Home > Publications Database > Perivascular space and white matter hyperintensities in Alzheimer's disease: associations with disease progression and cognitive function. > print

001     277548
005     20250323001012.0
024 7 _ |a 10.1186/s13195-025-01707-9
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037 _ _ |a DZNE-2025-00437
041 _ _ |a English
082 _ _ |a 610
100 1 _ |a Schirge, Philine Marie
|b 0
245 _ _ |a Perivascular space and white matter hyperintensities in Alzheimer's disease: associations with disease progression and cognitive function.
260 _ _ |a London
|c 2025
|b BioMed Central
336 7 _ |a article
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336 7 _ |a Journal Article
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336 7 _ |a ARTICLE
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336 7 _ |a Journal Article
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520 _ _ |a Alzheimer's disease (AD) is the leading cause of dementia, characterized by the accumulation of amyloid-beta (Aβ) and neurofibrillary tangles. Recent studies emphasize the role of vascular factors, including the glymphatic system, in AD pathogenesis, particularly in Aβ clearance. The diffusion tensor image analysis along the perivascular space (DTI-ALPS; ALPS-Index) has emerged as a novel, non-invasive method to evaluate the glymphatic system in vivo, showing glymphatic insufficiency in AD. This study aimed to investigate alterations in the function of the glymphatic system in individuals with AD versus healthy controls (HC), and to explore its association with Aβ, cerebrovascular disease (CVD), white matter hyperintensities (WMH), and cognitive function.DTI MRI data from three independent study cohorts (ActiGliA: AD n = 16, Controls n = 18; DELCODE: AD n = 54, Controls n = 67; ADNI: AD n = 43, Controls n = 49) were used to evaluate the perivascular space (PVS) integrity; a potential biomarker for glymphatic activity. The DTI-Along the Perivascular Space technique was used to measure water diffusion along PVS providing an index to assess the efficiency of the glymphatic system's waste clearance function. WMH load was quantified in FLAIR MRI using the lesion segmentation tool. We quantified WMHs volume within our defined region of interest (ROI) and excluded participants with any WMHs to avoid confounding the ALPS-Index. Associations with cerebrospinal fluid (CSF) AD hallmark biomarkers, cognitive performance (MMSE) and clinical severity (CDR) were assessed.AD patients had a significantly lower ALPS-Index vs. healthy controls (ActiGliA: AD: mean = 1.22, SD = 0.12; Controls: mean = 1.36, SD = 0.14, p = 0.004; DELCODE: AD: mean = 1.26, SD = 0.18; Controls: mean = 1.34, SD = 0.2, p = 0.035; ADNI: AD: mean = 1.08, SD = 0.24; Controls: mean = 1.19, SD = 0.13, p = 0.008). The ALPS-Index was associated with CSF Aβ concentration, WMH number and MMSE and CDR. WMH, found in the ROIs correlated negatively with the ALPS-Index.This study highlights the potential of the DTI-ALPS-Index as a biomarker for glymphatic dysfunction in AD. It underscores the importance of considering vascular factors and the glymphatic system in the pathogenesis and diagnosis of AD as WMHs in the ROI could cause disturbances and inaccurate indices.
536 _ _ |a 351 - Brain Function (POF4-351)
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588 _ _ |a Dataset connected to CrossRef, PubMed, , Journals: pub.dzne.de
650 _ 7 |a Alzheimer's disease
|2 Other
650 _ 7 |a Amyloid-beta
|2 Other
650 _ 7 |a Cognitive decline
|2 Other
650 _ 7 |a Dementia
|2 Other
650 _ 7 |a Diffusion tensor imaging
|2 Other
650 _ 7 |a Perivascular space
|2 Other
650 _ 7 |a Amyloid beta-Peptides
|2 NLM Chemicals
650 _ 2 |a Humans
|2 MeSH
650 _ 2 |a Alzheimer Disease: diagnostic imaging
|2 MeSH
650 _ 2 |a Alzheimer Disease: pathology
|2 MeSH
650 _ 2 |a Female
|2 MeSH
650 _ 2 |a Male
|2 MeSH
650 _ 2 |a White Matter: diagnostic imaging
|2 MeSH
650 _ 2 |a White Matter: pathology
|2 MeSH
650 _ 2 |a Aged
|2 MeSH
650 _ 2 |a Disease Progression
|2 MeSH
650 _ 2 |a Glymphatic System: diagnostic imaging
|2 MeSH
650 _ 2 |a Glymphatic System: pathology
|2 MeSH
650 _ 2 |a Diffusion Tensor Imaging: methods
|2 MeSH
650 _ 2 |a Cognition: physiology
|2 MeSH
650 _ 2 |a Aged, 80 and over
|2 MeSH
650 _ 2 |a Amyloid beta-Peptides: cerebrospinal fluid
|2 MeSH
650 _ 2 |a Amyloid beta-Peptides: metabolism
|2 MeSH
650 _ 2 |a Magnetic Resonance Imaging: methods
|2 MeSH
650 _ 2 |a Brain: diagnostic imaging
|2 MeSH
650 _ 2 |a Brain: pathology
|2 MeSH
700 1 _ |a Perneczky, Robert
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700 1 _ |a Taoka, Toshiaki
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700 1 _ |a Ruiz-Rizzo, Adriana L
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700 1 _ |a Ersoezlue, Ersin
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700 1 _ |a Forbrig, Robert
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700 1 _ |a Guersel, Selim
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700 1 _ |a Kurz, Carolin
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700 1 _ |a Brendel, Matthias
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700 1 _ |a Hellmann-Regen, Julian
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700 1 _ |a Priller, Josef
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700 1 _ |a Schneider, Anja
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700 1 _ |a Jessen, Frank
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700 1 _ |a Düzel, Emrah
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700 1 _ |a Buerger, Katharina
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700 1 _ |a Teipel, Stefan
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700 1 _ |a Laske, Christoph
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700 1 _ |a Peters, Oliver
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700 1 _ |a Spruth, Eike
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700 1 _ |a Fliessbach, Klaus
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700 1 _ |a Rostamzadeh, Ayda
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700 1 _ |a Glanz, Wenzel
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700 1 _ |a Janowitz, Daniel
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700 1 _ |a Kilimann, Ingo
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700 1 _ |a Sodenkamp, Sebastian
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700 1 _ |a Ewers, Michael
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700 1 _ |a Rauchmann, Boris-Stephan
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773 _ _ |a 10.1186/s13195-025-01707-9
|g Vol. 17, no. 1, p. 62
|0 PERI:(DE-600)2506521-X
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|t Alzheimer's research & therapy
|v 17
|y 2025
|x 1758-9193
856 4 _ |u https://pub.dzne.de/record/277548/files/DZNE-2025-00437%20SUP.docx
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