HighlightsCholinergic mechanisms modulate multiple stations of the pain pathway.Cholinomimetic drugs may provide a promising avenue in pain therapy.Bilateral cholinergic–opioidergic interactions are therapeutically important.Underlying circuits and mechanisms require further elucidation.Plasticity of cholinergic neurons and pathways is important to study.

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Transcranial, minimally-invasive stimulation of the primary motor cortex (M1) has recently emerged to show promise in treating clinically refractory neuropathic pain. However, there is a major need for improving efficacy, reducing variability and understanding mechanisms. Rodent models hold promise in helping to overcome these obstacles. However, there still remains a major divide between clinical and preclinical studies with respect to stimulation programs, analysis of pain as a multidimensional sensory-affective-motivational state and lack of focus on chronic phases of established pain. Here, we employed direct transcranial M1 stimulation (M1 tDCS) either as a single 5-day block or recurring blocks of repetitive stimulation over early or chronic phases of peripherally-induced neuropathic pain in mice. We report that repeated blocks of stimulation reverse established neuropathic mechanical allodynia more strongly than a single 5-day regime and also suppress cold allodynia, aversive behavior and anxiety without adversely affecting motor function over a long period. Activity mapping revealed highly selective alterations in the posterior insula, periaqueductal gray subdivisions and superficial spinal laminae in reversal of mechanical allodynia. Our preclinical data reveal multimodal analgesia and improvement in quality of life by multiple blocks of M1 tDCS and uncover underlying brain networks, thus helping promote clinical translation.

With the current unmet demand for effective analgesics and the opioid crisis, pain relief without major central adverse effects is highly appealing. In this issue of Neuron, Snyder et al. (2018) report on the localization, functions, and therapeutic potential of kappa opioid receptors in peripheral sensory neurons.

The basal nucleus of Meynert (NBM) subserves critically important functions in attention, arousal and cognition via its profound modulation of neocortical activity and is emerging as a key target in Alzheimer’s and Parkinson’s dementias. Despite the crucial role of neocortical domains in pain perception, however, the NBM has not been studied in models of chronic pain. Here, using in vivo tetrode recordings in behaving mice, we report that beta and gamma oscillatory activity is evoked in the NBM by noxious stimuli and is facilitated at peak inflammatory pain-like behavior. Optogenetic and chemogenetic cell-specific, reversible manipulations of NBM cholinergic-GABAergic neurons reveal their role in endogenous control of nociceptive hypersensitivity, which are manifest via projections to the prelimbic cortex, resulting in layer 5-mediated antinociception. Our data unravel the importance of the NBM in top-down control of neocortical processing of pain-like behavior.

The basal nucleus of Meynert (NBM) subserves critically important functions in attention, arousal and cognition via its profound modulation of neocortical activity and is emerging as a key target in Alzheimer’s and Parkinson’s dementias. Despite the crucial role of neocortical domains in pain perception, however, the NBM has not been studied in chronic pain. Here, using in vivo tetrode recordings in behaving mice, we report that beta and gamma oscillatory activity is evoked in the NBM by noxious stimuli and is facilitated at peak inflammatory pain. Optogenetic and chemogenetic cell-specific, reversible manipulations of NBM cholinergic-GABAergic neurons reveal their role in endogenous control of nociceptive hypersensitivity, which are manifest via projections to the prelimbic cortex, resulting in layer 5-mediated antinociception. Our data unravel the importance of the NBM in top-down control of neocortical processing of pain and suggest a potential for its therapeutic modulation via neurostimulation strategies in chronic pain disorders.

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Background: This ex vivo experimental study sought to compare screw planning accuracy of a self-derived deep-learning-based (DL) and a commercial atlas-based (ATL) tool and to assess robustness towards pathologic spinal anatomy. Methods: From a consecutive registry, 50 cases (256 screws in L1-L5) were randomly selected for experimental planning. Reference screws were manually planned by two independent raters. Additional planning sets were created using the automatic DL and ATL tools. Using Python, automatic planning was compared to the reference in 3D space by calculating minimal absolute distances (MAD) for screw head and tip points (mm) and angular deviation (degree). Results were evaluated for interrater variability of reference screws. Robustness was evaluated in subgroups stratified for alteration of spinal anatomy. Results: Planning was successful in all 256 screws using DL and in 208/256 (81%) using ATL. MAD to the reference for head and tip points and angular deviation was 3.93 ± 2.08 mm, 3.49 ± 1.80 mm and 4.46 ± 2.86° for DL and 7.77 ± 3.65 mm, 7.81 ± 4.75 mm and 6.70 ± 3.53° for ATL, respectively. Corresponding interrater variance for reference screws was 4.89 ± 2.04 mm, 4.36 ± 2.25 mm and 5.27 ± 3.20°, respectively. Planning accuracy was comparable to the manual reference for DL, while ATL produced significantly inferior results (p < 0.0001). DL was robust to altered spinal anatomy while planning failure was pronounced for ATL in 28/82 screws (34%) in the subgroup with severely altered spinal anatomy and alignment (p < 0.0001). Conclusions: Deep learning appears to be a promising approach to reliable automated screw planning, coping well with anatomic variations of the spine that severely limit the accuracy of ATL systems.