Characterizing Dynamic Brain Causal Networks with Instantaneous Frequency
Poster No:
1174
Submission Type:
Abstract Submission
Authors:
Marcus Zhan1, Siyuan Dai2, Lei Guo2, Pengfei Gu3, Alex Leow4, Haoteng Tang3
Institutions:
1Sewickley Academy, Pittsburgh, PA, 2University of Pittsburgh, Pittsburgh, PA, 3University of Texas Rio Grande Valley, Edinburg, TX, 4University of Illinois Chicago, Chicago, IL
First Author:
Co-Author(s):
Introduction:
Brain functionality relies on dynamic causal interactions between regions, revealing how these connections evolve [1]. These dynamics, crucial for understanding connectivity and its disruption in neurodegenerative conditions, reflect adaptability and resilience. Gender and sleep quality also modulate these patterns [2, 3]. This motivates the search for biomarkers to assess brain health and disease. We investigate dynamic causality using fMRI BOLD signals and dynamic causal modeling (DCM) [4]. We introduce a novel method using the instantaneous frequency (IF) of effective connectomes to capture temporal fluctuations in causal influences. Averaging IF across the network provides a global biomarker, reflecting overall network oscillatory behavior and its adaptability. This framework allows us to study differences between healthy individuals and those with neurodegenerative conditions and explore factors like lifestyle and demographics, aiming to identify biomarkers for diagnosis, monitoring, and potential therapeutic guidance.
Methods:
We constructed dynamic brain effective networks using the DCM framework. Since the analysis focuses on resting-state fMRI, external inputs are absent and α governs neuronal lag between brain nodes, the effective connectivity between nodes i and j at time t, denoted as Aij(t), is defined in Eq. 1, where β=1/α and α modulates neuronal lag between brain nodes, and Bi represents the BOLD signal at node i. This framework characterizes the causal interactions between brain regions, with each element Aij(t) reflecting the time-varying influence of node i on node j. To analyze oscillatory dynamics, we applied the Hilbert transform to each Aij(t) and derived the instantaneous frequency matrix F(t) [5,6]. The global instantaneous frequency, Ω(t), was then defined as the average instantaneous frequency (Eq. 2) across all nodes in the effective network. To compare Ω(t) between groups, we calculated the Dynamic Time Warping (DTW) distance as a measure of similarity between time series. For two distinct groups, we computed intra-group DTW distances and cross-group DTW distances. Statistical significance was evaluated using two Mann-Whitney U tests [7], comparing intra-group distances with the cross-group distance. If both tests showed significant differences, the groups were considered significantly distinct in their Ω(t) dynamics.
·Equations
Results:
In this study, we applied our proposed framework to a cohort of 1,206 young, healthy participants (mean age = 28.19±7.15, 603 females) from the Human Connectome Project. For each participant, we constructed a brain effective connectivity network and extracted the global instantaneous frequency, Ω(t), as a measure of temporal dynamics. We then conducted two experiments to evaluate the performance of Ω(t) in detecting group differences. Then we conducted two experiments to study the performance of Ω(t) in the group differences. In the first experiment, we compared Ω(t) between male and female participants. Our analysis revealed a statistically significant difference between sexes, with both comparisons yielding p<7.1e-6. The second experiment investigated differences in Ω(t) among groups categorized by sleep quality, as measured by the Pittsburgh Sleep Quality Index (PSQI). The PSQI scores were classified into three levels: good sleep quality (0 ≤PSQI ≤ 5), moderate sleep quality (6 ≤ PSQI ≤ 10), and poor sleep quality (PSQI ≥ 11). Our result indicated that Ω(t) exhibited significant group differences across these PSQI categories, with all p-values < 4.6e-4.
Conclusions:
We propose instantaneous frequency (IF) as a biomarker from dynamic brain networks. IF captures temporal activity changes, revealing sex and sleep-related patterns. IF differentiates clinical groups in the HCP dataset, suggesting utility for individual brain characterization. Future research will explore clinical and neurological applications.
Modeling and Analysis Methods:
Classification and Predictive Modeling 2
Connectivity (eg. functional, effective, structural) 1
Methods Development
Keywords:
ADULTS
Computing
Data analysis
FUNCTIONAL MRI
Machine Learning
MRI
1|2Indicates the priority used for review
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[2] Speck et al., Gender differences in the functional organization of the brain for working memory, Neuroreport 11 (2000) 2581–2585.
[3] Krause et al.,The sleep-deprived human brain, Nature Reviews Neuroscience 18 (2017) 404–418.
[4] Marreiros et al.,Dynamic causal modeling, Scholarpedia 5 (2010) 9568.
[5] B. Boashash, Estimating and interpreting the instantaneous frequency of a signal. i. fundamentals, Proceedings of the IEEE 80 (1992) 520–538.
[6] L. Cohen, Time-frequency analysis, volume 778, Prentice Hall PTR New Jersey,1995.
[7] Nachar, et al., The mann-whitney u: A test for assessing whether two independent samples come from the same distribution, Tutorials in quantitative Methods for Psychology 4 (2008) 13–20.