Machine-Learning Based EEG Biomarkers for Personalized Interventions
The goal of this observational study is to develop a machine learning model to predict the outcome of a transcranial direct current stimulation (tDCS) treatment in patients suffering from neuropathic pain derived from a spinal cord injury. The main question it aims to answer is: • Can electroencephalography (EEG) and clinical assessment data predict the success of tDCS treatment in neuropathic pain patients? Participants will: * Undergo EEG recording sessions to collect brain activity data before treatment. * Complete clinical assessments, including medical diagnostics and questionnaires focused on factors related to neuropathic pain before and after treatment.
• Age: Over 18 years old.
• Neuropathic Pain (NP): Subacute NP at or below the lesion level for at least 1 month following spinal cord injury or disease. Persistent NP is defined as pain in an area of sensory abnormality corresponding to the spinal cord injury according to international criteria (Bryce et al. 2012). The pain should not be primarily related to spasms or any other movement.
• Pain Intensity: At least 4 out of 10 on the Numerical Rating Scale (NRS) at the time of screening (rated during the previous 24 hours).
• Pharmacological Treatment: Stable treatment including antiepileptic, antidepressant, or antispastic drugs (Gabapentin (GBP) with a minimum dose of 900 mg/day, Pregabalin (PGB) with a minimum dose of 150 mg/day, Amitriptyline with a minimum dose of 25 mg/day). No dose changes for at least 2 weeks prior to treatment and no additional antiepileptic medication. The pharmacological regimen must be maintained without changes during the 10-day stimulation period and until the electrophysiological measurement. It is recommended to keep the regimen stable until the completion of the following two evaluations (4 and 12 weeks after the end of treatment). Only paracetamol or anti-inflammatory drugs are allowed as rescue treatment.