Network Analysis of Behaviors in the Depression and Autism Realms: Inter-Relationships and Clinical Implications

Factors predicting parenting stress in the autism spectrum disorder context: A network analysis approach

Elevated levels of parenting stress have been reported in parents raising an Autistic child. Previous studies have identified a multitude of predictors of parenting stress, including both child-related and parent-related factors, though findings across studies are not always in agreement. In the present study we investigate the determinants of parenting stress using a Network Analysis approach, which is then used to inform a subsequent structural equation model. New Zealand parents (n =  490) of a child diagnosed with Autism Spectrum Disorder (ASD) provided data on their Autistic child (e.g., ASD core symptoms, problem behaviours) and themselves (i.e., parenting stress). The analysis revealed that both child and parent demographic factors were poor predictors of parenting stress, while the child's current language and communication ability were correlated with diagnostic age and parenting stress. An earlier diagnostic age, in turn, suggested better behavioural and emotional outcomes for children. Overall, the Network Analysis showed itself to be an informative approach to understanding parenting stress in the ASD context. Findings further advocate for the implementation of ASD-related and language-related interventions as early as possible, and that language delays during early infancy justify prompt clinical assessment.

The Connection Between Sleep Problems and Emotional and Behavioural Difficulties in Autistic Children: A Network Analysis

The interactions between sleep problems, autism symptoms and emotional and behavioural difficulties were explored using network analysis in 240 autistic children (mean age: 8.8 years, range 5-13 years) with moderate to severe sleep problems. Findings revealed a highly connected and interpretable network, with three separate clusters identified of the modelled variables. Depression, anxiety and behavioural difficulties were the most central variables of the network. Depression, anxiety and restricted repetitive and stereotyped patterns behaviours (RRBs) were the strongest bridging variables in the network model, transmitting activation both within and between other symptom clusters. The results highlight that depression and anxiety were highly connected symptoms within the network, suggesting support in these areas could be helpful, as well as future research.

Understanding depression in autism: the role of subjective perception and anterior cingulate cortex volume

BACKGROUND

The prevalence of depression is elevated in individuals with autism spectrum disorder (ASD) compared to the general population, yet the reasons for this disparity remain unclear. While social deficits central to ASD may contribute to depression, it is uncertain whether social interaction behavior themselves or individuals' introspection about their social behaviors are more impactful. Although the anterior cingulate cortex (ACC) is frequently implicated in ASD, depression, and social functioning, it is unknown if it explains differences between ASD adults with and without co-occurring depression.

METHODS

The present study contrasted observed vs. subjective perception of autism symptoms and social interaction assessed with both standardized measures and a lab task, in 65 sex-balanced (52.24% male) autistic young adults. We also quantified ACC and amygdala volume with 7-Tesla structural neuroimaging to examine correlations with self-reported depression and social functioning.

RESULTS

We found that ASD individuals with self-reported depression exhibited differences in subjective evaluations including heightened self-awareness of ASD symptoms, lower subjective satisfaction with social relations, and less perceived affiliation during the social interaction task, yet no differences in corresponding observed measures, compared to those without depression. Larger ACC volume was related to depression, greater self-awareness of ASD symptoms, and worse subjective satisfaction with social relations. In contrast, amygdala volume, despite its association with clinician-rated ASD symptoms, was not related to depression.

LIMITATIONS

Due to the cross-sectional nature of our study, we cannot determine the directionality of the observed relationships. Additionally, we included only individuals with an IQ over 60 to ensure participants could complete the social task. We also utilized self-reported depression indices instead of clinically diagnosed depression, which may limit the comprehensiveness of the findings.

CONCLUSIONS

Our approach highlights the unique role of subjective perception of autism symptoms and social interactions, beyond the observable manifestation of social impairment in ASD, in contributing to self-reported depression, with the ACC playing a crucial role. These findings imply possible heterogeneity of ASD concerning co-occurring depression. Using neuroimaging, we were able to demarcate depressive phenotypes co-occurring alongside autistic phenotypes.

Symptom clusters and networks analysis in acute-phase stroke patients: a cross-sectional study

The symptoms of stroke jeopardize patients' health and increase the burden on society and caregivers. Although the traditional symptom cluster research paradigm can enhance management efficiency, it fails to provide targets for intervention, thereby hindering the development of patient-centered precision medicine. However, the symptom network paradigm, as a novel research approach, addresses the limitations of traditional symptom management by identifying core symptoms and determining intervention targets, thereby enhancing the efficiency and precision of symptom management. This study. aims to explore the symptom network and core symptoms of acute-phase stroke patients. A convenience sample of 505 stroke patients was selected for this study. Symptoms were assessed by the Stroke Symptom Experience Scale.Exploratory factor analysis was utilized to extract symptom clusters, and network analysis was conducted to construct the symptom network and characterize its nodes. In this study, four symptom clusters were extracted through exploratory factor analysis. Based on the results of node predictability(re) and node centrality such as strength centrality (rs), it was found that the symptoms of "No interest in surroundings" (rs = 1.299, re = 1.081), "Be disappointed about future" (rs = 0.922, re = 0.901), and "Unable to maintain body balance" (rs = 0.747, re = 0.744) had the highest centrality and predictability values, indicating their core positions within the symptom network. No interest in surroundings, Be disappointed about future, and Unable to maintain body balance are core symptoms in the symptom network. In the future, intervention methods for core symptoms can be constructed and validated for their intervention effects to further demonstrate the benefits of core symptoms.

Sleep disturbance and other co-occurring conditions in autistic children: A network approach to understanding their inter-relationships

Autistic children frequently have one or more co-occurring psychological, behavioral, or medical conditions. We examined relationships between child behaviors, sleep, adaptive behavior, autistic traits, mental health conditions, and health in autistic children using network analysis. Network analysis is hypothesis generating and can inform our understanding of relationships between multiple conditions and behaviors, directing the development of transdiagnostic treatments for co-occurring conditions. Participants were two child cohorts from the Autism Treatment Network registry: ages 2-5 years (n = 2372) and 6-17 years (n = 1553). Least absolute-shrinkage and selection operator (LASSO) regularized partial correlation network analysis was performed in the 2-5 years cohort (35 items) and the 6-17 years cohort (36 items). The Spinglass algorithm determined communities within each network. Two-step expected influence (EI2) determined the importance of network variables. The most influential network items were sleep difficulties (2 items) and aggressive behaviors for young children and aggressive behaviors, social problems, and anxious/depressed behavior for older children. Five communities were found for younger children and seven for older children. Of the top three most important bridge variables, night-waking/parasomnias and anxious/depressed behavior were in both age-groups, and somatic complaints and sleep initiation/duration were in younger and older cohorts respectively. Despite cohort differences, sleep disturbances were prominent in all networks, indicating they are a transdiagnostic feature across many clinical conditions, and thus a target for intervention and monitoring. Aggressive behavior was influential in the partial correlation networks, indicating a potential red flag for clinical monitoring. Other items of strong network importance may also be intervention targets or screening flags.

The interplay among burnout, and symptoms of depression, anxiety, and stress in Chinese clinical therapists

Burnout, depression, anxiety, and stress negatively impact the well-being and retention of healthcare professionals. The interplay of these symptoms is understudied. Utilizing network analysis, this study examined the interrelationships among these symptom clusters in clinical therapists in China. An anonymous survey was conducted among clinical therapists from 41 tertiary psychiatric hospitals in China. Burnout was assessed using the Maslach Burnout Inventory-Human Service Survey (MBI-HSS), while symptoms of depression, anxiety, and stress were assessed via the Depression, Anxiety, and Stress Scale-21 (DASS-21). Analyses were performed to identify central symptoms and bridge symptoms of this network. A total of 419 participants were included in this survey. The prevalence rate for burnout, depression, anxiety, and stress was 19.8%, 22.2%, 17.9%, and 8.6%, respectively. Network analysis indicated that stress symptoms had the highest expected influence values, closely followed by emotional exhaustion from MBI-HSS. Notably, emotional exhaustion emerged as the strongest bridge of expected influence. The stability of the expected influence and bridge expected influence was robust, with coefficients at 0.75. The study's findings underscore the importance of recognizing the central symptoms and bridge symptoms, which could lead to more effective early detection and intervention for burnout, depression, anxiety, and stress among clinical therapists.

Understanding Depression in Autism: The Role of Subjective Perception and Anterior Cingulate Cortex Volume

Background

The prevalence of depression is elevated in individuals with autism spectrum disorder (ASD) compared to the general population, yet the reasons for this disparity remain unclear. While social deficits central to ASD may contribute to depression, it is uncertain whether social interaction behavior themselves or individuals' introspection about their social behaviors are more impactful. Although the anterior cingulate cortex (ACC) and amygdala are frequently implicated in ASD, depression, and social functioning, it is unknown if these regions explain differences between ASD adults with and without co-occurring depression.

Methods

The present study contrasted observed vs. subjective perception of autism symptoms and social performances assessed with both standardized measures and a lab task, in 65 sex-balanced (52.24% male) autistic young adults. We also quantified ACC and amygdala volume with 7-Tesla structural neuroimaging to examine correlations with depression and social functioning.

Results

We found that ASD individuals with depression exhibited differences in subjective evaluations including heightened self-awareness of ASD symptoms, lower subjective satisfaction with social relations, and less perceived affiliation during the social interaction task, yet no differences in corresponding observed measures, compared to those without depression. Larger ACC volume was related to depression, greater self-awareness of ASD symptoms, and worse subjective satisfaction with social interactions. In contrast, amygdala volume, despite its association with clinician-rated ASD symptoms, was not related to depression.

Limitations

Due to the cross-sectional nature of our study, we cannot determine the directionality of the observed relationships. Additionally, we included only individuals with an IQ over 60 to ensure participants could complete the social task, which excluded many on the autism spectrum. We also utilized self-reported depression indices instead of clinically diagnosed depression, which may limit the comprehensiveness of the findings.

Conclusions

Our approach highlights the unique role of subjective perception of autism symptoms and social interactions, beyond the observable manifestation of social interaction in ASD, in contributing to depression, with the ACC playing a crucial role. These findings imply possible heterogeneity of ASD concerning co-occurring depression. Using neuroimaging, we were able to demarcate depressive phenotypes co-occurring alongside autistic phenotypes.

Contemporaneous symptom networks analysis in lymphoma patients during chemotherapy: protocol for a single-centre prospective cross-sectional study

BACKGROUND

Symptom networks offer a theoretical basis for developing personalised and precise symptom management strategies. However, symptom networks in lymphoma patients during chemotherapy have been rarely reported. This study intends to establish contemporaneous symptom networks in lymphoma patients during chemotherapy and explore the centrality indices and density in these symptom networks.

METHODS AND ANALYSIS

This is a single-centre prospective cross-sectional study. A total of 315 lymphoma patients admitted to the Lymphoma Department of Shanxi Bethune Hospital since 1 June 2024 will be selected as the study subjects. The patient-reported outcome measures of General Data Questionnaire and Lymphoma Symptom Assessment Scale will be assessed. R package will be used to construct a contemporaneous symptom network, explore the relationship between core and analysed symptoms and analyse the predictive role of network density on patient prognosis.

ETHICS AND DISSEMINATION

This study adheres to the principles of the Declaration of Helsinki and relevant ethical guidelines. Ethical approval has been obtained from Shanxi Bethune Hospital Ethics Committee (approval number: YXLL-2023-186). The final outcomes will be published in a peer-reviewed journal and disseminated through a conference.

Autistic Symptoms, Irritability, and Executive Dysfunctions: Symptom Dynamics from Multi-Network Models

Socio-cognitive difficulties in individuals with autism spectrum disorder (ASD) are heterogenuous and often co-occur with irritability symptoms, such as angry/grouchy mood and temper outbursts. However, the specific relations between individual symptoms are not well-represented in conventional methods analyzing aggregated autistic symptoms and ASD diagnosis. Moreover, the cognitive-behavioral mechanisms linking ASD to irritability are largely unknown. This study investigated the dynamics between autistic (Social Responsiveness Scale) and irritability (Affective Reactivity Index) symptoms and executive functions (Cambridge Neuropsychological Test Automated Battery) in a sample of children and adolescents with ASD, their unaffected siblings, and neurotypical peers (N = 345, aged 6-18 years, 78.6% male). Three complementary networks across the entire sample were computed: (1) Gaussian graphical network estimating the conditional correlations between symptom nodes; (2) Relative importance network computing relative influence between symptoms; (3) Bayesian directed acyclic graph estimating predictive directionality between symptoms. Networks revealed numerous partial correlations within autistic (rs = .07-.56) and irritability (rs = .01-.45) symptoms and executive functions (rs = -.83 to .67) but weak connections between clusters. This segregated pattern converged in all directed and supplementary networks. Plausible predictive paths were found between social communication difficulties to autism mannerisms and between "angry frequently" to "lose temper easily." Autistic and irritability symptoms are two relatively independent families of symptoms. It is unlikely that executive dysfunctions explain elevated irritability in ASD. Findings underscore the need for researching other mood and cognitive-behavioral bridge symptoms, which may inform individualized treatments for co-occurring irritability in ASD.

Stability and Validity of Self-Reported Depression and Anxiety in Autistic Youth

The aim of this study was to assess test-retest reliability and diagnostic validity of self-report instruments of depression and anxiety in autistic youth. Participants were 55 autistic youth aged 8-17 years presenting with depressive or anxiety symptoms. They were interviewed with the Kiddie Schedule for Affective Disorders and Schizophrenia for School-Age Children (K-SADS-PL) and completed the Children's Depression Inventory, Second Edition - Self Report Short (CDI 2:SR[S]) and the Revised Child Anxiety and Depression Scale (RCADS) twice, separated by a two-week interval. Test-retest reliability was measured with intraclass correlation coefficients (ICCs), and diagnostic validity was assessed using receiver operating characteristic (ROC) curves with the summary ratings on the K-SADS-PL as the criterion. The effect of participant characteristics was analyzed through a moderation analysis. Generalized anxiety (GAD) and social anxiety disorder (SOC) were the two most prevalent disorders in the sample. Test-retest reliability for most of the subscales was good (ICC = 0.74 - 0.87), with the exception of the RCADS obsessive-compulsive disorder (OCD) and GAD. The Adaptive Behavior conceptual score was a significant moderator of the reliability of the CDI 2:SR[S]. The ROC analysis suggested the RCADS SOC and the CDI 2:SR[S] to be good screening tools with inadequate specificity when appropriately sensitive cutoff scores are used. Optimal cutoff scores in this sample were lower than originally published. The findings suggest that autistic youth can provide stable reports of anxiety and depressive symptoms over time. Diagnostic validity varied according to the construct and instrument.

A network analysis of the interrelationships between depression, anxiety, insomnia and quality of life among fire service recruits

Background

Research on the mental health and quality of life (hereafter QOL) among fire service recruits after the end of the COVID-19 restrictions is lacking. This study explored the network structure of depression, anxiety and insomnia, and their interconnections with QOL among fire service recruits in the post-COVID-19 era.

Methods

This cross-sectional study used a consecutive sampling of fire service recruits across China. We measured the severity of depression, anxiety and insomnia symptoms, and overall QOL using the nine-item Patient Health Questionnaire (PHQ-9), seven-item Generalized Anxiety Disorder scale (GAD-7), Insomnia Severity Index (ISI) questionnaire, and World Health Organization Quality of Life-brief version (WHOQOL-BREF), respectively. We estimated the most central symptoms using the centrality index of expected influence (EI), and the symptoms connecting depression, anxiety and insomnia symptoms using bridge EI.

Results

In total, 1,560 fire service recruits participated in the study. The prevalence of depression (PHQ-9 ≥ 5) was 15.2% (95% CI: 13.5-17.1%), while the prevalence of anxiety (GAD-7 ≥ 5) was 11.2% (95% CI: 9.6-12.8%). GAD4 ("Trouble relaxing") had the highest EI in the whole network model, followed by ISI5 ("Interference with daytime functioning") and GAD6 ("Irritability"). In contrast, PHQ4 ("Fatigue") had the highest bridge EI values in the network, followed by GAD4 ("Trouble relaxing") and ISI5 ("Interference with daytime functioning"). Additionally, ISI4 "Sleep dissatisfaction" (average edge weight = -1.335), which was the central symptom with the highest intensity value, had the strongest negative correlation with QOL.

Conclusion

Depression and anxiety were important mental health issues to address among fire service recruits in the post-COVID-19 era in China. Targeting central and bridge symptoms identified in network analysis could help address depression and anxiety among fire service recruits in the post-COVID-19 era.

Comparison of network structures between autistic and non-autistic adults, and autism subgroups: A focus on demographic, psychological, and lifestyle factors

LAY ABSTRACT

There are large differences in the level of demographic, psychological, and lifestyle characteristics between autistic and non-autistic adults but also among autistic people. Our goal was to test whether these differences correspond to differences in underlying relationships between these characteristics-also referred to as network structure-to determine which characteristics (and relationships between them) are important. We tested differences in network structure in (1) autistic and non-autistic adults and (2) two previously identified subgroups of autistic adults. We showed that comparing networks of autistic and non-autistic adults provides subtle differences, whereas networks of the autism subgroups were similar. There were also no sex differences in the networks of the autism subgroups. Thus, the previously observed differences in the level of characteristics did not correspond to differences across subgroups in how these characteristics relate to one another (i.e. network structure). Consequently, a focus on differences in characteristics is not sufficient to determine which characteristics (and relationships between them) are of importance. Hence, network analysis provides a valuable tool beyond looking at (sub)group level differences. These results could provide hints for clinical practice, to eventually determine whether psychological distress, cognitive failures, and reduced quality of life in autistic adults can be addressed by tailored support. However, it is important that these results are first replicated before we move toward intervention or support.

Unpacking the overlap between Autism and ADHD in adults: A multi-method approach

The overlap between Autism and Attention-Deficit Hyperactivity Disorder (ADHD) is widely observed in clinical settings, with growing interest in their co-occurrence in neurodiversity research. Until relatively recently, however, concurrent diagnoses of Autism and ADHD were not possible. This has limited the scope for large-scale research on their cross-condition associations, further stymied by a dearth of open science practices in the neurodiversity field. Additionally, almost all previous research linking Autism and ADHD has focused on children and adolescents, despite them being lifelong conditions. Tackling these limitations in previous research, 5504 adults - including a nationally representative sample of the UK (Study 1; n = 504) and a large pre-registered study (Study 2; n = 5000) - completed well-established self-report measures of Autism and ADHD traits. A series of network analyses unpacked the associations between Autism and ADHD at the individual trait level. Low inter-item connectivity was consistently found between conditions, supporting the distinction between Autism and ADHD as separable constructs. Subjective social enjoyment and hyperactivity-impulsivity traits were most condition-specific to Autism and ADHD, respectively. Traits related to attention control showed the greatest Bridge Expected Influence across conditions, revealing a potential transdiagnostic process underlying the overlap between Autism and ADHD. To investigate this further at the cognitive level, participants completed a large, well-powered, and pre-registered study measuring the relative contributions of Autism and ADHD traits to attention control (Study 3; n = 500). We detected age- and sex-related effects, however, attention control did not account for the covariance between Autism and ADHD traits. We situate our findings and discuss future directions in the cognitive science of Autism, ADHD, and neurodiversity, noting how our open datasets may be used in future research.

Network Analysis of Internet Addiction, Online Social Anxiety, Fear of Missing Out, and Interpersonal Sensitivity among Chinese University Students

Background

Despite the growing prevalence of internet usage among young people, the relationships between internet addiction, online social anxiety, fear of missing out (FoMO), and interpersonal sensitivity remain uncertain, intricate, and multifaceted. To gain insight into the underlying psychological mechanisms, we employed network analysis to explore the interconnections between them. This endeavor may provide fresh opportunities for intervention and treatment.

Methods

In this study, 470 participants were assessed at age from 18 to 22 (M = 20.18 years, SD = 1.861) years. Network analysis was used to examine the connections between symptoms, and statistical measures were applied to assess the stability of the network model.

Results

Online social anxiety and interpersonal sensitivity had the strongest associations with other symptoms in the network, with "Evaluation anxiety" having the highest expected influence centrality, followed by "Privacy concern anxiety," "Need for approval," "Suspicion," and "vulnerability." The FoMO symptom, "Fear of missing information," had the strongest direct relation to internet addiction. "Evaluation anxiety" and "Fear of missing information" played a key role in bridging internet addiction and interpersonal sensitivity. Additionally, the structure distribution of edge weights had a significant difference between gender.

Conclusions

Our findings indicated that FoMO, interpersonal sensitivity, and online social anxiety likely play a significant role in the development and continuation of internet addiction. Interpersonal sensitivity seems to contribute to increased online social anxiety, FoMO, and the development of internet addiction, indicating that targeting these symptoms may help reduce negative online behavior and psychological burden.

Brief Report: Convergence and Discrepancy Between Self- and Informant-Reported Depressive Symptoms in Young Autistic Adults

PURPOSE

Autistic individuals exhibit elevated rates of depression; however, assessment is complicated by clinical presentations and limited validation in this population. Recent work has demonstrated the utility of the Beck Depression Inventory (BDI-II) in screening for depression in ASD. The current study extends this work by examining the convergence and divergence of self- and informant-reported depression in autistic (n = 258) and non-autistic (n = 255) young adults.

METHODS

Participants completed the BDI-II as a self-report measure of depression; informants completed the Achenbach Adult Behavior Checklist. Analyses probed for between-group differences in rates of depression symptoms, convergence between self- and informant-reported depression, and discrepancy between self- and informant-reported depression.

RESULTS

Results indicated significantly higher rates of depressive symptoms in the autistic group. Convergence was significant in both groups, with significantly greater agreement in the autistic group. There was differential divergence, with the autistic group reporting significantly lower scores relative to informants, and the non-autistic group reporting significantly higher scores relative to informants.

CONCLUSIONS

Consistent with prior reports, results suggest that depression rates are elevated in autism. Additionally, while the BDI-II may be adequate for screening depressive symptoms in speaking autistic young adults, eliciting information from a close adult informant provides valuable diagnostic information, due to clinically critical concerns about underreporting in this population. Although controlled in analyses, between-group differences in gender, age, race, and informant identity, and a predominantly White and non-Latinx sample, limit the generalizability of these results.

Depression and anxiety among caregivers of psychiatric patients during the late stage of the COVID-19 pandemic: A perspective from network analysis

BACKGROUND

Depressive and anxiety symptoms (depression and anxiety hereafter) are common among psychiatric patients and their caregivers during the COVID-19 pandemic. Network analysis is a novel method to assess the associations between psychiatric syndromes/disorders at the symptom level. This study examined depression and anxiety among caregivers of psychiatric inpatients during the late stage of the COVID-19 pandemic from the perspective of network analysis.

METHODS

A total of 1101 caregivers of psychiatric inpatients were included in this study. The severity of depression was assessed using the nine-item Patient Health Questionnaire (PHQ-9), while anxiety was assessed with the seven-item Generalized Anxiety Disorder Scale (GAD-7). The expected index (EI) and bridge EI index were used to identify the central and bridge symptoms, respectively. The stability of the network was evaluated via a case-dropping bootstrap procedure.

RESULTS

The prevalence of depression and anxiety were 32.4 % (95%CI: 29.7 %-35.3 %) and 28.0 % (95%CI: 25.4 %-30.7 %), respectively while the prevalence of comorbid depression and anxiety was 24.9 % (95%CI: 22.4 %-27.6 %). The most central symptom was "Fatigue", followed by "Trouble Relaxing" and "Restlessness". The highest bridge symptom was "Restlessness", followed by "Uncontrollable worry" and "Suicide ideation". The bootstrap test indicated that the whole network model was stable, and no network difference was detected between genders and between different education levels.

CONCLUSIONS

Depression, anxiety, and comorbid depression and anxiety were common among caregivers of psychiatric inpatients during the late stage of the COVID-19 pandemic. Central and bridge symptoms identified in this network analysis should be considered key target symptoms to address in caregivers of patients.

Neurocognitive Test Performance and Concussion-Like Symptom Reporting Among Adolescent Athletes With Self-Reported Autism on Preseason Assessments

OBJECTIVE

To examine baseline neurocognitive functioning and symptom reporting among adolescents with self-reported autism.

METHOD

Participants in this cross-sectional, observational study were 60,751 adolescents who completed preseason testing. There were 425 students (0.7%) who self-reported an autism spectrum disorder (ASD) diagnosis. Cognitive functioning was measured by Immediate Post-Concussion Assessment and Cognitive Testing and symptom ratings were obtained from the Post-Concussion Symptom Scale.

RESULTS

Groups differed significantly across all neurocognitive composites (p values <.002); effect size magnitudes for most differences were small, though among boys a noteworthy difference on visual memory and among girls differences on verbal memory and visual motor speed composites were noted. Among boys, the ASD group endorsed 21 of the 22 symptoms at a greater rate. Among girls, the ASD group endorsed 11 of the 22 symptoms at a greater rate. Some examples of symptoms that were endorsed at a higher rate among adolescents with self-reported autism were sensitivity to noise (girls: odds ratio, OR = 4.38; boys: OR = 4.99), numbness or tingling (girls: OR = 3.67; boys: OR = 3.25), difficulty remembering (girls: OR = 2.01; boys: OR = 2.49), difficulty concentrating (girls: OR = 1.82; boys: OR = 2.40), sensitivity to light (girls: OR = 1.82; boys: OR = 1.76), sadness (girls: OR = 1.72; boys: OR = 2.56), nervousness (girls: OR = 1.80; boys: OR = 2.27), and feeling more emotional (girls: OR = 1.79; boys: OR = 2.84).

CONCLUSION

Students with self-reported autism participating in organized sports likely experience a low degree of functional impairment, on average. If they sustain a concussion, their clinical management should be more intensive to maximize the likelihood of swift and favorable recovery.

Network Structure of Autism Spectrum Disorder Behaviors and Its Evolution in Preschool Children: Insights from a New Longitudinal Network Analysis Method

Network modeling of the social, communication and restrictive/repetitive behaviors (RRBs) included in the definition of Autism Spectrum Disorder was performed. The Autism Diagnostic Interview-Revised (ADI-R) assessed behaviors in 139 pre-school cases at two cross-sections that averaged 34.8 months apart. Cross-sectional networks were based on the correlation matrix of the ADI-R behavioral items and the "bootCross" method was developed and enabled the estimation of a longitudinal network. At both stages, RRB items/nodes formed a consistent peripheral cluster, while social and communication nodes formed a core cluster that diverged with time. These differences in the nature and evolution of the RRB and socio-communicative dimensions indicate that their inter-behavior dynamics are very different. The most central behaviors across stages are proposed as prime targets for efficient therapeutic intervention.

Infodemic Management for Social and Behavior Change: Youth Mobilization for Combating Disinformation During COVID-19

This study discusses an undergraduate elective university course as a notable case for youth mobilization in combatting misinformation during COVID-19 with positive social and behavior change outcomes of an indicative nature. Remote modality of the civic engagement course entailed students' voluntary work at partnering with society organizations specialized in new media technologies. Students' engagement with the civil society organizations' three different research and implementation projects as a form of voluntary work enabled them to mobilize in accordance with a vital dimension of infodemic management, namely engagement of communities to take positive action. Results derived from a mixed model research present that individual change observed on the students' knowledge, attitudes and practices as well as social change objectives of partnering institutions and the course are modestly positive, suggesting replication of adapted course design and implementation in relevant contexts.

Predictors of Treatment Response to a Community-Delivered Group Social Skills Intervention for Youth with ASD

Group social skills interventions (GSSIs) are among the most commonly used treatments for improving social competence in youth with ASD, however, results remain variable. The current study examined predictors of treatment response to an empirically-supported GSSI for youth with ASD delivered in the community (Ntotal=75). Participants completed a computer-based emotion recognition task and their parents completed measures of broad psychopathology, ASD symptomatology, and social skills. We utilized generalized estimating equations in an ANCOVA-of-change framework to account for nesting. Results indicate differential improvements in emotion recognition by sex as well as ADHD-specific improvements in adaptive functioning. Youth with both co-occurring anxiety and ADHD experienced iatrogenic effects, suggesting that SDARI may be most effective for youth with ASD without multiple co-occurring issues. Findings provide important directions for addressing variability in treatment outcomes for youth with ASD.

Mental health challenges faced by autistic people

Mental health challenges impede the well-being of autistic people. This Review outlines contributing neurodevelopmental and physical health conditions, rates and developmental trajectories of mental health challenges experienced by autistic people, as well as unique clinical presentations. A framework is proposed to consider four contributing themes to aid personalized formulation: social-contextual determinants, adverse life experiences, autistic cognitive features, and shared genetic and early environmental predispositions. Current evidence-based and clinical-knowledge-informed intervention guidance and ongoing development of support are highlighted for specific mental health areas. Tailored mental health support for autistic people should be neurodivergence-informed, which is fundamentally humanistic and compatible with the prevailing bio-psycho-social frameworks. The personalized formulation should be holistic, considering physical health and transdiagnostic neurodevelopmental factors, intellectual and communication abilities, and contextual-experiential determinants and their interplay with autistic cognition and biology, alongside resilience. Supporting family well-being is integral. Mutual empathic understanding is fundamental to creating societies in which people across neurotypes are all empowered to thrive.

A cross-sectional online survey of depression symptoms among New Zealand's Asian community in the first 10 months of the COVID-19 pandemic

The COVID-19 pandemic has elevated levels of distress and resulted in anti-Asian discrimination in many countries. We aimed to determine the 10-month prevalence of depression symptoms in Asian adults in New Zealand during the pandemic and to see if this was related to experience of racism. An online survey was conducted and a stratified sample of 402 respondents completed the brief Centre for Epidemiological Studies-Depression (CES-D) scale. Analyses included: descriptive statistics, depression scores by age/gender, factor analysis of the 10 item CES-D and partial correlation network analysis of CES-D items together with questions about experience of racism. Results show that half of the sample reported clinically significant symptoms of depression. Depression was higher among younger participants but there was no gender difference. Internal consistency was high (α = 0.85) for the CES-D which revealed a clear two-factor structure. Network analysis suggested that sleeping problems might be the bridge between experiences of racism and depression. The prevalence of low mood was high with clinically significant levels of depressive symptoms. Depression was higher in younger people and had a modest positive correlation with personal experience of racism.

Prevalence, correlates, and network analysis of depression and its associated quality of life among ophthalmology nurses during the COVID-19 pandemic

Background

Nurses in Ophthalmology Department (OD) had a high risk of infection during the novel coronavirus disease 2019 (COVID-19) pandemic. This study examined the prevalence, correlates, and network structure of depression, and explored its association with quality of life (QOL) in Chinese OD nurses.

Methods

Based on a cross-sectional survey, demographic and clinical data were collected. Depression was measured with the 9-item Self-reported Patient Health Questionnaire (PHQ-9), and QOL was measured using the World Health Organization Quality of Life Questionnaire-brief version (WHOQOL-BREF). Univariate analyses, multivariate logistic regression analyses, and network analyses were performed.

Results

Altogether, 2,155 OD nurses were included. The overall prevalence of depression among OD nurses was 32.71% (95%CI: 30.73-34.70%). Multiple logistic regression analysis revealed that having family or friends or colleagues who were infected (OR = 1.760, p = 0.003) was significantly associated with higher risk of depression. After controlling for covariates, nurses with depression reported lower QOL (F(1, 2,155) = 596.784, p < 0.001) than those without depression. Network analyses revealed that 'Sad Mood', 'Energy Loss' and 'Worthlessness' were the key central symptoms.

Conclusion

Depression was common among OD nurses during the COVID-19 pandemic. Considering the negative impact of depression on QOL and daily life, regular screening for depression, timely counselling service, and psychiatric treatment should be provided for OD nurses, especially those who had infected family/friends or colleagues. Central symptoms identified in network analysis should be targeted in the treatment of depression.

The role of anxiety and depression in suicidal thoughts for autistic and non-autistic people: A theory-driven network analysis

BACKGROUND

Autistic adults experience more frequent suicidal thoughts and mental health difficulties than non-autistic adults, but research has yet to explain how these experiences are connected. This study explored how anxiety and depression contribute to suicidal thoughts according to the Interpersonal Theory of Suicide for autistic and non-autistic adults.

METHODS

Participants (autistic adults n = 463, 61% female; non-autistic n = 342, 64% female) completed online measures of anxiety, depression, thwarted belonging, and perceived burdensomeness. Network analysis explored whether: (i) being autistic is a risk marker for suicide; and (ii) pathways to suicidal thoughts are consistent for autistic and non-autistic adults.

RESULTS

Being autistic connected closely with feeling like an outsider, anxiety, and movement, which connected to suicidal thoughts through somatic experiences, low mood, and burdensomeness. Networks were largely consistent for autistic and non-autistic people, but connections from mood symptoms to somatic and thwarted belonging experiences were absent for autistic adults.

CONCLUSION

Autistic people experience more life stressors than non-autistic people leading to reduced coping, low mood, and suicidal thoughts. Promoting belonging, reducing anxiety, and understanding the role of movement could inform suicide prevention for autistic people. Research should accurately capture autistic lived experience when modeling suicide to ensure suicide prevention meets autistic needs.

Childhood anxious/withdrawn behaviour and later anxiety disorder: a network outcome analysis of a population cohort

BACKGROUND

Several previous studies have identified a continuity between childhood anxiety/withdrawal and anxiety disorder (AD) in later life. However, not all children with anxiety/withdrawal problems will experience an AD in later life. Previous studies have shown that the severity of childhood anxiety/withdrawal accounts for some of the variability in AD outcomes. However, no studies to date have investigated how variation in features of anxiety/withdrawal may relate to continuity prognoses. The present research addresses this gap.

METHODS

Data were gathered as part of the Christchurch Health and Development Study, a 40-year population birth cohort of 1265 children born in Christchurch, New Zealand. Fifteen childhood anxiety/withdrawal items were measured at 7-9 years and AD outcomes were measured at various interviews from 15 to 40 years. Six network models were estimated. Two models estimated the network structure of childhood anxiety/withdrawal items independently for males and females. Four models estimated childhood anxiety/withdrawal items predicting adolescent AD (14-21 years) and adult AD (21-40 years) in both males and females.

RESULTS

Approximately 40% of participants met the diagnostic criteria for an AD during both the adolescent (14-21 years) and adult (21-40 years) outcome periods. Outcome networks showed that items measuring social and emotional anxious/withdrawn behaviours most frequently predicted AD outcomes. Items measuring situation-based fears and authority figure-specific anxious/withdrawn behaviour did not consistently predict AD outcomes. This applied across both the male and female subsamples.

CONCLUSIONS

Social and emotional anxious/withdrawn behaviours in middle childhood appear to carry increased risk for AD outcomes in both adolescence and adulthood.

Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review

BACKGROUND

Artificial intelligence (AI) is giving rise to a revolution in medicine and health care. Mental health conditions are highly prevalent in many countries, and the COVID-19 pandemic has increased the risk of further erosion of the mental well-being in the population. Therefore, it is relevant to assess the current status of the application of AI toward mental health research to inform about trends, gaps, opportunities, and challenges.

OBJECTIVE

This study aims to perform a systematic overview of AI applications in mental health in terms of methodologies, data, outcomes, performance, and quality.

METHODS

A systematic search in PubMed, Scopus, IEEE Xplore, and Cochrane databases was conducted to collect records of use cases of AI for mental health disorder studies from January 2016 to November 2021. Records were screened for eligibility if they were a practical implementation of AI in clinical trials involving mental health conditions. Records of AI study cases were evaluated and categorized by the International Classification of Diseases 11th Revision (ICD-11). Data related to trial settings, collection methodology, features, outcomes, and model development and evaluation were extracted following the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline. Further, evaluation of risk of bias is provided.

RESULTS

A total of 429 nonduplicated records were retrieved from the databases and 129 were included for a full assessment-18 of which were manually added. The distribution of AI applications in mental health was found unbalanced between ICD-11 mental health categories. Predominant categories were Depressive disorders (n=70) and Schizophrenia or other primary psychotic disorders (n=26). Most interventions were based on randomized controlled trials (n=62), followed by prospective cohorts (n=24) among observational studies. AI was typically applied to evaluate quality of treatments (n=44) or stratify patients into subgroups and clusters (n=31). Models usually applied a combination of questionnaires and scales to assess symptom severity using electronic health records (n=49) as well as medical images (n=33). Quality assessment revealed important flaws in the process of AI application and data preprocessing pipelines. One-third of the studies (n=56) did not report any preprocessing or data preparation. One-fifth of the models were developed by comparing several methods (n=35) without assessing their suitability in advance and a small proportion reported external validation (n=21). Only 1 paper reported a second assessment of a previous AI model. Risk of bias and transparent reporting yielded low scores due to a poor reporting of the strategy for adjusting hyperparameters, coefficients, and the explainability of the models. International collaboration was anecdotal (n=17) and data and developed models mostly remained private (n=126).

CONCLUSIONS

These significant shortcomings, alongside the lack of information to ensure reproducibility and transparency, are indicative of the challenges that AI in mental health needs to face before contributing to a solid base for knowledge generation and for being a support tool in mental health management.

Unraveling the central and bridge psychological symptoms of people living with HIV: A network analysis

Background

People living with HIV (PLWH) experience multiple psychological symptoms. Few studies have provided information on central and bridge psychological symptoms among PLWH. This information has implications for improving the efficiency and efficacy of psychological interventions. Our study aimed to identify the central and bridge psychological symptoms of PLWH and to explore the interconnectedness among symptoms and clusters.

Methods

Our study used data from the HIV-related Symptoms Monitoring Survey, a multisite, cross-sectional study conducted during 2017-2021. We used R to visualize the network of 16 symptoms and analyzed the centrality and predictability indices of the network. We further analyzed the bridge symptoms among the three symptom clusters.

Results

A total of 3,985 participants were included in the analysis. The results suggested that sadness had the highest strength (r S = 9.69) and predictability (70.7%) compared to other symptoms. Based on the values of bridge strength, feeling unsafe (r bs = 0.94), uncontrollable worry (r bs = 0.82), and self-abasement (r bs = 0.81) were identified as bridge symptoms. We also found a strong correlation between sadness and self-abasement (r = 0.753) and self-loathing and self-blame (r = 0.744).

Conclusion

We found that sadness was the central psychological symptom of PLWH, indicating that sadness was the center of the psychological symptom network from a mechanistic perspective and could be a target for intervention. Deactivating bridge symptoms, including "feeling unsafe," "self-abasement," and "uncontrollable worry," could be more effective in preventing symptom activation from spreading (e.g., one symptom activating another).

Practitioner Review: Attention-deficit hyperactivity disorder and autism spectrum disorder - the importance of depression

Young people with neurodevelopmental disorders, such as attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD), show high rates of mental health problems, of which depression is one of the most common. Given that depression in ASD and ADHD is linked with a range of poor outcomes, knowledge of how clinicians should assess, identify and treat depression in the context of these neurodevelopmental disorders is much needed. Here, we give an overview of the latest research on depression in young people with ADHD and ASD, including possible mechanisms underlying the link between ADHD/ASD and depression, as well as the presentation, assessment and treatment of depression in these neurodevelopmental disorders. We discuss the implications for clinicians and make recommendations for critical future research in this area.

Network Analysis of Well-Being Dimensions in Vaccinated and Unvaccinated Samples of University Students from Poland during the Fourth Wave of the COVID-19 Pandemic

Although numerous studies investigated the predictors of vaccination intention and decision, little is known about the relationship between vaccination and well-being. This study compares the physical and mental health dimensions among vaccinated and unvaccinated people. In a cross-sectional online survey, 706 university students from Poland (mean age of 23 years, 76% of women) participated in this study during the fourth pandemic wave (November–December 2021). Standardized questionnaires with a Likert response scale were included in the survey to measure spirituality, exposure to the COVID-19 pandemic, perceived physical health, stress, coronavirus-related PTSD, fear of COVID-19, anxiety, depression, and life satisfaction. Consistent with the fuzzy-trace theory, the unvaccinated sample was younger and scored significantly lower than the vaccinated group in exposure to COVID-19, perceived physical health, stress, coronavirus-related PTSD, fear of COVID-19, and depression, while higher in life satisfaction. The network analysis showed that mental health plays a crucial role in both groups, with the central influence of anxiety and stress on depression and life satisfaction. The message on vaccination to university students should focus on the benefits of vaccination in maintaining the status quo of good health and well-being. Campus prevention programs should primarily aim to reduce anxiety, stress, and negative emotions by teaching students coping strategies, relaxation techniques, and mindfulness.

Network analysis of depression, anxiety, insomnia and quality of life among Macau residents during the COVID-19 pandemic

BACKGROUND

Although the Coronavirus Disease 2019 (COVID-19) has greatly impacted individuals' mental health and quality of life, network analysis studies of associations between symptoms of common syndromes during the pandemic are lacking, particularly among Macau residents. This study investigated the network structure of insomnia, anxiety, and depression and explored their associations with quality of life in this population.

METHOD

This online survey was conducted in Macau between August 18 and November 9, 2020. Insomnia, anxiety, depressive symptoms, and quality of life were assessed with the Insomnia Severity Index, Generalized Anxiety Disorder Scale, Patient Health Questionnaire, and World Health Organization Quality of Life-brief version, respectively. Analyses were performed to identify central symptoms and bridge symptoms of this network and their links to quality of life.

RESULTS

975 participants enrolled in this survey. The prevalence of depressive, anxiety and insomnia symptoms were 38.5% (95% confidence interval (CI): 35.5%-41.5%), 28.8% (95%CI: 26.0%-31.7%), and 27.6% (95% CI: 24.8%-30.4%), respectively. "Sleep maintenance" had the highest expected influence centrality, followed by "Trouble relaxing", "Interference with daytime functioning", "Irritability", and "Fatigue". Five bridge symptoms were identified: "Sleep problems", "Restlessness", "Irritability", "Severity of sleep onset", and "Motor activity". The insomnia symptom, "Sleep dissatisfaction", had the strongest direct relation to quality of life.

CONCLUSION

Insomnia symptoms played a critical role in the distress symptom network regarding node and bridge centrality as well as associations with quality of life among Macau residents. Close attention to these symptoms may be critical to reducing risk and preventing exacerbations in common forms of distress in this population.

Depression, anxiety and worry in young Pacific adults in New Zealand during the COVID-19 pandemic

OBJECTIVE

To measure symptoms of anxiety, depression and hopelessness in a sample of young Pacific adults living in Auckland, New Zealand during the 2020/2021 COVID-19 pandemic and identify protective factors.

METHODS

Participants were 267 Pacific adults (58% female) who completed a survey online. Analyses included descriptive statistics, correlations, linear regression and symptom network analysis.

RESULTS

Around 25% of the sample scored in the range for moderate to severe anxiety and 10% for moderate to severe depression on standard measures. Almost 40% indicated that they found the first lockdown very stressful and 55% noted that some members of their family found it stressful. Only 16% worried about COVID-19 and their future quite a bit or constantly, while another 25% worried sometimes. Self-compassion and Pacific Identity had moderate, negative correlations, and Worry about COVID-19 had weak positive correlations, with anxiety, depression, hopelessness and perceived stress.

CONCLUSION

These results suggest that, while the prevalence of depression and anxiety are quite high among this population, fostering ethnic identity and self-compassion in Pacific children and adolescents might protect against developing depression and anxiety.

Intelligent Palliative Care Based on Patient-Reported Outcome Measures

CONTEXT

The growth of patient reported outcome measures data in palliative care provides an opportunity for machine learning to identify patterns in patient responses signifying different phases of illness.

OBJECTIVES

The study will explore if machine learning and network analysis can identify phases in patient palliative status through symptoms reported on the Integrated Palliative Care Outcome Scale (IPOS).

METHODS

A partly cross-sectional and partially longitudinal observational study was undertaken using the Australasian Karnofsky Performance Scale (AKPS); Integrated Palliative Care Outcome Scale (IPOS); Phase of Illness (POI). Patient palliative records (n = 1507, 65% stable, 20% unstable, 9% deteriorating, 2% terminal) from 804 adult patients enrolled in a New Zealand palliative care service were analysed using a combination of statistical, machine learning and network analysis techniques.

RESULTS

Data from IPOS showed considerable variation with phase. Also, network analysis showed clear associations between items by phase. Six machine learning techniques identified the most important variables for predicting possible transition between phases of illness. Network analysis for all patients showed that Poor Appetite and Loss of Energy were central IPOS items, with Loss of Energy linked to Drowsiness, Shortness of Breath and Lack of Mobility on the one hand, and Poor Appetite linked to Nausea, Vomiting, Constipation and Sore and Dry Mouth on the other.

CONCLUSION

These preliminary results, when coupled with the latest technological developments in mobile apps and wearable technology, could point the way to increased use of digital therapeutics in continuous palliative care monitoring.

Network analysis of depressive and anxiety symptoms in adolescents during and after the COVID-19 outbreak peak

BACKGROUND

This study examined the extent to which the network structure of anxiety and depression among adolescents identified during the peak of the COVID-19 pandemic could be cross-validated in a sample of adolescents assessed after the COVID-19 peak.

METHODS

Two cross-sectional surveys were conducted between February 20 and 27, 2020 and between April 11 and 19, 2020, respectively. Depressive and anxiety symptoms were assessed using the 20-item Center for Epidemiological Studies-Depression and 7-item Generalized Anxiety Disorder, respectively. Anxiety-depression networks of the first and second assessments were estimated separately using a sparse Graphical Gaussian Model combined with the graphical least absolute shrinkage and selection operator method. A Network Comparison Test was conducted to assess differences between the two networks.

RESULTS

The most central symptoms in the first and second survey networks were Depressed affect and Nervousness. Compared with connections in the first survey network, connections in the second survey network analysis between Relax-Nervousness-Depressed affect-Interpersonal problems (diff, contrast: second survey-first survey. diff=-0.04, P = 0.04; diff=-0.03, P = 0.03; diff=-0.03, P = 0.04), and Irritability-Somatic complaints (diff=-0.04, P = 0.02) were weaker while connections of Somatic complaints-Nervousness (diff=0.05, P<0.001), Somatic complaints-Depressed affect (diff=0.03, P = 0.009), and Irritability-Control worry-Restlessness (diff=0.02, P = 0.03; diff=0.05, P = 0.02) were stronger.

CONCLUSIONS

Depressed affect emerged as a robust central symptom and bridge symptom across Anxiety-Depression networks. Considering the negative impact of depression and anxiety on daily life, timely interventions targeting depressed affect should be implemented to reduce the co-occurrence of anxious and depressive symptoms among adolescents during the COVID-19 pandemic.

A systematic review of the rates of depression in autistic children and adolescents without intellectual disability

OBJECTIVES

Increasing evidence suggests that major depressive disorder (MDD) is highly prevalent in autism spectrum disorder (ASD). The current study is a systematic review of rates of depression in autistic children and adolescents, without intellectual disability.

DESIGN

Adhering to PRISMA guidelines, a total of 14,557 studies were identified through five databases (MEDLINE, EMBASE, Cinahl, ERIC, PsycINFO, and Web of Science).

METHODS

Articles were screened for inclusion and exclusion criteria and 10% double coded at each stage. Nineteen studies met criteria and were retained in the review.

RESULT

The reported rates of depression in autistic children and adolescents varied from 0% to 83.3%. We discuss these findings in relation to method of report (self/informant, interview/questionnaire), recruitment status (clinical/community recruited), and age (pre-pubertal/adolescent).

CONCLUSION

Rates of depression vary considerably across studies and do not show a particular pattern in relation to methodology, or age. Our research joins a crucial call to action from the research community for future research to improve the identification of depression in autism, which in turn will aid our understanding of the potentially different characterization and manifestation of depression in autism, to ultimately improve assessment and treatment of depression in autistic children and adolescents.

PRACTITIONER POINTS

Rates of depression in autistic children and adolescents vary and do not show a particular pattern in relation to methodology or age. Our research joins the call to action from the research community for future research to improve the identification of depression in autistic children and adolescents, which in turn will aid understanding of depression in autism, and ultimately improve assessment and treatment of depression in autistic children and young people. The development of new measures of depression, specifically designed with, and for, children and adolescents with autism, is warranted.

A network analysis of interoception, self-awareness, empathy, alexithymia, and autistic traits

Altered interoception has been consistently found in people with autism spectrum disorder (ASD), and this impairment may contribute to social cognitive dysfunctions. However, little is known regarding the intercorrelations between interoceptive sensibility, autistic, alexithymic, empathic, and self-related traits. We recruited 1360 non-clinical college students and adults to investigate the complex inter-relationship between these variables using network analysis. The resultant network revealed patterns connecting autistic traits to interoceptive sensibility, empathy, alexithymia, and self-awareness, with reasonable stability and test-retest consistency. The node of alexithymia exhibited the highest centrality and expected influence. As revealed by the network comparison test, networks constructed in high- and low-autistic subgroups were comparable in global strength and structure. Our findings suggested that alexithymia serves as an important node, bridging interoceptive deficits, self-awareness, and empathic impairments of autism spectrum disorder. The co-morbidity of alexithymia should be considered carefully in future studies of interoceptive impairments and social deficits in ASD.

ADHD and autism symptoms in youth: a network analysis

BACKGROUND

Previous research investigating the overlap between attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (henceforth, autism) symptoms in population samples have relied on latent variable modeling in which averaged scores representing dimensions were derived from observed symptoms. There are no studies evaluating how ADHD and autism symptoms interact at the level of individual symptom items.

METHODS

We aimed to address this gap by performing a network analysis on data from a school survey of children aged 6-17 years old (N = 7,405). ADHD and autism symptoms were measured via parent-report on the Swanson, Nolan, Pelham-IV questionnaire and the Childhood Autism Spectrum test, respectively.

RESULTS

A relatively low interconnectivity between ADHD and autism symptoms was found with only 10.06% of possible connections (edges) between one ADHD and one autism symptoms different than zero. Associations between ADHD and autism symptoms were significantly weaker than those between two symptoms pertaining to the same construct. Select ADHD symptoms, particularly those presenting in social contexts (e.g. 'talks excessively', 'does not wait turn'), showed moderate-to-strong associations with autism symptoms, but some were considered redundant to autism symptoms.

CONCLUSIONS

The present findings indicate that individual ADHD and autism symptoms are largely segregated in accordance with diagnostic boundaries corresponding to these conditions in children and adolescents from the community. These findings could improve our clinical conceptualization of ADHD and autism and guide advancements in diagnosis and treatment.

Sleep in Children with Autism Spectrum Disorder: A Narrative Review and Systematic Update

Purpose of Review

Sleep problems are a common comorbidity for children with autism spectrum disorder (ASD), and research in this area has a relatively long history. Within this review, we first outline historic patterns in the field of sleep and ASD. Second, we conducted a systematic update and coded these studies based on their alignment with historic patterns. Research on ASD and sleep over the past two decades has primarily focused on four principal areas: (1) documenting the prevalence and types of sleep problems; (2) sleep problem treatment options and efficacy; (3) how sleep problems are associated with other behavioral, contextual, or biological elements; and (4) the impact of child sleep problems on families and care providers. The systematic update in this paper includes empirical studies published between 2018 and 2021 with terms for sleep and ASD within the title, keywords, or abstract.

Recent Findings

In sum, 60 studies fit the inclusion/exclusion criteria and most fit within the historic patterns noted above. Notable differences included more global representation in study samples, studies on the impacts of COVID-19, and a growing body of work on sleep problems as an early marker of ASD. The majority of studies focus on correlates of sleep problems noting less optimal behavioral, contextual, and biological elements are associated with sleep problems across development for children with ASD.

Summary

Recommendations for future directions include continued expansion of global and age representation across samples, a shift toward more treatment and implementation science, and studies that inform our mechanistic understanding of how sleep and ASD are connected.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40675-022-00234-5.

A blind spot in mental healthcare? Psychotherapists lack education and expertise for the support of adults on the autism spectrum

Most adults on the autism spectrum have co-occurring mental health conditions, creating a high demand for mental health services – including psychotherapy – in autistic adults. However, autistic adults have difficulties accessing mental health services. The most-reported barriers to accessing treatment are therapists’ lack of knowledge and expertise surrounding autism, as well as unwillingness to treat autistic individuals. This study was conducted by a participatory autism research group and examined 498 adult-patient psychotherapists on knowledge about autism and self-perceived competency to diagnose and treat autistic patients without intellectual disability compared to patients with other diagnoses. Psychotherapists rated their education about autism in formal training, and competency in the diagnosis and treatment of patients with autism, lowest compared to patients with all other diagnoses surveyed in the study, including those with comparable prevalence rates. Many therapists had misconceptions and outdated beliefs about autism. Few had completed additional training on autism, but the majority were interested in receiving it. Greater knowledge about autism was positively linked to openness to accept autistic patients. The results point to an alarming gap in knowledge necessary for adequate mental health care for individuals with autism.

Identifying Central Symptoms and Bridge Pathways Between Autism Spectrum Disorder Traits and Suicidality Within an Active Duty Sample

OBJECTIVE

This study employed network analysis to characterize central autism spectrum disorder (ASD) traits and suicide symptoms within an active duty military sample as well as to identify symptoms that may bridge between ASD traits and suicidality (i.e., suicidal ideation and behaviors).

METHOD

Participants were active duty U.S. military service members (N = 287). Autism spectrum traits, suicidality, depression, and suicide related constructs were assessed online via self-report.

RESULTS

Within the combined ASD trait-suicidality network, suicide rumination, suicide behaviors, and depression had the highest strength centrality. The most central bridge symptoms between ASD and suicidality were thwarted belongingness, social skills deficits, and depressive symptoms.

CONCLUSIONS

Social skills deficits and thwarted belongingness may function as a meaningful bridge between ASD symptoms and suicidality within active duty members. Individuals with ASD symptoms who additionally present with high levels of thwarted belongingness and/or considerable social skills deficits may be at increased risk for suicidality.HIGHLIGHTSWithin an ASD-suicidality network, social skills deficits, low belonging, and depression had the greatest bridge strength.Although low belonging emerged as a bridge symptom, perceived burdensomeness did not.Suicide rumination, suicide behaviors, and depression were the most central symptom in an ASD-suicidality network.Symptoms related to social skills deficits may connect ASD traits and suicidality.

Investigating the structure of trait rumination in autistic adults: A network analysis

LAY ABSTRACT

Autistic adults are substantially more likely to develop depression than individuals in the general population, and recent research has indicated that certain differences in thinking styles associated with autism may play a role in this association. Rumination, the act of thinking about the same thing over and over without a functional outcome, is a significant risk factor for depression in both autistic and non-autistic adults. However, little is known about how different kinds of rumination relate to each other and to depressive symptoms in the autistic population specifically. To fill this gap in knowledge, we recruited a large online sample of autistic adults, who completed questionnaire measures of both the tendency to ruminate and symptoms of depression. By examining the interacting network of rumination and depression symptoms, this study was able to identify particular aspects of rumination-such as thinking repetitively about one's guilty feelings or criticizing oneself-that may be particularly important in maintaining these harmful thought patterns in autistic adults. Although further study is needed, it is possible that the symptoms identified as most "influential" in the network may be particularly good targets for future interventions for mood and anxiety disorders in the autistic population.

Connecting brain and behavior in clinical neuroscience: A network approach

In recent years, there has been an increase in applications of network science in many different fields. In clinical neuroscience and psychopathology, the developments and applications of network science have occurred mostly simultaneously, but without much collaboration between the two fields. The promise of integrating these network applications lies in a united framework to tackle one of the fundamental questions of our time: how to understand the link between brain and behavior. In the current overview, we bridge this gap by introducing conventions in both fields, highlighting similarities, and creating a common language that enables the exploitation of synergies. We provide research examples in autism research, as it accurately represents research lines in both network neuroscience and psychological networks. We integrate brain and behavior not only semantically, but also practically, by showcasing three methodological avenues that allow to combine networks of brain and behavioral data. As such, the current paper offers a stepping stone to further develop multi-modal networks and to integrate brain and behavior.

Network analysis of COVID-19-related PTSD symptoms in China: the similarities and differences between the general population and PTSD sub-population

BACKGROUND AND OBJECTIVES

Prevalent Post-traumatic Stress Disorder (PTSD) negatively affected individuals during the COVID-19 pandemic. Using network analyses, this study explored the construct of PTSD symptoms during the COVID-19 pandemic in China to identify similarities and differences in PTSD symptom network connectivity between the general Chinese population and individuals reporting PTSD.

METHODS

We conducted an online survey recruiting 2858 Chinese adults. PTSD symptoms were measured using the PCL-5 and PTSD was determined according to the DSM-5 criteria.

RESULTS

In the general population, self-destructive/reckless behaviours were on average the most strongly connected to other PTSD symptoms in the network. The five strongest positive connections were found between 1) avoidance of thoughts and avoidance of reminders, 2) concentration difficulties and sleep disturbance, 3) negative beliefs and negative trauma-related emotions, 4) irritability/anger and self-destructive/reckless behaviours, and 5) hypervigilance and exaggerated startle responses. Besides, negative connections were found between intrusive thoughts and trauma-related amnesia and between intrusive thoughts and self-destructive/reckless behaviours. Among individuals reporting PTSD, symptoms such as flashbacks and self-destructive/reckless behaviours were on average most strongly connected to other PTSD symptoms in the network. The five strongest positive connections were found between 1) concentration difficulty and sleep disturbance, 2) intrusive thoughts and emotional cue reactivity, 3) negative beliefs and negative trauma-related emotions, 4) irritability/anger and self-destructive/reckless behaviour, and 5) detachment and restricted affect. In addition, a negative connection was found between intrusive thoughts and self-destructive/reckless behaviours.

CONCLUSION

Our results indicate similarly positive connections between concentration difficulty and sleep disturbance, negative beliefs and negative trauma-related emotions, and irritability/anger and self-destructive/reckless behaviours in the general and PTSD-reported populations. We argue that self-destructive/reckless behaviours are a core symptom of COVID-19 related PTSD, worthy of more attention in future psychiatric programmers.

Network Analysis of Internet Addiction Symptoms Among a Clinical Sample of Japanese Adolescents with Autism Spectrum Disorder

In the present study, we employed network analysis that conceptualizes internet addiction (IA) as a complex network of mutually influencing symptoms in 108 adolescents with autism spectrum disorder (ASD) to examine the network architecture of IA symptoms and identify central/influential symptoms. Our analysis revealed that defensive and secretive behaviors and concealment of internet use were identified as central symptoms in this population, suggesting that mitigating these symptoms potentially prevent the development and/or maintenance of IA in adolescents with ASD. Providing adolescents and their caregivers with psychoeducation on the role of central symptoms above in IA can be a salient intervention. Doing so may facilitate nonconflicting conversations between them about adolescents' internet use and promote more healthy adolescents' internet use behavior.

The Differential Role of Central and Bridge Symptoms in Deactivating Psychopathological Networks

The network model of psychopathology suggests that central and bridge symptoms represent promising treatment targets because they may accelerate the deactivation of the network of interactions between the symptoms of mental disorders. However, the evidence confirming this hypothesis is scarce. This study re-analyzed a convenience sample of 51 cross-sectional psychopathological networks published in previous studies addressing diverse mental disorders or clinically relevant problems. In order to address the hypothesis that central and bridge symptoms are valuable treatment targets, this study simulated five distinct attack conditions on the psychopathological networks by deactivating symptoms based on two characteristics of central symptoms (degree and strength), two characteristics of bridge symptoms (overlap and bridgeness), and at random. The differential impact of the characteristics of these symptoms was assessed in terms of the magnitude and the extent of the attack required to achieve a maximum impact on the number of components, average path length, and connectivity. Only moderate evidence was obtained to sustain the hypothesis that central and bridge symptoms constitute preferential treatment targets. The results suggest that the degree, strength, and bridgeness attack conditions are more effective than the random attack condition only in increasing the number of components of the psychopathological networks. The degree attack condition seemed to perform better than the strength, bridgeness, and overlap attack conditions. Overlapping symptoms evidenced limited impact on the psychopathological networks. The need to address the basic mechanisms underlying the structure and dynamics of psychopathological networks through the expansion of the current methodological framework and its consolidation in more robust theories is stressed.