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Temporal Directionality Between Symptoms During Treatment of Depressed Inpatients: A Dynamic Time Warp Network

Daphne A Cocx-Swiebel¹, Gabrielle van Son¹, Anita Scheper¹

¹Mental Health Institute Rivierduinen, Leiden, Netherlands.

Clinical Psychology & Psychotherapy

|October 8, 2024

Summary

This study used dynamic time warp (DTW) analyses to explore depression symptom dynamics. Five key symptoms, including hopelessness and restlessness, were identified as having significant influence on others, suggesting potential targets for personalized treatment.

Keywords:

directionality dynamic time warp analysis idiographic approach major depressive disorder nomothetic approach symptom dynamics

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Area of Science:

Psychiatry
Network Science
Computational Psychology

Background:

Depression is often viewed through a common-cause disease model.
A network perspective highlights the mutual influence and dynamics of individual symptoms.
Understanding symptom dynamics is crucial for developing effective depression treatments.

Purpose of the Study:

To investigate the associations and directionality between 43 symptoms of depression.
To identify key symptoms that influence the dynamics of other symptoms using network analysis.
To explore the potential for personalized treatment strategies based on symptom dynamics.

Main Methods:

Employed exploratory longitudinal research design.
Utilized dynamic time warp (DTW) analyses on data from the Symptom Questionnaire-48 (SQ-48).
Analyzed individual and aggregated data to identify time-dependent patterns and symptom associations.

Main Results:

Group-level analyses revealed significant connections between symptoms, clustering them by pre-existing subscales.
Five symptoms demonstrated significant 'outstrength' in directed DTW analyses: 'hopeless', 'restless', 'down/depressed', 'feeling tense', and 'no enjoyment'.
These key symptoms' changes preceded changes in other symptoms, indicating a leading role in depression dynamics.

Conclusions:

The Symptom Questionnaire-48 (SQ-48) symptoms primarily capture internalizing problems in severely depressed inpatients, potentially limiting generalizability.
DTW network analysis identified five pivotal symptoms based on their dynamic influence.
Future research should investigate process-based therapies targeting high-outstrength symptoms for personalized depression treatment.