Objectives:
Current pharmacological antidepressant treatments suffer from low remission rates and slow initiation of therapeutic effects. In addition, the development of new antidepressant treatments is confounded by the lack of consensus on efficient and valid neurophysiological targets. Temporal complexity is an alternative measure of dynamic brain activity that estimates brain signal variability at several timescales. It can be easily extracted from non-invasive brain recordings and provides new insights into pathophysiological mechanisms. We aim to assess the potential of brain temporal complexity as a novel neuromarker to predict the effectiveness of antidepressant treatments.
Method:
We measured longitudinal changes in temporal complexity of electroencephalography signals in patients undergoing 8 weeks of escitalopram treatment through a Canadian Biomarker Integration Network in Depression (CAN-BIND) trial.
Results:
As early as 2 weeks after the start of treatment, reduction of complexity in fine timescales was associated with improvement in depressive symptoms. After 8 weeks of treatment, the treatment-related effect shifted towards an increase in coarse timescale complexity, linked to symptom improvement.
Conclusions:
These results suggest a relative shift away from local, segregated information processing, measured by complexity at fine timescales, in the short term, potentially in favour of a higher long-range communication across networks, as indicated by higher complexity measures at coarse timescales in the long term. Further research into the modulation of multiscale temporal complexity by antidepressant treatments could open new possibilities for faster-acting and more efficient treatments.