AIMC Topic: Motor Cortex
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Machine learning approaches to predict whether MEPs can be elicited via TMS.
Journal of neuroscience methods
Aug 9, 2024
BACKGROUND: Transcranial magnetic stimulation (TMS) is a valuable technique for assessing the function of the motor cortex and cortico-muscular pathways. TMS activates the motoneurons in the cortex, which after transmission along cortico-muscular pat...
A virtual rodent predicts the structure of neural activity across behaviours.
Nature
Jun 11, 2024
Animals have exquisite control of their bodies, allowing them to perform a diverse range of behaviours. How such control is implemented by the brain, however, remains unclear. Advancing our understanding requires models that can relate principles of ...
Oscillating latent dynamics in robot systems during walking and reaching.
Scientific reports
May 19, 2024
Sensorimotor control of complex, dynamic systems such as humanoids or quadrupedal robots is notoriously difficult. While artificial systems traditionally employ hierarchical optimisation approaches or black-box policies, recent results in systems neu...
Transcranial Magnetic Stimulation-Based Machine Learning Prediction of Tumor Grading in Motor-Eloquent Gliomas.
Neurosurgery
Mar 21, 2024
BACKGROUND: Navigated transcranial magnetic stimulation (nTMS) is a well-established preoperative mapping tool for motor-eloquent glioma surgery. Machine learning (ML) and nTMS may improve clinical outcome prediction and histological correlation.
Uncovering the Neural Mechanisms of Inter-Hemispheric Balance Restoration in Chronic Stroke Through EMG-Driven Robot Hand Training: Insights From Dynamic Causal Modeling.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Jan 12, 2024
EMG-driven robot hand training can facilitate motor recovery in chronic stroke patients by restoring the interhemispheric balance between motor networks. However, the underlying mechanisms of reorganization between interhemispheric regions remain unc...
Unsupervised learning of stationary and switching dynamical system models from Poisson observations.
Journal of neural engineering
Dec 12, 2023
. Investigating neural population dynamics underlying behavior requires learning accurate models of the recorded spiking activity, which can be modeled with a Poisson observation distribution. Switching dynamical system models can offer both explanat...
Increased functional connectivity coupling with supplementary motor area in blepharospasm at rest.
Brain research
Jun 22, 2023
OBJECTIVE: To explore the abnormalities of brain function in blepharospasm (BSP) and to illustrate its neural mechanisms by assuming supplementary motor area (SMA) as the entry point.
DELMEP: a deep learning algorithm for automated annotation of motor evoked potential latencies.
Scientific reports
May 22, 2023
The analysis of motor evoked potentials (MEPs) generated by transcranial magnetic stimulation (TMS) is crucial in research and clinical medical practice. MEPs are characterized by their latency and the treatment of a single patient may require the ch...
Reliability of active robotic neuro-navigated transcranial magnetic stimulation motor maps.
Experimental brain research
Dec 16, 2022
Transcranial magnetic stimulation (TMS) motor mapping is a safe, non-invasive method used to study corticomotor organization and intervention-induced plasticity. Reliability of resting maps is well established, but understudied for active maps and un...
A large-scale neural network training framework for generalized estimation of single-trial population dynamics.
Nature methods
Nov 28, 2022
Achieving state-of-the-art performance with deep neural population dynamics models requires extensive hyperparameter tuning for each dataset. AutoLFADS is a model-tuning framework that automatically produces high-performing autoencoding models on dat...
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