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Solute | TMS Resource Portal

Welcome to TMS Solutions' resource portal, Solute, where you will find the latest informative articles about mental health, neuromodulation techniques, and TMS.

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Aug 13, 2022 3:39:00 PM

Transcranial Magnetic Stimulation of the Brain: What is Stimulated? - A Consensus and Critical Position Paper [Review]

SOURCE: Clinical Neurophysiology. 140:59-97, 2022 Aug.

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Jul 24, 2022 12:49:00 PM

Human Neurogenesis in Ischemic Adult Brain: Effect of Repetitive Transcranial Magnetic Stimulation

SOURCE: European Stroke Journal. Conference: 8th European Stroke Organisation Conference. Lyon France. 7(1 SUPPL) (pp 492), 2022.

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May 5, 2022 5:42:00 PM

Transcranial Magnetic Stimulation and Neocortical Neurons: The Micro-Macro Connection

SOURCE: Frontiers in Neuroscience. 16 (no pagination), 2022. Article Number:
866245.

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Apr 28, 2022 1:51:00 PM

Metabolomics Changes After rTMS Intervention Reveal Potential Peripheral Biomarkers in Methamphetamine Dependence

SOURCE: European Neuropsychopharmacology. 56:80-88, 2022 03.

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Feb 22, 2022 4:43:00 PM

A Combination of P300 and Eye Movement Data Improves the Accuracy of Auxiliary Diagnoses of Depression

Source: Journal of Affective Disorders. 297:386-395, 2022 01 15.

Authors:
Diao Y; Geng M; Fu Y; Wang H; Liu C; Gu J; Dong J; Mu J; Liu X; Wang C

Abstract

BACKGROUND: Exploratory eye movements (EEMs) and P300 are often used to facilitate the clinical diagnosis of depression. However, There were few studies using the combination of EEMs and P300 to build a model for detecting depression and predicting a curative effect.

METHODS: Sixty patients were recruited for 2 groups: high frequency repetitive transcranial magnetic stimulation(rTMS) combined with paroxetine group and simple paroxetine group. Clinical efficacy was evaluated by the Hamilton Depression scale-24(HAMD-24), EEMs and P300. The classification model of the auxiliary diagnosis of depression and the prediction model of the two treatments were developed based on a machine learning algorithm.

RESULTS: The classification model with the greatest accuracy for patients with depression and healthy controls was 95.24% (AUC = 0.75, recall = 1.00, precision = 0.95, F1-score = 0.97). The root mean square error (RMSE) of the model for predicting the efficacy of high frequency rTMS combined with paroxetine was 3.54 (MAE [mean absolute error] = 2.56, R2 = -0.53). The RMSE of the model for predicting the efficacy of paroxetine was 4.97 (MAE = 4.00, R2 = -0.91).

CONCLUSION: Based on the machine learning algorithm, P300 and EEMs data
was suitable for modeling to distinguish depression patients and healthy individuals. However, it was not suitable for predicting the efficacy of high frequency rTMS combined with paroxetine or to predict the efficacy of paroxetine.

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