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Rising early warning signals in affect associated with future changes in depression: a dynamical systems approach

Published online by Cambridge University Press:  23 December 2021

Joshua E. Curtiss ,
David Mischoulon ,
Lauren B. Fisher ,
Cristina Cusin ,
Szymon Fedor ,
Rosalind W. Picard  and
Paola Pedrelli
Joshua E. Curtiss*
Affiliation:
Depression Clinical and Research Program at Massachusetts General Hospital, Boston, MA, USA Harvard Medical School, Boston, MA, USA
David Mischoulon
Affiliation:
Depression Clinical and Research Program at Massachusetts General Hospital, Boston, MA, USA Harvard Medical School, Boston, MA, USA
Lauren B. Fisher
Affiliation:
Depression Clinical and Research Program at Massachusetts General Hospital, Boston, MA, USA Harvard Medical School, Boston, MA, USA
Cristina Cusin
Affiliation:
Depression Clinical and Research Program at Massachusetts General Hospital, Boston, MA, USA Harvard Medical School, Boston, MA, USA
Szymon Fedor
Affiliation:
The Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
Rosalind W. Picard
Affiliation:
The Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
Paola Pedrelli
Affiliation:
Depression Clinical and Research Program at Massachusetts General Hospital, Boston, MA, USA Harvard Medical School, Boston, MA, USA
*
Author for correspondence: Joshua E. Curtiss, E-mail: [email protected]
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Abstract

Background

Predicting future states of psychopathology such as depressive episodes has been a hallmark initiative in mental health research. Dynamical systems theory has proposed that rises in certain ‘early warning signals’ (EWSs) in time-series data (e.g. auto-correlation, temporal variance, network connectivity) may precede impending changes in disorder severity. The current study investigates whether rises in these EWSs over time are associated with future changes in disorder severity among a group of patients with major depressive disorder (MDD).

Methods

Thirty-one patients with MDD completed the study, which consisted of daily smartphone-delivered surveys over 8 weeks. Daily positive and negative affect were collected for the time-series analyses. A rolling window approach was used to determine whether rises in auto-correlation of total affect, temporal standard deviation of total affect, and overall network connectivity in individual affect items were predictive of increases in depression symptoms.

Results

Results suggested that rises in auto-correlation were significantly associated with worsening in depression symptoms (r = 0.41, p = 0.02). Results indicated that neither rises in temporal standard deviation (r = −0.23, p = 0.23) nor in network connectivity (r = −0.12, p = 0.59) were associated with changes in depression symptoms.

Conclusions

This study more rigorously examines whether rises in EWSs were associated with future depression symptoms in a larger group of patients with MDD. Results indicated that rises in auto-correlation were the only EWS that was associated with worsening future changes in depression.

Keywords

Depressiontime seriesearly warning signalsdynamics
Type
Original Article
Information
Psychological Medicine , Volume 53 , Issue 7 , May 2023 , pp. 3124 - 3132
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

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