NeuroImage最新文献

{"title":"Differentiating atypical parkinsonian syndromes with hyperbolic few-shot contrastive learning","authors":"Won June Choi ,&nbsp;Jin HwangBo ,&nbsp;Quan Anh Duong ,&nbsp;Jae-Hyeok Lee ,&nbsp;Jin Kyu Gahm","doi":"10.1016/j.neuroimage.2024.120940","DOIUrl":"10.1016/j.neuroimage.2024.120940","url":null,"abstract":"<div><div>Differences in iron accumulation patterns have been observed in susceptibility-weighted images across different classes of atypical parkinsonian syndromes (APS). Deep learning methods have shown great potential in automatically detecting these differences. However, the models typically require extensively labeled training datasets, which are costly and pose patient privacy risks. To address the issue of limited training datasets, we propose a novel few-shot learning framework for classifying multiple system atrophy parkinsonian (MSA-P) and progressive supranuclear palsy (PSP) within the APS category using fewer data items. Our method identifies feature areas where iron accumulation patterns occur in classes other than the target classification (MSA-P vs. PSP) and enhances stability by leveraging a superior hyperbolic space embedding technique. Experimental results demonstrate significantly improved performance over conventional methods, as validated by ablation studies and visualizations.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"304 ","pages":"Article 120940"},"PeriodicalIF":4.7,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142715950","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":"Biological mechanism of sex differences in mental rotation: Evidence from multimodal MRI, transcriptomic and receptor/transporter data.","authors":"Haixia Long, Hao Wu, Chaoliang Sun, Xinli Xu, Xu-Hua Yang, Jie Xiao, Mingqi Lv, Qiuju Chen, Ming Fan","doi":"10.1016/j.neuroimage.2024.120955","DOIUrl":"https://doi.org/10.1016/j.neuroimage.2024.120955","url":null,"abstract":"<p><p>Sex differences in mental rotation are a well-documented phenomenon in cognitive research, with implications for the differing prevalence of neuropsychiatric disorders such as autism spectrum disorder (ASD), Alzheimer's disease (AD) and major depressive disorder (MDD) between the sexes. Despite extensive documentation, the biological mechanism underpinning these differences remain elusive. This study aimed to elucidate neural, genetic, and molecular bases of these disparities in mental rotation by integrating data from multimodal magnetic resonance imaging (MRI), transcriptomic and receptor/transporter. We first calculated the dynamic regional homogeneity (dReHo), gray matter volume (GMV) and fractional anisotropy (FA) in voxel-wise manner and parceled them into 246 brain regions based on Brainnetome Atlas. Subsequent analyses involved Pearson Correlations to examine the association between mental rotation performance and dReHo/GMV/FA and two-sample t-tests to delineate gender differences in these indices. Based on the above results, further mediation analysis was conducted to explore the relationship between sex, brain biomarkers and mental rotation. In addition, transcriptome-neuroimaging association analysis and correlation analysis between brain biomarkers and neurotransmitter receptor/transporter distribution were also performed to uncover genetic and molecular mechanisms contributing to the observed sex differences in mental rotation. We found correlations between mental rotation performance and dReHo, GMV and FA of the inferior parietal lobule (IPL) and superior temporal gyrus (STG) and sex effects on these brain biomarkers. Notably, the dReHo of the left IPL mediated the relationship between sex and mental rotation. Further correlation analysis revealed that the proton-coupled oligopeptide transporter PEPT2 (SLC15A2) and interleukin 17 receptor D (IL17RD) were associated with sex-related t-statistic maps and mental rotation-related r-statistic maps of dReHo. Moreover, γ-aminobutyric acid subtype A (GABA_A) receptor availability was correlated with the r-statistic of dReHo, while norepinephrine transporter (NET) availability was correlated with its t-statistic. Serial mediation models revealed the indirect effect of these genes on the r-statistic maps through the transporter/receptor and t-statistic maps. Our findings provide novel insights into the biological mechanism underlying sex differences in mental rotation, identifying potential biomarkers for cognitive impairment and explaining variations in prevalence of certain mental disorders between the sexes. These results highlight the necessity of considering sex in research on mental health disorders.</p>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":" ","pages":"120955"},"PeriodicalIF":4.7,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142715944","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":"Exploring the relationship between hallucination proneness and brain morphology","authors":"Abdullah M. Alotaibi ,&nbsp;Manal H. Alosaimi ,&nbsp;Nawal S. Alshammari ,&nbsp;Razan S. Orfali ,&nbsp;Adnan Z. Alwatban ,&nbsp;Roaa A. Alsharif ,&nbsp;Georg F. Meyer ,&nbsp;Richard P. Bentall","doi":"10.1016/j.neuroimage.2024.120942","DOIUrl":"10.1016/j.neuroimage.2024.120942","url":null,"abstract":"<div><h3>Background</h3><div>Hallucinations, including both auditory and visual forms, are often associated with alterations in brain structure, particularly in specific language-related cortical areas. Existing models propose different frameworks for understanding the relationship between brain volume and hallucination proneness, but practical evidence supporting these models is limited.</div></div><div><h3>Methods</h3><div>This study investigated the relationship between hallucination proneness and brain volume in language-related cortical regions, specifically the superior temporal gyrus and Broca's area. A total of 68 participants, primarily university students, completed the Launay-Slade Hallucination Scale (LSHS) to assess hallucination proneness for both auditory and visual experiences. Structural MRI scans were used to measure brain volume in the targeted regions.</div></div><div><h3>Results</h3><div>The results indicated significant positive correlations between LSHS scores and brain volume in the superior temporal gyrus and Broca's area regions previously linked to volume reductions in patients with clinically diagnosed hallucinations. Participants reporting high hallucination proneness for both auditory and visual hallucinations exhibited higher brain volumes in these language areas compared to those experiencing hallucinations rarely or never.</div></div><div><h3>Conclusions</h3><div>These findings challenge existing models by suggesting that higher brain volumes in language-related cortical areas may be associated with increased proneness to both auditory and visual hallucinations in non-clinical populations. This contrasts with the volume reductions seen in patients with clinical hallucinations and highlights the need for further research into the complex interplay between brain structure and hallucinatory experiences.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"304 ","pages":"Article 120942"},"PeriodicalIF":4.7,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142715875","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":"Generative Modeling of the Circle of Willis Using 3D-StyleGAN.","authors":"Orhun Utku Aydin, Adam Hilbert, Alexander Koch, Felix Lohrke, Jana Rieger, Satoru Tanioka, Dietmar Frey","doi":"10.1016/j.neuroimage.2024.120936","DOIUrl":"https://doi.org/10.1016/j.neuroimage.2024.120936","url":null,"abstract":"<p><p>The circle of Willis (CoW) is a network of cerebral arteries with significant inter-individual anatomical variations. Deep learning has been used to characterize and quantify the status of the CoW in various applications for the diagnosis and treatment of cerebrovascular disease. In medical imaging, the performance of deep learning models is limited by the diversity and size of training datasets. To address medical data scarcity, generative AI models have been applied to generate synthetic vessel neuroimaging data. However, the proposed methods produce synthetic data with limited anatomical fidelity or downstream utility in tasks concerning vessel characteristics. We adapted the StyleGANv2 architecture to 3D to synthesize Time-of-Flight Magnetic Resonance Angiography (TOF MRA) volumes of the CoW. For generative modeling, we used 1782 individual TOF MRA scans from 6 open source datasets. To train the adapted 3D StyleGAN model with limited data we employed differentiable data augmentations, used mixed precision and a cropped region of interest of size 32 × 128 × 128 to tackle computational constraints. The performance was evaluated quantitatively using the Fréchet Inception Distance (FID), MedicalNet distance (MD) and Area Under the Curve of the Precision and Recall Curve for Distributions (AUC-PRD). Qualitative analysis was performed via a visual Turing test. We demonstrated the utility of generated data in a downstream task of multiclass semantic segmentation of CoW arteries. Vessel segmentation performance was assessed quantitatively using the Dice coefficient and the Hausdorff distance. The best-performing 3D StyleGANv2 architecture generated high-quality and diverse synthetic TOF MRA volumes (FID: 12.17, MD: 0.00078, AUC-PRD: 0.9610). Multiclass vessel segmentation models trained on synthetic data alone achieved comparable performance to models trained using real data in most arteries. The addition of synthetic data to a baseline training set improved segmentation performance in underrepresented artery segments, similar to the addition of real data. In conclusion, generative modeling of the Circle of Willis via synthesis of 3D TOF MRA data paves the way for generalizable deep learning applications in cerebrovascular disease. In the future, the extensions of the provided methodology to other medical imaging problems or modalities with the inclusion of pathological datasets has the potential to advance the development of more robust AI models for clinical applications.</p>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":" ","pages":"120936"},"PeriodicalIF":4.7,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142716234","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":"Different oscillatory mechanisms of dementia-related diseases with cognitive impairment in closed-eye state.","authors":"Talifu Zikereya, Yuchen Lin, Zhizhen Zhang, Ignacio Taguas, Kaixuan Shi, Chuanliang Han","doi":"10.1016/j.neuroimage.2024.120945","DOIUrl":"https://doi.org/10.1016/j.neuroimage.2024.120945","url":null,"abstract":"<p><p>The escalating global trend of aging has intensified the focus on health concerns prevalent among the elderly. Notably, Dementia related diseases, including Alzheimer's disease (AD) and frontotemporal dementia (FTD), significantly impair the quality of life for both affected seniors and their caregivers. However, the underlying neural mechanisms of these diseases remain incompletely understood, especially in terms of neural oscillations. In this study, we leveraged an open dataset containing 36 AD, 23 FTD, and 29 healthy controls (HC) to investigate these mechanisms. We accurately and clearly identified three stable oscillation targets (theta, ∼5Hz, alpha, ∼10Hz, and beta, ∼18Hz) that facilitate differentiation between AD, FTD, and HC both statistically and through classification using machine learning algorithms. Overall, the differences between AD and HC were the most pronounced, with FTD exhibiting intermediate characteristics. The differences in the theta and alpha bands showed a global pattern, whereas the differences in the beta band were localized to the central-temporal region. Moreover, our analysis revealed that the relative theta power was significantly and negatively correlated with the Mini Mental State Examination (MMSE) scores, while the relative alpha and beta power showed a significant positive correlation. This study is the first to pinpoint multiple robust and effective neural oscillation targets to distinguish AD, offering a simple and convenient method that holds promise for future applications in the early screening of large-scale dementia-related diseases.</p>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":" ","pages":"120945"},"PeriodicalIF":4.7,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142715948","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":"Association between low-frequency oscillation and cognitive compensation in high-performance group: An fNIRS mapping study","authors":"Xiang Ji ,&nbsp;Qiwei Dong ,&nbsp;Zhanxu Liu ,&nbsp;Jiangbo Pu ,&nbsp;Ting Li","doi":"10.1016/j.neuroimage.2024.120944","DOIUrl":"10.1016/j.neuroimage.2024.120944","url":null,"abstract":"<div><div>Brain lateralization is known to enhance cognitive efficiency by reducing redundant processing. Theories such as HAROLD and CRUNCH propose that cognitive decline with age can be compensated by the recruitment of additional bilateral brain regions. However, cognitive compensation is not always effective, and the underlying mechanisms remain unclear, particularly those not related to aging. Low-frequency oscillation (LFO) may be a potential factor in this process. This study investigated the relationship between LFO and cognitive compensation in the prefrontal cortex (PFC) of 28 young adults during a visual verbal working memory task, utilizing functional near-infrared spectroscopy (fNIRS). The participants were categorized into high- and low-performance groups. Changes in oxygenated hemoglobin (Δ[oxy-Hb]), deoxygenated hemoglobin (Δ[deoxy-Hb]), and total hemoglobin (Δ[tot-Hb]) were measured. Both groups exhibited reduced lateralization and increased PFC activation under cognitive load. The results show that only the high-performance group displayed enhanced Δ[oxy-Hb] LFO power, which correlated with behavioral performance. In conclusion, this study found that insufficient LFO is associated with a lack of cognitive resources, which may be due to a deficiency in cerebral autoregulation (CA). This deficiency results in an absence of low-frequency rhythms during cognitive processes, hindering effective coordination between distant brain regions. This provides new insights into the non-aging-related cognitive compensation mechanism.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"304 ","pages":"Article 120944"},"PeriodicalIF":4.7,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142702511","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":"Deep learning applied to the segmentation of rodent brain MRI data outperforms noisy ground truth on full-fledged brain atlases.","authors":"Jonas Kohler, Thomas Bielser, Stanislaw Adaszewski, Basil Künnecke, Andreas Bruns","doi":"10.1016/j.neuroimage.2024.120934","DOIUrl":"https://doi.org/10.1016/j.neuroimage.2024.120934","url":null,"abstract":"<p><p>Translational magnetic resonance imaging of the rodent brain provides invaluable information for preclinical drug development. However, the automated segmentation of such images for quantitative analyses is limited compared to human brain imaging mainly due to the inferior anatomical contrast and the resulting less advanced registration and atlasing tools. Here, we investigated the potential of deep learning models for the segmentation of magnetic resonance images of rat brains into an entire set of multiple regions of interest (rather than individual loci), focusing on the development of a robust method that accommodates changes in the input based on differences in animal strain (genotype) and size. Manually generated labels are expensive, so we tested the ability of neural networks to learn brain structures from noisy but inexpensive registration-based labels, allowing very large datasets to be leveraged for training. We compared three distinct model architectures (U-Net, Attention-U-Net and DeepLab) by training them on a dataset of >10,000 magnetic resonance images of rat brains and found that each model was able to segment the entire brain into predefined sets of 29 and 58 regions, respectively, with the Attention U-Net achieving the best performance. The models canceled out unstructured label noise in the imperfect training data to provide smoother and more symmetric segmentations than registration-based labeling, and were more robust when presented with input variations, thus outperforming the noisy ground truth. Our pipeline also includes uncertainty estimation and an explainability mechanism, hence providing features essential for anomaly detection and quality assurance. In summary, our study shows that deep learning models do achieve accurate brain segmentation in high-throughput quantitative preclinical imaging without the need for expensive expert-generated labels.</p>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":" ","pages":"120934"},"PeriodicalIF":4.7,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142693226","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":"Cerebellar representation during phonetic processing in tonal and non-tonal language speakers: An ALE meta-analysis","authors":"Xiaotong Zhang ,&nbsp;Zhaowen Zhou ,&nbsp;Ying Wang ,&nbsp;Jinyi Long ,&nbsp;Zhuoming Chen","doi":"10.1016/j.neuroimage.2024.120950","DOIUrl":"10.1016/j.neuroimage.2024.120950","url":null,"abstract":"<div><div>The role of the cerebellum in phonetic processing has been discovered and widely discussed for decades. However, with the idea that the cerebral representation of phonetic processing is different in tonal language and non-tonal language speakers, whether the cerebellar representation of phonetic processing differs based on language background remains unknown. In the present study, we conducted an activation likelihood estimation (ALE) analysis among 33 functional neuroimaging studies involving 541 healthy adults (213 tonal language speakers and 328 non-tonal language speakers). The aim was to explore the cerebellar representation of phonetic perception and phonetic production in these two language backgrounds. Our results demonstrated the involvement of cerebellum left Crus I, right Crus II, lobules VI, and VIIb in phonetic perception among tonal language speakers, whereas only one focal cluster (right Crus I and Crus II) was demonstrated in non-tonal language speakers. Conjunction analysis revealed overlapping regions located in the right Crus II both in tonal and non-tonal language speakers during phonetic perception. During phonetic production, no significant cluster was detected among tonal language speakers, whereas one focal cluster (within right lobule VI) was detected in non-tonal language speakers. These results highlight the specific cerebellar representation of phonetic processing in tonal and non-tonal languages. Overall, this ALE analysis provides a profound view of the neural mechanism of phonetic processing.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"303 ","pages":"Article 120950"},"PeriodicalIF":4.7,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142693224","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":"VAEEG: Variational auto-encoder for extracting EEG representation","authors":"Tong Zhao ,&nbsp;Yi Cui ,&nbsp;Taoyun Ji ,&nbsp;Jiejian Luo ,&nbsp;Wenling Li ,&nbsp;Jun Jiang ,&nbsp;Zaifen Gao ,&nbsp;Wenguang Hu ,&nbsp;Yuxiang Yan ,&nbsp;Yuwu Jiang ,&nbsp;Bo Hong","doi":"10.1016/j.neuroimage.2024.120946","DOIUrl":"10.1016/j.neuroimage.2024.120946","url":null,"abstract":"<div><div>The electroencephalogram (EEG) exhibits characteristics of complexity and strong randomness. Existing deep learning models for EEG typically target specific objectives and datasets, with their scalability constrained by the size of the dataset, resulting in limited perceptual and generalization abilities. In order to obtain more intuitive, concise, and useful representations of brain activity, we constructed a reconstruction-based self-supervised learning model for EEG based on Variational Autoencoder (VAE) with separate frequency bands, termed variational auto-encoder for EEG (VAEEG). VAEEG achieved outstanding reconstruction performance. Furthermore, we validated the efficacy of the latent representations in three clinical tasks concerning pediatric brain development, epileptic seizure, and sleep stage classification. We discovered that certain latent features: 1) correlate with adolescent brain developmental changes; 2) exhibit significant distinctions in the distribution between epileptic seizures and background activity; 3) show significant variations across different sleep cycles. In corresponding downstream fitting or classification tasks, models constructed based on the representations extracted by VAEEG demonstrated superior performance. Our model can extract effective features from complex EEG signals, serving as an early feature extractor for downstream classification tasks. This reduces the amount of data required for downstream tasks, simplifies the complexity of downstream models, and streamlines the training process.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"304 ","pages":"Article 120946"},"PeriodicalIF":4.7,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142687553","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":"Sex differences of negative emotions in adults and infants along the prefrontal-amygdaloid brain pathway","authors":"Leiming Wu ,&nbsp;Zilong Hong ,&nbsp;Shujun Wang ,&nbsp;Jia Huang ,&nbsp;Jixin Liu","doi":"10.1016/j.neuroimage.2024.120948","DOIUrl":"10.1016/j.neuroimage.2024.120948","url":null,"abstract":"<div><div>The neural basis of sex-related differences in processing negative emotions remains poorly understood. The amygdala-related fiber pathways serve as the neuroanatomical foundation for emotion processing. However, the precise sex-related variations within these pathways remain largely elusive. Using diffusion magnetic resonance imaging data from 418 healthy individuals, we identified sex differences in white-matter microstructures of the striato-amygdaloid-prefrontal tracts, particularly the amygdala (Amy)-medial prefrontal cortex (mPFC) pathway. These differences were associated with various neurobiological factors, including pain-related negative emotions, pain sensitivity, neurotransmitter receptors, and gene expressions in the human brain. Our findings suggested that the Amy-mPFC pathway may serve as a neuroanatomical foundation for sex-specific negative emotion processing, driven by specific genetic and neurotransmitter profiles. Notably, we also found similar sex differences in this pathway in an infant imaging dataset, hinting at its developmental significance as a precursor to sex differences in adulthood. These findings underscore the importance of the striato-amygdaloid-prefrontal tracts in sex-related differences in processing negative emotions. This may enhance our understanding of sex-specific emotion regulation and potentially inform future research on strategies for preventing and diagnosing emotional regulation disorders across sexes.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"304 ","pages":"Article 120948"},"PeriodicalIF":4.7,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142687550","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}