
==== Front
Hum Brain Mapp
Hum Brain Mapp
10.1002/(ISSN)1097-0193
HBM
Human Brain Mapping
1065-9471
1097-0193
John Wiley & Sons, Inc. Hoboken, USA

10.1002/hbm.70019
HBM70019
Research Article
Research Article
The macroscale routing mechanism of structural brain connectivity related to body mass index
Kim et al.
Kim Chae Yeon 1
Park Yunseo 1
Namgung Jong Young 1
Park Yeongjun 2
Park Bo‐yong 3 4 5 boyongpark@korea.ac.kr

1 Department of Data Science Inha University Incheon South Korea
2 Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea
3 Department of Brain and Cognitive Engineering Korea University Seoul South Korea
4 Center for Neuroscience Imaging Research Institute for Basic Science Suwon South Korea
5 Research Center for Small Businesses Ecosystem Inha University Incheon South Korea
* Correspondence
Bo‐yong Park, Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea.
Email: boyongpark@korea.ac.kr

04 9 2024
9 2024
45 13 10.1002/hbm.v45.13 e7001905 8 2024
20 10 2023
20 8 2024
© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Abstract

Understanding the brain's mechanisms in individuals with obesity is important for managing body weight. Prior neuroimaging studies extensively investigated alterations in brain structure and function related to body mass index (BMI). However, how the network communication among the large‐scale brain networks differs across BMI is underinvestigated. This study used diffusion magnetic resonance imaging of 290 young adults to identify links between BMI and brain network mechanisms. Navigation efficiency, a measure of network routing, was calculated from the structural connectivity computed using diffusion tractography. The sensory and frontoparietal networks indicated positive associations between navigation efficiency and BMI. The neurotransmitter association analysis identified that serotonergic and dopaminergic receptors, as well as opioid and norepinephrine systems, were related to BMI‐related alterations in navigation efficiency. The transcriptomic analysis found that genes associated with network routing across BMI overlapped with genes enriched in excitatory and inhibitory neurons, specifically, gene enrichments related to synaptic transmission and neuron projection. Our findings suggest a valuable insight into understanding BMI‐related alterations in brain network routing mechanisms and the potential underlying cellular biology, which might be used as a foundation for BMI‐based weight management.

We associated the body mass index with navigation efficiency, a measure of network routing calculated from diffusion tractography‐based structural connectivity.

body mass index
navigation efficiency
network routing
neurotransmitter
transcriptomic analysis
National Research Foundation of Korea 10.13039/501100003725 NRF‐2022R1A5A7033499 Institute for Basic Science 10.13039/501100010446 IBS‐R015‐D1 Institute for Information and Communications Technology Promotion 10.13039/501100010418 2022‐0‐00448 RS‐2021‐II212068 source-schema-version-number2.0
cover-dateSeptember 2024
details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.4.8 mode:remove_FC converted:04.09.2024
Kim, C. Y. , Park, Y. , Namgung, J. Y. , Park, Y. , & Park, B. (2024). The macroscale routing mechanism of structural brain connectivity related to body mass index. Human Brain Mapping, 45 (13 ), e70019. 10.1002/hbm.70019
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pmc1 INTRODUCTION

Managing body mass index (BMI) is a crucial issue worldwide because individuals with a high BMI often suffer from severe health problems, including cardiovascular diseases, diabetes, and stroke (Apovian et al., 2014; Malik et al., 2013; World Health Organization, 2015). Previous studies observed alterations in brain morphology and functional activity patterns in individuals with a high BMI. These patterns are related to errant eating behaviors, leading to weight gain (Donofry et al., 2020; Park et al., 2016; Val‐Laillet et al., 2015). However, most work focused on identifying regional abnormalities in brain structure or functional activity in individuals with a high BMI, and thus, understanding network‐level BMI‐related brain mechanisms is relatively underinvestigated.

Previous neuroimaging studies widely adopted magnetic resonance imaging (MRI) to identify the relationships between BMI variations and the brain. Structural MRI research found that compared to lean individuals, people with obesity showed gray matter atrophy in the frontal lobe, anterior cingulate cortex, hippocampus, and thalamus, and reductions in cortical thickness in the superior and medial frontal cortices (Marqués‐Iturria et al., 2013; Raji et al., 2010). In addition, in diffusion MRI studies, white matter integrity was reduced in the fiber tracts, including the external capsule, sagittal stratum, and inferior fronto‐occipital regions in individuals with obesity (Herrmann et al., 2019; Shott et al., 2015). Moreover, altered functional brain activations have been observed in individuals with a high BMI when performing impulse control and reward‐related tasks (Brooks et al., 2013; Goldstone et al., 2009; Opel et al., 2015; Stoeckel et al., 2008). These studies indicate that BMI variations are highly related to structural and functional brain alterations, particularly in cognitive control and reward systems (Doucet et al., 2018; Farruggia et al., 2020; García‐García et al., 2013; Park et al., 2016, 2020, 2021).

Prior works showed that BMI‐related brain networks are reorganized along the cortical hierarchy, expanding from low‐level sensory to higher‐order frontoparietal and default mode networks (Lee et al., 2022; Namgung et al., 2024; Park et al., 2020, 2021). Particularly, our previous structure–function coupling study on BMI showed that the hierarchical organization of the brain networks is associated with polysynaptic communication (Namgung et al., 2024). The studies collectively indicate that BMI‐related alterations in brain networks might be related to atypical network communication mechanisms. Indeed, understanding network communication provides insights for investigating large‐scale brain networks because the communication strategies delineate signaling pathways between different brain regions (Avena‐Koenigsberger et al., 2019; Seguin et al., 2019). Recent approaches to network routing and diffusion models enabled the indirect stratification of network communication along a spectrum of communication processes (Avena‐Koenigsberger et al., 2019; Seguin et al., 2019). The shortest path routing mechanism is anchored on one end, and the random walk‐based diffusion mechanism is located on the other (Avena‐Koenigsberger et al., 2019; Seguin et al., 2019). The communicability and spreading models reside between these extremes (Estrada & Hatano, 2008; Mišić et al., 2015). Among various types of network communications, navigation is a decentralized (i.e., greedy routing) network communication strategy (Seguin et al., 2018, 2019), and it is known as a near‐optimal communication strategy explaining real‐world complex networks across multiple species (Allard & Serrano, 2018; Boguna et al., 2009; Seguin et al., 2018, 2019). Therefore, we hypothesized that navigation might be the optimal communication measure to investigate the BMI–brain relationship.

Examining neurobiological aspects of large‐scale brain connectivity is another critical issue for understanding brain–BMI relationships. One aspect is the neurotransmitter system. For example, communication between nerve cells is mediated by the chemical transmission of neurotransmitters from presynaptic neurons and reception at postsynaptic neurons (Lovinger, 2008). Recent works on positron emission tomography (PET) and single photon computed emission tomography (SPECT) aggregated spatial maps of neurotransmitter receptors and transporters, allowing the decoding of large‐scale brain maps according to different neurotransmitter systems (Dukart et al., 2021; Hansen et al., 2022). Transcriptomic analysis identifies gene expression potentially implicated in cellular and biological processes (Fornito et al., 2019; Hawrylycz et al., 2015; Thompson et al., 2013). Advancements in imaging–transcriptomics analysis facilitate interpreting macroscale brain networks with respect to gene regulation (Arnatkeviciute et al., 2019; Ashburner et al., 2000; Carbon et al., 2019; Chen et al., 2013; Kuleshov et al., 2016; Subramanian et al., 2005).

In this study, we aimed to identify altered brain network mechanisms related to BMI. First, a decentralized network communication measure was calculated to quantify the degree of signal transmission using the diffusion MRI tractography‐based structural connectivity data. Based on prior works that showed alterations in neural circuits involving food intake, reward, and inhibitory control in individuals with high BMI (Donofry et al., 2020; Kenny, 2011), we hypothesized that the brain regions involved in the reward and cognitive control systems might show significant associations with BMI. Additionally, it has been shown that individuals with high BMI displayed altered dopaminergic and serotonergic systems (van Galen et al., 2018, 2021), and the BMI‐related alterations in connectome organization were related to the gene expressions in the striatum, hypothalamus, and cortex cells (Namgung et al., 2024). Thus, we linked the BMI–communication relation to multiple neurotransmitter systems and gene expression patterns to investigate the biological underpinnings of the BMI–communication associations.

2 METHODS

2.1 Participants

This study is a retrospective study, and we obtained T1‐weighted structural MRI and diffusion MRI data from the S1200 release of the Human Connectome Project (HCP) database (Van Essen et al., 2013). Among 1206 participants, we excluded those who were genetically related (i.e., twins) and had a family history of mental illness and drug ingestion. Ultimately, the study included 290 participants (mean ± standard deviation [SD] age = 28.3 ± 3.9, 51% female; Table 1). Participants comprised five underweight individuals (BMI < 18.5 kg/m2), 131 of healthy weight (18.5 ≤ BMI < 25), 98 overweight (25 ≤ BMI < 30), and 56 with obesity (BMI ≥ 30). The participant recruitment procedures and informed consent forms, which included the consent to share de‐identified data, were approved by the Washington University Institutional Review Board as part of the HCP.

TABLE 1 Demographic information of study participants.

Group	N	Age (years)	Sex (% female)	
Underweight (BMI ≤ 18.5)	5	28.40 ± 5.27	100	
Healthy weight (18.5 ≤ BMI < 25)	131	28.06 ± 4.03	56.49	
Overweight (25 ≤ BMI < 30)	98	28.20 ± 3.76	80	
Obesity (BMI ≥ 30)	56	29.34 ± 4.01	53.57	
p‐value a	N/A	0.04	<0.001 b	
Abbreviations: BMI, body mass index; N/A, not available.

a p‐value was reported as the lowest value among six possible combinations from four groups.

b Chi‐square test.

2.2 MRI data acquisition

The T1‐ and T2‐weighted MRI were acquired using a Siemens Skyra 3 T scanner at Washington University. The T1‐weighted images were obtained using the magnetization‐prepared rapid gradient echo (MPRAGE) sequence (repetition time [TR] = 2400 ms; echo time [TE] = 2.14 ms; field of view [FOV] = 224 × 224 mm2; voxel size = 0.7 mm2; number of slices = 256). The T2‐weighted data were scanned with the T2‐Sampling Perfection with Application optimized Contrast (SPACE) sequence; the parameters were the same as the T1‐weighted data except for TR (3200 ms) and TE (565 ms). The diffusion MRI data were obtained using spin‐echo echo‐planar imaging (EPI) sequences (TR = 5195 ms; TE = 89.5 ms; FOV = 210 × 180 mm2; voxel size = 1.25 mm3; b‐value = three different shells of 1000, 2000, and 3000 s/mm2; number of directions = 270; number of b0 images = 18).

2.3 Data preprocessing and structural connectome generation

The MRI data were previously preprocessed based on the HCP minimal preprocessing pipeline (Glasser et al., 2013). In brief, the T1‐ and T2‐weighted images underwent gradient nonlinearity and b0 distortion correction. Bias field correction was performed based on the inverse intensities from the T1‐ and T2‐weighting, and the data were registered nonlinearly to the MNI152 standard space. The white and pial surfaces were generated by following the boundaries between different tissues (Dale et al., 1999; Fischl, Sereno, & Dale, 1999; Fischl, Sereno, Tootell, & Dale, 1999). A mid‐thickness surface generated by averaging the white and pial surfaces was used to generate the inflated surface. The spherical surface was registered to the Conte69 template with 164 k vertices using MSMAll (Glasser et al., 2016; Van Essen et al., 2012) and downsampled to a 32 k vertex mesh. The diffusion MRI data were corrected for EPI distortions using the reversed‐phase‐encoded directions, and the eddy current and head motion distortions were corrected. Structural connectomes were generated from the preprocessed diffusion MRI data using MRtrix3 (Tournier et al., 2019). Tissue types were defined using T1‐weighted MRI, including cortical and subcortical gray matter, white matter, and cerebrospinal fluid (Smith et al., 2012). Multishell and multitissue response functions were estimated (Christiaens et al., 2015), and constrained spherical‐deconvolution and intensity normalization were performed (Jeurissen et al., 2014). The tractogram was generated with 40 million streamlines, a maximum tract length of 250, and a fractional anisotropy cutoff of 0.06. Whole‐brain streamlines weighted by cross‐section multipliers were reconstructed using spherical‐deconvolution informed filtering of tractograms (SIFT2) (Smith et al., 2015). The reconstructed cross‐section streamlines were mapped onto the Schaefer atlas with 300 parcels (Schaefer et al., 2018). In addition, seven subcortical regions of the thalamus, caudate, putamen, pallidum, hippocampus, amygdala, and accumbens were parcellated from the T1‐weighted data using FSL's FIRST (Patenaude et al., 2011) to construct the structural connectivity matrix. Details of the parcellation can be found in Data S1, Supporting Information.

2.4 Network routing associated with BMI

As a measure of the network communication, we calculated navigation efficiency from the structural connectivity matrix using the Brain Connectivity Toolbox (https://sites.google.com/site/bctnet/) without thresholding (Seguin et al., 2018, 2019, 2020). Briefly, navigation efficiency measures the signal propagation efficiency of a given brain region to reach the target region based on the greedy routing algorithm (Seguin et al., 2018). Specifically, the navigation path length from node i to j (Λij) was defined by calculating the Euclidean distance between region centroids through the path. If node i fails to reach j, then Λij=∞. Otherwise, Λij=Liu+…+Lvj, where u…v is the sequence of nodes visited during the navigation, Lij is the length of the connection between nodes i and j. Navigation efficiency is defined as Enavi,j=1/Λij, where Enavi,j is the efficiency of the navigation path from node i to j. After controlling for age and sex, the navigation efficiency was associated with the BMI using a general linear model implemented in the BrainStat toolbox (https://github.com/MICA-MNI/BrainStat) (Larivière et al., 2023). The significance of the association of cortical regions was assessed based on the 1000 spin permutation tests to adjust for the spatial autocorrelation (Alexander‐Bloch et al., 2018), and that of subcortical areas was evaluated by shuffling the subcortical indices 1000 times using the ENIGMA toolbox (https://github.com/MICA-MNI/ENIGMA) (Larivière et al., 2021). Multiple comparisons across brain regions were corrected using a false discovery rate (FDR) (Benjamini & Hochberg, 1995). As a sensitivity analysis, we performed the association analysis using alternative network communication measures, including search information and communicability. Search information is a measure quantifying the amount of information to reach a target node from a source node, and a high value indicates that a large amount of information is required to transfer the information through the path (Avena‐Koenigsberger et al., 2018; Goni et al., 2014). Communicability measures how a perturbation in a node is transmitted to another, and it is defined as the sum of all walk lengths between pairs of regions (Estrada & Hatano, 2008). We associated search information and communicability with BMI using a general linear model implemented in the BrainStat toolbox (Larivière et al., 2021).

2.5 Associations with neurotransmitter systems

It has been shown that individuals with a high BMI are associated with alterations in dopaminergic and serotonergic systems (van Galen et al., 2018). To explore the neurobiological foundations of BMI‐related alterations in network routing patterns, we associated the BMI–navigation efficiency association map with 18 neurotransmitter distribution maps obtained from the previous study (Hansen et al., 2022). The maps included the D1 and D2 dopamine receptors and DAT transporter, 5HT1a, 5HT1b, 5HT2a, 5HT4, and 5HT6 serotonin receptors and 5HTT transporter, GABAa transmitter, mGluR5 and NMDA glutamate receptors, CB1 cannabinoid receptor, VAChT, M1, and α4𝛽2 acetylcholine receptors, H3 histamine receptor, and NET norepinephrine transporter. The volumetric neurotransmitter data were mapped to the Conte69 template space with 32 k vertices and subsequently mapped onto the Schaefer atlas with 300 parcels (Schaefer et al., 2018). The association between the BMI–navigation efficiency association map and each neurotransmitter distribution map was assessed using permutation tests. We randomly shuffled brain regions of each neurotransmitter distribution map 1000 times and calculated spatial correlations with the BMI–navigation efficiency association map. The null distribution was constructed, and if the actual correlation coefficient did not belong to 95% of the null distribution, it was deemed significant. Multiple comparisons across neurotransmitter distribution maps were corrected using an FDR procedure (Benjamini & Hochberg, 1995).

2.6 Transcriptomic analysis

In addition to the neurotransmitter contextualization, the BMI–navigation efficiency association map was analyzed using the post‐mortem gene expression data provided by the Allen Human Brain Atlas (AHBA). Among all genes from AHBA, we selected genes expressed consistently across donors using the abagen toolbox (https://github.com/rmarkello/abagen) (Markello et al., 2021). The abagen toolbox mapped the gene expression data onto the parcels and interpolated missing data by assigning the expression to the nearest tissue sample. The expression maps of two of the six donors, which provided data only from the left hemisphere, were mirrored from the left to the right hemispheres. The differential stability method was used to select probes with multiple probe‐indexed expressions, and the probe with the highest correlation of the expression data for every pair of donors was retained. Expression data were normalized using a scaled robust sigmoid function to mitigate potential differences in expression values among donors. For each gene, the whole‐brain gene expression map was compared between all pairs of donors; genes that showed FDR < 0.05 were considered for subsequent analyses. Finally, the expression data from six donors were averaged. We spatially correlated the BMI–navigation efficiency association map with the gene expression maps to identify associated gene lists. Correlation analysis was performed for each gene using 1000 spin tests (Alexander‐Bloch et al., 2018), and the p‐values were corrected using FDR (Benjamini & Hochberg, 1995). To address cell‐type gene enrichment, the identified genes were compared with cell‐specific genes proposed in prior work, including excitatory and inhibitory neuronal subtypes in the cortex and non‐neuronal cells of endothelial cells, pericytes, astrocytes, microglia, and oligodendrocytes and their precursor cells (Lake et al., 2016, 2018). The overlap ratio was calculated to assess how many genes expressed for BMI–navigation efficiency associations were included in each cell‐type‐specific gene set as follows: Overlap ratio = Number of overlapped genes/Total number of genes. The identified genes were entered into the gene ontology analysis (http://geneontology.org/) to evaluate biological processes, molecular functions, and cellular components (Ashburner et al., 2000; Carbon et al., 2019). Significances were assessed using Fisher's exact test and FDR correction.

3 RESULTS

3.1 Navigation efficiency according to BMI

After calculating the navigation efficiency for each participant, the metric was stratified according to seven functional networks of visual, somatomotor, dorsal attention, ventral attention, limbic, frontoparietal, and default mode networks (Yeo et al., 2011) (Figure 1). We assessed the between‐group differences in the navigation efficiency between individuals with healthy weight (18.5 ≤ BMI < 25) and individuals with overweight and obesity (BMI ≥ 25) using two‐sample t tests with FDR correction (Benjamini & Hochberg, 1995). Although no statistical significance was observed after the FDR correction, the frontoparietal (t = −2.043, uncorrected p = 0.028) and ventral attention networks (t = −2.211, uncorrected p = 0.042) showed marginal differences.

FIGURE 1 Body mass index (BMI) and navigation efficiency. (a) BMI distribution. (b) Schema of navigation efficiency. Navigation efficiency measures how efficiently the shortest paths are found from the seed to the target regions. (c) The navigation efficiency values of the underweight, healthy weight, overweight, and obesity groups are visualized on brain surfaces (top). The spider plot stratifies the values according to seven functional networks (bottom).

3.2 Association between BMI and navigation efficiency

Potential associations between BMI and navigation efficiency were assessed using a general linear model while controlling for age and sex. Significant positive associations were found in the ventrolateral and dorsolateral prefrontal (r = 0.279, p spin‐FDR < 0.001; frontoparietal network), somatomotor (r = 0.279, p spin‐FDR < 0.001; somatomotor network), visual (r = 0.299, p spin‐FDR < 0.001; visual network), medial temporal (r = 0.220, p spin‐FDR < 0.001; default mode network), and inferior parietal cortices (r = 0.273, p spin‐FDR <0.001; frontoparietal and default mode networks), as well as the accumbens (r = 0.170, p spin‐FDR = 0.004), and thalamus (r = 0.181, p spin‐FDR = 0.002; Figure 2). The findings indicate that greater navigation efficiency in the sensory, reward, and executive control‐related brain regions is observed in individuals with a high BMI. The robustness of our findings was evaluated for the association between BMI and navigation efficiency after excluding underweight individuals (n = 5), where the findings were consistent (linear correlation between the t‐statistic maps: r = 0.98, p < 0.001; Figure S1). When we additionally controlled for the head motion from the navigation efficiency, largely consistent results were observed (Figure S2), suggesting that head motion did not affect their relationships. When we associated search information and communicability with BMI, consistent findings were observed, where default mode and frontoparietal networks showed higher associations (Figure S3). Specifically, the correlation between navigation efficiency and communicability was r = 0.181 and p FDR < 0.001, and that between navigation efficiency and search information was r = 0.158 and p FDR = 0.003. Additionally, we performed the association analysis between BMI and navigation efficiency for each functional network (Yeo et al., 2011). We found the strongest associations in the ventral attention network (r = 0.213, p spin‐FDR < 0.001) followed by the frontoparietal (r = 0.187, p spin‐FDR = 0.001), default‐mode (r = 0.167, p spin‐FDR = 0.003), dorsal attention (r = 0.159, p spin‐FDR = 0.003), and limbic networks (r = 0.126, p spin‐FDR = 0.130; Figure S4). The correlation results for other communication measures are reported in Table S1.

FIGURE 2 Association between body mass index (BMI) and navigation efficiency. (a) We describe a schema of the association between BMI and navigation efficiency (top). The association effects of the whole brain (middle) and the regions that showed significant (p spin‐FDR < 0.05) effects are visualized (bottom). (b) Scatter plots of the correlations of the navigation efficiency of the identified regions with BMI. FDR, false discovery rate.

3.3 Neurobiological analysis

The BMI–navigation efficiency association map (Figure 2a) was contextualized with neurotransmitter distribution maps (Figure 3a). The D1 (r = 0.148, p spin‐FDR = 0.01), 5HT2a (r = 0.154, p spin‐FDR = 0.008), 5HT1a (r = 0.178, p spin‐FDR = 0.002), and CB1 (r = 0.161, p spin‐FDR = 0.005) receptors, and NET norepinephrine transporter (r = −0.118, p spin‐FDR = 0.042) showed significant correlations, suggesting that BMI‐related alterations in navigation efficiency might be related to the serotonergic and dopaminergic systems as well as opioid and norepinephrine systems. Furthermore, cell‐type‐specific transcriptomic association and enrichment analyses were performed. The genes associated with the BMI–navigation efficiency association map overlapped more with excitatory (2.31 ± 0.87%) and inhibitory neurons (1.47 ± 0.89%) than endothelial cells (0%), pericytes (1.56%), astrocytes (1.40%), oligodendrocytes (0.60%), oligodendrocyte precursor cells (1.53%), and microglia (0%; Figure 3b). Although the overlap ratio was small, the findings suggest potential associations with excitatory and inhibitory neurons. The gene ontology analysis showed enrichment for synaptic transmission and neuron projection (Figure 3c).

FIGURE 3 Neurotransmitter and transcriptomic analyses. (a) Schema of neurotransmitter systems (left). Spatial correlations between the body mass index (BMI)–navigation efficiency association map and neurotransmitter distributions are performed. The correlation coefficients are reported with bar plots (right), and the asterisks indicate significant associations (FDR < 0.05). (b) Schema of different cell types (left) and the overlap ratio between the genes associated with the BMI–navigation efficiency association map and cell‐specific genes are shown (right). (c) Gene ontology analysis of biological processes and cellular components enriched in cortically expressed gene sets with BMI‐related alterations in navigation efficiency. FDR, false discovery rate.

4 DISCUSSION

This study investigated the BMI‐related network routing and associated the macroscale findings with neurotransmitter and transcriptomic data. We found that individuals with a high BMI showed greater navigation efficiency, particularly in the sensory, reward, and executive control brain regions. The BMI‐related differences in navigation efficiency were associated with the serotonergic and dopaminergic systems and synaptic transmission. Although more in‐depth investigation incorporating macroscale network alterations and microscale gene enrichments is required, our findings provide potential links for understanding the network‐level mechanisms of the brain related to the variations in BMI.

Large‐scale network routing was assessed using a navigation efficiency measure that quantifies the information transfer based on a greedy routing algorithm (Seguin et al., 2018). Navigation efficiency recapitulates the shortest path by forwarding signals to physically close neighbor nodes (Seguin et al., 2018). A prior study showed that routing‐based network communication is highly associated with cortical hierarchical patterns (Vázquez‐Rodríguez et al., 2020). Here, BMI‐related brain network communication was studied utilizing the navigation efficiency measure, identifying significant associations in the regions involved in multiple brain regions, particularly in the reward and executive control networks. Reward processing and executive control systems are related to eating behaviors promoting obesity (Donofry et al., 2020). For example, the nucleus accumbens receives the dopaminergic signal from the ventral tegmental area, which plays a role in reward processing and influences the regulation of food consumption (Kenny, 2011). Excessive food consumption may yield maladaptive responses in reward and executive control circuits, affecting eating behaviors (Donofry et al., 2020; Kenny, 2011). In addition, the thalamocortical circuit is another important system for understanding variations in BMI. The thalamus is a key region transferring brain signals through the loop linking the prefrontal cortex and basal ganglia (Biezonski et al., 2016). Indeed, the abnormal pattern was observed in the thalamo‐frontal connectivity in individuals with eating disorders, suggesting dysfunction in the cognitive control systems (Biezonski et al., 2016). These studies indicate that the identified brain regions are associated with altered brain connectome organization in individuals with a high BMI. Our findings complement these prior findings and expand the knowledge regarding BMI‐related information flow within the brain circuits at the macroscale.

Elucidating the underlying biology of imaging‐based findings is essential for considering clinical implications. Therefore, we associated multiple neurotransmitter systems with the map of BMI‐related alterations in navigation efficiency and found significant effects for the D1, 5HT1a, 5HT2a, and CB1 receptors and NET. The identified receptors affect food intake (Rossi & Stuber, 2018; van Galen et al., 2021). Specifically, food intake increased when 5HT1a was activated and decreased when 5HT2a was activated (van Galen et al., 2021). The D1 receptor‐expressing neurons in the nucleus accumbens control food consumption, where its activity inhibits GABA neurons in the lateral hypothalamus, stopping food consumption (O'Connor et al., 2015). The studies collectively suggest serotonin and dopamine modulate motivation and reward circuits (van Galen et al., 2021; Wang et al., 2001). In contrast to the dopamine and serotonin receptors, the transporters did not show significant associations. Lower correlations in the transporters may be due to functional differences in signal transduction between transporters and receptors. For example, while dopamine transporter functions in reuptaking dopamine to terminate dopamine signaling, receptors respond to these signals by eliciting cellular responses (Chen & Reith, 2000; Vallone et al., 2000). Similarly, the serotonin transporter functions in reuptaking serotonin between synaptic gaps of neurons, which is crucial for terminating serotonin signaling. On the other hand, serotonin receptors respond to signals by activating signal transduction pathways (McCorvy & Roth, 2015; Rudnick, 2006). These functional disparities might contribute to distinct associations, but the precise mechanisms of dopamine and serotonin transporter signaling associated with BMI should be systematically elucidated in future studies.

In addition to the neurotransmitter analysis, transcriptomic analysis was performed to detect gene expression patterns in specific cell types related to the BMI–navigation efficiency association map. We found that the associated genes were more involved in the excitatory and inhibitory neurons than other cell types, such as astrocytes and oligodendrocytes. It has been shown that food intake is controlled by excitatory and inhibitory signaling systems (Blundell & Gillett, 2001). The excitatory process is related to food cravings, which may reflect an individual's hunger feelings, and the inhibitory process is more related to satiation and hunger suppression (Blundell & Gillett, 2001). The studies suggest an imbalance between excitation and inhibition may break body weight regulation and lead to obesity. Moreover, synaptic transmission is also associated with food intake and appetite control (Schlegel et al., 2016). The food intake circuit comprises feeding and reward circuits, and synaptic transmissions between neurons facilitate the communication of food‐related signals in the feeding circuit and influence neuron activity, affecting appetite (Rossi & Stuber, 2018). The neurotransmitter and gene enrichment findings suggest associations between the network‐level alterations and underlying biology at the molecular scale.

In summary, our investigation of the relationship between BMI variations and navigation efficiency provides new biological insights based on in‐depth microscopic data analyses. However, our study has several limitations. First, we opted for navigation efficiency, as well as search information and communicability, as network communication measures to assess relationships with BMI. Other communication measures, such as path transitivity and spreading models, might be considered in future studies for a comprehensive understanding of BMI–communication relationships. Second, as our study is cross‐sectional, we could not assess causal relationships between network communication and changes in BMI. The current findings only provide insights about the association between BMI and communication efficiency. We will collect longitudinal datasets to unveil their causal relationships in future works. Third, we could not consider factors related to eating disorders as our study is retrospective. Future works need to explore eating disorder‐related brain network alterations. Last, it should be noted that the interpretations regarding the analyses of transmitters and genes need caution because these analyses were conducted using the data from independent subjects. In future research, we will obtain the multiscale data from the same subject to provide more reliable biological interpretation.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

Supporting information

Figure S1. Association between BMI and navigation efficiency excluding individuals with underweight (BMI < 18.5; n = 5).

Figure S2. Head motion effects.

Figure S3. Associations between communication measures and body mass index (BMI).

Figure S4. Spatial correlations between network‐level navigation efficiency and BMI.

Table S1. Brain network‐wise associations between communication measures and body mass index (BMI).

Data S1. Supporting Information.

ACKNOWLEDGMENTS

Bo‐yong Park was funded by the Institute for Information and Communications Technology Planning and Evaluation (IITP) funded by the Korea Government (MSIT) (No. 2022‐0‐00448, Deep Total Recall: Continual Learning for Human‐Like Recall of Artificial Neural Networks; No. RS‐2021‐II212068, Artificial Intelligence Innovation Hub), the Institute for Basic Science (IBS‐R015‐D1), and the National Research Foundation of Korea (NRF‐2022R1A5A7033499).

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are openly available in Human Connetome Project at http://www.humanconnectome.org. The codes for navigation efficiency calculation are available at https://sites.google.com/site/bctnet/; statistical analyses are at https://github.com/MICA-MNI/BrainStat and https://github.com/MICA-MNI/ENIGMA; transcriptomic analyses are at https://github.com/rmarkello/abagen.
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