Ignite Creation Date:
2024-10-25 @ 7:58 PM
Last Modification Date:
2024-10-26 @ 3:36 PM
Study NCT ID:
NCT06531317
Status:
RECRUITING
Last Update Posted:
None
First Post:
2024-06-11
Brief Title:
Machine-Learning Based EEG Biomarkers for Personalized Interventions
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Machine-Learning Based EEG Biomarkers for Personalized Interventions
Status:
RECRUITING
Status Verified Date:
2024-07
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
EEG-INSIGHT
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
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 1020 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 006 mAcm²
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 brains 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 1020 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
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 1020 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 006 mAcm²
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 brains 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 1020 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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None