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This study registered consecutive cases to elucidate the efficacy of rTMS treatment for depression within the Japanese public health insurance system. Of the 102 patients with depression who received rTMS over the left dorsolateral prefrontal cortex, 44 (43.1 %) patients reached remission and 14 (13.7 %) patients did not reach remission but responded to treatment. No serious adverse events occurred. Low baseline HAMD-17 score was associated with remission after rTMS treatment. Favorable outcomes of rTMS treatment were shown in this cohort within the Japanese public insurance system. Our results provide insights into rTMS treatment for depression in real-world clinical setting., Competing Interests: Declaration of Competing Interest Authors Imazu, Ikeda, Shinosaki, and Takahashi have received speaker honoraria from Teijin Pharma Limited. Author Kanazawa has received materials supervision fee from Teijin Pharma Limited., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background and Purpose: An early and accurate diagnosis of Dementia with Lewy bodies (DLB) is critical because treatments and prognosis of DLB are different from Alzheimer's disease (AD). This study was carried out in Japan to validate an Electroencephalography (EEG)-derived machine learning algorithm for discriminating DLB from AD which developed based on a database of EEG records from two different European countries., Methods: In a prospective multicenter study, patients with probable DLB or with probable AD were enrolled in a 1:1 ratio. A continuous EEG segment of 150 seconds was recorded, and the EEG data was processed using MC-004, the EEG-based machine learning algorithm, with all clinical information blinded except for age and gender., Results: Eighteen patients with probable DLB and 21 patients with probable AD were the included for the analysis. The performance of MC-004 differentiating probable DLB from probable AD was 72.2% (95% CI 46.5-90.3%) for sensitivity, 85.7% (63.7-97.0%) for specificity, and 79.5% (63.5-90.7%) for accuracy. When limiting to subjects taking ≤5 mg donepezil, the sensitivity was 83.3% (95% CI 51.6-97.9), the specificity 89.5% (66.9-98.7), and the accuracy 87.1% (70.2-96.4)., Conclusions: MC-004, the EEG-based machine learning algorithm, was able to discriminate between DLB and AD with fairly high accuracy. MC-004 is a promising biomarker for DLB, and has the potential to improve the detection of DLB in a diagnostic process., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: YS, MS,TA and EM report grants and personal fees from Mentis Cura (https://www.mentiscura.com) during the conduct of the study. KShig, KShin, YA, HK, TB and MH report grants from Mentis Cura, during the conduct of the study. KJ is an employee of Mentis Cura. All the authors have sufficiently participated in the work and take responsibility for the content and agree to transfer copyright to PLOS. We disclose potential conflicts of interest and describe their financial support. This does not alter our adherence to PLOS ONE policies on sharing data and materials. Our manuscript contains no extractions from other copyrighted works. We confirm that this manuscript has not been accepted for publication elsewhere, is not being considered for publication elsewhere, and does not duplicate previously published material. I confirm that all the authors consent to publication of this manuscript.