Poster No:
184
Submission Type:
Abstract Submission
Authors:
Beatriz Padrela1, Sandra Tecelão2, Oliver Geier3, Markus Sneve4, David Vallez Garcia1, Amnah Mahroo5, Lene Pålhaugen2, Bjørn-Eivind Kirsebom6, Klaus Eickel5, David Thomas7, Atle Bjørnerud4, Anders Fjell4, Kristine Beate Walhovd4, Frederik Barkhof1, Per Selnes2, Matthias Günther5, Jan Petr8, Tormod Fladby2, Henk Mutsaerts1
Institutions:
1Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Location VUmc, Amsterdam, Netherlands, 2Department of Neurology, Akershus University Hospital, Oslo, Norway, 3Department of Physics and Computational Radiology, Division of Radiology and Nuclear Medicine, Oslo, Norway, 4Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway, 5Fraunhofer-Institute for Digital Medicine MEVIS, Bremen, Germany, 6Institute of Clinical Medicine, Campus Ahus, University of Oslo, Oslo, Norway, 7Queen Square Institute of Neurology, University College London, London, UK, 8Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiopharmaceutical Cancer Research, Dresden, Germany
First Author:
Beatriz Padrela
Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Location VUmc
Amsterdam, Netherlands
Co-Author(s):
Sandra Tecelão
Department of Neurology, Akershus University Hospital
Oslo, Norway
Oliver Geier
Department of Physics and Computational Radiology, Division of Radiology and Nuclear Medicine
Oslo, Norway
Markus Sneve
Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo
Oslo, Norway
David Vallez Garcia
Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Location VUmc
Amsterdam, Netherlands
Amnah Mahroo
Fraunhofer-Institute for Digital Medicine MEVIS
Bremen, Germany
Lene Pålhaugen
Department of Neurology, Akershus University Hospital
Oslo, Norway
Klaus Eickel
Fraunhofer-Institute for Digital Medicine MEVIS
Bremen, Germany
David Thomas
Queen Square Institute of Neurology, University College London
London, UK
Atle Bjørnerud
Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo
Oslo, Norway
Anders Fjell
Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo
Oslo, Norway
Kristine Beate Walhovd
Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo
Oslo, Norway
Frederik Barkhof, MD, Ph. D
Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Location VUmc
Amsterdam, Netherlands
Per Selnes
Department of Neurology, Akershus University Hospital
Oslo, Norway
Matthias Günther
Fraunhofer-Institute for Digital Medicine MEVIS
Bremen, Germany
Jan Petr
Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiopharmaceutical Cancer Research
Dresden, Germany
Tormod Fladby
Department of Neurology, Akershus University Hospital
Oslo, Norway
Henk Mutsaerts
Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Location VUmc
Amsterdam, Netherlands
Introduction:
Blood-brain barrier (BBB) dysfunction is potentially one of the earliest microvascular changes in Alzheimer's disease (AD) and related dementias[1]. An emerging technique to image the time of exchange (Tex) of water across the BBB is multi-echo[2,3] arterial spin labeling (ASL) which obviates the need for exogenous contrast, making it a less invasive and less costly alternative to existing contrast-based agents, and may even be more sensitive to subtle BBB changes. Tex has been shown to provide reproducible values of BBB integrity in healthy volunteers[2]. Here, we investigate the associations of Tex values with amyloid positivity and cognitive status. As cerebral blood flow (CBF) can be seen as an established ASL biomarker, we repeated all analyses for CBF for comparison.
Methods:
Data from 116 participants older than 50 years were selected from the Center for Lifespan Changes in Brain and Cognition (LCBC) and the Dementia Disease Initiation (DDI) cohorts. LCBC comprises a population-based cohort including only cognitively normal (CN) participants (n=77, CNLCBC), while DDI is a clinical outpatient cohort including CN and subjective cognitive decline patients (n=24, merged here into a single cohort CNDDI) and mild cognitive impairment (n=15, MCIDDI)[4] patients.
Amyloid status was defined as positive (A+) or negative (A-) from the CSF amyloid-beta 42/40 ratio (cut-off ≤ 0.077) or amyloid-PET by visual read, when available.
All cohorts were scanned on the same 3T Siemens Prisma scanner with a 32-channel head coil. Two recently developed multi-post-labeling delay (PLD) Hadamard-encoded (HAD) 3D GRASE PCASL sequences were used to estimate Tex and CBF: 1) HAD-8 with a labeling duration (LD) 400 ms, PLD [600:400:3400] ms, and single echo time (TE) 12.5 ms; 2) multi-TE HAD-4 with LD 1000 ms, PLD [1500:1000:3500] ms, and 8 TEs [14.4:28.9:217.2] ms. Data were analyzed with ExploreASL 1.11.0 beta[5], and gray matter (GM) CBF and Tex were quantified with FSL-FABBER[6]. Tex and CBF associations with amyloid and cognitive status were assessed using linear regression adjusted for age and sex.
Results:
Of the 116 participants, 77 were from the LCBC (64.6±8.4 years, 64% female) and 39 from the DDI (67.7±7.9 years, 51% female) cohorts. DDI included 15 MCIs and 28 A+, of which 12 were both MCI and A+. Across the whole population, GM Tex was negatively correlated with age (r = -0.38, p < 0.001), whereas for GM CBF, this correlation was not statistically significant (r = -0.26, p = 0.069).
Whole-brain group average Tex (Figure 1A) and CBF (Figure 1B) maps show data from CN A- controls, MCI patients, and A+ subjects, where Tex and CBF appear higher in the NC A- group than in the MCI or A+ groups.
Tex was 15% lower in A+ compared to A- (t=2.75, p=0.01; Figure 2A). CBF was 5% higher in the A+ group than the A- group but did borderline not reach statistical significance (t=-1.94, p=0.06; Figure 2B)[7,8]. The linear regression analysis showed that amyloid status was associated with BBB water permeability (given by Tex), with higher permeability in A+ compared with A- groups when correcting for age, sex, and CBF (β = -35 s, p < 0.001; Figure 2C).
Moreover, cognitive staging was related to Tex, even when correcting for age and sex (βMCI = -31.3 s, p < 0.01). A similar relationship was not found for CBF.
Conclusions:
Interestingly, both amyloid positivity and cognitive status were associated with increased BBB water permeability, even when correcting for age and sex. In agreement with previous studies, BBB water permeability was shown to increase with age. These permeability increases might be explained by a normal aging process of increased brain clearance or by BBB dysfunction promoting leakage of toxins to cross from the capillary site to the brain parenchyma. These findings encourage the use of BBB-ASL to non-invasively investigate BBB integrity in the early stages of dementia.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Neuroinformatics and Data Sharing:
Brain Atlases 2
Novel Imaging Acquisition Methods:
Imaging Methods Other
Keywords:
Cerebral Blood Flow
Degenerative Disease
MRI
Other - Blood-brain barrier
1|2Indicates the priority used for review
Provide references using author date format
1. Montagne, A. et al. (2020) 'APOE4 leads to blood-brain barrier dysfunction predicting cognitive decline'. Nature 581, 71–76
2. Mahroo, A. et al. (2021) 'Robust Multi-TE ASL-Based Blood-Brain Barrier Integrity Measurements'. Front. Neurosci. 15, 719676
3. Gregori, J., Schuff, N., Kern, R. & Günther, M. (2013) 'T2-based arterial spin labeling measurements of blood to tissue water transfer in human brain'. J. Magn. Reson. Imaging 37, 332–342
4. Fladby, T. et al. (2017) 'Detecting At-Risk Alzheimer’s Disease Cases. J. Alzheimers'. Dis. 60, 97–105
5. Mutsaerts, H. J. M. M. et al. (2020) 'ExploreASL: an image processing pipeline for multi-center ASL perfusion MRI studies'. Neuroimage 117031
6. Chappell, M. A., Groves, A. R., Whitcher, B. & Woolrich, M. W. (2009) 'Variational Bayesian Inference for a Nonlinear Forward Model. IEEE Trans'. Signal Process. 57, 223–236
7. Fazlollahi, A. et al. (2020) 'Increased cerebral blood flow with increased amyloid burden in the preclinical phase of alzheimer’s disease'. J. Magn. Reson. Imaging 51, 505–513
8. Padrela, B. E. et al. (2023) 'Genetic, vascular, and amyloid components of cerebral blood flow in a preclinical population'. J. Cereb. Blood Flow Metab. 271678X231178993