRF compared to Benson et al. (2018) was negligible, with a median of 0.025% (R² units being between 0% and 100%). In general, the qPRF yielded a slightly better fitting solution, achieving a greater R² 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.</p>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"306 ","pages":"120994"},"PeriodicalIF":4.7,"publicationDate":"2025-01-04","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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extended homogeneous field correction method based on oblique projection in OPM-MEG.","authors":"Fulong Wang, Fuzhi Cao, Yujie Ma, Ruochen Zhao, Ruonan Wang, Nan An, Min Xiang, Dawei Wang, Xiaolin Ning","doi":"10.1016/j.neuroimage.2024.120991","DOIUrl":"10.1016/j.neuroimage.2024.120991","url":null,"abstract":"<p><p>Optically pumped magnetometer-based magnetoencephalography (OPM-MEG) is an novel non-invasive functional imaging technique that features more flexible sensor configurations and wearability; however, this also increases the requirement for environmental noise suppression. Subspace projection algorithms are widely used in MEG to suppress noise. However, in OPM-MEG systems with a limited number of channels, subspace projection methods that rely on spatial oversampling exhibit reduced performance. The homogeneous field correction (HFC) method resolves this problem by constructing a low-rank spatial model; however, it cannot address complex non-homogeneous noise. The spatiotemporal extended homogeneous field correction (teHFC) method uses multiple orthogonal projections to suppress disturbances. However, the signal and noise subspace are not completely orthogonal, limiting enhancement in the capabilities of the teHFC. Therefore, we propose an extended homogeneous field correction method based on oblique projection (opHFC), which overcomes the issue of non-orthogonality between the signal and noise subspace, enhancing the ability to suppress complex interferences. The opHFC constructs an oblique projection operator that divides the signals into internal and external components, eliminating complex interferences through temporal extension. We compared the opHFC with four benchmark methods by simulations and auditory and somatosensory evoked OPM-MEG experiments. The results demonstrate that opHFC provides superior noise suppression with minimal distortion, enhancing the signal quality at the sensor and source levels. Our method offers a novel approach to reducing interference in OPM-MEG systems, expanding their application scenarios, and providing high-quality signals for scientific research and clinical applications based on OPM-MEG.</p>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":" ","pages":"120991"},"PeriodicalIF":4.7,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142932164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Local structural-functional coupling with counterfactual explanations for epilepsy prediction.","authors":"Jiashuang Huang, Shaolong Wei, Zhen Gao, Shu Jiang, Mingliang Wang, Liang Sun, Weiping Ding, Daoqiang Zhang","doi":"10.1016/j.neuroimage.2024.120978","DOIUrl":"10.1016/j.neuroimage.2024.120978","url":null,"abstract":"<p><p>The structural-functional brain connections coupling (SC-FC coupling) describes the relationship between white matter structural connections (SC) and the corresponding functional activation or functional connections (FC). It has been widely used to identify brain disorders. However, the existing research on SC-FC coupling focuses on global and regional scales, and few studies have investigated the impact of brain disorders on this relationship from the perspective of multi-brain region cooperation (i.e., local scale). Here, we propose the local SC-FC coupling pattern for brain disorders prediction. Compared with previous methods, the proposed patterns quantify the relationship between SC and FC in terms of subgraphs rather than whole connections or single brain regions. Specifically, we first construct structural and functional connections using diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) data, subsequently organizing them into a multimodal brain network. Then, we extract subgraphs from these multimodal brain networks and select them based on their frequencies to generate local SC-FC coupling patterns. Finally, we employ these patterns to identify brain disorders while refining abnormal patterns to generate counterfactual explanations. Results on a real epilepsy dataset suggest that the proposed method not only outperforms existing methods in accuracy but also provides insights into the local SC-FC coupling pattern and their changes in brain disorders. Code available at https://github.com/UAIBC-Brain/Local-SC-FC-coupling-pattern.</p>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":" ","pages":"120978"},"PeriodicalIF":4.7,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142927533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Two distinct neural pathways for mechanical versus digital technology.","authors":"Giovanni Federico, Mathieu Lesourd, Arnaud Fournel, Alexandre Bluet, Chloé Bryche, Maximilien Metaireau, Dario Baldi, Maria Antonella Brandimonte, Andrea Soricelli, Yves Rossetti, François Osiurak","doi":"10.1016/j.neuroimage.2024.120971","DOIUrl":"10.1016/j.neuroimage.2024.120971","url":null,"abstract":"<p><p>Technology pervades every aspect of our lives, making it crucial to investigate how the human mind deals with it. Here we examine the cognitive and neural foundations of technological cognition. In the first fMRI experiment, participants viewed videos depicting the use of mechanical tools (e.g., a screwdriver) and digital tools (e.g., a smartphone) compared to simple object movements. Results revealed a key dissociation: mechanical tools extensively activated the dorsal and ventro-dorsal visual streams, which are motor- and action-oriented brain systems. Conversely, digital tools largely engaged the ventral visual stream, associated with conceptual and social cognition. A second behavioral experiment showed a pronounced tendency to anthropomorphize digital tools. A third experiment involving a priming task confirmed that digital tools activate the social brain. The discovery of two different neurocognitive systems for mechanical versus digital technology offers new insights into human-technology interaction and its implications for the evolution of the human mind.</p>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":" ","pages":"120971"},"PeriodicalIF":4.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142818746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Similarity or complementarity? Understanding marital relationships in terms of sexual dimorphism in brain morphometry and gender roles.","authors":"Lei Li, Xinyue Huang, Qingyu Zheng, Jinming Xiao, Xiaolong Shan, Huafu Chen, Xujun Duan","doi":"10.1016/j.neuroimage.2024.120974","DOIUrl":"10.1016/j.neuroimage.2024.120974","url":null,"abstract":"<p><p>\"Birds of a feather flock together\" and \"opposites attract\" are two contrasting statements regarding interpersonal relationships. Sex differences provide a theoretical integration of these two conflicting statements. Here, we explored the relationship between marital satisfaction and sex differences in social attributes and neuroanatomical characteristics in 48 married couples. Sexually dimorphic neuroanatomy was investigated for gray matter volume (GMV), which was estimated by voxel-based morphometry. The brain regions that showed typically larger GMV in males compared with that in females were defined as the male-typical brain regions; otherwise, they were defined as the female-typical brain regions. We found that masculine gender roles and the individual deviation index (IDI) of the GMV in the male-typical brain region were positively correlated with marital satisfaction in males but were negatively correlated in females, demonstrating the \"complementarity\" nature of masculine characteristics, which was further supported by the negative correlation between couple-wise morphological similarity in male-typical brain region and marital satisfaction. Conversely, feminine characteristics reflected the \"similarity\" nature of married couples; i.e., feminine gender roles and IDI in the female-typical brain region were positively correlated with marital satisfaction in both males and females, and couple-wise morphological similarity in the female-typical brain region was positively correlated with marital satisfaction. The actor-partner interdependence model also supports the similarity/complementarity hypothesis. Additionally, the sexual dimorphism of brain morphometry and marital satisfaction were found to share a similar transcriptional mechanism. Our findings highlight the relationship among gender roles, brain morphology, and marital satisfaction, providing important implications for understanding marital bonding.</p>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":" ","pages":"120974"},"PeriodicalIF":4.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142829461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RegBoost: Enhancing mouse brain image registration using geometric priors and Laplacian interpolation.","authors":"Atchuth Naveen Chilaparasetti, Andy Thai, Pan Gao, Xiangmin Xu, M Gopi","doi":"10.1016/j.neuroimage.2024.120981","DOIUrl":"10.1016/j.neuroimage.2024.120981","url":null,"abstract":"<p><p>We show in this work that incorporating geometric features and geometry processing algorithms for mouse brain image registration broadens the applicability of registration algorithms and improves the registration accuracy of existing methods. We introduce the preprocessing and postprocessing steps in our proposed framework as RegBoost. We develop a method to align the axis of 3D image stacks by detecting the central planes that pass symmetrically through the image volumes. We then find geometric contours by defining external and internal structures to facilitate image correspondences. We establish Dirichlet boundary conditions at these correspondences and find the displacement map throughout the volume using Laplacian interpolation. We discuss the challenges in our standalone framework and demonstrate how our new approaches can improve the results of existing image registration methods. We expect our new approach and algorithms will have critical applications in brain mapping projects.</p>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":" ","pages":"120981"},"PeriodicalIF":4.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142896296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ACTION: Augmentation and computation toolbox for brain network analysis with functional MRI.","authors":"Yuqi Fang, Junhao Zhang, Linmin Wang, Qianqian Wang, Mingxia Liu","doi":"10.1016/j.neuroimage.2024.120967","DOIUrl":"10.1016/j.neuroimage.2024.120967","url":null,"abstract":"<p><p>Functional magnetic resonance imaging (fMRI) has been increasingly employed to investigate functional brain activity. Many fMRI-related software/toolboxes have been developed, providing specialized algorithms for fMRI analysis. However, existing toolboxes seldom consider fMRI data augmentation, which is quite useful, especially in studies with limited or imbalanced data. Moreover, current studies usually focus on analyzing fMRI using conventional machine learning models that rely on human-engineered fMRI features, without investigating deep learning models that can automatically learn data-driven fMRI representations. In this work, we develop an open-source toolbox, called Augmentation and Computation Toolbox for braIn netwOrk aNalysis (ACTION), offering comprehensive functions to streamline fMRI analysis. The ACTION is a Python-based and cross-platform toolbox with graphical user-friendly interfaces. It enables automatic fMRI augmentation, covering blood-oxygen-level-dependent (BOLD) signal augmentation and brain network augmentation. Many popular methods for brain network construction and network feature extraction are included. In particular, it supports constructing deep learning models, which leverage large-scale auxiliary unlabeled data (3,800+ resting-state fMRI scans) for model pretraining to enhance model performance for downstream tasks. To facilitate multi-site fMRI studies, it is also equipped with several popular federated learning strategies. Furthermore, it enables users to design and test custom algorithms through scripting, greatly improving its utility and extensibility. We demonstrate the effectiveness and user-friendliness of ACTION on real fMRI data and present the experimental results. The software, along with its source code and manual, can be accessed online.</p>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":" ","pages":"120967"},"PeriodicalIF":4.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11726259/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142882558","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}