Cortical neurodegeneration influences annual white matter hyperintensity progression
Poster No:
192
Submission Type:
Abstract Submission
Authors:
Jose Bernal1, Inga Menze1, Renat Yakupov1, Oliver Peters2, Josef Priller2, Anja Schneider3, Klaus Fliessbach3, Jens Wiltfang4, Frank Jessen3, Katharina Buerger5, Robert Perneczky5, Stefan Teipel6, Christoph Laske7, Annika Spottke3, Michael Heneka8, Stefanie Schreiber1, Emrah Düzel1, Gabriel Ziegler1
Institutions:
1German Centre for Neurodegenerative Diseases (DZNE), Magdeburg, Germany, 2German Centre for Neurodegenerative Diseases (DZNE), Berlin, Germany, 3German Centre for Neurodegenerative Diseases (DZNE), Bonn, Germany, 4German Centre for Neurodegenerative Diseases (DZNE), Göttingen, Germany, 5German Centre for Neurodegenerative Diseases (DZNE), Munich, Germany, 6German Centre for Neurodegenerative Diseases (DZNE), Rostock, Germany, 7German Centre for Neurodegenerative Diseases (DZNE), Tübingen, Germany, 8University of Luxembourg, Luxembourg, Luxembourg
First Author:
Co-Author(s):
Introduction:
During late adulthood, the cortical thickness decreases as white matter hyperintensities (WMH) emerge(Shirzadi et al. 2023)-a co-incidence that has prompted consideration of coupled temporal dynamics for over three decades(Garnier-crussard et al. 2023). Longitudinal evidence substantiating such a coupling remains nonetheless scarce(Ter Telgte et al. 2018).
We integrated surface-based morphometry and bivariate latent change score modelling (BLCSM) to examine the interrelationships between WMH and cortical thickness over a one-year period in older individuals without objective cognitive impairment and with a low vascular profile.
We integrated surface-based morphometry and bivariate latent change score modelling (BLCSM) to examine the interrelationships between WMH and cortical thickness over a one-year period in older individuals without objective cognitive impairment and with a low vascular profile.
Methods:
We used baseline and 12-month follow-up data from cognitively unimpaired participants enrolled in DELCODE (n=393; median age 70.31 [IQR 66.06, 74.87] years; 52% females; median years of education 15 [IQR 13, 17]; European origins). We segmented WMH using the Lesion Prediction Algorithm and T2w FLAIR data(Schmidt and Wink 2019). We estimated WMH volumes in two regions which we previously found associated with amyloid pathology and cardiovascular risk: a posterior one, comprising both parietal and occipital lobes, and a periventricular one, respectively(Bernal et al. 2023) (Figure 1A). We also obtained cortical thickness using the CAT12 longitudinal pipeline (ageing workflow; default parameters; final resolution 1 mm3; 12-mm Gaussian smoothing; resampling to 32k HCP surface template)(Gaser et al., n.d.) and T1w MPRAGE data.
Using BLCSM in a vertex-wise fashion (Figure 1B), we tested two hypotheses: (a) ischemic or hypoxic damage-operationalised as WMH-causes a depletion of oxygen, nutrients, and tropic support, thereby affecting both neighbouring and distant cortical regions and leading to cortical atrophy7; (b) cortical neurodegeneration-especially when in conjunction with amyloid or tau pathologies(Garnier-crussard et al. 2023; Salvadores, Gerónimo-Olvera, and Court 2020)-initiates degenerative axonal loss and contributes to the emergence and progression of WMH(Garnier-crussard et al. 2023). Note that the second association should be more evident when considering the posterior WMH pattern as opposed to the periventricular one. We adjusted the model for age, sex, years of education, total cardiovascular risk factors, and CSF-derived amyloid-β (Aβ) 42/40 ratio. We log-10 transformed WMH volumes and corrected WMH volumes and thickness measurements for TICV via residualisation.
Using BLCSM in a vertex-wise fashion (Figure 1B), we tested two hypotheses: (a) ischemic or hypoxic damage-operationalised as WMH-causes a depletion of oxygen, nutrients, and tropic support, thereby affecting both neighbouring and distant cortical regions and leading to cortical atrophy7; (b) cortical neurodegeneration-especially when in conjunction with amyloid or tau pathologies(Garnier-crussard et al. 2023; Salvadores, Gerónimo-Olvera, and Court 2020)-initiates degenerative axonal loss and contributes to the emergence and progression of WMH(Garnier-crussard et al. 2023). Note that the second association should be more evident when considering the posterior WMH pattern as opposed to the periventricular one. We adjusted the model for age, sex, years of education, total cardiovascular risk factors, and CSF-derived amyloid-β (Aβ) 42/40 ratio. We log-10 transformed WMH volumes and corrected WMH volumes and thickness measurements for TICV via residualisation.
Results:
The BLCSM converged at all vertices and fitted the data well (χ^2(15)=8.857, p=0.885, RMSEA=0.000 90%-CI[0.000;0.000], CFI=1.000, SRMR=0.034). On average, individuals with thinner cortices at baseline had greater baseline WMH volumes (Figure 2A; σThick↔WMH=-0.239, SE=0.048, Z=-4.968, p<0.001) and showed stronger WMH volume increase over the course of a year (Figure 2B; βThick->ΔWMH=-0.168, SE=0.056, Z=-3.001, p=0.003). The annual progression of WMH in posterior regions, as opposed to periventricular ones, was associated with cortical thickness at baseline, especially that spanning posterior cortices (Figure 2B; peak at parieto-occipital sulcus: βThick->ΔWMH=-0.064, SE=0.015, Z=-4.409, p<0.001). Further investigation of this relationship revealed that subjects with a lower Aβ42/40 ratio and thinner baseline cuneal, precuneal, and superior parietal cortices experienced larger annual changes in posterior WMH volumes (Figure 2C; interaction between baseline cortical thickness and the Aβ42/40 ratio on posterior WMH changes: β=0.028, SE=0.010, Z=2.718, p=0.007).
Conclusions:
The progression of WMH in parietal and occipital regions within a year can in part be explained by the extent of cortical neurodegeneration occurring in those very same regions at baseline; an association that becomes stronger with higher retention of amyloid in the brain. Our work therefore suggests that posterior WMH might be influenced by cortical neurodegeneration and amyloidosis, and these alterations occur prior to the onset of any detectable cognitive deficits.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Lifespan Development:
Aging
Modeling and Analysis Methods:
Methods Development 2
Keywords:
Aging
Cerebrovascular Disease
Data analysis
Degenerative Disease
Modeling
MRI
Statistical Methods
STRUCTURAL MRI
White Matter
1|2Indicates the priority used for review
Provide references using author date format
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Gaser, Christian, Robert Dahnke, Paul M Thompson, Florian Kurth, Eileen Luders, and Disease Neuroimaging Initiative. n.d. ‘CAT - A Computational Anatomy Toolbox for the Analysis of Structural MRI Data’. https://doi.org/10.1101/2022.06.11.495736.
Salvadores, Natalia, Cristian Gerónimo-Olvera, and Felipe A. Court. 2020. ‘Axonal Degeneration in AD: The Contribution of Aβ and Tau’. Frontiers in Aging Neuroscience. Frontiers Media S.A. https://doi.org/10.3389/fnagi.2020.581767.
Schmidt, Paul, and Lucie Wink. 2019. ‘LST : A Lesion Segmentation Tool for SPM’.
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