Parenting practices and child irritability across diverse racial\u2013ethnic backgrounds: A temporal network analysis

Children with irritability are vulnerable to negative parenting. Negative parenting and parenting stress, in turn, may exacerbate and predict increases in child symptoms (Kiff et al., 2011). Cross-cultural similarities emerge in mothers’ self-efficacy about anger regulation (Di Giunta et al., 2020). However, little is known about cross-cultural similarities and differences in the associations between child irritability and parenting practices. To examine whether there are differences in maternal parenting practices and ideologies across different cultures and investigate their correlations with irritability symptoms in early childhood. N = 3003 mothers from the Fragile Families study at Wave 4 and their report of their children (age 5). Cross-Sectional Network Analysis in R with the following nodes: Aggravation in Parenting (from Child Development Supplement of Panel Study of Income Dynamics), Child Irritability (Child Behavior Checklist-Irritability items), Parent-Child Conflict Tactics Scale: Non-Violent Discipline, Psychological Aggression, Physical Assault, Neglect. Cultural Attachment: attachment to racial/ethnic heritage and participation in cultural practicesResults provide initial evidence of both similarities and differences in parenting practices and ideologies in different cultures. While parental psychological aggression and neglect are associated with child irritability across races/ethnicities, parenting stress is associated with child irritability in White participants only. It is important to better understand parenting practices and parenting related stress, and their specific impact on child outcomes across cultures. Further directions include longitudinal network studies on parenting and child irritability as well as paternal parenting practices across cultures.

Developmental Trajectories of Irritability and Bidirectional Associations With Maternal Depression

Chronic child irritability is a core dimension of externalizing behavior associated with significant short- and long-term risk. Mothers of children with externalizing disorders show considerable variability in mental health, parenting practices, and family functioning, yet links between these parental characteristics and elevated levels of child irritability and impairment remain unclear. Latent profile analysis was used to identify parent profiles based on self-report data from 385 mothers of clinic-referred children with disruptive behavior aged 6-12years. Three profiles emerged: Mental Health Difficulties, Relatively Low Difficulties, and High Difficulties, varying in positive parenting, discipline practices, supervision, family functioning, and maternal mental health. The High Difficulties and Mental Health Difficulties groups showed the highest mother- and teacher-reported child irritability and the highest mother-reported child functional impairment. Child irritability did not mediate associations between parental profile membership and functional impairment. Findings highlight the importance of identifying parental and family-level factors as treatment targets related to elevated child irritability.

Irritability can be a prominent characteristic of various psychopathologies, including childhood psychiatric and neurodevelopmental disorders. Genetic, environmental, and prenatal factors influence the development and progression of childhood irritability. This review aims to highlight the biological and behavioral pathways associated with childhood irritability, examine the relationship between irritability and childhood psychopathology, identify the existing gap in the literature, review these connections, and provide guidance for future research. Articles published on PubMed and Google Scholar from 2000 to 2023 were reviewed using a combination of search terms such as "childhood irritability,” “maternal stress,” and “prenatal stress.” The literature search yielded roughly 2,800 articles using the predefined search terms, of which 65 were deemed relevant to this scoping review. The articles reviewed identified a link between prenatal stress, childhood irritability, and the development of adult psychopathology. Pathological irritability and its emerging connection to maternal stress pose a risk factor for developing neurodevelopmental disorders and psychopathology in the pediatric population. Much of the current literature addresses the biopathophysiologic pathway linking maternal stress to childhood irritability in offspring. However, no interventional research studies have reported on how to interrupt this pathway or mitigate its progression with predictable outcomes. Therefore, identifying a critical period during childhood or adolescence when the progression from childhood irritability to adult psychopathology can be recognized may reduce the risk of developing neurodevelopmental disorders or psychopathology in childhood and throughout life.

Environmental sensitivity moderates the longitudinal effect of fathers' positive parenting on mental disorders in Chinese adolescents

BackgroundChildhood irritability and harsh parenting are associated with youth suicide attempts. Parents’ harsh reactions have been associated with children’s irritable behavior. While studies have shown individual associations of irritability and parenting behaviors with suicide risk, few have considered these factors jointly. We aimed to identify profiles of children based on irritability and parenting during childhood and examine their associations with youth suicide attempt.MethodsParticipants (N = 1626) were from the Québec Longitudinal Study of Child Development. Mothers reported on childhood irritability, harsh parenting, and positive parenting between ages 3.5 and 8; youth self-reported suicide attempt between ages 13 and 23.ResultsWe identified four profiles based on the joint development of irritability and parenting during childhood: (1) low irritability, low harsh parenting, and high positive parenting (30.3%); (2) moderate irritability, moderate harsh parenting, and high positive parenting (28.4%); (3) moderate irritability, moderate harsh parenting, and low positive parenting (26.6%); and (4) high irritability, high harsh parenting, and low positive parenting (14.8%). In logistic regression analyses, only children in the high irritability, high harsh parenting, and low positive parenting profile had higher odds of attempting suicide (OR = 2.51; 95% CI = 1.55–4.09) compared to those in the low irritability, low harsh parenting, and high positive parenting profile. This association remained significant (OR = 1.80; 95% CI = 1.03–3.15) in models adjusting for covariates.ConclusionChildren with chronically high irritability were also those experiencing the harshest parenting and the least positive parenting, as well as those most at risk of suicide attempt. Targeting both child and parental behavior may maximize suicide prevention efforts among children with high irritability.

While network science provides a powerful framework for deciphering complex biological systems, static models often fail to capture the dynamic nature of cellular processes. Temporal network analysis addresses this by modelling biological relationships as time-indexed graphs, offering a more realistic representation of evolving biological interactions. But, biological data are often sparse, noisy, and heterogeneous, making temporal network reconstruction highly sensitive to modelling and preprocessing choices. This review synthesises temporal network analysis for systems biology with an emphasis on practical interpretability and trustworthy inference. We highlight how different notions of “time” (e.g., longitudinal measurements, condition/stage progression, or inferred trajectories) and different meanings of “edges” (e.g., physical interactions, statistical associations, or model-based influences) support different biological claims and therefore demand different validation strategies. Using a multi-scale perspective, we survey approaches for characterising local dynamics, tracking mesoscale reorganisation such as module and community changes, and quantifying global shifts in network topology, alongside common tasks including rewiring detection, network comparison, and community evolution. A central message is that inference is often the bottleneck, while prediction is the temptation. We therefore foreground validation and benchmarking practices needed to distinguish genuine biological dynamics from artefacts of sampling, windowing, or model class. Finally, we discuss temporal graph learning, including temporal graph neural networks. We highlight when these methods can enable meaningful forecasting in biology, and when performance is inflated by sensitivity to network construction choices or information leakage in evaluation.

In this chapter, we use temporal network analysis to model longitudinal associations among six variables. We present three different networks to illustrate the threefold framework that temporal network analysis offers for analysing time-series data ( Blanchard et al., 2023 ): a temporal network (how variables predict one another from one timepoint to the next), a contemporaneous network (how variables are related within the same timeframe), and a between-subjects network (how variables are related across individuals). Conceptually, a network approach has many affordances for CDST-inspired research. Temporal network analysis is an exploratory, data-driven method that is conceptually aligned with many aspects of CDST. Methodologically however, temporal network analysis presents several challenges, including the large number of data points needed, assumptions of linearity, and considerations for handling missing data.

Childhood psychiatric disorders and their symptoms evince both within-disorder (homotypic) and between-disorder (heterotypic) continuities. These continuities may be due to earlier symptoms causing later symptoms or, alternatively, that the same (unknown) causes (e.g., genetics) are operating across time. Applying a novel data analytic approach, we disentangle these two explanations. Participants in a Norwegian community study were assessed biennially from 4 to 10years of age with clinical interviews (n=1,042). Prospective reciprocal relations between symptoms of disorders were analyzed with a dynamic panel model within a structural equationframework, adjusting for all unmeasured time-invariant confounders and time-varying negative life-events. Homotypic continuities in symptoms characterized all disorders; strongest for attention-deficit/hyperactivity disorder (ADHD) (r=.32-.62), moderate for behavioral disorders (r=.31-.48) and for anxiety and depression (r=.15-.40), and stronger between 8 and 10 than between 4 and 6years. Heterotypic continuity also characterized all disorders. A dynamic panel model showed that most continuities were due to unmeasured time-invariant factors rather than effects of earlier symptoms on later symptoms, although symptoms of behavioral disorders, which evinced two-year homotypic continuity (B=.14, 95% CI: .04, .25), did influence later symptoms of ADHD (B=.13, CI: .03, .23), and earlier ADHD symptoms influenced later anxiety disorder symptoms (B=.07, CI: .01, .12). Homotypic and heterotypic continuities of symptoms of childhood psychiatric disorders are mostly due to unobserved time-invariant factors. Nonetheless, symptoms of earlier behavioral disorders may affect later symptoms of such disorders and of ADHD, and ADHD may increase the risk of later anxiety. Thus, even if interventions do not alter basic etiological factors, symptom reduction may itself cause later symptom reduction.

Associations between childhood irritability and neural reactivity to maternal feedback in adolescence

While research on social network analysis is abundant and less frequently so temporal network analysis, research that uses inferential temporal network methods is barely existent. This paper aims to fill this gap by conducting a comparative analysis of temporal networks and inferential longitudinal network methods in the context of learner interactions in Massive Open Online Courses (MOOCs). We focus on three prominent methods: Temporal Network Analysis (TNA), Temporal Exponential Random Graph Models (TERGM) and Simulation Investigation for Empirical Network Analysis (SIENA). Using a five-week Nature Education MOOC as a case study, we compared the features, metrics of each method as well as their understanding of using network to analyze learner interactions. TNA focuses on describing and visualizing temporal changes in network structure, while TERGM and SIENA view networks as evolving systems influenced by individual behaviors and structural dependencies. TERGM treats network changes as a joint of random processes, while SIENA emphasizes the agency of learners and analyzes continuous network evolution. The findings provide guidelines for researchers and educators to select appropriate network analysis methods for temporal studies in educational contexts.

BackgroundDisruptive mood dysregulation disorder (DMDD) was introduced in (DSM-5) as a new diagnostic category to get control on the exaggerated diagnosis of bipolar disorder in children and adolescents which was elevated more than 40 times in the last decade. Few studies were done recently to explore the role of family history of bipolar disorder in the occurrence of DMDD in children and adolescents. Unfortunately, there is limited number of studies about the familial transmission of DMDD.The aim of this study is to examine the presence of a relation between DMDD and the presence of a family history of bipolar disorder through comparing the diagnosis of DMDD in offspring of parents with (a) bipolar disorder and (b) a control group.ResultsThe distribution of psychiatric disorders among the studied groups highlighted that DMDD and depressive disorder are significantly present in the offsprings of bipolar parents more than in the offsprings of control parents. DMDD is significantly present among offsprings of bipolar parents aged ≤ 11 years old (p = 0.008*). Also, DMDD is significantly (p = 0.02) present among male offsprings of bipolar parents. Also, depressive disorder is significantly (p = 0.002) present among female offsprings of control parents. While significant comorbidity between DMDD and depressive disorders (p = 0.018*) was detected.ConclusionsThis study revealed that DMDD and depressive disorders are significantly present in the offsprings of bipolar parents higher than in the offsprings of control parents. DMDD is significantly present among the offsprings of bipolar parents aged ≤ 11 years old.The relation between sex and affective disorders among the offsprings was also assessed revealing that DMDD is significantly present among male offsprings of bipolar parents. Also, depressive disorders are significantly present among female offsprings of control parents.Potential implicationsThis study has several clinical implications. This study draws attention to the importance of the careful assessment of the offsprings of bipolar patients to check the presence of different childhood psychiatric disorders such as DMDD. Also, symptoms of temper outbursts and persistent irritability in childhood require the necessary of psychiatric evaluation not only for the patients but also for their families. In addition, this study highlights the need for accurate updated diagnostic instruments to differentiate between these overlapping symptoms.As regards implications for future research, following-up of children diagnosed with DMDD to recognize the prognostic outcome of this new diagnostic category is necessary.

In a group of crime victims recruited from the community, the authors investigated the ability of both a diagnosis of acute stress disorder and its component symptoms to predict posttraumatic stress disorder (PTSD) at 6 months. A mixed-sex group of 157 victims of violent assaults were interviewed within 1 month of the crime. At 6-month follow-up 88% were reinterviewed by telephone and completed further assessments generating estimates of the prevalence of PTSD. The rate of acute stress disorder was 19%, and the rate of subsequent PTSD was 20%. Symptom clusters based on the DSM-IV criteria for acute stress disorder were moderately strongly interrelated. All symptom clusters predicted subsequent PTSD, but not as well as an overall diagnosis of acute stress disorder, which correctly classified 83% of the group. Similar predictive power could be achieved by classifying the group according to the presence or absence of at least three reexperiencing or arousal symptoms. Logistic regression indicated that both a diagnosis of acute stress disorder and high levels of reexperiencing or arousal symptoms made independent contributions to predicting PTSD. This exploratory study provides evidence for the internal coherence of the new acute stress disorder diagnosis and for the symptom thresholds proposed in DSM-IV. As predicted, acute stress disorder was a strong predictor of later PTSD, but similar predictive power may be possible by using simpler criteria.

BackgroundWhile bidirectional associations among sleep duration, cognitive function, and depression are established, the symptom-level temporal interactions among these factors in China’s aging population, which is experiencing unprecedented growth, remain poorly characterized.ObjectiveWe aim to use a novel temporal network analysis to clarify these dynamics and guide targeted interventions, with a focus on sex-specific dynamic pathways.MethodsWe conducted a longitudinal temporal network analysis on 3136 Chinese adults aged ≥45 years from the China Health and Retirement Longitudinal Study (CHARLS) across 5 waves (2011, 2013, 2015, 2018, and 2020). A graphical vector autoregressive (GVAR) model delineated the interdependencies among sleep duration, cognitive performance (assessed via the Mini-Mental State Examination [MMSE]), and depressive symptoms (evaluated with the 10-item Center for Epidemiologic Studies Depression Scale [CESD-10]). We also examined sex-specific differences in network structures.ResultsThe symptom “bothered” was found to predict all other CESD-10 symptoms. There were significant predictive links between sleep and the CESD-10 node (ie, bothered, drained, and depressed), along with sleep and the MMSE functions (ie, numerical ability). Furthermore, sleep duration served as a bridge between depression symptoms and cognitive functions. There were significant differences in longitudinal network structure between sexes. Sex-specific analyses revealed distinct network patterns. Among female participants, the “bothered” node significantly predicted several outcomes over time. In contrast, the temporal network for male participants was sparser, with the “stuck” node in the depression domain being predominantly influenced by other nodes.ConclusionsOur study revealed that emotional distress, especially the “bothered” symptom, plays a central role in depressive symptoms and cognitive decline. The bridging effect of short sleep duration underscores the potential of interventions targeting both sleep and emotional distress for alleviating depressive symptoms and delaying cognitive deterioration in older adults.

Modelling the prodrome to severe mental disorders (SMD), including unipolar mood disorders (UMD), bipolar mood disorders (BMD) and psychotic disorders (PSY), should consider both the evolution and interactions of symptoms and substance use (prodromal features) over time. Temporal network analysis can detect causal dependence between and within prodromal features by representing prodromal features as nodes, with their connections (edges) indicating the likelihood of one feature preceding the other. In SMD, node centrality could reveal insights into important prodromal features and potential intervention targets. Community analysis can identify commonly occurring feature groups to define SMD at-risk states. This retrospective (2-year) cohort study aimed to develop a global transdiagnostic SMD network of the temporal relationships between prodromal features and to examine within-group differences with sub-networks specific to UMD, BMD and PSY. Electronic health records (EHRs) from South London and Maudsley (SLaM) NHS Foundation Trust were included from 6462 individuals with SMD diagnoses (UMD:2066; BMD:740; PSY:3656). Validated natural language processing algorithms extracted the occurrence of 61 prodromal features every three months from two years to six months before SMD onset. Temporal networks of prodromal features were constructed using generalised vector autoregression panel analysis, adjusting for covariates. Edge weights (partial directed correlation coefficients, z) were reported in autocorrelative, unidirectional and bidirectional relationships. Centrality was calculated as the sum of (non-autoregressive) connections leaving (out-centrality, cout) or entering (in-centrality, cin) a node. The three sub-networks (UMD, BMD, PSY) were compared using permutation analysis, and community analysis was performed using Spinglass. The SMD network revealed strong autocorrelations (0.04 ≤ z ≤ 0.10), predominantly positive connections, and identified aggression (cout = 0.103) and tearfulness (cin = 0.134) as the most central features. Sub-networks for UMD, BMD, and PSY showed minimal differences, with 3.5% of edges differing between UMD and PSY, 0.8% between UMD and BMD, and 0.4% between BMD and PSY. Community analysis identified one positive psychotic community (delusional thinking-hallucinations-paranoia) and two behavioural communities (aggression-cannabis use-cocaine use-hostility, aggression-agitation-hostility) as the most common. This study represents the most extensive temporal network analysis conducted on the longitudinal interplay of SMD prodromal features. The findings provide further evidence to support transdiagnostic early detection services across SMD, refine assessments to detect individuals at risk and identify central features as potential intervention targets.