Machine-Learning Based EEG Biomarkers for Personalized Interventions (EEG-INSIGHT)

July 30, 2024 updated by: Dolors Soler, Institut Guttmann

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.

Study Overview

Status

Recruiting

Conditions

Intervention / 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.

Study Type

Observational

Enrollment (Estimated)

58

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • Spain
    • Barcelona
      • Badalona, Barcelona, Spain, 08196
        • Recruiting
        • Institut Guttmann
        • Contact:
        • Principal Investigator:
          • Dolors Soler, PhD

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

Patients with spinal cord injury (SCI) suffering from neuropathic pain (NP).

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
NP Subjects
Subjects suffering from NP pain after an SCI. Will receive a tDCS treatment.
Device: transcranial Direct Current Stimulation
The treatment follows an approved neuromodulation protocol at our center (approved on Nov. 4 2021, valid until Nov. 4 2024). tDCS will be administered with a battery-powered DC stimulator (Sooma tDCS, Helsinki, Finland), using saline-saturated circular electrodes with a diameter of 6 cm². The anode will be positioned over C3 to stimulate the primary motor cortex (M1), and the cathode over the contralateral supraorbital area (FP2). For asymmetric pain, stimulation targets the M1 contralateral to the more painful side, and for symmetric pain, the dominant hemisphere (C3) is stimulated. The maximum current is 2 mA (current density: 0.06 mA/cm²). Each session lasts 30 minutes, conducted daily for two weeks (Monday to Friday), totaling 10 sessions. All stimulation parameters adhere to general safety guidelines for transcranial electrical stimulation (Bikson et al., 2016).
Other Names:
  • tDCS
  • tCS
Diagnostic test: Electroencephalography
64-channel active-electrode EEG with impedances kept ~5KOhm
Other Names:
  • EEG

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Patient Improvement as assessed by a composite score
Time Frame: After tDCS treatment (compared to score before tDCS treatment)

Composite Score Breakdown:

Neuropathic Pain Symptom Inventory (NPSI)

Items:

  • Constant pain intensity: Questions 1, 2, 3, 11, 12 (sum)
  • Constant pain persistence/frequency: Question 4
  • Pain crisis intensity: Questions 5, 6 (sum)
  • Pain crisis frequency: Question 7
  • Allodynia: Questions 8, 9, 10 (sum)

Criteria:

  • Numerical NPSI items are only used where pre-treatment pain ratings are 4/10 or higher.
  • Responder: Any NPSI item shows a 50% or larger reduction.

Brief Pain Inventory (BPI)

Items:

  • Pain Interference: Questions 3a - 3g (average)

Criteria:

  • Responder: 30% improvement or more.

Summary:

A subject is considered a responder if BOTH of these conditions are met:

  1. Improvement of 50% or more in pain intensity or frequency (NPSI).
  2. Improvement of 30% or more in pain interference (BPI).
After tDCS treatment (compared to score before tDCS treatment)

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Institut Guttmann

Collaborators

Castellers de la Vila de Gràcia

Investigators

  • Principal Investigator: Dolors Soler, PhD, Institut Guttmann

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

October 5, 2023

Primary Completion (Estimated)

November 7, 2025

Study Completion (Estimated)

November 7, 2025

Study Registration Dates

First Submitted

June 11, 2024

First Submitted That Met QC Criteria

July 30, 2024

First Posted (Actual)

July 31, 2024

Study Record Updates

Last Update Posted (Actual)

July 31, 2024

Last Update Submitted That Met QC Criteria

July 30, 2024

Last Verified

July 1, 2024

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2023412

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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