• F Guetarni and P Rigoard.
• N(3)Lab (Neuromodulation & Neural Networks), Poitiers university hospital, 86021 Poitiers cedex, France. Electronic address: f.guetarni@chu-poitiers.fr.
• Neurochirurgie. 2015 Mar 1;61 Suppl 1:S90-8.

IntroductionConventional spinal cord stimulation (SCS) generates paraesthesia, as the efficacy of this technique is based on the relationship between the paraesthesia provided by SCS on the painful zone and an analgesic effect on the stimulated zone. Although this basic postulate is based on clinical evidence, it is clear that this relationship has never been formally demonstrated by scientific studies. There is a need for objective evaluation tools ("transducers") to transpose electrical signals to clinical effects and to guide therapeutic choices.Material And MethodsWe have developed a software at Poitiers University hospital allowing real-time objective mapping of the paraesthesia generated by SCS lead placement and programming during the implantation procedure itself, on a touch screen interface.ObjectivesThe purpose of this article is to describe this intraoperative mapping software, in terms of its concept and technical aspects.Results And DiscussionThe Neuro-Mapping Locator (NML) software is dedicated to patients with failed back surgery syndrome, candidates for SCS lead implantation, to actively participate in the implantation procedure. Real-time geographical localization of the paraesthesia generated by percutaneous or multicolumn surgical SCS lead implanted under awake anaesthesia allows intraoperative lead programming and possibly lead positioning to be modified with the patient's cooperation. Software updates should enable us to refine objectives related to the use of this tool and minimize observational biases. The ultimate goals of NML software should not be limited to optimize one specific device implantation in a patient but also allow to compare instantaneously various stimulation strategies, by characterizing new technical parameters as "coverage efficacy" and "device specificity" on selected subgroups of patients. Another longer-term objective would be to organize these predictive factors into computer science ontologies, which could constitute robust and helpful data for device selection and programming of tomorrow's neurostimulators.Copyright © 2014 Elsevier Masson SAS. All rights reserved.

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