AIMC Topic: Transcranial Magnetic Stimulation
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Artificial neural network for evaluating sleep spindles and slow waves after transcranial magnetic stimulation in a child with autism.
Neurocase
Dec 4, 2024
Sleep spindles (SS) and slow waves (SW) serve as indicators of the integrity of thalamocortical connections, which are often compromised in individuals with autism spectrum disorder (ASD). Transcranial magnetic stimulation (TMS) can modulate brain ac...
Real-time estimation of the optimal coil placement in transcranial magnetic stimulation using multi-task deep learning.
Scientific reports
Aug 21, 2024
Transcranial magnetic stimulation (TMS) has emerged as a promising neuromodulation technique with both therapeutic and diagnostic applications. As accurate coil placement is known to be essential for focal stimulation, computational models have been ...
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...
Multimodal workflows optimally predict response to repetitive transcranial magnetic stimulation in patients with schizophrenia: a multisite machine learning analysis.
Translational psychiatry
Apr 25, 2024
The response variability to repetitive transcranial magnetic stimulation (rTMS) challenges the effective use of this treatment option in patients with schizophrenia. This variability may be deciphered by leveraging predictive information in structura...
Robotic-electronic platform for autonomous and accurate transcranial magnetic stimulation targeting.
Brain stimulation
Apr 4, 2024
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.
Effects of robotic therapy associated with noninvasive brain stimulation on motor function in individuals with incomplete spinal cord injury: A systematic review of randomized controlled trials.
The journal of spinal cord medicine
Jan 24, 2024
CONTEXT: Motor deficits are among the most common consequences of incomplete spinal cord injury (SCI). These impairments can affect patients' levels of functioning and quality of life. Combined robotic therapy and non-invasive brain stimulation (NIBS...
verified anatomically aware deep learning for real-time electric field simulation.
Journal of neural engineering
Nov 28, 2023
Transcranial magnetic stimulation (TMS) has emerged as a prominent non-invasive technique for modulating brain function and treating mental disorders. By generating a high-precision magnetically evoked electric field (E-field) using a TMS coil, it en...
Robotic transcranial magnetic stimulation in the treatment of depression: a pilot study.
Scientific reports
Aug 28, 2023
There has been an increasing demand for robotic coil positioning during repetitive transcranial magnetic stimulation (rTMS) treatment. Accurate coil positioning is crucial because rTMS generally targets specific brain regions for both research and cl...
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...
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