Brain Activation During Visually Guided Finger Movements

Brand, Johannes; Piccirelli, Marco; Hepp-Reymond, Marie-Claude; Eng, Kynan; Michels, Lars

doi:10.3389/fnhum.2020.00309

Cited by 10 publications

(7 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the study of Perani et al, different degree of realism in real and VR hands, including high (realistic) and low (coarse) VR hands, led to different activation patterns [ 11 ]. However, in the study of Brand et al, they found that there was no significant difference in brain activation between virtual (more realistic) hand and shadow (less realistic) hand while observing [ 30 ], which was in line with our findings. Thus, between the VR and real context, the much closer physical characteristics of visual stimuli perceived, the much similar effects of brain activations evoked.…”

Section: Discussionsupporting

confidence: 92%

Motor Cortical Activity during Observing a Video of Real Hand Movements versus Computer Graphic Hand Movements: An MEG Study

et al. 2020

View full text Add to dashboard Cite

Both action observation (AO) and virtual reality (VR) provide visual stimuli to trigger brain activations during the observation of actions. However, the mechanism of observing video movements performed by a person’s real hand versus that performed by a computer graphic hand remains uncertain. We aimed to investigate the differences in observing the video of real versus computer graphic hand movements on primary motor cortex (M1) activation by magnetoencephalography. Twenty healthy adults completed 3 experimental conditions: the resting state, the video of real hand movements (VRH), and the video of computer graphic hand movements (CGH) conditions with the intermittent electrical stimuli simultaneously applied to the median nerve by an electrical stimulator. The beta oscillatory activity (~20 Hz) in the M1 was collected, lower values indicating greater activations. To compare the beta oscillatory activities among the 3 conditions, the Friedman test with Bonferroni correction (p-value < 0.017 indicating statistical significance) were used. The beta oscillatory activities of the VRH and CGH conditions were significantly lower than that of the resting state condition. No significant difference in the beta oscillatory activity was found between the VRH and CGH conditions. Observing hand movements in a video performed by a real hand and those by a computer graphic hand evoked comparable M1 activations in healthy adults. This study provides some neuroimaging support for the use of AO and VR in rehabilitation, but no differential activations were found.

show abstract

Section: Discussionsupporting

confidence: 92%

Motor Cortical Activity during Observing a Video of Real Hand Movements versus Computer Graphic Hand Movements: An MEG Study

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The immersive experience the VR offered to the patient demonstrates improvements to the performance of tasks and functional abilities of the patient due to brain plasticity [15,24,52]. The above finding can be explained based on brain imaging approaches and how the perceived visual feedback activates the mirror neurons reaching an improvement on prosthetics control ability [5,18,62]. Therefore, the ability to correctly perform a grabbing task with the missing limb through the embodied virtual arm ensured it is perceived by the patient as a real part of the body.…”

Section: Attitudes and Effective Responses Of The Vr Myoelectric-powered Prosthetic Trainingmentioning

confidence: 88%

“…Visual feedback (e.g., naturally reaching out and grabbing a virtual object with an amputated limb) activates the neuron networks in the brain that are involved in sensorimotor learning, called mirror neurons [54,55]. Mirror neurons can be activated through the reproduction or observation of movements [5,18,53].…”

Section: Introductionmentioning

confidence: 99%

Designing a Virtual Reality Myoelectric Prosthesis Training System for Amputees

Phelan

Arden

Matsangidou

et al. 2021

Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems

View full text Add to dashboard Cite

Electrical signals produced by muscle contractions are found to be effective in controlling accurately artificial limbs. Myoelectric-powered can be more functional and advantageous compared to passive or body-powered prostheses, however extensive training is required to take full advantage of the myoelectric prosthesis' usability. In recent years, computer technology has brought new opportunities for improving patients' training, resulting in more usable and functional solutions.Virtual Reality (VR) is a representative example of this type of technology. These preliminary findings suggested that myoelectric-powered training enhanced with VR can simulate a pain-free, natural, enjoyable, and realistic experience for the patient. It was also suggested that VR can complement prosthesis training, by improving the functionality of the missing body part. Finally, it was shown that VR can resolve one of the most common challenges for a new prosthesis user, which is to accept the fitting of the prosthetic device to their own body.

show abstract

“…Furthermore, in humans, TP8 region contributes to the global processing of visual information [165]. The connection between this two regions, TP8 and C3, is involved in visual and tactile perceptions, and finger movements [166, 167].…”

Section: Discussionmentioning

confidence: 99%

Application of machine learning and complex network measures to an EEG dataset from DMT experiments

Alves

Toutain

Porto

et al. 2022

Preprint

View full text Add to dashboard Cite

There is a growing interest in the medical use of psychedelic substances as preliminary studies using them for psychiatric disorders have shown positive results. In particularly, one of these substances is N,N-dimethyltryptamine (DMT) an agonist serotonergic psychedelic that can induce profound alterations in state of consciousness. In this work, we propose a computational method based on machine learning as an exploratory tool to reveal DMT-induced changes in brain activity using EEG data and provide new insights into the mechanisms of action of this psychedelic substance. To answer these questions, we propose a two-class classification based on (A) the connectivity matrix or (B) complex network measures derived from it as input to a support vector machine We found that both approaches were able to automatically detect changes in the brain activity, with case (B) showing the highest AUC (89%), indicating that complex network measurements best capture the brain changes that occur due to DMT use. In a second step, we ranked the features that contributed most to this result. For case (A) we found that differences in the high alpha, low beta, and delta frequency band were most important to distinguish between the state before and after DMT inhalation, which is consistent with results described in the literature. Further, the connection between the temporal (TP8) and central cortex (C3) and between the precentral gyrus (FC5) and the lateral occipital cortex (T8) contributed most to the classification result. The connection between regions TP8 and C3 has been found in the literature associated with finger movements that might have occurred during DMT consumption. However, the connection between cortical regions FC5 and P8 has not been found in the literature and is presumably related to emotional, visual, sensory, perceptual, and mystical experiences of the volunteers during DMT consumption. For case (B) closeness centrality was the most important complex network measure. Moreover, we found larger communities and a longer average path length with the use of DMT and the opposite in its absence indicating that the balance between functional segregation and integration was disrupted. This findings supports the idea that cortical brain activity becomes more entropic under psychedelics. Overall, a robust computational workflow has been developed here with an interpretability of how DMT (or other psychedelics) modify brain networks and insights into their mechanism of action. Finally, the same methodology applied here may be useful in interpreting EEG time series from patients who consumed other psychedelic drugs and can help obtain a detailed understanding of functional changes in the neural network of the brain as a result of drug administration.

show abstract

Brain Activation During Visually Guided Finger Movements

Cited by 10 publications

References 59 publications

Motor Cortical Activity during Observing a Video of Real Hand Movements versus Computer Graphic Hand Movements: An MEG Study

Motor Cortical Activity during Observing a Video of Real Hand Movements versus Computer Graphic Hand Movements: An MEG Study

Designing a Virtual Reality Myoelectric Prosthesis Training System for Amputees

Application of machine learning and complex network measures to an EEG dataset from DMT experiments

Contact Info

Product

Resources

About