Frontiers in neuroimaging最新文献

{"title":"Hypereosinophilic syndrome with central nervous system involvement: a case report.","authors":"Wenjuan Xu, Chao Zhang, Fan Wang, Xiaomin Liu, Xiao Zhang, Fei Mao, Xinyi Wang, Xiaoyu Zhang","doi":"10.3389/fnimg.2026.1783329","DOIUrl":"10.3389/fnimg.2026.1783329","url":null,"abstract":"<p><p>Cerebral infarction is the most common neurological complication in patients with hypereosinophilic syndromes (HES), typically occurring in border-zone regions. However, intracranial artery stenosis is rarely observed in HES, and the underlying mechanisms of cerebral infarction remain largely unknown. Here, we report a case of HES complicated by acute ischemic stroke secondary to severe stenosis of left middle cerebral artery (MCA). A diagnosis of idiopathic HES was established based on eosinophilia (14.08%) in bone marrow aspiration and negative genetic testing. Without contraindications, intravenous thrombolysis with alteplase was administered, resulting in a decrease of the National Institutes of Health Stroke Scale score from 13 to 2. High-resolution magnetic resonance imaging (HR-MRI) showed homogeneous, concentric wall thickening and enhancement in the terminal segments of the left internal carotid artery and at the origin of the MCA, indicating an inflammatory process. Follow-up HR-MRI at 17 months demonstrated a reduction in vessel wall enhancement after immunosuppressive therapy. Over the two-year follow-up period, the eosinophil count remained within the range of 0.22-1.09 × 10⁹/L, and no stroke recurrence was observed. In the literature review, only three cases of stroke associated with HES reported intracranial stenosis, all located in the M1 segment of the MCA. Their clinical outcomes improved following immunosuppressive therapy. Thus, intracranial large artery stenosis is a rare etiology of stroke in patients with HES. Homogeneous vessel wall enhancement on HR-MRI suggests an underlying vasculitis, which appears responsive to immunosuppressive therapy.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"5 ","pages":"1783329"},"PeriodicalIF":2.5,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13099763/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147791009","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":"Comparison of commercial 1Tx32Rx vs. 8Tx32Rx head coils for routine 7T neuroimaging.","authors":"Carina Graf, Belinda Ding, Catarina Rua, Krzysztof Klodowski, Christopher T Rodgers","doi":"10.3389/fnimg.2026.1736950","DOIUrl":"10.3389/fnimg.2026.1736950","url":null,"abstract":"<p><strong>Introduction: </strong>Human 7T MRI systems are manufactured by three vendors (Siemens, Philips, and GE) who all provide equivalent head coils from the same 3rd party manufacturer. Furthermore, many 7T MRI sites have two head coils available for neuroimaging: a 1Tx32Rx head coil for conventional single-channel transmit imaging and an 8Tx32Rx head coil for parallel-transmit (pTx) imaging.</p><p><strong>Methods: </strong>We compared the performance of these coils in six healthy volunteers. All scans were done on a 7T MRI (MAGNETOM Terra, Siemens, Germany). We tested seven sequences in wide use at our centre: B₀ and B₁ ⁺ mapping, anatomical T₁-weighted MP2RAGE, R₂*-mapping, single-voxel spectroscopy (MRS), echo-planar imaging time series, and diffusion tensor imaging (DTI). Sequences were run unmodified and without any pTx pulses.</p><p><strong>Results: </strong>Data quality is comparable for both coils. The 8Tx32Rx coil had improved B₁ ⁺ in inferior brain regions, enhanced spinal cord visibility in the cervical spine on anatomical MP2RAGE, higher SNR in MRS of the brainstem, and more defined fitted white matter tracts in the DTI images. All sequences showed acceptable data quality with the 8Tx32Rx coil.</p><p><strong>Conclusion: </strong>It is reasonable to substitute the 8Tx32Rx coil for the 1Tx32Rx coil for standard neuroimaging protocols. This will enable advanced parallel transmit sequences to be added to protocols with minimal disruption.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"5 ","pages":"1736950"},"PeriodicalIF":2.5,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13056848/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147647475","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":"Retraction: Initial insights into post-contrast enhancement in ultra-low-field MRI: Case Report.","authors":"","doi":"10.3389/fnimg.2026.1816667","DOIUrl":"10.3389/fnimg.2026.1816667","url":null,"abstract":"<p><p>[This retracts the article DOI: 10.3389/fnimg.2025.1507522.].</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"5 ","pages":"1816667"},"PeriodicalIF":0.0,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13015187/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147522633","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":"Unique presentation of superficial siderosis of the central nervous system following pituitary tumor surgery: a case report and literature review.","authors":"Tengyao Liang, Jiangqin Song, Fengzhu Zhao, Weifang Zhu, Hui Wei","doi":"10.3389/fnimg.2026.1751864","DOIUrl":"10.3389/fnimg.2026.1751864","url":null,"abstract":"<p><p>Superficial Siderosis of the Central Nervous System is an infrequent neurological disorder resulting from hemosiderin deposition due to chronic and recurrent subarachnoid hemorrhage, leading to significant neurological impairments including sensorineural hearing loss, cerebellar ataxia, and pyramidal signs. This case report presents a 50-year-old male patient with a history of pituitary tumor surgery, manifesting progressive neurological symptoms over 2 years, thereby highlighting the potential long-term complications associated with SSCNS. The atypical clinical presentation, coupled with a surgical background, underscores the diagnostic challenges faced by clinicians, who may misattribute symptoms to more common neurological conditions. Advanced imaging modalities, particularly susceptibility-weighted imaging (SWI), have proven essential in enhancing the diagnostic accuracy for SSCNS, revealing characteristic patterns of iron deposition that are often subtle and can lead to delayed recognition. This case not only contributes to the existing literature by documenting a rare presentation of SSCNS but also emphasizes the necessity for increased awareness and vigilance among healthcare providers regarding this condition's complex manifestations. The findings advocate for interdisciplinary collaboration between neurologists and radiologists to improve recognition and management strategies, ultimately leading to better patient outcomes. Despite the rarity and variability of SSCNS, which complicates the establishment of standardized treatment protocols, this case highlights the critical need for continued research into its underlying mechanisms and therapy efficacy, particularly in patients with previous neurological interventions. Enhanced educational initiatives may be pivotal in addressing the diagnostic challenges associated with this debilitating condition.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"5 ","pages":"1751864"},"PeriodicalIF":2.5,"publicationDate":"2026-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12979144/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147464496","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":"Diffusion MRI sampling schemes bias diffusion metrics and tractography.","authors":"Ivanei Bramati, Diego Szczupak, Marina Carneiro Monteiro, Fernanda Meireles, Daniel Menezes Guimarães, Ryan J Dean, Lynn K Paul, Fernanda Tovar-Moll","doi":"10.3389/fnimg.2026.1670604","DOIUrl":"10.3389/fnimg.2026.1670604","url":null,"abstract":"<p><strong>Introduction: </strong>Diffusion MRI is increasingly used to study white-matter architecture, but tractography and diffusion metrics can be biased by different sampling schemes. We assessed systematic differences across four common protocols-single-shell high-angular resolution diffusion imaging (HARDI), Siemens clinical multi-shell (Sms), diffusion spectrum imaging (DSI), and Human Connectome Project multi-shell (HCPms)-in healthy adults and individuals with corpus callosum dysgenesis (CCD).</p><p><strong>Methods: </strong>All data were acquired on a single 3 T scanner and processed uniformly to extract fractional anisotropy (FA), mean diffusivity (MD), effective contrast-to-noise ratio (eCNR), and orientation dispersion within the corpus callosum (CC), corona radiata (CR), and centrum semiovale (CSO). In controls, we measured tract volumes for CC, bilateral CR, anterior commissure (AC) and posterior commissure (PC), and streamline counts for AC and PC; in CCD, we quantified volumes of the Probst and sigmoid bundles.</p><p><strong>Results: </strong>Across participants, FA and MD showed moderate cross-scheme correlations for most ROIs, but matched means were rare (only Sms-HARDI in CC). eCNR and dispersion exhibited few cross-scheme correlations; however, means were similar for eCNR between Sms and HCPms and for dispersion among HARDI, DSI, and HCPms. Tract-based volumes correlated across Sms, DSI, and HCPms for CC in controls and for the right sigmoid and both Probst bundles in CCD. DSI and HCPms yielded similar volumes in all ROIs (controls and CCD). In controls, Sms volumes agreed with DSI/HCPms in CR, but were lower in CC and in all CCD ROIs. HARDI produced higher volumes in CC and bilateral CR in controls and in all CCD ROIs. For AC and PC in controls, tract-based means (volumes, streamlines, streamlines/volume) were consistent across schemes; nonetheless, correlations were limited-streamlines and streamlines/volume correlated for Sms, DSI, and HARDI in AC, and for DSI and HCPms in PC.</p><p><strong>Discussion: </strong>These findings demonstrate systematic differences in voxel-wise metrics and tractography outcomes from four diffusion-sampling schemes. In addition to qualitatively informing attempts to consolidate or contrast data across schemes, future work could explore regression-based harmonization-and other methods-to reduce residual bias and enable pooled analyses across diverse protocols.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"5 ","pages":"1670604"},"PeriodicalIF":2.5,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12975589/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147446308","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":"Post COVID-19 condition is associated with altered regional cerebral blood volume as revealed by dynamic susceptibility contrast MRI.","authors":"Bradley J MacIntosh, S Shirley Lin, Finn O'Hara, Nathan W Churchill, Fred Tam, Alexandra Pavel, Eugenie Roudaia, Allison B Sekuler, Ivy Cheng, Fuqiang Gao, Benjamin Lam, Chris Heyn, Mario Masellis, J Jean Chen, Tom A Schweizer, Sandra E Black, Simon J Graham","doi":"10.3389/fnimg.2025.1688973","DOIUrl":"10.3389/fnimg.2025.1688973","url":null,"abstract":"<p><strong>Background: </strong>Coronavirus disease 2019 (COVID-19) has been associated with central nervous system dysfunction implicating cerebrovascular and neurovascular units, as reflected in lower regional cerebral blood flow among non-hospitalized individuals that experienced post COVID-19 condition (PCC). This study investigates whether PCC is associated with altered regional cerebral blood volume assessed using Dynamic Susceptibility Contrast (DSC) Magnetic Resonance Imaging (MRI). The comparison control group are individuals without PCC who previously experienced cold or flu-like symptoms, or COVID-19.</p><p><strong>Methods: </strong>Fifty-seven participants were recruited: 36 with PCC (mean age: 42.7, standard deviation: 10.4, 26 females) and 21 controls (mean age: 41.6, standard deviation: 14.7, 13 females). T2*-weighted DSC MRI was performed at 3 Tesla to image the first passage of the bolus. A total of 22 regions of interest (ROIs) were considered. Group differences in DSC-derived cerebral blood volume (rCBV) and cerebral blood flow (rCBF) were evaluated using Bayesian regression, providing median group differences, highest density interval (HDI), and the probability of direction (PD) estimates.</p><p><strong>Results: </strong>The two groups (PCC and controls) were matched for age, sex, days from symptom onset, and number of previous vaccines, but had different degrees of self-report illness severity. The rCBV analysis showed median group differences (range: -0.05 to -0.13), with PD > 0.90, indicating a high probability of decreased rCBV in the PCC group, involving the superior frontal gyrus, thalamus, paracentral lobule, cingulate gyrus, postcentral gyrus, middle frontal gyrus, inferior frontal gyrus, and superior temporal gyrus ROIs. By comparison, group differences in rCBF were muted and did not reach PD > 0.90.</p><p><strong>Discussion: </strong>We found group-level differences that were reflected by lower regional rCBV in PCC relative to controls. The imaging findings are suggestive of cerebrovascular alterations several months after the initial illness.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"4 ","pages":"1688973"},"PeriodicalIF":2.5,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12956688/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147367398","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":"A systematic study on the integration of MRI connectivity metrics for Alzheimer's diagnosis, staging, and cognitive decline prediction.","authors":"Shahzad Ali, Wendy Kreshpa, Nicola Rosso, Michele Piana, Luca Roccatagliata, Alessio Cirone, Lorenzini Luigi, Cristina Campi, Matteo Pardini, Sara Garbarino","doi":"10.3389/fnimg.2026.1746464","DOIUrl":"10.3389/fnimg.2026.1746464","url":null,"abstract":"<p><p>Alzheimer's disease (AD) is a degenerative neurological disorder marked by cognitive decline and functional disability. Despite the extensive use of magnetic resonance imaging (MRI) in machine learning (ML)-based AD studies, the relative and combined contributions of MRI-derived morphometric (MO), microstructural (MS), and graph-theoretical (GT) features are still not well explored in a unified, comparative framework. It remains unclear whether adding multimodal MRI-derived features consistently improves the predictive performance of ML-based approaches for AD diagnosis and cognitive decline. Addressing this gap, this study systematically analyzed the individual (MO, MS, GT) and combined (MO+MS, MO+GT, MS+GT, MO+MS+GT) utility of MRI-based feature sets. We developed an ensemble-based ML framework with a nested cross-validation module for two key tasks: (i) Alzheimer's disease cognitive stage classification (DSC) and (ii) longitudinal cognitive decline prediction (LCDP) in terms of mini-mental state examination (MMSE) score. In this study, we conducted feature ablation and statistical analysis to evaluate performance improvements resulting from the incremental addition of feature sets. The results of the study indicated that the proposed ensemble-based ML approach achieved the best predictive performance (balanced accuracy [BACC]: 0.898 ± 0.051) using a combination of MO and MS feature sets for cognitively normal (CN) vs. AD dementia (CN-ADD). In contrast, the best results for mild cognitive impairment (MCI) vs. ADD (MCI-ADD) and CN-MCI were achieved using the MO feature set alone, with BACC of 0.769 ± 0.116 and 0.652 ± 0.044, respectively. Likewise, for the LCDP task, the MO-based ensemble learner achieved an R² of 0.212 ± 0.177. These results demonstrate that MO features capture the most robust disease-related information, while multimodal integration offers task-specific and limited benefits. In addition, these findings demonstrate the potential of integrated MRI-derived features in ML frameworks for enhancing ADD diagnosis and cognitive decline prediction and underscore the importance of feature selection based on task complexity.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"5 ","pages":"1746464"},"PeriodicalIF":2.5,"publicationDate":"2026-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12953077/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147358071","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":"Study protocol for the Champaign-Urbana population study.","authors":"Paul B Camacho, Aaron T Anderson, Rong Guo, Yuhui Chai, Sina Tafti, Ian Hall, Dominika M Pindus, Chris Lockwood, Paul M Arnold, Sheeba Arnold-Anteraper, Zhi-Pei Liang, Hacene Serrai, Andrew G Webb, Bansari Upadhyay, Diane Beck, Mark D Whiting, Bruce M Damon, Tracey M Wszalek, Brad P Sutton","doi":"10.3389/fnimg.2026.1728970","DOIUrl":"10.3389/fnimg.2026.1728970","url":null,"abstract":"<p><p>Superior signal-to-noise ratio, enhanced and novel forms of contrast, and improved spectral resolution made possible by 7 Tesla (7 T) magnetic resonance imaging (MRI) offer great promise for both neuroimaging research and clinical practice. To characterize these gains, it is essential to acquire structural, functional, and biochemical 7 T MRI data from a large sample of adults. The Champaign Urbana Population Study (CUPS) will collect and publish a database of 7 T MRI data, including raw MRI data, from a cohort of up to 200 adults. Here, we describe the study design and provide example images from the initial round of data collection for CUPS.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"5 ","pages":"1728970"},"PeriodicalIF":2.5,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12929149/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147313186","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":"Deep learning to predict future cognitive decline: a multimodal approach using brain MRI and clinical data.","authors":"Tamoghna Chattopadhyay, Pavithra Senthilkumar, Rahul H Ankarath, Christopher Patterson, Emma J Gleave, Sophia I Thomopoulos, Heng Huang, Li Shen, Lei You, Degui Zhi, Paul M Thompson","doi":"10.3389/fnimg.2026.1726037","DOIUrl":"10.3389/fnimg.2026.1726037","url":null,"abstract":"<p><p>Predicting the trajectory of clinical decline in aging individuals is a pressing challenge, especially for people with mild cognitive impairment, Alzheimer's disease, Parkinson's disease, or vascular dementia. Accurate predictions can guide treatment decisions, identify risk factors, and optimize clinical trials. In this study, we compared two deep learning approaches for forecasting changes, over a 2-year interval, in the Clinical Dementia Rating scale 'sum of boxes' score (sobCDR), as a continuous outcome (regression). This is a key metric in dementia research and clinical trials, and scores range from 0 (no impairment) to 18 (severe impairment). To predict decline, we trained a hybrid convolutional neural network (CNN) that integrates 3D T1-weighted brain MRI scans with tabular clinical and demographic features (including age, sex, body mass index (BMI), and baseline sobCDR). We benchmarked its performance against AutoGluon, an automated multimodal machine learning framework that selects an appropriate neural network architecture (an 'autoML' approach). We evaluated the models using data from 2,319 unique participants drawn from three independent cohorts-ADNI, OASIS-3, and NACC. For each participant, we used one T1-weighted brain MRI scan along with corresponding clinical and demographic information. Our results demonstrate the importance of combining image and tabular data in predictive modeling for this clinical application. Deep learning algorithms can fuse information from image-based brain signatures and tabular clinical data, with potential for personalized prognostics in aging and dementia. Rather than concluding that multimodal fusion uniformly improves performance, our results show that deep learning applied to volumetric MRI data may struggle to add predictive value, particularly when clinical covariates explain substantial variance and provide a strong baseline. In other conditions and tasks, it may help to have a hybrid system that can learn from both data types, and their relative value may be different. Conversely, AutoML-based multimodal fusion provides a robust baseline when tabular data already provide strong predictive value for the task. These insights clarify how different multimodal strategies could be selected in clinical prognostic applications.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"5 ","pages":"1726037"},"PeriodicalIF":2.5,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12913073/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146230067","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":"Clinical and radiographic intersection of cerebral amyloid angiopathy with euglycemic diabetic ketoacidosis in the development of transient focal neurologic deficits: case report.","authors":"John Paul Aboubechara, Michael Saggio, Olivia Campa, Kader Karli Oguz, Ivy Nguyen","doi":"10.3389/fnimg.2025.1743623","DOIUrl":"10.3389/fnimg.2025.1743623","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to describe a case of transient neurologic deficits triggered by euglycemic diabetic ketoacidosis (DKA) in brain tissue at risk due to heavy cerebral amyloid angiopathy (CAA) microbleed burden, while demonstrating the rare imaging finding of reversible T2 fluid-attenuated inversion recovery (FLAIR) subcortical hypointensity.</p><p><strong>Methods: </strong>We present the clinical course, laboratory findings, and neuroimaging features of an 81-year-old man who presented with acute altered mental status and transient focal neurologic deficits.</p><p><strong>Results: </strong>The patient presented with encephalopathy, headache, left hemianopsia, left sensory neglect, and mild left upper extremity weakness. Laboratory examination showed euglycemic DKA. Brain MRI revealed findings consistent with probable CAA according to Boston Criteria 2.0, including innumerable cortical microbleeds predominantly in the right temporo-parieto-occipital lobes, with superimposed diffuse T2 FLAIR-weighted hypointensity in this region.</p><p><strong>Discussion: </strong>Reversible T2 FLAIR hypointensity has been described in hyperglycemia-associated syndromes. In this case, T2 FLAIR hypointensity likely represented metabolic dysregulation that triggered cortical dysfunction within brain regions at risk due to heavy CAA-related microbleed burden. We speculate that a common pathway for the development of the patient's transient deficits resulted from cortical spreading depolarization (CSD), which has been associated with both CAA and hyperglycemia.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"4 ","pages":"1743623"},"PeriodicalIF":0.0,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12855084/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146108981","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}