Distraction From Rumination As An Underlying Mechanism Of The Antidepressant Effect Of Exercise: Using Machine Learning Algorithms To Decode Rumination From EEG Data During Exercise - Info and Reading Options

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Rumination is associated with the onset, duration and severity of a depression. Being distracted from ruminative thoughts (“distraction hypothesis”) is discussed as a possible mechanism of action of the antidepressant effect of moderate to vigorous exercise, which is well-established (Heissel et al., 2023; Morres et al., 2019). In this project, we decode rumination from electroencephalography (EEG) data using machine learning algorithms. Decoded rumination and self-reports are used to predict possible changes in rumination through exercise. Decoded rumination provides a more objective measure of rumination, additional to and beyond self-reports, that might be less biased and shed light into the underlying neurophysiological correlates of rumination. In this project, we will investigate whether moderate-intensity exercise (ME) reduces rumination compared to a sedentary control condition (SED). ME will be performed as continuous exercise at 100-110% of the individual first lactate threshold. In the sedentary control condition, participants sit inactive in a chair. Each condition is performed for 30 minutes. Participants will complete a single factor (ME vs. SED) within-subject design in randomised order while EEG is measured. EEG is applied with 59 electrodes according to the 10-20 system. Additionally, data is measured from 4 EOG electrodes, 1 electrode at muscle risorius, 4 bipolar electrodes at muscle trapezius and 4 bipolar electrodes at sternocleidomastoid muscle. In a previous part of the project (https://doi.org/10.17605/OSF.IO/C5JF9), decoders (i.e., support-vector classification models) are trained to predict rumination (versus distraction) from EEG data during experimentally induced rumination or distraction. In the current project, the trained decoders predict the class (i.e., rumination vs. distraction) and class probability of rumination from continuous EEG data features (i.e., alpha and theta power across the 59 channels and a connectivity matrix between all channels) measured during the exercises. The class probability for rumination is analysed in 7.5 s data segments across the time course of ME or SED, respectively. Furthermore, self-reported rumination will be assessed before and after each condition using the Perseverative Thinking Questionnaire state (PTQ-S; Ehring et al., 2011) and during the conditions using visual analogue scales (VAS). We hypothesize that the mean change of self-reported rumination as well as the mean decoded probability of rumination is significantly lower in the ME condition compared to the SED condition. By implementing a novel, more objective measurement of rumination in combination with validated and well-established self-reports, this project will help to understand whether distraction mediates the antidepressant effect of exercise.

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