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.