Interpretability of Spatiotemporal Dynamics of the Brain Processes Followed by Mindfulness Intervention in a Brain-Inspired Spiking Neural Network Architecture

被引:15
|
作者
Doborjeh, Zohreh [1 ,2 ,3 ]
Doborjeh, Maryam [4 ]
Crook-Rumsey, Mark [5 ]
Taylor, Tamasin [6 ]
Wang, Grace Y. [7 ]
Moreau, David [3 ,8 ]
Krageloh, Christian [7 ]
Wrapson, Wendy [9 ]
Siegert, Richard J. [7 ]
Kasabov, Nikola [10 ,11 ]
Searchfield, Grant [1 ,2 ,3 ]
Sumich, Alexander [5 ]
机构
[1] Univ Auckland, Sect Audiol, Sch Populat Hlth, Fac Med & Hlth Sci, Auckland 1142, New Zealand
[2] Univ Auckland, Eisdell Moore Ctr, Auckland 1142, New Zealand
[3] Univ Auckland, Ctr Brain Res, Auckland 1142, New Zealand
[4] Auckland Univ Technol, Informat Technol & Software Engn Dept, Auckland 1010, New Zealand
[5] Nottingham Trent Univ, Sch Psychol, Nottingham NG25 0QF, England
[6] Univ Auckland, Fac Med & Hlth Sci, Auckland 1142, New Zealand
[7] Auckland Univ Technol, Dept Psychol & Neurosci, Auckland 0627, New Zealand
[8] Univ Auckland, Sch Psychol, Auckland 1142, New Zealand
[9] Auckland Univ Technol, Sch Publ Hlth & Interdisciplinary Studies, Auckland 0627, New Zealand
[10] Ulster Univ, Intelligent Syst Res Ctr, Derry BT48 7JL, Londonderry, North Ireland
[11] Auckland Univ Technol, Sch Engn Comp & Math Sci, Auckland 1010, New Zealand
关键词
mindfulness; oddball-paradigm event-related potential (ERP) data; target and distractor stimuli; dynamic spatiotemporal brain data; computational modelling; spiking neural network; STATE FUNCTIONAL CONNECTIVITY; RESPONSE-INHIBITION; MEDITATION EXPERIENCE; ATTENTIONAL CONTROL; EMOTION REGULATION; EXECUTIVE CONTROL; EEG SIGNALS; P300; IMPACT; STRESS;
D O I
10.3390/s20247354
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Mindfulness training is associated with improvements in psychological wellbeing and cognition, yet the specific underlying neurophysiological mechanisms underpinning these changes are uncertain. This study uses a novel brain-inspired artificial neural network to investigate the effect of mindfulness training on electroencephalographic function. Participants completed a 4-tone auditory oddball task (that included targets and physically similar distractors) at three assessment time points. In Group A (n = 10), these tasks were given immediately prior to 6-week mindfulness training, immediately after training and at a 3-week follow-up; in Group B (n = 10), these were during an intervention waitlist period (3 weeks prior to training), pre-mindfulness training and post-mindfulness training. Using a spiking neural network (SNN) model, we evaluated concurrent neural patterns generated across space and time from features of electroencephalographic data capturing the neural dynamics associated with the event-related potential (ERP). This technique capitalises on the temporal dynamics of the shifts in polarity throughout the ERP and spatially across electrodes. Findings support anteriorisation of connection weights in response to distractors relative to target stimuli. Right frontal connection weights to distractors were associated with trait mindfulness (positively) and depression (inversely). Moreover, mindfulness training was associated with an increase in connection weights to targets (bilateral frontal, left frontocentral, and temporal regions only) and distractors. SNN models were superior to other machine learning methods in the classification of brain states as a function of mindfulness training. Findings suggest SNN models can provide useful information that differentiates brain states based on distinct task demands and stimuli, as well as changes in brain states as a function of psychological intervention.
引用
收藏
页码:1 / 29
页数:29
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