Clinical trial NCT06531317: Machine-Learning Based EEG Biomarkers for Personalized Interventions
Official title: Machine-Learning Based EEG Biomarkers for Personalized Interventions
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Neuropathic Pain
Spinal Cord Injuries
Central Neuropathic Pain
Keywords: Neuropathic Pain
EEG
Machine Learning
Spinal Cord Injury
tDCS
Lead sponsor: Institut Guttmann
Sponsor class: OTHER
Collaborators: name: Castellers de la Vila de Gràcia
First posted: 2024-07-31
Enrollment: 58
Sex: ALL
Minimum age (years): 18
Maximum age (years): 99
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients with spinal cord injury (SCI) suffering from neuropathic pain (NP).

Brief summary: 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.

Detailed description: This project aims to develop an artificial intelligence model to predict the response to a neuromodulation treatment (transcranial Direct Current Stimulation, tDCS) for neuropathic pain (NP) following spinal cord injury (SCI), based on electroencephalographic (EEG) signals and clinical assessments. The project consists of two stages:

Stage 1 involves an open trial where participants with SCI and NP will receive neuromodulation treatment at our center, with data collected before and after treatment.

Pre-Treatment Evaluation:

* Clinical assessment through interviews and validated questionnaires targeted at factors associated with neuropathic pain, depression, and other relevant components.
* EEG recording using a 64-channel device (Brain Products GmbH, Germany). EEG will be recorded in a soundproof room with participants in a resting state, first with eyes open for 5 minutes and then with eyes closed for another 5 minutes. Participants will be asked to avoid alcohol 12 hours prior and caffeine 3 hours before the recording.

Neuromodulation Treatment:

* The treatment protocol involves 10 sessions of non-invasive stimulation, each lasting 30 minutes.
* tDCS will be administered using a battery-powered DC stimulator (Sooma tDCS, Helsinki, Finland) with 6 cm² saline-saturated circular electrodes.
* The anode will be placed over C3 (EEG 10/20 system) to stimulate the primary motor cortex (M1) and the cathode over the contralateral supraorbital area (FP2).
* For asymmetric pain, stimulation will be applied to the M1 contralateral to the more painful hemibody. For symmetric pain, the dominant hemisphere (C3) will be stimulated.
* Maximum current delivered will be 2 mA (current density: 0.06 mA/cm²).
* Sessions will be held once daily for two weeks (Monday to Friday), totaling 10 sessions. All stimulation parameters adhere to general safety guidelines for transcranial electrical stimulation .

Post-Treatment Evaluation:

• Conducted through interviews and the same validated questionnaires used in the pre-treatment assessment.

As part of the intervention, participants will undergo EEG recording to study the brain's bioelectrical activity non-invasively. Active surface electrodes with electrode gel will be used to enhance skin conductivity. EEG recordings will be conducted at rest, with participants looking at a blank wall in a soundproof room, for 5 minutes with eyes open and 5 minutes with eyes closed.

Stage 2 involves developing a predictive model to classify patients based on their response to the neuromodulation treatment. The model will use metrics derived from pre-treatment EEG recordings and clinical assessments conducted before and after the treatment, with the goal of predicting which patients will respond favorably to tDCS.

EEG preprocessing will be performed by means of the Python programming language, using a custom-made preprocessing pipeline based on the MNE-Python library including: selective outlier channel and segment elimination, frequency filters, supervised auto-labeled independent component analysis for the elimination of muscular and ocular activity, and detection of bridged electrodes.

The EEG recordings will be analyzed using metrics derived from the frequency, complexity and connectivity of the EEG signal. These metrics were selected due to their demonstrated potential in related publications, which highlight the capability of these features to capture differences between groups, either between treatment responders and non-responders, or between healthy subjects and those suffering from NP, among others. Based on these EEG features and other features derived from patient questionnaires, a feature selection process based on metric independence and relevance in previous literature will be carried out in order to maximize model generalizability.

A machine learning (ML) model, with the main candidate model being a support vector machine (SVM), will be used in order to classify between responders and non-responders. The model will be validated by means of k-fold cross-validation. Given satisfactory results, an undersampling of EEG channels (adhering to typical 10:20 setups) will be used to evaluate whether an EEG with less electrodes can yield similar predictive results, thus reducing the need for EEG systems with a high electrode count.

Eligibility criteria: Inclusion Criteria:

* 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.

Exclusion Criteria:

* Patients with severe pain (NRS \> 7) from other sources, such as musculoskeletal pain, inflammatory pain, or cancer-related pain.
* Subjects with traumatic brain injury.
* Subjects with alcohol abuse.
* Subjects with neurological diseases other than the specified spinal cord injury.
* Subjects with substance abuse.
* Subjects with any other chronic medical condition where transcranial tDCS is relatively contraindicated, such as pregnancy or epilepsy.

Primary outcomes:
measure: Patient Improvement as assessed by a composite score
timeFrame: After tDCS treatment (compared to score before tDCS treatment)
Secondary outcomes:
Other outcomes:
Locations:
city: Badalona
country: Spain
facility: Institut Guttmann
state: Barcelona
Study officials:
affiliation: Institut Guttmann
name: Dolors Soler, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Gewandter JS, McDermott MP, Evans S, Katz NP, Markman JD, Simon LS, Turk DC, Dworkin RH. Composite outcomes for pain clinical trials: considerations for design and interpretation. Pain. 2021 Jul 1;162(7):1899-1905. doi: 10.1097/j.pain.0000000000002188. No abstract available.
citation: Bikson M, Grossman P, Thomas C, Zannou AL, Jiang J, Adnan T, Mourdoukoutas AP, Kronberg G, Truong D, Boggio P, Brunoni AR, Charvet L, Fregni F, Fritsch B, Gillick B, Hamilton RH, Hampstead BM, Jankord R, Kirton A, Knotkova H, Liebetanz D, Liu A, Loo C, Nitsche MA, Reis J, Richardson JD, Rotenberg A, Turkeltaub PE, Woods AJ. Safety of Transcranial Direct Current Stimulation: Evidence Based Update 2016. Brain Stimul. 2016 Sep-Oct;9(5):641-661. doi: 10.1016/j.brs.2016.06.004. Epub 2016 Jun 15.
citation: Zhdanov A, Atluri S, Wong W, Vaghei Y, Daskalakis ZJ, Blumberger DM, Frey BN, Giacobbe P, Lam RW, Milev R, Mueller DJ, Turecki G, Parikh SV, Rotzinger S, Soares CN, Brenner CA, Vila-Rodriguez F, McAndrews MP, Kleffner K, Alonso-Prieto E, Arnott SR, Foster JA, Strother SC, Uher R, Kennedy SH, Farzan F. Use of Machine Learning for Predicting Escitalopram Treatment Outcome From Electroencephalography Recordings in Adult Patients With Depression. JAMA Netw Open. 2020 Jan 3;3(1):e1918377. doi: 10.1001/jamanetworkopen.2019.18377.
citation: Vuckovic A, Gallardo VJF, Jarjees M, Fraser M, Purcell M. Prediction of central neuropathic pain in spinal cord injury based on EEG classifier. Clin Neurophysiol. 2018 Aug;129(8):1605-1617. doi: 10.1016/j.clinph.2018.04.750. Epub 2018 May 23.
citation: Mussigmann T, Bardel B, Lefaucheur JP. Resting-state electroencephalography (EEG) biomarkers of chronic neuropathic pain. A systematic review. Neuroimage. 2022 Sep;258:119351. doi: 10.1016/j.neuroimage.2022.119351. Epub 2022 Jun 2.
citation: Mari T, Henderson J, Maden M, Nevitt S, Duarte R, Fallon N. Systematic Review of the Effectiveness of Machine Learning Algorithms for Classifying Pain Intensity, Phenotype or Treatment Outcomes Using Electroencephalogram Data. J Pain. 2022 Mar;23(3):349-369. doi: 10.1016/j.jpain.2021.07.011. Epub 2021 Aug 21.