Mathematical model of nerve fiber activation during low back peripheral nerve field stimulation: analysis of electrode implant depth.

OBJECTIVES: The lower back is the most common location of pain experienced by one-fifth of the European population reporting chronic pain. A peripheral nerve field stimulation system, which involves electrodes implanted subcutaneously in the painful area, has been shown to be efficacious for low back pain. Moreover, the predominant analgesic mechanism of action is thought to be via activation of peripheral Aß fibers. Unfortunately, electrical stimulation also might coactivate Aδ fibers, causing pain or unpleasantness itself. The aim of this study was to investigate at which implant depth Aß-fiber stimulation is maximized, and Aδ-fiber minimized, which in turn should lead to therapy optimization. MATERIALS AND METHODS: A finite element model was used to estimate the electrical potential generated by a bipolar single-lead electrode implanted in the subcutaneous adipose tissue at depths of 5 mm to 30 mm below the skin surface. The model includes low back tissue; the epidermis, dermis, adipose, and muscle layers, and nerve fibers, which were programmed to branch randomly in the model in a fiber type-specific manner. Likewise, activation thresholds were specific to Aß- and Aδ-fiber types and were estimated using a passive cable model. RESULTS: The stimulus-response functions showed that the skin area covered by Aß-fiber activation was larger than the area covered by Aδ-fiber activation at all depths and all intensities. The skin area covered by Aδ-fiber activation was largest when the electrode was modeled to have a superficial location (5 mm below the skin surface), while the skin area covered by Aß-fiber activation was largest at lower depths. CONCLUSIONS: The present mathematical model predicts an optimal implantation depth of 10 to 15 mm below the skin surface to achieve activation of the greatest area of Aß fibers and the smallest area of Aδ fibers. This finding may act as a guide for peripheral nerve field stimulation implant depth to treat low back pain.

Probabilistic model for individual assessment of central hyperexcitability using the nociceptive withdrawal reflex: a biomarker for chronic low back and neck pain.

BACKGROUND: The nociceptive withdrawal reflex (NWR) has been proven to be a valuable tool in the objective assessment of central hyperexcitability in the nociceptive system at spinal level that is present in some chronic pain disorders, particularly chronic low back and neck pain. However, most of the studies on objective assessment of central hyperexcitability focus on population differences between patients and healthy individuals and do not provide tools for individual assessment. In this study, a prediction model was developed to objectively assess central hyperexcitability in individuals. The method is based on statistical properties of the EMG signals associated with the nociceptive withdrawal reflex. The model also supports individualized assessment of patients, including an estimation of the confidence of the predicted result. RESULTS: up to 80% classification rates were achieved when differentiating between healthy volunteers and chronic low back and neck pain patients. EMG signals recorded after stimulation of the anterolateral and heel regions and of the sole of the foot presented the best prediction rates. CONCLUSIONS: A prediction model was proposed and successfully tested as a new approach for objective assessment of central hyperexcitability in the nociceptive system, based on statistical properties of EMG signals recorded after eliciting the NWR. Therefore, the present statistical prediction model constitutes a first step towards potential applications in clinical practice.