Karim Jerbi

Combining virtual reality and hypnosis to alleviate chronic pain in elderly with hand arthritis: protocol for a randomised phase II clinical trial

Valentyn Fournier

Marie-Fania Simard

Sai Yan Yuen

Joséphine Guiné

Floriane Rousseaux

Julie Lebeau

Karim Jerbi

Philippe Richebé

Mathieu Landry

Pierre Rainville

David Ogez

Abstract Introduction Chronic pain is a common health condition that significantly impacts the quality of life of those affected, affecting … (voir plus)one in five people in Canada. The prevalence of this condition tends to increase with age, making it a major health issue given the ageing population. However, its management remains inadequate and requires significant mobilisation of healthcare professionals as well as the development of multiple therapeutic solutions. Among these, non-pharmacological interventions such as hypnosis and virtual reality have proven effective. Nevertheless, while the existing literature seems promising, it presents methodological limitations. Therefore, this study aims to assess the effectiveness of an intervention combining virtual reality and hypnosis in an ageing population suffering from a widespread chronic pain condition, that is, hand arthritis. Methods and analysis This study will be a single-centre randomised clinical trial. Participants will be randomly assigned to one of two conditions: one receiving an intervention combining virtual reality and hypnosis, and the other receiving only virtual reality. The effectiveness of the intervention on current perceived pain before and after the intervention (primary outcome) will be evaluated. Secondary outcomes will include anxiety and depressive symptoms, quality of life, relaxation and fatigue. Exploratory analyses will also be conducted to contribute to the emerging literature by examining physiological variables such as heart rate variability, respiratory rate and electrodermal activity during the intervention, and their relationship with primary and secondary outcomes. Ethics and dissemination The project was approved by the Research Ethical Committee of the Hospital Maisonneuve-Rosemont (Project no 2024-3539). Participants will be asked to provide written consent for their participation. Results from this study will be shared through peer-reviewed publications, as well as oral and poster presentations at scientific events. The protocol for this study was preregistered on Open Science Framework and raw anonymised data will be available on this platform (https://osf.io/vbh72/?view_only=1d17c5708f894faab6669d85e1fde75d). Trial registration number NCT06833905.

2025-10-05

BMJ Open (publié)

doi.org

Combining virtual reality and hypnosis to alleviate chronic pain in elderly with hand arthritis: protocol for a randomised phase II clinical trial

Valentyn Fournier

Marie-Fania Simard

Sai Yan Yuen

Joséphine Guiné

Floriane Rousseaux

Julie Lebeau

Karim Jerbi

Philippe Richebé

Mathieu Landry

Pierre Rainville

David Ogez

2025-10-05

BMJ Open (publié)

doi.org

Combining virtual reality and hypnosis to alleviate chronic pain in elderly with hand arthritis: protocol for a randomised phase II clinical trial

Valentyn Fournier

Marie-Fania Simard

Sai Yan Yuen

Joséphine Guiné

Floriane Rousseaux

Julie Lebeau

Karim Jerbi

Philippe Richebé

Mathieu Landry

Pierre Rainville

David Ogez

Abstract Introduction Chronic pain is a common health condition that significantly impacts the quality of life of those affected, affecting … (voir plus)one in five people in Canada. The prevalence of this condition tends to increase with age, making it a major health issue given the ageing population. However, its management remains inadequate and requires significant mobilisation of healthcare professionals as well as the development of multiple therapeutic solutions. Among these, non-pharmacological interventions such as hypnosis and virtual reality have proven effective. Nevertheless, while the existing literature seems promising, it presents methodological limitations. Therefore, this study aims to assess the effectiveness of an intervention combining virtual reality and hypnosis in an ageing population suffering from a widespread chronic pain condition, that is, hand arthritis. Methods and analysis This study will be a single-centre randomised clinical trial. Participants will be randomly assigned to one of two conditions: one receiving an intervention combining virtual reality and hypnosis, and the other receiving only virtual reality. The effectiveness of the intervention on current perceived pain before and after the intervention (primary outcome) will be evaluated. Secondary outcomes will include anxiety and depressive symptoms, quality of life, relaxation and fatigue. Exploratory analyses will also be conducted to contribute to the emerging literature by examining physiological variables such as heart rate variability, respiratory rate and electrodermal activity during the intervention, and their relationship with primary and secondary outcomes. Ethics and dissemination The project was approved by the Research Ethical Committee of the Hospital Maisonneuve-Rosemont (Project no 2024-3539). Participants will be asked to provide written consent for their participation. Results from this study will be shared through peer-reviewed publications, as well as oral and poster presentations at scientific events. The protocol for this study was preregistered on Open Science Framework and raw anonymised data will be available on this platform (https://osf.io/vbh72/?view_only=1d17c5708f894faab6669d85e1fde75d). Trial registration number NCT06833905.

2025-10-01

BMJ Open (publié)

doi.org

Intrinsic Neural Oscillations Predict Verbal Learning Performance and Encoding Strategy Use

Victor Oswald

Mathieu Landry

Hamza Abdelhedi

Sarah Lippé

Philippe Robaey

Karim Jerbi

2025-09-28

bioRxiv (prépublication)

doi.org

Hitting the right pitch: Cortical tracking of speech fundamental frequency in auditory and somatomotor regions

Yorguin-Jose Mantilla-Ramos

Ana-Sofía Hincapié-Casas

Annalisa Pascarella

Tarek Lajnef

Richard M. Leahy

Emily B.J. Coffey

Karim Jerbi

Véronique Boulenger

Low-frequency neural oscillations contribute to the parsing of continuous speech into linguistic units. Little is known however on the coupl… (voir plus)ing of brain rhythms to higher-frequencies in speech such as fundamental frequency (F0) or pitch. Using magnetoencephalography, we investigated whole-brain cortical tracking of F0 while participants listened to sentences produced at normal rate or fast rate, where pitch naturally increases, and to artificially accelerated sentences, where F0 remains unchanged. Our results revealed significant brain-to-F0 coupling across all speech rates not only in right auditory but also in right parietal, insular, and pre- and postcentral regions, likely including the ventral larynx area. Importantly, the cortico-acoustic coupling peak frequency was higher for natural fast speech to reflect the corresponding F0 increase compared to normal rate and time-compressed speech. These findings demonstrate the engagement of an auditory-somato-motor network in F0 tracking, supporting its role in facilitating phonemic processing during the perception of naturally-produced speech.

2025-09-13

bioRxiv (prépublication)

doi.org

Uncovering executive function profiles within interindividual variability: A data driven clustering exploration of design fluency in school-aged children

Myriam Sahraoui

Karim Jerbi

Vanessa Hadid

Bruno Gauthier

2025-08-24

bioRxiv (prépublication)

doi.org

The 2025 PNPL Competition: Speech Detection and Phoneme Classification in the LibriBrain Dataset

Gilad Landau

Miran Ozdogan

Gereon Elvers

Francesco Mantegna

Pratik Somaiya

Dulhan Hansaja Jayalath

Luisa Kurth

Teyun Kwon

Brendan Shillingford

Greg Farquhar

Minqi Jiang

Karim Jerbi

Hamza Abdelhedi

Yorguin Mantilla Ramos

Caglar Gulçehre

M. Woolrich

Natalie Voets

Oiwi Parker Jones

The advance of speech decoding from non-invasive brain data holds the potential for profound societal impact. Among its most promising appli… (voir plus)cations is the restoration of communication to paralysed individuals affected by speech deficits such as dysarthria, without the need for high-risk surgical interventions. The ultimate aim of the 2025 PNPL competition is to produce the conditions for an"ImageNet moment"or breakthrough in non-invasive neural decoding, by harnessing the collective power of the machine learning community. To facilitate this vision we present the largest within-subject MEG dataset recorded to date (LibriBrain) together with a user-friendly Python library (pnpl) for easy data access and integration with deep learning frameworks. For the competition we define two foundational tasks (i.e. Speech Detection and Phoneme Classification from brain data), complete with standardised data splits and evaluation metrics, illustrative benchmark models, online tutorial code, a community discussion board, and public leaderboard for submissions. To promote accessibility and participation the competition features a Standard track that emphasises algorithmic innovation, as well as an Extended track that is expected to reward larger-scale computing, accelerating progress toward a non-invasive brain-computer interface for speech.

2025-06-11

ArXiv (prépublication)

arxiv.org

The 2025 PNPL Competition: Speech Detection and Phoneme Classification in the LibriBrain Dataset

Gilad Landau

Miran Ozdogan

Gereon Elvers

Francesco Mantegna

Pratik Somaiya

Dulhan Hansaja Jayalath

Luisa Kurth

Teyun Kwon

Brendan Shillingford

Greg Farquhar

Minqi Jiang

Karim Jerbi

Hamza Abdelhedi

Yorguin Mantilla Ramos

Caglar Gulçehre

M. Woolrich

Natalie Voets

Oiwi Parker Jones

The advance of speech decoding from non-invasive brain data holds the potential for profound societal impact. Among its most promising appli… (voir plus)cations is the restoration of communication to paralysed individuals affected by speech deficits such as dysarthria, without the need for high-risk surgical interventions. The ultimate aim of the 2025 PNPL competition is to produce the conditions for an"ImageNet moment"or breakthrough in non-invasive neural decoding, by harnessing the collective power of the machine learning community. To facilitate this vision we present the largest within-subject MEG dataset recorded to date (LibriBrain) together with a user-friendly Python library (pnpl) for easy data access and integration with deep learning frameworks. For the competition we define two foundational tasks (i.e. Speech Detection and Phoneme Classification from brain data), complete with standardised data splits and evaluation metrics, illustrative benchmark models, online tutorial code, a community discussion board, and public leaderboard for submissions. To promote accessibility and participation the competition features a Standard track that emphasises algorithmic innovation, as well as an Extended track that is expected to reward larger-scale computing, accelerating progress toward a non-invasive brain-computer interface for speech.

2025-06-11

ArXiv (prépublication)

doi.org

arxiv.org

Artificial Neural Networks for Magnetoencephalography: A review of an emerging field

Vanessa Hadid

Magnetoencephalography (MEG) is a cutting-edge neuroimaging technique that measures the intricate brain dynamics underlying cognitive proces… (voir plus)ses with an unparalleled combination of high temporal and spatial precision. MEG data analytics has always relied on advanced signal processing and mathematical and statistical tools for various tasks ranging from data cleaning to probing the signals' rich dynamics and estimating the neural sources underlying the surface-level recordings. Like in most domains, the surge in Artificial Intelligence (AI) has led to the increased use of Machine Learning (ML) methods for MEG data classification. More recently, an emerging trend in this field is using Artificial Neural Networks (ANNs) to address many MEG-related tasks. This review provides a comprehensive overview of how ANNs are being used with MEG data from three vantage points: First, we review work that employs ANNs for MEG signal classification, i.e., for brain decoding. Second, we report on work that has used ANNs as putative models of information processing in the human brain. Finally, we examine studies that use ANNs as techniques to tackle methodological questions in MEG, including artifact correction and source estimation. Furthermore, we assess the current strengths and limitations of using ANNs with MEG and discuss future challenges and opportunities in this field. Finally, by establishing a detailed portrait of the field and providing practical recommendations for the future, this review seeks to provide a helpful reference for both seasoned MEG researchers and newcomers to the field who are interested in using ANNs to enhance the exploration of the complex dynamics of the human brain with MEG.

2025-05-27

Journal of Neural Engineering (publié)

doi.org

arxiv.org

Caffeine induces age-dependent increases in brain complexity and criticality during sleep

Philipp Thölke

Maxine Arcand-Lavigne

Tarek Lajnef

Sonia Frenette

Julie Carrier

Karim Jerbi

2025-04-30