ArticleFrontiers in neuroscience2019
Non-motor Brain Regions in Non-dominant Hemisphere Are Influential in Decoding Movement Speed.
Article in Frontiers in neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Deep Learning-Based Decoding and Feature Visualization of Motor Imagery Speeds From EEG Signals.IEEE open journal of engineering in medicine and biology · 2026Article
- Tactile exploration and imagery elicit distinct neural dynamics in the parietal cortical network.Frontiers in neuroscience · 2025Article
- Internal states as a source of subject-dependent movement variability are represented by large-scale brain networks.Nature communications · 2023Article
- Stereo-EEG recordings extend known distributions of canonical movement-related oscillations.Journal of neural engineering · 2023Article
- Static and dynamic resting-state brain activity patterns of table tennis players in 7-Tesla MRI.Frontiers in neuroscience · 2023Article
- Baseline hippocampal beta band power is lower in the presence of movement uncertainty.Journal of neural engineering · 2022Article
- Shared and distinct voxel-based lesion-symptom mappings for spasticity and impaired movement in the hemiparetic upper limb.Scientific reports · 2022Article
- Neuromodulation in Beta-Band Power Between Movement Execution and Inhibition in the Human Hippocampus.Neuromodulation : journal of the International Neuromodulation Society · 2022Article
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Authors and funding
6 authors.
Funding
Abstract
Sensorimotor control studies have predominantly focused on how motor regions of the brain relay basic movement-related information such as position and velocity. However, motor control is often complex, involving the integration of sensory information, planning, visuomotor tracking, spatial mapping, retrieval and storage of memories, and may even be emotionally driven. This suggests that many more regions in the brain are involved beyond premotor and motor cortices. In this study, we exploited an experimental setup wherein activity from over 87 non-motor structures of the brain were recorded in eight human subjects executing a center-out motor task. The subjects were implanted with depth electrodes for clinical purposes. Using training data, we constructed subject-specific models that related spectral power of neural activity in six different frequency bands as well as a combined model containing the aggregation of multiple frequency bands to movement speed. We then tested the models by evaluating their ability to decode movement speed from neural activity in the test data set. The best models achieved a correlation of 0.38 ± 0.03 (mean ± standard deviation). Further, the decoded speeds matched the categorical representation of the test trials as correct or incorrect with an accuracy of 70 ± 2.75% across subjects. These models included features from regions such as the right hippocampus, left and right middle temporal gyrus, intraparietal sulcus, and left fusiform gyrus across multiple frequency bands. Perhaps more interestingly, we observed that the non-dominant hemisphere (ipsilateral to dominant hand) was most influential in decoding movement speed.
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