Psychiatry Research: Neuroimaging最新文献_第3页

Formal thought disorder and familial risk in first-episode psychosis: A study of cortical thickness and neuroimaging-transcriptomic association analysis 首发精神病的形式思维障碍和家族性风险：皮质厚度和神经成像-转录组学关联分析的研究

IF 2.1 4区医学 Q3 CLINICAL NEUROLOGY

Psychiatry Research: Neuroimaging

Pub Date : 2026-01-16 DOI: 10.1016/j.pscychresns.2026.112148

Tuğçe Çabuk , Yuanchao Zhang , Lena Palaniyappan , Didenur Şahin-Çevik , Hanife Avcı , Işık Batuhan Çakmak , Helin Yılmaz Kafalı , Bedirhan Şenol , Kader Karlı Oğuz , Timothea Toulopoulou

Formal thought disorder (FTD), a prominent feature of schizophrenia, encompasses disruptions in thought, language, and communication. This study examines cortical thickness (CT) alterations in first-episode psychosis (FEP) patients (N = 24), their siblings (SIB) (N = 21), and healthy controls (CON) (N = 21) to explore potential neural correlates of FTD. Using structural MRI, we analyzed whole-brain CT and its relationship with positive and negative FTD measured by Thought and Language Index. Out-of-sample spatial correlations of gene expression with regional CT were also performed using a transcriptomic dataset. FEP had significant CT reductions in right middle frontal gyrus (MFG) compared with SIB and CON and in superior frontal gyrus (SFG) compared to CON; but SIB did not differ from CON. GLM analyses demonstrated that negative FTD exerted a significant main effect on CT in the MFG and SFG. By contrast, positive FTD showed no significant associations with CT. Neuroimaging-transcriptomic association analysis identified key biological pathways linked to cortical morphology. These findings emphasize the specific association between negative FTD and CT alterations in frontal brain regions, confirming prior reports. Future research should examine larger cohorts and investigate additional FTD subtypes to further elucidate neural correlates and potential familial risks of schizophrenia.

形式思维障碍（FTD）是精神分裂症的一个显著特征，包括思维、语言和交流的中断。本研究检测了首发精神病（FEP）患者（N = 24）、他们的兄弟姐妹（SIB）（N = 21）和健康对照（CON）（N = 21）的皮质厚度（CT）改变，以探索FTD的潜在神经相关性。利用结构MRI分析全脑CT及其与思维语言指数测量的FTD阳性和阴性的关系。样本外基因表达与区域CT的空间相关性也使用转录组数据集进行。与SIB和CON相比，FEP在右侧额叶中回（MFG）和额叶上回（SFG）中有显著的CT减少；但SIB与con没有差异。GLM分析表明，阴性FTD对MFG和SFG的CT有显著的主要影响。相反，FTD阳性与CT无显著相关性。神经成像-转录组关联分析确定了与皮质形态相关的关键生物学途径。这些发现强调了FTD阴性与额叶脑区CT改变之间的特定关联，证实了先前的报道。未来的研究应该检查更大的队列，并调查更多的FTD亚型，以进一步阐明精神分裂症的神经相关性和潜在的家族性风险。

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引用次数: 0

Dual-representation structural MRI classification of psychiatric disorders using deep learning and large language models 基于深度学习和大型语言模型的双表征结构MRI精神疾病分类

IF 2.1 4区医学 Q3 CLINICAL NEUROLOGY

Psychiatry Research: Neuroimaging

Pub Date : 2026-01-16 DOI: 10.1016/j.pscychresns.2026.112146

Hidir Selcuk Nogay , Hojjat Adeli

Accurate differentiation among psychiatric disorders such as schizophrenia and bipolar disorder remains a significant clinical challenge due to overlapping symptoms and subtle neuroanatomical variations. This study proposes a dual‐representation structural MRI framework in which raw T1-weighted MRI slices and their corresponding color-coded tissue segmentation maps—both derived from the same imaging modality—are analyzed using two independently trained ResNet-18 CNNs. The four diagnostic groups examined include Healthy Controls, Schizophrenia Spectrum, Bipolar Disorder with Psychosis, and Bipolar Disorder without Psychosis. TL and DA techniques were employed to address the limited dataset (N= 103).

Following model training, a Large Language Model (LLM) was used as a post-hoc analysis tool to contextualize the CNN outputs and provide interpretative insights into the relative contributions of the two MRI-derived representations. The results indicate that the dual-representation approach improves four-way classification performance and enables systematic comparison of structural information captured by raw versus segmentation-based inputs. These findings highlight the potential of combining deep learning models with LLM-assisted interpretability to support more transparent and informative diagnostic tools in psychiatric neuroimaging.

由于精神分裂症和双相情感障碍的症状重叠和微妙的神经解剖学差异，准确区分精神疾病仍然是一个重大的临床挑战。本研究提出了一种双表征结构MRI框架，其中原始t1加权MRI切片及其相应的彩色编码组织分割图（均来自相同的成像模式）使用两个独立训练的ResNet-18 cnn进行分析。检查的四个诊断组包括健康对照组、精神分裂症谱系、双相情感障碍伴精神病和双相情感障碍无精神病。采用TL和DA技术来处理有限的数据集（N= 103）。在模型训练之后，使用大型语言模型（LLM）作为事后分析工具，将CNN输出上下文化，并提供对两种mri衍生表示的相对贡献的解释性见解。结果表明，双表示方法提高了四向分类性能，并能够系统地比较由原始输入和基于分割的输入捕获的结构信息。这些发现强调了将深度学习模型与llm辅助的可解释性相结合的潜力，以支持精神神经成像中更透明和信息丰富的诊断工具。

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引用次数: 0

Resting-state functional magnetic resonance imaging study of voxel-mirrored homotopy connections in patients with schizophrenia. 精神分裂症患者体素镜像同伦连接静息状态功能磁共振成像研究。

IF 2.1 4区医学 Q3 CLINICAL NEUROLOGY

Psychiatry Research: Neuroimaging

Pub Date : 2026-01-15 DOI: 10.1016/j.pscychresns.2026.112143

Tangyu Gao, Wei Liu

Background: This resting-state functional magnetic resonance imaging (rs-fMRI) study investigated alterations in voxel-mirrored homotopic connectivity between schizophrenia patients and healthy controls. It further explored the associations between these neural alterations and clinical profiles. The findings aim to enhance the understanding of interhemispheric dysconnectivity in schizophrenia and may offer clues for identifying potential neurobiological substrates of the disorder.

Methods: A total of 38 schizophrenic individuals who attended the psychiatric department were recruited as the experimental group, and 35 healthy volunteers from the medical examination centre were enrolled as the control group during the same time period. Scanning of the subject's entire brain using 3.0T MRI. we finally analysed the correlation between voxel-mirrored homotopic connectivity (VMHC) values and disease severity, disease duration and cognitive function.

Results: (1) VMHC values were significantly lower in the bilateral lingual gyrus in the case group compared to the control group(p<0.05). (2)After applying rigorous False Discovery Rate (FDR) correction for multiple comparisons, the reduction in lingual gyrus VMHC remained specifically and positively correlated with poorer performance in delayed memory (p<0.05,Cohen's d = -1.09). Nominal associations with illness duration and overall symptom severity did not survive this statistical correction. (3) The VMHC values were positively correlated with the total cognitive scale score and the delayed memory factor score (p<0.05, q< 0.015).

Conclusions: This study identifies a robust reduction in interhemispheric functional connectivity within the lingual gyrus of chronic, medicated schizophrenia patients. Critically, the extent of this reduction is specifically linked to the severity of memory impairment, rather than to general symptom profiles. These findings highlight the role of aberrant homotopic connectivity in visual association cortex in the cognitive pathophysiology of schizophrenia and provide a focused neurobiological correlate for future mechanistic and longitudinal investigations.

背景：这项静息状态功能磁共振成像（rs-fMRI）研究调查了精神分裂症患者和健康对照者体素镜像同位连通性的改变。它进一步探讨了这些神经改变与临床表现之间的联系。这些发现旨在加强对精神分裂症的半球间连接障碍的理解，并可能为识别该疾病潜在的神经生物学基础提供线索。方法：选取在精神科就诊的精神分裂症患者38例作为实验组，同时选取医学检查中心健康志愿者35例作为对照组。使用3.0T核磁共振扫描受试者的整个大脑。我们最后分析了体素镜像同位连通性（VMHC）值与疾病严重程度、病程和认知功能之间的相关性。结果：(1)与对照组相比，病例组双侧舌回的VMHC值显著降低(结论：本研究确定了慢性药物性精神分裂症患者舌回半球间功能连通性的显著降低。关键的是，这种减少的程度与记忆障碍的严重程度有关，而不是与一般症状有关。这些发现强调了视觉关联皮层异常同位连通性在精神分裂症认知病理生理学中的作用，并为未来的机制和纵向研究提供了重点的神经生物学关联。

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引用次数: 0

An effective alzheimer disease diagnosis using resting state fmri images and broad learning system 利用静息状态fmri图像和广泛学习系统有效诊断阿尔茨海默病

IF 2.1 4区医学 Q3 CLINICAL NEUROLOGY

Psychiatry Research: Neuroimaging

Pub Date : 2026-01-14 DOI: 10.1016/j.pscychresns.2025.112133

Sali Issa , Qi Wang , Ruinan Qi , Guangxi Peng , Shi Yin , Qinmu Peng

In this paper, a new multiclass Alzheimer diagnosis system is proposed using Broad Learning (BL) and the combination of Local Coherence (LCOR) and Intrinsic Connectivity Contrast (ICC) parameters. A public resting state fMRI database; including healthy elderly subjects (HC), Alzheimer Disease (AD) and Mild Cognitive Impairment (MCI) patients; was chosen in this study. All rs-fMRI pre-processing and analysis were performed by CONN toolbox. Three contrast cases of AD, MCI and HC were implemented within the group-level analysis, then both LCOR and ICC parameters of the effected brain clusters were combined and collected. For diagnosis system, Broad Learning (BL) classifier is trained to classify three stages of AD, MCI and HC, respectively. Referring to the experimental results and compared with other current studies, the proposed system achieved high average accuracy of 99.6% with low training time of 2 s. Furthermore, a mapping between effected brain regions and their functions is given to interprets the common symptoms for AD and MCI patients.

本文提出了一种基于广义学习（BL）和局部相干（LCOR）与内在连通性对比（ICC）相结合的多类阿尔茨海默病诊断系统。公共静息状态fMRI数据库；包括健康老年受试者（HC）、阿尔茨海默病（AD）和轻度认知障碍（MCI）患者；在本研究中被选中。所有rs-fMRI预处理和分析均由CONN工具箱完成。在组水平分析中对3例AD、MCI和HC进行对比分析，然后将受影响脑簇的LCOR和ICC参数合并收集。在诊断系统中，训练广义学习（BL）分类器分别对AD、MCI和HC三个阶段进行分类。结合实验结果并与现有研究结果进行对比，该系统在训练时间仅为2 s的情况下，平均准确率达到99.6%。此外，受影响的大脑区域及其功能之间的映射可以解释AD和MCI患者的共同症状。

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引用次数: 0

Advancing precision psychiatry: Machine learning integration with neuroimaging for early detection and diagnosis of Obsessive-Compulsive Disorder 推进精确精神病学：机器学习与神经成像的集成，用于强迫症的早期检测和诊断

IF 2.1 4区医学 Q3 CLINICAL NEUROLOGY

Psychiatry Research: Neuroimaging

Pub Date : 2026-01-12 DOI: 10.1016/j.pscychresns.2026.112137

Wijdan S. Aljebreen , Norah A. Alturaiqi , Muna I. Almushyti , Haifa F. Alhasson , Shuaa S. Alharbi

Early detection of Obsessive-Compulsive Disorder (OCD), a chronic mental health condition characterized by intrusive thoughts and repetitive behaviors, is crucial for improving patient outcomes. Traditional diagnostic methods rely heavily on subjective assessments, which are often delayed or inaccurate. This review explores recent advancements in machine learning (ML) models – particularly hybrid and explainable AI (XAI) approaches – integrated with neuroimaging modalities such as structural (sMRI) and functional MRI (fMRI), as well as biochemical and clinical biomarkers. Studies demonstrate that ML techniques, including support vector machines (SVM), convolutional neural networks (CNN), and deep learning hybrids, can achieve diagnostic accuracies exceeding 90%. XAI methods like SHAP enhance interpretability and clinical trust. Despite promising results, challenges such as dataset variability, limited generalizability, and ethical concerns remain. The review highlights the need for multimodal data integration, scalable models, and privacy-preserving frameworks to support clinical adoption. By shifting from subjective assessments to ML-driven diagnostics, this paradigm promotes earlier detection, personalized treatment, and improved outcomes—advancing precision psychiatry in OCD care.

强迫症（OCD）是一种以侵入性思想和重复性行为为特征的慢性精神健康状况，早期发现对改善患者的治疗效果至关重要。传统的诊断方法严重依赖于主观评估，这往往是延迟或不准确的。本综述探讨了机器学习（ML）模型的最新进展-特别是混合和可解释的AI （XAI）方法-与神经成像模式（如结构（sMRI）和功能MRI (fMRI)，以及生化和临床生物标志物）相结合。研究表明，ML技术，包括支持向量机（SVM）、卷积神经网络（CNN）和深度学习混合，可以实现超过90%的诊断准确率。像SHAP这样的XAI方法提高了可解释性和临床信任度。尽管取得了可喜的结果，但数据集可变性、有限的可泛化性和伦理问题等挑战仍然存在。该综述强调需要多模式数据集成、可扩展模型和隐私保护框架来支持临床应用。通过从主观评估转向机器学习驱动的诊断，这种模式促进了早期发现、个性化治疗和改善的结果——推进了强迫症治疗的精确精神病学。

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引用次数: 0

Identification of major depressive disorder based on Triple-GCN model constructed with multimodal elastic network from higher-order brain connectivity 基于高阶脑连通性多模态弹性网络构建的三重gcn模型识别重度抑郁症

IF 2.1 4区医学 Q3 CLINICAL NEUROLOGY

Psychiatry Research: Neuroimaging

Pub Date : 2026-01-11 DOI: 10.1016/j.pscychresns.2026.112142

Ying Zou , Helu Shan , Yuan Li

Major Depressive Disorder (MDD) is the common mental health disease threatening human well-being. Several neuroimaging studies show that analyzing neural connectivity patterns improved diagnostic accuracy, though most approaches overlook node-edge interactions. Our study proposed an integrated approach combining LASSO and Ridge regression algorithms with brain connectivity features. Using both sMRI and fMRI data, we constructed a Multi-feature(gray matter volume/ALFF/Reho) Fusion Elastic Net (MFEN) framework to enhance MDD identification. Furthermore, we improved the Graph Convolutional Neural Network (GCN) algorithm by incorporating a self-attention mechanism and applied a triple Siamese Network to enhance feature extraction. Our proposed method of MDD identification was experimented on 2048 first-episode drug-naive MDD patients and 2562 healthy controls, using rs-fMRI data and sMRI features from the UK Biobank database. Results demonstrated that the extracted features significantly enhanced discriminative capability, establishing the foundation for identifying more reliable biomarkers in MDD patients. By integrating these techniques with elastic networks, the classification accuracy for MDD detection improved substantially to 89%, highlighting the framework's superior performance in mental health diagnostics. In summary, this MDD identification framework proved highly effective and may offer novel insights for auxiliary diagnosis of other neuropsychiatric disorders in clinical practice.

重度抑郁症（MDD）是威胁人类健康的常见精神疾病。一些神经影像学研究表明，分析神经连接模式提高了诊断的准确性，尽管大多数方法忽略了节点-边缘的相互作用。我们的研究提出了一种结合LASSO和Ridge回归算法与脑连接特征的综合方法。利用sMRI和fMRI数据，我们构建了一个多特征（灰质体积/ALFF/Reho）融合弹性网（MFEN）框架来增强MDD识别。此外，我们通过引入自关注机制改进了图卷积神经网络（GCN）算法，并应用三重暹罗网络来增强特征提取。我们采用来自UK Biobank数据库的rs-fMRI数据和sMRI特征，对2048名首次用药的MDD患者和2562名健康对照者进行了MDD鉴定方法的实验。结果表明，提取的特征显著增强了识别能力，为识别更可靠的MDD患者生物标志物奠定了基础。通过将这些技术与弹性网络相结合，MDD检测的分类准确率大大提高到89%，突出了该框架在精神健康诊断方面的优越性能。综上所述，该MDD识别框架被证明是非常有效的，并可能在临床实践中为其他神经精神疾病的辅助诊断提供新的见解。

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引用次数: 0

Structural connectivity correlates of response to electroconvulsive therapy in treatment-resistant depression 难治性抑郁症患者对电休克治疗反应的结构连通性相关。

IF 2.1 4区医学 Q3 CLINICAL NEUROLOGY

Psychiatry Research: Neuroimaging

Pub Date : 2026-01-09 DOI: 10.1016/j.pscychresns.2026.112141

María Eugenia Samman , Leticia Fiorentini , Delfina Lahitou Herlyn , Aki Tsuchiyagaito , Mariana N. Castro , Elsa Costanzo , Luis Ignacio Brusco , Joan A. Camprodon , Cecilia Forcato , Salvador M. Guinjoan , Mirta F. Villarreal

Electroconvulsive therapy (ECT) remains the most effective intervention for treatment-resistant depression (TRD). We investigated whether cortico-limbic structural connectivity is associated with ECT response. Twenty-nine TRD patients underwent bifrontal ECT and baseline probabilistic tractography assessing connectivity between amygdala, anterior insula (aINS), orbitofrontal cortex (OFC), posterior cingulate cortex (PCC), posterior ventrolateral (VLPFC) and dorsolateral prefrontal cortex (DLPFC), subgenual cingulate cortex (SGCC) and thalamus. Stronger connectivity within a subnetwork comprising bilateral OFC, bilateral aINS, left SGCC and right DLPFC was associated with poorer ECT response showing predictive value for treatment outcome. Conversely, thalamus–PCC connectivity is related to greater baseline severity and better ECT response. In TRD, depressive symptom scores negatively correlated with fronto-limbic-thalamic connectivity. Our findings highlight a fronto-limbic subnetwork whose hyperconnectivity may reflect maladaptive regulation limiting neuromodulation efficacy. In contrast, thalamic connectivity may act as a hub linking fronto-limbic and default mode network circuits, potentially facilitating receptive pathways supporting therapeutic response and guiding anatomically precise neuromodulation strategies.

电休克疗法（ECT）仍然是治疗难治性抑郁症（TRD）最有效的干预手段。我们调查了皮质-边缘结构连接是否与ECT反应相关。29例TRD患者接受了双额电刺激和基线概率神经束造影，评估杏仁核、前岛（aINS）、眶额皮质（OFC）、后扣带皮质（PCC）、后腹外侧（VLPFC）和背外侧前额叶皮质（DLPFC）、亚膝扣带皮质（SGCC）和丘脑之间的连通性。子网络（包括双侧OFC、双侧aINS、左侧SGCC和右侧DLPFC）内更强的连通性与较差的ECT反应相关，显示出对治疗结果的预测价值。相反，丘脑- pcc连通性与更大的基线严重程度和更好的ECT反应有关。在TRD中，抑郁症状得分与额边缘丘脑连通性呈负相关。我们的研究结果强调了一个额边缘子网络，其超连通性可能反映了限制神经调节功效的不适应调节。相比之下，丘脑的连通性可能作为连接额边缘和默认模式网络回路的枢纽，潜在地促进支持治疗反应的接受通路，并指导解剖学上精确的神经调节策略。

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引用次数: 0

EEG-based schizophrenia classification using attention-integrated deep convolutional networks 基于脑电图的精神分裂症分类使用注意整合深度卷积网络。

IF 2.1 4区医学 Q3 CLINICAL NEUROLOGY

Psychiatry Research: Neuroimaging

Pub Date : 2026-01-08 DOI: 10.1016/j.pscychresns.2026.112138

Anjali Sagar Jangde, Gyanendra Kumar Verma

Schizophrenia is a complex psychiatric disorder marked by cognitive and perceptual disruptions, for which electroencephalography (EEG) provides a valuable non-invasive biomarker. In this study, we propose a convolutional attention-based deep learning framework for the automatic detection of schizophrenia from EEG signals. The model integrates spatial feature extraction via convolutional layers with an attention mechanism that adaptively focuses on discriminative temporal patterns within the EEG. We have conducted experiments on two publicly available datasets, the Moscow EEG dataset and the IBIB PAN dataset. Performance on the Moscow dataset was lower, with an accuracy of 73.98%, likely due to age-related neural variability and limited recording duration. The model achieved a classification accuracy of 98.45% on the IBIB PAN dataset, demonstrating strong generalization and discriminative capability. These results highlight the potential of attention-augmented convolutional networks for schizophrenia detection, while also underscoring the challenges of generalizing across datasets with differing demographic and acquisition characteristics.

精神分裂症是一种以认知和知觉障碍为特征的复杂精神疾病，脑电图（EEG）为其提供了一种有价值的非侵入性生物标志物。在这项研究中，我们提出了一个基于卷积注意的深度学习框架，用于从脑电图信号中自动检测精神分裂症。该模型将卷积层的空间特征提取与自适应关注EEG内判别时间模式的注意机制相结合。我们在两个公开的数据集上进行了实验，莫斯科脑电图数据集和IBIB PAN数据集。莫斯科数据集的性能较低，准确率为73.98%，可能是由于与年龄相关的神经变异性和有限的记录时间。该模型在IBIB PAN数据集上的分类准确率达到98.45%，具有较强的泛化和判别能力。这些结果强调了注意力增强卷积网络在精神分裂症检测中的潜力，同时也强调了在具有不同人口统计学和习得特征的数据集上进行泛化的挑战。

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引用次数: 0

Early effects of oral naltrexone on craving, resting state and cue-induced brain activation in opioid use disorder: a prospective fMRI study 口服纳曲酮对阿片类药物使用障碍的渴望、静息状态和线索诱导的脑激活的早期影响：一项前瞻性fMRI研究

IF 2.1 4区医学 Q3 CLINICAL NEUROLOGY

Psychiatry Research: Neuroimaging

Pub Date : 2026-01-08 DOI: 10.1016/j.pscychresns.2026.112139

Malsawmkima Kullai , Sourav Khanra , Justin Raj , Chandramouli Roy

Background

Opioid use disorder (OUD) is characterized by intense cue-induced craving and high relapse risk. This longitudinal fMRI study investigated whether oral naltrexone (50mg/day) (oral-NTX) modulates neural and behavioral responses to opioid cues, as well as resting-state brain connectivity.

Methods

Thirty male patients with moderate–severe OUD underwent fMRI during an opioid-versus-neutral image task at baseline and after 2 weeks of oral-NTX treatment. The DDQ and OCDUS were administered for craving assessment. Task fMRI data was analyzed with linear mixed-effects models (3dLME). Resting-state fMRI was analyzed for ROI-to-ROI functional connectivity changes in key craving-related regions.

Results

Oral_NTX significantly reduced both DDQ and OCDUS scores (p<0.01). Task-based fMRI revealed significant reductions in cue-induced activation in the anterior cingulate cortex (ACC) and cerebellum (whole-brain p<0.05, cluster-corrected). ROI analyses confirmed pre-to-post decreases in ACC and cerebellar activation (t>3.5, p<0.05). Larger craving reductions correlated with greater left superior temporal deactivation (t≈1.97, p<0.05). Resting-state connectivity analysis showed significant attenuation of intrinsic functional coupling between ACC–insula, nucleus accumbens (NAc)–amygdala, and cerebellum–hippocampus (p<0.05). These decreases complement task findings, indicating widespread dampening of salience and reward network interactions following oral-NTX.

Conclusion

Oral-NTX reduces cue-driven activation in cortical and cerebellar regions while dampening resting-state connectivity within craving circuits.

背景：阿片类药物使用障碍（OUD）的特点是强烈的线索诱导渴望和高复发风险。这项纵向功能磁共振成像研究调查了口服纳曲酮（50mg/天）（口服ntx）是否调节对阿片类药物线索的神经和行为反应，以及静息状态下的大脑连接。方法30例男性中重度OUD患者在基线和口服ntx治疗2周后分别在阿片类与中性图像任务期间进行fMRI检查。用DDQ和OCDUS评估渴望程度。任务fMRI数据采用线性混合效应模型（3dLME）进行分析。静息状态fMRI分析了关键渴望相关区域的ROI-to-ROI功能连接变化。结果soral_ntx显著降低DDQ和OCDUS评分（p<0.01）。基于任务的fMRI显示，前扣带皮层（ACC）和小脑的线索诱导激活显著减少（全脑p<；0.05，簇校正）。ROI分析证实了前后ACC和小脑激活减少（t>3.5, p<0.05）。更大的渴望减少与更大的左颞上失活相关（t≈1.97,p<0.05）。静息状态连通性分析显示acc -脑岛、伏隔核(NAc) -杏仁核和小脑-海马之间的内在功能耦合显著减弱（p<0.05）。这些减少补充了任务结果，表明口服ntx后显著性和奖励网络相互作用的广泛抑制。结论口服ntx减少了皮层和小脑区域的线索驱动激活，同时抑制了渴望回路的静息状态连接。

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引用次数: 0

Dysfunction of the superior occipital gyrus in individuals with subclinical social anxiety and its mediating effect on gray matter structure 亚临床社交焦虑患者枕上回功能障碍及其对灰质结构的调节作用

IF 2.1 4区医学 Q3 CLINICAL NEUROLOGY

Psychiatry Research: Neuroimaging

Pub Date : 2026-01-07 DOI: 10.1016/j.pscychresns.2026.112140

Fangfang Huang , Shuai Ren , Yuan Huang , Yuqi Chen , MingZhu Wang , Xiaoyi Chang , Kaile Liu , Siying Guo , Xingnuo Liu

The investigation of neuroimaging abnormalities of young adults with subclinical social anxiety will contribute to understand the brain mechanism of social anxiety during developing on early stage. In this study, we recruited twenty-six young adults with subclinical social anxiety and matched healthy controls to examine their resting-state brain functional changes reflected by amplitude of low-frequency fluctuation (ALFF), functional connectivity (FC) and effective connectivity (EC). Mediating effect models were used to investigate the mediation of brain functional alterations between gray matter structure and social anxiety. Subjects with subclinical social anxiety exhibited increased ALFF in the left superior occipital gyrus (SOG), heightened FC between the left SOG and the right orbital part inferior frontal gyrus, decreased EC from the left SOG to bilateral postcentral gyrus (PCG), and increased EC from bilateral PCG and the right precuneus to the left SOG. A complete mediating effect of gray matter volume in the left SOG on subclinical social anxiety through ALFF in the left SOG was observed. In conclusion, dysfunction of the left SOG plays an important role in subclinical social anxiety. Additionally, the spontaneous neural hyperactivities in the left SOG function on social anxiety as a complete mediation of gray matter structure.

研究青少年亚临床社交焦虑的神经影像学异常，有助于了解社交焦虑发展早期的脑机制。在这项研究中，我们招募了26名患有亚临床社交焦虑的年轻成年人和匹配的健康对照者，研究了低频波动幅值（ALFF）、功能连通性（FC）和有效连通性（EC）所反映的静息状态脑功能变化。采用中介效应模型探讨脑灰质结构与社交焦虑之间脑功能改变的中介作用。亚临床社交焦虑受试者表现为左侧枕上回ALFF增高，左侧枕上回与右侧眶部额下回之间的FC增高，左侧枕上回至双侧中央后回的EC降低，双侧枕上回和右侧楔前叶至左侧枕上回的EC增高。我们观察到左侧SOG灰质体积通过左侧SOG ALFF对亚临床社交焦虑的完全中介作用。综上所述，左侧SOG功能障碍在亚临床社交焦虑中起重要作用。此外，左侧SOG自发性神经亢进对社交焦虑的作用是灰质结构的完全中介。

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引用次数: 0