Altered white matter functional network in nicotine addiction

Investigation of white matter functional networks in young smokers

AIMS

This study investigated the changes in the organizational and intrinsical activities of the white matter functional networks (WMFNs) in young smokers using resting-state functional magnetic resonance imaging.

METHODS

A data-driven approach was used to characterize the WMFNs of 30 young smokers and 30 non-smokers. We applied K-means clustering to the neuroimaging data to delineate the WMFNs. Functional neural activities of the WMFNs were compared between the two groups. Correlation analyses were also conducted for the WMFNs neural activities of and clinical indicators of smoking.

RESULTS

Eight WMFNs were identified in both groups. Compared to non-smokers, young smokers demonstrated a different dorsal attention network and lack of a frontostriatal network. The neural activities in the frontal network, deep frontoparietal network, and visual network were reduced in young smokers. Further correlation analyses showed that the decreased neural activity in the deep frontal network and deep frontoparietal network were significantly negatively correlated with the Fagerström Test for Nicotine Dependence.

CONCLUSION

Young smokers exhibited differences in the organizational structure and neural activity intensities of the WMFNs. The present findings may indicate the importance of WMFNs in young smokers, which can help in obtaining a comprehensive understanding of the neural mechanisms underlying smoking addiction.

Brain dysfunctions and neurotoxicity induced by psychostimulants in experimental models and humans: an overview of recent findings

Preclinical and clinical studies indicate that psychostimulants, in addition to having abuse potential, may elicit brain dysfunctions and/or neurotoxic effects. Central toxicity induced by psychostimulants may pose serious health risks since the recreational use of these substances is on the rise among young people and adults. The present review provides an overview of recent research, conducted between 2018 and 2023, focusing on brain dysfunctions and neurotoxic effects elicited in experimental models and humans by amphetamine, cocaine, methamphetamine, 3,4-methylenedioxymethamphetamine, methylphenidate, caffeine, and nicotine. Detailed elucidation of factors and mechanisms that underlie psychostimulant-induced brain dysfunction and neurotoxicity is crucial for understanding the acute and enduring noxious brain effects that may occur in individuals who use psychostimulants for recreational and/or therapeutic purposes.

Distinct brain network patterns in complete and incomplete spinal cord injury patients based on graph theory analysis

AIMS

To compare the changes in brain network topological properties and structure-function coupling in patients with complete spinal cord injury (CSCI) and incomplete spinal cord injury (ICSCI), to unveil the potential neurobiological mechanisms underlying the different effects of CSCI and ICSCI on brain networks and identify objective neurobiological markers to differentiate between CSCI and ICSCI patients.

METHODS

Thirty-five SCI patients (20 CSCI and 15 ICSCI) and 32 healthy controls (HCs) were included in the study. Here, networks were constructed using resting-state functional magnetic resonance imaging to analyze functional connectivity (FC) and diffusion tensor imaging for structural connectivity (SC). Then, graph theory analysis was used to examine SC and FC networks, as well as to estimate SC-FC coupling values.

RESULTS

Compared with HCs, CSCI patients showed increased path length (Lp), decreased global efficiency (Eg), and local efficiency (Eloc) in SC. For FC, ICSCI patients exhibited increased small-worldness, clustering coefficient (Cp), normalized clustering coefficient, and Eloc. Also, ICSCI patients showed increased Cp and Eloc than CSCI patients. Additionally, ICSCI patients had reduced SC-FC coupling values compared to HCs. Moreover, in CSCI patients, the SC network's Lp and Eg values were significantly correlated with motor scores, while in ICSCI patients, the FC network's Cp, Eloc, and SC-FC coupling values were related to sensory/motor scores.

CONCLUSIONS

These results suggest that CSCI patients are characterized by decreased efficiency in the SC network, while ICSCI patients are distinguished by increased local connections and SC-FC decoupling. Moreover, the differences in network metrics between CSCI and ICSCI patients could serve as objective biological markers, providing a basis for diagnosis and treatment strategies.

Altered executive control network and default model network topology are linked to acute electronic cigarette use: A resting-state fNIRS study

In recent years, electronic cigarettes (e-cigs) have gained popularity as stylish, safe, and effective smoking cessation aids, leading to widespread consumer acceptance. Although previous research has explored the acute effects of combustible cigarettes or nicotine replacement therapy on brain functional activities, studies on e-cigs have been limited. Using fNIRS, we conducted graph theory analysis on the resting-state functional connectivity of 61 male abstinent smokers both before and after vaping e-cigs. And we performed Pearson correlation analysis to investigate the relationship between alterations in network metrics and changes in craving. E-cig use resulted in increased degree centrality, nodal efficiency, and local efficiency within the executive control network (ECN), while causing a decrease in these properties within the default model network (DMN). These alterations were found to be correlated with reductions in craving, indicating a relationship between differing network topologies in the ECN and DMN and decreased craving. These findings suggest that the impact of e-cig usage on network topologies observed in male smokers resembles the effects observed with traditional cigarettes and other forms of nicotine delivery, providing valuable insights into their addictive potential and effectiveness as aids for smoking cessation.

Altered White-Matter Functional Network in Children with Idiopathic Generalized Epilepsy

RATIONALE AND OBJECTIVES

The white matter (WM) functional network changes offers insights into the potential pathological mechanisms of certain diseases, the alterations of WM functional network in idiopathic generalized epilepsy (IGE) remain unclear. We aimed to explore the topological characteristics changes of WM functional network in childhood IGE using resting-state functional Magnetic resonance imaging (MRI) and T1-weighted images.

METHODS

A total of 84 children (42 IGE and 42 matched healthy controls) were included in this study. Functional and structural MRI data were acquired to construct a WM functional network. Group differences in the global and regional topological characteristics were assessed by graph theory and the correlations with clinical and neuropsychological scores were analyzed. A support vector machine algorithm model was employed to classify individuals with IGE using WM functional connectivity as features, and the model's accuracy was evaluated using leave-one-out cross-validation.

RESULTS

In IGE group, at the network level, the WM functional network exhibited increased assortativity; at the nodal level, 17 nodes presented nodal disturbances in WM functional network, and nodal disturbances of 11 nodes were correlated with cognitive performance scores, disease duration and age of onset. The classification model achieved the 72.6% accuracy, 0.746 area under the curve, 69.1% sensitivity, 76.2% specificity.

CONCLUSION

Our study demonstrated that the WM functional network topological properties changes in childhood IGE, which were associated with cognitive function, and WM functional network may help clinical classification for childhood IGE. These findings provide novel information for understanding the pathogenesis of IGE and suggest that the WM function network might be qualified as potential biomarkers.