Presented During:
Tuesday, June 25, 2024: 12:00 PM - 1:15 PM
COEX
Room:
Grand Ballroom 104-105
Poster No:
1739
Submission Type:
Abstract Submission
Authors:
Huan Liu1, Tingting Dan1, defu yang1, Won Hwa Kim2, Minjeong Kim3, Paul Laurienti4, Guorong Wu1
Institutions:
1University of North Carolina at Chapel Hill, Chapel Hill, NC, 2Pohang University of Science and Technology (POSTECH), Pohang, Korea, Republic of, 3University of North Carolina at Greensboro, Greensboro, NC, 4Wake Forest School of Medicine, Winston Salem, NC
First Author:
Huan Liu
University of North Carolina at Chapel Hill
Chapel Hill, NC
Co-Author(s):
Tingting Dan
University of North Carolina at Chapel Hill
Chapel Hill, NC
defu yang
University of North Carolina at Chapel Hill
Chapel Hill, NC
Won Hwa Kim
Pohang University of Science and Technology (POSTECH)
Pohang, Korea, Republic of
Minjeong Kim
University of North Carolina at Greensboro
Greensboro, NC
Guorong Wu
University of North Carolina at Chapel Hill
Chapel Hill, NC
Introduction:
Human brain is a complex wiring system in which diverse behaviors are supported by ubiquitous functional fluctuations. Although striking efforts have been made to investigate the association between brain structure connectivity (SC) and function connectivity (FC), the SC-FC coupling mechanism is largely elusive. Recently, we have developed a novel analytic approach, called HoloBrain (Liu, Dan et al. 2023), to characterize the interference patterns formed by the BOLD signals modulated by a collection of harmonic wavelets (stemming from wirings of white matter fibers) across a widespread graph spectrum. In this work, we have applied our HoloBrain technique to task-fMRI data from HCP-YA. Compared to conventional network analysis methods, HoloBrain offers a new window to investigate the task-specific footprint of SC-FC coupling through the lens of cross-frequency coupling (CFC), demonstrating great potential in discovering novel neurobiological biomarkers for resting-state fMRI studies.
Methods:
Method overview of HoloBrain. First, we construct harmonic wavelets from the Laplacian matrix of SC by (1) calculating harmonic waves based on the Laplacian matrix, (2) localizing each harmonic wave to each brain region and form region-specific (indexed by i) and frequency-specific (denoted by s) harmonic wavelet ψis using graph signal processing techniques (Hammond, Vandergheynst et al. 2011). Second, we modulate the snapshot of BOLD signal f(t) at time t with each harmonic wavelets and generate a time series of harmonic power pis. Third, we construct a collection of local CFC patterns CFCijsr by the inner product of harmonic power between i-th and j-th brain regions and across frequencies s and r. Supposing we break down the whole graph spectrum into four frequency bands (Fig. 1(c)), the output of HoloBrain is a 4×4 CFC matrix for each node/edge.
Neuroscience insight of HoloBrain. Following the concept of wave-to-wave interference (Gabor 1948), we have developed a proof-of-concept approach to computationally "record" the CFC of time-evolving interference patterns that are formed by superimposing the harmonic wavelets on the subject-specific neural activities.
Group comparison using HoloBrain. For each CFC value, we apply a linear regression model to examine whether the underlying CFC manifests significant difference between two cognitive tasks, where age and gender are confounders.
![Supporting Image: OHBM_fig1.png](https://files.aievolution.com/prd/hbm2401/abstracts/abs_1980/OHBM_fig1.png)
Results:
We evaluate the statistical power of HoloBrain on task-based fMRI of HCP-YA dataset, where each subject is partitioned into 360 brain regions (HCP atlas). In the following experiments, we only show the significant motor (1) vs. language (0) result at the significance level of 10-8, along with the effect size greater than 0.2 and adjusted R2>0.2. In Fig. 2 top, it is evident that the identified brain regions and connections are aligned with current findings in the neuroscience field (Glasser, Coalson et al. 2016), which are located at the somatosensory, motor cortex, and Auditory Association cortex. Compared to the conventional network analysis using FC matrix only (Fig. 2(a)), HoloBrain provides a new dimension of brain connectome in the frequency domain. Furthermore, we apply the same analysis to the re-test scans (shown in Fig. 2 bottom). In terms of replicability, the group comparison results by HoloBrain are more consistent than conventional FC analysis, as indicated by circles.
![Supporting Image: OHBM_fig2.png](https://files.aievolution.com/prd/hbm2401/abstracts/abs_1980/OHBM_fig2.png)
Conclusions:
We evaluate the statistical power and replicability performance of our recently developed HoloBrain technique on task-fMRI data in HCP-YA. Since HoloBrain characterizes the SC-FC coupling mechanism via the interference between SC-modulated BOLD signals, we are able to examine the groupwise SC-FC coupling differences on both brain regions and connections across the graph spectrum.
Modeling and Analysis Methods:
Classification and Predictive Modeling 2
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 1
Keywords:
Computational Neuroscience
Data analysis
FUNCTIONAL MRI
Modeling
Statistical Methods
STRUCTURAL MRI
Other - Cross Frequency Coupling
1|2Indicates the priority used for review
Provide references using author date format
Gabor, D. (1948). "A new microscopic principle." Nature 161(4098): 777.
Glasser, M. F., T. S. Coalson, E. C. Robinson, C. D. Hacker, J. Harwell, E. Yacoub, K. Ugurbil, J. Andersson, C. F. Beckmann, M. Jenkinson, S. M. Smith and D. C. Van Essen (2016). "A multi-modal parcellation of human cerebral cortex." Nature 536(7615): 171-178.
Hammond, D. K., P. Vandergheynst and R. Gribonval (2011). "Wavelets on graphs via spectral graph theory." Applied and Computational Harmonic Analysis 30(2): 129-150.
Liu, H., T. Dan, Z. Huang, D. Yang, W. H. Kim, M. Kim, P. Laurienti and G. Wu (2023). HoloBrain: A Harmonic Holography for Stereotyped Brain Function. Information Processing in Medical Imaging 2023. N. Navab and D. Wassermann. Argentina, LNCS.