Mindfulness-Based Cognitive Therapy in Recurrent MDD Patients With Residual Symptoms: Alterations in Resting-State Theta Oscillation Dynamics Associated With Changes in Depression and Rumination

Effects of self-help mindfulness-based cognitive therapy on mindfulness, symptom change, and suicidal ideation in patients with depression: a randomized controlled study

Objective

This study aimed to evaluate the effects of self-help mindfulness-based cognitive therapy (MBCT-SH) on mindfulness, symptom change, and suicidal ideation in patients with depression.

Methods

For this randomized controlled study, 97 patients were randomly assigned to either the MBCT-SH (n = 48) or control (n = 49) group. The Five Facet Mindfulness Questionnaire (FFMQ), Hamilton Depression Rating Scale (HAMD-24), and Suicide Attitude Questionnaire (SAQ) were used to assess mindfulness, depression symptoms, and suicidal ideation, respectively, at baseline (T0), intervention week 4 (T1), intervention week 8 (T2), and 3-month follow-up (T3). The groups were also compared on treatment costs and readmission rates at a 6-month follow-up.

Results

In the MBCT-SH group, 46 of 48 participants (96%) completed the eight-week program. At T0, there were no statistically significant between-group differences in demographics, clinical characteristics, FFMQ, HAMD-24, or SAQ. Nor were there statistically significant differences on the HAMD-24 or SAQ between the MBCT-SH and control groups at T1 (p = 0.18 and p = 0.59, respectively), while mindfulness was significantly higher in the MBCT-SH group (t = 2.383, p = 0.019). At T2, there were significant between-group differences on the FFMQ, HAMD-24, and SAQ, all of which remained significant at T3. At the 6-month follow-up, per capita treatment costs were 5,298 RMB lower in the MBCT-SH group compared with the control group, while their readmission rates (6.1% and 4.2%, respectively) did not differ significantly.

Conclusion

These findings support the feasibility and effectiveness of MBCT-SH among patients with depression.

Clinical trial registration

http://www.chictr.org.cn, ChiCTR2300077850.

EEG-based investigation of effects of mindfulness meditation training on state and trait by deep learning and traditional machine learning

Introduction

This study examines the state and trait effects of short-term mindfulness-based stress reduction (MBSR) training using convolutional neural networks (CNN) based deep learning methods and traditional machine learning methods, including shallow and deep ConvNets as well as support vector machine (SVM) with features extracted from common spatial pattern (CSP) and filter bank CSP (FBCSP).

Methods

We investigated the electroencephalogram (EEG) measurements of 11 novice MBSR practitioners (6 males, 5 females; mean age 35.7 years; 7 Asians and 4 Caucasians) during resting and meditation at early and late training stages. The classifiers are trained and evaluated using inter-subject, mix-subject, intra-subject, and subject-transfer classification strategies, each according to a specific application scenario.

Results

For MBSR state effect recognition, trait effect recognition using meditation EEG, and trait effect recognition using resting EEG, from shallow ConvNet classifier we get mix-subject/intra-subject classification accuracies superior to related previous studies for both novice and expert meditators with a variety of meditation types including yoga, Tibetan, and mindfulness, whereas from FBSCP + SVM classifier we get inter-subject classification accuracies of 68.50, 85.00, and 78.96%, respectively.

Conclusion

Deep learning is superior for state effect recognition of novice meditators and slightly inferior but still comparable for both state and trait effects recognition of expert meditators when compared to the literatures. This study supports previous findings that short-term meditation training has EEG-recognizable state and trait effects.