The macroscale routing mechanism of structural brain connectivity related to body mass index
Poster No:
1503
Submission Type:
Abstract Submission
Authors:
ChaeYeon Kim1, YEONGJUN PARK2, Jong Young Namgung1, Yunseo Park1, Bo-yong Park1,3,4
Institutions:
1Department of Data Science, Inha University, Incheon, Republic of Korea, 2Department of Computer Engineering, Inha University, Incheon, Republic of Korea, 3Department of Statistics and Data Science, Inha University, Incheon, Republic of Korea, 4Center for Neuroscience Imaging and Research, Institute for Basic Science, Suwon, Republic of Korea
First Author:
Co-Author(s):
Bo-yong Park
Department of Data Science, Inha University|Department of Statistics and Data Science, Inha University|Center for Neuroscience Imaging and Research, Institute for Basic Science
Incheon, Republic of Korea|Incheon, Republic of Korea|Suwon, Republic of Korea
Department of Data Science, Inha University|Department of Statistics and Data Science, Inha University|Center for Neuroscience Imaging and Research, Institute for Basic Science
Incheon, Republic of Korea|Incheon, Republic of Korea|Suwon, Republic of Korea
Introduction:
Previous neuroimaging studies observed that morphological and functional activity patterns are associated with body mass index (BMI) [1], [2]. However, most studies focused on identifying regional abnormalities in gray matter volume and functional activation according to BMI [3], and how the network communication altered is relatively underinvestigated. In this study, we aimed to identify the link between the network-level communication mechanisms and BMI using a measure of network routing.
Methods:
We studied T1-weighted structural magnetic resonance imaging (MRI) and diffusion MRI data from the S1200 release of the Human Connectome Project (HCP) database [4]. Among 1,206 subjects, we excluded participants who were genetically related (i.e., twins) and had a family history of mental illness and history of drug ingestion, resulting in 290 participants (mean ± standard deviation age = 28.3 ± 3.9 years, 51% female). The MRI data were preprocessed using the HCP minimal preprocessing pipeline [5]. Navigation efficiency was calculated from structural connectivity using the Brain Connectivity Toolbox to assess network communication ability. This measure estimates the signal propagation efficiency of a given brain region to reach the target region based on the greedy routing algorithm [6]. While controlling for age and sex, we assessed the association between navigation efficiency and BMI. The significance of the association was assessed based on the 1,000 spin permutation tests to adjust for the spatial autocorrelation [7] and further corrected for multiple comparisons using a false discovery rate (FDR) [8]. To examine neurobiological aspects, we linked the BMI-navigation efficiency association map to the neurotransmitter distribution maps of serotonin and dopamine, where the significance was assessed using 1,000 spin permutations [7] and FDR correction [8]. In addition, we identified gene lists associated with the BMI-navigation efficiency association map using the post-mortem gene expression data provided by the Allen Human Brain Atlas (AHBA) using the abagen toolbox (https://github.com/rmarkello/abagen) [9]. Significances were assessed using Fisher's exact test and FDR [8]. We compared the identified gene lists with cell-specific genes and calculated the overlap ratio of how many genes expressed for BMI-navigation efficiency associations were included in each cell-type–specific gene set.
Results:
We found significant positive associations between BMI and navigation efficiency in the sensory, reward, and executive control-related brain regions. Specifically, ventrolateral and dorsolateral prefrontal (r = 0.279, pFDR < 0.001), somatomotor (r = 0.279, pFDR < 0.001), visual (r = 0.299, pFDR < 0.001), medial temporal (r = 0.220, pFDR < 0.001), and inferior parietal cortices (r = 0.273, pFDR < 0.001), as well as the accumbens (r = 0.170, pFDR = 0.004) and thalamus (r = 0.181, pFDR = 0.002) showed significant effects (Fig. 1A). The associations with the neurotransmitter maps exhibited significant correlations with the D1 (r = 0.148, pFDR = 0.01), 5HT2a (r = 0.154, pFDR = 0.008), and 5HT1a (r = 0.178, pFDR = 0.002) receptors (Fig. 1B). The genes associated with the BMI-navigation efficiency association map were more highly overlapped with excitatory (2.31 ± 0.87%) and inhibitory neurons (1.47 ± 0.89%) (Fig. C).
·Alterations in navigation efficiency according to body mass index (BMI)
Conclusions:
We studied network routing mechanisms related to BMI and associated with the neurotransmitter and transcriptomic data. Our findings provide potential links for understanding the network-level mechanisms of the brain related to the variations in BMI.
Genetics:
Genetic Association Studies 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
Keywords:
Other - Body mass index; navigation efficiency; network routing; neurotransmitter; transcriptomic analysis.
1|2Indicates the priority used for review
Provide references using author date format
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