To assess the cognitive effects and explore the neural signatures of analgesic limbic deep brain stimulation in patients with chronic neuropathic pain.

Patients with chronic neuropathic pain experience deficits in attention and cognitive flexibility. fMRI studies have shown the anterior cingulate (ACC) and orbitofrontal cortices (OFC), regions associated with attention and cognitive processing respectively, are important during acute and chronic pain conditions. Studies using deep brain stimulation (DBS) in the ACC have shown analgesic effects by modulating pain unpleasantness. However, despite efforts to modulate the affective dimension of pain, it remains unclear how analgesic DBS influences cognitive circuits and behavior.

We used a visual oddball paradigm to assess selective attention in chronic pain patients implanted with bilateral DBS electrodes targeting the ACC and OFC during ACC stimulation. Participants respond to the detection of a novel shape while local field potentials were recorded. Responses were time-locked to electrophysiological recordings and analyzed.

General task response rates (true positive rates) were unchanged between stimulation ON and OFF conditions for each individual patient. However, analgesic DBS significantly slowed reaction times by an average of ~70ms. Immediately following task responses, there were also response-associated low frequency signals in both the ACC and OFC. These signals were significantly increased in the stimulation ON condition.

Increased reaction time due to analgesic DBS could be explained by increased instances of attentional lapses. Since the ACC is an integral region for attention allocation, ACC stimulation drives changes in attentional demand that affects both attention to pain intensity and task-related attention. Global increases in low-frequency oscillations post-stimulation indicate increased attentional demand in response to a target stimulus in a distracted attentional state. These neural signatures of pain can be used in conjunction with other acute and chronic pain biomarkers to predict individual chronic pain states (abstract by Shirvalkar et. al.)