Journal of Neuroimaging最新文献

{"title":"A Task-Based Functional MRI Double Dissociation of Visual Language and Feature Processing in Healthy Adults","authors":"Solana Redway,&nbsp;Helen Hsiao,&nbsp;Erica Zeng,&nbsp;John Shahki,&nbsp;Todd S. Woodward","doi":"10.1111/jon.70139","DOIUrl":"10.1111/jon.70139","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background/Purpose</h3>\u0000 \u0000 <p>Task-based functional MRI (fMRI) can identify spatial and temporal brain configurations of blood-oxygen-level-dependent (BOLD) activity for dissociable cognitive functions. Evidence for the discriminability of spatial/temporal/functional combinations (i.e., cognitive modes) can be achieved through the dissociation of task-based BOLD signal changes following whole-brain dimension reduction. A double dissociation can be achieved by task-induced BOLD-change activation of mode A in condition A but not B, and vice versa for activation in mode B. In this study, we analyzed the Coherence–Semantic task from the Midnight Scan Club dataset to test for a double dissociation of Focus on Visual Features (FoVF) from Language (LAN) modes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The Coherence–Semantic task involved 10 healthy participants scanned over 10 sessions on a 3-Tesla MRI scanner. The task included (1) identifying coherent or random movements of dots (Coherence condition) and (2) identifying a noun versus a verb (Semantic condition). fMRI data were analyzed by combining finite impulse response multivariate multiple regression with whole-brain dimensionality reduction. The dissociations were evaluated at the level of subject-specific task-induced BOLD changes using repeated-measures ANOVAs comparing the Coherence and Semantic conditions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>A double dissociation was observed: FoVF showed task-induced increases in BOLD activation during the Coherence condition, with no significant task-related activation during the Semantic condition, and vice versa for LAN. The default mode (DM) also showed task-related deactivation in both conditions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This double dissociation provides evidence for the temporal and functional discriminability of FoVF and LAN, contributing to evidence supporting the discriminability of cognitive modes detectable by fMRI.</p>\u0000 </section>\u0000 </div>","PeriodicalId":16399,"journal":{"name":"Journal of Neuroimaging","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jon.70139","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148436815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning for Distal Medium-Vessel Occlusion Detection: Advances, Challenges, and Future Directions","authors":"Omar M. Hamam,&nbsp;Adyasha M. Pradhan,&nbsp;Hamza A. Salim,&nbsp;Andrew Cho,&nbsp;Dhairya A. Lakhani,&nbsp;Risheng Xu,&nbsp;Shyam Majmundar,&nbsp;Vaibhav Vagal,&nbsp;Ferdinand Hui,&nbsp;Adam A. Dmytriw,&nbsp;Adrien Guenego,&nbsp;Kambiz Nael,&nbsp;Gregory W. Albers,&nbsp;Jeremy J. Heit,&nbsp;Tobias D. Faizy,&nbsp;Max Wintermark,&nbsp;Vivek Yedavalli","doi":"10.1111/jon.70138","DOIUrl":"10.1111/jon.70138","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>Distal medium-vessel occlusions (DMVOs) account for roughly 25%–40% of acute ischemic strokes and often evade early detection, delaying treatment, and worsening outcomes. Conventional imaging (non-contrast CT, CT angiography [CTA], MR angiography [MRA]) can miss smaller distal thrombi, and even experienced readers have limited sensitivity, which can be as low as 35%. Recent studies highlight that advanced neuroimaging (CT perfusion, multiphase CTA, magnetic resonance imaging [MRI]) and automated analysis improve DMVO identification. In particular, machine learning (ML) and deep learning algorithms have shown promise in detecting subtle occlusions on multimodal stroke imaging. This review summarizes current imaging approaches for DMVOs, surveys ML-based detection methods, and examines validation studies and clinical evidence. We discuss barriers to clinical integration, including the need for large, annotated datasets and regulatory validation. Finally, we outline future directions: improved algorithms (explainable AI, multimodal networks), prospective trials, and workflow integration in the neurovascular service. In sum, ML-driven DMVO detection holds potential to augment rapid stroke care, but further research and collaboration are needed to translate these tools into routine practice.</p>\u0000 </section>\u0000 </div>","PeriodicalId":16399,"journal":{"name":"Journal of Neuroimaging","volume":"36 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148301996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Synthetic Lesional MR Images in Automated Focal Cortical Dysplasia Detection in Low-Data Scenarios","authors":"Prabhjot Kaur,&nbsp;Hakim Ouaalam,&nbsp;Sedat Kandemirli,&nbsp;Sanjay P. Prabhu,&nbsp;Simon K. Warfield","doi":"10.1111/jon.70137","DOIUrl":"10.1111/jon.70137","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background and Purpose</h3>\u0000 \u0000 <p>Automated detection of focal cortical dysplasia (FCD) requires large volumes of voxelwise-lesion-delineated MRI data, which are difficult to acquire. This study aims to generate synthetic MRI data exhibiting FCD, assess its realism, and evaluate its impact on automated FCD detection—particularly in reducing the need for manual annotations.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>T1-weighted (T1w) and T2-weighted-fluid-attenuated inversion recovery (FLAIR) MRI scans from 131 FCD patients and 90 healthy controls from multiple (3) sites were retrospectively studied. Synthetic MRIs were generated by conditioning a generative network on binary FCD mask. Two neuroradiologists identified real images from a random set of 14 real and 14 synthetic scans. Three nnU-Net models were trained to detect FCD using (i) real-only (35-FCD/35-controls), (ii) real (35-FCD/35-controls) + synthetic augmentation, and (iii) expanded real data (70-FCD/70 controls).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Experts showed limited ability to distinguish real from synthetic images, with classification accuracy of 60% for T1w and 70% for FLAIR (inter-rater agreement <i>κ</i> = 0.86). Augmenting automated FCD detection with synthetic data increased sensitivity by 8.14% (<i>p</i> = 0.12) and improved model confidence at true lesion sites (0.83 ± 0.11 to 0.89 ± 0.12; <i>p</i> = 0.02). The expanded real-data model further improved sensitivity to 73.8% (<i>p</i> &lt; 0.001) and confidence to 0.90 ± 0.14 (<i>p</i> = 0.01).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Conditional generative networks can generate realistic synthetic FCD-MRIs, reducing labeled data needs by ∼20% while maintaining equivalent sensitivity. Equivalent amounts of real data, when available, remain more effective than synthetic augmentation.</p>\u0000 </section>\u0000 </div>","PeriodicalId":16399,"journal":{"name":"Journal of Neuroimaging","volume":"36 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148156914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":16399,"journal":{"name":"Journal of Neuroimaging","volume":"36 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148066641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":16399,"journal":{"name":"Journal of Neuroimaging","volume":"36 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148066642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":16399,"journal":{"name":"Journal of Neuroimaging","volume":"36 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148066639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":16399,"journal":{"name":"Journal of Neuroimaging","volume":"36 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148066640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association of Acute Ischemic Stroke Volume With Post-Stroke Epilepsy Development.","authors":"Vijaya Dasari, Kunio Nakamura, Bhaskar Thoomukuntla, Nicolas Thompson, Shumei Man, Ken Uchino, Andrew Russman, M Shazam Hussain, Vineet Punia","doi":"10.1111/jon.70134","DOIUrl":"10.1111/jon.70134","url":null,"abstract":"<p><strong>Background and purpose: </strong>Stroke is a leading cause of epilepsy, especially in older adults. The SeLECT score remains the standard among post-stroke epilepsy (PSE) prediction tools. However, its broader validation is limited by the need to manually extract neuroimaging predictors (cortical and middle cerebral artery [MCA] involvement). Unlike the CAVE score, SeLECT did not evaluate acute stroke volume, which can now be quantified automatically. We aimed to determine whether stroke volume independently predicts PSE and compare its predictive contribution to SeLECT's neuroimaging variables.</p><p><strong>Methods: </strong>SeLECT variables were manually extracted. Diffusion-weighted imaging volume was quantified using a validated convolutional neural network. Cox proportional hazards models for time to PSE were built by adding stroke volume (per 10 mL) and then removing cortical and/or MCA involvement. For each model, we analyzed variable significance, discrimination, and calibration.</p><p><strong>Results: </strong>Among 221 patients, 35 (15.8%) developed PSE. In our cohort, the original SeLECT score and the refit model had a C-index of 0.669 and 0.642, respectively. Adding stroke volume resulted in a C-index of 0.656. Retaining volume while removing cortical and MCA involvement resulted in C-indices of 0.664 and 0.668, respectively. Keeping stroke volume and removing both variables increased the C-index to 0.679. Calibration was good for all models. Stroke volume in crease by 10 mL was an independent predictor of 12% increased PSE risk across all models.</p><p><strong>Conclusions: </strong>Acute stroke volume is an independent PSE predictor. Stroke volume offered comparable discrimination to the neuroimaging components of the SeLECT score, supporting its use as a scalable and automated alternative.</p>","PeriodicalId":16399,"journal":{"name":"Journal of Neuroimaging","volume":"36 3","pages":"e70134"},"PeriodicalIF":2.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13145306/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147838993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":16399,"journal":{"name":"Journal of Neuroimaging","volume":"36 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148125084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":16399,"journal":{"name":"Journal of Neuroimaging","volume":"36 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148125085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}