Identifying robust neural signatures of acupuncture modulation in healthy brains: a multimodal meta-analysis mapping core network.

UNLABELLED

Acupuncture, a cornerstone of Chinese integrative medicine, demonstrates clinical efficacy for pain- and inflammation-related conditions through extensive clinical practices. Using multimodal neuroimaging techniques (fMRI and PET), numerous studies have investigated acupuncture's impact on brain structure and function. However, a systematic integration of these findings is still lacking. To address this issue, we conducted a meta-analysis using the activation likelihood estimation (ALE) approach to identify brain regions that consistently modulated by verum acupuncture in both healthy populations and patients. Critically, establishing neural signatures in healthy populations provides a foundational baseline for normative brain responses, which is essential to detect pathological deviations in patient cohorts. Subsequent behavioral domain analyses elucidated the functional profiles of these significant clusters. To characterize whole brain functional connectivity architecture that associated with identified regions, we employed meta-analytic connectivity modeling (MACM) and seed-based resting-state functional connectivity (rsFC) analyses. Our analyses consistently implicated the bilateral inferior parietal lobule (extending into postcentral gyrus) and right thalamus, key regions within the somatosensory pain processing network, as core neural substrates that modulated by acupuncture in healthy populations. Functional connectivity analyses further demonstrated that these brain clusters engaged a distributed pain-processing network, including bilateral supplementary motor area (SMA), superior temporal gyrus (STG), and insular cortex. Taken together, these findings provide a mechanistic neurobiological basis for acupuncture-induced pain processing and identify potential therapeutic targets within the pain-associated neural circuitry.

SYSTEMATIC REVIEW REGISTRATION

https://www.crd.york.ac.uk/PROSPERO/view/CRD42024562566, identifier CRD42024562566.