AJNR. American journal of neuroradiology最新文献

{"title":"CT-Based Intrathrombus and Perithrombus Radiomics for Prediction of Prognosis after Endovascular Thrombectomy: A Retrospective Study across 2 Centers.","authors":"Minda Li, Jingxuan Jiang, Hongmei Gu, Su Hu, Jingli Wang, Chunhong Hu","doi":"10.3174/ajnr.A8522","DOIUrl":"10.3174/ajnr.A8522","url":null,"abstract":"<p><strong>Background and purpose: </strong>Complications from endovascular thrombectomy (EVT) can negatively affect clinical outcomes, making the development of a more precise and objective prediction model essential. This research aimed to assess the effectiveness of radiomics features derived from presurgical CT scans in predicting the prognosis post-EVT in patients with acute ischemic stroke.</p><p><strong>Materials and methods: </strong>This investigation included 336 patients with acute ischemic stroke from 2 medical centers from March 2018 to March 2024. The participants were split into a training cohort of 161 patients and a validation cohort of 175 patients. Patient outcomes were rated with the mRS: 0-2 for good, 3-6 for poor. A total of 428 radiomics features were derived from intrathrombus and perithrombus regions in noncontrast CT and CTA images. Feature selection was conducted using a least absolute shrinkage and selection operator regression model. The efficacy of 8 different supervised learning models was assessed using the area under the curve (AUC) of the receiver operating characteristic curve.</p><p><strong>Results: </strong>Among all models tested in the validation cohort, the logistic regression algorithm for the combined model achieved the highest AUC (0.87; 95% CI, 0.81-0.92), outperforming other algorithms. The combined use of radiomics features from both the intrathrombus and perithrombus regions significantly enhanced diagnostic accuracy over models using features from a single region (0.81 versus 0.70, 0.77), highlighting the benefit of integrating data from both regions for improved prediction.</p><p><strong>Conclusions: </strong>The findings suggest that a combined radiomics model based on CT serves as a potent approach to assessing the prognosis following EVT. The logistic regression model, in particular, proved to be both effective and stable, offering critical insights for the management of stroke.</p>","PeriodicalId":93863,"journal":{"name":"AJNR. American journal of neuroradiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376413","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":"Hypersensitivity Reactions to Fibrin Glue During Epidural Blood Patching.","authors":"Jennifer L Smith, Myoung J Kim, Linda Gray, Michael D Malinzak, Samantha Morrison, Amy P Stallings, Alaattin Erkanli, Peter G Kranz, Timothy J Amrhein","doi":"10.3174/ajnr.A8568","DOIUrl":"https://doi.org/10.3174/ajnr.A8568","url":null,"abstract":"<p><strong>Background and purpose: </strong>Fibrin glue is increasingly incorporated as a component in epidural blood patching (EBP) for the treatment of spinal CSF leaks. Hypersensitivity reactions are a potential complication of its use but are not well studied in the setting of EBP. The purpose of this study was to determine the incidence of hypersensitivity reactions to fibrin glue during EBP and to identify any predisposing factors associated with increased patient risk.</p><p><strong>Materials and methods: </strong>A single-center retrospective cohort study with nested case-control design included patients who received fibrin glue EBP for the treatment of iatrogenic CSF leaks or spontaneous intracranial hypotension over 13 years. Patient demographics and multiple procedure-specific variables were collected. Cases were identified from the total cohort as those with hypersensitive reactions and matched with controls in a 1:3 ratio. The incidence of hypersensitivity reactions in the total cohort was calculated. Logistic regression models were fit to test for associations between variables and the development of a hypersensitivity reaction.</p><p><strong>Results: </strong>A total of 3065 CT-guided EBPs with fibrin glue were identified in 1574 individual patients. The incidence of hypersensitivity reactions was 0.49% per procedure and 0.95% per patient and never occurred during the first EBP with fibrin glue. Case-control analysis found higher odds for hypersensitivity reactions in patients with a lower BMI (OR 0.82 [0.71-0.96], <i>P</i> = .003), younger age (OR 0.95 [0.91-0.99], <i>P</i> = .011), and during procedures with inadvertent intravenous injections (OR 5.44 [1.34-22.01], <i>P</i> = .014).</p><p><strong>Conclusions: </strong>We found a 0.49% incidence of hypersensitivity reactions during EBP with fibrin glue, none occurring during the first exposure. Younger age, lower BMI, and inadvertent intravenous injection during the procedure were associated with a higher likelihood of reactions. This study provides data useful for counseling patients on procedural risk and identifies variables for physicians to be aware of to help prevent life-threatening reactions to fibrin glue during EBP.</p>","PeriodicalId":93863,"journal":{"name":"AJNR. American journal of neuroradiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143672015","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":"Additional Diagnostic Value of Conebeam CT Myelography Performed After Digital Subtraction Myelography for Detecting CSF-Venous Fistulas.","authors":"Ajay A Madhavan, Niklas Lutzen, Jeremy K Cutsforth-Gregory, Wouter I Schievink, Michelle L Kodet, Ian T Mark, Pearse P Morris, Steven A Messina, John T Wald, Waleed Brinjikji","doi":"10.3174/ajnr.A8535","DOIUrl":"10.3174/ajnr.A8535","url":null,"abstract":"<p><strong>Background and purpose: </strong>CSF-venous fistulas (CVFs) are a common cause of spontaneous intracranial hypotension. The diagnosis and precise localization of these fistulas hinges on specialized myelographic techniques, which mainly include decubitus digital subtraction myelography and decubitus CT myelography (by using either energy-integrating or photon-counting detector CT). A previous case series showed that conebeam CT myelography (CB-CTM), performed as an adjunctive tool with digital subtraction myelography, increased the detection of CVFs. Here, we sought to determine the additive yield of CB-CTM for CVF detection in a consecutive series of patients with spontaneous intracranial hypotension who underwent concurrent decubitus digital subtraction myelography and CB-CTM.</p><p><strong>Materials and methods: </strong>We retrospectively searched our institutional database for all consecutive patients who underwent decubitus digital subtraction myelography with adjunctive CB-CTM between August 5, 2021 and August 5, 2024. We excluded any patients harboring extradural CSF on spine imaging, not meeting International Classification of Headache Disorders, 3rd edition criteria for spontaneous intracranial hypotension, or not having undergone technically successful CB-CTM in combination with digital subtraction myelography. All myelographic images were independently reviewed by 2 neuroradiologists. We calculated the diagnostic yield of both myelographic tests for localizing a CVF.</p><p><strong>Results: </strong>We identified 100 patients who underwent decubitus digital subtraction myelography with adjunctive conebeam CT. We excluded 15 patients based on above criteria. Fifty-nine of 85 patients had a single definitive CVF. Among positive cases, the fistula was visible on digital subtraction myelography in 38 of 59 patients and visible on CB-CTM in 59 of 59 patients. In 26 of 85 patients, no definitive fistula was identified by either technique.</p><p><strong>Conclusions: </strong>CB-CTM increased the diagnostic yield for CVF detection and may be a useful addition to digital subtraction myelography.</p>","PeriodicalId":93863,"journal":{"name":"AJNR. American journal of neuroradiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482790","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":"Embolization of Posterior Fossa Meningiomas Supplied with Meningohypophyseal Trunk by Using <i>n</i>-BCA and Dual Balloon Protection.","authors":"Jota Tega, Koichiro Takemoto, Takayuki Koga, Dai Kawano, Shintaro Yoshinaga, Hideaki Tanaka, Kei Yamashiro, Toshiyuki Enomoto, Hironori Fukumoto, Yoshinobu Horio, Hiromasa Kobayashi, Takashi Morishita, Mitsutoshi Iwaasa, Hiroshi Abe","doi":"10.3174/ajnr.A8536","DOIUrl":"10.3174/ajnr.A8536","url":null,"abstract":"<p><strong>Background and purpose: </strong>Efficacy of tumor embolization for posterior fossa meningioma is controversial due to the lack of adequate embolization for dangerous feeders. Of these, a meningohypophyseal trunk (MHT) has high therapeutic value despite the high risks associated with embolization.</p><p><strong>Materials and methods: </strong>To analyze the utility of preoperative MHT embolization for posterior fossa meningiomas by using <i>n-</i>BCA with dual balloon protection, a single-center retrospective record review was performed on 8 consecutive patients who underwent preoperative tumor embolization via the MHT for posterior fossa meningiomas between 2020 and 2024.</p><p><strong>Results: </strong>In all cases the MHT was successfully embolized by using <i>n-</i>BCA. Complete obliteration was achieved in 5 cases, which is related to the tentorial artery alone as the feeding vessel. None of the patients had cerebral infarction associated with distal embolization. One patient experienced worsening of preoperatively observed abducens nerve palsy due to cranial nerve ischemia. Gross total resection was achieved in 7 of 8 cases. The mean estimated blood loss during surgical resection was 186 mL (range, 39-392 mL). The mean operative time was 431 minutes (range, 317-767 minutes).</p><p><strong>Conclusions: </strong>The MHT embolization of posterior fossa meningiomas by using <i>n</i>-BCA is technically feasible with a high success rate and an acceptable complication rate.</p>","PeriodicalId":93863,"journal":{"name":"AJNR. American journal of neuroradiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482792","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":"Comprehensive Segmentation of Gray Matter Structures on T1-Weighted Brain MRI: A Comparative Study of Convolutional Neural Network, Convolutional Neural Network Hybrid-Transformer or -Mamba Architectures.","authors":"Yujia Wei, Jaidip Manikrao Jagtap, Yashbir Singh, Bardia Khosravi, Jason Cai, Jeffrey L Gunter, Bradley J Erickson","doi":"10.3174/ajnr.A8544","DOIUrl":"10.3174/ajnr.A8544","url":null,"abstract":"<p><strong>Background and purpose: </strong>Recent advances in deep learning have shown promising results in medical image analysis and segmentation. However, most brain MRI segmentation models are limited by the size of their data sets and/or the number of structures they can identify. This study evaluates the performance of 6 advanced deep learning models in segmenting 122 brain structures from T1-weighted MRI scans, aiming to identify the most effective model for clinical and research applications.</p><p><strong>Materials and methods: </strong>A total of 1510 T1-weighted MRIs were used to compare 6 deep learning models for the segmentation of 122 distinct gray matter structures: nnU-Net, SegResNet, SwinUNETR, UNETR, U-Mamba_BOT, and U-Mamba_ Enc. Each model was rigorously tested for accuracy by using the dice similarity coefficient (DSC) and the 95th percentile Hausdorff distance (HD95). Additionally, the volume of each structure was calculated and compared between normal controls (NCs) and patients with Alzheimer disease (AD).</p><p><strong>Results: </strong>U-Mamba_Bot achieved the highest performance with a median DSC of 0.9112 (interquartile range [IQR]: 0.8957, 0.9250). nnU-Net achieved a median DSC of 0.9027 [IQR: 0.8847, 0.9205], and had the highest HD95 of 1.392 [IQR: 1.174, 2.029]. The value of each HD95 (<3 mm) indicates its superior capability in capturing detailed brain structures accurately. Following segmentation, volume calculations were performed, and the resultant volumes of NCs and patients with AD were compared. The volume changes observed in 13 brain substructures were all consistent with those reported in existing literature, reinforcing the reliability of the segmentation outputs.</p><p><strong>Conclusions: </strong>This study underscores the efficacy of U-Mamba_Bot as a robust tool for detailed brain structure segmentation in T1-weighted MRI scans. The congruence of our volumetric analysis with the literature further validates the potential of advanced deep learning models to enhance the understanding of neurodegenerative diseases such as AD. Future research should consider larger data sets to validate these findings further and explore the applicability of these models in other neurologic conditions.</p>","PeriodicalId":93863,"journal":{"name":"AJNR. American journal of neuroradiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482791","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":"Concordance between Centiloid quantification and visual interpretation of amyloid PET scans across the Alzheimer's disease continuum.","authors":"Mohammad Khalafi, Seyed Hani Hojjati, Xiuyuan H Wang, Liangdong Zhou, Anna S Nordvig, Yi Li, Tracy A Butler, Qolamreza R Razlighi, Emily B Tanzi, Silky Pahlajani, Lidia Glodzik, Nancy S Foldi, Mony J de Leon, Gloria C Chiang","doi":"10.3174/ajnr.A8743","DOIUrl":"https://doi.org/10.3174/ajnr.A8743","url":null,"abstract":"<p><strong>Background and purpose: </strong>Accurate identification of cerebral beta-amyloid (Aβ) accumulation is crucial for diagnosing Alzheimer's disease (AD) and determining eligibility for anti-Aβ therapies. The Centiloid (CL) scale has emerged as a standardized method to harmonize Aβ positron emission tomography (PET) quantification across different tracers and sites. We aimed to evaluate the concordance between CL quantification and visual interpretation in a cohort of cognitively impaired (CI) and unimpaired (CU) participants who underwent Aβ PET.</p><p><strong>Materials and methods: </strong>Two hundred twenty-one participants (mean age 69 ± 12.3 years) were prospectively enrolled in AD studies and underwent 247 Aβ PET scans, including 157 with [¹¹C]Pittsburgh Compound B (PiB) and 90 with [¹⁸F]Florbetaben (FBB). Standardized uptake value ratios (SUVRs) were converted to the CL scale following Global Alzheimer's Association Interactive Network (GAAIN) guidelines. Percent agreement and kappa statistics were used to evaluate the concordance between CL thresholds and visual interpretation in determining Aβ positivity.</p><p><strong>Results: </strong>The highest concordance rate for the whole cohort was 93% using a CL cutoff of 18 (kappa coefficient 0.84). Using FBB, the concordance rate was highest using a CL cutoff of 24 (97%), whereas the concordance rate for PiB peaked at 94% at a CL cutoff of 18. Concordance was higher in negative than positive Aβ PET cases, 98% versus 90%. Concordance was slightly higher in CI participants, compared to CU (96% versus 93%). Disagreement commonly occurred when focal areas of Aβ positivity were identified on visual interpretation but did not meet the threshold globally by CL quantification.</p><p><strong>Conclusions: </strong>Global CL quantification of Aβ PET scans is highly concordant with visual interpretation. Combining both methods may provide a more complete assessment of the extent of Aβ deposition in the brain.</p><p><strong>Abbreviations: </strong>AD = Alzheimer's disease; Aβ = beta-amyloid; PET = positron emission tomography; PiB = Pittsburgh Compound B; FBB = Florbetaben; CL = Centiloid.</p>","PeriodicalId":93863,"journal":{"name":"AJNR. American journal of neuroradiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143659898","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":"Erratum.","authors":"","doi":"10.3174/ajnr.A8711","DOIUrl":"https://doi.org/10.3174/ajnr.A8711","url":null,"abstract":"","PeriodicalId":93863,"journal":{"name":"AJNR. American journal of neuroradiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143652639","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":"Dissociation of Structural and Functional Connectivity and Metabolism in the Neocortex of Idiopathic Generalized Epilepsy: A Simultaneous PET/MRI Multimodal Study.","authors":"Chenyang Yao, Jie Hu, Bixiao Cui, Jingjuan Wang, Zhen-Ming Wang, Yaqin Hou, Kharel Sudeep, Hongwei Yang, Yihe Wang, Yongzhi Shan, Jie Lu","doi":"10.3174/ajnr.A8612","DOIUrl":"https://doi.org/10.3174/ajnr.A8612","url":null,"abstract":"<p><strong>Background and purpose: </strong>Idiopathic generalized epilepsy (IGE) accounts for approximately 20% of epilepsy cases. Characterized by generalized spike-wave discharge, IGE is increasingly recognized as a network disorder with potential metabolic underpinnings. This study leverages the advantages of simultaneous PET/MRI, which enables the concurrent acquisition of MRI and PET data, to integrate structural connectivity (SC), functional connectivity (FC), and glucose metabolism into a unified framework. This study aims to elucidate the multimodal abnormalities of the neocortex in IGE, to analyze the correlations between these abnormalities and clinical presentations, and to investigate the interactions among different imaging modalities.</p><p><strong>Materials and methods: </strong>Twenty-one patients with IGE and 34 healthy controls (HCs) were recruited. Simultaneous PET/MRI scans were performed, incorporating DTI, resting-state fMRI, and [¹⁸F]FDG-PET. DTI generated a neocortical connectivity blueprint, while resting-state fMRI provided a whole-brain connectivity matrix. [¹⁸F]FDG-PET data were processed to obtain standardized uptake value ratios (SUVRs). Multivariate distance matrix regression was used to identify abnormal neocortical regions in SC and FC. Differences in SUVRs were identified by using least absolute shrinkage and selection operator regression. Statistical analyses, including <i>t</i> tests, linear models, mediation analysis, and Pearson correlations, were conducted to compare values of each technique between groups and explore relationships with clinical features.</p><p><strong>Results: </strong>SC abnormalities were primarily found in the limbic (40% of all abnormal neocortical regions) and visual networks (31%), while FC abnormalities were mostly in the default mode network (DMN, 45%). Metabolic abnormalities were predominantly in the frontoparietal (26%) and somatomotor (22%) networks. SC in the limbic was positively correlated with onset age, while seizure frequency was negative correlated with DMN FC and positively correlated with frontoparietal metabolism. Mediation analysis showed that DMN FC mediated the relationship between limbic SC and frontoparietal and somatomotor metabolism.</p><p><strong>Conclusions: </strong>A multimodal approach reveals distinct and interrelated abnormalities in IGE, with different modalities reflecting various aspects of the disease, thus enhancing our understanding of its complex mechanisms. This integrative analysis could inform more effective treatments.</p>","PeriodicalId":93863,"journal":{"name":"AJNR. American journal of neuroradiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143627145","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":"Predicting White Matter Hyperintensity: Leveraging Portable Magnetic Resonance Imaging for Accessible Brain Health Screening.","authors":"Ian P Johnson, Hailey Brigger, Joel Smith, Emma Peasley, Alison Champagne, Lauren Littig, Dheeraj Lalwani, Gordon Sze, Seyedmehdi Payabvash, Basmah Safdar, Gail D'Onofrio, Charles Wira, Juan Eugenio Iglesias, Matthew S Rosen, Annabel Sorby-Adams, W Taylor Kimberly, Kevin N Sheth, Adam de Havenon","doi":"10.3174/ajnr.A8734","DOIUrl":"https://doi.org/10.3174/ajnr.A8734","url":null,"abstract":"<p><strong>Background and purpose: </strong>Portable MRI (pMRI) has emerged as a cost-effective and accessible tool for the identification of white matter hyperintensities (WMH), an independent risk factor for stroke and dementia. Our objective was to confirm that pMRI can produce accurate WMH measurements and to develop and validate a risk model to predict WMH on pMRI for the purpose of identifying patients who may benefit from pMRI screening.</p><p><strong>Materials and methods: </strong>The development (N=143) and validation (N=127) cohorts included patients without acute neurologic pathology who received a pMRI at a tertiary care hospital between May 2020 and July 2024. The development cohort included pMRIs collected as part of a prospective WMH screening pilot program in the emergency department. The validation cohort was a retrospective collection of pMRIs obtained for separate research purposes. Conventional MRIs (cMRIs) in the validation cohort obtained within 3 months of pMRIs were used for additional validation and device agreement. The primary outcome was WMH burden greater than 10 mL, assessed via an axial T2-FLAIR sequence acquired on a 0.064 T pMRI and quantified using a WMH segmentation software developed to process sequences of any resolution. We used backwards selection to screen candidate variables and report the area under the curve of the resulting model.</p><p><strong>Results: </strong>The final model, which included age, systolic blood pressure >140, atrial fibrillation, and tobacco use, achieved an AUC of 0.83 (95% CI 0.75-0.90) in the development cohort (N=143, 62.4±12.6 years, 44% female, 36% non-white race) and 0.85 (95% CI 0.77-0.92) in the validation cohort (N=127, 65.2±16.8 years, 51% female, 34% non-white race), with similar results using WMH measurements derived from cMRI (N=120, p=0.98, AUC=0.86, 95% CI 0.77-0.93). Additionally, we confirmed agreement in WMH volumes between pMRI and cMRI (N=120, r=0.93, 95% CI 0.90-0.95, p<0.001).</p><p><strong>Conclusions: </strong>The WMH risk score demonstrated accurate performance and reproducibility across cohorts, supporting its potential as a screening tool for identifying patients at risk of significant WMH burden. Appropriately targeted pMRI screening in high-risk individuals could allow providers and patients to proactively manage vascular risk factors and improve neurological outcomes.</p><p><strong>Abbreviations: </strong>pMRI = portable magnetic resonance imaging; cMRI = conventional magnetic resonance imaging; WMH = white matter hyperintensity; hypertension = HTN; diabetes = DM; atrial fibrillation = AFib; systolic blood pressure = SBP; hyperlipidemia = HLD; area under the curve = AUC; receiver operating characteristic = ROC.</p>","PeriodicalId":93863,"journal":{"name":"AJNR. American journal of neuroradiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143627159","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":"Language and Memory Network Alterations in Temporal Lobe Epilepsy: A Functional and Structural Connectivity Study.","authors":"Alireza Fallahi, Mohammad-Reza Nazem-Zadeh, Narges Hosseini-Tabatabaei, Jafar Mehvari Habibabadi, Seyed-Sohrab Hashemi-Fesharaki, Hamid Soltanian-Zadeh","doi":"10.3174/ajnr.A8737","DOIUrl":"https://doi.org/10.3174/ajnr.A8737","url":null,"abstract":"<p><strong>Background and purpose: </strong>This study evaluated preoperative alterations and postoperative reorganization of the joint language-memory network (LMN) from the perspective of resting-state functional and structural connectivity in Temporal lobe epilepsy (TLE). Graph theory and machine learning approaches were employed to explore automatic lateralization.</p><p><strong>Materials and methods: </strong>Resting-state fMRI and DTI data were obtained from 20 healthy subjects and 35 patients with TLE. Functional and structural connectivity were calculated within the LMN before and after temporal lobectomy. ANOVA was performed to identify significant connectivity differences between groups. Four local graph measures were extracted from functional and structural connectivity matrices. Standard feature selection techniques and genetic algorithm (GA) methods were applied to select the optimal features. Subsequently, the K-nearest neighbor, support vector machine (SVM), Naive Bayes, and logistic regression classification methods were used to classify healthy controls (HCs) and pre-surgical TLE groups, as well as pre-surgical left TLE (LTLE) and right TLE (RTLE) groups. Also, relationships between psychological scores and the selected features were evaluated using a linear regression method.</p><p><strong>Results: </strong>The results demonstrated increased functional and decreased structural connectivity in TLE patients before surgery. After surgery, significant connections revealed reduced functional connectivity and increased structural connectivity in TLE patients. Functional analysis identified the left parahippocampal region in LTLE and the right temporal regions in RTLE as key areas. Structural connectivity analysis showed that memory-related areas in the bilateral occipital region and the left language-related area were the origins of alterations. The GA method achieved the highest classification performance using SVM for fMRI and DTI graph measures, with accuracy rates of 97% and 88% for distinguishing LTLE from RTLE, and 93% and 87% for distinguishing TLE from HC, respectively. Moreover, a significant relationship was observed between the best-selected features and memory-assisted cognitive tests.</p><p><strong>Conclusions: </strong>Pre-surgical functional hyperconnectivity and post-surgical hypoconnectivity and also newly observed bilateral postsurgical structural connectivity, highlighting functional and structural alterations in the LMN network. Additionally, the study underscores the potential of machine learning for TLE diagnosis and lateralization. A limited sample size, particularly in the postsurgical group was one of the constraints of this study.</p><p><strong>Abbreviations: </strong>TLE=Temporal lobe epilepsy; LMN=Language-memory network; GA=Genetic algorithm; HC=Healthy controls; LTLE=Left TLE; RTLE=Right TLE; AUC=Area under the curve.</p>","PeriodicalId":93863,"journal":{"name":"AJNR. American journal of neuroradiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143627146","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}