Ignite Creation Date:
2024-07-17 @ 11:28 AM
Last Modification Date:
2024-10-26 @ 3:32 PM
Study NCT ID:
NCT06469463
Status:
NOT_YET_RECRUITING
Last Update Posted:
2024-06-21
First Post:
2024-05-28
Brief Title:
Decoding Motor Imagery From Non-invasive Brain Recordings as a Prerequisite for Innovative Motor Rehabilitation Therapies
Sponsor:
Hospices Civils de Lyon
Organization:
Hospices Civils de Lyon
Study Overview
Official Title:
Decoding Motor Imagery From Non-invasive Brain Recordings as a Prerequisite for Innovative Motor Rehabilitation Therapies
Status:
NOT_YET_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:
MODECO
Brief Summary:
Seminal studies in motor neuroscience involving healthy subjects have revealed time-locked changes in induced power within specific frequency bands Brain recordings were shown to exhibit a gradual reduction in signal power relative to baseline in the mu and beta frequency bands during an action or during motor imagery the event-related desynchronization ERD This is considered to reflect processes related to movement preparation and execution and is particularly pronounced in the contralateral sensorimotor cortex Shortly following the completion of the task a relative increase in power the event-related synchronization ERS could be observed in the beta band ERS is thought to reflect the re-establishment of inhibition in the same area
Ever since the characterization of the ERD and ERS phenomena there has been little to no discussion in the field of non-invasive Brain Computer Interfaces BCI as to whether these features accurately capture the task-related modulations of brain activity Recent studies in neurophysiology have demonstrated that the ERD and ERS patterns only emerge as a result of averaging signal power over multiple trials On a single trial level beta band activity occurs in short transient events bursts rather than as sustained oscillations This indicates that the ERD and ERS patterns reflect accumulated time-varying changes in the burst probability during each trial Thus beta bursts may carry more behaviourally relevant information than averaged beta band power Studies in humans involving arm movements have established a link between the timing of sensorimotor beta bursts and response times before movement as well as behavioural errors post-movement Beta burst activity in frontal areas has also been shown to correlate with movement cancellation and recent studies show that activity at the motor unit level also occurs in a transient manner which is time-locked to sensorimotor beta bursts
Although beta burst rate has been shown to carry significant information it still comprises a rather simplistic representation of the underlying activity Indeed complex burst waveforms are embedded in the raw signals and can be characterized by a stereotypical average shape with large variability around it The waveform features are neglected in standard BCI approaches because conventional signal processing methods generally presuppose sustained oscillatory and stationary signals and are thus inherently unsuitable for analysing transient activity
In contrast to beta activity in the mu frequency band is oscillatory even in single trials This activity is typically analysed using time-frequency decomposition techniques which assume that the underlying signal is sinusoidal However there is now growing consensus that oscillatory neural activity is often non-sinusoidal and that the raw waveform shape can be informative of movement
In this project the design of a subject-specific neurophysiological model to guide motor BCI training will be optimized using Magnetic Resonance Imaging MRI and Magnetoencephalography MEG for high spatial and biophysical specificity in the experimental group Anatomical MR volumes will be used to design and 3D-print an individual head cast that will be used in the MEG scanner to stabilize the head position and minimize movements This high-precision approach hpMEG has been proven to significantly improve source localization up to the level of distinguishing laminar activity which makes it superior to EEG recording technique An individualized hpMEG approach as well as the widely adopted EEG will be used to study bursts of oscillatory activity in the beta and mu frequency bands related to motor imagery and motor execution hpMEG will yield subject-specific models of motor imagery that will be used to constrain online decoding of EEG data This approach will be applied and validated on a group of healthy adult subjects and will then be compared against another feasibility group of patients and age-matched healthy participants The proposed approach will be compared with a classic EEG-based BCI approach
The information will be used to optimally guide subsequent EEG-based BCI training in the control group After a thorough investigation in healthy subjects in this project the feasibility of the approach will be evaluated in a few stroke patients with upper-limb motor deficits Tasks 11 and 12 aim to develop subject-specific generative models decoding movement onset and offset the type of movement as well as finely discretized movement amplitude during both real and imagined wrist extensionsflexions Task 12 investigates how lesions of patients alter our ability to decode attempted wrist movements
Ever since the characterization of the ERD and ERS phenomena there has been little to no discussion in the field of non-invasive Brain Computer Interfaces BCI as to whether these features accurately capture the task-related modulations of brain activity Recent studies in neurophysiology have demonstrated that the ERD and ERS patterns only emerge as a result of averaging signal power over multiple trials On a single trial level beta band activity occurs in short transient events bursts rather than as sustained oscillations This indicates that the ERD and ERS patterns reflect accumulated time-varying changes in the burst probability during each trial Thus beta bursts may carry more behaviourally relevant information than averaged beta band power Studies in humans involving arm movements have established a link between the timing of sensorimotor beta bursts and response times before movement as well as behavioural errors post-movement Beta burst activity in frontal areas has also been shown to correlate with movement cancellation and recent studies show that activity at the motor unit level also occurs in a transient manner which is time-locked to sensorimotor beta bursts
Although beta burst rate has been shown to carry significant information it still comprises a rather simplistic representation of the underlying activity Indeed complex burst waveforms are embedded in the raw signals and can be characterized by a stereotypical average shape with large variability around it The waveform features are neglected in standard BCI approaches because conventional signal processing methods generally presuppose sustained oscillatory and stationary signals and are thus inherently unsuitable for analysing transient activity
In contrast to beta activity in the mu frequency band is oscillatory even in single trials This activity is typically analysed using time-frequency decomposition techniques which assume that the underlying signal is sinusoidal However there is now growing consensus that oscillatory neural activity is often non-sinusoidal and that the raw waveform shape can be informative of movement
In this project the design of a subject-specific neurophysiological model to guide motor BCI training will be optimized using Magnetic Resonance Imaging MRI and Magnetoencephalography MEG for high spatial and biophysical specificity in the experimental group Anatomical MR volumes will be used to design and 3D-print an individual head cast that will be used in the MEG scanner to stabilize the head position and minimize movements This high-precision approach hpMEG has been proven to significantly improve source localization up to the level of distinguishing laminar activity which makes it superior to EEG recording technique An individualized hpMEG approach as well as the widely adopted EEG will be used to study bursts of oscillatory activity in the beta and mu frequency bands related to motor imagery and motor execution hpMEG will yield subject-specific models of motor imagery that will be used to constrain online decoding of EEG data This approach will be applied and validated on a group of healthy adult subjects and will then be compared against another feasibility group of patients and age-matched healthy participants The proposed approach will be compared with a classic EEG-based BCI approach
The information will be used to optimally guide subsequent EEG-based BCI training in the control group After a thorough investigation in healthy subjects in this project the feasibility of the approach will be evaluated in a few stroke patients with upper-limb motor deficits Tasks 11 and 12 aim to develop subject-specific generative models decoding movement onset and offset the type of movement as well as finely discretized movement amplitude during both real and imagined wrist extensionsflexions Task 12 investigates how lesions of patients alter our ability to decode attempted wrist movements
Detailed Description:
Seminal studies in motor neuroscience involving healthy subjects have since a long time revealed time-locked changes in induced power within specific frequency band Brain recordings were shown to exhibit a gradual reduction in signal power relative to baseline in the mu 8-12 Hz and beta 13-30 Hz frequency bands during an action or during motor imagery MI the so-called event-related desynchronization ERD This phenomenon is considered to reflect processes related to movement preparation and execution and is particularly pronounced in the contralateral sensorimotor cortex Moreover shortly following the completion of the task a relative increase in power the event-related synchronization ERS also referred to as the beta rebound could be observed in the beta band ERS is thought to reflect the re-establishment of inhibition in the same area
Ever since the characterization of the ERD and ERS phenomena there has been little to no discussion in the non-invasive BCI field as to whether these features accurately capture the task-related modulations of brain activity Recent studies in neurophysiology have challenged this view and have demonstrated that the ERD and ERS patterns only emerge as a result of averaging signal power over multiple trials On a single trial level beta band activity occurs in short transient events termed bursts rather than as sustained oscillations This indicates that the ERD and ERS patterns reflect accumulated time-varying changes in the burst probability during each trial Thus beta bursts may carry more behaviorally relevant information than averaged beta band power Indeed studies in humans involving arm movements have established a link between the timing of sensorimotor beta bursts and response times prior to movement as well as behavioral errors post-movement Beta burst activity in frontal areas has also been shown to correlate with movement cancellation and recent studies show that activity at the motor unit level also occurs in a transient manner which is time-locked to sensorimotor beta bursts
Although beta burst rate has been shown to carry significant information it still comprises a rather simplistic representation of the underlying activity Every burst can be characterized by a set of TF-based features the burst peak time and peak frequency as well as its duration and its span in the frequency axis In turn all these descriptors are extracted using a particular time-frequency transformation and constitute simpler representations of the more complex burst waveform that is embedded in the raw signals and which is characterized by a stereotypical average shape with large variability around it The waveform features are neglected in standard BCI approaches because conventional signal processing methods generally presuppose sustained oscillatory and stationary signals and are thus inherently unsuitable for analyzing transient activity
In contrast to beta activity in the mu frequency band is oscillatory even in single trials This activity is typically analyzed using time-frequency decomposition techniques which assume that the underlying signal is sinusoidal However there is now growing consensus that oscillatory neural activity is often non-sinusoidal and that the raw waveform shape can be informative of movement Future efforts could take advantage of this possibility by using recently developed non-parametric cycle-by-cycle analyzes
In this project the investigators will optimize the design of a subject-specific neurophysiological model to guide motor BCI training by using Magnetic Resonance Imaging MRI and Magnetoencephalography MEG for high spatial and biophysical specificity in the experimental group Anatomical MR volumes will be used to design and 3D-print an individual head-cast that will be used in the MEG scanner in order to stabilize the subjects head position and minimize movements This high precision approach hpMEG has been proven to significantly improve source localization up to the level of distinguishing laminar activity which makes it a superior-to-EEG recording technique In MODECO the investigators will use an individualized hpMEG approach as well as the widely adopted EEG to study bursts of oscillatory activity in the beta and mu frequency bands related to motor imagery and motor execution hpMEG will yield subject-specific models of motor imagery that will be used to constrain online decoding of EEG data This approach will be applied and validated on a group of healthy adult subjects control group and will then be compared against another feasibility group of patients patient group and age-matched healthy participants control group the investigators will attempt to recruit patients relatives the investigators will compare the proposed approach with a classic EEG-based BCI approach
the investigators will investigate how to use this information to optimally guide subsequent EEG-based BCI training in the control group After a thorough investigation in healthy subjects in this project the investigators will be able to evaluate this approach on a population of stroke patients with upper-limb motor deficits Two tasks have been designed in this project tasks 11 and task 12 The aim of Task 11 is to develop subject-specific generative models decoding movement onset and offset the type of movement left versus right as well as finely discretized movement amplitude during both real and imagined wrist extensionflexion movements In task 12 the investigators aim to investigate how the lesions of patients alter our ability to decode attempted wrist movements control vs patients group
Ever since the characterization of the ERD and ERS phenomena there has been little to no discussion in the non-invasive BCI field as to whether these features accurately capture the task-related modulations of brain activity Recent studies in neurophysiology have challenged this view and have demonstrated that the ERD and ERS patterns only emerge as a result of averaging signal power over multiple trials On a single trial level beta band activity occurs in short transient events termed bursts rather than as sustained oscillations This indicates that the ERD and ERS patterns reflect accumulated time-varying changes in the burst probability during each trial Thus beta bursts may carry more behaviorally relevant information than averaged beta band power Indeed studies in humans involving arm movements have established a link between the timing of sensorimotor beta bursts and response times prior to movement as well as behavioral errors post-movement Beta burst activity in frontal areas has also been shown to correlate with movement cancellation and recent studies show that activity at the motor unit level also occurs in a transient manner which is time-locked to sensorimotor beta bursts
Although beta burst rate has been shown to carry significant information it still comprises a rather simplistic representation of the underlying activity Every burst can be characterized by a set of TF-based features the burst peak time and peak frequency as well as its duration and its span in the frequency axis In turn all these descriptors are extracted using a particular time-frequency transformation and constitute simpler representations of the more complex burst waveform that is embedded in the raw signals and which is characterized by a stereotypical average shape with large variability around it The waveform features are neglected in standard BCI approaches because conventional signal processing methods generally presuppose sustained oscillatory and stationary signals and are thus inherently unsuitable for analyzing transient activity
In contrast to beta activity in the mu frequency band is oscillatory even in single trials This activity is typically analyzed using time-frequency decomposition techniques which assume that the underlying signal is sinusoidal However there is now growing consensus that oscillatory neural activity is often non-sinusoidal and that the raw waveform shape can be informative of movement Future efforts could take advantage of this possibility by using recently developed non-parametric cycle-by-cycle analyzes
In this project the investigators will optimize the design of a subject-specific neurophysiological model to guide motor BCI training by using Magnetic Resonance Imaging MRI and Magnetoencephalography MEG for high spatial and biophysical specificity in the experimental group Anatomical MR volumes will be used to design and 3D-print an individual head-cast that will be used in the MEG scanner in order to stabilize the subjects head position and minimize movements This high precision approach hpMEG has been proven to significantly improve source localization up to the level of distinguishing laminar activity which makes it a superior-to-EEG recording technique In MODECO the investigators will use an individualized hpMEG approach as well as the widely adopted EEG to study bursts of oscillatory activity in the beta and mu frequency bands related to motor imagery and motor execution hpMEG will yield subject-specific models of motor imagery that will be used to constrain online decoding of EEG data This approach will be applied and validated on a group of healthy adult subjects control group and will then be compared against another feasibility group of patients patient group and age-matched healthy participants control group the investigators will attempt to recruit patients relatives the investigators will compare the proposed approach with a classic EEG-based BCI approach
the investigators will investigate how to use this information to optimally guide subsequent EEG-based BCI training in the control group After a thorough investigation in healthy subjects in this project the investigators will be able to evaluate this approach on a population of stroke patients with upper-limb motor deficits Two tasks have been designed in this project tasks 11 and task 12 The aim of Task 11 is to develop subject-specific generative models decoding movement onset and offset the type of movement left versus right as well as finely discretized movement amplitude during both real and imagined wrist extensionflexion movements In task 12 the investigators aim to investigate how the lesions of patients alter our ability to decode attempted wrist movements control vs patients group
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 |
|---|---|---|---|
| 2024-A00161-46 | OTHER | None | None |