Cortical neurodegeneration influences annual posterior white matter hyperintensity progression

Abstract Background For over three decades, the concomitance of cortical neurodegeneration and white matter hyperintensities (WMH) has sparked discussion about their coupled temporal dynamics (Garnier‐Crussard et al. 2023). Longitudinal evidence supporting this hypothesis remains nonetheless scarce (Ter Telgte et al. 2018). We integrated surface‐based morphometry and bivariate latent change score modelling (BLCSM) to examine interrelationships between individual WMH and cortical thickness changes over a one‐year period in cognitively unimpaired participants. Method We analysed baseline and 12‐month follow‐up data from cognitively unimpaired DELCODE participants (n=393; median age 70.31 [IQR 66.06‐74.87] years; 52% females). We used T2w FLAIR and T1w MPRAGE data to segment WMH (LST; Schmidt and Wink 2019) and estimate cortical thicknesses (CAT12; Gaser et al., n.d.). Using BLCSM in a vertex‐wise fashion, we tested whether baseline WMH volumes predicted cortical thinning rates and whether baseline cortical thickness predicted WMH volumes increases (Figure 1). Due to the posterior dominance of WMH in AD (Bernal et al. 2023), we focussed on parietal and occipital WMH. All models included age, sex, years of education, and total cardiovascular risk factors as covariates on baseline and change scores. We log‐10 transformed WMH volumes and corrected WMH volumes and thicknesses measurements for TICV via residualisation. We finally tested for moderation effects of CSF‐derived Aβ42/40 and pTau181 on cross‐domain coupling. Result BLCSM generally provided good data fits (RMSEA≤0.05, CFI≥0.095, SRMR≤0.05) across all analyses. The mean thickness across precuneal and superiorparietal cortices at baseline predicted the progression of WMH better than any other cortical region (Figure 2B; β Thick→ΔWMH =‐0.051, SE=0.011, Z=‐4.535, p‐value<0.001). This was especially evident with lower CSF‐derived Aβ42/40 (β Thick*Aβ42/40→ΔWMH =0.176, SE=0.053, Z=3.309, p‐value=0.001). WMH volume at baseline explained, in part, the level of thinning of parts of the fusiform cortex (Figure 2D; β WMH→ΔThick =‐0.139, SE=0.028, Z=‐5.030, p‐value<0.001). This interrelationship was moderated by CSF‐derived pTau181 (β WMH*pTau181→ΔThick =‐0.144, SE=0.053, Z=‐2.711, p‐value=0.007). Conclusion Cortical thinning and WMH progression may be mutually reinforcing processes that become reciprocally coupled prior to any detectable cognitive deficits. Hallmark AD proteins appear to moderate their interrelationships.

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Journal
Alzheimer s & Dementia
Published
2024-12-01
DOI
https://doi.org/10.1002/alz.086414
Primary Topic
Advanced Neuroimaging Techniques and Applications
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article
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article

Cortical neurodegeneration influences annual posterior white matter hyperintensity progression

Emrah Düzel, Jens Wiltfang, Robert Perneczky, Annika Spottke et al.
Alzheimer s & Dementia
Advanced Neuroimaging Techniques and Applications
article

Cortical neurodegeneration influences annual posterior white matter hyperintensity progression

Emrah Düzel, Jens Wiltfang, Robert Perneczky, Annika Spottke, Oliver Peters, Stefan Johannes Teipel, Gabriel Ziegler, Wiesje M. van der Flier, Renat Yakupov, Katharina Büerger, Klaus Fließbach, Josef Priller, Inga Menze, Frank Jessen, Anja Schneider, José Bernal Moyano, Stefanie Schreiber, Christoph Laske
article en

Abstract

Abstract Background For over three decades, the concomitance of cortical neurodegeneration and white matter hyperintensities (WMH) has sparked discussion about their coupled temporal dynamics (Garnier‐Crussard et al. 2023). Longitudinal evidence supporting this hypothesis remains nonetheless scarce (Ter Telgte et al. 2018). We integrated surface‐based morphometry and bivariate latent change score modelling (BLCSM) to examine interrelationships between individual WMH and cortical thickness changes over a one‐year period in cognitively unimpaired participants. Method We analysed baseline and 12‐month follow‐up data from cognitively unimpaired DELCODE participants (n=393; median age 70.31 [IQR 66.06‐74.87] years; 52% females). We used T2w FLAIR and T1w MPRAGE data to segment WMH (LST; Schmidt and Wink 2019) and estimate cortical thicknesses (CAT12; Gaser et al., n.d.). Using BLCSM in a vertex‐wise fashion, we tested whether baseline WMH volumes predicted cortical thinning rates and whether baseline cortical thickness predicted WMH volumes increases (Figure 1). Due to the posterior dominance of WMH in AD (Bernal et al. 2023), we focussed on parietal and occipital WMH. All models included age, sex, years of education, and total cardiovascular risk factors as covariates on baseline and change scores. We log‐10 transformed WMH volumes and corrected WMH volumes and thicknesses measurements for TICV via residualisation. We finally tested for moderation effects of CSF‐derived Aβ42/40 and pTau181 on cross‐domain coupling. Result BLCSM generally provided good data fits (RMSEA≤0.05, CFI≥0.095, SRMR≤0.05) across all analyses. The mean thickness across precuneal and superiorparietal cortices at baseline predicted the progression of WMH better than any other cortical region (Figure 2B; β Thick→ΔWMH =‐0.051, SE=0.011, Z=‐4.535, p‐value<0.001). This was especially evident with lower CSF‐derived Aβ42/40 (β Thick*Aβ42/40→ΔWMH =0.176, SE=0.053, Z=3.309, p‐value=0.001). WMH volume at baseline explained, in part, the level of thinning of parts of the fusiform cortex (Figure 2D; β WMH→ΔThick =‐0.139, SE=0.028, Z=‐5.030, p‐value<0.001). This interrelationship was moderated by CSF‐derived pTau181 (β WMH*pTau181→ΔThick =‐0.144, SE=0.053, Z=‐2.711, p‐value=0.007). Conclusion Cortical thinning and WMH progression may be mutually reinforcing processes that become reciprocally coupled prior to any detectable cognitive deficits. Hallmark AD proteins appear to moderate their interrelationships.

Alzheimer s & DementiaVol. 20(S2)
University of Bonn (DE), University of Cologne (DE), University of Luxembourg (LU), German Center for Neurodegenerative Diseases (DE), LMU Klinikum (DE), Humboldt-Universität zu Berlin (DE), Munich Cluster for Systems Neurology (DE), UK Dementia Research Institute (GB), Cologne Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases (DE), Hertie Institute for Clinical Brain Research (DE), University of Rostock (DE), University of Aveiro (PT), Technical University of Munich (DE), University of Göttingen (DE), Freie Universität Berlin (DE), Charité - Universitätsmedizin Berlin (DE), Ludwig-Maximilians-Universität München (DE), Otto-von-Guericke-Universität Magdeburg (DE), University of Edinburgh (GB)
Openalex Percentile: Top 58%
Advanced Neuroimaging Techniques and Applications
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