White matter integrity of right frontostriatal circuit predicts internet addiction severity among internet gamers.

Excessive use of the internet, which is a typical scenario of self-control failure, could lead to potential consequences such as anxiety, depression, and diminished academic performance. However, the underlying neuropsychological mechanisms remain poorly understood. This study aims to investigate the structural basis of self-control and internet addiction. In a cohort of 96 internet gamers, we examined the relationships among grey matter volume and white matter integrity within the frontostriatal circuits and internet addiction severity, as well as self-control measures. The results showed a significant and negative correlation between dACC grey matter volume and internet addiction severity (p < 0.001), but not with self-control. Subsequent tractography from the dACC to the bilateral ventral striatum (VS) was conducted. The fractional anisotropy (FA) and radial diffusivity of dACC-right VS pathway was negatively (p = 0.011) and positively (p = 0.020) correlated with internet addiction severity, respectively, and the FA was also positively correlated with self-control (p = 0.036). These associations were not observed for the dACC-left VS pathway. Further mediation analysis demonstrated a significant complete mediation effect of self-control on the relationship between FA of the dACC-right VS pathway and internet addiction severity. Our findings suggest that the dACC-right VS pathway is a critical neural substrate for both internet addiction and self-control. Deficits in this pathway may lead to impaired self-regulation over internet usage, exacerbating the severity of internet addiction.

A domain-general frontoparietal network interacts with domain-preferential intermediate pathways to support working memory task.

Working memory (WM) is essential for cognition, but the underlying neural mechanisms remain elusive. From a hierarchical processing perspective, this paper proposed and tested a hypothesis that a domain-general network at the top of the WM hierarchy can interact with distinct domain-preferential intermediate circuits to support WM. Employing a novel N-back task, we first identified the posterior superior temporal gyrus (pSTG), middle temporal area (MT), and postcentral gyrus (PoCG) as intermediate regions for biological motion and shape motion processing, respectively. Using further psychophysiological interaction analyses, we delineated a frontal-parietal network (FPN) as the domain-general network. These results were further verified and extended by a delayed match to sample (DMS) task. Although the WM load-dependent and stimulus-free activations during the DMS delay phase confirm the role of FPN as a domain-general network to maintain information, the stimulus-dependent activations within this network during the DMS encoding phase suggest its involvement in the final stage of the hierarchical processing chains. In contrast, the load-dependent activations of intermediate regions in the N-back task highlight their further roles beyond perception in WM tasks. These results provide empirical evidence for a hierarchical processing model of WM and may have significant implications for WM training.

The multidimensional assessment of interoceptive awareness, version 2: Translation and psychometric properties of the Chinese version.

Background: The Multidimensional Assessment of Interoceptive Awareness (MAIA) is a self-report questionnaire developed by Dr. Mehling that has been widely used to assess multiple dimensions of interoceptive awareness. To further improve the MAIA, Mehling developed the Multidimensional Assessment of Interoceptive Awareness, Version 2 (MAIA-2). The goal of this study is to systematically translate the MAIA-2 into Chinese and to investigate the psychometric properties of the Chinese version (MAIA-2C). Materials and methods: The translation and adaptation of the questionnaire was conducted according to Beaton's method. A total number of 627 participants were enrolled and completed the survey. The entire sample was randomly divided into a training sample (n = 300, 47.8%) and a validation sample (n = 327, 52.2%) for a cross-validation. Exploratory factor analysis (EFA) was used to identify the factor structure of the MAIA-2C in the training sample while confirmatory factor analysis (CFA) was used to test the factor structure obtained by EFA. The reliability of the MAIA-2C was indicated by Cronbach's alpha. The convergent and discriminant validity were assessed by Pearson intercorrelations between the MAIA-2C and the Five-Facet Mindfulness Questionnaire (FFMQ) and State-Trait Anxiety Inventory-Trait anxiety (STAI-T). Results: The EFA results showed an initial 10-factor model, but some items (1, 2, 3, 4, 15, and 16) were deleted because they did not yield the original subscale construct, eventually resulting in a 7-factor model. The CFA results represented a good model fit (χ2/df = 2.170, RMSEA = 0.060, SRMR = 0.0810, CFI = 0.890). The Cronbach's alpha was 0.822 for the total scale and ranged from 0.656 to 0.838 for the subscales. The results of convergent and discriminant validity showed that most MAIA-2C subscales were correlated with the average score and subscales of FFMQ (r = -0.342∼0.535, p < 0.05), and all of the subscales of the MAIA-2C showed negative correlations with the STAI-T total score (r = -0.352∼-0.080, p < 0.05). Conclusion: The MAIA-2C is a valid and reliable instrument for evaluating multiple dimensions of interoceptive awareness in a Chinese population.

Self-control impacts symptoms defining Internet gaming disorder through dorsal anterior cingulate-ventral striatal pathway.

Self-control is important for long-term success and could be a protective factor against maladaptive behaviours such as excessive gaming activity or Internet gaming disorder (IGD). However, the neurobiological basis of self-control and its relationship to IGD remain elusive. Using resting-state fMRI data from 89 participants aged from 18 to 26, we found that self-control and the number of IGD symptoms (IGD-S) were positively and negatively correlated with functional connectivity between right ventral striatum (rVS) and dorsal anterior cingulate cortex (dACC), respectively. A mediation analysis indicated that self-control influenced IGD-S partially through the rVS-dACC connectivity. In addition, step-wise regression analyses revealed that the rVS connectivity in a reward-anticipation limbic pathway contributed to IGD-S but not self-control, independent of the dACC pathway. These results suggest that the cingulate-ventral striatal functional connectivity may serve as an important neurobiological underpinning of self-control to regulate maladaptive behaviours such as these manifesting IGD through striatal circuitry balance.

Medial prefrontal cortex and anteromedial thalamus interaction regulates goal-directed behavior and dopaminergic neuron activity.

The prefrontal cortex is involved in goal-directed behavior. Here, we investigate circuits of the PFC regulating motivation, reinforcement, and its relationship to dopamine neuron activity. Stimulation of medial PFC (mPFC) neurons in mice activated many downstream regions, as shown by fMRI. Axonal terminal stimulation of mPFC neurons in downstream regions, including the anteromedial thalamic nucleus (AM), reinforced behavior and activated midbrain dopaminergic neurons. The stimulation of AM neurons projecting to the mPFC also reinforced behavior and activated dopamine neurons, and mPFC and AM showed a positive-feedback loop organization. We also found using fMRI in human participants watching reinforcing video clips that there is reciprocal excitatory functional connectivity, as well as co-activation of the two regions. Our results suggest that this cortico-thalamic loop regulates motivation, reinforcement, and dopaminergic neuron activity.

Individualized video recommendation modulates functional connectivity between large scale networks.

With the emergence of AI-powered recommender systems and their extensive use in the video streaming service, questions and concerns also arise. Why can recommended video content continuously capture users' attention? What is the impact of long-term exposure to personalized video content on one's behaviors and brain functions? To address these questions, we designed an fMRI experiment presenting participants with personally recommended videos and generally recommended ones. To examine how large-scale networks were modulated by personalized video content, graph theory analysis was applied to investigate the interaction between seven networks, including the ventral and dorsal attention networks (VAN, DAN), frontal-parietal network (FPN), salience network (SN), and three subnetworks of default mode network (dorsal medial prefrontal (dMPFC), Core, and medial temporal lobe (MTL)). Our results showed that viewing nonpersonalized video content mainly enhanced the connectivity in the DAN-FPN-Core pathway, whereas viewing personalized ones increased not only the connectivity in this pathway but also the DAN-VAN-dMPFC pathway. In addition, both personalized and nonpersonalized short videos decreased the couplings between SN and VAN as well as between two DMN subsystems, Core and MTL. Collectively, these findings uncovered distinct patterns of network interactions in response to short videos and provided insights into potential neural mechanisms by which human behaviors are biased by personally recommended content.

Viewing personalized video clips recommended by TikTok activates default mode network and ventral tegmental area.

Cutting-edge recommendation algorithms have been widely used by media platforms to suggest users with personalized content. While such user-specific recommendations may satisfy users' needs to obtain intended information, some users may develop a problematic use pattern manifested by addiction-like undesired behaviors. Using a popular video sharing and recommending platform (TikTok) as an example, the present study first characterized use-related undesired behaviors with a questionnaire, then investigated how personally recommended videos modulated brain activity with an fMRI experiment. We found more undesired symptoms were related to lower self-control ability among young adults, and about 5.9% of TikTok users may have significant problematic use. The fMRI results showed higher brain activations in sub-components of the default mode network (DMN), ventral tegmental area, and discrete regions including lateral prefrontal, anterior thalamus, and cerebellum when viewing personalized videos in contrast to non-personalized ones. Psychophysiological interaction analyses revealed stronger coupling between activated DMN subregions and neural pathways underlying auditory and visual processing, as well as the frontoparietal network. This study highlights the functional heterogeneity of DMN in viewing personalized videos and may shed light on the neural underpinnings of how recommendation algorithms are able to keep the user's attention to suggested contents.