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Yanxiang Niu

Showing results (11-20 of 14) with videos related to

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Frontiers in Medicine|April 17, 2023
The effects of ECMO on neurological function recovery of critical patients: A double-edged swordJinxia Cai, Halidan Abudou, Yuansen Chen, et al.
Journal of Affective Disorders|September 12, 2024
Common and differential EEG microstate of major depressive disorder patients with and without response to rTMS treatmentZongya Zhao, Xiangying Ran, Junming Wang, et al.
BMC Psychiatry|November 19, 2025
Exploring potential resting-state EEG biomarkers of obsessive-compulsive disorder based on explainable machine learning analysis of independent training and test samplesZongya Zhao, Junming Wang, Yanxiang Niu, et al.
Biological Psychiatry. Cognitive Neuroscience and Neuroimaging|February 20, 2025
Predicting Treatment Response of Repetitive Transcranial Magnetic Stimulation in Major Depressive Disorder Using an Explainable Machine Learning Model Based on Electroencephalography and Clinical FeaturesZongya Zhao, Xiangying Ran, Yanxiang Niu, et al.
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Showing results (11-20 of 14) with videos related to

Sort By:
Pageof 2
You have reached the last page of results.This site can display upto 14 results.
Frontiers in Medicine|April 17, 2023
The effects of ECMO on neurological function recovery of critical patients: A double-edged swordJinxia Cai, Halidan Abudou, Yuansen Chen, et al.
Journal of Affective Disorders|September 12, 2024
Common and differential EEG microstate of major depressive disorder patients with and without response to rTMS treatmentZongya Zhao, Xiangying Ran, Junming Wang, et al.
BMC Psychiatry|November 19, 2025
Exploring potential resting-state EEG biomarkers of obsessive-compulsive disorder based on explainable machine learning analysis of independent training and test samplesZongya Zhao, Junming Wang, Yanxiang Niu, et al.
Biological Psychiatry. Cognitive Neuroscience and Neuroimaging|February 20, 2025
Predicting Treatment Response of Repetitive Transcranial Magnetic Stimulation in Major Depressive Disorder Using an Explainable Machine Learning Model Based on Electroencephalography and Clinical FeaturesZongya Zhao, Xiangying Ran, Yanxiang Niu, et al.
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