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, 9 (1), 6367

Spiking Neural Network Modelling Approach Reveals How Mindfulness Training Rewires the Brain

Affiliations

Spiking Neural Network Modelling Approach Reveals How Mindfulness Training Rewires the Brain

Zohreh Doborjeh et al. Sci Rep.

Abstract

There has been substantial interest in Mindfulness Training (MT) to understand how it can benefit healthy individuals as well as people with a broad range of health conditions. Research has begun to delineate associated changes in brain function. However, whether measures of brain function can be used to identify individuals who are more likely to respond to MT remains unclear. The present study applies a recently developed brain-inspired Spiking Neural Network (SNN) model to electroencephalography (EEG) data to provide novel insight into: i) brain function in depression; ii) the effect of MT on depressed and non-depressed individuals; and iii) neurobiological characteristics of depressed individuals who respond to mindfulness. Resting state EEG was recorded from before and after a 6 week MT programme in 18 participants. Based on self-report, 3 groups were formed: non-depressed (ND), depressed before but not after MT (responsive, D+) and depressed both before and after MT (unresponsive, D-). The proposed SNN, which utilises a standard brain-template, was used to model EEG data and assess connectivity, as indicated by activation levels across scalp regions (frontal, frontocentral, temporal, centroparietal and occipitoparietal), at baseline and follow-up. Results suggest an increase in activation following MT that was site-specific as a function of the group. Greater initial activation levels were seen in ND compared to depressed groups, and this difference was maintained at frontal and occipitoparietal regions following MT. At baseline, D+ had great activation than D-. Following MT, frontocentral and temporal activation reached ND levels in D+ but remained low in D-. Findings support the SNN approach in distinguishing brain states associated with depression and responsiveness to MT. The results also demonstrated that the SNN approach can be used to predict the effect of mindfulness on an individual basis before it is even applied.

Conflict of interest statement

Auckland University of Technology (AUT) Strategic Research Investment Fund (SRIF) funded this research but had no influence on any part of research process or preparation of this manuscript.

Figures

Figure 1
Figure 1
A block diagram of the proposed method, consisting of: encoding EEG data into spike sequences; a brain-inspired 3D SNN structure for data mapping; learning; visualisation in 3D SNN; and output classification of patterns. (NB. Model used in photograph). (The person in this figure is the original picture of M. Doborjeh and taken by Z. Doborjeh who are the authors of this paper).
Figure 2
Figure 2
SNN connectivity trained on EEG samples that measured at T1 (before MT) and T2 (after MT), related to (a) non-depressed group (ND), (b) responsive-depressed group (D+) and (c) unresponsive-depressed group (D).
Figure 3
Figure 3
Differences between the connectivity in the trained SNN models of T1 (prior to MT) and T2 (post-training) in (a) non-depressed (ND) group, (b) responsive-depressed (D+) group, and (c) unresponsive-depressed (D) group. The connections in each neural cluster represent the areas of main changes in the EEG data at post-MT.
Figure 4
Figure 4
The Feature Interaction Network (FIN) captured the total spike interaction between the areas in the SNN models representing 62 EEG channels as input neurons during the STDP learning for: (a) non-depressed (ND); (b) responsive-depressed (D+); and (c) unresponsive depressed (D). FIN nodes represent the input neuronal areas of the SNN model and lines represent the amount of spike transmission between these areas (clusters) of neurons that correspond to the input neurons (EEG channels).
Figure 5
Figure 5
Spatio-temporal connectivity generated in the SNN models for responsive-depressed (D+) participants. The SNN models are visualised in both 3D (x, y, z) and 2-D (x, y) projections. (a) Delta frequency sub-band at T1 and delta at T2 (b) theta at T1 and theta at T2.
Figure 6
Figure 6
Spatio-temporal connectivity generated in the SNN models for responsive-depressed (D+) participants. The SNN models are visualised in both 3D (x, y, z) and 2-D (x, y) projections. (a) Alpha at T1 and alpha at T2, (b) beta at T1 and beta at T2.
Figure 7
Figure 7
Histogram of the number of connections and the connection weights in the SNN models for the D+ group trained on data corresponding to four EEG frequency sub-bands before (T1) and after (T2) MT. (a) Delta, (b) Theta, (c) Alpha, and (d) Beta.
Figure 8
Figure 8
The SNN connection weights prior to MT (T1) and after following 6 weeks of training (T2) in (a) ND group, (b) D+ group and (c) D group at Frontal, Temporal, Frontocentral, Centroparietal and Occipitoparietal clusters. Blue line represents the connectivity values in the SNN model of T1 (before mindfulness training) and green line represents T2 (after the mindfulness training).

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References

    1. Creswell JD. Mindfulness interventions. Annual review of psychology. 2017;68:491–516. doi: 10.1146/annurev-psych-042716-051139. - DOI - PubMed
    1. Keng S-L, Smoski MJ, Robins CJ. Effects of mindfulness on psychological health: A review of empirical studies. Clinical psychology review. 2011;31:1041–1056. doi: 10.1016/j.cpr.2011.04.006. - DOI - PMC - PubMed
    1. Lomas T, Ivtzan I, Fu CH. A systematic review of the neurophysiology of mindfulness on EEG oscillations. Neuroscience & Biobehavioral Reviews. 2015;57:401–410. doi: 10.1016/j.neubiorev.2015.09.018. - DOI - PubMed
    1. Long J, Briggs M, Astin F. Overview of Systematic Reviews of Mindfulness Meditation-based Interventions for People With Long-term Conditions. Advances in mind-body medicine. 2017;31:26–36. - PubMed
    1. Gouda S, Luong MT, Schmidt S, Bauer J. Students and teachers benefit from mindfulness-based stress reduction in a school-embedded pilot study. Frontiers in psychology. 2016;7:590. doi: 10.3389/fpsyg.2016.00590. - DOI - PMC - PubMed
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