Reflections on an emerging new science of mental disorders

Borsboom, Denny

doi:10.1016/j.brat.2022.104127

Cited by 9 publications

(6 citation statements)

References 44 publications

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“…This is important since excessive clustering of symptoms in different domains can lead to a lack of clarity in the representation of the two mental health diagnoses considered by the DSM-V, which are represented as the interaction of all their signs and symptoms together within a given period 67 . The results refer to greater precision and interpretation based on transdiagnostic models between two emotional disorders such as anxiety and depression 28 , 68 , given that the methodology used allows studying these mental health problems as systems 17 , 69 , 70 .…”

Section: Discussionmentioning

confidence: 91%

Network analysis of anxiety and depressive symptoms during the COVID-19 pandemic in older adults in the United Kingdom

Ramos-Vera,

García O’Diana,

Basauri-Delgado

et al. 2024

Sci Rep

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The health crisis caused by COVID-19 in the United Kingdom and the confinement measures that were subsequently implemented had unprecedented effects on the mental health of older adults, leading to the emergence and exacerbation of different comorbid symptoms including depression and anxiety. This study examined and compared depression and anxiety symptom networks in two specific quarantine periods (June–July and November–December) in the older adult population in the United Kingdom. We used the database of the English Longitudinal Study of Aging COVID-19 Substudy, consisting of 5797 participants in the first stage (54% women) and 6512 participants in the second stage (56% women), all over 50 years of age. The symptoms with the highest centrality in both times were: “Nervousness (A1)” and “Inability to relax (A4)” in expected influence and predictability, and “depressed mood (D1”; bridging expected influence). The latter measure along with "Irritability (A6)" overlapped in both depression and anxiety clusters in both networks. In addition, a the cross-lagged panel network model was examined in which a more significant influence on the direction of the symptom "Nervousness (A1)" by the depressive symptoms of "Anhedonia (D6)", "Hopelessness (D7)", and "Sleep problems (D3)" was observed; the latter measure has the highest predictive capability of the network. The results report which symptoms had a higher degree of centrality and transdiagnostic overlap in the cross-sectional networks (invariants) and the cross-lagged panel network model of anxious and depressive symptomatology.

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Section: Discussionmentioning

confidence: 91%

Network analysis of anxiety and depressive symptoms during the COVID-19 pandemic in older adults in the United Kingdom

Ramos-Vera,

García O’Diana,

Basauri-Delgado

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Since that network theory posits that symptom associations are not caused by a latent variable, but they are “real interactions,” comorbidity cannot be considered as a methodological problem, but “the substantiative flesh and bones of psychopathology” ( 40 , 54 , 78 ). Although meta-analytical research displayed the presence of poor functioning and debilitating comorbid mental disorders in CHR-P individuals ( 3 , 4 , 28 ), relatively little is known about their causal interplay and co-occurrence–which supported the adoption of the network theory insights and methods instead of other methodological approaches.…”

Section: Discussionmentioning

confidence: 99%

“…Although meta-analytical research displayed the presence of poor functioning and debilitating comorbid mental disorders in CHR-P individuals ( 3 , 4 , 28 ), relatively little is known about their causal interplay and co-occurrence–which supported the adoption of the network theory insights and methods instead of other methodological approaches. Moreover, in understating and assessing emerging mental disorders, network theory as an organizing principle which embodies a more refined understating of how symptoms tend to engage in feedback loops ( 53 , 78 ) may offer relevant insights into which symptom domains tend to co-occur and reinforce each other–ultimately, we consider that such a framework may inform preventive strategies aiming of limiting the spreading of early psychopathology manifestations.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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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.

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“…A second source of new theories of depression comes from psychology. Moving away from a somewhat stale debate about the values of categorial versus dimensional categorization, Borsboom proposed a network theoretical description of depression [ 140 , 141 ], arguing that the probability of a change from a normal to a depressed state is related to elevated temporal autocorrelation, variance, and correlation between emotions in fluctuations of autorecorded emotions [ 142 ]. Translating these concepts into genetically testable ideas is an important challenge to the field.…”

Section: Turning Silver Into Goldmentioning

confidence: 99%

The genetic basis of major depressive disorder

Flint

2023

Mol Psychiatry

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The genetic dissection of major depressive disorder (MDD) ranks as one of the success stories of psychiatric genetics, with genome-wide association studies (GWAS) identifying 178 genetic risk loci and proposing more than 200 candidate genes. However, the GWAS results derive from the analysis of cohorts in which most cases are diagnosed by minimal phenotyping, a method that has low specificity. I review data indicating that there is a large genetic component unique to MDD that remains inaccessible to minimal phenotyping strategies and that the majority of genetic risk loci identified with minimal phenotyping approaches are unlikely to be MDD risk loci. I show that inventive uses of biobank data, novel imputation methods, combined with more interviewer diagnosed cases, can identify loci that contribute to the episodic severe shifts of mood, and neurovegetative and cognitive changes that are central to MDD. Furthermore, new theories about the nature and causes of MDD, drawing upon advances in neuroscience and psychology, can provide handles on how best to interpret and exploit genetic mapping results.

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Reflections on an emerging new science of mental disorders

Cited by 9 publications

References 44 publications

Network analysis of anxiety and depressive symptoms during the COVID-19 pandemic in older adults in the United Kingdom

Network analysis of anxiety and depressive symptoms during the COVID-19 pandemic in older adults in the United Kingdom

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

The genetic basis of major depressive disorder

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