Alterations in Amygdala Connectivity in Internet Addiction Disorder

Identification of patients with internet gaming disorder via a radiomics-based machine learning model of subcortical structures in high-resolution T1-weighted MRI

It is of vital importance to establish an objective and reliable model to facilitate the early diagnosis and intervention of internet gaming disorder (IGD). A total of 133 patients with IGD and 110 healthy controls (HCs) were included. We extracted radiomic features of subcortical structures in high-resolution T1-weighted MRI. Different combinations of four feature selection methods (analysis of variance, Kruskal-Wallis, recursive feature elimination and relief) and ten classification algorithms were used to identify the most robust combined models for distinguishing IGD patients from HCs. Furthermore, a nomogram incorporating radiomic signatures and independent clinical factors was developed. Calibration curve and decision curve analyses were used to evaluate the nomogram. The combination of analysis of variance selector and logistic regression classifier identified that the radiomic model constructed with 20 features from the right caudate nucleus and amygdala showed better IGD screening performance. The radiomic model produced good areas under the curves (AUCs) in the training, validation and test cohorts (AUCs of 0.961, 0.903 and 0.895, respectively). In addition, sex, internet addiction test scores and radiomic scores were included in the nomogram as independent risk factors for IGD. Analysis of the correction curve and decision curve showed that the clinical-radiomic model has good reliability (C-index: 0.987). The nomogram incorporating radiomic features of subcortical structures and clinical characteristics achieved satisfactory classification performance and could serve as an effective tool for distinguishing IGD patients from HCs.

Amygdala functional connectivity in borderline personality disorder

Borderline personality disorder (BPD) is characterised by structural and functional brain alterations. Yet, there is little data on functional connectivity (FC) across different levels of brain networks and parameters. In this study, we applied a multi-level approach to analyse abnormal functional connectivity. We analysed resting-state functional magnetic resonance imaging (fMRI) data sets of 69 subjects: 17 female BPD patients and 51 age-matched psychiatrically healthy female controls. fMRI was analysed using CONN toolbox including: a) seed-based FC analysis of amygdala connectivity, b) independent component analysis (ICA) based network analysis of intra- and inter-network FC of selected resting-state networks (DMN, SN, FPN), as well as c) graph-theory based measures of network-level characteristics. We show group-level seed FC differences with higher amygdala to contralateral (superior) occipital cortex connectivity in BPD, which correlated with schema-therapy derived measures of symptoms/traits across the entire cohort. While there was no significant group effect on DMN, SN, or FPN intra-network or inter-network FC, we show a significant group difference for local efficiency and cluster coefficient for a DMN-linked cerebellum cluster. Our findings demonstrate BPD-linked changes in FC across multiple levels of observation, which supports a multi-level analysis for future studies to consider different aspects of functional connectome alterations.

Effects of different interventions on internet addiction: a systematic review and network meta-analysis

BACKGROUND

Globally, Internet is a recognized form of leisure, but there are growing apprehensions about the increasing number of individuals developing an addiction to it. Recent research has focused on social issues associated with internet addiction (IA). However, the treatment of IA is currently unclear. This study aimed to explore the relationship between IA treatment outcomes and different intervention strategies through systematic review and data analysis of patients who received different intervention modes.

METHODS

A meta-analysis was conducted using RevMan 5.4 and Stata 14.2 on 57 literature research data from five Chinese and English databases, PubMed, Embase, Web of Science, Wanfang and CNKI.

RESULT

A total of 57 randomized controlled trials (RCTs) were included in this network meta-analysis involving 3538 IA patients and 13 different interventions. The network meta-analysis results demonstrated that the top four interventions were: rTMS + CBT, drug + others, rTMS, and electro-acupuncture + CBT.

CONCLUSION

Our study indicated that comprehensive therapy had an optimal therapeutic effect on IA patients and rTMS + CBT ranked first among all therapeutic indicators of intervention, indicating optimal clinical effectiveness.

State-dependent memory retrieval: insights from neural dynamics and behavioral perspectives

Memory retrieval is strikingly susceptible to external states (environment) and internal states (mood states and alcohol), yet we know little about the underlying mechanisms. We examined how internally generated states influence successful memory retrieval using the functional magnetic resonance imaging (fMRI) of laboratory mice during memory retrieval. Mice exhibited a strong tendency to perform memory retrieval correctly only in the reinstated mammillary body-inhibited state, in which mice were trained to discriminate auditory stimuli in go/no-go tasks. fMRI revealed that distinct auditory cues engaged differential brain regions, which were primed by internal state. Specifically, a cue associated with a reward activated the lateral amygdala, while a cue signaling no reward predominantly activated the postsubiculum. Modifying these internal states significantly altered the neural activity balance between these regions. Optogenetic inhibition of those regions in the precue period blocked the retrieval of type-specific memories. Our findings suggest that memory retrieval is under the control of two interrelated neural circuits underlying the neural basis of state-dependent memory retrieval.

Neuroprotection in metabolic syndrome by environmental enrichment. A lifespan perspective

Metabolic syndrome (MetS) is defined by the concurrence of different metabolic conditions: obesity, hypertension, dyslipidemia, and hyperglycemia. Its incidence has been increasingly rising over the past decades and has become a global health problem. MetS has deleterious consequences on the central nervous system (CNS) and neurological development. MetS can last several years or be lifelong, affecting the CNS in different ways and treatments can help manage condition, though there is no known cure. The early childhood years are extremely important in neurodevelopment, which extends beyond, encompassing a lifetime. Neuroplastic changes take place all life through - childhood, adolescence, adulthood, and old age - are highly sensitive to environmental input. Environmental factors have an important role in the etiopathogenesis and treatment of MetS, so environmental enrichment (EE) stands as a promising non-invasive therapeutic approach. While the EE paradigm has been designed for animal housing, its principles can be and actually are applied in cognitive, sensory, social, and physical stimulation programs for humans. Here, we briefly review the central milestones in neurodevelopment at each life stage, along with the research studies carried out on how MetS affects neurodevelopment at each life stage and the contributions that EE models can provide to improve health over the lifespan.

Opposing associations of Internet Use Disorder symptom domains with structural and functional organization of the striatum: A dimensional neuroimaging approach

BACKGROUND

Accumulating evidence suggests brain structural and functional alterations in Internet Use Disorder (IUD). However, conclusions are strongly limited due to the retrospective case-control design of the studies, small samples, and the focus on general rather than symptom-specific approaches.

METHODS

We here employed a dimensional multi-methodical MRI-neuroimaging design in a final sample of n = 203 subjects to examine associations between levels of IUD and its symptom-dimensions (loss of control/time management, craving/social problems) with brain structure, resting state and task-based (pain empathy, affective go/no-go) brain function.

RESULTS

Although the present sample covered the entire range of IUD, including normal, problematic as well as pathological levels, general IUD symptom load was not associated with brain structural or functional alterations. However, the symptom-dimensions exhibited opposing associations with the intrinsic and structural organization of the brain, such that loss of control/time management exhibited negative associations with intrinsic striatal networks and hippocampal volume, while craving/social problems exhibited a positive association with intrinsic striatal networks and caudate volume.

CONCLUSIONS

Our findings provided the first evidence for IUD symptom-domain specific associations with progressive alterations in the intrinsic structural and functional organization of the brain, particularly of striatal systems involved in reward, habitual and cognitive control processes.

Adolescent mobile phone addiction during the COVID-19 pandemic predicts subsequent suicide risk: a two-wave longitudinal study

Both the rate of mobile phone addiction and suicidality among adolescents have increased during the pandemic lockdown. However, the relationship between mobile phone addiction and suicide risk and the underlying psychological mechanisms remains unknown. This study examined the associations between mobile phone addiction in adolescents during the first month of lockdown and the suicide risk in the subsequent five months. A two-wave short-term longitudinal web-based survey was conducted on 1609 senior high school students (mean age = 16.53 years, SD = 0.97 years; 63.5% female). At Time 1 (T1), the severity of mobile phone addiction and basic demographic information was collected from Feb 24 to 28, 2020 in Sichuan Province, China (at the pandemic’s peak). Five months later, between July 11 and July 23 (Time 2, T2), mobile phone addiction, daytime sleepiness, depression, and suicidality were measured within the past five months. The regression analysis revealed that mobile phone addiction during quarantine directly predicted suicidality within the next five months, even after controlling for the effect of depression and daytime sleepiness. Meanwhile, mobile phone addiction at T1 also indirectly predicted suicidality at T2, with depression and daytime sleepiness mediating this association. Programs targeting improvement of daytime sleepiness and depressive symptoms may be particularly effective in reducing suicide risk among adolescents with mobile phone addiction.

Functional Neural Alterations in Pathological Internet Use: A Meta-Analysis of Neuroimaging Studies

Previous resting-state functional MRI (fMRI) studies found spontaneous neural activity in the brains of Pathological Internet Use (PIU) subjects. However, the findings were inconsistent in studies using different neuroimaging analyses. This meta-analytic study aimed to identify a common pattern of altered brain activity from different studies. Resting-state fMRI studies, based on whole-brain analysis methods published before July 1, 2021, were searched in multiple databases (PubMed, EMBASE, MEDLINE, and Web of Science). A voxel-based signed differential mapping (SDM) method was used to clarify brain regions, which showed anomalous activity in PIU subjects compared with healthy controls (HCs). Ten eligible publications consisting of 306 PIU subjects and 314 HCs were included in the SDM meta-analysis. Compared with HCs, subjects with PIU showed increased spontaneous neural functional activity in the left temporal pole of the superior temporal cortex, left amygdala, bilateral median cingulate cortex, and right insula. Meanwhile, a decreased spontaneous neural activity was identified in the left dorsolateral superior frontal gyrus and right middle frontal gyrus in the subjects with PIU. These abnormal brain regions are associated with cognitive executive control and emotional regulation. The consistent changes under different functional brain imaging indicators found in our study may provide important targets for the future diagnosis and intervention of PIU.

Systematic Review Registration:www.crd.york.ac.uk/PROSPERO, identifier: CRD42021258119.

More utilitarian judgment in Internet addiction? An exploration using process dissociation and the CNI model

BACKGROUND

Internet addiction (IA), which is disadvantageous for decision making, such as moral judgment, is a pernicious threat to contemporary societies. However, few studies consider social cognition abilities as an important variable in IA.

OBJECTIVES

This study explores the psychological mechanism of IA facing the moral dilemma.

METHODS

Forty participants with IA and 89 healthy participants were recruited. They finished the Internet Addiction Test and completed the moral judgment task. The process dissociation (PD) method and the consequences, norms, and generalized inaction (CNI) model were used to analyze moral judgment data.

RESULTS

Compared with the healthy control (HC) group, the traditional analysis showed that the IA group made more utilitarian judgment regarding moral dilemmas. PD analysis showed that the IA group had decreased deontological inclination, without utilitarian inclination. The CNI model further showed that the sensitivity of the IA group to moral rules was significantly lower than that of the HC group, while there was no significant difference between groups in the sensitivity to the consequences and the general preference for action.

CONCLUSIONS

Individuals with IA make more utilitarian judgment when faced with a moral dilemma, which is related to their weak sensitivity to moral norms.

Neuropsychological Correlates of Internet Addiction

The Internet, mobile phones, and other similar tools are often necessary for the current functioning of both private life and business. During these two years of pandemic (2019-2021), Internet use, especially different games and mobile phones, were indispensable for the global population. Internet addiction is defined as a psychological dependence on the internet, regardless of the type of activity once logged on. Many studies have confirmed the correlation of stress, depression, and anxiety with internet addiction. It has also been proven that internet addiction, per se, increases the risk of depression, anxiety, and stress. Moreover, the COVID-19 pandemic has generated and promoted social isolation along with unmonitored and increased screen time, all of which are the main causes of internet addiction. The aim of this article is to give a short review of internet addiction research, terminology, and symptoms related to brain functioning. The source material was articles cited in the database, PubMed. Our interest was especially oriented towards the personality characteristics of users and addicted persons as well as neuroimage findings among affected people. We selected the newest articles, published in the period of 2012-2021, of which there are more than 2000. The selected obtained results will be presented and discussed.

Identifying Internet Addiction and Evaluating the Efficacy of Treatment Based on Functional Connectivity Density: A Machine Learning Study

Although mounting neuroimaging studies have greatly improved our understanding of the neurobiological mechanism underlying internet addiction (IA), the results based on traditional group-level comparisons are insufficient in guiding individual clinical practice directly. Specific neuroimaging biomarkers are urgently needed for IA diagnosis and the evaluation of therapy efficacy. Therefore, this study aimed to develop support vector machine (SVM) models to identify IA and assess the efficacy of cognitive behavior therapy (CBT) based on unbiased functional connectivity density (FCD). Resting-state fMRI data were acquired from 27 individuals with IA before and after 8-week CBT sessions and 30 demographically matched healthy controls (HCs). The discriminative FCDs were computed as the features of the support vector classification (SVC) model to identify individuals with IA from HCs, and the changes in these discriminative FCDs after treatment were further used as features of the support vector regression (SVR) model to evaluate the efficacy of CBT. Based on the informative FCDs, our SVC model successfully differentiated individuals with IA from HCs with an accuracy of 82.5% and an area under the curve (AUC) of 0.91. Our SVR model successfully evaluated the efficacy of CBT using the FCD change ratio with a correlation efficient of 0.59. The brain regions contributing to IA classification and CBT efficacy assessment were the left inferior frontal cortex (IFC), middle frontal cortex (MFC) and angular gyrus (AG), the right premotor cortex (PMC) and middle cingulate cortex (MCC), and the bilateral cerebellum, orbitofrontal cortex (OFC) and superior frontal cortex (SFC). These findings confirmed the FCDs of hyperactive impulsive habit system, hypoactive reflecting system and sensitive interoceptive reward awareness system as potential neuroimaging biomarkers for IA, which might provide objective indexes for the diagnosis and efficacy evaluation of IA.

Association between Internet Use Behavior and Palpitation among Adolescents: A Cross-Sectional Study of Middle School Children from Northwest Romania

PURPOSE

The use of the internet is a tool and media literacy has become an essential skill among adolescents. Related to this behavior, some adolescents evoke cardiovascular effects. The purpose of this study was to explore a possible correlation between internet use behavior and occurrence of palpitations and related symptoms among a representative cohort of adolescents from the north-west region of Romania.

METHOD

The study included students of seven middle schools from Northwest Romania. Participants completed an anonymous questionnaire consisting of 18 questions about internet use.

RESULTS

In total, 1147 students responded to the study. Mean duration of daily internet usage was 2.57 h during school time and 3.57 h during the holidays. A total of 77% of adolescents had more than one symptom related to internet use, and 11% of them reported palpitations and related symptoms. We found an independent relation between palpitation and urban background, palpitations and the internet usage time interval 20:00-24:00, and palpitations and tobacco smoking. Strong heartbeats were independently associated with the time interval 12:00-16:00, tobacco smoking, and energy drink consumption.

CONCLUSION

In our cohort, the most important factors associated with the occurrence of palpitations and related symptoms were the timeframe of internet usage and smoking.