Cortical diffusion MRI microstructure associations with amyloid PET in cognitively normal people
Presented During:
Thursday, June 26, 2025: 11:30 AM - 12:45 PM
Brisbane Convention & Exhibition Centre
Room:
Great Hall
Poster No:
211
Submission Type:
Abstract Submission
Authors:
Talia Nir1, Siddharth Narula1, Sunanda Somu1, Kevin Low1, Julio Villalón-Reina1, Sophia Thomopoulos1, Noelle Lee1, Meredith Braskie1, Paul Thompson1, Neda Jahanshad1
Institutions:
1University of Southern California, Los Angeles, CA
First Author:
Co-Author(s):
Introduction:
Diffusion MRI (dMRI) is sensitive to small changes in brain microstructure and may offer sensitivity to early Alzheimer's disease (AD) neuropathology that precedes macrostructural brain changes. Subtle pathology associated with early amyloid (Aβ) deposition may be better captured by dMRI measures in cortical gray matter (GM), where the earliest AD histopathological changes occur, compared to GM volume or thickness, the most commonly used MRI biomarkers. Recently, single-shell adaptations of multi-shell NODDI [1] and mean apparent propagator (MAP)-MRI [2] dMRI models have been proposed: NODDI-DTI [3] and MAP-AMURA [4], respectively. These models may mimic the sensitivity of multi-shell models to sources of non-Gaussianity in the GM, including dispersion and restriction, and may help to identify early correlates of Aβ accumulation before neurodegeneration and cognitive impairment occur. Here, we evaluated relationships between Aβ PET and advanced single-shell cortical microstructural measures in cognitively normal (CN) individuals from 3 AD studies. For comparison, more conventional DTI and cortical thickness (CTh) measures were also evaluated.
Methods:
T1w, dMRI, and Aβ PET data were analyzed in 654 CN people from ADNI3 [5] (N=182; age: 72.6±6.9 yrs; 72 male; 7 dMRI protocols; FBB/FBP PET), HABS-HD [6] (N=340; age: 62.1±7.7 yrs; 97 male; 1 dMRI protocol; FBB PET), and OASIS3 [7] (N=132; age: 71.3±8.1 yrs; 64 male; 1 dMRI protocol; FBP/PiB PET). Across studies, 309 participants also had tau PET data (ADNI3/OASIS3: FTP, HABS-HD: PI2620). After dMRI were preprocessed, 4 DTI, 4 MAP-AMURA and 3 NODDI-DTI maps were estimated (Fig 1A) and warped to respective T1w images. CTh and mean dMRI measures were extracted from 34 cortical regions parcellated from T1w with FreeSurfer, and the full cortex. Mean cortical Aβ PET SUVRs for each study were converted to centiloids (CL) [8]. Random-effects linear regressions were used to test for associations between regional cortical MRI measures and Aβ-CL, adjusting for age, sex, education, ethnicity, race, total CTh (excluded from CTh analyses), intracranial volume, and grouping by study protocol. We also tested the interactive effects of Aβ-CL and tau positivity ('+' or '-') on cortical measures. Tau PET positivity was defined for each respective study. FDR (q=0.05) was used to correct for multiple comparisons across 35 regions.
Results:
Significant Aβ-CL associations were detected with 4 dMRI measures and CTh (Fig 1b). Limited regional associations were found between greater Aβ and lower cortical ODI, greater FA, and both greater occipital and lower middle temporal CTh. More widespread associations were detected between Aβ and higher APA and Τ, including the full cortex. The relationship between 5 cortical dMRI measures and Aβ were significantly moderated by tau positivity; compared to tau- individuals, tau+ individuals showed steeper positive AxD and Τ, and negative RTOP, RTPP, and ODI slopes with respect to Aβ burden (Fig 2).
Conclusions:
Compared to more conventional CTh and DTI measures, advanced dMRI measures showed more widespread associations with Aβ load and moderately larger effects. Greater restricted diffusion (i.e., higher APA) associations with greater Aβ burden, could be attributed to factors such as cellular hypertrophy or inflammatory microglia infiltration increasing the number of diffusion barriers in early stages. In contrast, lower neurite dispersion (ODI) with greater Aβ could be driven by subsequent neurite loss and neurodegeneration. This biphasic pattern has previously been identified in preclinical AD dMRI studies [9]. A non-monotonic trajectory is further supported by dMRI interactions showing lower diffusivity (i.e., lower AxD, higher RTPP/RTOP) with greater Aβ in tau- individuals and vice-versa in tau+; in contrast, no CTh interactions were found. Single-shell dMRI models may offer insight into pathological disease processes beyond more widely used CTh measures.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Lifespan Development:
Aging
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis 2
Keywords:
Aging
Cortex
STRUCTURAL MRI
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - Alzheimer's disease, amyloid PET, microstructure
1|2Indicates the priority used for review
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