Decoding Motor Imagery From Non-invasive Brain Recordings as a Prerequisite for Innovative Motor Rehabilitation Therapies

NCT06469463 | Recruiting

• 2 other identifiers: 69HCL23_13462024-A00161-46
• Study Type: interventional
• Target: 35
• Locations: 1 country
• Sites: 1

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 1.1 and 1.2 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 extensions/flexions. Task 1.2 investigates how lesions of patients alter our ability to decode attempted wrist movements.

Conditions

Stroke Sequelae; Motor Imagery; Upper Limb Deficit

Keywords

Motor imagery; motor rehabilitation; magnetoencephalography; electroencephalography

Outcome Measures

Primary Outcomes (1)

• Develop subject-specific generative models

  Movement Onset and Offset Detected During Real and Imagined Wrist Extension/Flexion Movements. Direction of Movement Detected During Real and Imagined Wrist Extension/Flexion Movements. Amplitude of Movement Measured During Real and Imagined Wrist Extension/Flexion Movements. Each of these outcome measures focuses on a specific aspect of the movement, providing all the necessary information to developp subject-specific generative models.

  2 years

Secondary Outcomes (1)

• Beta burst characterization.

  2 years

Study Arms (2)

Control group

PLACEBO COMPARATOR

Control group of healthy subjects

Device: MRI; Device: MEG; Device: EEG

Patients group

ACTIVE COMPARATOR

Patients group of post-stroke with upper-limb deficits on one side

Device: EEG

Interventions

MEG DEVICE

The healthy subjects will undergo hpMEG data while wearing 3D-printed headcasts created from high resolution MRI

Control group

EEG DEVICE

The healthy participants will undergo a similar session using EEG recording, using a Polhemus Fastrak system for localization of EEG electrodes and precise co-registration with anatomy and hpMEG data. The patients group will take part in the same EEG recording session.

Control group; Patients group

MRI DEVICE

The healthy subjects in the control group will perform an MRI head scan, which will be used to construct 3D head models and headcasts.

Control group

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Control group:
• Healthy subjects with age\> 18, male or female.
• Right handed (Due to the fact that unlike right-handed people, left-handed people tend to have more complicated somatotopic organization of the motor cortex, we will avoid confounding observed differences to differences attributable to anatomic factors).
• Registered with the French healthcare system.
• Motivated to participate in the study.
• Normal or corrected vision.
• Adequate knowledge of French language to be able to follow directions.
• Subjects must be able to listen and understand the study instructions.
• Subjects must be able to give written informed consent before participation.
• No history of neurological or psychiatric disease.
• No physical disability of the arms or wrists.
• No use of drugs affecting the central nervous system or self-reported abuse of any drugs.
• Patient group :
• Male or female stroke patients over 18 years old.
• Stroke patients must be not be in the acute phase of stroke.

You may not qualify if:

• Subjects with characteristics incompatible with MEG and MRI :
• \. Claustrophobia.
• Subjects with motor impairment (only applies to control group), severe traumatic brain injury.
• Subjects with chronic stroke (applies to both groups).
• Subjects history of skin disease or skin allergies (multiple or severe).
• Subjects who:
• have had an MRI within 2 weeks prior to the experiment.
• have implanted materials (any dental apparatus containing metal including or root canals or any metallic object, pacemaker, cochlear implanted in the body).
• Subjects working with metals in their professional lives.
• Pregnant or lactating women Pregnant or lactating women (check with a urinary test).
• Subjects who are not able to tolerate sitting for longer than 2 hours (the estimated length of an experimental session is about 2h30mins).
• Subjects with alcohol dependence (no consumption of alcohol or drugs at least 24 hours prior to the day of experiments).
• Subjects currently taking a medication that may have a strong effect on MEG or EEG recordings (e.g., antidepressants, stimulant medication, etc).
• Subjects who, in the opinion of the investigator, are not able or willing to comply with the protocol.
• Persons under guardianship or curatorship.

Sponsors & Collaborators

• Hospices Civils de Lyon: lead

Study Sites (1)

Centre de Recherche en Neurosciences de Lyon - INSERM U1028

Bron, 69675, France

RECRUITING

MeSH Terms

Interventions

Electroencephalography

Intervention Hierarchy (Ancestors)

Diagnostic Techniques, Neurological; Diagnostic Techniques and Procedures; Diagnosis; Electrodiagnosis

Central Study Contacts

Jérémie MATTOUT, MD

CONTACT

04 72 13 89 00; jeremie.mattout@inserm.fr

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: PARTICIPANT
• Purpose: BASIC SCIENCE
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: May 28, 2024
• First Posted: June 21, 2024
• Study Start: March 17, 2026
• Primary Completion (Estimated): August 1, 2028
• Study Completion (Estimated): August 1, 2028
• Last Updated: March 19, 2026

Record last verified: 2026-03

Locations

FR (1)