Manual acupuncture is a traditional needle-based stimulation method that delivers dynamic mechanical input to peripheral tissues through direct needle manipulation and has been widely used in both clinical and experimental settings1,2. Unlike electroacupuncture (EA), manual acupuncture generates complex mechanical stimulation, including tissue deformation and activation of peripheral mechanoreceptors, through physical manipulation of the inserted needle2. These mechanical components are considered important contributors to the characteristic physiological effects of acupuncture and are thought to engage somatosensory afferent pathways differently from electrical stimulation3,4.
Despite its physiological relevance and broad use, the experimental application of manual acupuncture remains limited by poor reproducibility2. The therapeutic effects of manual acupuncture largely depend on operator-dependent factors, including needle manipulation techniques, stimulation intensity, frequency, and duration, which can vary substantially among practitioners and experimental conditions1. This variability presents a major obstacle for mechanistic studies attempting to establish causal relationships between acupuncture stimulation and neurophysiological outcomes.
To overcome these limitations, EA has frequently been adopted as an alternative method because it enables precise control of stimulation parameters such as frequency, amplitude, and duration. However, EA introduces electrical input that may recruit neural pathways distinct from those activated by manual needling5. Consequently, it remains difficult to isolate and evaluate the specific contribution of mechanical stimulation, which represents a fundamental component of traditional manual acupuncture. Therefore, a reproducible method capable of delivering controlled mechanical stimulation without electrical confounds is needed for mechanistic acupuncture research.
To address this limitation, a mechanical acupuncture instrument (MAI) was developed in 2013 to reproduce vibration-based mechanical stimulation while enabling quantitative control of stimulation parameters3,5. This device delivers controlled vibratory stimulation directly to an inserted acupuncture needle, thereby standardizing the mechanical input applied to tissue. Using this approach, previous studies demonstrated that mechanical acupuncture modulates somatosensory, reward-related, and autonomic neural circuits across multiple experimental models4,6,7,8. For example, mechanical stimulation at HT7 acupoints attenuated cocaine-induced behavioral responses and alcohol dependence through modulation of mesolimbic dopamine circuitry, endogenous opioid pathways, and somatosensory relay circuits6,7. In addition, combined mechanical and electrical stimulation applied to neurogenic spots suppressed hypertension through opioid-mediated mechanisms in central autonomic regions5. Collectively, approximately 30 studies using this platform have contributed to the mechanistic understanding of acupuncture in addiction, autonomic regulation, and other experimental conditions.
Unlike general vibration-only stimulation systems, this protocol provides an integrated workflow for needle-coupled, non-electrical, vibration-based mechanical acupuncture in awake rats. It combines device assembly, needle-motor coupling, insertion-depth standardization, accelerometer-based calibration, acupuncture point localization, and representative behavioral validation using HT7 stimulation in a rat cocaine-induced locomotor activity model. Thus, this article provides a reproducible experimental protocol for controlled mechanical acupuncture stimulation rather than presenting a new therapeutic device.
The standardized and quantitatively controlled output of the MAI may also provide a useful platform for future integration with data-driven analytical approaches. Because stimulation parameters such as vibration frequency, acceleration, insertion depth, and stimulation duration can be precisely defined and reproduced, these variables may serve as structured inputs for computational modeling. Recent studies have shown that machine learning, deep learning, and transfer learning approaches can improve feature extraction, signal classification, pattern recognition, and optimization in complex biomedical images and vibration-based signal datasets9,10,11,12. Similar approaches could potentially be applied to MAI-based acupuncture research by integrating stimulation parameters with behavioral, neurophysiological, and vibration-output data to support signal analysis, behavioral classification, stimulation-parameter optimization, and prediction of acupuncture-related responses. Thus, future studies combining MAI-based stimulation with machine learning methodologies may further enhance the precision, reproducibility, and mechanistic interpretation of acupuncture research.
Importantly, the representative validation data presented in this article are reproduced, adapted, or summarized from previously published studies using the MAI platform. The primary objective of this article is not to report new experimental findings, but rather to provide a detailed, step-by-step protocol for the reproducible application of standardized mechanical acupuncture stimulation in experimental animal models.