Jan Kufer, Christine Preibisch, Jens Göttler, Lena Schmitzer, Gabriel Hoffmann, Michael Kallmayer, Claus Zimmer, Fahmeed Hyder, Stephan Kaczmarz
Background and Purpose
Hemodynamic impairment may contribute to stroke risk and cognitive decline in asymptomatic internal carotid artery stenosis (ICAS). Therefore, multimodal MRI-based quantification of hemodynamic impairment could inform improved treatment decisions. While gross interhemispheric hemodynamic imbalances have been reported in ICAS, identifying more spatially resolved patterns of disease-related alterations may be promising to harness the full potential of hemodynamic MRI.
Methods
In this feasibility study, we investigated the spatial topography of ICAS-related impairments by applying scaled subprofile model principal component analysis (SSM-PCA) to cerebral blood flow (CBF), relative oxygen extraction fraction (rOEF), and oxygen extraction capacity (OEFmax) data of 21 unilateral ICAS patients and 25 healthy controls (HC).
Results
We found spatially extended, partly overlapping disease-related patterns for CBF and OEFmax, but not rOEF. CBF (area under the curve [AUC] = 0.95) but not OEFmax (AUC = 0.72) SSM-PCA scores distinguished ICAS patients and HC better than interhemispheric lateralizations (AUC = 0.75/0.73). SSM-PCA scores were only partly explained by interhemispheric lateralization (R2 = −0.27/0.38), indicating complementary information. Critically, ICAS patients with higher OEFmax SSM-PCA scores (z ≥ 1) demonstrated higher stenotic degrees and lower cognitive performance (p < 0.05) without differing in interhemispheric lateralization (p > 0.05).
Conclusions
We demonstrated the feasibility of SSM-PCA in ICAS and obtained novel insights into complex hemodynamic impairment patterns and their association with cognitive function.
{"title":"Multivariate Pattern Analysis of Perfusion and Oxygenation Impairment in Asymptomatic Carotid Artery Stenosis","authors":"Jan Kufer, Christine Preibisch, Jens Göttler, Lena Schmitzer, Gabriel Hoffmann, Michael Kallmayer, Claus Zimmer, Fahmeed Hyder, Stephan Kaczmarz","doi":"10.1111/jon.70084","DOIUrl":"https://doi.org/10.1111/jon.70084","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background and Purpose</h3>\u0000 \u0000 <p>Hemodynamic impairment may contribute to stroke risk and cognitive decline in asymptomatic internal carotid artery stenosis (ICAS). Therefore, multimodal MRI-based quantification of hemodynamic impairment could inform improved treatment decisions. While gross interhemispheric hemodynamic imbalances have been reported in ICAS, identifying more spatially resolved patterns of disease-related alterations may be promising to harness the full potential of hemodynamic MRI.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>In this feasibility study, we investigated the spatial topography of ICAS-related impairments by applying scaled subprofile model principal component analysis (SSM-PCA) to cerebral blood flow (CBF), relative oxygen extraction fraction (rOEF), and oxygen extraction capacity (OEF<sup>max</sup>) data of 21 unilateral ICAS patients and 25 healthy controls (HC).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We found spatially extended, partly overlapping disease-related patterns for CBF and OEF<sup>max</sup>, but not rOEF. CBF (area under the curve [AUC] = 0.95) but not OEF<sup>max</sup> (AUC = 0.72) SSM-PCA scores distinguished ICAS patients and HC better than interhemispheric lateralizations (AUC = 0.75/0.73). SSM-PCA scores were only partly explained by interhemispheric lateralization (<i>R</i><sup>2</sup> = −0.27/0.38), indicating complementary information. Critically, ICAS patients with higher OEF<sup>max</sup> SSM-PCA scores (<i>z</i> ≥ 1) demonstrated higher stenotic degrees and lower cognitive performance (<i>p</i> < 0.05) without differing in interhemispheric lateralization (<i>p</i> > 0.05).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>We demonstrated the feasibility of SSM-PCA in ICAS and obtained novel insights into complex hemodynamic impairment patterns and their association with cognitive function.</p>\u0000 </section>\u0000 </div>","PeriodicalId":16399,"journal":{"name":"Journal of Neuroimaging","volume":"35 5","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jon.70084","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145062596","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}
Jeffrey Lambe, Nicolas R. Thompson, Yadi Li, Kunio Nakamura, Daniel Ontaneda
Background and Purpose
Spinal cord pathology underpins disability accumulation in people with multiple sclerosis (pwMS). Visual inspection of spinal cord magentic resonance imaging (MRI) often fails to reliably detect injury. Radiomics analyzes signal intensities in images to identify pathological changes that may be imperceptible to the human eye. This study evaluated the application of radiomics to spinal cord MRI to distinguish subgroups of pwMS and disability correlations.
Methods
Radiomic features were extracted from upper cervical cord coverage on cross-sectional 3.0T brain noncontrasted T1-weighted MRI scans in pwMS and healthy controls (HCs). Ninety-three radiomic features—predominantly gray-level matrices—were extracted using Pyradiomics, with pixel heterogeneity considered to reflect neuroaxonal pathology. T2 lesion and brain substructure volumes were segmented from 3D fluid-attenuated inversion recovery and magnetization-prepared rapid gradient-echo sequences using an in-house 2.5D U-Net convolutional neural network to encapsulate neuroinflammation and neurodegeneration. Cervical cross-sectional area (C1−C3) was measured using in-house atlas-based segmentation. Imaging features were compared between pwMS and HCs, and pwMS by phenotype (relapsing vs. progressive), age, and race. Associations of imaging features with Patient-Determined Disease Steps (PDDS) were examined.
Results
Among 2966 pwMS and 41 HCs, we identified radiomic features distinguishing pwMS from HCs, and pwMS by phenotype, age, and race. Radiomic features exhibited stronger correlations with PDDS than conventional MRI measures.
Conclusions
Radiomics identified pathological changes in pwMS in varying stages of the disease course that are undetectable by conventional spinal cord MRI. Radiomics may increase the yield of spinal cord MRI in pwMS and serve as biomarkers predicting disability worsening.
{"title":"Association of Spinal Cord Radiomic Features and Disability in Multiple Sclerosis","authors":"Jeffrey Lambe, Nicolas R. Thompson, Yadi Li, Kunio Nakamura, Daniel Ontaneda","doi":"10.1111/jon.70089","DOIUrl":"https://doi.org/10.1111/jon.70089","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background and Purpose</h3>\u0000 \u0000 <p>Spinal cord pathology underpins disability accumulation in people with multiple sclerosis (pwMS). Visual inspection of spinal cord magentic resonance imaging (MRI) often fails to reliably detect injury. Radiomics analyzes signal intensities in images to identify pathological changes that may be imperceptible to the human eye. This study evaluated the application of radiomics to spinal cord MRI to distinguish subgroups of pwMS and disability correlations.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Radiomic features were extracted from upper cervical cord coverage on cross-sectional 3.0T brain noncontrasted T1-weighted MRI scans in pwMS and healthy controls (HCs). Ninety-three radiomic features—predominantly gray-level matrices—were extracted using Pyradiomics, with pixel heterogeneity considered to reflect neuroaxonal pathology. T2 lesion and brain substructure volumes were segmented from 3D fluid-attenuated inversion recovery and magnetization-prepared rapid gradient-echo sequences using an in-house 2.5D U-Net convolutional neural network to encapsulate neuroinflammation and neurodegeneration. Cervical cross-sectional area (C1−C3) was measured using in-house atlas-based segmentation. Imaging features were compared between pwMS and HCs, and pwMS by phenotype (relapsing vs. progressive), age, and race. Associations of imaging features with Patient-Determined Disease Steps (PDDS) were examined.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Among 2966 pwMS and 41 HCs, we identified radiomic features distinguishing pwMS from HCs, and pwMS by phenotype, age, and race. Radiomic features exhibited stronger correlations with PDDS than conventional MRI measures.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Radiomics identified pathological changes in pwMS in varying stages of the disease course that are undetectable by conventional spinal cord MRI. Radiomics may increase the yield of spinal cord MRI in pwMS and serve as biomarkers predicting disability worsening.</p>\u0000 </section>\u0000 </div>","PeriodicalId":16399,"journal":{"name":"Journal of Neuroimaging","volume":"35 5","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jon.70089","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145062597","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}
Emma Dereskewicz, Francesco La Rosa, Jonadab Dos Santos Silva, Edward Sizer, Amit Kohli, Maxence Wynen, William A. Mullins, Pietro Maggi, Sarah Levy, Kamso Onyemeh, Batuhan Ayci, Andrew J. Solomon, Jakob Assländer, Omar Al-Louzi, Daniel S. Reich, James Sumowski, Erin S. Beck
Background and Purpose
Assessment of brain lesions on magnetic resonance imaging (MRI) is crucial for research in multiple sclerosis (MS). Manual segmentation is time-consuming and inconsistent. We aimed to develop an automated MS lesion segmentation algorithm for T2-weighted fluid-attenuated inversion recovery (FLAIR) MRI.
Methods
We developed FLAIR Lesion Analysis in Multiple Sclerosis (FLAMeS), a deep learning-based MS lesion segmentation algorithm based on the nnU-Net 3D full-resolution U-Net and trained on 668 FLAIR 1.5 and 3 tesla scans from persons with MS. FLAMeS was evaluated on three external datasets: MSSEG-2 (n = 14), MSLesSeg (n = 51), and a clinical cohort (n = 10), and compared to SAMSEG, LST-LPA, and LST-AI. Performance was assessed qualitatively by two blinded experts and quantitatively by comparing automated and ground truth lesion masks using standard segmentation metrics.
Results
In a blinded qualitative review of 20 scans, both raters selected FLAMeS as the most accurate segmentation in 15 cases, with one rater favoring FLAMeS in two additional cases. Across all testing datasets, FLAMeS achieved a mean Dice score of 0.74, a true positive rate of 0.84, and an F1 score of 0.78, consistently outperforming the benchmark methods. For other metrics, including positive predictive value, relative volume difference, and false positive rate, FLAMeS performed similarly to or better than benchmark methods. Most lesions missed by FLAMeS were smaller than 10 mm3, whereas the benchmark methods missed larger lesions in addition to smaller ones.
Conclusions
FLAMeS is an accurate, robust method for MS lesion segmentation that outperforms other publicly available methods.
{"title":"A Novel Convolutional Neural Network for Automated Multiple Sclerosis Brain Lesion Segmentation","authors":"Emma Dereskewicz, Francesco La Rosa, Jonadab Dos Santos Silva, Edward Sizer, Amit Kohli, Maxence Wynen, William A. Mullins, Pietro Maggi, Sarah Levy, Kamso Onyemeh, Batuhan Ayci, Andrew J. Solomon, Jakob Assländer, Omar Al-Louzi, Daniel S. Reich, James Sumowski, Erin S. Beck","doi":"10.1111/jon.70085","DOIUrl":"10.1111/jon.70085","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background and Purpose</h3>\u0000 \u0000 <p>Assessment of brain lesions on magnetic resonance imaging (MRI) is crucial for research in multiple sclerosis (MS). Manual segmentation is time-consuming and inconsistent. We aimed to develop an automated MS lesion segmentation algorithm for T2-weighted fluid-attenuated inversion recovery (FLAIR) MRI.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We developed FLAIR Lesion Analysis in Multiple Sclerosis (FLAMeS), a deep learning-based MS lesion segmentation algorithm based on the nnU-Net 3D full-resolution U-Net and trained on 668 FLAIR 1.5 and 3 tesla scans from persons with MS. FLAMeS was evaluated on three external datasets: MSSEG-2 (<i>n</i> = 14), MSLesSeg (<i>n</i> = 51), and a clinical cohort (<i>n</i> = 10), and compared to SAMSEG, LST-LPA, and LST-AI. Performance was assessed qualitatively by two blinded experts and quantitatively by comparing automated and ground truth lesion masks using standard segmentation metrics.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>In a blinded qualitative review of 20 scans, both raters selected FLAMeS as the most accurate segmentation in 15 cases, with one rater favoring FLAMeS in two additional cases. Across all testing datasets, FLAMeS achieved a mean Dice score of 0.74, a true positive rate of 0.84, and an F1 score of 0.78, consistently outperforming the benchmark methods. For other metrics, including positive predictive value, relative volume difference, and false positive rate, FLAMeS performed similarly to or better than benchmark methods. Most lesions missed by FLAMeS were smaller than 10 mm<sup>3</sup>, whereas the benchmark methods missed larger lesions in addition to smaller ones.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>FLAMeS is an accurate, robust method for MS lesion segmentation that outperforms other publicly available methods.</p>\u0000 </section>\u0000 </div>","PeriodicalId":16399,"journal":{"name":"Journal of Neuroimaging","volume":"35 5","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jon.70085","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145040383","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}
To review the existing evidence on multiple timepoint assessments of optic nerve sheath diameter (ONSD) as an indicator of intraindividual variation of intracranial pressure (ICP).
Methods
A systematic search identified studies assessing intraindividual variation in ICP through multiple timepoint measurements of ONSD using ultrasonography. Meta-analysis of studies assessing intraindividual correlation coefficients between ONSD and ICP was performed using a random effects model, and we calculated the weighted correlation coefficient for the expected change in ICP associated with variations in ONSD.
Results
A total of five studies, comprising 157 patients, were included in the review. ONSD was compared with invasive ICP measurement methods at multiple timepoints. Meta-analysis of intraindividual ONSD–ICP correlation demonstrated a correlation coefficient of 0.62 (CI: 0.50–0.71). Individual linear correlation analyses were performed in two of the studies, yielding correlation coefficients ranging from 0.79 to 1.00; however, widely variable individual slopes were found (1.51–41.43 mm/mmHg). ONSD variations ranged from 0.12 to 3.30 mm per 5 mmHg change in ICP, with a variation of 0.55 mm in adults with hypoxic brain injury and 0.77 mm in children with idiopathic intracranial hypertension.
Conclusions
Our findings indicate that ONSD significantly correlates with ICP, and longitudinal intraindividual assessment shows a predominantly linear correlation between both variables. A personalized ONSD–ICP correlation equation may enable accurate ICP prediction, making ONSD a useful tool for follow-up in patients with previous invasive ICP measurements, when adjusted to each patient's characteristics and pathologies.
{"title":"Intraindividual Optic Nerve Sheath Variation and Intracranial Pressure Changes: A Systematic Review and Meta-Analysis","authors":"Henrique Azevedo, Lia Lucas Neto, David Berhanu","doi":"10.1111/jon.70083","DOIUrl":"https://doi.org/10.1111/jon.70083","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background and Purpose</h3>\u0000 \u0000 <p>To review the existing evidence on multiple timepoint assessments of optic nerve sheath diameter (ONSD) as an indicator of intraindividual variation of intracranial pressure (ICP).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A systematic search identified studies assessing intraindividual variation in ICP through multiple timepoint measurements of ONSD using ultrasonography. Meta-analysis of studies assessing intraindividual correlation coefficients between ONSD and ICP was performed using a random effects model, and we calculated the weighted correlation coefficient for the expected change in ICP associated with variations in ONSD.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>A total of five studies, comprising 157 patients, were included in the review. ONSD was compared with invasive ICP measurement methods at multiple timepoints. Meta-analysis of intraindividual ONSD–ICP correlation demonstrated a correlation coefficient of 0.62 (CI: 0.50–0.71). Individual linear correlation analyses were performed in two of the studies, yielding correlation coefficients ranging from 0.79 to 1.00; however, widely variable individual slopes were found (1.51–41.43 mm/mmHg). ONSD variations ranged from 0.12 to 3.30 mm per 5 mmHg change in ICP, with a variation of 0.55 mm in adults with hypoxic brain injury and 0.77 mm in children with idiopathic intracranial hypertension.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Our findings indicate that ONSD significantly correlates with ICP, and longitudinal intraindividual assessment shows a predominantly linear correlation between both variables. A personalized ONSD–ICP correlation equation may enable accurate ICP prediction, making ONSD a useful tool for follow-up in patients with previous invasive ICP measurements, when adjusted to each patient's characteristics and pathologies.</p>\u0000 </section>\u0000 </div>","PeriodicalId":16399,"journal":{"name":"Journal of Neuroimaging","volume":"35 5","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jon.70083","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145012376","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}
Hector Acosta-Rodriguez, Pratheek Bobba, Alicia Stephan, Tal Zeevi, Ajay Malhotra, Anh Tuan Tran, Simone Kaltenhauser, Laura Ment, Seyedmehdi Payabvash
Background and Purpose
Socioeconomic determinants of health impact childhood development and adult health outcomes. One key aspect is the physical environment and neighborhood where children live and grow. Emerging evidence suggests that neighborhood deprivation, often measured by the Area Deprivation Index (ADI), may influence neurodevelopment, but longitudinal and multimodal neuroimaging analyses remain limited.
Methods
We examined the association between childhood neighborhoods and brain white matter (WM) microstructural integrity using a large, demographically representative cohort from the Adolescent Brain Cognitive Development Study. We analyzed the relationship between ADI and MRI metrics of WM microstructural integrity and resting-state funtional magnetic resonance imaging (rs-fMRI) connectivity in children with data at baseline (mean age of 9.9 years) and follow-up (mean age 12.0 years), with a sample size of n = 2615.
Results
Children living in poorer neighborhoods (higher ADI) showed lower brain WM microstructural integrity at baseline and follow-up, even after adjusting for age, sex, race/ethnicity, head size, body mass index, parental education, and income levels. This reduced microstructure was seen in critical tracts, such as the superior longitudinal fasciculus, corpus callosum, and the uncinate. Additionally, baseline and follow-up rs-fMRI analysis revealed that children living in poorer neighborhoods had decreased connectivity within the retrosplenial-temporal network and between higher-order networks, such as the cingulo-opercular network.
Conclusions
These findings highlight the influence of neighborhood socioeconomic disadvantage on both WM microstructural integrity and functional brain connectivity in the preadolescent brain. Children from more deprived neighborhoods showed reduced integrity in key WM tracts and disrupted connectivity within and between higher-order networks.
{"title":"Impact of Childhood Neighborhood Deprivation on White Matter and Functional Connectivity During Adolescence","authors":"Hector Acosta-Rodriguez, Pratheek Bobba, Alicia Stephan, Tal Zeevi, Ajay Malhotra, Anh Tuan Tran, Simone Kaltenhauser, Laura Ment, Seyedmehdi Payabvash","doi":"10.1111/jon.70087","DOIUrl":"https://doi.org/10.1111/jon.70087","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background and Purpose</h3>\u0000 \u0000 <p>Socioeconomic determinants of health impact childhood development and adult health outcomes. One key aspect is the physical environment and neighborhood where children live and grow. Emerging evidence suggests that neighborhood deprivation, often measured by the Area Deprivation Index (ADI), may influence neurodevelopment, but longitudinal and multimodal neuroimaging analyses remain limited.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We examined the association between childhood neighborhoods and brain white matter (WM) microstructural integrity using a large, demographically representative cohort from the Adolescent Brain Cognitive Development Study. We analyzed the relationship between ADI and MRI metrics of WM microstructural integrity and resting-state funtional magnetic resonance imaging (rs-fMRI) connectivity in children with data at baseline (mean age of 9.9 years) and follow-up (mean age 12.0 years), with a sample size of <i>n</i> = 2615.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Children living in poorer neighborhoods (higher ADI) showed lower brain WM microstructural integrity at baseline and follow-up, even after adjusting for age, sex, race/ethnicity, head size, body mass index, parental education, and income levels. This reduced microstructure was seen in critical tracts, such as the superior longitudinal fasciculus, corpus callosum, and the uncinate. Additionally, baseline and follow-up rs-fMRI analysis revealed that children living in poorer neighborhoods had decreased connectivity within the retrosplenial-temporal network and between higher-order networks, such as the cingulo-opercular network.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>These findings highlight the influence of neighborhood socioeconomic disadvantage on both WM microstructural integrity and functional brain connectivity in the preadolescent brain. Children from more deprived neighborhoods showed reduced integrity in key WM tracts and disrupted connectivity within and between higher-order networks.</p>\u0000 </section>\u0000 </div>","PeriodicalId":16399,"journal":{"name":"Journal of Neuroimaging","volume":"35 5","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145012377","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}
Enchao Qiu, Joga Chaganti, Phillip Phan, Mahdi Alizadeh, Devon Middleton, Kiran Talekar, Prabath K. Mondel, Scott H. Faro, Feroze B. Mohamed, Hsiangkuo Yuan
Background and Purpose
Diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) has emerged as a promising noninvasive method for evaluating water motion that may reflect glymphatic system function. However, the reliability of DTI-ALPS measurements across different region-of-interest (ROI) selection methods remains underinvestigated. This study aimed to assess the interrater reliability among three neuroradiologists in native space and compare DTI-ALPS indices derived from ROIs placed in subjects’ native space versus standardized Montreal Neurological Institute (MNI) space.
Methods
DTI-ALPS indices from 16 healthy subjects were calculated from both left and right hemispheres using two ROI placement approaches: (1) native space ROIs manually placed by three neuroradiologists, and (2) standardized ROIs in MNI space based on the fractional anisotropy template. Interrater reliability was assessed using intraclass correlation coefficients (ICCs). The proportion of ROI overlaps among the three neuroradiologists was also evaluated. Differences between native and MNI space measurements were evaluated using related-samples Friedman's analysis with post hoc pairwise comparisons.
Results
Interrater reliability for native space ROI placement was moderate for left-sided DTI-ALPS indices (ICC = 0.599) and good for right-sided DTI-ALPS indices (ICC = 0.807). Spatial overlap analysis revealed poor Dice similarity coefficients across all ROI types (range: 0.047–0.312), with right association ROIs showing higher spatial consistency. Significant differences were found between native and MNI space measurements for left-sided DTI-ALPS indices (p = 0.002) but not for right-sided DTI-ALPS indices (p = 0.913).
Conclusion
These findings highlight the importance of standardized ROI selection approaches for clinical applications of DTI-ALPS.
{"title":"Reliability and Spatial Consistency of MR Diffusion Tensor Imaging Measures Along the Cerebral Perivascular Space","authors":"Enchao Qiu, Joga Chaganti, Phillip Phan, Mahdi Alizadeh, Devon Middleton, Kiran Talekar, Prabath K. Mondel, Scott H. Faro, Feroze B. Mohamed, Hsiangkuo Yuan","doi":"10.1111/jon.70086","DOIUrl":"https://doi.org/10.1111/jon.70086","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background and Purpose</h3>\u0000 \u0000 <p>Diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) has emerged as a promising noninvasive method for evaluating water motion that may reflect glymphatic system function. However, the reliability of DTI-ALPS measurements across different region-of-interest (ROI) selection methods remains underinvestigated. This study aimed to assess the interrater reliability among three neuroradiologists in native space and compare DTI-ALPS indices derived from ROIs placed in subjects’ native space versus standardized Montreal Neurological Institute (MNI) space.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>DTI-ALPS indices from 16 healthy subjects were calculated from both left and right hemispheres using two ROI placement approaches: (1) native space ROIs manually placed by three neuroradiologists, and (2) standardized ROIs in MNI space based on the fractional anisotropy template. Interrater reliability was assessed using intraclass correlation coefficients (ICCs). The proportion of ROI overlaps among the three neuroradiologists was also evaluated. Differences between native and MNI space measurements were evaluated using related-samples Friedman's analysis with post hoc pairwise comparisons.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Interrater reliability for native space ROI placement was moderate for left-sided DTI-ALPS indices (ICC = 0.599) and good for right-sided DTI-ALPS indices (ICC = 0.807). Spatial overlap analysis revealed poor Dice similarity coefficients across all ROI types (range: 0.047–0.312), with right association ROIs showing higher spatial consistency. Significant differences were found between native and MNI space measurements for left-sided DTI-ALPS indices (<i>p</i> = 0.002) but not for right-sided DTI-ALPS indices (<i>p</i> = 0.913).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>These findings highlight the importance of standardized ROI selection approaches for clinical applications of DTI-ALPS.</p>\u0000 </section>\u0000 </div>","PeriodicalId":16399,"journal":{"name":"Journal of Neuroimaging","volume":"35 5","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jon.70086","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144934855","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}
Jonadab dos Santos Silva, Claudia C. F. Vasconcelos, Carina T. Spedo, Carolina Alvarez Azevedo, Larissa C. S. Lopes, Francesco La Rosa, Carolina B. Moura, Osvaldo Nascimento, Fernanda Cristina Rueda Lopes
Background and Purpose
Cognitive impairment is common and disabling in multiple sclerosis (MS), yet poorly explained by lesion burden. This study aimed to determine whether the indirect impact of lesions, quantified through disconnectomes, explains multidomain cognitive deficits more effectively than lesion load, and to identify specific white matter tracts underlying these deficits.
Methods
Thirty adults with MS completed the Brief International Cognitive Assessment for MS, covering processing speed (Symbol Digit Modalities Test; SDMT), verbal memory (California Verbal Learning Test-II; CVLT-II), and visuospatial memory (Brief Visuospatial Memory Test–Revised; BVMT-R). Lesions were segmented using 3 Tesla fluid-attenuated inversion recovery images and used to estimate tract-specific disconnections and generate voxel-wise disconnectome maps. Tract-specific and voxel-wise analyses were used to identify disconnection patterns associated with cognitive performance.
Results
Cognition was impaired across all domains (all p < 0.001). Disconnectome volume was a significant independent determinant of cognitive performance (β = –0.41, p = 0.004), whereas lesion volume was not. Tract-specific analyses revealed distinct disconnection patterns: slower SDMT was associated with left cingulum posterior (β = −0.310, 95% confidence interval [CI] [−0.599, −0.021]); poorer CVLT-II with left arcuate fasciculus (β = −0.232, 95% CI [−0.435, −0.030]); and lower BVMT-R with right cingulum posterior (β = −0.218, 95% CI [−0.383, −0.054]). Voxel-wise analyses identified where the strongest associations between disconnectomes and cognitive performance were located.
Conclusion
Lesion-driven disconnection is a more robust determinant of cognitive impairment in MS than lesion burden alone, and disconnectome mapping may help understand the indirect network-level mechanisms underlying cognitive deficits in MS.
{"title":"Lesion-Based Disconnectome Explains Cognitive Outcomes in Multiple Sclerosis","authors":"Jonadab dos Santos Silva, Claudia C. F. Vasconcelos, Carina T. Spedo, Carolina Alvarez Azevedo, Larissa C. S. Lopes, Francesco La Rosa, Carolina B. Moura, Osvaldo Nascimento, Fernanda Cristina Rueda Lopes","doi":"10.1111/jon.70081","DOIUrl":"https://doi.org/10.1111/jon.70081","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background and Purpose</h3>\u0000 \u0000 <p>Cognitive impairment is common and disabling in multiple sclerosis (MS), yet poorly explained by lesion burden. This study aimed to determine whether the indirect impact of lesions, quantified through disconnectomes, explains multidomain cognitive deficits more effectively than lesion load, and to identify specific white matter tracts underlying these deficits.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Thirty adults with MS completed the Brief International Cognitive Assessment for MS, covering processing speed (Symbol Digit Modalities Test; SDMT), verbal memory (California Verbal Learning Test-II; CVLT-II), and visuospatial memory (Brief Visuospatial Memory Test–Revised; BVMT-R). Lesions were segmented using 3 Tesla fluid-attenuated inversion recovery images and used to estimate tract-specific disconnections and generate voxel-wise disconnectome maps. Tract-specific and voxel-wise analyses were used to identify disconnection patterns associated with cognitive performance.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Cognition was impaired across all domains (all <i>p</i> < 0.001). Disconnectome volume was a significant independent determinant of cognitive performance (β = –0.41, <i>p</i> = 0.004), whereas lesion volume was not. Tract-specific analyses revealed distinct disconnection patterns: slower SDMT was associated with left cingulum posterior (β = −0.310, 95% confidence interval [CI] [−0.599, −0.021]); poorer CVLT-II with left arcuate fasciculus (β = −0.232, 95% CI [−0.435, −0.030]); and lower BVMT-R with right cingulum posterior (β = −0.218, 95% CI [−0.383, −0.054]). Voxel-wise analyses identified where the strongest associations between disconnectomes and cognitive performance were located.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Lesion-driven disconnection is a more robust determinant of cognitive impairment in MS than lesion burden alone, and disconnectome mapping may help understand the indirect network-level mechanisms underlying cognitive deficits in MS.</p>\u0000 </section>\u0000 </div>","PeriodicalId":16399,"journal":{"name":"Journal of Neuroimaging","volume":"35 5","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jon.70081","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144929631","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}
Mahla Radmard, Mona Gad, Ali Sheikhy, Richard Dagher, Vivek Yedavalli, Licia P. Luna
Background
Alzheimer's disease (AD) and mild cognitive impairment (MCI) are two common conditions associated with cognitive decline. With global dementia cases rising, identifying the most accurate imaging method for diagnosis is essential.
Methods
Following Preferred Reporting Items for Systematic review and Meta-Analysis Protocols, we systematically reviewed studies utilizing arterial spin labeling magnetic resonance imaging (ASL-MRI) and [18F]-2-fluoro-2-deoxy-D-glucose positron emission tomography (FDG-PET) for neurodegenerative disorders. Searches in PubMed/MEDLINE, Scopus, and Embase led to 17 studies directly comparing these modalities for MCI/AD diagnosis. Seven studies met criteria for meta-analysis based on interreader agreement and area under the curve (AUC) data. Meta-analysis was performed using the R “Mada” package, with study heterogeneity assessed via the Zhou and Dendukuri approach.
Results
Meta-analysis of seven studies showed FDG-PET had a slightly higher AUC (0.864, sensitivity 0.81, specificity 0.80) compared to ASL-MRI (AUC 0.836, sensitivity 0.73, specificity 0.80), but no statistically significant difference. Low heterogeneity was observed (I2: 9.4% for FDG-PET, 6.5% for ASL-MRI). In MCI subgroup analysis, FDG-PET outperformed ASL-MRI with significantly higher sensitivity (0.90 vs. 0.75), specificity (0.91 vs. 0.73), and AUC (0.92 vs. 0.80, p < 0.001).
Conclusion
FDG-PET and ASL-MRI demonstrate comparable diagnostic accuracy for AD and MCI. Selection between modalities may depend on availability, cost, and safety considerations.
Clinical Relevance/Application
Both FDG-PET and MRI-ASL are effective at identifying MCI and AD, enabling accurate diagnosis. The choice of which modality to use may be addressed by cost, availability, and consideration of other potential diagnoses.
阿尔茨海默病（AD）和轻度认知障碍（MCI）是两种与认知能力下降相关的常见疾病。随着全球痴呆症病例的增加，确定最准确的诊断成像方法至关重要。方法根据系统评价和荟萃分析方案的首选报告项目，我们系统地回顾了利用动脉自旋标记磁共振成像（ASL-MRI）和[18F]-2-氟-2-脱氧-d -葡萄糖正电子发射断层扫描（FDG-PET）治疗神经退行性疾病的研究。在PubMed/MEDLINE、Scopus和Embase中搜索，有17项研究直接比较了这些模式对MCI/AD的诊断。7项研究符合基于解读者一致性和曲线下面积（AUC）数据的meta分析标准。采用R“Mada”软件包进行meta分析，通过Zhou和Dendukuri方法评估研究异质性。结果7项研究荟萃分析显示，FDG-PET的AUC（0.864，敏感性0.81，特异性0.80）略高于ASL-MRI (AUC 0.836，敏感性0.73，特异性0.80)，但差异无统计学意义。观察到低异质性（I2: FDG-PET为9.4%，ASL-MRI为6.5%）。在MCI亚组分析中，FDG-PET的灵敏度（0.90 vs. 0.75）、特异性（0.91 vs. 0.73）和AUC （0.92 vs. 0.80, p < 0.001）明显优于ASL-MRI。结论FDG-PET和ASL-MRI对AD和MCI的诊断准确性相当。模式之间的选择可能取决于可用性、成本和安全性考虑。FDG-PET和MRI-ASL均能有效识别MCI和AD，实现准确诊断。使用哪种方式的选择可以根据成本、可获得性和其他潜在诊断的考虑来解决。
{"title":"Functional Imaging in Alzheimer's Disease: A Systematic Review and Meta-Analysis of ASL-MRI and FDG-PET","authors":"Mahla Radmard, Mona Gad, Ali Sheikhy, Richard Dagher, Vivek Yedavalli, Licia P. Luna","doi":"10.1111/jon.70080","DOIUrl":"https://doi.org/10.1111/jon.70080","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Alzheimer's disease (AD) and mild cognitive impairment (MCI) are two common conditions associated with cognitive decline. With global dementia cases rising, identifying the most accurate imaging method for diagnosis is essential.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Following Preferred Reporting Items for Systematic review and Meta-Analysis Protocols, we systematically reviewed studies utilizing arterial spin labeling magnetic resonance imaging (ASL-MRI) and [18F]-2-fluoro-2-deoxy-D-glucose positron emission tomography (FDG-PET) for neurodegenerative disorders. Searches in PubMed/MEDLINE, Scopus, and Embase led to 17 studies directly comparing these modalities for MCI/AD diagnosis. Seven studies met criteria for meta-analysis based on interreader agreement and area under the curve (AUC) data. Meta-analysis was performed using the R “Mada” package, with study heterogeneity assessed via the Zhou and Dendukuri approach.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Meta-analysis of seven studies showed FDG-PET had a slightly higher AUC (0.864, sensitivity 0.81, specificity 0.80) compared to ASL-MRI (AUC 0.836, sensitivity 0.73, specificity 0.80), but no statistically significant difference. Low heterogeneity was observed (<i>I</i><sup>2</sup>: 9.4% for FDG-PET, 6.5% for ASL-MRI). In MCI subgroup analysis, FDG-PET outperformed ASL-MRI with significantly higher sensitivity (0.90 vs. 0.75), specificity (0.91 vs. 0.73), and AUC (0.92 vs. 0.80, <i>p</i> < 0.001).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>FDG-PET and ASL-MRI demonstrate comparable diagnostic accuracy for AD and MCI. Selection between modalities may depend on availability, cost, and safety considerations.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Clinical Relevance/Application</h3>\u0000 \u0000 <p>Both FDG-PET and MRI-ASL are effective at identifying MCI and AD, enabling accurate diagnosis. The choice of which modality to use may be addressed by cost, availability, and consideration of other potential diagnoses.</p>\u0000 </section>\u0000 </div>","PeriodicalId":16399,"journal":{"name":"Journal of Neuroimaging","volume":"35 5","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144929630","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}
Jun Kanzawa, Shiori Amemiya, Hidemasa Takao, Osamu Abe
Background and Purpose
This study aims to characterize hemodynamic alterations in gliomas by analyzing global signal metrics and amplitude of low-frequency fluctuations (ALFF).
Methods
We analyzed resting-state functional MRI data from an open dataset. A total of 29 glioma patients (17 men; mean age: 44.2 ± 13.9 years; 16 high-grade) were included. Global signal cross-correlation (GSXC), representing the maximum cross-correlation value between each voxel's time series and the average gray matter signal of the contralateral hemisphere, and ALFF were calculated. Regions of interest for tumor were defined and mirrored to the contralateral side for comparison. Linear mixed-effects models were used to examine effects of tumor grade and measurement side on GSXC and ALFF. Post hoc analyses compared tumor versus contralateral regions.
Results
GSXC was significantly reduced in tumor regions compared to the contralateral hemisphere, regardless of tumor grade (low-grade: 0.31 ± 0.21 vs. 0.45 ± 0.16, p = 0.005; high-grade: 0.39 ± 0.17 vs. 0.55 ± 0.13, p < 0.001). ALFF was significantly lower in low-grade tumors compared to the contralateral side (0.65 ± 0.15 vs. 0.77 ± 0.14, p = 0.005), while no significant difference was found in high-grade tumors (0.80 ± 0.18 vs. 0.77 ± 0.10, p = 0.46).
Conclusions
Our findings suggest that GSXC and ALFF provide complementary pathological information on glioma. GSXC indicates impaired cerebrovascular reactivity due to gliovascular uncoupling, whereas ALFF shows an increase in high-grade gliomas likely reflecting greater total vascularity.
背景与目的本研究旨在通过分析神经胶质瘤的整体信号指标和低频波动（ALFF）的幅度来表征血液动力学的改变。方法对开放数据集的静息状态功能MRI数据进行分析。共纳入29例胶质瘤患者（男性17例，平均年龄44.2±13.9岁，高级别16例）。计算各体素时间序列与对侧半球平均灰质信号的最大互相关值Global signal cross-correlation （GSXC）和ALFF。确定肿瘤感兴趣的区域，并镜像到对侧进行比较。采用线性混合效应模型检验肿瘤分级和测量侧对GSXC和ALFF的影响。事后分析比较肿瘤和对侧区域。结果与对侧半球相比，GSXC在肿瘤区域显著减少，与肿瘤分级无关（低分级：0.31±0.21 vs 0.45±0.16,p = 0.005；高分级：0.39±0.17 vs 0.55±0.13,p < 0.001）。低级别肿瘤的ALFF明显低于对侧（0.65±0.15比0.77±0.14,p = 0.005），而高级别肿瘤的ALFF差异无统计学意义（0.80±0.18比0.77±0.10,p = 0.46）。结论GSXC和ALFF为胶质瘤提供了互补的病理信息。GSXC表明由于胶质血管解偶联导致脑血管反应性受损，而ALFF显示高级别胶质瘤的增加可能反映了更大的总血管。
{"title":"Characterizing Hemodynamic Alterations in Glioma: Insights From Resting-State Functional MRI","authors":"Jun Kanzawa, Shiori Amemiya, Hidemasa Takao, Osamu Abe","doi":"10.1111/jon.70082","DOIUrl":"https://doi.org/10.1111/jon.70082","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background and Purpose</h3>\u0000 \u0000 <p>This study aims to characterize hemodynamic alterations in gliomas by analyzing global signal metrics and amplitude of low-frequency fluctuations (ALFF).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We analyzed resting-state functional MRI data from an open dataset. A total of 29 glioma patients (17 men; mean age: 44.2 ± 13.9 years; 16 high-grade) were included. Global signal cross-correlation (GSXC), representing the maximum cross-correlation value between each voxel's time series and the average gray matter signal of the contralateral hemisphere, and ALFF were calculated. Regions of interest for tumor were defined and mirrored to the contralateral side for comparison. Linear mixed-effects models were used to examine effects of tumor grade and measurement side on GSXC and ALFF. Post hoc analyses compared tumor versus contralateral regions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>GSXC was significantly reduced in tumor regions compared to the contralateral hemisphere, regardless of tumor grade (low-grade: 0.31 ± 0.21 vs. 0.45 ± 0.16, <i>p</i> = 0.005; high-grade: 0.39 ± 0.17 vs. 0.55 ± 0.13, <i>p</i> < 0.001). ALFF was significantly lower in low-grade tumors compared to the contralateral side (0.65 ± 0.15 vs. 0.77 ± 0.14, <i>p</i> = 0.005), while no significant difference was found in high-grade tumors (0.80 ± 0.18 vs. 0.77 ± 0.10, <i>p</i> = 0.46).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Our findings suggest that GSXC and ALFF provide complementary pathological information on glioma. GSXC indicates impaired cerebrovascular reactivity due to gliovascular uncoupling, whereas ALFF shows an increase in high-grade gliomas likely reflecting greater total vascularity.</p>\u0000 </section>\u0000 </div>","PeriodicalId":16399,"journal":{"name":"Journal of Neuroimaging","volume":"35 5","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jon.70082","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144929597","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}
Masoud Hassanzadeh, Alyssa Ailion, Simon K. Warfield, Taha Gholipour, Shan Siddiqi, Steven M. Stufflebeam, Elmira Hassanzadeh
Background and Purpose
Resting-state fMRI (rs-fMRI) is increasingly used to map brain networks in patients under anesthesia, but technical factors can affect its utility. We evaluated the effects of sevoflurane, multiband acceleration, and scan duration on rs-fMRI signal quality and within-subject reliability under anesthesia.
Methods
We retrospectively analyzed 64 clinical rs-fMRI scans acquired under anesthesia, with or without sevoflurane and multiband factor 5 acceleration. Temporal signal-to-noise ratio (tSNR) was used as a measure of signal quality. For each patient, the scan was split in half, and seed-based connectivity maps were generated for the primary motor cortex (M1), posterior cingulate cortex (PCC), and subgenual anterior cingulate cortex (sgACC). Split-half spatial correlations were used to assess within-subject reliability. Group comparisons examined differences in tSNR and reliability across conditions, and correlations with scan duration were tested.
Results
Multiband acceleration was associated with significantly lower tSNR (U = 652.0, p = 8.9 × 10─6) and reduced split-half reliability for M1 (p = 0.019), PCC (p = 0.010), and sgACC (p = 0.0064). Sevoflurane showed no significant effect on tSNR or reliability. Longer scan duration correlated with improved reliability for M1 (r = 0.38, p = 0.003) but not for PCC or sgACC. No correlation was found between tSNR and reliability.
Conclusion
Hight multiband acceleration reduces both signal quality and reliability of rs-fMRI under anesthesia. Sevoflurane had no measurable effect. The lack of correlation between tSNR and reliability underscores the need for more robust metrics when evaluating scan quality.
{"title":"High Multiband Acceleration Degrades Resting-State Functional MRI Reliability and Signal Quality Under Anesthesia","authors":"Masoud Hassanzadeh, Alyssa Ailion, Simon K. Warfield, Taha Gholipour, Shan Siddiqi, Steven M. Stufflebeam, Elmira Hassanzadeh","doi":"10.1111/jon.70075","DOIUrl":"https://doi.org/10.1111/jon.70075","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background and Purpose</h3>\u0000 \u0000 <p>Resting-state fMRI (rs-fMRI) is increasingly used to map brain networks in patients under anesthesia, but technical factors can affect its utility. We evaluated the effects of sevoflurane, multiband acceleration, and scan duration on rs-fMRI signal quality and within-subject reliability under anesthesia.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We retrospectively analyzed 64 clinical rs-fMRI scans acquired under anesthesia, with or without sevoflurane and multiband factor 5 acceleration. Temporal signal-to-noise ratio (tSNR) was used as a measure of signal quality. For each patient, the scan was split in half, and seed-based connectivity maps were generated for the primary motor cortex (M1), posterior cingulate cortex (PCC), and subgenual anterior cingulate cortex (sgACC). Split-half spatial correlations were used to assess within-subject reliability. Group comparisons examined differences in tSNR and reliability across conditions, and correlations with scan duration were tested.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Multiband acceleration was associated with significantly lower tSNR (<i>U</i> = 652.0, <i>p</i> = 8.9 × 10─<sup>6</sup>) and reduced split-half reliability for M1 (<i>p</i> = 0.019), PCC (<i>p</i> = 0.010), and sgACC (<i>p</i> = 0.0064). Sevoflurane showed no significant effect on tSNR or reliability. Longer scan duration correlated with improved reliability for M1 (<i>r</i> = 0.38, <i>p</i> = 0.003) but not for PCC or sgACC. No correlation was found between tSNR and reliability.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Hight multiband acceleration reduces both signal quality and reliability of rs-fMRI under anesthesia. Sevoflurane had no measurable effect. The lack of correlation between tSNR and reliability underscores the need for more robust metrics when evaluating scan quality.</p>\u0000 </section>\u0000 </div>","PeriodicalId":16399,"journal":{"name":"Journal of Neuroimaging","volume":"35 4","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891680","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}
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