NeuroImage最新文献

{"title":"Transcranial brain atlas based on photon measurement density function in a triple-parameter standard channel space","authors":"Lijiang Wei ,&nbsp;Yang Zhao ,&nbsp;Farui Liu ,&nbsp;Yuanyuan Chen ,&nbsp;Yilong Xu ,&nbsp;Zheng Li ,&nbsp;Chaozhe Zhu","doi":"10.1016/j.neuroimage.2025.121026","DOIUrl":"10.1016/j.neuroimage.2025.121026","url":null,"abstract":"<div><div>Functional near-infrared spectroscopy (fNIRS) is a widely-used transcranial brain imaging technique in neuroscience research. Nevertheless, the lack of anatomical information from recordings poses challenges for designing appropriate optode montages and for localizing fNIRS signals to underlying anatomical regions. The photon measurement density function (PMDF) is often employed to address these issues, as it accurately measures the sensitivity of an fNIRS channel to perturbations of absorption coefficients at any brain location. However, existing PMDF-based localization methods have two limitations: (1) limited channel space, and (2) estimation based on a single standard head model, which usually differ anatomically from individuals. To overcome these limitations, this study proposes a continuous standard channel space for fNIRS and constructs a PMDF-based transcranial brain atlas (PMDF-TBA) by calculating PMDFs using MRI data from 48 adults. The PMDF-TBA contains group-averaged sensitivities of channels to gray matter and brain regions as defined in 3 atlases: Brodmann, AAL2, and LPBA40. We evaluated the prediction ability of PMDF-TBA for sensitivity of unseen individuals. The results show that it outperformed PMDFs based on single standard head models, making PMDF-TBA a more generalizable fNIRS spatial localization tool. Therefore, in the absence of individual sMRI data, PMDF-TBA can optimize optode montage design, enhance channel sensitivity in target brain regions, and assist in source localization for fNIRS data, thereby facilitating the application of fNIRS in neuroscience research.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"307 ","pages":"Article 121026"},"PeriodicalIF":4.7,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143009064","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":"Frontostriatal connectivity dynamically modulates the adaptation to environmental volatility","authors":"Yuxuan Zhang ,&nbsp;Nicholas T. Van Dam ,&nbsp;Hui Ai ,&nbsp;Pengfei Xu","doi":"10.1016/j.neuroimage.2025.121027","DOIUrl":"10.1016/j.neuroimage.2025.121027","url":null,"abstract":"<div><div>Humans adjust their learning strategies in changing environments by estimating the volatility of the reinforcement conditions. Here, we examine how volatility affects learning and the underlying functional brain organizations using a probabilistic reward reversal learning task. We found that the order of states was critically important; participants adjusted learning rate going from volatile to stable, but not from stable to volatile environments. Subjective volatility of the environment was encoded in the striatal reward system and its dynamic connections with the prefrontal control system. Flexibility, which captures the dynamic changes of network modularity in the brain, was higher in the environmental transition from volatile to stable than from stable to volatile. These findings suggest that behavioral adaptations and dynamic brain organizations in transitions between stable and volatile environments are asymmetric, providing critical insights into the way that people adapt to changing environments.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"307 ","pages":"Article 121027"},"PeriodicalIF":4.7,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143008978","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":"Multi-channel spatio-temporal graph attention contrastive network for brain disease diagnosis","authors":"Chaojun Li ,&nbsp;Kai Ma ,&nbsp;Shengrong Li ,&nbsp;Xiangshui Meng ,&nbsp;Ran Wang ,&nbsp;Daoqiang Zhang ,&nbsp;Qi Zhu","doi":"10.1016/j.neuroimage.2025.121013","DOIUrl":"10.1016/j.neuroimage.2025.121013","url":null,"abstract":"<div><div>Dynamic brain networks (DBNs) can capture the intricate connections and temporal evolution among brain regions, becoming increasingly crucial in the diagnosis of neurological disorders. However, most existing researches tend to focus on isolated brain network sequence segmented by sliding windows, and they are difficult to effectively uncover the higher-order spatio-temporal topological pattern in DBNs. Meantime, it remains a challenge to utilize the structure connectivity prior in the DBNs analysis. To address these problems, we propose a multi-channel spatio-temporal graph attention contrastive network for DBNs analysis. Specifically, we first construct dynamic brain functional networks from fMRI data with sliding windows, and embed the structural connectivity derived from diffusion tensor imaging (DTI) to the dynamic functional connectivity graph representation to construct multi-modal brain network. Second, we develop a multi-channel spatial attention contrastive network to extract topological features from the brain network within each time window. This network incorporates an intra-window graph contrastive constraint to enhance the discriminative ability of the extracted features. Moreover, temporal dependencies across windows are captured by integrating feature embeddings through a self-attention mechanism, and the inter-window recurrent contrastive constraint is devised to extract higher-order spatio-temporal topological features. Finally, a multi-layer perceptron (MLP) is used to classify the brain networks. Experiments on epilepsy and ADNI datasets show that our method outperforms several state-of-the-art approaches in diagnosing performance, and it provides discriminative graph features for related brain diseases.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"307 ","pages":"Article 121013"},"PeriodicalIF":4.7,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143009056","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":"Zero-echo time imaging achieves whole brain activity mapping without ventral signal loss in mice","authors":"Ayako Imamura ,&nbsp;Rikita Araki ,&nbsp;Yukari Takahashi ,&nbsp;Koichi Miyatake ,&nbsp;Fusao Kato ,&nbsp;Sakiko Honjoh ,&nbsp;Tomokazu Tsurugizawa","doi":"10.1016/j.neuroimage.2025.121024","DOIUrl":"10.1016/j.neuroimage.2025.121024","url":null,"abstract":"<div><div>Functional MRI (fMRI) is an important tool for investigating functional networks. However, the widely used fMRI with T2*-weighted imaging in rodents has the problem of signal lack in the lateral ventral area of forebrain including the amygdala, which is essential for not only emotion but also noxious pain. Here, we scouted the zero-echo time (ZTE) sequence, which is robust to magnetic susceptibility and motion-derived artifacts, to image activation in the whole brain including the amygdala following the noxious stimulation to the hind paw. ZTE exhibited higher temporal signal-to-noise ratios than conventional fMRI sequences. Electrical sensory stimulation of the hind paw evoked ZTE signal increase in the primary somatosensory cortex. Formalin injection into the hind paw evoked early and latent change of ZTE signals throughout the whole brain including the subregions of amygdala. Furthermore, resting-state fMRI using ZTE demonstrated the functional connectivity, including that of the amygdala. These results indicate the feasibility of ZTE for whole brain fMRI including the amygdala and we first show acute and latent activity in different subnuclei of the amygdala complex after nociceptive stimulation.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"307 ","pages":"Article 121024"},"PeriodicalIF":4.7,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142979473","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":"Left-Right Brain-Wide Asymmetry of Neuroanatomy in the Mouse Brain","authors":"Andrew Silberfeld ,&nbsp;James M. Roe ,&nbsp;Jacob Ellegood ,&nbsp;Jason P. Lerch ,&nbsp;Lily Qiu ,&nbsp;Yongsoo Kim ,&nbsp;Jong Gwan Lee ,&nbsp;William D. Hopkins ,&nbsp;Joanes Grandjean ,&nbsp;Yangming Ou ,&nbsp;Olivier Pourquié","doi":"10.1016/j.neuroimage.2025.121017","DOIUrl":"10.1016/j.neuroimage.2025.121017","url":null,"abstract":"<div><div>Left-right asymmetry of the human brain is widespread through its anatomy and function. However, limited microscopic understanding of it exists, particularly for anatomical asymmetry where there are few well-established animal models. In humans, most brain regions show subtle, population-average regional asymmetries in thickness or surface area, alongside a macro-scale twisting called the cerebral petalia in which the right hemisphere protrudes past the left. Here, we ask whether neuroanatomical asymmetries can be observed in mice, leveraging 6 mouse neuroimaging cohorts from 5 different research groups (∼3,500 animals). We found an anterior-posterior pattern of volume asymmetry with anterior regions larger on the right and posterior regions larger on the left. This pattern appears driven by similar trends in surface area and positional asymmetries, with the results together indicating a small brain-wide twisting pattern, similar to the human cerebral petalia. Furthermore, the results show no apparent relationship to known functional asymmetries in mice, emphasizing the complexity of the structure-function relationship in brain asymmetry. Our results recapitulate and extend previous patterns of asymmetry from two published studies as well as capture well-established, bilateral male-female differences in the mouse brain as a positive control. By establishing a signature of anatomical brain asymmetry in mice, we aim to provide a foundation for future studies to probe the mechanistic underpinnings of brain asymmetry seen in humans – a feature of the brain with extremely limited understanding.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"307 ","pages":"Article 121017"},"PeriodicalIF":4.7,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142971722","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":"Exploring in vivo human brain metabolism at 10.5 T: Initial insights from MR spectroscopic imaging","authors":"Lukas Hingerl ,&nbsp;Bernhard Strasser ,&nbsp;Simon Schmidt ,&nbsp;Korbinian Eckstein ,&nbsp;Guglielmo Genovese ,&nbsp;Edward J. Auerbach ,&nbsp;Andrea Grant ,&nbsp;Matt Waks ,&nbsp;Andrew Wright ,&nbsp;Philipp Lazen ,&nbsp;Alireza Sadeghi-Tarakameh ,&nbsp;Gilbert Hangel ,&nbsp;Fabian Niess ,&nbsp;Yigitcan Eryaman ,&nbsp;Gregor Adriany ,&nbsp;Gregory Metzger ,&nbsp;Wolfgang Bogner ,&nbsp;Małgorzata Marjańska","doi":"10.1016/j.neuroimage.2025.121015","DOIUrl":"10.1016/j.neuroimage.2025.121015","url":null,"abstract":"<div><h3>Introduction</h3><div>Ultra-high-field magnetic resonance (MR) systems (7 T and 9.4 T) offer the ability to probe human brain metabolism with enhanced precision. Here, we present the preliminary findings from 3D MR spectroscopic imaging (MRSI) of the human brain conducted with the world's first 10.5 T whole-body MR system.</div></div><div><h3>Methods</h3><div>Employing a custom-built 16-channel transmit and 80-channel receive MR coil at 10.5 T, we conducted MRSI acquisitions in six healthy volunteers to map metabolic compounds in the human cerebrum <em>in vivo</em>. Three MRSI protocols with different matrix sizes and scan times (4.4 × 4.4 × 4.4 mm³: 10 min, 3.4 × 3.4 × 3.4 mm³: 15 min, and 2.75×2.75×2.75 mm³: 25 min) were tested. Concentric ring trajectories were utilized for time-efficient encoding of a spherical 3D k-space with ∼4 kHz spectral bandwidth. B₀/B₁ shimming was performed based on respective field mapping sequences and anatomical T₁-weighted MRI were obtained.</div></div><div><h3>Results</h3><div>By combining the benefits of an ultra-high-field system with the advantages of free-induction-decay (FID-)MRSI, we present the first metabolic maps acquired at 10.5 T in the healthy human brain at both high (voxel size of 4.4³ mm³) and ultra-high (voxel size of 2.75³ mm³) isotropic spatial resolutions. Maps of 13 metabolic compounds (aspartate, choline compounds and creatine + phosphocreatine, γ-aminobutyric acid (GABA), glucose, glutamine, glutamate, glutathione, <em>myo</em>-inositol, <em>scyllo</em>-inositol, N-acetylaspartate (NAA), N-acetylaspartylglutamate (NAAG), taurine) and macromolecules were obtained individually. The spectral quality was outstanding in the parietal and occipital lobes, but lower in other brain regions such as the temporal and frontal lobes. The average total NAA (tNAA = NAA + NAAG) signal-to-noise ratio over the whole volume of interest was 12.1± 8.9 and the full width at half maximum of tNAA was 24.7± 9.6 Hz for the 2.75 × 2.75 × 2.75 mm³ resolution. The need for an increased spectral bandwidth in combination with spatio-spectral encoding imposed significant challenges on the gradient system, but the FID approach proved very robust to field inhomogeneities of ∆<em>B</em>₀ = 45 ± 38 Hz (frequency offset ± spatial STD) and <em>B</em>₁⁺ = 65 ± 11° within the MRSI volume of interest.</div></div><div><h3>Discussion</h3><div>These preliminary findings highlight the potential of 10.5 T MRSI as a powerful imaging tool for probing cerebral metabolism. By providing unprecedented spatial and spectral resolution, this technology could offer a unique view into the metabolic intricacies of the human brain, but further technical developments will be necessary to optimize data quality and fully leverage the capabilities of 10.5 T MRSI.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"307 ","pages":"Article 121015"},"PeriodicalIF":4.7,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142966109","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":"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":"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":"Similarity or complementarity? Understanding marital relationships in terms of sexual dimorphism in brain morphometry and gender roles","authors":"Lei Li ,&nbsp;Xinyue Huang ,&nbsp;Qingyu Zheng ,&nbsp;Jinming Xiao ,&nbsp;Xiaolong Shan ,&nbsp;Huafu Chen ,&nbsp;Xujun Duan","doi":"10.1016/j.neuroimage.2024.120974","DOIUrl":"10.1016/j.neuroimage.2024.120974","url":null,"abstract":"<div><div>“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.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"305 ","pages":"Article 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":"OA","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 ,&nbsp;Mathieu Lesourd ,&nbsp;Arnaud Fournel ,&nbsp;Alexandre Bluet ,&nbsp;Chloé Bryche ,&nbsp;Maximilien Metaireau ,&nbsp;Dario Baldi ,&nbsp;Maria Antonella Brandimonte ,&nbsp;Andrea Soricelli ,&nbsp;Yves Rossetti ,&nbsp;François Osiurak","doi":"10.1016/j.neuroimage.2024.120971","DOIUrl":"10.1016/j.neuroimage.2024.120971","url":null,"abstract":"<div><div>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 (<em>e.g.</em>, a screwdriver) and digital tools (<em>e.g.</em>, 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.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"305 ","pages":"Article 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":"OA","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 ,&nbsp;Junhao Zhang ,&nbsp;Linmin Wang ,&nbsp;Qianqian Wang ,&nbsp;Mingxia Liu","doi":"10.1016/j.neuroimage.2024.120967","DOIUrl":"10.1016/j.neuroimage.2024.120967","url":null,"abstract":"<div><div>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 <strong>A</strong>ugmentation and <strong>C</strong>omputation <strong>T</strong>oolbox for bra<strong>I</strong>n netw<strong>O</strong>rk a<strong>N</strong>alysis (<strong>ACTION</strong>), 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 <span><span>online</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"305 ","pages":"Article 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}