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
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Abstract	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-to-cortex distance (CCD), gray matter volume (GMV), depolarized GMV (DGMV), and maximum electric field (E-field) value, neuroanatomy, and connectivity derived from functional magnetic resonance imaging (fMRI) are all associated with RMT. For 54 subjects with schizophrenia and 43 healthy subjects, fMRI blood oxygen-level detection (BOLD) in 25 brain regions was turned into time series and fed into a long short-term memory (LSTM) model. The outputs of the LSTM are concatenated with the schizophrenia status, CCD, GMV, percentage of gray matter voxels depolarized over 50 V/m (DGMV50) and 100 V/M (DGMV100), and maximum E-field value and then fed into an artificial neural network (ANN) that predicted the RMT. The training and testing mean absolute errors (MAEs) are 0.1176 and 0.0845, respectively, corresponding to the errors of 3.6456% and 2.6195% of the maximum stimulator output (%MSO) in the predicted RMT values. Our novel hybrid LSTM-ANN neural network can be used as a pretreatment procedure to reduce the number of trials needed to measure RMT for patients and increase patient comfort and confidence in the procedure administered.
AbstractList	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-to-cortex distance (CCD), gray matter volume (GMV), depolarized GMV (DGMV), and maximum electric field (E-field) value, neuroanatomy, and connectivity derived from functional magnetic resonance imaging (fMRI) are all associated with RMT. For 54 subjects with schizophrenia and 43 healthy subjects, fMRI blood oxygen-level detection (BOLD) in 25 brain regions was turned into time series and fed into a long short-term memory (LSTM) model. The outputs of the LSTM are concatenated with the schizophrenia status, CCD, GMV, percentage of gray matter voxels depolarized over 50 V/m (DGMV50) and 100 V/M (DGMV100), and maximum E-field value and then fed into an artificial neural network (ANN) that predicted the RMT. The training and testing mean absolute errors (MAEs) are 0.1176 and 0.0845, respectively, corresponding to the errors of 3.6456% and 2.6195% of the maximum stimulator output (%MSO) in the predicted RMT values. Our novel hybrid LSTM-ANN neural network can be used as a pretreatment procedure to reduce the number of trials needed to measure RMT for patients and increase patient comfort and confidence in the procedure administered.
Author	Saxena, Yash R. Mehta, Urvakhsh M. Atulasimha, Jayasimha Sabbir Alam, Muhammad Lewis, Connor J. Hadimani, Ravi L.
Author_xml	– sequence: 1 givenname: Yash R. surname: Saxena fullname: Saxena, Yash R. organization: Department of Mechanical and Nuclear Engineering, Virginia Commonwealth University, Richmond, VA, USA – sequence: 2 givenname: Connor J. orcidid: 0000-0002-8204-5848 surname: Lewis fullname: Lewis, Connor J. organization: Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, VA, USA – sequence: 3 givenname: Muhammad orcidid: 0000-0001-7520-5308 surname: Sabbir Alam fullname: Sabbir Alam, Muhammad organization: Department of Mechanical and Nuclear Engineering, Virginia Commonwealth University, Richmond, VA, USA – sequence: 4 givenname: Jayasimha orcidid: 0000-0002-5681-0884 surname: Atulasimha fullname: Atulasimha, Jayasimha organization: Department of Mechanical and Nuclear Engineering, Virginia Commonwealth University, Richmond, VA, USA – sequence: 5 givenname: Urvakhsh M. surname: Mehta fullname: Mehta, Urvakhsh M. organization: National Institute of Mental Health and Neurosciences, Bengaluru, India – sequence: 6 givenname: Ravi L. orcidid: 0000-0001-5939-556X surname: Hadimani fullname: Hadimani, Ravi L. email: rhadimani@vcu.edu organization: Department of Mechanical and Nuclear Engineering, Virginia Commonwealth University, Richmond, VA, USA
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Cites_doi	10.1001/archgenpsychiatry.2010.46 10.1063/1.4913937 10.1093/schbul/sbt155 10.1016/b978-0-12-805353-9.00137-6 10.1016/j.brs.2011.10.001 10.1109/tmag.2022.3148214 10.1016/j.biopsych.2014.05.020 10.1063/1.4974981 10.1016/j.brs.2021.11.010 10.1016/j.biopsych.2024.01.027 10.1109/tmag.2014.2326819 10.1063/9.0000697 10.1109/tmag.2018.2846521 10.1186/1743-0003-11-40 10.1109/tmag.2023.3282784 10.1109/tmag.2017.2711962 10.1002/hbm.25968 10.1371/journal.pone.0186007 10.1109/lmag.2019.2903993 10.1109/tmag.2014.2316479 10.1063/9.0000567 10.1016/j.cortex.2008.10.002
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SubjectTerms	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)
Title	A Hybrid Machine Learning Algorithm for Predicting Resting Motor Thresholds in Patients With Schizophrenia and Healthy Individuals Undergoing Transcranial Magnetic Stimulation
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