Association between fALFF of white matter and postmenstrual age in newborns
Poster No:
1217
Submission Type:
Abstract Submission
Authors:
Yali Huang1, Xiaoxu Na1, Xiawei Ou1,2
Institutions:
11. Department of Radiology, University of Arkansas for Medical Sciences, LITTLE ROCK, AR, 22. Department of Pediatrics, University of Arkansas for Medical Sciences, LITTLE ROCK, AR
First Author:
Co-Author(s):
Xiawei Ou
1. Department of Radiology, University of Arkansas for Medical Sciences|2. Department of Pediatrics, University of Arkansas for Medical Sciences
LITTLE ROCK, AR|LITTLE ROCK, AR
1. Department of Radiology, University of Arkansas for Medical Sciences|2. Department of Pediatrics, University of Arkansas for Medical Sciences
LITTLE ROCK, AR|LITTLE ROCK, AR
Introduction:
Blood oxygenation level dependent (BOLD) contrast has been exploited for detecting neural activity in the brain using fMRI. While BOLD signals have been reliably detected in gray matter, such signals have long been ignored in white matter (Ding et al. 2018, Gore et al. 2019). The objective of the study is to investigate the relationships between fractional amplitude of low-frequency fluctuations (fALFF) in both white and gray matter and postmenstrual age in term-born infants using resting-state BOLD functional magnetic resonance imaging (BOLD-fMRI).
Methods:
A total of 40 newborns (average postmenstrual age: 289 days) were analyzed. We initially performed independent component analysis (ICA) on the fMRI white and gray matter data of newborns to obtain group-level components. White matter BOLD data were decomposed into functional networks (independent components) using a group-level spatial ICA as implemented in the GIFT toolbox, and Group-ICA was performed to generate a set of group-average parcels (Calhoun et al. 2001). Subject-specific spatial maps and time courses were estimated using the GICA back-reconstruction method (Allen et al. 2014). A similar processing scheme was also applied to the gray matter BOLD signal.
Here we calculated the ratio of power spectrum of three frequency bands (Frequency band: 0.001~0.027 Hz; Frequency band: 0.027~0.073 Hz; Frequency band: 0.073~0.198 Hz) to that of the frequency range (0.001~0.198 Hz), which denote the power of BOLD signal fluctuation.
For each group spatial component, time course was extracted to compute fALFF at different frequency band (0.001~0.027 Hz, 0.027~0.073 Hz, 0.001~0.198 Hz) for every subject. Specifically, as the ICA decomposition order is set as 12, 12(components)*3(frequency bands)*40(subjects) fALFF values were calculated for all subjects. These 1440 (12*3*40) features represent spontaneous brain activity energy (the power of low frequency fluctuation) of the subjects, as shown in Fig. 1.
Here we calculated the ratio of power spectrum of three frequency bands (Frequency band: 0.001~0.027 Hz; Frequency band: 0.027~0.073 Hz; Frequency band: 0.073~0.198 Hz) to that of the frequency range (0.001~0.198 Hz), which denote the power of BOLD signal fluctuation.
For each group spatial component, time course was extracted to compute fALFF at different frequency band (0.001~0.027 Hz, 0.027~0.073 Hz, 0.001~0.198 Hz) for every subject. Specifically, as the ICA decomposition order is set as 12, 12(components)*3(frequency bands)*40(subjects) fALFF values were calculated for all subjects. These 1440 (12*3*40) features represent spontaneous brain activity energy (the power of low frequency fluctuation) of the subjects, as shown in Fig. 1.
Results:
To examine potential relationships between white matter fALFF and postmenstrual age of newborns, the Pearson correlation between fALFF (including 12 components, and each component comprise 3 frequency bands) and postmenstrual age was calculated. A 12*3 correlation matrix between the fALFF and postmenstrual age was obtained. To further evaluate the statistical significance of the correlation matrix, a permutation test was performed. The age labels were randomly permuted 10000 times to generate the null organization. Permutation analysis revealed that there is significant relationship between the fALFF of one component and postmenstrual age. Specifically, the fALFF of an independent component from white matter in frequency range 0.027 - 0.073 Hz is positively correlated with postmenstrual age (r=0.35, p = 0.018), while the fALFF in frequency range 0.073 - 0.198 Hz is negatively correlated with postmenstrual age (r = -0.43, p = 0.007). The spatial map of this white matter component is shown in Fig. 2A, which is located in the parietal lobe.
The similar processing were conducted to the gray matter. The non-parametric permutation also demonstrated that there is one significant correlation between the fALFF of one independent component in grey matter and postmenstrual age. Specifically, it was found that there is positively correlation with postmenstrual age (r=0.40, p = 0.005) in frequency range 0.027 - 0.073 Hz , while negatively correlation with postmenstrual age (r = -0.43, p = 0.004) in frequency range 0.073 - 0.198 Hz . The spatial map of this gray matter component is shown in Fig. 2B, which is located in the parietal lobe, similar to that in Fig. 2A.
The similar processing were conducted to the gray matter. The non-parametric permutation also demonstrated that there is one significant correlation between the fALFF of one independent component in grey matter and postmenstrual age. Specifically, it was found that there is positively correlation with postmenstrual age (r=0.40, p = 0.005) in frequency range 0.027 - 0.073 Hz , while negatively correlation with postmenstrual age (r = -0.43, p = 0.004) in frequency range 0.073 - 0.198 Hz . The spatial map of this gray matter component is shown in Fig. 2B, which is located in the parietal lobe, similar to that in Fig. 2A.
Conclusions:
Our studies showed that fALFF in the parietal lobe in both white and gray matter is significantly correlated with postmenstrual age in neonates, suggesting that it may serve as an indicator of normal brain development during the perinatal age window. Further study will be needed to understand the implication of this finding.
Lifespan Development:
Early life, Adolescence, Aging 1
Modeling and Analysis Methods:
Methods Development 2
Keywords:
Data analysis
Development
FUNCTIONAL MRI
White Matter
1|2Indicates the priority used for review
·Figure 1. A) the fALFF of white matter (including 12 independent components) in different frequency bands; B) the fALFF of grey matter (including 12 independent components) in different frequency band
·Figure 2. A) The age-related components in white matter; B) the age-related components in gray matter.
Provide references using author date format
Calhoun, V. D., T. Adali, G. Pearlson and J. J. Pekar (2001). "Spatial and temporal independent component analysis of functional MRI data containing a pair of task‐related waveforms." Human brain mapping 13(1): 43-53.
Ding, Z., Y. Huang, S. K. Bailey, Y. Gao, L. E. Cutting, B. P. Rogers, A. T. Newton and J. C. Gore (2018). "Detection of synchronous brain activity in white matter tracts at rest and under functional loading." Proc Natl Acad Sci USA 115(3): 595-600.
Gore, J. C., M. Li, Y. Gao, T.-L. Wu, K. G. Schilling, Y. Huang, A. Mishra, A. T. Newton, B. P. Rogers and L. M. Chen (2019). "Functional MRI and resting state connectivity in white matter-a mini-review." Magn Reson Imaging 63: 1-11.