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The current paper presents an integrated formal model of typical and atypical development based on the mechanisms of mutualism and resource competition. The mutualistic network model is extended with the dynamics of competition for limited resources, such as time and environmental factors. The proposed model generates patterns that resemble established phenomena in cognitive development: the positive manifold, developmental phases, developmental delays and lack of early indicators in atypical development, developmental regression, and "quasi-autism" caused by extreme environmental deprivation. The presented modeling framework fits a general movement towards formal theory construction in psychology. The model is easy to replicate and develop further, and we offer several avenues for future work.
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Transtorno Autístico , Cognição , Humanos , SimbioseRESUMO
LAY ABSTRACT: More and more members of the autistic community and the research field are moving away from the idea that there will be a single biological or cognitive explanation for autistic characteristics. However, little is known about the complex dynamic processes that could explain why early difficulties in the language and motor domain often go hand-in-hand. We here study how language and motor skills develop simultaneously in the British Autism Study of Infant Siblings cohort of infants, and compare the way they are linked between children with and without developmental delays. Our results suggest that improvements in one domain go hand-in-hand with improvements in the other in both groups and show no compelling evidence for group differences in how motor skills relate to language and vice versa. We did observe a larger diversity in motor and language skills at 6 months, and because we found the motor and language development to be tightly linked, this suggests that even very small early impairments can result in larger developmental delays in later childhood. Greater variability at baseline, combined with very strong correlations between the slopes, suggests that dynamic processes may amplify small differences between individuals at 6months to result into large individual differences in autism symptomatology at 36 months.
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Transtorno do Espectro Autista , Destreza Motora , Lactente , Criança , Humanos , Transtorno do Espectro Autista/psicologia , Idioma , Desenvolvimento Infantil , Desenvolvimento da LinguagemRESUMO
Background: There is an urgent need to better understand and prevent relapse in major depressive disorder (MDD). We explored the differential impact of various MDD relapse prevention strategies (pharmacological and/or psychological) on affect fluctuations and individual affect networks in a randomised setting, and their predictive value for relapse. Methods: We did a secondary analysis using experience sampling methodology (ESM) data from individuals with remitted recurrent depression that was collected alongside a randomised controlled trial that ran in the Netherlands, comparing: (I) tapering antidepressants while receiving preventive cognitive therapy (PCT), (II) combining antidepressants with PCT, or (III) continuing antidepressants without PCT, for the prevention of depressive relapse, as well as ESM data from 11 healthy controls. Participants had multiple past depressive episodes, but were remitted for at least 8 weeks and on antidepressants for at least six months. Exclusion criteria were: current (hypo)mania, current alcohol or drug abuse, anxiety disorder that required treatment, psychological treatment more than twice per month, a diagnosis of organic brain damage, or a history of bipolar disorder or psychosis. Fluctuations (within-person variance, root mean square of successive differences, autocorrelation) in negative and positive affect were calculated. Changes in individual affect networks during treatment were modelled using time-varying vector autoregression, both with and without applying regularisation. We explored whether affect fluctuations or changes in affect networks over time differed between treatment conditions or relapse outcomes, and predicted relapse during 2-year follow-up. This ESM study was registered at ISRCTN registry, ISRCTN15472145. Findings: Between Jan 1, 2014, and Jan 31, 2015, 72 study participants were recruited, 42 of whom were included in the analyses. We found no indication that affect fluctuations differed between treatment groups, nor that they predicted relapse. We observed large individual differences in affect network structure across participants (irrespective of treatment or relapse status) and in healthy controls. We found no indication of group-level differences in how much networks changed over time, nor that changes in networks over time predicted time to relapse (regularised models: hazard ratios [HR] 1063, 95% CI <0.0001->10 000, p = 0.65; non-regularised models: HR 2.54, 95% CI 0.23-28.7, p = 0.45) or occurrence of relapse (regularised models: odds ratios [OR] 22.84, 95% CI <0.0001->10 000, p = 0.90; non-regularised models: OR 7.57, 95% CI 0.07-3709.54, p = 0.44) during complete follow-up. Interpretation: Our findings should be interpreted with caution, given the exploratory nature of this study and wide confidence intervals. While group-level differences in affect dynamics cannot be ruled out due to low statistical power, visual inspection of individual affect networks also revealed no meaningful patterns in relation to MDD relapse. More studies are needed to assess whether affect dynamics as informed by ESM may predict relapse or guide personalisation of MDD relapse prevention in daily practice. Funding: The Netherlands Organisation for Health Research and Development, Dutch Research Council, University of Amsterdam.
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Adolescence is a period of rapid change, with cognitive, mental wellbeing, environmental biological factors interacting to shape lifelong outcomes. Large, longitudinal phenotypically rich data sets available for reuse (secondary data) have revolutionized the way we study adolescence, allowing the field to examine these unfolding processes across hundreds or even thousands of individuals. Here, we outline the opportunities and challenges associated with such secondary data sets, provide an overview of particularly valuable resources available to the field, and recommend best practices to improve the rigor and transparency of analyses conducted on large, secondary data sets.
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Desenvolvimento do Adolescente , Adolescente , HumanosRESUMO
Heterogeneity within autism spectrum disorder (ASD) is recognized as a challenge to both biological and psychological research, as well as clinical practice. To reduce unexplained heterogeneity, subtyping techniques are often used to establish more homogeneous subtypes based on metrics of similarity and dissimilarity between people. We review the ASD literature to create a systematic overview of the subtyping procedures and subtype validation techniques that are used in this field. We conducted a systematic review of 156 articles (2001-June 2020) that subtyped participants (range N of studies = 17-20,658), of which some or all had an ASD diagnosis. We found a large diversity in (parametric and non-parametric) methods and (biological, psychological, demographic) variables used to establish subtypes. The majority of studies validated their subtype results using variables that were measured concurrently, but were not included in the subtyping procedure. Other investigations into subtypes' validity were rarer. In order to advance clinical research and the theoretical and clinical usefulness of identified subtypes, we propose a structured approach and present the SUbtyping VAlidation Checklist (SUVAC), a checklist for validating subtyping results.
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Transtorno do Espectro Autista , Psiquiatria , Transtorno do Espectro Autista/diagnóstico , HumanosRESUMO
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.
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Encéfalo , Neurociências , HumanosRESUMO
Urbanisation and common mental disorders (CMDs; ie, depressive, anxiety, and substance use disorders) are increasing worldwide. In this Review, we discuss how urbanicity and risk of CMDs relate to each other and call for a complexity science approach to advance understanding of this interrelationship. We did an ecological analysis using data on urbanicity and CMD burden in 191 countries. We found a positive, non-linear relationship with a higher CMD prevalence in more urbanised countries, particularly for anxiety disorders. We also did a review of meta-analytic studies on the association between urban factors and CMD risk. We identified factors relating to the ambient, physical, and social urban environment and showed differences per diagnosis of CMDs. We argue that factors in the urban environment are likely to operate as a complex system and interact with each other and with individual city inhabitants (including their psychological and neurobiological characteristics) to shape mental health in an urban context. These interactions operate on various timescales and show feedback loop mechanisms, rendering system behaviour characterised by non-linearity that is hard to predict over time. We present a conceptual framework for future urban mental health research that uses a complexity science approach. We conclude by discussing how complexity science methodology (eg, network analyses, system-dynamic modelling, and agent-based modelling) could enable identification of actionable targets for treatment and policy, aimed at decreasing CMD burdens in an urban context.
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COVID-19/psicologia , Transtornos Mentais/epidemiologia , Saúde Mental/normas , Saúde da População Urbana/normas , Adulto , Transtornos de Ansiedade/diagnóstico , Transtornos de Ansiedade/epidemiologia , COVID-19/diagnóstico , COVID-19/epidemiologia , COVID-19/virologia , Transtorno Depressivo/diagnóstico , Transtorno Depressivo/epidemiologia , Ecossistema , Feminino , Indicadores Básicos de Saúde , Humanos , Masculino , Transtornos Mentais/diagnóstico , Transtornos Mentais/psicologia , Transtornos Mentais/terapia , Saúde Mental/tendências , Metanálise como Assunto , Prevalência , SARS-CoV-2/genética , Análise de Rede Social , Transtornos Relacionados ao Uso de Substâncias/diagnóstico , Transtornos Relacionados ao Uso de Substâncias/epidemiologia , Análise de Sistemas , Saúde da População Urbana/tendênciasRESUMO
Irritability and aggression (IA) are highly prevalent in individuals with autism spectrum disorder (ASD). Although clinical correlates of IA in this population have been previously examined, findings from existing studies capturing symptoms as a set of latent variables do not fully explain meaningful associations between the symptoms themselves. In the present study, we conducted network analysis which conceptualizes mental health difficulties as a complex network of directly associated symptoms in 2612 individuals who were diagnosed with ASD through rigorous diagnostic assessment and who were enrolled in the Simons Simplex Collection. Using the Aberrant Behavior Checklist, a validated scale, we investigated the network structure of IA and tried to identify bridge symptoms that link IA and other symptom domains. In our analysis, irritability symptoms had stronger and more direct associations with other nodes than aggression symptoms did. Additionally, depressed mood and oppositionality were identified to function as bridge symptoms. The network structures did not differ between individuals with and without intellectual disability. Our findings indicate that addressing these bridge symptoms through integrated care combining different modalities of treatment could ease the complicated symptom network and thereby reduce IA symptoms in individuals with ASD.
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Agressão/fisiologia , Agressão/psicologia , Transtorno do Espectro Autista/psicologia , Humor Irritável/fisiologia , Adolescente , Transtorno do Espectro Autista/diagnóstico , Criança , Pré-Escolar , Feminino , Humanos , Deficiência Intelectual/diagnóstico , Deficiência Intelectual/psicologia , MasculinoRESUMO
Autism and depression often co-occur. Through network analysis, we seek to gain a better understanding of this co-occurrence by investigating whether (1) autism and depression share overlapping groups of symptoms and/or (2) are connected through a bridge of mastery or worry symptoms. This is addressed in two complimentary studies: (1) Study 1 focusing on depressed (N = 258) and non-depressed adults (N = 117), aged 60-90 years; (2) Study 2 focusing on autistic (N = 173) and non-autistic adults (N = 70), aged 31-89 years. Self-report questionnaire data were collected on autistic traits (AQ-28), depression symptoms (Study 1: Inventory of Depressive Symptomatology Self Report; Study 2: Symptom Checklist 90-Revised depression subscale), worry (Worry Scale-R) and mastery (the Pearlin Mastery Scale). For both studies, data were analysed by creating glasso networks and subsequent centrality analyses to identify the most influential variables in the respective networks. Both depressed and autistic adults are highly similar in the perceived amount of worries and lack of control. While caution is needed when interpreting the pattern of findings given the bootstrapping results, findings from both studies indicate that overlapping symptoms do not fully explain the co-occurrence of autism and depression and the perception of having control over your life, that is, mastery seems a relevant factor in connecting autism and depression.
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Transtorno Autístico/epidemiologia , Depressão/epidemiologia , Adulto , Idoso , Idoso de 80 Anos ou mais , Estudos de Casos e Controles , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Autorrelato , Inquéritos e QuestionáriosRESUMO
The network approach to psychological phenomena advances our understanding of the interrelations between autism and well-being. We use the Perceived Causal Relations methodology in order to (i) identify perceived causal pathways in the well-being system, (ii) validate networks based on self-report data, and (iii) quantify and integrate clinical expertise in autism research. Trained clinicians served as raters (N = 29) completing 374 cause-effects ratings of 34 variables on well-being and symptomatology. A subgroup (N = 16) of raters chose intervention targets in the resulting network which we found to match the respective centrality of nodes. Clinicians' perception of causal relations was similar to the interrelatedness found in self-reported client data (N = 323). We present a useful tool for translating clinical expertise into quantitative information enabling future research to integrate this in scientific studies.
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Transtorno Autístico/psicologia , Felicidade , Adolescente , Adulto , Idoso , Transtorno Autístico/terapia , Criança , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , AutorrelatoRESUMO
Many individuals with autism report generally low quality of life (QoL). Identifying predictors for pathways underlying this outcome is an urgent priority. We aim to examine multivariate patterns that predict later subjective and objective QoL in autistic individuals. Autistic characteristics, comorbid complaints, aspects of daily functioning, and demographics were assessed online in a 2-year longitudinal study with 598 autistic adults. Regression trees were fitted to baseline data to identify factors that could predict QoL at follow-up. We found that sleep problems are an important predictor of later subjective QoL, while the subjective experience of a person's societal contribution is important when it comes to predicting the level of daily activities. Sleep problems are the most important predictor of QoL in autistic adults and may offer an important treatment target for improving QoL. Our results additionally suggest that social satisfaction can buffer this association. Autism Research 2019, 12: 794-801. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Many individuals with autism report generally low quality of life (QoL). In this study, we looked at factors that predict long-term QoL and found that sleep problems are highly influential. Our results additionally suggest that social satisfaction can buffer this influence. These findings suggest that sleep and social satisfaction could be monitored to increase QoL in autistic adults.
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Transtorno do Espectro Autista/complicações , Transtorno do Espectro Autista/psicologia , Qualidade de Vida/psicologia , Transtornos do Sono-Vigília/complicações , Transtornos do Sono-Vigília/psicologia , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Feminino , Humanos , Estudos Longitudinais , Masculino , Pessoa de Meia-Idade , Sono , Adulto JovemRESUMO
Network theory, as a theoretical and methodological framework, is energizing many research fields, among which clinical psychology and psychiatry. Fundamental to the network theory of psychopathology is the role of specific symptoms and their interactions. Current statistical tools, however, fail to fully capture this constitutional property. We propose community detection tools as a means to evaluate the complex network structure of psychopathology, free from its original boundaries of distinct disorders. Unique to this approach is that symptoms can belong to multiple communities. Using a large community sample and spanning a broad range of symptoms (Symptom Checklist-90-Revised), we identified 18 communities of interconnected symptoms. The differential role of symptoms within and between communities offers a framework to study the clinical concepts of comorbidity, heterogeneity and hallmark symptoms. Symptoms with many and strong connections within a community, defined as stabilizing symptoms, could be thought of as the core of a community, whereas symptoms that belong to multiple communities, defined as communicating symptoms, facilitate the communication between problem areas. We propose that defining symptoms on their stabilizing and/or communicating role within and across communities accelerates our understanding of these clinical phenomena, central to research and treatment of psychopathology.
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Transtornos Mentais/diagnóstico , Modelos Psicológicos , Psicopatologia/métodos , Algoritmos , Comorbidade , Biologia Computacional , Humanos , Transtornos Mentais/epidemiologiaRESUMO
Given the heterogeneity of autism spectrum disorder, an important limitation of much autism spectrum disorder research is that outcome measures are statistically modeled as separate dependent variables. Often, their multivariate structure is either ignored or treated as a nuisance. This study aims to lift this limitation by applying network analysis to explicate the multivariate pattern of risk and success factors for subjective well-being in autism spectrum disorder. We estimated a network structure for 27 potential factors in 2341 individuals with autism spectrum disorder to assess the centrality of specific life domains and their importance for well-being. The data included both self- and proxy-reported information. We identified social satisfaction and societal contribution as the strongest direct paths to subjective well-being. The results suggest that an important contribution to well-being lies in resources that allow the individual to engage in social relations, which influence well-being directly. Factors most important in determining the network's structure include self-reported IQ, living situation, level of daily activity, and happiness. Number of family members with autism spectrum disorder and openness about one's diagnosis are least important of all factors for subjective well-being. These types of results can serve as a roadmap for interventions directed at improving the well-being of individuals with autism spectrum disorder.
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Transtorno do Espectro Autista/psicologia , Felicidade , Satisfação Pessoal , Atividades Cotidianas/psicologia , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Atitude Frente a Saúde , Criança , Pré-Escolar , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Países Baixos , Inquéritos e Questionários , Adulto JovemRESUMO
OBJECTIVE: Recent developments in psychometrics enable the application of network models to analyze psychological disorders, such as PTSD. Instead of understanding symptoms as indicators of an underlying common cause, this approach suggests symptoms co-occur in syndromes due to causal interactions. The current study has two goals: (1) examine the network structure among the 20 DSM-5 PTSD symptoms, and (2) incorporate clinically relevant variables to the network to investigate whether PTSD symptoms exhibit differential relationships with suicidal ideation, depression, anxiety, physical functioning/quality of life (QoL), mental functioning/QoL, age, and sex. METHOD: We utilized a nationally representative U.S. military veteran's sample; and analyzed the data from a subsample of 221 veterans who reported clinically significant DSM-5 PTSD symptoms. Networks were estimated using state-of-the-art regularized partial correlation models. Data and code are published along with the paper. RESULTS: The 20-item DSM-5 PTSD network revealed that symptoms were positively connected within the network. Especially strong connections emerged between nightmares and flashbacks; blame of self or others and negative trauma-related emotions, detachment and restricted affect; and hypervigilance and exaggerated startle response. The most central symptoms were negative trauma-related emotions, flashbacks, detachment, and physiological cue reactivity. Incorporation of clinically relevant covariates into the network revealed paths between self-destructive behavior and suicidal ideation; concentration difficulties and anxiety, depression, and mental QoL; and depression and restricted affect. CONCLUSION: These results demonstrate the utility of a network approach in modeling the structure of DSM-5 PTSD symptoms, and suggest differential associations between specific DSM-5 PTSD symptoms and clinical outcomes in trauma survivors. Implications of these results for informing the assessment and treatment of this disorder, are discussed.