Machine learning identifies different related factors associated with depression and suicidal ideation in Chinese children and adolescents.

BACKGROUND: Depression and suicidal ideation often co-occur in children and adolescents, yet they possess distinct characteristics. This study sought to identify the different related factors associated with depression and suicidal ideation. METHODS: A nationwide cross-sectional survey collected data from Chinese children and adolescents aged 8 to 18 (N = 160,962; 48.91 % girls). The survey included inquiries about demographics, depression, suicidal ideation, anxiety, perceived stress, academic burnout, internet addiction, non-suicidal self-injury, bullying, and being bullied. Fifteen machine learning algorithms were conducted to identify the different related factors associated with depression and suicidal ideation. Additionally, we conducted external validation on an independent sample of 1,812,889 children and adolescents. RESULTS: Our findings revealed seven related factors linked to depression and six associated with suicidal ideation, with average accuracy rates of 86.86 % and 85.82 %, respectively. For depression, the most influential factors were anxiety, perceived stress, academic burnout, internet addiction, non-suicidal self-injury, experience of bullying, and age. Similarly, anxiety, non-suicidal self-injury, perceived stress, internet addiction, academic burnout, and age emerged as paramount factors for suicidal ideation. Moreover, these related factors showed notable variations in their predictive capacities for depression and suicidal ideation across different subgroups. CONCLUSION: Anxiety emerged as the predominant shared factor for both depression and suicidal ideation, whereas the other related factors displayed distinct predictive patterns for each condition. These findings highlight the critical need for tailored strategies from public mental health service providers and policymakers to address the pressing concerns of depression and suicidal ideation.

The dorsomedial prefrontal cortex represents subjective value across effort-based and risky decision-making.

Decisions that require taking effort costs into account are ubiquitous in real life. The neural common currency theory hypothesizes that a particular neural network integrates different costs (e.g., risk) and rewards into a common scale to facilitate value comparison. Although there has been a surge of interest in the computational and neural basis of effort-related value integration, it is still under debate if effort-based decision-making relies on a domain-general valuation network as implicated in the neural common currency theory. Therefore, we comprehensively compared effort-based and risky decision-making using a combination of computational modeling, univariate and multivariate fMRI analyses, and data from two independent studies. We found that effort-based decision-making can be best described by a power discounting model that accounts for both the discounting rate and effort sensitivity. At the neural level, multivariate decoding analyses indicated that the neural patterns of the dorsomedial prefrontal cortex (dmPFC) represented subjective value across different decision-making tasks including either effort or risk costs, although univariate signals were more diverse. These findings suggest that multivariate dmPFC patterns play a critical role in computing subjective value in a task-independent manner and thus extend the scope of the neural common currency theory.

Youth Screen Media Activity Patterns and Associations With Behavioral Developmental Measures and Resting-state Brain Functional Connectivity.

OBJECTIVE: Screen media activity (SMA) consumes considerable time in youth's lives, raising concerns about the effects it may have on youth development. Disentangling mixed associations between SMA of youth and developmental measures should move beyond overall screen time and consider types and patterns of SMA. This study aimed to identify reliable and generalizable SMA patterns among youth and examine their associations with behavioral developmental measures and developing brain functional connectivity. METHOD: Three waves of Adolescent Brain and Cognitive Development (ABCD) data were examined. The Lifespan Human Connectome Project in Development (HCP-D) was interrogated as an independent sample. ABCD participants included 11,876 children at baseline. HCP-D participants included 652 children and adolescents. Youth-reported SMA and behavioral developmental measures (neurocognitive performance, behavioral problems, psychotic-like experiences, impulsivity, and sensitivities to punishment/reward) were assessed with validated instruments. We identified SMA patterns in the ABCD baseline data using K-means clustering and sensitivity analyses. Generalizability and stability of the identified SMA patterns were examined in HCP-D data and ABCD follow-up waves, respectively. Relations between SMA patterns and behavioral and brain (resting-state brain functional connectivity) measures were examined using linear mixed effects modeling with false discovery rate (FDR) correction. RESULTS: SMA data from 11,815 children (mean [SD] age = 119.0 [7.5] months; 6,159 [52.1%] boys) were examined; 3,151 (26.7%) demonstrated a video-centric higher-frequency SMA pattern, and 8,664 (73.3%) demonstrated a lower-frequency pattern. SMA patterns were validated in similarly aged HCP-D youth. Compared with the lower-frequency SMA pattern group, the video-centric higher-frequency SMA pattern group showed poorer neurocognitive performance (ß = -.12, 95% CI [-0.08, -0.16], FDR-corrected p < .001), more total behavioral problems (ß = .13, 95% CI [0.09, 0.18], FDR-corrected p < .001), and more psychotic-like experiences (ß = .31, 95% CI [0.27, 0.36], FDR-corrected p < .001). The video-centric higher-frequency SMA pattern group demonstrated higher impulsivity, more sensitivity to punishment/reward, and altered resting-state brain functional connectivity among brain areas implicated previously in cognitive processes. Most of the associations persisted with age in the ABCD data, with more participants (n = 3,378, 30.4%) in the video-centric higher-frequency SMA group at 1-year follow-up. A social communication-centric SMA pattern was observed in HCP-D adolescents. CONCLUSION: Video-centric SMA patterns are reliable and generalizable during late childhood. A higher-frequency video entertainment SMA pattern group showed altered resting-state brain functional connectivity and poorer developmental measures that persisted longitudinally. The findings suggest that public health strategies to decrease excessive time spent by children on video entertainment-related SMA are needed. Further studies are needed to examine potential video-centric/social communication-centric SMA bifurcation to understand dynamic changes and trajectories of SMA patterns and related outcomes developmentally. DIVERSITY & INCLUSION STATEMENT: We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We worked to ensure that the study questionnaires were prepared in an inclusive way. We actively worked to promote sex and gender balance in our author group. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. While citing references scientifically relevant for this work, we also actively worked to promote sex and gender balance in our reference list. The author list of this paper includes contributors from the location and/or community where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work.

Subgroups of internet gaming disorder based on addiction-related resting-state functional connectivity.

AIMS: To identify subgroups of people with internet gaming disorder (IGD) based on addiction-related resting-state functional connectivity and how these subgroups show different clinical correlates and responses to treatment. DESIGN: Secondary analysis of two functional magnetic resonance imaging (fMRI) data sets. SETTING: Zhejiang province and Beijing, China. PARTICIPANTS: One hundred and sixty-nine IGD and 147 control subjects. MEASUREMENTS: k-Means algorithmic and support-vector machine-learning approaches were used to identify subgroups of IGD subjects. These groups were examined with respect to assessments of craving, behavioral activation and inhibition, emotional regulation, cue-reactivity and guessing-related measures. FINDINGS: Two groups of subjects with IGD were identified and defined by distinct patterns of connectivity in brain networks previously implicated in addictions: subgroup 1 ('craving-related subgroup') and subgroup 2 ('mixed psychological subgroup'). Clustering IGD on this basis enabled the development of diagnostic classifiers with high sensitivity and specificity for IGD subgroups in 10-fold validation (n = 218) and out-of-sample replication (n = 98) data sets. Subgroup 1 is characterized by high craving scores, cue-reactivity during fMRI and responsiveness to a craving behavioral intervention therapy. Subgroup 2 is characterized by high craving, behavioral inhibition and activations scores, non-adaptive emotion-regulation strategies and guessing-task fMRI measures. Subgroups 1 and 2 showed largely opposite functional-connectivity patterns in overlapping networks. CONCLUSIONS: There appear to be two subgroups of people with internet gaming disorder, each associated with differing patterns of brain functional connectivity and distinct clinical symptom profiles and gender compositions.

Neural activations to loss anticipation mediates the association between difficulties in emotion regulation and screen media activities among early adolescent youth: A moderating role for depression.

BACKGROUND: Screen media activities (SMAs; e.g., watching videos, playing videogames) have become increasingly prevalent among youth as ways to alleviate or escape from negative emotional states. However, neural mechanisms underlying these processes in youth are incompletely understood. METHOD: Seventy-nine youth aged 11-15 years completed a monetary incentive delay task during fMRI scanning. Neural correlates of reward/loss processing and their associations with SMAs were explored. Next, brain activations during reward/loss processing in regions implicated in the processing of emotions were examined as potential mediating factors between difficulties in emotion regulation (DER) and engagement in SMAs. Finally, a moderated mediation model tested the effects of depressive symptoms in such relationships. RESULT: The emotional components associated with SMAs in reward/loss processing included activations in the left anterior insula (AI) and right dorsolateral prefrontal cortex (DLPFC) during anticipation of working to avoid losses. Activations in both the AI and DLPFC mediated the relationship between DER and SMAs. Moreover, depressive symptoms moderated the relationship between AI activation in response to loss anticipation and SMAs. CONCLUSION: The current findings suggest that DER link to SMAs through loss-related brain activations implicated in the processing of emotions and motivational avoidance, particularly in youth with greater levels of depressive symptoms. The findings suggest the importance of enhancing emotion-regulation tendencies/abilities in youth and, in particular, their regulatory responses to negative emotional situations in order to guide moderate engagement in SMAs.

Emotional bias modification weakens game-related compulsivity and reshapes frontostriatal pathways.

Addiction is characterized by compulsive engagement despite adverse consequences. Psychobehavioural interventions targeting compulsivity in addictions are relatively rare, particularly for behavioural addictions like internet gaming disorder (IGD). Free from confounding drug-on-brain effects, IGD provides a promising model for understanding neuropsychological processes of addictions. IGD is a global concern in the setting of increasing internet use worldwide. Thus, developing interventions and understanding their mechanisms of action are important. Positive emotional association biases (EABs) towards addiction cues based on reward conditioning may underlie addiction-associated compulsivity. Here, we developed an EAB modification (EABM) protocol and examined whether modifying EABs via cognitive training would alter neurocognitive aspects of addiction-associated compulsivity in IGD. We recruited 90 IGD participants who were randomly assigned to receive EABM or sham training in a 1:1 ratio (clinicaltrials.gov identifier: NCT04068064). The EABM intervention involved six consecutive days of exposure to negative emotional terms linked to gaming stimuli and positive terms linked to non-gaming healthy-alternative stimuli. The sham training involved similar stimuli linked to neutral words. Participants underwent event-related functional MRI while performing a regulation-of-craving task and received several behavioural assessments pretraining and post-training. Primary efficacy measures were changes in gaming-related positive EABs, and compulsive gaming thoughts and behaviours. Behaviourally, EABM (versus sham) training decreased gaming-related positive EABs and compulsive gaming thoughts and behaviours. Neurally, EABM training involved decreased activation in the bilateral dorsal striatum in the regulation-of-craving task and altered left dorsal striatum-centric functional connectivity with ventral prefrontal cortical regions, which correlated with decreases in gaming-related EABs or compulsive gaming thoughts and behaviours. EABM training also implicated activation changes in the right medial frontal gyrus and posterior insula. EABM may reduce compulsive gaming thoughts and behaviours via reshaping functional organization of frontostriatal pathways and insular activity in IGD. The therapeutic potential of EABM should be examined in larger, longer-term studies, as should its application to other addictive disorders.

Connectome-based prediction of marital quality in husbands' processing of spousal interactions.

Marital quality may decrease during the early years of marriage. Establishing models predicting individualized marital quality may help develop timely and effective interventions to maintain or improve marital quality. Given that marital interactions have an important impact on marital well-being cross-sectionally and prospectively, neural responses during marital interactions may provide insight into neural bases underlying marital well-being. The current study applies connectome-based predictive modeling, a recently developed machine-learning approach, to functional magnetic resonance imaging (fMRI) data from both partners of 25 early-stage Chinese couples to examine whether an individual's unique pattern of brain functional connectivity (FC) when responding to spousal interactive behaviors can reliably predict their own and their partners' marital quality after 13 months. Results revealed that husbands' FC involving multiple large networks, when responding to their spousal interactive behaviors, significantly predicted their own and their wives' marital quality, and this predictability showed gender specificity. Brain connectivity patterns responding to general emotional stimuli and during the resting state were not significantly predictive. This study demonstrates that husbands' differences in large-scale neural networks during marital interactions may contribute to their variability in marital quality and highlights gender-related differences. The findings lay a foundation for identifying reliable neuroimaging biomarkers for developing interventions for marital quality early in marriages.

Gender-related differences in involvement of addiction brain networks in internet gaming disorder: Relationships with craving and emotional regulation.

BACKGROUND: Abnormal interactions among addiction brain networks associated with intoxication, negative affect, and anticipation may have relevance for internet gaming disorder (IGD). Despite prior studies having identified gender-related differences in the neural correlates of IGD, gender-related differences in the involvement of brain networks remain unclear. METHODS: One-hundred-and-nine individuals with IGD (54 males) and 111 with recreational game use (RGU; 58 males) provided resting-state fMRI data. We examined gender-related differences in involvement of addiction brain networks in IGD versus RGU subjects. We further compared the strength between and within addiction brain networks and explored possible relationships between the strength of functional connectivities within and between addiction brain networks and several relevant behavioral measures. RESULTS: The addiction brain networks showed high correct classification rates in distinguishing IGD and RGU subjects in men and women. Male subjects with versus without IGD showed stronger functional connectivities between and within addiction brain networks. Moreover, the strength of the connectivity within the anticipation network in male IGD subjects was positively related to subjective craving. However, female subjects with versus without IGD showed decreased functional connections between and within addiction brain networks. The strength of connectivity between the anticipation and negative-affect brain networks in female IGD subjects was negatively related to maladaptive cognitive emotion-regulation strategies. CONCLUSIONS: Addiction brain networks have potential for distinguishing IGD and RGU individuals. Importantly, this study identified novel gender-related differences in brain-behavior relationships in IGD. These results help advance current neuroscientific theories of IGD and may inform gender-informed treatment strategies.

Neural classification of internet gaming disorder and prediction of treatment response using a cue-reactivity fMRI task in young men.

BACKGROUND: Neural mechanisms underlying internet gaming disorder (IGD) are important for diagnostic considerations and treatment development. However, neurobiological underpinnings of IGD remain relatively poorly understood. METHODS: We employed multi-voxel pattern analysis (MVPA), a machine-learning approach, to examine the potential of neural features to statistically predict IGD status and treatment outcome (percentage change in weekly gaming time) for IGD. Cue-reactivity fMRI-task data were collected from 40 male IGD subjects and 19 male healthy control (HC) subjects. 23 IGD subjects received 6 weeks of craving behavioral intervention (CBI) treatment. MVPA was applied to classify IGD subjects from HCs and statistically predict clinical outcomes. RESULTS: MVPA displayed a high (92.37%) accuracy (sensitivity of 90.00% and specificity of 94.74%) in the classification of IGD and HC subjects. The most discriminative brain regions that contribute to classification were the bilateral middle frontal gyrus, precuneus, and posterior lobe of the right cerebellum. MVPA statistically predicted clinical outcomes in the craving behavioral intervention (CBI) group (r = 0.48, p = 0.0032). The most strongly implicated brain regions in the prediction model were the right middle frontal gyrus, superior frontal gyrus, supramarginal gyrus, anterior/posterior lobes of the cerebellum and left postcentral gyrus. CONCLUSIONS: The findings about cue-reactivity neural correlates could help identify IGD subjects and predict CBI-related treatment outcomes provide mechanistic insight into IGD and its treatment and may help promote treatment development efforts.

Efficient Brain Connectivity Reconfiguration Predicts Higher Marital Quality and Lower Depression.

Social-information processing is important for successful romantic relationships and protecting against depression, and depends on functional connectivity (FC) within and between large-scale networks. Functional architecture evident at rest is adaptively reconfigured during task and there were two possible associations between brain reconfiguration and behavioral performance during neurocognitive tasks (efficiency effect and distraction-based effect). This study examined relationships between brain reconfiguration during social-information processing and relationship-specific and more general social outcomes in marriage. Resting-state FC was compared with FC during social-information processing (watching relationship-specific and general emotional stimuli) of 29 heterosexual couples, and the FC similarity (reconfiguration efficiency) was examined in relation to marital quality and depression 13 months later. The results indicated wives' reconfiguration efficiency (globally and in visual association network) during relationship-specific stimuli processing was related to their own marital quality. Higher reconfiguration efficiency (globally and in medial frontal, frontal-parietal, default mode, motor/sensory and salience networks) in wives during general emotional stimuli processing was related to their lower depression. These findings suggest efficiency effects on social outcomes during social cognition, especially among married women. The efficiency effects on relationship-specific and more general outcome are respectively higher during relationship-specific stimuli or general emotional stimuli processing.

A review of psychological interventions for internet addiction.

Internet addiction (IA) may constitute a widespread and serious mental problem. Previous reviews have not fully considered potential factors that may contribute to therapeutic outcomes or predict behavioral changes. Such information is relevant to understand the active ingredients of interventions and to develop more efficacious treatments that target features of IA. This systematic review was designed to relate theories of IA to treatments, describe studies of psychotherapies for IA, and propose a model of addiction and interventions based on extant studies. A computer database search of PubMed, PsychINFO, ScienceDirect, China National Knowledge Infrastructure, and Google Scholar was conducted to identify all available research evidence on psychological treatments for IA (N = 31 studies). Among these psychological interventions, the targeted reduction of addiction-related impulsivity and craving, improvement of cognitive maladjustment, and alleviation of family problems have been investigated in IA interventions. The targeted domains and intervention methods are not mutually exclusive, and further research is needed to demonstrate the effective components and mechanisms of action for treatments of IA. Such research will help generate more efficacious evidence-based interventions.

Resting-state connectome-based support-vector-machine predictive modeling of internet gaming disorder.

Internet gaming disorder (IGD), a worldwide mental health issue, has been widely studied using neuroimaging techniques during the last decade. Although dysfunctions in resting-state functional connectivity have been reported in IGD, mapping relationships from abnormal connectivity patterns to behavioral measures have not been fully investigated. Connectome-based predictive modeling (CPM)-a recently developed machine-learning approach-has been used to examine potential neural mechanisms in addictions and other psychiatric disorders. To identify the resting-state connections associated with IGD, we modified the CPM approach by replacing its core learning algorithm with a support vector machine. Resting-state functional magnetic resonance imaging (fMRI) data were acquired in 72 individuals with IGD and 41 healthy comparison participants. The modified CPM was conducted with respect to classification and regression. A comparison of whole-brain and network-based analyses showed that the default-mode network (DMN) is the most informative network in predicting IGD both in classification (individual identification accuracy = 78.76%) and regression (correspondence between predicted and actual psychometric scale score: r = 0.44, P < 0.001). To facilitate the characterization of the aberrant resting-state activity in the DMN, the identified networks have been mapped into a three-subsystem division of the DMN. Results suggest that individual differences in DMN function at rest could advance our understanding of IGD and variability in disorder etiology and intervention outcomes.

Impaired regulation of both addiction-related and primary rewards in individuals with internet gaming disorder.

Poor executive function (EF) has been implicated in addictions. Among "hot" EFs (i.e., those involving motivations and emotions), poor regulation of craving has been proposed to underlie addiction development in substance-use disorders (SUDs), making such regulation a potential treatment target. However, regulation of craving remains poorly understood in internet gaming disorder (IGD). Additionally, prior studies of cold EFs (e.g., inhibition and cognitive flexibility under neutral conditions) in IGD have provided mixed results and mostly included only male subjects. We addressed these issues by instructing 54 participants (26 with IGD including males and females, and 28 control subjects) to perform a regulation-of-craving (ROC) task and a Stroop color-word-interference task. Compared to control subjects, individuals with IGD revealed deficits in regulation for both gaming- and food-related craving, but no differences in Stroop performance. The current study provides initial empirical support suggesting regulation impairments for both addiction-related and primary rewards among individuals with IGD. The findings are consistent with studies in SUDs, suggesting that impaired regulation of craving may be a relevant transdiagnostic construct across SUDs and behavioral addictions. The findings suggest targeting regulation of "hot" processes should be considered in IGD treatment development.