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Testing Cold and Hot Cognitive Control As Moderators of a Network CPXXXX10.1177/2167702619842466Madole et al.Adolescent Symptom Network 842466research-article2019 ASSOCIATION FOR Empirical Article PSYCHOLOGICAL SCIENCE Clinical Psychological Science 2019, Vol. 7(4) 701 –718 Testing Cold and Hot Cognitive Control © The Author(s) 2019 Article reuse guidelines: sagepub.com/journals-permissions as Moderators of a Network of Comorbid DOI:https://doi.org/10.1177/2167702619842466 10.1177/2167702619842466 Psychopathology Symptoms in Adolescence www.psychologicalscience.org/CPS James W. Madole1 , Mijke Rhemtulla2 , Andrew D. Grotzinger1, Elliot M. Tucker-Drob1, and K. Paige Harden1 1Department of Psychology, The University of Texas at Austin, and 2Department of Psychology, University of California, Davis Abstract Comorbidity is pervasive across psychopathological symptoms, diagnoses, and domains. Network analysis is a method for investigating symptom-level associations that underlie comorbidity, particularly through bridge symptoms connecting diagnostic syndromes. We applied network analyses of comorbidity to data from a population-based sample of adolescents (N = 849). We implemented a method for assessing nonparametric moderation of psychopathology networks to evaluate differences in network structure across levels of intelligence and emotional control. Symptoms generally clustered by clinical diagnoses, but specific between-cluster bridge connections emerged. Internalizing symptoms demonstrated unique connections with aggression symptoms of interpersonal irritability, whereas externalizing symptoms showed more diffuse interconnections. Aggression symptoms identified as bridge nodes in the cross-sectional network were enriched for longitudinal associations with internalizing symptoms. Cross-domain connections did not significantly vary across intelligence but were weaker at lower emotional control. Our findings highlight transdiagnostic symptom relationships that may underlie co-occurrence of clinical diagnoses or higher-order factors of psychopathology. Keywords psychopathology, comorbidity, network analysis, cognitive control, open materials Received 6/4/18; Revision accepted 11/14/18 Mental disorders are widely comorbid (Hasin & Kilcoyne, cases meet for at least one comorbid disorder (Arcelus 2012; Kessler, Chiu, Demler, & Walters, 2005). Complicating & Vostanis, 2005; Kessler, Berglund, et al., 2005). understanding of comorbidity is that clinical diagnoses aggregate over heterogeneous symptom presentations (e.g., Network Models of Comorbidity Fair, Bathula, Nikolas, & Nigg, 2012; Lindhiem, Bennett, Hipwell, & Pardini, 2015; Wright et al., 2013). For exam- Network analysis is a methodological tool for modeling ple, one study of 3,703 outpatients with major depressive unique relationships between psychopathology symp- disorder found more than 1,000 unique symptom profiles toms. Although multiple types of network modeling (Fried & Nesse, 2015a). Given this diversity of symptom exist (for a review, see McNally, 2016), the most com- presentation, symptomics, defined as a focus on studying monly employed version of this tool is the concentra- individual symptoms of psychopathology, has been cham- tion network because of its suitability for cross-sectional, pioned as a promising avenue for understanding psychi- correlational data (Epskamp & Fried, 2018; Epskamp, atric comorbidity (Armour, Fried, & Olff, 2017; Fried et al., Waldorp, Mõttus, & Borsboom, 2018). In concentration 2015). Here, we take a symptomics approach to identify- ing granular pathways through which mental disorders Corresponding Author: covary during adolescence, a critical developmental James W. Madole, Department of Psychology, The University of Texas period when more than half of all lifetime cases of at Austin, 108 E. Dean Keeton St., Stop A8000, Austin, TX 78712-1043 psychopathology begin and more than a quarter of E-mail: [email protected] 702 Madole et al. networks, the partial pairwise correlations between of multiple disorders, are particularly important given symptoms are estimated, controlling for all other symp- the growing awareness that widespread comorbidity toms (Epskamp & Fried, 2018). These partial correla- exists even across domains of psychopathology that tions are then typically graphed to allow for both an appear quite distinct (such as internalizing and exter- easily interpretable visualization of the relationships nalizing problems; Caspi & Moffitt, 2018). among symptoms and a formal quantification of these relationships using graph theory (Borsboom, 2017; Individual Differences in Network Borsboom & Cramer, 2013). Results from network anal- Structure ysis have informed a burgeoning conceptualization of psychopathology, network theory, which suggests that The ubiquitous comorbidity among DSM-defined and mental disorders are an upstream reflection of activa- International Classification of Diseases–defined clinical tion patterns among symptoms (Borsboom, 2017; Bors- diagnoses has motivated interest in identifying transdi- boom & Cramer, 2013). Within this conceptualization, agnostic risk factors for psychiatric disorders and refining comorbidity is understood as the occurrence of symp- psychiatric nosology accordingly (Clark, Cuthbert, Lewis- toms from two distinct symptom clusters. Such symp- Fernández, Narrow, & Reed, 2017). However, hypothe- tom co-occurrence can arise via bridge symptoms, sized transdiagnostic risk factors have not generally been defined as symptoms from one cluster that have con- integrated into network analyses of symptom relation- nections with symptoms from another cluster or another ships. Here, we propose a novel conceptualization of clinical disorder (Cramer, Waldorp, van der Maas, & transdiagnostic risk factors in the context of network Borsboom, 2010; Fried et al., 2017). theory as psychological or neurobiological background Previous network analyses investigating the large- conditions that strengthen or exacerbate the symptom- scale organization (topology; Costantini & Perugini, to-symptom connections across psychiatric disorders or 2016) of psychiatric symptom networks have primarily domains. No previous network analysis study has exam- evaluated (a) how symptoms cluster together and (b) ined how transdiagnostic symptom relationships might the strength and number of the connections that symp- vary as a function of other individual differences (Fried toms display both within and across clusters (Boschloo, et al., 2015; Fried & Nesse, 2015b), in part because sta- Schoevers, van Borkulo, Borsboom, & Oldehinkel, 2016; tistical methods to evaluate moderation of networks by Boschloo et al., 2015; Cramer et al., 2010; Goekoop & continuously varying individual differences have not Goekoop, 2014). Few studies, however, have used net- been implemented. work analysis to address comorbidity across the broad We addressed this methodological and substantive range of mental health disorders (for a review, see Fried gap by examining individual differences in cognitive et al., 2017). Instead, most network analyses of comor- control as a moderator of symptom coactivation. Cogni- bidity have examined only two disorders (Robinaugh, tive control is broadly defined as the ability to coordi- Leblanc, Vuletich, & McNally, 2014; Ruzzano, Borsboom, nate thoughts or actions in relation to internal goals & Geurts, 2015). For example, longitudinal and cross- (Koechlin, Ody, & Kouneiher, 2003), and it can be dif- sectional studies have found symptoms of major depres- ferentiated into “cold” and “hot” forms. Cold cognitive sion and generalized anxiety to be densely interconnected, control is defined as the regulatory ability to monitor, although the precise nature of these interconnections direct, and manipulate basic information processing varied by study (Beard et al., 2016; Cramer et al., 2010; (Zelazo & Müller, 2002), whereas hot cognitive control Curtiss, Ito, Takebayashi, & Hofmann, 2018; Curtiss & is defined as the regulatory ability to monitor, direct, Klemanski, 2016). When examining symptoms across and manipulate affective processing (Roiser & Sahakian, more than two disorders, one study, notable for its size 2013). Intelligence-test performance is a robust indica- (~34,000 adult patients), found that the network struc- tor of cold cognitive control (Chuderski & Ne˛cka, 2010). ture of 120 symptoms from 12 disorders generally Our previous research found that intelligence-test per- cohered to clinical boundaries defined by the fourth formance is highly correlated, both phenotypically and edition of the Diagnostic and Statistical Manual of Men- genetically, with a general factor of performance on tal Disorders (DSM–IV; American Psychiatric Associa- executive-functioning tests, which measure the ability tion, 1994) but that individual symptoms differed to inhibit responses, shift attention, and update infor- substantially in their cross-disorder relationships mation in working memory (Engelhardt et al., 2016). (Boschloo et al., 2015). For example, all diagnoses were Research in a latent variable framework has found connected via specific symptoms to at least three other that intelligence and executive functioning are negatively diagnoses (Boschloo et al., 2015). Similar results were associated with a general factor of psychopathology found in a community sample of 2,175 preadolescents (Caspi et al., 2014; Harden et al., 2017; Lahey et al., 2015; ages 10 to 12 years (Boschloo et al., 2016). Studies in Neumann et al., 2016) and with an array of clinically this vein, which apply network analysis to
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