Spatial Encoding and Network Information via Hamming Weights Reveals Multi-connectivity
Poster No:
1412
Submission Type:
Abstract Submission
Authors:
Biozid Bostami1, Oktay Agcaoglu2, Vince Calhoun3
Institutions:
1Georgia Institute of Technology, Atlanta, GA, 2Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, 3GSU/GATech/Emory, Atlanta, GA
First Author:
Co-Author(s):
Oktay Agcaoglu, PhD
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)
Atlanta, GA
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)
Atlanta, GA
Introduction:
For decades, pairwise partial or full correlations of temporal fluctuations in the BOLD signal, commonly referred to as functional connectivity (FC), have been a fundamental tool for studying interactions among brain regions and understanding brain function. However, given the brain's densely interconnected nature, examining interactions in isolation fails to account for the complex interplay between all elements within the system. To address this, we propose moving beyond traditional FC approaches. We present a multi-network encoding framework which posits that brain elements communicate and interact through multiple pathways spanning various levels of the brain's complex structure. This suggests that the brain operates with a multi-connective functional architecture, enabling elements to engage through more than one pathway. For example, information can flow between networks through multiple voxels, facilitating effective resource utilization and seamless interactions between voxel-network. In this work, we introduce a novel framework to estimate the brain's functional encoding by capturing the multi-way entanglement between networks and voxels.
Methods:
In this study, we used an age- and gender-matched dataset including 160 controls (CON) and 151 individuals with schizophrenia (SZ) from the FBIRN project [1]. Resting state fMRI (rsfMRI) data were collected with 3-Tesla MRI machines with a repetition time (TR) of 2 sec, voxel size of 3.4375 × 3.4375 × 4 mm, a slice gap of 1 mm, and a total of 162 volumes. Subjects were instructed to keep their eyes open during the resting-state scan and stare passively at a central cross. The preprocessing step includes brain extraction, slice-timing, and motion correction. The preprocessed data of each subject was then registered into structural MNI space, resampled to 3 mm3 isotropic voxels, and spatially smoothed using a Gaussian kernel with a 6 mm full width at half-maximum. Finally, voxel time courses were z-scored. Further details on the dataset and preprocessing steps can be found in [1] and [2], respectively. Next we use our NeuroMark pipeline to estimate individual subject estimates of each of 53 intrinsic connectivity networks (components). To capture the voxel and network interaction we binarized the voxels value for each component of each subject based on a global threshold z-value > 1. To capture the global voxel-network information we calculated Hamming weights at each voxel for all the 53 networks resulting in a spatial map where each voxels represents how many networks does it contributes.
HW (v) = ∑d(v)
Where, v is the voxel position and d(v) is the bit-stream containing information from all k networks at voxel position v.
After calculating the Hamming weight for all the subjects, we performed group difference t-tests between the healthy controls and participants diagnosed with schizophrenia. Followed by the false discovery rate (FDR) correction at p < 0.05.
HW (v) = ∑d(v)
Where, v is the voxel position and d(v) is the bit-stream containing information from all k networks at voxel position v.
After calculating the Hamming weight for all the subjects, we performed group difference t-tests between the healthy controls and participants diagnosed with schizophrenia. Followed by the false discovery rate (FDR) correction at p < 0.05.
Results:
Initial group test reveals significant group difference between the two groups(HC vs SC) in the Default Mode, Visual, Auditory and Thalamus regions. Our initial results show that these areas contain significantly higher multi-connectivity in the patient groups, which we speculate may be related to inefficient information processing.
Conclusions:
In our initial experiment, Hamming weight was used to capture the voxel-network interaction which is simple way to measure information. This method can be extended to leverage additional information theory measures such as mutual information to describe the voxel-network interactions more comprehensively.
Brain Stimulation:
Non-invasive Magnetic/TMS
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia)
Higher Cognitive Functions:
Space, Time and Number Coding 2
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 1
Keywords:
Design and Analysis
FUNCTIONAL MRI
Schizophrenia
1|2Indicates the priority used for review
·Figure 1
Abstract Information
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Provide references using APA citation style.
2. A. Iraji et al., "Spatial dynamics within and between brain functional domains: A hierarchical approach to study time-varying brain function", Human brain mapping, vol. 40, no. 6, pp. 1969-1986, Apr 2019.