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Intraindividual Dynamic Network Analysis - Implications for Clinical Assessment
Sarah Jo David1, Andrew J Marshall1, Emma K Evanovich1
1Department of Psychological Sciences, Texas Tech University, Psychological Sciences Building MS 2051, Lubbock, TX 79409-2051 USA.
Journal of Psychopathology and Behavioral Assessment
|June 26, 2018
Summary
This study introduces intraindividual dynamic network analysis (IDNA) to map symptom interactions over time. IDNA offers a personalized approach for understanding complex, comorbid mental health conditions.
Area of Science:
- Psychology
- Network Science
- Psychiatry
Background:
- Psychopathology is increasingly viewed as a system of interacting symptoms.
- Existing network analyses primarily use cross-sectional data, limiting understanding of dynamic symptom relationships.
- A dynamic, intraindividual approach is needed for complex comorbid cases.
Purpose of the Study:
- To introduce and describe an intraindividual dynamic network analysis (IDNA) method.
- To apply IDNA to examine day-to-day symptom dynamics in an individual with comorbid mood and anxiety disorders.
- To explore the clinical utility of IDNA for assessment and personalized treatment.
Main Methods:
- Utilized multivariate time series data to construct a lag-1 network.
- Analyzed dynamic lead-lag interrelations between symptoms within an individual.
- Examined network characteristics such as centrality and stability.
Main Results:
- Developed and demonstrated the IDNA approach for a complex comorbid case.
- Characterized the specific dynamic network of symptoms for the individual.
- Identified implications of network properties for clinical assessment.
Conclusions:
- Intraindividual dynamic network analysis (IDNA) provides a novel framework for understanding individual psychopathology.
- IDNA may offer incremental validity for idiographic assessment and personalized treatment planning.
- This person-specific approach is particularly valuable for complex and comorbid presentations.
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