Neuroimage. Reports最新文献

{"title":"Corrigendum to “Diffusion MRI-based analysis of functional alterations of the glymphatic system in children with non-lesional epilepsy” [Neuroimag. Rep. 2025 5(1) 100237]","authors":"Bixia Wu, Gengbiao Zhang, Yanting Wang, Hongyi Zheng, Hui Tan, Wenbin Zheng","doi":"10.1016/j.ynirp.2026.100387","DOIUrl":"10.1016/j.ynirp.2026.100387","url":null,"abstract":"","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"6 3","pages":"Article 100387"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148934359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"3D brain tumor segmentation using an improved V-Net architecture and 3D attention gate","authors":"Sima Esmaeilzadeh Asl ,&nbsp;Mehdi Chehel Amirani ,&nbsp;Hadi Seyedarabi","doi":"10.1016/j.ynirp.2026.100364","DOIUrl":"10.1016/j.ynirp.2026.100364","url":null,"abstract":"<div><div>Tumor segmentation involves the identification and definition of tumors in medical images, which is crucial for cancer diagnosis and treatment as it enables accurate measurement of the size and shape of the tumor. This can be done manually or automatically with computer algorithms. Automated segmentation reduces the time and effort of manual methods while enhancing accuracy and stability. This article uses BraTS2021 data for 3D brain tumor segmentation. Initially, an N4 BiasField Correction Filter preprocesses MRI data. An improved V-Net is used for segmenting enhanced tumors, whole tumors, and tumor cores. The V-Net processes image data in three dimensions, with enhancements in the decoder and encoder sections, improving overall performance. The encoder benefits from residual and dilated convolution layer, while the Attention Gate enhances the decoder. In the encoder, the dilated convolution layer enhances performance, while residual layers ensure that adding more layers doesn't harm the network. All four MRI types are input into the 3D improved V-Net, achieving Dice coefficients of 87%, 81.2%, and 74.43% for whole tumors, tumor cores, and enhanced tumors respectively. Results indicate this method effectively segments 3D brain MRI images.</div></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"6 2","pages":"Article 100364"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148230740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"“Tract-specific white matter microstructure associated with early cognitive development in infants” [Neuroimage Rep 6 (2026) 100336]","authors":"Eun Soo Kim ,&nbsp;Martin A. Styner ,&nbsp;Weili Lin ,&nbsp;Jed T. Elison ,&nbsp;Sooyeon Sung ,&nbsp;Mi Jung Kwon ,&nbsp;Dae Young Yoon","doi":"10.1016/j.ynirp.2026.100348","DOIUrl":"10.1016/j.ynirp.2026.100348","url":null,"abstract":"","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"6 2","pages":"Article 100348"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148230741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-Powered MRI Radiomics and Deep Learning for Preoperative Prediction of Cavernous Sinus Invasion in Pituitary Adenomas: A Clinically Oriented Review of Current Evidence","authors":"Farzan Asadirad ,&nbsp;Mohammad Rezaei ,&nbsp;Parna Ghannadikhosh ,&nbsp;Hadi Salehpour ,&nbsp;Alireza Motamedi ,&nbsp;Mobin Mobadersani ,&nbsp;Niloofar soleimannezhad ,&nbsp;Sevil Ghaffarzadeh Rad ,&nbsp;Esmaeil Gharepapagh ,&nbsp;Sahar Rezaei ,&nbsp;Mahsa Karbasi ,&nbsp;Hossein Arabi","doi":"10.1016/j.ynirp.2026.100354","DOIUrl":"10.1016/j.ynirp.2026.100354","url":null,"abstract":"<div><h3>Background</h3><div>Pituitary adenomas represent one of the most common intracranial tumors, and cavernous sinus invasion (CSI remains a major challenge for surgical management. Although the Knosp grading system provides a widely used radiological framework, its subjective nature and inter-observer variability limit diagnostic reliability. In recent years, advanced computational methods have been investigated to improve the preoperative prediction of invasion.</div></div><div><h3>Objective</h3><div>This review synthesizes current evidence on the use of radiomics, machine learning (ML), and deep learning (DL) approaches in the detection and assessment of CSI in pituitary adenomas, with particular emphasis on their comparative performance against traditional imaging methods.</div></div><div><h3>Methods</h3><div>Studies employing MRI-based radiomic feature extraction, ML classifiers, and convolutional neural networks were analyzed. Reported models commonly incorporated intensity, texture, and shape descriptors, or applied end-to-end DL architectures for automated prediction. Performance metrics such as accuracy, sensitivity, specificity, AUC, and Dice similarity coefficients were compared across studies, with Knosp grade serving as a frequent benchmark.</div></div><div><h3>Results</h3><div>Evidence suggests that ML and DL models consistently outperform conventional MRI interpretation in predicting CSI. Radiomics pipelines integrating quantitative imaging features with clinical variables achieved high diagnostic accuracy, while CNN-based models trained on contrast-enhanced MRI often exceeded AUC values of 0.85. Furthermore, automated segmentation frameworks demonstrated reliable delineation of tumor boundaries, facilitating improved assessment of invasive behavior. Despite promising outcomes, limitations such as small sample sizes, single-center designs, and lack of external validation restrict broad clinical adoption.</div></div><div><h3>Conclusions</h3><div>Radiomics and AI-driven approaches show substantial potential for enhancing preoperative evaluation of pituitary adenomas with CSI. Standardized imaging protocols, multicenter collaborations, and transparent model validation are essential for future integration into neurosurgical decision-making.</div></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"6 2","pages":"Article 100354"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148167416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative MRI analysis of deep gray matter aging: Volumetric, T1/T2 ratio, and blood perfusion alterations","authors":"Miaoqi Zhang ,&nbsp;Zhe Sun ,&nbsp;Chao Wang ,&nbsp;Chenyang Li ,&nbsp;Jiangyang Zhang ,&nbsp;Yulin Ge","doi":"10.1016/j.ynirp.2026.100355","DOIUrl":"10.1016/j.ynirp.2026.100355","url":null,"abstract":"<div><h3>Background</h3><div>The deep gray matter nuclei (DGMN)—including the thalamus, caudate, putamen, and pallidum—are essential for cognitive, motor, and affective functions and are highly susceptible to age-related degeneration. However, the combined effects of aging and sex on DGMN volume, microstructure, and perfusion remain incompletely characterized.</div></div><div><h3>Methods</h3><div>Using data from 652 healthy adults (36–89 years) from the Human Connectome Project-Aging cohort, we examined volumetric and perfusion changes across the DGMN. High-resolution T1- and T2-weighted MRI and multi-delay pseudo-continuous arterial spin labeling (pCASL) were used to quantify normalized volume, T1/T2 ratio, cerebral blood flow (CBF), and arterial transit time (ATT) in DGMN regions after perivascular spaces were removed. Age-related trajectories were modeled using linear and quadratic regression, and differences between sexes were examined, with false discovery rate correction applied.</div></div><div><h3>Results</h3><div>Advancing age was associated with significant volumetric decline in all DGMN regions (p &lt; 0.001), prolonged ATT, and reduced CBF, particularly in the caudate and thalamus. The T1/T2 ratio exhibited region-specific nonlinear trajectories, peaking in mid-adulthood and declining thereafter, reflecting its underlying age-related processes of demyelination and iron accumulation. Sex-stratified analyses suggested modest differences in T1/T2, ATT, and CBF trajectories; however, no significant age × sex interactions were observed after correction for multiple comparisons. DGMN volumes correlated negatively with ATT, while T1/T2 ratio correlated inversely with CBF, indicating more complex interactions between structure, tissue properties, and perfusion.</div></div><div><h3>Conclusions</h3><div>This large-scale quantitative MRI study delineates distinct age-related trajectories of DGMN over adult lifespan. Integrating volumetric, T1/T2 ratio, and multi-delay ASL metrics—while correcting for perivascular spaces—enhances sensitivity to subtle changes and provides normative benchmarks for detecting early neurodegenerative alterations.</div></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"6 2","pages":"Article 100355"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148152533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fully automated volumetry of ventricular subregions on computed tomography using object detection and semantic segmentation","authors":"Raffaele Da Mutten ,&nbsp;Olivier Zanier ,&nbsp;Alessandro Carretta ,&nbsp;Giorgio Palandri ,&nbsp;Massimo Bottini ,&nbsp;Daniel de Wilde ,&nbsp;Ulf C. Schneider ,&nbsp;Luca Regli ,&nbsp;Carlo Serra ,&nbsp;Victor E. Staartjes","doi":"10.1016/j.ynirp.2026.100325","DOIUrl":"10.1016/j.ynirp.2026.100325","url":null,"abstract":"<div><h3>Background</h3><div>Our goal was to develop and validate machine learning models that are capable of fully automatic identification and segmentation of frontal, temporal, and posterior horns, the body of the lateral ventricle, the third and fourth ventricle, as well as the atrium on either side.</div></div><div><h3>Methods</h3><div>Patients shunted for hydrocephalus were included. Data from two centers was used for development/external validation, respectively. Manual labelling of ventricular subregions on computed tomography (CT) was performed. First, an object detection algorithm (YOLOv5) was trained. This allowed for precise cropping of the subregions that could then be used as input for a 2D U-Net. For comparison, a nnU-Net was also trained. Precision, recall, mean average precision 50 and 50–95 (mAP50; mAP50-95) were used as performance metrics for the YOLO algorithm. Dice score, Jaccard score, and 95th percentile Hausdorff distance assessed performance for the U-Net.</div></div><div><h3>Results</h3><div>80 CTs from patients at our center were included, as well as 50 from a second center. The mean age was 68.59 ± 15.89 and 75.94 ± 4.17 for the first and second centers, and 43 (52.5%) and 30 (60%) were male. MAP 50, mAP50-95 was 0.728, 0.453 for internal and 0.274, 0.124 for external validation across all classes. Best mean Dice scores were 0.92 ± 0.1 and 0.90 ± 0.05 for the body of the left lateral ventricle.</div></div><div><h3>Conclusions</h3><div>Automatic segmentation and volumetry of ventricles including their subregions was feasible with high precision on computed tomography, potentially helping the clinical evaluation of even subtle changes in ventricular volume.</div></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"6 1","pages":"Article 100325"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146214458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Functional connectivity of youth in family-like residential care in Japan: Impact of reactive attachment disorder and disinhibited social engagement disorder symptoms","authors":"Shoko Shimada ,&nbsp;Toshiki Iwabuchi ,&nbsp;Motofumi Sumiya ,&nbsp;Koji Shimada ,&nbsp;Shinichiro Takiguchi ,&nbsp;Kai Makita ,&nbsp;Akiko Yao ,&nbsp;Takashi X. Fujisawa ,&nbsp;Atsushi Senju ,&nbsp;Akemi Tomoda","doi":"10.1016/j.ynirp.2026.100323","DOIUrl":"10.1016/j.ynirp.2026.100323","url":null,"abstract":"<div><div>Adverse childhood experiences are a risk factor for attachment disorders. While several neuroimaging studies have shown changes in functional networks in children who have experienced institutional care, the results are inconsistent. Furthermore, no research has been conducted on how structured residential care, such as Japan's small-group family-style care, influences attachment-related symptoms and functional connectivity. This study compared attachment-related symptoms (reactive attachment disorder [RAD] and disinhibited social engagement disorder [DSED] symptoms) between youth aged 9–18 years raised in Japanese small-group residential care (RC; n = 31) and those raised in birth families but not in RC (NRC; n = 37). Group differences in resting-state functional connectivity were also analyzed using multivariate pattern analysis (MVPA) on functional magnetic resonance imaging (fMRI) data. MVPA revealed group differences in whole-brain functional connectivity patterns from the right occipital pole and the left lingual gyrus (LLG). Functional connectivity between the LLG and the frontal medial cortex (FMC) was reduced in RC youth. LLG-FMC connectivity was positively correlated with RAD scores, while longer duration of stay in RC was negatively correlated with RAD symptoms. This study highlights caregiving environment's influence on attachment-related symptoms and functional connectivity, higher levels of RAD and DSED symptoms and reduced LLG-FMC functional connectivity in the RC group. However, this study further demonstrated not only the association between longer stays in family-like RC and the reduction of RAD symptoms but also changes in the connectivity. These findings suggest that stable, high-quality care may have the potential to mitigate adverse developmental outcomes.</div></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"6 1","pages":"Article 100323"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"One is not like the other: Examining the neural response to repetitive low-level blast exposure in experienced military personnel","authors":"Timothy K. Lam ,&nbsp;Shawn G. Rhind ,&nbsp;Catherine Tenn ,&nbsp;Ann Nakashima ,&nbsp;Maria Y. Shiu ,&nbsp;Kristen King ,&nbsp;Norleen Caddy ,&nbsp;Janani Vallikanthan ,&nbsp;Mike Crouzat ,&nbsp;Laura Lester ,&nbsp;Oshin Vartanian","doi":"10.1016/j.ynirp.2026.100335","DOIUrl":"10.1016/j.ynirp.2026.100335","url":null,"abstract":"<div><h3>Background</h3><div>Military members often report concussion-like symptoms from repetitive low-level blast (LLB) exposure, defined as overpressure from outgoing munitions like rifles and explosive breaching. Typically, the early stages of concussion and other neurological conditions (e.g., Alzheimer's Disease) lead to hyperconnectivity which is a transient and adaptive brain response to strengthen and establish neural connections to restore brain function. Over time, however, chronic hyperconnectivity can contribute to neurodegeneration. To determine whether LLB exposure also exhibits this connectivity trajectory, this study investigated the neural signature of LLB at two time points: At the chronic stage extrapolated from the duration of an individual's occupational career, and following a recent and concentrated blast regimen.</div></div><div><h3>Methods</h3><div>Forty-six military breachers and snipers underwent a resting state functional magnetic resonance imaging brain scan before and after a training course. Graph theory was used to study the whole-brain network, cross-validated by a principal components analysis (PCA) conducted <em>post-hoc</em>. The pre-course scan was analyzed separately to examine the neural effects of chronic LLB exposure. The pre- and post-course scans were compared to examine the neural effects of recent blast exposure. Military controls without occupational breaching and/or sniping experience underwent the same protocol.</div></div><div><h3>Results</h3><div>At pre-course, breachers and snipers exhibited hyperconnectivity compared to controls. However, after undergoing a recent LLB regimen, only breachers showed hypoconnectivity post-course relative to pre-course compared to controls.</div></div><div><h3>Conclusion</h3><div>The mechanism of repeated LLB overpressure and its associated neural response to this exposure appear to be specific to this condition. Characterizing LLB exposure can help refine assessment and treatment.</div></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"6 1","pages":"Article 100335"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SHAP-based explainable machine learning analysis of reward-related neural connectivity to predict preadolescent irritability","authors":"Faith M. Wariri ,&nbsp;Johanna C. Walker ,&nbsp;Jillian Lee Wiggins ,&nbsp;Jinbo Bi","doi":"10.1016/j.ynirp.2026.100322","DOIUrl":"10.1016/j.ynirp.2026.100322","url":null,"abstract":"<div><div>Preadolescent irritability is a robust transdiagnostic neurodevelopmental predictor of later psychopathology, linked to altered reward processing—a common neurocognitive substrate across psychiatric disorders. However, its neurobiological mechanisms remain unclear. Deep learning (DL) excels in predicting neurodevelopmental vulnerabilities and detecting nonlinear brain-behavior relationships but often lacks explainability. Here, we integrate optimized prediction with explainability to characterize neural mechanisms of irritability using task-based fMRI from a large preadolescent sample (N = 1934; mean age = 9.95 years, 101 Persistently High Irritability [PHI], 1833 Persistently Low Irritability [PLI]). We trained three classifiers—artificial neural network (ANN), random forest (RF), XGBoost—to distinguish PHI from PLI using functional connectivity (FC) during reward anticipation. FC was assessed between four seeds (bilateral amygdala, ventral striatum) and 18 cortical/subcortical regions. Shapley additive explanations (SHAP) identified key connectivity predictors accounting for nonlinear effects. ANN (AUC = 0.73, p &lt; .001) outperformed RF (AUC = 0.63, p = .03), XGBoost (AUC = 0.65, p = .01). SHAP revealed increased contralateral FC (e.g., right amygdala-left middle frontal gyrus) and decreased ipsilateral FC (e.g., left ventral striatum-left insula) generally predicted PHI, except amygdala connectivity, where higher ipsilateral FC predicted PHI. These findings highlight interplay between reward and emotion regulation circuits in persistent irritability, underscoring the potential of explainable DL to improve irritability prediction and enhance understanding of its neural mechanisms.</div></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"6 1","pages":"Article 100322"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of SNAP-25 MnlI polymorphism on brain activity patterns in children with ADHD: Insights from fractional amplitude of low-frequency fluctuation analysis","authors":"Diangang Fang ,&nbsp;Wenxian Huang ,&nbsp;Tong Mo ,&nbsp;Xiaojing Lv ,&nbsp;Guohua Liang ,&nbsp;Binrang Yang ,&nbsp;Hongwu Zeng","doi":"10.1016/j.ynirp.2026.100321","DOIUrl":"10.1016/j.ynirp.2026.100321","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Objective&lt;/h3&gt;&lt;div&gt;SNAP-25, a synaptic vesicle docking protein, carries a polymorphism (rs3746544) in its 3′-UTR region that is associated with ADHD, yet its functional mechanism remains unknown. The purpose of this study is to evaluate the impact of synaptosomal-associated protein 25 (SNAP-25) gene MnlI polymorphism (rs3746544) on spontaneous brain activity in children with attention deficit hyperactivity disorder (ADHD), employing the fractional amplitude of low-frequency fluctuation (fALFF) analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data, to explore its potential neurobiological mechanisms and neuroimaging biomarkers.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;This study enrolled 56 boys with ADHD (aged 8–10 years) and 21 age-matched healthy boys as healthy controls (HCs). According to the SNAP-25 MnlI genotype, ADHD patients were divided into two groups: the TT homozygote group (TT group, n = 36) and the G-allele carrier group (TG group, n = 20). Rs-fMRI data were acquired and analyzed using fALFF to measure spontaneous brain activity.&lt;/div&gt;&lt;div&gt;One-sample &lt;em&gt;t&lt;/em&gt;-tests were performed to calculate fALFF maps for each group, setting the threshold as a cluster greater than 20 voxels, with &lt;em&gt;P&lt;/em&gt; &lt; 0.01 after AlphaSim correction. Two-sample &lt;em&gt;t&lt;/em&gt;-tests were performed to calculate the differences in fALFF values among the TT, TG, and HCs groups, with age as a covariate. A cluster of greater than 20 voxels, with &lt;em&gt;P&lt;/em&gt; &lt; 0.01 after AlphaSim correction, was considered to have statistically significant differences. Assessed the Working Memory Index (WMI) using the Wechsler Intelligence Scale for Children-IV (WISC-IV) in children with ADHD from the TT and TG groups.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;One-sample &lt;em&gt;t&lt;/em&gt;-tests revealed that children with ADHD group (both TT and TG group) exhibited significantly lower fALFF values in the default mode network (DMN) and parieto-occipital cortex compared to HCs, while showing increased fALFF located in the posterior cerebellar lobe; Two-sample &lt;em&gt;t&lt;/em&gt;-tests demonstrated that: (a) Compared to HCs, the ADHD group (both TT and TG group) showed widespread reductions of fALFF values across multiple brain regions, including the posterior cingulate cortex and precuneus. The TG group showed more pronounced decreases when compared with the TT group. (b) In comparison to the TG group, the TT group exhibited higher fALFF values in higher-order cognitive regions, such as the right superior frontal gyrus and left medial frontal gyrus, but lower fALFF values in the posterior cerebellar lobe and posterior cingulate cortex. The TT group had significantly higher WMI compared to the TG group (&lt;em&gt;t&lt;/em&gt; = 2.098, &lt;em&gt;P&lt;/em&gt; &lt; 0.05).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusions&lt;/h3&gt;&lt;div&gt;The SNAP-25 gene MnlI polymorphism has an impact on spontaneous brain activity in children with ADHD, as measured by fALFF. This study reveals the potential mechanisms from the perspective ","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"6 1","pages":"Article 100321"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}