A Hybrid Machine Learning Algorithm for Predicting Resting Motor Thresholds in Patients With Schizophrenia and Healthy Individuals Undergoing Transcranial Magnetic Stimulation

Due to the complex and varied neuroanatomy and functional states of brains, it is difficult to predict the resting motor threshold (RMT) needed as a dose parameter for treatment with transcranial magnetic stimulation (TMS). Our prior publications have shown that anatomical parameters, such as coil-t...

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Veröffentlicht in:	IEEE transactions on magnetics Jg. 61; H. 9; S. 1 - 6
Hauptverfasser:	Saxena, Yash R., Lewis, Connor J., Sabbir Alam, Muhammad, Atulasimha, Jayasimha, Mehta, Urvakhsh M., Hadimani, Ravi L.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.09.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Artificial neural network (ANN) Artificial neural networks Biological neural networks Brain modeling Charge coupled devices Depolarization Electric fields Errors Functional magnetic resonance imaging functional magnetic resonance imaging (fMRI) Grey matter hybrid neural network Long short term memory long short-term memory (LSTM) Machine learning Magnetic resonance imaging Motors Neural networks neuromodulation Parameters Schizophrenia Stimulators Testing Training Transcranial magnetic stimulation transcranial magnetic stimulation (TMS)
ISSN:	0018-9464, 1941-0069
Online-Zugang:	Volltext
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