Abnormal developmental of structural covariance networks in young adults with heavy cannabis use: a 3-year follow-up study

Altered gray matter structural covariance networks in young adults with obesity

BACKGROUND

Overwhelming evidence showed that obesity was associated with abnormal brain functional networks. However, the changes of structural covariance networks (SCNs) based on cortical thickness (CT) and cortical surface area (CSA) in obesity is still unclear.

METHODS

In this study, 243 young adults with obesity and matched 243 lean individuals were enrolled from the Human Connectome Project Release S1200 dataset. All participants underwent magnetic resonance imaging scans following clinical and neuropsychological assessments. SCNs matrices were constructed by Brain Connectivity Toolbox based on both CT and CSA. Nonparametric permutation tests were adopted to examine group differences of these matrices.

RESULTS

Young adults with obesity exhibited lower CSA of left entorhinal cortex, but higher CT of both left rostral anterior cingulate cortex and right superior parietal lobule, as well as lower CT of left temporal pole. While in terms of global network measures, there were no significant group differences; in terms of nodal network measures, young adults with obesity exhibited alterations in widespread brain regions including left posterior cingulate cortex, bilateral superior frontal gyrus, left entorhinal cortex and right insula.

CONCLUSIONS

Young adults with obesity exhibited abnormal nodal network measures in widespread brain regions involved in default mode network, central executive network and salience network. These findings indicate the adverse effects of obesity on young adults might be associated with the altered triple network.

How short video addiction affects risk decision-making behavior in college students based on fNIRS technology

Introduction

Short video addiction is an emerging form of Internet behavioral addiction characterized by dependent, inappropriate, or excessive use of short video applications. This phenomenon significantly affects decision-making processes and warrants further investigation. Despite the growing prevalence of short video addiction, research on its impact on risky decision-making abilities remains limited. To address this gap, the present study contributes to this critical issue by incorporating neurophysiological data.

Methods

Using functional Near-Infrared Spectroscopy (fNIRS) brain imaging technology and the Balloon Analogue Risk Task (BART) experimental paradigm, this study explored the decision-making behaviors and brain activity of individuals with short video addiction (Individuals with SVA) under varying short video background cues. Adopting a mixed experimental design, the study examined decision-making ability and brain activation as dependent variables across two groups (addiction and control), two outcomes (gain and loss), and two background cue conditions (with and without cues). A total of 45 participants were included in the study: 23 in the addiction group and 22 in the control group.

Results

Behavioral results revealed that individuals with short video addiction demonstrated higher risk-taking tendencies, shorter reaction times, and higher rates of balloon explosions, particularly when exposed to short video cues. Neural data indicated heightened sensitivity to short video cues among individuals with addiction compared to healthy controls. Specifically, the right orbitofrontal cortex (OFC) and right frontopolar area (FPA) exhibited increased activation during risk decision-making with short video cues in the addiction group, while the control group showed no activation in these regions. Additionally, Individuals with SVA displayed greater sensitivity to loss outcomes, with significant OFC activation observed in response to losses but not gains. In risk decision-making scenarios involving short video cues, notable activation was observed in the FPA and left dorsolateral prefrontal cortex (DLPFC) among Individuals with SVA when encountering reward loss. However, no significant differences were observed between the two groups in risk decision-making under the background without short video cue conditions, regardless of gain or loss outcomes.

Conclusion

These findings suggest that Individuals with SVA are more susceptible to the influence of short video-related cues, more sensitive to income loss, and more likely to pursue higher rewards, resulting in higher-risk decisions. The fNIRS results provide critical insights into encouraging healthy short video consumption, informing psychological clinical therapy, and advancing research on addiction-related brain mechanisms.

Abnormal topological structure of structural covariance networks based on fractal dimension in noise induced hearing loss

The topological attributes of structural covariance networks (SCNs) based on fractal dimension (FD) and changes in brain network connectivity were investigated using graph theory and network-based statistics (NBS) in patients with noise-induced hearing loss (NIHL). High-resolution 3D T1 images of 40 patients with NIHL and 38 healthy controls (HCs) were analyzed. FD-based Pearson correlation coefficients were calculated and converted to Fisher's Z to construct the SCNs. Topological attributes and network hubs were calculated using the graph theory. Topological measures between groups were compared using nonparametric permutation tests. Abnormal connection networks were identified using NBS analysis. The NIHL group showed a significantly increased normalized clustering coefficient, normalized characteristic path length, and decreased nodal efficiency of the right medial orbitofrontal gyrus. Additionally, the network hubs based on betweenness centrality and degree centrality were both the right transverse temporal gyrus and left parahippocampal gyrus in the NIHL group. The NBS analysis revealed two subnetworks with abnormal connections. The subnetwork with enhanced connections was mainly distributed in the default mode, frontoparietal, dorsal attention, and somatomotor networks, whereas the subnetwork with reduced connections was mainly distributed in the limbic, visual, default mode, and auditory networks. These findings demonstrate the abnormal topological structure of FD-based SCNs in patients with NIHL, which may contribute to understand the complex mechanisms of brain damage at the network level, providing a new theoretical basis for neuropathological mechanisms.

Abnormal structural covariance networks in young adults with recent cannabis use

BACKGROUND

Recent cannabis use (RCU) exerts adverse effects on the brain. However, the effect of RCU on structural covariance networks (SCNs) is still unclear. This retrospective cross-sectional study aimed to explore the effects of RCU on SCNs in young adults in terms of whole cerebral cortical thickness (CT) and cortical surface area (CSA).

METHODS

A total of 117 participants taking tetrahydrocannabinol (RCU group) and 896 participants not using cannabis (control group) were included in this study. All participants underwent MRI scanning following urinalysis screening, after which FreeSurfer 5.3 was used to calculate the CT and CSA, and SCNs matrices were constructed by Brain Connectivity Toolbox. Subsequently, the global and nodal network measures of the SCNs were computed based on these matrices. A nonparametric permutation test was used to investigate the group differences by Matlab.

RESULTS

Regarding global network measures of CT, young adults with RCU exhibited altered small-worldness (P = 0.020) and clustering coefficient (P = 0.031) compared to controls, whereas there were no significant group differences in terms of SCNs constructed with CSA. Additionally, SCNs based on CT and CSA displayed abnormal nodal degree, nodal efficiency, and nodal betweenness centrality in vital brain regions of the triple network, including the dorsolateral and ventrolateral prefrontal cortex, and anterior cingulate cortex.

CONCLUSION

The effects of RCU on brain structure in young adults can be detected by SCNs, in which structural abnormalities in the triple network are dominant, indicating that RCU can be detrimental to brain function.

Abnormal longitudinal changes of structural covariance networks of cortical thickness in mild traumatic brain injury with posttraumatic headache

BACKGROUND

It is widely acknowledged that mild traumatic brain injury (MTBI) leads to either functionally or anatomically abnormal brain regions. Structural covariance networks (SCNs) that depict coordinated regional maturation patterns are commonly employed for investigating brain structural abnormalities. However, the dynamic nature of SCNs in individuals with MTBI who suffer from posttraumatic headache (PTH) and their potential as biomarkers have hitherto not been investigated.

METHODS

This study included 36 MTBI patients with PTH and 34 well-matched healthy controls (HCs). All participants underwent magnetic resonance imaging scans and were assessed with clinical measures during the acute and subacute phases. Structural covariance matrices of cortical thickness were generated for each group, and global as well as nodal network measures of SCNs were computed.

RESULTS

MTBI patients with PTH demonstrated reduced headache impact and improved cognitive function from the acute to subacute phase. In terms of global network metrics, MTBI patients exhibited an abnormal normalized clustering coefficient compared to HCs during the acute phase, although no significant difference in the normalized clustering coefficient was observed between the groups during the subacute phase. Regarding nodal network metrics, MTBI patients displayed alterations in various brain regions from the acute to subacute phase, primarily concentrated in the prefrontal cortex (PFC).

CONCLUSIONS

These findings indicate that the cortical thickness topography in the PFC determines the typical structural-covariance topology of the brain and may serve as an important biomarker for MTBI patients with PTH.