ADHD and autism symptoms in youth: a network analysis

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.

Cumulative comorbidity between neurodevelopmental, internalising, and externalising disorders in childhood: a network approach

Cumulative comorbidity of mental disorders is common, but the extent and patterns of comorbid psychopathology in childhood are not well established. The current study aimed to elucidate the emergent patterns of cumulative mental disorder comorbidity in children using network analysis of diagnoses recorded between birth and age 12 years. Participants were 90,269 children (mean age 12.7 years; 51.8% male) within the New South Wales Child Development Study (NSW-CDS)-a longitudinal record-linkage cohort study of Australian children born in NSW between 2002 and 2005. Binary indicators for eight types of mental disorder were derived from administrative health records. Patterns of conditional association between mental disorders were assessed utilising network analysis. Of 90,269 children, 2268 (2.5%) had at least one mental disorder by age 12 years; of the 2268 children who had at least one mental disorder by age 12 years, 461 (20.3%) were diagnosed with two or more different disorders out of the eight disorder types included in analyses. All disorders were either directly or indirectly interconnected, with childhood affective and emotional disorders and developmental disorders being most central to the network overall. Mental disorder nodes aggregated weakly (modularity = 0.185) into two communities, representative of internalising and externalising disorders, and neurodevelopmental and sleep disorders. Considerable sex differences in the structure of the mental disorder comorbidity networks were also observed. Developmental and childhood affective and emotional disorders appear to be key to mental disorder comorbidity in childhood, potentially reflecting that these disorders share symptoms in common with many other disorders.

Prevalence and network analysis of internet addiction, depression and their associations with sleep quality among commercial airline pilots: A national survey in China

OBJECTIVE

Airline pilots are members of a unique occupational group that is often confronted with sleep routine disruptions, yet relatively few studies have examined their mental health status. This study assessed the prevalence and network structure of internet addiction, depression and sleep quality problems in commercial airline pilots.

METHOD

A total of 7055 airline pilots were included in analyses. Internet addiction and depression were measured with the Internet Addiction Test (IAT) and 9-item Patient Health Questionnaire (PHQ-9), respectively. Sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI). The network model was constructed based on an Ising model and its association with sleep quality was evaluated using a flow procedure.

RESULTS

Internet addiction, depression and sleep quality were common among airline pilots. The prevalence of internet addiction was 8.0 % (95 % CI: 7.3-8.6 %), while the rates of depression and poor sleep quality were 23.3 % (95 % CI: 22.3-24.2 %) and 33.0 % (95 % CI: 31.9-34.1 %), respectively. In the depression and internet addiction network model, "Fatigue" (PHQ4; Expected Influence (EI): 2.04) and "Depressed/moody/nervous only while being offline" (IAT20; EI: 1.76) were most central symptoms while "Fatigue" (PHQ4; Bridge EI: 1.30) was also the most important bridge symptom. The flow network model of sleep quality with internet addiction and depression showed that "Appetite" (PHQ5) had the strongest positive association with poor sleep quality.

CONCLUSION

Internet addiction, depression and sleep quality were common among airline pilots and warrant regular screening and timely treatment. Strategies to improve sleep hygiene may be useful in preventing onsets or exacerbations in depression and internet addiction among airline pilots.

The distribution of parent-reported attention-deficit/hyperactivity disorder and subclinical autistic traits in children with and without an ADHD diagnosis

Background

Autistic traits are often reported to be elevated in children diagnosed with attention-deficit/hyperactivity disorder (ADHD). However, the distribution of subclinical autistic traits in children with ADHD has not yet been established; knowing this may have important implications for diagnostic and intervention processes. The present study proposes a preliminary model of the distribution of parent-reported ADHD and subclinical autistic traits in two independent samples of Australian children with and without an ADHD diagnosis.

Methods

Factor mixture modelling was applied to Autism Quotient and Conners' Parent Rating Scale - Revised responses from parents of Australian children aged 6-15 years who participated in one of two independent studies.

Results

A 2-factor, 2-class factor mixture model with class varying factor variances and intercepts demonstrated the best fit to the data in both discovery and replication samples. The factors corresponded to the latent constructs of 'autism' and 'ADHD', respectively. Class 1 was characterised by low levels of both ADHD and autistic traits. Class 2 was characterised by high levels of ADHD traits and low-to-moderate levels of autistic traits. The classes were largely separated along diagnostic boundaries. The largest effect size for differences between classes on the Autism Quotient was on the Social Communication subscale.

Conclusions

Our findings support the conceptualisation of ADHD as a continuum, whilst confirming the utility of current categorical diagnostic criteria. Results suggest that subclinical autistic traits, particularly in the social communication domain, are unevenly distributed across children with clinically significant levels of ADHD traits. These traits might be profitably screened for in assessments of children with high ADHD symptoms and may also represent useful targets for intervention.

Prevalence and network structure of depression, insomnia and suicidality among mental health professionals who recovered from COVID-19: a national survey in China

Psychiatric syndromes are common following recovery from Coronavirus Disease 2019 (COVID-19) infection. This study investigated the prevalence and the network structure of depression, insomnia, and suicidality among mental health professionals (MHPs) who recovered from COVID-19. Depression and insomnia were assessed with the Patient Health Questionnaire (PHQ-9) and Insomnia Severity Index questionnaire (ISI7) respectively. Suicidality items comprising suicidal ideation, suicidal plan and suicidal attempt were evaluated with binary response (no/yes) items. Network analyses with Ising model were conducted to identify the central symptoms of the network and their links to suicidality. A total of 9858 COVID-19 survivors were enrolled in a survey of MHPs. The prevalence of depression and insomnia were 47.10% (95% confidence interval (CI) = 46.09-48.06%) and 36.2% (95%CI = 35.35-37.21%), respectively, while the overall prevalence of suicidality was 7.8% (95%CI = 7.31-8.37%). The key central nodes included "Distress caused by the sleep difficulties" (ISI7) (EI = 1.34), "Interference with daytime functioning" (ISI5) (EI = 1.08), and "Sleep dissatisfaction" (ISI4) (EI = 0.74). "Fatigue" (PHQ4) (Bridge EI = 1.98), "Distress caused by sleep difficulties" (ISI7) (Bridge EI = 1.71), and "Motor Disturbances" (PHQ8) (Bridge EI = 1.67) were important bridge symptoms. The flow network indicated that the edge between the nodes of "Suicidality" (SU) and "Guilt" (PHQ6) showed the strongest connection (Edge Weight= 1.17, followed by "Suicidality" (SU) - "Sad mood" (PHQ2) (Edge Weight = 0.68)). The network analysis results suggest that insomnia symptoms play a critical role in the activation of the insomnia-depression-suicidality network model of COVID-19 survivors, while suicidality is more susceptible to the influence of depressive symptoms. These findings may have implications for developing prevention and intervention strategies for mental health conditions following recovery from COVID-19.

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.

The role of stimulant washout status in functional connectivity of default mode and fronto-parietal networks in children with neurodevelopmental conditions

BACKGROUND

Stimulant medication is the primary pharmacological treatment for attention dysregulation and is commonly prescribed for children with Attention-Deficit/Hyperactivity Disorder (ADHD) and Autism. Neuroimaging studies of these groups commonly use a 24-48-hour washout period to mediate the effects of stimulant medication on functional connectivity (FC) metrics. However, the impact of washout on functional connectivity has received limited study.

METHODS

We used fMRI data from participants with diagnosis of Autism and ADHD (and an off stimulant control) from the Adolescent Brain and Cognitive Development (ABCD) and Autism Brain Imaging Data Exchange (ABIDE) databases to explore the effect of simulant washout on FC. Connectivity within and between the default mode (DMN) and fronto-parietal networks (FPN) was examined, as these networks have previously been implicated in attention dysregulation and associated with stimulant medication usage. For each diagnostic group, we assessed effects in interconnectivity between DMN and FPN, intraconnectivity within DMN, and intraconnectivity within FPN.

RESULTS

We found no significant effect of medication status in intra- and inter-connectivity of the DMN and the FPN in either diagnostic group.

IMPLICATIONS

Our findings suggest that more information is needed about the effect of stimulant medication, and washout, on the FC of attention networks in clinical populations.

Comorbidity of autism spectrum and attention deficit/hyperactivity disorder symptoms and their associations with 1-year mental health outcomes in adolescents and young adults

Autism spectrum (ASD) and attention deficit/hyperactivity disorders (ADHD) share genetic, neurological, and behavioural features. However, related research in Asia is limited. We collected self-reported ASD and ADHD symptoms from 2186 Hong Kong adolescents and young adults aged 15-24 years, among whom, 1200 provided 1-year data on mental health-related outcomes. Comparative and network analyses were performed. Rating scale cutoff scores were used to divide participants into ASD, ADHD, comorbid, and control groups. The prevalence rates of ASD, ADHD, and comorbidities in Hong Kong were 13.3 %, 10.6 %, and 2.7 %, respectively. Compared with the control group, the comorbid group experienced more psychotic-like experiences (PLEs), the ASD group had poorer functioning, and the ADHD group had higher depression and anxiety symptoms and a lower quality of life after 1 year. The ability to switch attention, preference for routines and difficulty with change, and problems with organisation and planning were positively associated with depressive symptoms, forgetfulness and working memory issues with anxiety symptoms, and heightened sensory input and difficulties in sustaining attention and task completion with PLEs after 1 year. Our findings provide insight into support strategies to address the needs of young Asians to improving their well-being and long-term outcomes.

Depression and anxiety among Macau residents during the COVID-19 outbreak: A network analysis perspective

Background

The 2019 novel coronavirus disease (COVID-19) outbreak affected people's lifestyles and increased their risk for depressive and anxiety symptoms (depression and anxiety, respectively hereafter). We assessed depression and anxiety in residents of Macau during "the 6.18 COVID-19 outbreak" period and explored inter-connections of different symptoms from the perspective of network analysis.

Methods

In this cross-sectional study, 1,008 Macau residents completed an online survey comprising the nine-item Patient Health Questionnaire (PHQ-9) and seven-item Generalized Anxiety Disorder Scale (GAD-7) to measure depression and anxiety, respectively. Central and bridge symptoms of the depression-anxiety network model were evaluated based on Expected Influence (EI) statistics, while a bootstrap procedure was used to test the stability and accuracy of the network model.

Results

Descriptive analyses indicated the prevalence of depression was 62.5% [95% confidence interval (CI) = 59.47-65.44%], the prevalence of anxiety was 50.2% [95%CI = 47.12-53.28%], and 45.1% [95%CI = 42.09-48.22%] of participants experienced comorbid depression and anxiety. "Nervousness-Uncontrollable worry" (GADC) (EI = 1.15), "Irritability" (GAD6) (EI = 1.03), and "Excessive worry" (GAD3) (EI = 1.02) were the most central symptoms, while "Irritability" (GAD6) (bridge EI = 0.43), "restlessness" (GAD5) (bridge EI = 0.35), and "Sad Mood" (PHQ2) (bridge EI = 0.30) were key bridge symptoms that emerged in the network model.

Conclusion

Nearly half of residents in Macau experienced comorbid depression and anxiety during the 6.18 COVID-19 outbreak. Central and bridge symptoms identified in this network analysis are plausible, specific targets for treatment and prevention of comorbid depression and anxiety related to this outbreak.

Convergence mechanism of mindfulness intervention in treating attention deficit hyperactivity disorder: Clues from current evidence

This paper reviews the underlying evidence for various aspects of the convergence mechanism of mindfulness intervention in attention deficit hyperactivity disorder (ADHD). There may be compatibility among various ADHD remission models and the therapeutic mechanism of mindfulness intervention in ADHD may be mainly via the convergence mechanism. However, neuroimaging-based analysis of the mechanisms of mindfulness intervention in treating ADHD is lacking. Differences in the efficacy of various subtypes of mindfulness intervention, and corresponding specific imaging changes need further investigation. Future research may focus on the neuroimaging features of specific mindfulness intervention subtypes.

Diagnosis of children with attention-deficit/hyperactivity disorder (ADHD) comorbid autistic traits (ATs) by applying quantitative magnetic resonance imaging techniques

OBJECTIVE

To explore the feasibility of applying quantitative magnetic resonance imaging techniques for the diagnosis of children with attention-deficit/hyperactivity disorder (ADHD) comorbid autistic traits (ATs).

METHODS

A prospective study was performed by selecting 56 children aged 4-5 years with ADHD-ATs as the study group and 53 sex- and age-matched children with ADHD without ATs as the control group. All children underwent magnetic resonance scans with enhanced T2^*- weighted magnetic resonance angiography (ESWAN), 3D-PCASL, and 3D-T1 sequences. Iron content and cerebral blood flow parameters were obtained via subsequent software processing, and the parameter values in particular brain regions in both groups were compared and analyzed to determine the characteristics of these parameters in children with ADHD-ATs.

RESULTS

Iron content and cerebral blood flow in the frontal lobe, temporal lobe, hippocampus, and caudate nucleus of children with ADHD-ATs were lower than those of children with ADHD without ATs (p < 0.05). Iron content and CBF values in the frontal lobe, temporal lobe and caudate nucleus could distinguish children with ADHD-ATs from those without ATs (AUC > 0.5, p < 0.05).

CONCLUSIONS

Quantitative magnetic resonance techniques could distinguish children with ADHD-ATs.

TRIAL REGISTRATION

This study protocol was registered at the Chinese clinical trial registry (ChiCTR2100046616).

Using network analysis to improve understanding and utility of the 10-item Autism-Spectrum Quotient

The 10-item Autism-Spectrum Quotient (AQ10) is a measure of autistic traits used in research and clinical practice. Recently, the AQ10 has garnered critical attention, with research questioning its psychometric properties and clinical cutoff value. To help inform the utility of the measure, we conducted the first network analysis of the AQ10, with a view to gain a better understanding of its individual items. Using a large dataset of 6,595 participants who had completed the AQ10, we found strongest inter-subscale connections between communication, imagination, and socially relevant items. The nodes with greatest centrality concerned theory of mind differences. Together, these findings align with cognitive explanations of autism and provide clues about which AQ10 items show greatest utility for informing autism-related clinical practice.