title: An n=1 Clinical Network Analysis of Symptoms and Treatment in Psychosis.
abstract: BACKGROUND
  Dynamic relationships between the symptoms of psychosis can be shown in individual networks of psychopathology. In a single patient, data collected with the Experience Sampling Method (ESM-a method to construct intensive time series of experience and context) can be used to study lagged associations between symptoms in relation to illness severity and pharmacological treatment.
  
  
  METHODS
  The patient completed, over the course of 1 year, for 4 days per week, 10 daily assessments scheduled randomly between 10 minutes and 3 hours apart. Five a priori selected symptoms were analysed: 'hearing voices', 'down', 'relaxed', 'paranoia' and 'loss of control'. Regression analysis was performed including current level of one symptom as the dependent variable and all symptoms at the previous assessment (lag) as the independent variables. Resulting regression coefficients were printed in graphs representing a network of symptoms. Network graphs were generated for different levels of severity: stable, impending relapse and full relapse.
  
  
  RESULTS
  ESM data showed that symptoms varied intensely from moment to moment. Network representations showed meaningful relations between symptoms, e.g. 'down' and 'paranoia' fuelling each other, and 'paranoia' negatively impacting 'relaxed'. During relapse, symptom levels as well as the level of clustering between symptoms markedly increased, indicating qualitative changes in the network. While 'hearing voices' was the most prominent symptom subjectively, the data suggested that a strategic focus on 'paranoia', as the most central symptom, had the potential to bring about changes affecting the whole network.
  
  
  CONCLUSIONS
  Construction of intensive ESM time series in a single patient is feasible and informative, particularly if represented as a network, showing both quantitative and qualitative changes as a function of relapse.
authors.affiliation: Department of Psychiatry and Psychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, The Netherlands.
authors.forename: Maarten
authors.identifier: http://orcid.org/0000-0003-1822-5542
authors.initials: M
authors.lastname: Bak
authors.affiliation: Department of Psychiatry and Psychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, The Netherlands.
authors.forename: Marjan
authors.initials: M
authors.lastname: Drukker
authors.affiliation: Department of Psychiatry and Psychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, The Netherlands.
authors.forename: Laila
authors.initials: L
authors.lastname: Hasmi
authors.affiliation: Department of Psychiatry and Psychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, The Netherlands.|King's College London, King's Health Partners, Department of Psychosis Studies, Institute of Psychiatry, London, United Kingdom.
authors.forename: Jim
authors.initials: J
authors.lastname: van Os
mesh_terms: D014150:Antipsychotic Agents / Q000008:administration & dosage / Q000627:therapeutic use*; D003024:Clozapine / Q000008:administration & dosage / Q000627:therapeutic use*; D004305:Dose-Response Relationship, Drug; D005260:Female; D006212:Hallucinations / Q000150:complications; D006801:Humans; D008875:Middle Aged; D010259:Paranoid Disorders / Q000150:complications; D011618:Psychotic Disorders / Q000150:complications / Q000188:drug therapy* / Q000503:physiopathology* / Q000523:psychology; D012044:Regression Analysis
chemical_list: D014150:Antipsychotic Agents; D003024:Clozapine