Poster No:
290
Submission Type:
Abstract Submission
Authors:
Youjin Jung1,2, Andrew Bender3,4, Scott Counts5,4, Benjamin Hampstead6,4,7, Scott Peltier8,4, Ana Daugherty1,2,4, Jessica Damoiseaux1,2,4
Institutions:
1Department of Psychology, Wayne State University, Detroit, MI, 2Institute of Gerontology, Wayne State University, Detroit, MI, 3Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, 4Michigan Alzheimer’s Disease Research Center, Ann Arbor, MI, 5Departments of Translational Neuroscience and Family Medicine, Michigan State University, Grand Rapids, MI, 6Research Program on Cognition & Neuromodulation Based Interventions, University of Michigan, Ann Arbor, MI, 7VA Ann Arbor Healthcare System, Ann Arbor, MI, 8Functional MRI Laboratory, University of Michigan, Ann Arbor, MI
First Author:
Youjin Jung
Department of Psychology, Wayne State University|Institute of Gerontology, Wayne State University
Detroit, MI|Detroit, MI
Co-Author(s):
Andrew Bender, Ph.D.
Cleveland Clinic Lou Ruvo Center for Brain Health|Michigan Alzheimer’s Disease Research Center
Las Vegas, NV|Ann Arbor, MI
Scott Counts, Ph.D.
Departments of Translational Neuroscience and Family Medicine, Michigan State University|Michigan Alzheimer’s Disease Research Center
Grand Rapids, MI|Ann Arbor, MI
Benjamin Hampstead, Ph.D.
Research Program on Cognition & Neuromodulation Based Interventions, University of Michigan|Michigan Alzheimer’s Disease Research Center|VA Ann Arbor Healthcare System
Ann Arbor, MI|Ann Arbor, MI|Ann Arbor, MI
Scott Peltier
Functional MRI Laboratory, University of Michigan|Michigan Alzheimer’s Disease Research Center
Ann Arbor, MI|Ann Arbor, MI
Ana Daugherty, Ph.D.
Department of Psychology, Wayne State University|Institute of Gerontology, Wayne State University|Michigan Alzheimer’s Disease Research Center
Detroit, MI|Detroit, MI|Ann Arbor, MI
Jessica Damoiseaux
Department of Psychology, Wayne State University|Institute of Gerontology, Wayne State University|Michigan Alzheimer’s Disease Research Center
Detroit, MI|Detroit, MI|Ann Arbor, MI
Introduction:
Plasma neurofilament light (pNfL) is a promising marker of neurodegeneration for Alzheimer's disease (AD), predicting gray matter (GM) atrophy and white matter (WM) microstructural changes. However, its sensitivity to microstructural characteristics of the GM and WM in early AD requires further investigation. Moreover, it remains unclear whether microstructural changes in specific WM regions exhibit stronger associations with pNfL levels. Here, we investigated how pNfL associates with different microstructural indices in different regions of the WM, and how the associations between pNfL and the neurite density of the WM and GM vary with memory functioning in older adults with and without AD-related cognitive decline.
Methods:
A total of 97 older adults were included in this study – 44 with very mild to mild dementia (age: 71.3 ± 7.7; sex (m/f): 14/30; CDR (0.5/1): 39/5) and 53 cognitively unimpaired individuals (age: 70.7 ± 7.2; sex (m/f): 13/40; CDR (0/0.5): 29/24). pNfL was measured using a single-molecule array (Simoa) assay. We computed neurite orientation dispersion and density imaging (NODDI) measures from diffusion-weighted images (b = 0, 700, 2000 s/mm2), including intracellular volume fraction (VIC), orientation dispersion index (ODI), and CSF volume fraction (VISO). Our examination of the voxel-wise association between pNfL and the NODDI indices in WM employed a partial least squares correlation (PLSC) to examine the covariance between pNfL and the 3 NODDI images, VIC, ODI, and VISO. Prior to analysis, age and body mass index (BMI) were regressed out of pNfL, and age and sex were regressed out of the NODDI images. Next, to examine how the associations between pNfL and the neurite densities (VIC) of the WM and the GM vary with memory functioning, we fit a path model. The model included a memory factor score as the moderator for the link between BMI-adjusted pNfL, and the VIC of the entorhinal cortex, as well as the mean VIC of the WM clusters that reliably contributed to the covariance between pNfL and NODDI images in the first component of the PLSC analysis (Fig. 1A). The model included age and sex as covariates predicting the WM and GM VIC. The memory factor score was computed via a confirmatory factor analysis on delayed recall scores from four different memory tests.
Results:
The first component of the PLSC analysis showed the association between pNfL and the NODDI indices, where higher pNfL associated with lower VIC and VISO, and higher ODI. These effects were mainly observed in the anterior temporal lobe, WM regions near the precuneus, and the inferior parietal and superior frontal WM areas. The second component showed the association between pNfL and the NODDI indices mainly in the centrum semiovale, where higher pNfL associated with lower ODI and VISO (Fig. 2). In the path analysis, the memory factor moderated the link between pNfL and the entorhinal VIC: higher pNfL levels predicted lower entorhinal VIC in older adults with lower memory function, but not in those with higher memory function. However, despite the significant main effect of pNfL on WM VIC, memory function did not moderate their relationship, indicating that higher pNfL predicted lower WM VIC regardless of memory function (Fig. 2BC).
Conclusions:
The PLSC analysis demonstrated that higher pNfL reflects different WM microstructural characteristics, including lower axonal density mainly in temporoparietal WM likely due to AD pathology, and lower fiber orientation variability in WM regions abundant in crossing fibers. The differing moderating effects of memory for the GM and WM VIC in the path model suggest that the pNfL concentration reflects the neurite density in both GM and WM and may be more sensitive to the WM than the GM in early AD, given the significant pNfL – WM VIC relationship irrespective of the memory score. Our findings illuminate WM microstructural changes underlying elevated pNfL and the varied sensitivity of pNfL to different neurodegenerative aspects in early AD.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Lifespan Development:
Aging 2
Keywords:
Aging
Blood
Degenerative Disease
Memory
Multivariate
Neurological
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - Neurofilament light
1|2Indicates the priority used for review
Provide references using author date format
Damoiseaux, J. S., Smith, S. M., Witter, M. P., Sanz-Arigita, E. J., Barkhof, F., Scheltens, P., Stam, C. J., Zarei, M., & Rombouts, S. A. R. B. (2009). White matter tract integrity in aging and alzheimer’s disease. Human Brain Mapping, 30(4), 1051–1059.
McIntosh, A. R., & Lobaugh, N. J. (2004). Partial least squares analysis of neuroimaging data: Applications and advances. NeuroImage, 23(SUPPL. 1), 250–263.
Vogt, N. M., Hunt, J. F., Adluru, N., Dean, D. C., Johnson, S. C., Asthana, S., Yu, J. P. J., Alexander, A. L., & Bendlin, B. B. (2020). Cortical Microstructural Alterations in Mild Cognitive Impairment and Alzheimer’s Disease Dementia. Cerebral Cortex, 30(5), 2948–2960. https://doi.org/10.1093/cercor/bhz286
Zhang, H., Schneider, T., Wheeler-Kingshott, C. A., & Alexander, D. C. (2012). NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage, 61(4), 1000–1016. https://doi.org/10.1016/j.neuroimage.2012.03.072