Psychopathology Network Analysis Workshop Eiko Fried Marie Deserno Sacha Epskamp Department of Psychological Research Methods University of Amsterdam Harvard University, June 2nd – 3rd 2016

Marie Deserno Eiko Fried Sacha Epskamp Marie.Deserno@gmail.com Twitter @MarieDeserno  Eiko Fried www.eiko-fried.com Twitter @EikoFried Sacha Epskamp www.sachaepskamp.com Twitter @SachaEpskamp Department of Psychological Research Methods University of Amsterdam

Introduction to Psychopathological Networks Network Estimation Outline day 1 Introduction to Psychopathological Networks Network Estimation Network Inference Network Stability Practical with Borderline Data Advanced Techniques FAQ

Complexity trumps reductionism Introduction Complexity trumps reductionism Why symptoms cluster in syndromes: common cause vs. network models

Complexity trumps reductionism Chapter 1 Complexity trumps reductionism

What are networks (preview) A network is a set of nodes connected by a set of edges Nodes are also called vertices Edges are also called links Networks are also called graphs

Why symptoms cluster in syndromes Chapter 2 Why symptoms cluster in syndromes

Natural kinds Robert Koch, 1905: discovery that specific diseases have specific causative agents (tuberculosis & syphilis) Diseases understood as natural kinds: Unchanging and ahistoric entities with sharp boundaries that have a specific set of properties (e.g., symptoms) both necessary and sufficient for classification Measles: infection of the respiratory system caused by a specific virus, accompanied by specific symptoms like red eyes, fever, generalized rash, and Koplik's spots Gold: atomic number 79, and everything with this atomic number is gold At the turn of the 19th century, Koch discovered …

Natural kinds 1910: discovery of syphilitic bacteria in brains of deceased patients diagnosed with "general paralysis of the insane" Neuropsychiatric syndrome of late-stage syphilis Clear "essence" identified for a mental disorder Disease model applied to the rest of medicine, including psychiatry 1912, Alfred Roche: "The main example of a happy final definition of a disease condition […] has been general paresis. The success achieved here has perhaps been a misfortune in its side effects because it nourished the illusion that something similar might soon be repeated." At the turn of the 19th century, Koch discovered …

Natural kinds 1959, Kurt Schneider: "General paresis […] became the model for forming disease entities. It was thought it would continue thus, it was hoped that with time more and more such disease entities would emerge from the multifarious conditions of the mentally ill. In fact, however, this did not happen." At the turn of the 19th century, Koch discovered …

Mental disorders as natural kinds The assumption of mental disorders as natural kinds has been present throughout the history of psychiatry Gerald Klerman, chief of the US national mental health agency, 1978: "there is a boundary between the normal and the sick" "there are discrete mental disorders" Aim of developing specific treatments for particular disorders, and of finding specific underlying biological abnormalities At the turn of the 19th century, Koch discovered …

Mental disorders as natural kinds This is more than just a belief—reflected in everyday research practices Disparate symptoms added to sum-scores, thresholds distinguish between case and controls Search for potential causes then proceeds as if disorders are natural kinds, similar to measles This perspective has discouraged attention to specific depression symptoms and their dynamic interactions

Common cause framework Related to notion of natural kinds Disorders itself are latent—we cannot observe measles directly M

Common cause framework Related to notion of natural kinds Disorders itself are latent—we cannot observe measles directly We can only observe the symptoms of measles We can use symptoms to indicate the presence of measles s1 M s2 s3

Common cause framework Related to notion of natural kinds Disorders itself are latent—we cannot observe measles directly We can only observe the symptoms of measles We can use symptoms to indicate the presence of measles This works because measles causes measles symptoms s1 M s2 s3

Common cause framework The CC framework is responsible for symptom checklists in the rest of medicine and psychiatry We use symptom lists to determine the presence of an underlying disease The CC framework explains why symptoms cluster: they have the same causal origin Fever, generalized rash, Koplik's spots  measles! s1 s2 s3 M

Common cause framework What does this mean for symptoms? Symptoms are equivalent & interchangeable indicators of underlying disease ("assumption of symptom equivalence") Symptom number, not symptom nature is relevant Symptoms are "locally independent"; since they are derived from the same common cause, their correlations are spurious 72 74 73 W

Common cause framework What does this mean for symptoms? Symptoms are equivalent & interchangeable indicators of underlying disease ("assumption of symptom equivalence") Symptom number, not symptom nature is relevant Symptoms are "locally independent"; since they are derived from the same common cause, their correlations are spurious 72 74 73 W

Network perspective Traditional: symptoms cluster because of a shared origin Network view: symptoms cluster because they influence each other.

Network perspective Symptoms have autonomous causal power and are not mere passive consequences of a common cause

Network perspective Symptoms are separate entities that can differ in important aspects Symptoms are not interchangeable indicators of an underlying disorder.

Standard argument Analyzing sums of depression symptoms is problematic because: Depression is highly heterogeneous (10.1016/j.jad.2014.10.010) Depression scales are not unidimensional (10.1037/pas0000275) Individual symptoms are differentially related to important variables like impaired functioning, risk factors, or biomarkers (10.1186/s12916-015-0325-4; 10.3389/fpsyg.2015.00309; 10.1017/S0033291713002900; 10.1371/journal.pone.0090311) We need to focus on the analysis of individual symptoms and their causal dynamics instead of sum-scores (10.3389/fpsyt.2015.00117)

Disclaimer There is nothing wrong with the idea that common causes trigger symptoms Trauma → PTSD symptoms Traumatic brain injury → affective symptoms Particular life events → particular depression symptoms There is nothing wrong with structural equation models / factor models, and not all models imply a common cause (e.g., formative models) BUT: it may be interesting to explore to what degree problems of mental health may be better described as a causal system instead of an underlying common cause.

Network perspective Network research may help us gain important insights in a number of research domains Comorbidity Centrality & clinical relevance of symptoms Nomothetic & idiographic processes Missing heritability Network research may help us gain important insights into a number of disorders

Comorbidity DOI 10.1017/S0140525X09991567

Comorbidity DOI 10.1037/abn0000002

Comorbidity DOI 10.1371/journal.pone.0027407

Centrality A central symptom is one that exhibits a large number of connections in a network; switching on this symptom will likely spread symptom activation throughout the network A peripheral symptoms is on the corner of a network and has few connections Clinicians … judge causally central symptoms to be more typical symptoms recall central symptoms with greater accuracy than peripheral symptoms use central symptoms more for diagnosing patients DOI 10.1037//0096-3445.131.4.451

DOI 10.1016/j.jad.2015.09.005

Nomothetic vs idiographic information DOI 10.1017/S0033291714001809; Master Thesis by Renske Kroeze, 2014

Missing heritability Genetic liability in edges instead of nodes?

Network perspective Useful to gain insights into a number of disorders

Substance abuse networks DOI 10.1016/j.drugalcdep.2016.02.005

Substance abuse networks DOI 10.1016/j.drugalcdep.2016.02.005

Substance abuse networks DOI 10.1016/j.drugalcdep.2016.02.005

PTSD DOI 10.1177/2167702614553230

Bereavement DOI 10.1037/abn0000028