Neuropathic PainEEG-INSIGHTNCT06531317
Machine-Learning Based EEG Biomarkers for Personalized Interventions
An observational study of Neuropathic Pain and Spinal Cord Injuries, run by Institut Guttmann at 1 site in Spain.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 99 years
- Conditions the study is for
- Neuropathic Pain, Spinal Cord Injuries, Central Neuropathic Pain
- What is being tested?
- transcranial Direct Current Stimulation and Electroencephalography
- Type of treatment
- Medical device
- How many people
- 58 participants
- Where
- 1 siteSpain
- Study dates
- 5 Oct 2023to 7 Nov 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 99 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients with spinal cord injury (SCI) suffering from neuropathic pain (NP).
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
Spain1 site, 1 recruiting
- Recruiting
Institut Guttmann
Badalona, Barcelona, 08196
Dolors Soler, PhD, +34934977700, dsoler@guttmann.com
Dolors Soler, PhD
About this study
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.
Read the 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.
- Being tested
- transcranial Direct Current Stimulation, Electroencephalography
- Keywords
- Neuropathic Pain, EEG, Machine Learning, Spinal Cord Injury, tDCS
- Medical subject headings
- Neuralgia, Spinal Cord Injuries
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
transcranial Direct Current StimulationDevice
Also called tDCS, tCS
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).
ElectroencephalographyDiagnostic test
Also called EEG
64-channel active-electrode EEG with impedances kept ~5KOhm
| Study group | Role | What they receive |
|---|---|---|
| NP Subjects | Subjects suffering from NP pain after an SCI. Will receive a tDCS treatment. Receives: Device: transcranial Direct Current Stimulation; Diagnostic Test: Electroencephalography |
What is being measured
The primary outcome is the main question the study is designed to answer.
Patient Improvement as assessed by a composite score
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:
- Improvement of 50% or more in pain intensity or frequency (NPSI).
- Improvement of 30% or more in pain interference (BPI).
Measured: After tDCS treatment (compared to score before tDCS treatment)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted31 Jul 2024
- Study start5 Oct 2023
- Primary completion7 Nov 2025 (estimated)
- Study completion7 Nov 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (6)Papers the sponsor has linked to this study.
- Other trials for Neuropathic PainStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 58 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Institut Guttmann
- Sponsor type
- University, hospital or other
- Collaborators
- Castellers de la Vila de Gràcia
- Responsible party
- Principal investigator, Dolors Soler, Institut Guttmann
- Investigators
- Dolors Soler, PhD, Principal investigator, Institut Guttmann
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06531317
- Sponsor's protocol ID
- 2023412
- Acronym
- EEG-INSIGHT
- Status last verified
- Jul 2024
- First submitted
- 11 Jun 2024
- Last updated
- 31 Jul 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
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.
PubMed 33449513Bikson 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.
PubMed 27372845Zhdanov 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.
PubMed 31899530Vuckovic 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.
PubMed 29886266Mussigmann 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.
PubMed 35659993Mari 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.
PubMed 34425248
Study at a glance
From the registry record, last updated by the sponsor on 31 Jul 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Machine-Learning Based EEG Biomarkers for Personalized Interventions
- ClinicalTrials.gov ID
- NCT06531317
- Acronym / sponsor ID
- EEG-INSIGHT / 2023412
- Keywords
- Neuropathic Pain, EEG, Machine Learning, Spinal Cord Injury, tDCS
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 58 participants (estimated)
- Lead sponsor
- Institut GuttmannUniversity, hospital or other
- Collaborators
- Castellers de la Vila de Gràcia
- Study start
- 5 Oct 2023 (actual)
- Primary completion
- 7 Nov 2025 (estimated)
- Study completion
- 7 Nov 2025 (estimated)
- First posted
- 31 Jul 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| NP Subjects | – | Device: transcranial Direct Current Stimulation, Diagnostic Test: ElectroencephalographySubjects suffering from NP pain after an SCI. Will receive a tDCS treatment. |
Who may qualify
- Ages
- 18 to 99 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients with spinal cord injury (SCI) suffering from neuropathic pain (NP).
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (6 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| transcranial Direct Current StimulationAlso: tDCS, tCSThe 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). | Device | NP Subjects |
| ElectroencephalographyAlso: EEG64-channel active-electrode EEG with impedances kept ~5KOhm | Diagnostic test | NP Subjects |
Primary outcome
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).
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 31 Jul 2024.
- Background: PubMed 33449513Gewandter 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.
- Background: PubMed 27372845Bikson 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.
- Background: PubMed 31899530Zhdanov 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.
- Background: PubMed 29886266Vuckovic 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.
- Background: PubMed 35659993Mussigmann 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.
- Background: PubMed 34425248Mari 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Dolors Soler, PhD principal investigator
Institut Guttmann
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Neuropathic Pain and Spinal Cord Injuries, run by Institut Guttmann at 1 site in Spain.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 31 Jul 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Spain. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Dolors Soler Fernandez, PhD (+34934977700 ext. 2195, dsoler@guttmann.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Patient Improvement as assessed by a composite score (time frame: After tDCS treatment (compared to score before tDCS treatment)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06531317, which also lists 6 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 31 Jul 2024.