Altered evoked low-frequency connectivity from SI to ACC following nerve injury in rats

Abstract

Objective. Despite decades of research on central processing of pain, there are still several unanswered questions, in particular regarding the brain regions that may contribute to this alerting sensation. Since it is generally accepted that more than one cortical area is responsible for pain processing, there is an increasing focus on the interaction between areas known to be involved. Approach. In this study, we aimed to investigate the bidirectional information flow from the primary somatosensory cortex (SI) to the anterior cingulate cortex (ACC) in an animal model of neuropathic pain.19 rats (nine controls and ten intervention) had an intracortical electrode implanted with six pins in SI and six pins in ACC, and a cuff stimulation electrode around the sciatic nerve. The intervention rats were subjected to the spared nerve injury after baseline recordings. Electrical stimulation at three intensities of both noxious and non-noxious stimulation was used to record electrically evoked cortical potentials. To investigate information flow, two connectivity measures were used: phase lag index (PLI) and granger prediction (GP). The rats were anesthetized during the entire study. Main results. Immediately after the intervention (<5 minutes after intervention), the high frequency (γ and γ+) PLI was significantly decreased compared to controls. In the last recording cycle (3-4 hours after intervention), the GP increased consistently in the intervention group. Peripheral nerve injury, as a model of neuropathic pain, resulted in an immediate decrease in information flow between SI and ACC, possibly due to decreased sensory input from the injured nerve. Hours after injury, the connectivity between SI and ACC increased, likely indicating hypersensitivity of this pathway. Significance. We have shown that both a directed and nondirected connectivity between SI and ACC approach can be used to show the acute changes resulting from the SNI model.