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. 2023 Jan 30:16:1090224.
doi: 10.3389/fnins.2022.1090224. eCollection 2022.

Graph analysis of diffusion tensor imaging-based connectome in young men with internet gaming disorder

Affiliations

Graph analysis of diffusion tensor imaging-based connectome in young men with internet gaming disorder

Jiaolong Qin et al. Front Neurosci. .

Abstract

Although recent evidence suggests that dysfunctional brain organization is associated with internet gaming disorder (IGD), the neuroanatomical alterations related to IGD remain unclear. In this diffusion tensor imaging (DTI) study, we aimed to examine alterations in white matter (WM) structural connectomes and their association with IGD characteristics in 47 young men with IGD and in 34 well-matched healthy controls. Two approaches [namely, network-based statistics (NBS) and graph theoretical measures] were applied to assess differences in the specific topological features of the networks and to identify the potential changes in the topological properties, respectively. Furthermore, we explored the association between the alterations and the severity of internet addiction. An NBS analysis revealed widespread alterations of the cortico-limbic-striatal structural connectivity networks in young people with IGD: (1) an increased subnet1 comprising the insula and the regions responsible for visual, auditory, and sensorimotor functions and (2) two decreased subnet2 and subnet3 comprising the insula, striatum, and limbic regions. Additional correlation analysis showed a significant positive association between the mean fractional anisotropy- (FA-) weighted connectivity strength of subnet1 and internet addiction test (IAT) scores in the IGD group. The present study extends our knowledge of the neuroanatomical correlates in IGD and highlights the role of the cortico-limbic-striatal network in understanding the neurobiological mechanisms underlying this disorder.

Keywords: DTI; connectome; cortico-limbic-striatal network; internet gaming disorder; structural connectivity.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
A flowchart for the construction of structural brain networks. (A) The 90 nodes of the brain network obtained from the automated anatomical labeling (AAL) atlas. (B) White matter (WM) fiber information prepared to check which pairs of nodes remain connected. (C) Region-based information extracted from the abovementioned image data. All pairwise connections between the nodes are calculated to generate a connection matrix. (D,E) A list of network analysis entries that are applied.
FIGURE 2
FIGURE 2
The three subnetwork results from the network-based statistics (NBS) analysis in a coronal view.
FIGURE 3
FIGURE 3
Each group has its own hub distribution. It showed the hub with a probability of being a hub greater than 30%.
FIGURE 4
FIGURE 4
(A) Between-group comparison results of the mean fractional anisotropy- (FA–) weighted structural connectivity strength in each subnetwork between young people with internet gaming disorder (IGD) and healthy controls (HC). (B) An illustration of the correlation result between the mean FA-weighted structural connectivity strength of subnet1 and the IAT score in the IGD group.
FIGURE 5
FIGURE 5
Summarizing the results of network analysis. It represented three subnetwork results (i.e., subnetworks 1–3) whose connections are marked with different colors in the connectogram. If a nodal measure showed the existence of a significant between-group difference, the location of the outer ring of the circle corresponding to this node was labeled as the nodal measure’s name (namely Di, Ei, and Ci). If the nodal measure’s name was colored with red, it meant that the corresponding value increased. Hubs’ name was marked with orange color and with a larger font size in the ring. FRON, frontal cortex; INS, insula; SUBCOR, subcortical region; CING, cingulum; PAR, parietal cortex; OCC, occipital cortex; TEM, temporal cortex; L, left hemisphere; R, right hemisphere.

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