Cortical and Juxtacortical Layer Profiles Reveal Microstructural Changes at Early stage of Dementia
Poster No:
108
Submission Type:
Abstract Submission
Authors:
Ximu He1, Chun-Yi Lo2, Qihao Guo3, Chu-Chung Huang4
Institutions:
1East China Normal University, Shanghai, Shanghai, China, 2Department of Biomedical Engineering, Chung Yuan Christian University, Taoyuan, Taoyuan, 3Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong Univers, Shanghai, Shanghai, China, 4East China Normal University, Shanghai, Shanghai
First Author:
Co-Author(s):
Qihao Guo
Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong Univers
Shanghai, Shanghai, China
Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong Univers
Shanghai, Shanghai, China
Introduction:
Alzheimer's Disease is associated with extensive microstructural changes in gray and white matter, but layer-specific neurodegeneration and its impact on superficial white matter (SWM) remain relatively understudied. Previous studies suggest that cortical layers may exhibit specific vulnerabilities to dementia-related neuropathology. Among them, the third cortical layer showed highest accumulation of amyloid-beta [4], while the superficial layers of sensory cortices are the most severely affected [5], indicating that cytoarchitectonic subregions may undergo distinct changes during AD-related neurodegeneration. However, the differences in microstructural changes patterns across cortical depth and juxtacortical regions remain unclear. A deeper understanding of microstructural degeneration from cortical layers to SWM could provide insights into early stage neuroimaging biomarker, beyond the traditional focus on regional volume atrophy.
Methods:
We collected data 330 participants' magnetic resonance imaging (MRI), including normal control (NC, n=126, age: 63.49±7.54), mild cognitive impairment (MCI, n=117, age: 67.02±7.38) and Alzheimer's disease (AD, n=87, age: 70.28±8.45). We acquired T1-weighted image (T1WI) and T2-weighted image (T2WI) of all of them. To reveal the microstructural changes of dementia, we use sT1w/T2w ratio [1] as the proxy of myelin in cortical and juxtacortical region and sample to surfaces of each subject in native space (Fig.1a). We calculate the mean profile of each cortical type defined by Von Economo atlas (Fig.1b) [3] and use double-sigmoid curve to quantify T1w/T2w ratio profile near the white surface and pial surface [2]. Three main parameters used to quantify this profile are included in statistical analysis: a1 and b1 respectively represents the sigmoid curve centered around the white surface and pial surface scaling along the y-axis and c represents the entire curve translation along the y-axis (Fig.1c). We also used healthy adult participants' T1WI and T2WI from Wu-Minn Human Connectome Project (HCP) dataset to do the same thing to show the model can reflect the profile's features of different cortical types.
Results:
Compared to NC group, MCI group has mainly significant lower a1 value especially in cortical type 1~3. AD group also have significant higher b1 value and lower c value than MCI, which almost covers all cortical type. The differences between NC and AD were more pronounced and globally distributed in SWM. The AD group showed lower c value compared with NC, particularly in cortical type 3 (Fig.2b). The effects on the left and right hemispheres are relatively symmetrical. In addition, HCP dataset result shows that different profile pattern between cortical types (Fig.2a).
Conclusions:
Our findings suggest that in cortical and juxtacortical region, microstructural changes of early stage primarily observed in SWM than gray matter for dementia. At more advanced stage, microstructural changes may become more prominent in superficial cortex. These evidences suggest that different cortical layers or SWM microstructural changes may lead to varying degrees or types of neurobiological disruption, influencing the progression of dementia. Meanwhile, white matter indicators may offer greater sensitive for the clinical prediction and early identification of dementia risk.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Lifespan Development:
Aging
Modeling and Analysis Methods:
Methods Development 2
Neuroinformatics and Data Sharing:
Workflows
Novel Imaging Acquisition Methods:
Anatomical MRI
Keywords:
Aging
Cortex
Degenerative Disease
Demyelinating
DISORDERS
MRI
STRUCTURAL MRI
Workflows
Other - Cortical Profile; T1w/T2w Ratio
1|2Indicates the priority used for review
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[2] Hutsler, J. J., & Avino, T. A. (2013). Sigmoid fits to locate and characterize cortical boundaries in human cerebral cortex. Journal of Neuroscience Methods, 212(2), 242-246.
[3] Pijnenburg, R., Scholtens, L. H., Ardesch, D. J., de Lange, S. C., Wei, Y., & van den Heuvel, M. P. (2021). Myelo- and cytoarchitectonic microstructural and functional human cortical atlases reconstructed in common MRI space. NeuroImage, 239, 118274.
[4] Willumsen, N., Poole, T., Nicholas, J. M., Fox, N. C., Ryan, N. S., & Lashley, T. (2022). Variability in the type and layer distribution of cortical Aβ pathology in familial Alzheimer’s disease. Brain Pathology, 32(3), e13009.
[5] Zhang, N. K., Zhang, S. K., Zhang, L. I., Tao, H. W., & Zhang, G. W. (2023). Sensory processing deficits and related cortical pathological changes in Alzheimer’s disease. Frontiers in Aging Neuroscience, 15, 1213379.