Closed-loop Non-invasive Brain Stiumlation

Développement De La Stimulation Non-invasive En Boucle Fermée

Status

Not yet recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06600217

Acronym

CLOSEDLOOP

Enrollment

Registered

2024-09-19

Start date

2024-10-31

Completion date

2027-09-30

Last updated

2024-09-19

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Methodologic Study, Non-invasive Brain Stimulation

Brief summary

Transcranial magnetic stimulation (TMS) is a widely used tool for exploring brain function in humans (Siebner et al. et al., 2022), which has led to new therapeutics for various psychiatric and neurological disorders (Lefaucheur et al., 2020). However, the open-loop use of this technique has raised questions about its operating principle, due to the high degree of heterogeneity of results and the small to medium observed effect sizes (Zrenner and Ziemann, 2023). To increase the response rate, it has been suggested to individualize stimulation, by adapting the TMS parameters (i.e. delivered dose, target dose, targeting, timing, etc.) to instantaneous estimates of brain brain state. Such an approach, known as closed-loop closed-loop stimulation, is currently one of the main challenges challenges in this field (closed-loop brain state-dependent stimulation). To this end, we are focusing on the combination of robotic TMS and electroencephalography (EEG) (Hernandez-Pavon et al. al., 2023). The closed-loop stimulations using this combination developed to date have two limitations: (i) they are not adaptive and focus focus mainly on calculating the phase of brain oscillations to trigger stimulation and (ii) are limited to central cortical (sensorimotor) areas, where the EEG signal-to-noise ratio is optimal. This project aims to develop closed-loop TMS-EEG protocols that overcome these two limitations: (i) by incorporating adaptive decision modeling (AutoHS model, Harquel et al. 2017) to optimize several parameters in parallel (coil location, orientation, intensity) while using a wider range of EEG markers (evoked potentials, oscillatory activity strength, connectivity, etc.), and (ii) by integrating real-time EEG pre-processing to access any cortical target (including frontal, temporal and occipital lobes).

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to 40 Years

Healthy volunteers

Yes

Inclusion criteria

* Signed informed consent * Having undergone a medical examination prior to participation in research * Affiliation with or beneficiary of a social security scheme

Exclusion criteria

* Contraindications (CI) to MRI, EEG, TMS practice * Existence of a severe general medical condition: cardiac, respiratory, hematological, renal, hepatic, cancerous, * Regular use of anxiolytics, sedatives, antidepressants, neuroleptics, * Characterized psychiatric pathology, * Suspicion of alcohol ingestion prior to the examination, * Participation in other interventional research protocols in progress with exclusion period or in the preceding week. * Persons covered by articles L1121-5 to L1121-8 of the CSP (French Public Health Code) (corresponds to all protected persons: pregnant women, parturients, nursing mothers, persons deprived of their liberty by judicial or administrative decision, persons under psychiatric care under articles L. 3212-1 and L. 3213-1 who are not covered by the provisions of article L. 1121-8, persons admitted to a health or social establishment for purposes other than research, minors, persons under legal protection or unable to express their consent). \- Personnel with a hierarchical link to the principal investigator

Design outcomes

Primary

Measure	Time frame	Description
Quality of EEG markers extracted in real-time	During the two TMS-EEG experimental sessions, at day 0 and up to day 30	Quality of EEG markers extracted in real-time (evoked potentials, power and phase of brain oscillations and functional connectivity)
Quality of EEG marker modulation	During the two TMS-EEG experimental sessions, at day 0 and up to day 30	Quality of EEG marker modulation induced by TMS parameters selected by the AutoHS decision model

Secondary

Measure	Time frame	Description
Intra-individual (inter-session) reproducibility of primary endpoint quality markers	Contrast between the 2 TMS-EEG experimental sessions, at day 0 up to day 30	Intra-individual (inter-session) reproducibility of primary endpoint quality markers

Contacts

Primary ContactSylvain Harquel, Dr.

sylvain.harquel@univ-grenoble-alpes.fr+33 (0) 4 76 74 81 56

Backup ContactMircea Polosan, Prof.

mpolosan@chu-grenoble.fr

Outcome results