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Computing Local Functional Connectivity in 4D Dynamic fMRI Networks: A New Method

Poster No:

993

Submission Type:

Abstract Submission

Authors:

Rekha Saha¹, Debbrata Kumar Saha², Zening Fu³, Vince Calhoun⁴

Institutions:

¹Georgia State University, Atlanta, GA, ²Georgia Institute of Technology, Atlanta, GA, ³Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, ⁴GSU/GATech/Emory, Atlanta, GA

First Author:

Rekha Saha
Georgia State University
Atlanta, GA

Co-Author(s):

Debbrata Kumar Saha
Georgia Institute of Technology
Atlanta, GA

Zening Fu
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)
Atlanta, GA

Vince Calhoun
GSU/GATech/Emory
Atlanta, GA

Introduction:

Functional magnetic resonance imaging (fMRI) has become a cornerstone for studying brain functional network connectivity (FNC). Most research has centered on static or dynamic FNC between predefined networks or regions of interest, often overlooking temporal dynamics within these regions. While recent approaches have explored voxel-level spatial dynamic networks, no method has examined FNC between these dynamic spatial networks. Here, we introduce a novel approach to study FNC within spatially dynamic brain networks using resting-state fMRI (rsfMRI) data. This technique calculates FNC across localized voxel subsets within each network. We applied our method to rsfMRI data from the Adolescent Brain and Cognitive Development (ABCD) study. Our voxel-based FNC approach demonstrates reliable results consistent with traditional static FNC (sFNC), showing significant overlapping modularity in both matrices. Additionally, we observed a reduction in anticorrelations within the average local voxel FNC (LvFNC) as fewer voxels were included. The primary innovation of our method lies in its ability to analyze local FNC across different voxel subsets.

Methods:

In our study, we applied spatially constrained independent component analysis (sICA) on the rsfMRI data of 100 ABCD subjects using MOO-ICAR with the NeuroMark_fMRI_1.0 template as a reference [1]. This automated ICA method extracts intrinsic connectivity networks (ICNs) and their time courses. sFNC was computed as Pearson correlations between full-time courses, yielding 53x53 matrices. Next, we used a sliding-window approach with sICA to capture spatially dynamic ICNs and time courses for each window. Correlations between windowed time courses produced windowed FNC, which was averaged across windows to obtain awFNC. To assess the impact of different voxel subsets, we calculated LvFNC using masks that included 90%, 50%, and 25% of the voxels. A spatio-temporal covariation analysis was performed by averaging the voxels for each component and generating a matrix based on the number of components and windows. Finally, we computed the average LvFNC across all windows and subjects. This method is a preliminary test but can be further refined for flexibility and additional analyses.

Results:

Figure 1 shows the sFNC, awFNC, and voxel-based LvFNC using 90%, 50%, and 25% of the total voxels across 100 ABCD study datasets. Both sFNC and awFNC revealed stronger anticorrelations between domains like visual (VSN) and cognitive control (CCN), and sensorimotor (SMN) and cerebellar (CBN) networks. Additionally, internetwork connectivity was observed between VSN, CBN, SMN, and subcortical (SCN) networks, reflecting functional segregation. These findings demonstrate the consistency and reliability of our approach in capturing brain network dynamics. We also explored how varying voxel inclusion affects connectivity patterns. As the number of voxels decreased, anticorrelations in LvFNC diminished, highlighting the impact of voxel inclusion on connectivity. Including more voxels revealed broader interactions and stronger connectivity, including anticorrelations, while fewer voxels captured more localized connectivity. This voxel-level analysis allows for a finer examination of brain connectivity, revealing patterns that traditional global FNC may miss, which could be crucial for understanding individual differences, age-related changes, or disease progression.

·Figure 1. Average sFNC, awFNC, GvFNC, and LvFNC plots. It highlights the impact of voxel inclusion on LvFNC, showing a reduction in anticorrelations as the number of included voxels decreases

Conclusions:

In this study, we developed a novel method for analyzing FNC within spatially dynamic brain networks using rsfMRI data. Our approach allows for the calculation of network-specific FNC at the voxel level, providing more detailed connectivity patterns. Applying this method we replicated traditional static FNC results while uncovering new insights into voxel-level network interactions. The introduction of voxel-level FNC marks a significant advancement in understanding brain connectivity, offering a more detailed and dynamic view of network interactions.

Lifespan Development:

Early life, Adolescence, Aging ¹

Modeling and Analysis Methods:

Connectivity (eg. functional, effective, structural)

fMRI Connectivity and Network Modeling

Methods Development ²

Novel Imaging Acquisition Methods:

BOLD fMRI

Keywords:

Data analysis

Design and Analysis

FUNCTIONAL MRI

^1|2Indicates the priority used for review

Abstract Information

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Please indicate below if your study was a "resting state" or "task-activation” study.

Resting state

Healthy subjects only or patients (note that patient studies may also involve healthy subjects):

Healthy subjects

Was this research conducted in the United States?

Yes

Are you Internal Review Board (IRB) certified? Please note: Failure to have IRB, if applicable will lead to automatic rejection of abstract.

No, I do not have IRB or AUCC approval

Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.

Not applicable

Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.

Not applicable

Please indicate which methods were used in your research:

Functional MRI

For human MRI, what field strength scanner do you use?

1.5T

Provide references using APA citation style.

[1] Du, Y. and Y. Fan, Group information guided ICA for fMRI data analysis. Neuroimage, 2013. 69: p. 157-197.

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