Calming the mind: spiking neural networks reveal how Havening touch to reduce persistent distress attenuates left temporal electroencephalographic connectivity

Sumich, A ORCID: https://orcid.org/0000-0003-4333-8442, Doborjeh, Z, Heym, N ORCID: https://orcid.org/0000-0003-2414-8854, Scott, A, Hunter, K, Burgess, T, French, J, Sarkar, M ORCID: https://orcid.org/0000-0002-8338-8500, Doborjeh, M and Kasabov, N, 2025. Calming the mind: spiking neural networks reveal how Havening touch to reduce persistent distress attenuates left temporal electroencephalographic connectivity. In: Mahmud, M, Doborjeh, M, Wong, K, Leung, ACS, Doborjeh, Z and Tanveer, M, eds., Neural information processing: 31st International Conference, ICONIP 2024, Auckland, New Zealand, December 2–6, 2024, proceedings, part XI. Lecture notes in computer science, 11 . Singapore: Springer, pp. 61-71. ISBN 9789819666058

Text 2492448_Sumich.pdf - Post-print Full-text access embargoed until 23 June 2026. Download (908kB)

Abstract

Havening is an innovative psychosensory intervention that uses touch to facilitate recovery from psychological trauma. Whilst Havening is commonly employed by practitioners worldwide, only a few empirical studies have investigated efficacy or mechanism. The current study applies explainable machine learning methods to brain data to better understand mechanisms underpinning the role of Havening touch in recovery from trauma. Participants (n = 27) who had experienced an event that caused persistent psychological distress underwent a single Havening session that either did (H+, n = 15) or did not (H−, n = 12) include a touch component. Resting-state electroencephalography (EEG) data was recorded before (T1) and after (T2) the intervention. Two H+ subgroups were formed based on self-reported-response to the intervention. A recently developed brain-inspired machine-learning model, the Spiking Neural Network (SNN) was applied to compare groups and time points on functional connectivity. Results suggest region-specific reductions (left temporal, left frontocentral) in connection weights following Havening touch. Differentiation of H+ and H− was more accurate at T2 than T1; and in H+, brain states at T2 were more accurately classified than at T1, particularly for participants who had a greater response to the intervention. Findings support the SNN in distinguishing brain states associated with response to Havening. Reduction in left temporal connectivity may reflect downregulation of anterior temporal lobe activity, downstream from the amygdala.

Item Type:	Chapter in book
Description:	Paper presented at the 31st International Conference on Neural Information Processing (ICONIP 2024), Auckland, New Zealand, 2-6 December 2024.
Creators:	Sumich, A., Doborjeh, Z., Heym, N., Scott, A., Hunter, K., Burgess, T., French, J., Sarkar, M., Doborjeh, M. and Kasabov, N.
Publisher:	Springer
Place of Publication:	Singapore
Date:	24 June 2025
Volume:	11
ISBN:	9789819666058
Identifiers:	Number Type 10.1007/978-981-96-6606-5_5 DOI 2492448 Other
Rights:	This version of the conference paper has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-981-96-6606-5_5.
Divisions:	Schools > School of Social Sciences
Record created by:	Melissa Cornwell
Date Added:	14 Nov 2025 15:21
Last Modified:	14 Nov 2025 15:21
URI:	https://irep.ntu.ac.uk/id/eprint/54739

Actions (login required)

Edit View

Statistics

Views

Downloads