A network analysis of anxiety, depressive, and psychotic symptoms and functioning in children and adolescents at clinical high risk for psychosis

Abstract

ObjectiveYouths at clinical high risk for psychosis (CHR-P) are characterized by a high prevalence of anxiety and depressive disorders. The present study aimed at developing and analyzing a network structure of CHR-P symptom domains (i.e., positive, negative, disorganization, and general subclinical psychotic symptoms), depressive and anxiety symptoms, and general functioning.MethodsNetwork analysis was applied to data on 111 CHR-P children and adolescents (Mage = 14.1), who were assessed using the Structured Interview for Prodromal Syndromes, the Children’s Depression Inventory, the Children’s Global Assessment Scale, and the Multidimensional Anxiety Scale for Children.ResultsIn the network, negative and disorganization symptoms showed the strongest association (r = 0.71), and depressive and anxiety symptoms showed dense within-domain connections, with a main bridging role played by physical symptoms of anxiety. The positive symptom cluster was not associated with any other node. The network stability coefficient (CS) was slightly below 0.25, and observed correlations observed ranged from 0.35 to 0.71.ConclusionThe lack of association between subclinical positive symptoms and other network variables confirmed the independent nature of subclinical positive symptoms from comorbid symptoms, which were found to play a central role in the analyzed network. Complex interventions should be developed to target positive and comorbid symptoms, prioritizing those with the most significant impact on functioning and the most relevance for the young individual, through a shared decision-making process. Importantly, the results suggest that negative and disorganization symptoms, as well as depressive and anxiety symptoms, may be targeted simultaneously.

Clinical utility of the at-risk for psychosis state beyond transition: A multidimensional network analysis

“…In fact, the "isolation" of positive symptoms may be a defining feature of CHR-P status, possibly developing over time. This observation is consistent with previous findings from our research group showing that positive symptoms do not always display meaningful connections with other nodes in the network, marking them as potential evaluation and intervention targets [21]. A similar tendency for psychotic symptoms to cluster separately from non-psychotic dimensions (e.g., risk of developing depressive, bipolar, or borderline personality disorder) has been observed in recent research adopting a transdiagnostic approach [62].…”

“…An initial sample of N = 291 met the study criteria. However, 42 CHR-P youth (15.5%) were excluded due to missing data, in line with previous studies [21,52]. Table S2 provides the characteristics of the included and excluded participants.…”

“…Innovative insights from the network theory of mental disorders [15][16][17] may further clarify the potential clinical specificities of CHR-P patients and identify relevant intervention targets to improve clinical outcomes-focusing on both the diagnostic validity and the clinical utility of the CHR-P concept. In fact, a growing body of research suggests that conceptualizing psychopathology in terms of dimensions, rather than categories, and adopting approaches aimed at comprehending complex system structures may improve our knowledge of mental disorders and early manifestations of psychopathology [18][19][20][21]. This conceptual framework posits that, rather than a "common cause" generating manifestations of psychopathology, it is the causal interplay among mutually reinforcing symptoms (i.e., symptoms that cause and reinforce each other) in a network structure (i.e., a web of associations among symptoms) that leads to what is phenomenologically identified as "mental disorder" [15,22].…”

Harmonizing early intervention strategies: scoping review of clinical high risk for psychosis and borderline personality disorder

“…Fourth, there is little consensus on the best intervention for CHR-P youth with BPD (or vice-versa ). Research efforts conceptualizing comorbid conditions as a complex system (e.g., network analysis) may improve understanding of early psychopathology manifestations and potentially suggest relevant intervention targets ( Nelson et al, 2017 ; Borsboom et al, 2021 ; Ong et al, 2021 ; Lo Buglio et al, 2022 ).…”

Bridging Perspectives: Exploring the Convergence of Clinimetrics and Network Theory in Mental Health Assessment and Conceptualization

“…In the last decade, network psychometrics has received ever increasing attention from clinicians and researchers and, since its inception, the number of published studies using network psychometrics studies in different psychology specialties has been growing steadily [ 30 ], though the sweeping enthusiasm for networks raised some questions about the methodological robustness of the studies [ 31 , 32 ]. Notwithstanding these limits, network psychometrics has already established itself as a powerful method to analyze psychological data from a variety of clinical conditions [ 30 , 33 ], such as depression [ 34 , 35 ], eating disorders [ 35 , 36 ], anxiety [ 35 ], psychosis [ 35 , 37 , 38 , 39 ], and PTSD [ 40 , 41 , 42 ].…”