Quelles modulations cérébrales devrait-on renforcer pendant les procédures d''entraînement neurofeedback ciblant les capacités d'imagerie motrice ?

International audienceMotor imagery (MI) can be defined as a « dynamic state during which one simulates an action mentally without any body movement » [1]. The aim of MI is to optimise learning (e.g., in athletic training) or re-learning (e.g., in motor rehabilitation after stroke) by mastering the technique of new motor skills but also through attentional focus [2] thanks to brain plasticity mechanisms. Indeed, similarities exist between MI and motor execution with regards to the solicitation of certain brain regions, including premotor, parietal, and somatosensory regions [3]. Furthermore, MI can also be used to manage emotions (e.g., stress and anxiety) [1]. Improvements in EEG equipment make cognitive training procedures based on neurofeedback (NF) possible, notably in contexts of MI training. Current NF protocols targeting MI consist in positively reinforcing the maximum modulation on sensorimotor rhythms (SMRs) from baseline levels, meaning that we consider that the growing expertise in the MI task will induce a higher desynchronisation of neurons in the sensorimotor cortices [4].Yet, experiments investigating the neural efficiency hypothesis have shown that experts happen to have a reduced modulation of neural activity in comparison to novices, which can be attributed to a more efficient resource distribution [2, 5, 6].In this context it would be interesting to compare cerebral activity patterns in MI experts and non-experts tested under different NF protocol.Therefore, prior to a future project, we would like to discuss the following question with the community: what should we reinforce during SMR-NF training procedures? Is the percentage of (de-)synchronisation, which is the metric that is most often used, actually relevant? We argue that more caution should be devoted to the selection of brain patterns to be targeted if we want to improve the efficiency of NF and BCI training procedures.References[1] Guillot, A. and Collet, C. (2008). Construction of the Motor Imagery Integrative Model in Sport: A review and theoretical investigation of motor imagery use. International Review of Sport and Exercise Psychology.[2] Budnik-Przybylska, D. et al. (2021). Neural Oscillation During Mental Imagery in Sport: An Olympic Sailor Case Study. Frontiers in Human Neuroscience 15, 669422.[3] Hardwick, R. M. et al. (2018). Neural Correlates of Action: Comparing Meta-Analyses of Imagery, Observation, and Execution. Neuroscience & Biobehavioral Reviews 94, 31‑44.[4] Ono, T., Akio K., et Junichi U. (2013). Daily Training with Realistic Visual Feedback Improves Reproducibility of Event-Related Desynchronisation Following Hand Motor Imagery. Clinical Neurophysiology 124(9), 1779 86.[5] Del Percio, C. et al. (2008). Is There a “Neural Efficiency” in Athletes? A High-Resolution EEG Study. NeuroImage 42 (4), 1544‑53.[6] Del Percio, C. et al. (2009). “Neural Efficiency” of Athletes’ Brain for Upright Standing: A High-Resolution EEG Study. Brain Research Bulletin 79(3), 193‑200

Identifying profiles of patients to personalise BCI-based procedures for motor rehabilitation after stroke

International audienceIntroduction: Stroke leaves around 40% of surviving patients dependent in their activities, notably due to severe motor disabilities[1]. BCIs have been shown to favour motor recovery after stroke [2], but this efficiency has not reached yet the level required to achieve a clinical usage. We hypothesise that improving BCI acceptability, notably by personalising BCI-based rehabilitation procedures to each patient, will reduce anxiety and favour engagement in the rehabilitation process, thereby increasing the efficiency of those procedures. To test this hypothesis, we need to understand how to adapt BCI procedures to each patient depending on their profile. Thus, we constructed a model of BCI acceptability based on the literature [3], adapted it in a questionnaire, and distributed the latter to post-stroke patients (N=140).Methods: The questionnaire consisted of i) 3 target factors used as a proxy of BCI acceptability, namely the perceived usefulness (PU), perceived ease of use (PEoU) intention to use (IU) and ii) 23 explanatory factors that could influence acceptability. First, k-mean clustering analyses were performed to identify different profiles of patients. Then, for each cluster, elastic net regressions were used to identify the explanatory factors that predicted PU, PEoU and IU the best, i.e., to identify the factors that are the most important to personalise for each patient.Results: Five clusters (c1 to c5) were identified. The regression analyses indicated that the following factors had to be considered: (c1 & c5) “scientific relevance” & “ease of learning”; (c2) “benefits/risks ratio”, “ease of learning”, “visual aesthetic” & “result demonstrability”; (c3) “scientific relevance” & “benefits/risks ratio”;(c4) none.Perspectives: We will use those results in a clinical study to personalise the BCI procedures to each patient. We expect lower anxiety and better motivation, acceptability and motor recovery with this personalised setting than with a standard one

Quelles modulations cérébrales devrait-on renforcer pendant les procédures d''entraînement neurofeedback ciblant les capacités d'imagerie motrice ?

International audienceMotor imagery (MI) can be defined as a « dynamic state during which one simulates an action mentally without any body movement » [1]. The aim of MI is to optimise learning (e.g., in athletic training) or re-learning (e.g., in motor rehabilitation after stroke) by mastering the technique of new motor skills but also through attentional focus [2] thanks to brain plasticity mechanisms. Indeed, similarities exist between MI and motor execution with regards to the solicitation of certain brain regions, including premotor, parietal, and somatosensory regions [3]. Furthermore, MI can also be used to manage emotions (e.g., stress and anxiety) [1]. Improvements in EEG equipment make cognitive training procedures based on neurofeedback (NF) possible, notably in contexts of MI training. Current NF protocols targeting MI consist in positively reinforcing the maximum modulation on sensorimotor rhythms (SMRs) from baseline levels, meaning that we consider that the growing expertise in the MI task will induce a higher desynchronisation of neurons in the sensorimotor cortices [4].Yet, experiments investigating the neural efficiency hypothesis have shown that experts happen to have a reduced modulation of neural activity in comparison to novices, which can be attributed to a more efficient resource distribution [2, 5, 6].In this context it would be interesting to compare cerebral activity patterns in MI experts and non-experts tested under different NF protocol.Therefore, prior to a future project, we would like to discuss the following question with the community: what should we reinforce during SMR-NF training procedures? Is the percentage of (de-)synchronisation, which is the metric that is most often used, actually relevant? We argue that more caution should be devoted to the selection of brain patterns to be targeted if we want to improve the efficiency of NF and BCI training procedures.References[1] Guillot, A. and Collet, C. (2008). Construction of the Motor Imagery Integrative Model in Sport: A review and theoretical investigation of motor imagery use. International Review of Sport and Exercise Psychology.[2] Budnik-Przybylska, D. et al. (2021). Neural Oscillation During Mental Imagery in Sport: An Olympic Sailor Case Study. Frontiers in Human Neuroscience 15, 669422.[3] Hardwick, R. M. et al. (2018). Neural Correlates of Action: Comparing Meta-Analyses of Imagery, Observation, and Execution. Neuroscience & Biobehavioral Reviews 94, 31‑44.[4] Ono, T., Akio K., et Junichi U. (2013). Daily Training with Realistic Visual Feedback Improves Reproducibility of Event-Related Desynchronisation Following Hand Motor Imagery. Clinical Neurophysiology 124(9), 1779 86.[5] Del Percio, C. et al. (2008). Is There a “Neural Efficiency” in Athletes? A High-Resolution EEG Study. NeuroImage 42 (4), 1544‑53.[6] Del Percio, C. et al. (2009). “Neural Efficiency” of Athletes’ Brain for Upright Standing: A High-Resolution EEG Study. Brain Research Bulletin 79(3), 193‑200

Identifying profiles of patients to personalise BCI-based procedures for motor rehabilitation after stroke

International audienceIntroduction: Stroke leaves around 40% of surviving patients dependent in their activities, notably due to severe motor disabilities[1]. BCIs have been shown to favour motor recovery after stroke [2], but this efficiency has not reached yet the level required to achieve a clinical usage. We hypothesise that improving BCI acceptability, notably by personalising BCI-based rehabilitation procedures to each patient, will reduce anxiety and favour engagement in the rehabilitation process, thereby increasing the efficiency of those procedures. To test this hypothesis, we need to understand how to adapt BCI procedures to each patient depending on their profile. Thus, we constructed a model of BCI acceptability based on the literature [3], adapted it in a questionnaire, and distributed the latter to post-stroke patients (N=140).Methods: The questionnaire consisted of i) 3 target factors used as a proxy of BCI acceptability, namely the perceived usefulness (PU), perceived ease of use (PEoU) intention to use (IU) and ii) 23 explanatory factors that could influence acceptability. First, k-mean clustering analyses were performed to identify different profiles of patients. Then, for each cluster, elastic net regressions were used to identify the explanatory factors that predicted PU, PEoU and IU the best, i.e., to identify the factors that are the most important to personalise for each patient.Results: Five clusters (c1 to c5) were identified. The regression analyses indicated that the following factors had to be considered: (c1 & c5) “scientific relevance” & “ease of learning”; (c2) “benefits/risks ratio”, “ease of learning”, “visual aesthetic” & “result demonstrability”; (c3) “scientific relevance” & “benefits/risks ratio”;(c4) none.Perspectives: We will use those results in a clinical study to personalise the BCI procedures to each patient. We expect lower anxiety and better motivation, acceptability and motor recovery with this personalised setting than with a standard one

Acceptability of BCI-based procedures for motor rehabilitation after stroke: A questionnaire study among patients

International audienceIntroduction: Stroke leaves around 40% of surviving patients dependent in their activities, notably due to severe motor disabilities[1]. BCIs have been shown to favour motor recovery after stroke [2], but this efficiency has not reached yet the level required to achieve a clinical usage. We hypothesise that improving BCI acceptability, notably by personalising BCI-based rehabilitation procedures to each patient, will reduce anxiety and favour engagement in the rehabilitation process, thereby increasing the efficiency of those procedures. To test this hypothesis, we need to understand how to adapt BCI procedures to each patient depending on their profile. Thus, we constructed a model of BCI acceptability based on the literature [3], adapted it in a questionnaire, and distributed the latter to post-stroke patients (N=140). Methods: The questionnaire consisted of i) 3 target factors used as a proxy of BCI acceptability, namely the perceived usefulness (PU), perceived ease of use (PEoU) intention to use (IU) and ii) 23 explanatory factors that could influence acceptability. First, k-mean clustering analyses were performed to identify different profiles of patients. Then, for each cluster, elastic net regressions were used to identify the explanatory factors that predicted PU, PEoU and IU the best, i.e., to identify the factors that are the most important to personalise for each patient. Results: Five clusters (c1 to c5) were identified. The regression analyses indicated that the following factors had to be considered: (c1 & c5) "scientific relevance" & "ease of learning"; (c2) "benefits/risks ratio", "ease of learning", "visual aesthetic" & "result demonstrability"; (c3) "scientific relevance" & "benefits/risks ratio";(c4) none. Perspectives: We will use those results in a clinical study to personalise the BCI procedures to each patient. We expect lower anxiety and better motivation, acceptability and motor recovery with this personalised setting than with a standard one. Sources [1] Inserm, 2019 [2] Cervera, María A., et al. "Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis.

Etudier l’acceptabilité des interfaces cerveau-ordinateur en rééducation motrice post-AVC pour proposer des protocoles personnalisés en fonction du profil du patient

International audienceMalgré leur intérêt, les interfaces cerveau-ordinateur (ICO) ne sont pas utilisées en soins courants dans le domaine de la rééducation post-accident vasculaire cérébral. Nous faisons l’hypothèse que l’amélioration de l’acceptabilité des ICO, obtenue par une personnalisation des protocoles, permettra aux patients d’être moins anxieux et plus engagés, et ainsi d’optimiser l’efficacité en termes de récupération motrice, mais aussi l’utilisabilité.Nous avons conçu un questionnaire basé sur notre modèle théorique d’acceptabilité des ICO, auquel 140 sujets post-AVC ont répondu. Une identification des profils de sujets avec une analyse en composante principale (ACP) puis une clusterisation (méthode Elbow et K-mean clustering) a été réalisée. Un arbre de classification a été construit pour classer les nouveaux sujets dans leur cluster. Il a été validé par « leave-one-out cross validation » (LOOV). Pour chaque cluster, nous avons effectué des régressions et des corrélations pour identifier les facteurs à personnaliser.L’ACP a permis de passer de 27 à 15 facteurs, à partir desquels nous avons obtenu 5 clusters (Fig.1A, 1B). La performance de classification en LOOV était de 65,00 % (niveau de hasard pour α=5% : 30,75%) (Fig. 1C). Les facteurs d’importance majeurs sont (N = nombre de clusters les intégrant) : pertinence scientifique (4), facilité d’apprentissage (3), balance bénéfices/risques (2), esthétique et démonstrabilité (1). Ces résultats sont intégrés dans un logiciel « plug&play » utilisable en soins courants.Un essai contrôlé randomisé multicentrique permettra d’évaluer et d’optimiser les protocoles personnalisés proposés afin que les ICO soient plus utilisables/acceptables pour les patients et les soignants

Acceptability of BCI-based procedures for motor rehabilitation after stroke: A questionnaire study among patients

International audienceIntroduction: Stroke leaves around 40% of surviving patients dependent in their activities, notably due to severe motor disabilities[1]. BCIs have been shown to favour motor recovery after stroke [2], but this efficiency has not reached yet the level required to achieve a clinical usage. We hypothesise that improving BCI acceptability, notably by personalising BCI-based rehabilitation procedures to each patient, will reduce anxiety and favour engagement in the rehabilitation process, thereby increasing the efficiency of those procedures. To test this hypothesis, we need to understand how to adapt BCI procedures to each patient depending on their profile. Thus, we constructed a model of BCI acceptability based on the literature [3], adapted it in a questionnaire, and distributed the latter to post-stroke patients (N=140). Methods: The questionnaire consisted of i) 3 target factors used as a proxy of BCI acceptability, namely the perceived usefulness (PU), perceived ease of use (PEoU) intention to use (IU) and ii) 23 explanatory factors that could influence acceptability. First, k-mean clustering analyses were performed to identify different profiles of patients. Then, for each cluster, elastic net regressions were used to identify the explanatory factors that predicted PU, PEoU and IU the best, i.e., to identify the factors that are the most important to personalise for each patient. Results: Five clusters (c1 to c5) were identified. The regression analyses indicated that the following factors had to be considered: (c1 & c5) "scientific relevance" & "ease of learning"; (c2) "benefits/risks ratio", "ease of learning", "visual aesthetic" & "result demonstrability"; (c3) "scientific relevance" & "benefits/risks ratio";(c4) none. Perspectives: We will use those results in a clinical study to personalise the BCI procedures to each patient. We expect lower anxiety and better motivation, acceptability and motor recovery with this personalised setting than with a standard one. Sources [1] Inserm, 2019 [2] Cervera, María A., et al. "Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis.

Acceptability of BCI-based procedures for motor rehabilitation after stroke: A questionnaire study among patients

International audienceIntroduction: Stroke leaves around 40% of surviving patients dependent in their activities, notably due to severe motor disabilities[1]. BCIs have been shown to favour motor recovery after stroke [2], but this efficiency has not reached yet the level required to achieve a clinical usage. We hypothesise that improving BCI acceptability, notably by personalising BCI-based rehabilitation procedures to each patient, will reduce anxiety and favour engagement in the rehabilitation process, thereby increasing the efficiency of those procedures. To test this hypothesis, we need to understand how to adapt BCI procedures to each patient depending on their profile. Thus, we constructed a model of BCI acceptability based on the literature [3], adapted it in a questionnaire, and distributed the latter to post-stroke patients (N=140). Methods: The questionnaire consisted of i) 3 target factors used as a proxy of BCI acceptability, namely the perceived usefulness (PU), perceived ease of use (PEoU) intention to use (IU) and ii) 23 explanatory factors that could influence acceptability. First, k-mean clustering analyses were performed to identify different profiles of patients. Then, for each cluster, elastic net regressions were used to identify the explanatory factors that predicted PU, PEoU and IU the best, i.e., to identify the factors that are the most important to personalise for each patient. Results: Five clusters (c1 to c5) were identified. The regression analyses indicated that the following factors had to be considered: (c1 & c5) "scientific relevance" & "ease of learning"; (c2) "benefits/risks ratio", "ease of learning", "visual aesthetic" & "result demonstrability"; (c3) "scientific relevance" & "benefits/risks ratio";(c4) none. Perspectives: We will use those results in a clinical study to personalise the BCI procedures to each patient. We expect lower anxiety and better motivation, acceptability and motor recovery with this personalised setting than with a standard one. Sources [1] Inserm, 2019 [2] Cervera, María A., et al. "Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis.

Acceptability of BCI-based procedures for motor rehabilitation after stroke: A questionnaire study among patients

International audienceIntroduction: Stroke leaves around 40% of surviving patients dependent in their activities, notably due to severe motor disabilities[1]. BCIs have been shown to favour motor recovery after stroke [2], but this efficiency has not reached yet the level required to achieve a clinical usage. We hypothesise that improving BCI acceptability, notably by personalising BCI-based rehabilitation procedures to each patient, will reduce anxiety and favour engagement in the rehabilitation process, thereby increasing the efficiency of those procedures. To test this hypothesis, we need to understand how to adapt BCI procedures to each patient depending on their profile. Thus, we constructed a model of BCI acceptability based on the literature [3], adapted it in a questionnaire, and distributed the latter to post-stroke patients (N=140). Methods: The questionnaire consisted of i) 3 target factors used as a proxy of BCI acceptability, namely the perceived usefulness (PU), perceived ease of use (PEoU) intention to use (IU) and ii) 23 explanatory factors that could influence acceptability. First, k-mean clustering analyses were performed to identify different profiles of patients. Then, for each cluster, elastic net regressions were used to identify the explanatory factors that predicted PU, PEoU and IU the best, i.e., to identify the factors that are the most important to personalise for each patient. Results: Five clusters (c1 to c5) were identified. The regression analyses indicated that the following factors had to be considered: (c1 & c5) "scientific relevance" & "ease of learning"; (c2) "benefits/risks ratio", "ease of learning", "visual aesthetic" & "result demonstrability"; (c3) "scientific relevance" & "benefits/risks ratio";(c4) none. Perspectives: We will use those results in a clinical study to personalise the BCI procedures to each patient. We expect lower anxiety and better motivation, acceptability and motor recovery with this personalised setting than with a standard one. Sources [1] Inserm, 2019 [2] Cervera, María A., et al. "Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis.

Etudier l’acceptabilité des interfaces cerveau-ordinateur en rééducation motrice post-AVC pour proposer des protocoles personnalisés en fonction du profil du patient

International audienceMalgré leur intérêt, les interfaces cerveau-ordinateur (ICO) ne sont pas utilisées en soins courants dans le domaine de la rééducation post-accident vasculaire cérébral. Nous faisons l’hypothèse que l’amélioration de l’acceptabilité des ICO, obtenue par une personnalisation des protocoles, permettra aux patients d’être moins anxieux et plus engagés, et ainsi d’optimiser l’efficacité en termes de récupération motrice, mais aussi l’utilisabilité.Nous avons conçu un questionnaire basé sur notre modèle théorique d’acceptabilité des ICO, auquel 140 sujets post-AVC ont répondu. Une identification des profils de sujets avec une analyse en composante principale (ACP) puis une clusterisation (méthode Elbow et K-mean clustering) a été réalisée. Un arbre de classification a été construit pour classer les nouveaux sujets dans leur cluster. Il a été validé par « leave-one-out cross validation » (LOOV). Pour chaque cluster, nous avons effectué des régressions et des corrélations pour identifier les facteurs à personnaliser.L’ACP a permis de passer de 27 à 15 facteurs, à partir desquels nous avons obtenu 5 clusters (Fig.1A, 1B). La performance de classification en LOOV était de 65,00 % (niveau de hasard pour α=5% : 30,75%) (Fig. 1C). Les facteurs d’importance majeurs sont (N = nombre de clusters les intégrant) : pertinence scientifique (4), facilité d’apprentissage (3), balance bénéfices/risques (2), esthétique et démonstrabilité (1). Ces résultats sont intégrés dans un logiciel « plug&play » utilisable en soins courants.Un essai contrôlé randomisé multicentrique permettra d’évaluer et d’optimiser les protocoles personnalisés proposés afin que les ICO soient plus utilisables/acceptables pour les patients et les soignants