Ignite Creation Date:
2024-05-06 @ 7:29 PM
Last Modification Date:
2024-10-26 @ 3:07 PM
Study NCT ID:
NCT06025201
Status:
RECRUITING
Last Update Posted:
2024-06-24
First Post:
2023-08-24
Brief Title:
Detection of EEG-Based Biomarkers of Chronic Low Back Pain
Sponsor:
Stanford University
Organization:
Stanford University
Study Overview
Official Title:
Characterization of Longitudinal EEG Biomarkers in Chronic Low Back Pain
Status:
RECRUITING
Status Verified Date:
2024-06
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
Chronic low back pain CLBP is a pervasive disorder affecting up to one-fifth of adults globally and is the single greatest cause of disability worldwide Despite the high prevalence and detrimental impact of CLBP its treatments and mechanisms remain largely unclear Biomarkers that predict symptom progression in CLBP support precision-based treatments and ultimately aid in reducing suffering Longitudinal brain-based resting-state neuroimaging of patients with CLBP has revealed neural networks that predict pain chronification and its symptom progression Although early findings suggest that measurements of brain networks can lead to the development of prognostic biomarkers the predictive ability of these models is strongest for short-term follow-up Measurements of different neural systems may provide additional benefits with better predictive power
Emotional and cognitive dysfunction is common in CLBP occurring at the behavioral and cerebral level presenting a unique opportunity to detect prognostic brain-based biomarkers Likewise improvements in electroencephalogram EEG neuroimaging strategies have led to increased spatial resolution enabling researchers to overcome the limitations of classically used neuroimaging modalities eg magnetic resonance imaging MRI and functional MRI such as high cost and limited accessibility Using longitudinal EEG this patient-oriented research project will provide a comprehensive neural picture of emotional cognitive and resting-state networks in patients with CLBP which will aid in predicting symptom progression in CLBP Through this award the investigators will use modern EEG source analysis strategies to track biomarkers at baseline and 1- and 2-month follow-ups and their covariance with markers for pain and emotional and cognitive dysfunction A 5-month follow up will also be used to only assess patient reported outcomes In Aim 1 the investigators will identify and characterize differences in resting-state emotional and cognitive networks between patients with CLPB and agesex-matched controls In Aim 2 the investigators will identify within-subject changes across time and their relationship with clinical symptoms In Aim 3 as an exploratory aim the investigators will apply machine- and deep-learning strategies to detect a comprehensive signature of CLBP using EEG features from resting-state emotional and cognitive networks
Emotional and cognitive dysfunction is common in CLBP occurring at the behavioral and cerebral level presenting a unique opportunity to detect prognostic brain-based biomarkers Likewise improvements in electroencephalogram EEG neuroimaging strategies have led to increased spatial resolution enabling researchers to overcome the limitations of classically used neuroimaging modalities eg magnetic resonance imaging MRI and functional MRI such as high cost and limited accessibility Using longitudinal EEG this patient-oriented research project will provide a comprehensive neural picture of emotional cognitive and resting-state networks in patients with CLBP which will aid in predicting symptom progression in CLBP Through this award the investigators will use modern EEG source analysis strategies to track biomarkers at baseline and 1- and 2-month follow-ups and their covariance with markers for pain and emotional and cognitive dysfunction A 5-month follow up will also be used to only assess patient reported outcomes In Aim 1 the investigators will identify and characterize differences in resting-state emotional and cognitive networks between patients with CLPB and agesex-matched controls In Aim 2 the investigators will identify within-subject changes across time and their relationship with clinical symptoms In Aim 3 as an exploratory aim the investigators will apply machine- and deep-learning strategies to detect a comprehensive signature of CLBP using EEG features from resting-state emotional and cognitive networks
Detailed Description:
None
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None
Secondary IDs
| Secondary ID | Type | Domain | Link |
|---|---|---|---|
| 1K23AR083171-01 | NIH | None | httpsreporternihgovquickSearch1K23AR083171-01 |