Katerina Deike, Andreas Decker, Paul Scheyhing, Julia Harten, Nadine Zimmermann, Daniel Paech, Oliver Peters, S. D. Freiesleben, Luisa-Sophie Schneider, L. Preis, J. Priller, E. Spruth, S. Altenstein, A. Lohse, Klaus Fliessbach, O. Kimmich, Jens Wiltfang, C. Bartels, Niels Hansen, Frank Jessen, A. Rostamzadeh, E. Düzel, W. Glanz, E. Incesoy, M. Butryn, K. Buerger, D. Janowitz, Michael Ewers, Robert Perneczky, B. Rauchmann, Stefan J Teipel, I. Kilimann, Doreen Goerss, C. Laske, M. Munk, A. Spottke, N. Roy, Michael Wagner, S. Roeske, Michael T. Heneka, F. Brosseron, Alfredo Ramirez, L. Dobisch, S. Wolfsgruber, L. Kleineidam, R. Yakupov, Melina Stark, Matthias C Schmid, Moritz Berger, S. Hetzer, Peter Dechent, Klaus Scheffler, G. Petzold, Anja Schneider, Alexander Effland, Alexander Radbruch
{"title":"Machine Learning-Based Perivascular Space Volumetry in Alzheimer Disease.","authors":"Katerina Deike, Andreas Decker, Paul Scheyhing, Julia Harten, Nadine Zimmermann, Daniel Paech, Oliver Peters, S. D. Freiesleben, Luisa-Sophie Schneider, L. Preis, J. Priller, E. Spruth, S. Altenstein, A. Lohse, Klaus Fliessbach, O. Kimmich, Jens Wiltfang, C. Bartels, Niels Hansen, Frank Jessen, A. Rostamzadeh, E. Düzel, W. Glanz, E. Incesoy, M. Butryn, K. Buerger, D. Janowitz, Michael Ewers, Robert Perneczky, B. Rauchmann, Stefan J Teipel, I. Kilimann, Doreen Goerss, C. Laske, M. Munk, A. Spottke, N. Roy, Michael Wagner, S. Roeske, Michael T. Heneka, F. Brosseron, Alfredo Ramirez, L. Dobisch, S. Wolfsgruber, L. Kleineidam, R. Yakupov, Melina Stark, Matthias C Schmid, Moritz Berger, S. Hetzer, Peter Dechent, Klaus Scheffler, G. Petzold, Anja Schneider, Alexander Effland, Alexander Radbruch","doi":"10.1097/RLI.0000000000001077","DOIUrl":null,"url":null,"abstract":"OBJECTIVES\nImpaired perivascular clearance has been suggested as a contributing factor to the pathogenesis of Alzheimer disease (AD). However, it remains unresolved when the anatomy of the perivascular space (PVS) is altered during AD progression. Therefore, this study investigates the association between PVS volume and AD progression in cognitively unimpaired (CU) individuals, both with and without subjective cognitive decline (SCD), and in those clinically diagnosed with mild cognitive impairment (MCI) or mild AD.\n\n\nMATERIALS AND METHODS\nA convolutional neural network was trained using manually corrected, filter-based segmentations (n = 1000) to automatically segment the PVS in the centrum semiovale from interpolated, coronal T2-weighted magnetic resonance imaging scans (n = 894). These scans were sourced from the national German Center for Neurodegenerative Diseases Longitudinal Cognitive Impairment and Dementia Study. Convolutional neural network-based segmentations and those performed by a human rater were compared in terms of segmentation volume, identified PVS clusters, as well as Dice score. The comparison revealed good segmentation quality (Pearson correlation coefficient r = 0.70 with P < 0.0001 for PVS volume, detection rate in cluster analysis = 84.3%, and Dice score = 59.0%). Subsequent multivariate linear regression analysis, adjusted for participants' age, was performed to correlate PVS volume with clinical diagnoses, disease progression, cerebrospinal fluid biomarkers, lifestyle factors, and cognitive function. Cognitive function was assessed using the Mini-Mental State Examination, the Comprehensive Neuropsychological Test Battery, and the Cognitive Subscale of the 13-Item Alzheimer's Disease Assessment Scale.\n\n\nRESULTS\nMultivariate analysis, adjusted for age, revealed that participants with AD and MCI, but not those with SCD, had significantly higher PVS volumes compared with CU participants without SCD (P = 0.001 for each group). Furthermore, CU participants who developed incident MCI within 4.5 years after the baseline assessment showed significantly higher PVS volumes at baseline compared with those who did not progress to MCI (P = 0.03). Cognitive function was negatively correlated with PVS volume across all participant groups (P ≤ 0.005 for each). No significant correlation was found between PVS volume and any of the following parameters: cerebrospinal fluid biomarkers, sleep quality, body mass index, nicotine consumption, or alcohol abuse.\n\n\nCONCLUSIONS\nThe very early changes of PVS volume may suggest that alterations in PVS function are involved in the pathophysiology of AD. Overall, the volumetric assessment of centrum semiovale PVS represents a very early imaging biomarker for AD.","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":null,"pages":null},"PeriodicalIF":7.0000,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Investigative Radiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/RLI.0000000000001077","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0
Abstract
OBJECTIVES
Impaired perivascular clearance has been suggested as a contributing factor to the pathogenesis of Alzheimer disease (AD). However, it remains unresolved when the anatomy of the perivascular space (PVS) is altered during AD progression. Therefore, this study investigates the association between PVS volume and AD progression in cognitively unimpaired (CU) individuals, both with and without subjective cognitive decline (SCD), and in those clinically diagnosed with mild cognitive impairment (MCI) or mild AD.
MATERIALS AND METHODS
A convolutional neural network was trained using manually corrected, filter-based segmentations (n = 1000) to automatically segment the PVS in the centrum semiovale from interpolated, coronal T2-weighted magnetic resonance imaging scans (n = 894). These scans were sourced from the national German Center for Neurodegenerative Diseases Longitudinal Cognitive Impairment and Dementia Study. Convolutional neural network-based segmentations and those performed by a human rater were compared in terms of segmentation volume, identified PVS clusters, as well as Dice score. The comparison revealed good segmentation quality (Pearson correlation coefficient r = 0.70 with P < 0.0001 for PVS volume, detection rate in cluster analysis = 84.3%, and Dice score = 59.0%). Subsequent multivariate linear regression analysis, adjusted for participants' age, was performed to correlate PVS volume with clinical diagnoses, disease progression, cerebrospinal fluid biomarkers, lifestyle factors, and cognitive function. Cognitive function was assessed using the Mini-Mental State Examination, the Comprehensive Neuropsychological Test Battery, and the Cognitive Subscale of the 13-Item Alzheimer's Disease Assessment Scale.
RESULTS
Multivariate analysis, adjusted for age, revealed that participants with AD and MCI, but not those with SCD, had significantly higher PVS volumes compared with CU participants without SCD (P = 0.001 for each group). Furthermore, CU participants who developed incident MCI within 4.5 years after the baseline assessment showed significantly higher PVS volumes at baseline compared with those who did not progress to MCI (P = 0.03). Cognitive function was negatively correlated with PVS volume across all participant groups (P ≤ 0.005 for each). No significant correlation was found between PVS volume and any of the following parameters: cerebrospinal fluid biomarkers, sleep quality, body mass index, nicotine consumption, or alcohol abuse.
CONCLUSIONS
The very early changes of PVS volume may suggest that alterations in PVS function are involved in the pathophysiology of AD. Overall, the volumetric assessment of centrum semiovale PVS represents a very early imaging biomarker for AD.
期刊介绍:
Investigative Radiology publishes original, peer-reviewed reports on clinical and laboratory investigations in diagnostic imaging, the diagnostic use of radioactive isotopes, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasound, digital subtraction angiography, and related modalities. Emphasis is on early and timely publication. Primarily research-oriented, the journal also includes a wide variety of features of interest to clinical radiologists.