Age-Related Changes in Resting-State fMRI White Matter Engagement in Elderlies: A Longitudinal Study
Poster No:
2331
Submission Type:
Abstract Submission
Authors:
Hui Zhang1, Chun Liang Hsu1, Henry K.F. Mak2, Edward S. Hui3, David H.K. Shum1
Institutions:
1The Hong Kong Polytechnic University, Hong Kong, China, 2The University of Hong Kong, Hong Kong, China, 3The Chinese University of Hong Kong, Hong Kong, China
First Author:
Co-Author(s):
Introduction:
The decrease in white matter (WM) volume starts to become evident around the age of 40 (Ge et al., 2002). The application of diffusion tensor imaging (DTI) (Assaf & Pasternak, 2008) and T2-weighted-fluid-attenuated inversion recovery (Bakshi et al., 2001) provide valuable insights into the microstructure of white matter, including features such as lesions and tracts. However, due to differences in the biophysical origins of the signals and the tissue in question, there has been no direct integration of these techniques to date. Recent studies have indicated that blood oxygenation level dependent (BOLD) effects in WM can reflect neural activities, offering an additional complementary perspective on the brain's functional organization (Li et al., 2020). Our study involved the analysis of 170 healthy elderly participants from the Harvard Aging Brain Study, with data collected at two separate time points, spaced three years apart (Dagley et al., 2017).
Methods:
Preprocessing of fMRI data followed the procedure by Li et al (Li et al., 2020). The engagement maps began with the definition of the population-based (PB)-averaged WM and grey matter (GM) masks. The PB GM mask was generated by normalizing the probability of the average for all subjects, which was then be binarized with a loose threshold (> 0.6), whereas the PB WM mask was binarized with a tight threshold (> 0.95). After multiplying the time series of each subject by the threshold WM and GM masks, the images were used to calculate the full correlation matrix M and partial correlation M'x. M was obtained by averaging all voxels of the GM node within the 90 AAL atlas with a dimension of 90 x 90. M'x represented the connectivity with the same pairwise value as matrix M, but with the time series of each voxel x in WM in the time series serving as the control. The time series of the voxel x were computed by the average signal of 5 x 5 x 5 voxels surrounding x. The global network connectivity metric G(M) represented the mean of matrix M, and G(M'x) represented the mean of M'x for each voxel x. Finally, the method mapped the difference between G(M) and G(M'x) onto the WM voxel map concerning the whole network and defined a rough WM engagement map for each subject. Finally, the Fisher Z-transformation was applied to transform the WM engagement map so that the data in the map were normally distributed.
A paired t-test was used to compare between two visits (2nd –1st). Statistical significance was established at a p-value < 0.05 after Alphasim correction, with a cluster size > 54 voxels.
A paired t-test was used to compare between two visits (2nd –1st). Statistical significance was established at a p-value < 0.05 after Alphasim correction, with a cluster size > 54 voxels.
Results:
Table 1 presents the demographic information of the participants. According to the Johns Hopkins University White Matter Tractography Atlas (Wakana et al., 2004), we observed a significant decrease in engagement within the bilateral brain stem and the right corticospinal tract between two visits. Conversely, there was a notable increase in engagement within the right superior longitudinal fasciculus (SLF). (Figure 1)
Conclusions:
The study found decreased WM engagement in bilateral brain stem and the right corticospinal tract. Consistent with previous findings (Bouhrara et al., 2021; Jang & Seo, 2015), decrease in these specific regions implies a process of tissue maturation and degeneration occurring within the brainstem and corticospinal tract. On the other hand, the right SLF tract is more active with increase in age, which might compensate for executive dysfunction (Amemiya et al., 2021). Further investigation will be needed to determine the exact implications of these findings.
Modeling and Analysis Methods:
Task-Independent and Resting-State Analysis 2
Novel Imaging Acquisition Methods:
BOLD fMRI 1
Keywords:
Aging
FUNCTIONAL MRI
White Matter
1|2Indicates the priority used for review
Provide references using author date format
Assaf, Y., & Pasternak, O. (2008). 'Diffusion tensor imaging (DTI)-based white matter mapping in brain research: a review'. Journal of molecular neuroscience, 34, 51-61.
Bakshi, R.(2001). 'Fluid-attenuated inversion recovery magnetic resonance imaging detects cortical and juxtacortical multiple sclerosis lesions'. Archives of neurology, 58(5), 742-748.
Bouhrara, M. (2021). 'Maturation and degeneration of the human brainstem across the adult lifespan'. Aging (Albany NY), 13(11), 14862.
Dagley, A. (2017). 'Harvard aging brain study: dataset and accessibility'. Neuroimage, 144, 255-258.
Ge, Y. (2002). 'Age-related total gray matter and white matter changes in normal adult brain. Part I: volumetric MR imaging analysis'. American journal of neuroradiology, 23(8), 1327-1333.
Jang, S. H., & Seo, J. P. (2015). 'Aging of corticospinal tract fibers according to the cerebral origin in the human brain: a diffusion tensor imaging study'. Neuroscience Letters, 585, 77-81.
Li, M. (2020). 'Functional engagement of white matter in resting-state brain networks'. Neuroimage, 220, 117096.
Wakana, S.(2004). 'Fiber tract–based atlas of human white matter anatomy'. Radiology, 230(1), 77-87.