NeuroImage最新文献

{"title":"Parietal cortex is recruited by frontal and cingulate areas to support action monitoring and updating during stopping","authors":"Jung Uk Kang ,&nbsp;Layth Mattar ,&nbsp;José Vergara ,&nbsp;Victoria E. Gobo ,&nbsp;Hernan G. Rey ,&nbsp;Sarah R. Heilbronner ,&nbsp;Andrew J. Watrous ,&nbsp;Benjamin Y. Hayden ,&nbsp;Sameer A. Sheth ,&nbsp;Eleonora Bartoli","doi":"10.1016/j.neuroimage.2025.121288","DOIUrl":"10.1016/j.neuroimage.2025.121288","url":null,"abstract":"<div><div>Recent evidence indicates that the intraparietal sulcus (IPS) may play a causal role in action stopping, potentially representing a novel neuromodulation target for inhibitory control dysfunctions. Here, we leverage intracranial recordings in human subjects to establish the timing and directionality of information flow between IPS and prefrontal and cingulate regions during action stopping. Prior to successful inhibition, information flows primarily from the inferior frontal gyrus (IFG), a critical inhibitory control node, to IPS. In contrast, during stopping errors the communication between IPS and IFG is lacking, and IPS is engaged by posterior cingulate cortex, an area outside of the classical inhibition network and typically associated with default mode. Anterior cingulate and orbitofrontal cortex also display performance-dependent connectivity with IPS. Our functional connectivity results provide direct electrophysiological evidence that IPS is recruited by frontal and anterior cingulate areas to support action plan monitoring and updating, and by posterior cingulate during control failures.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"315 ","pages":"Article 121288"},"PeriodicalIF":4.7,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144132476","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":"DCSLK: Combined large kernel shared convolutional model with dynamic channel Sampling","authors":"Zongren Li ,&nbsp;Shuping Luo ,&nbsp;Hongwei Li ,&nbsp;Yanbin Li","doi":"10.1016/j.neuroimage.2025.121280","DOIUrl":"10.1016/j.neuroimage.2025.121280","url":null,"abstract":"<div><div>This study centers around the competition between Convolutional Neural Networks (CNNs) with large convolutional kernels and Vision Transformers in the domain of computer vision, delving deeply into the issues pertaining to parameters and computational complexity that stem from the utilization of large convolutional kernels. Even though the size of the convolutional kernels has been extended up to 51×51, the enhancement of performance has hit a plateau, and moreover, striped convolution incurs a performance degradation. Enlightened by the hierarchical visual processing mechanism inherent in humans, this research innovatively incorporates a shared parameter mechanism for large convolutional kernels. It synergizes the expansion of the receptive field enabled by large convolutional kernels with the extraction of fine-grained features facilitated by small convolutional kernels. To address the surging number of parameters, a meticulously designed parameter sharing mechanism is employed, featuring fine-grained processing in the central region of the convolutional kernel and wide-ranging parameter sharing in the periphery. This not only curtails the parameter count and mitigates the model complexity but also sustains the model's capacity to capture extensive spatial relationships. Additionally, in light of the problems of spatial feature information loss and augmented memory access during the 1 × 1 convolutional channel compression phase, this study further puts forward a dynamic channel sampling approach, which markedly elevates the accuracy of tumor subregion segmentation. To authenticate the efficacy of the proposed methodology, a comprehensive evaluation has been conducted on three brain tumor segmentation datasets, namely BraTs2020, BraTs2024, and Medical Segmentation Decathlon Brain 2018. The experimental results evince that the proposed model surpasses the current mainstream ConvNet and Transformer architectures across all performance metrics, proffering novel research perspectives and technical stratagems for the realm of medical image segmentation.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"315 ","pages":"Article 121280"},"PeriodicalIF":4.7,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124301","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":"Decoding the Narcissistic Brain","authors":"Zhiwei Zhou ,&nbsp;Chengli Huang ,&nbsp;Esther M. Robins ,&nbsp;Douglas J. Angus ,&nbsp;Constantine Sedikides ,&nbsp;Nicholas J. Kelley","doi":"10.1016/j.neuroimage.2025.121284","DOIUrl":"10.1016/j.neuroimage.2025.121284","url":null,"abstract":"<div><div>There is a substantial knowledge gap in the narcissism literature: &lt;1 % of the nearly 12,000 articles on narcissism have addressed its neural basis. To help fill this gap, we asked whether the multifacetedness of narcissism could be decoded from spontaneous neural oscillations. We attempted to do so by applying a machine learning approach (multivariate pattern analysis) to the resting-state EEG data of 162 participants who also completed a comprehensive battery of narcissism scales assessing agentic, admirative, rivalrous, communal, and vulnerable forms. Consistent with the agency-communion model of narcissism, agentic and communal forms of grandiose narcissism were reflected in distinct, non-overlapping patterns of spontaneous neural oscillations. Furthermore, consistent with a narcissistic admiration and rivalry concept model of narcissism, we observed largely non-overlapping patterns of spontaneous neural oscillations for admirative and rivalrous forms of narcissism. Vulnerable narcissism was negatively associated with power across fast and slow wave frequency bands. Taken together, the results suggest that the diverse forms of narcissism can be reliably predicted from spontaneous neural oscillations. The findings contribute to the burgeoning field of personality neuroscience.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"315 ","pages":"Article 121284"},"PeriodicalIF":4.7,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144128301","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":"Mapping GABA+/Glx in experimental temporal lobe epilepsy using edited-MRSI at 9.4T","authors":"Alicia Plaindoux ,&nbsp;Yann Le Fur ,&nbsp;Clothilde Courivaud ,&nbsp;Camille Beets ,&nbsp;Loan Samalens ,&nbsp;Julien Valette ,&nbsp;Benjamin Lemasson ,&nbsp;Emmanuel L. Barbier ,&nbsp;Vasile Stupar ,&nbsp;Florence Fauvelle","doi":"10.1016/j.neuroimage.2025.121274","DOIUrl":"10.1016/j.neuroimage.2025.121274","url":null,"abstract":"<div><div>Mesial temporal lobe epilepsy (MTLE) is a common drug-resistant epilepsy, often requiring surgery to remove the epileptogenic zone (EZ). The mean efficiency of the surgery is 50-70 %, so the accurate spatial localization of the EZ remains a challenge. In a previous study from <span><span>Hamelin et al. (2021)</span></span>, gamma-aminobutyric acid (GABA), the main inhibitory neurotransmitter, was shown to be highly increased in the EZ of a MTLE mouse model, with a concomitant decrease of Glx (Glutamate + glutamine). The authors proposed the GABA/Glx ratio as a potential specific biomarker of the EZ.</div><div>As it is the only way to measure GABA and Glx non-invasively <em>in vivo,</em> we propose to use Magnetic Resonance Spectroscopy (MRS) methods to improve the non-invasive localization of the EZ.</div><div>Our study introduces an original GABA-edited Magnetic Resonance Spectroscopic Imaging (MRSI) method, i.e. the MEGA-LASER CSI, and the dedicated data processing pipeline, to map GABA and Glx in pre-clinical settings.</div><div>The MEGA-LASER CSI sequence was validated using multi-compartment phantoms with varying GABA concentrations, demonstrating a high accuracy to spatially discriminate the different compartments. <em>In vivo</em> experiments revealed a significant increase of the GABA+/Glx ratio in the EZ of epileptic animals (n=30) compared to SHAM ones (n=15), strongly correlated with <em>ex vivo</em> data. Preliminary immunohistochemistry experiments revealed astrocytic localization of GAD65 in the EZ, suggesting a shift in GABA synthesis from nerve endings to astrocytes.</div><div>Phantom and <em>in vivo</em> experiments prove that our original workflow for GABA and Glx mapping is suitable for use in neuroscience and pre-clinical applications.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"315 ","pages":"Article 121274"},"PeriodicalIF":4.7,"publicationDate":"2025-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144111321","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":"Flexible ability-willingness trade-offs in cooperative partner choice: Evidence from a drift-diffusion model and ERP data","authors":"Qiang Xu ,&nbsp;Jing Wang ,&nbsp;Jing Wang ,&nbsp;Peng Li","doi":"10.1016/j.neuroimage.2025.121282","DOIUrl":"10.1016/j.neuroimage.2025.121282","url":null,"abstract":"<div><div>Partner choice plays a crucial role in human social behavior, with previous research highlighting the importance of both partner ability and willingness to cooperate in decision-making processes. However, the dynamic interplay between these factors across different contexts and how they are influenced by individual characteristics remain poorly understood. This study investigated how task demands and individual traits modulate the tradeoff between partner ability and willingness to make social decisions. We employed a novel experimental paradigm combining three tasks (prisoner's dilemma, time estimation, and a combined task) with computational modeling and EEG recording. Fifty-three participants completed partner-selection trials across these tasks, while their behavioral and neural responses were recorded. Drift diffusion model (DDM) analysis revealed that decision weights for partner ability and willingness were systematically modulated by task demands, with willingness prioritized in cooperative contexts, but ability prioritized in skill-based tasks. Using these model-estimated weights, we found that participants with high self-perceived ability had higher ability weights, whereas those with high self-perceived willingness had lower ability weights in their decision making. EEG analysis, utilizing trial-level weights computed by the DDM, showed that the processing of willingness and ability information began approximately 300 ms post-stimulus, with distinct spatiotemporal patterns across tasks. In the combined task, EEG data indicated earlier and more extensive processing of willingness than of ability information. These findings reveal that individuals flexibly adjust their weighting of partner willingness and ability based on task demands, challenging the notion of a fixed \"willingness priority\" and demonstrating the dynamic nature of social decision-making across contexts.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"315 ","pages":"Article 121282"},"PeriodicalIF":4.7,"publicationDate":"2025-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144111316","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":"Blood-brain barrier leakage in renovascular hypertensive rats: A quantitative MRI analysis","authors":"María Luz Alonso-Alonso ,&nbsp;Ana Sampedro-Viana ,&nbsp;Ivan Fernandez-Bueno ,&nbsp;María Pérez-Mato ,&nbsp;Clara Correa-Paz ,&nbsp;Lara Pérez-Gayol ,&nbsp;Daniel Romaus-Sanjurjo ,&nbsp;Alberto Ouro ,&nbsp;Javier Blanco-Santero ,&nbsp;Crhistian-Mario Oblitas ,&nbsp;José Castillo ,&nbsp;Pablo Hervella ,&nbsp;Ramón Iglesias-Rey","doi":"10.1016/j.neuroimage.2025.121283","DOIUrl":"10.1016/j.neuroimage.2025.121283","url":null,"abstract":"<div><div>Hypertension is a modifiable risk factor for cerebral small vessel disease (cSVD) which leads to blood-brain barrier (BBB) dysfunction. This study used gadolinium-enhanced MRI T₁ and T₂ in stroke-prone renovascular hypertensive rats (RHRSP) to quantify BBB leakage and brain lesions. Serum inflammation and endothelial disruption biomarkers were assessed. Brain aquaporin 4 (AQ4) and retinal morphology were evaluated by histology. RHRSP showed higher systolic blood pressure from the first post-surgical week (134.1 ± 16.6 vs 113.5 ± 11.4 mmHg; <em>p</em> = 0.041). Gadolinium extravasation was increased in the whole brain of RHRSP (<em>p</em> &lt; 0.05), observing cerebral lesions in 50 % of them. Regarding biomarkers, TNF-α was increased (13.5 ± 2.2 vs 11.1 ± 3.5 pg/ml, <em>p</em> = 0.031) while soluble tumor necrosis factor-like weak inducer of apoptosis (sTWEAK) was lower (14.5 ± 2.2 vs 18.9 ± 2.5 pg/ml, <em>p</em> &lt; 0.001) in the acute hypertension phase. AQ4 was increased in the RHRSP brain (1.6 ± 0.7 vs 1.0 ± 0.2 NFU; <em>p</em> = 0.001). Both retinal nuclear layers were reduced in RHRSP (<em>p</em> &lt; 0.05). Gadolinium-enhanced MRI allows the detection of BBB leakage during the establishment of RHRSP. BBB dysfunction is consistent with increased AQ4 and decreased sTWEAK release. Therefore, this animal model is useful for testing potential treatments at cSVD initial stages.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"315 ","pages":"Article 121283"},"PeriodicalIF":4.7,"publicationDate":"2025-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144111386","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":"Odense-Oxford PET Image Analysis (OPETIA): An FSL-based toolbox for multimodal neuroimaging","authors":"Mohammadtaha Parsayan ,&nbsp;Sasan Andalib ,&nbsp;Thomas Lund Andersen ,&nbsp;Habib Ganjgahi ,&nbsp;Poul Flemming Høilund-Carlsen ,&nbsp;Abass Alavi ,&nbsp;Mojtaba Zarei","doi":"10.1016/j.neuroimage.2025.121278","DOIUrl":"10.1016/j.neuroimage.2025.121278","url":null,"abstract":"<div><div>Advanced analysis of MRI and PET images provides quantitative and accurate information about the brain structure and function, allowing differential diagnosis, prognosis, and personalized treatment. Most clinical software lack accurate quantification. Here we developed a user-friendly multimodal neuroimage analysis toolbox, named Odense-Oxford PET Image Analysis (OPETIA), based on Functional Magnetic Resonance Imaging of the Brain Software Library (FSL) and Python programming language. FSL is a strong toolbox library for MRI analysis but has not been widely used for PET image analysis. OPETIA includes a graphical user interface that facilitates automatic multimodal neuroimage analysis. OPETIA can automatically pre-process magnetic resonance, and PET images and calculates maximum, mean, and standard deviation of Standardized Uptake Value (SUV) and Standardized Uptake Value Ratio (SUVR) in the volumes of interest (VOI). To assess the efficacy of OPETIA, we analysed a set of static ¹⁸F-fluorodeoxyglucose (FDG) PET and MRIs of healthy subjects and patients with Alzheimer’s disease (AD) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset using OPETIA and compared the SUVR measurements with those obtained from Statistical Parametric Mapping, version 12 (SPM12). The result of this comparison showed a close association between OPETIA and SPM12 results (p-value 〈 0.01, r 〉 0.8). OPETIA measurements were significantly (p-value &lt; 0.01) larger than those of SPM12 in all brain regions (according to the Harvard-Oxford brain atlas), indicating a systematic difference between these tools. The Cronbach’s Alpha values for both tools were &gt; 0.9, indicating a high reproducibility. We compared the group difference (control vs Alzheimer’s disease) obtained from each toolbox using two-sample <em>t</em>-test and found significantly (p-value &lt; 0.01) larger Cohen’s d values for SUVRs from OPETIA (<em>d</em> = 0.22) than SPM12 (<em>d</em> = 0.04). We suggest that OPETIA is a user-friendly and robust tool for quantitative analysis of multimodal neuroimaging such as cerebral PET and MR images.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"314 ","pages":"Article 121278"},"PeriodicalIF":4.7,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144088984","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":"The role of the human cerebellum in representing social behavior sequences: An SDM-PSI meta-analysis","authors":"Yuting Zhang,&nbsp;Yidan Qiu,&nbsp;Shuting Lin,&nbsp;Xiaoyu Zheng,&nbsp;Liwei Tan,&nbsp;Xia Liu,&nbsp;Ruiwang Huang","doi":"10.1016/j.neuroimage.2025.121277","DOIUrl":"10.1016/j.neuroimage.2025.121277","url":null,"abstract":"<div><div>Although the cerebellum has been widely considered to be a motor structure, recent studies have shown that it is also involved in constructing sequences of social events. However, little is known about (1) whether explicit sequencing processes elicit stronger cerebellar activation than non-sequencing processes, (2) whether the cerebellar sequence detection function is more applicable to social or non-social contexts, or (3) whether the cerebellum exhibits consistent or heterogeneous activation patterns in representing behavioral sequences across contexts. Thus, we conducted a meta-analysis of 13 neuroimaging studies by using a seed-based <em>d</em> mapping with permutation of subject images (SDM-PSI) approach. The results showed that the cerebellar Crus I and II were activated more strongly in social sequencing processes than in social non-sequencing, indicating that sequence detection is a basic function of the cerebellum. In sequencing processes, the cerebellar posterior Crus II responded more strongly to social than to non-social events, suggesting that the sequencing function of this cerebellar sub-region is more applicable to social contexts. The posterior cerebellum exhibited heterogeneous activation patterns, with distinct functional specializations in Crus I and Crus II. These findings provide a deeper understanding of the functions of the cerebellar regions in social cognition.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"315 ","pages":"Article 121277"},"PeriodicalIF":4.7,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144102209","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":"A robust automated segmentation method for white matter hyperintensity of vascular-origin","authors":"Haoying He ,&nbsp;Jiu Jiang ,&nbsp;Sisi Peng ,&nbsp;Chu He ,&nbsp;Tianqi Sun ,&nbsp;Fan Fan ,&nbsp;Hao Song ,&nbsp;Dong Sun ,&nbsp;Zhipeng Xu ,&nbsp;Shenjia Wu ,&nbsp;Dongwei Lu ,&nbsp;Junjian Zhang","doi":"10.1016/j.neuroimage.2025.121279","DOIUrl":"10.1016/j.neuroimage.2025.121279","url":null,"abstract":"<div><div>White matter hyperintensity (WMH) is a primary manifestation of small vessel disease (SVD), leading to vascular cognitive impairment and other disorders. Accurate WMH quantification is vital for diagnosis and prognosis, but current automatic segmentation methods often fall short, especially across different datasets. The aims of this study are to develop and validate a robust deep learning segmentation method for WMH of vascular-origin. In this study, we developed a transformer-based method for the automatic segmentation of vascular-origin WMH using both 3D T1 and 3D T2-FLAIR images. Our initial dataset comprised 126 participants with varying WMH burdens due to SVD, each with manually segmented WMH masks used for training and testing. External validation was performed on two independent datasets: the WMH Segmentation Challenge 2017 dataset (170 subjects) and an in-house vascular risk factor dataset (70 subjects), which included scans acquired on eight different MRI systems at field strengths of 1.5T, 3T, and 5T This approach enabled a comprehensive assessment of the method’s generalizability across diverse imaging conditions. We further compared our method against LGA, LPA, BIANCA, UBO-detector and TrUE-Net in optimized settings. Our method consistently outperformed others, achieving a median Dice coefficient of 0.78 ± 0.09 in our primary dataset, 0.72 ± 0.15 in the external dataset 1, and 0.72 ± 0.14 in the external dataset 2. The relative volume errors were 0.15 ± 0.14, 0.50 ± 0.86, and 0.47 ± 1.02, respectively. The true positive rates were 0.81 ± 0.13, 0.92 ± 0.09, and 0.92 ± 0.12, while the false positive rates were 0.20 ± 0.09, 0.40 ± 0.18, and 0.40 ± 0.19. None of the external validation datasets were used for model training; instead, they comprise previously unseen MRI scans acquired from different scanners and protocols. This setup closely reflects real-world clinical scenarios and further demonstrates the robustness and generalizability of our model across diverse MRI systems and acquisition settings. As such, the proposed method provides a reliable solution for WMH segmentation in large-scale cohort studies.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"315 ","pages":"Article 121279"},"PeriodicalIF":4.7,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144102158","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":"Overlaps of fMRI activation patterns of the anxiety-emotional and the vestibular-sensory networks","authors":"M. Lotze ,&nbsp;K. Klepzig ,&nbsp;T. Stephan ,&nbsp;M. Domin ,&nbsp;T. Brandt ,&nbsp;M. Dieterich","doi":"10.1016/j.neuroimage.2025.121275","DOIUrl":"10.1016/j.neuroimage.2025.121275","url":null,"abstract":"<div><div>Clinical and meta-analytic imaging data suggest a considerable overlap between vestibular-sensory and anxiety-emotional processing networks. We therefore examined functional MRI activation using galvanic vestibular stimulation (GVS) and a fear conditioning paradigm in the same 28 healthy individuals. This study was to proof the effects of both stimulations in the same individual whereas our earlier meta-analytical analysis compared groups of participants who had received only one or the other stimulation. In the actual study we further assessed subjective experience (expectancy ratings, questionnaires) and autonomic arousal (skin conductance response; SCR). Activation patterns during vestibular stimulation confirmed previous findings showing highest fMRI-activation in the parieto-insular vestibular cortex. Fear conditioning activated the anterior insula, secondary somatosensory cortex (S2) and thalamus. A conjunction of fMRI-activation maps for both stimulation paradigms revealed bilateral anterior and posterior insula, dorsolateral prefrontal cortex and S2 as well as cerebellar hemisphere fMRI-activation. Regression analyses showed a high positive association of left anterior insular activation during the fear extinction period with trait anxiety. The vestibular intensity during GVS was positively associated with right ventro-lateral prefrontal cortex (PFC) fMRI-activation. This is compatible with the earlier hypothesized top-down regulation of vestibular perception which involves the PFC beneficial for suppression of unusual vestibular excitation or vertigo related to vestibular disorders.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"315 ","pages":"Article 121275"},"PeriodicalIF":4.7,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144102164","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}