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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Machine learning approaches and non-linear processing of extracted components in frontal region to predict rTMS treatment response in major depressive disorder
Elias Ebrahimzadeh, Farahnaz Fayaz, Lila Rajabion, et al.
Frontiers in Systems Neuroscience (2023) Vol. 17
Open Access | Times Cited: 22

Showing 22 citing articles:

Stroke classification based on deep reinforcement learning over stroke screening imbalanced data
Ting Zuo, Fenglian Li, Xueying Zhang, et al.
Computers & Electrical Engineering (2024) Vol. 114, pp. 109069-109069
Closed Access | Times Cited: 8
Neuroenhancement by repetitive transcranial magnetic stimulation (rTMS) on DLPFC in healthy adults
Elias Ebrahimzadeh, Seyyed Mostafa Sadjadi, Mostafa Asgarinejad, et al.
Cognitive Neurodynamics (2025) Vol. 19, Iss. 1
Closed Access
Embracing Internal States: A Review of Optimization of Repetitive Transcranial Magnetic Stimulation for Treating Depression
Tingting Wu, Qi Yu, Ximei Zhu, et al.
Neuroscience Bulletin (2025)
Closed Access
Network-based predictive models for artificial intelligence: an interpretable application of machine learning techniques in the assessment of depression in stroke patients
Wenwei Zuo, Xuelian Yang
BMC Geriatrics (2025) Vol. 25, Iss. 1
Open Access
Predicting Treatment Response of rTMS in Major Depressive Disorder Using a Explainable Machine Learning Model Based on EEG and Clinical Features
Zongya Zhao, Xiangying Ran, Yanxiang Niu, et al.
Biological Psychiatry Cognitive Neuroscience and Neuroimaging (2025)
Closed Access
A comprehensive review of neurotransmitter modulation via artificial intelligence: A new frontier in personalized neurobiochemistry
Jaleh Bagheri Hamzyan Olia, Arasu Raman, Chou‐Yi Hsu, et al.
Computers in Biology and Medicine (2025) Vol. 189, pp. 109984-109984
Closed Access
Neurophysiological and neuroimaging markers of repetitive transcranial magnetic stimulation treatment response in major depressive disorder: A systematic review and meta-analysis of predictive modeling studies
Min Xia Jin, Penny Ping Qin, Adam W.L. Xia, et al.
Neuroscience & Biobehavioral Reviews (2024) Vol. 162, pp. 105695-105695
Closed Access | Times Cited: 3
Common and differential EEG microstate of major depressive disorder patients with and without response to rTMS treatment
Zongya Zhao, Xiangying Ran, Junming Wang, et al.
Journal of Affective Disorders (2024) Vol. 367, pp. 777-787
Closed Access | Times Cited: 3
Repetitive transcranial magnetic stimulation (rTMS) as a tool for cognitive enhancement in healthy adults: a review study
Mostafa Asgarinejad, Marzieh Saviz, Seyyed Mostafa Sadjadi, et al.
Medical & Biological Engineering & Computing (2023) Vol. 62, Iss. 3, pp. 653-673
Closed Access | Times Cited: 8
Bayesian networks for feature selection and patient pre-screening for depressive symptomatology: a prototype (Preprint)
Eduardo Maekawa, Eoin Martino Grua, Carina Akemi Nakamura, et al.
JMIR Mental Health (2024) Vol. 11, pp. e52045-e52045
Open Access | Times Cited: 2
Non-linear processing and reinforcement learning to predict rTMS treatment response in depression
Elias Ebrahimzadeh, Amin Dehghani, Mostafa Asgarinejad, et al.
Psychiatry Research Neuroimaging (2023) Vol. 337, pp. 111764-111764
Closed Access | Times Cited: 6
Identifying predictive biomarkers for repetitive transcranial magnetic stimulation response in depression patients with explainability
Matthew Squires, Xiaohui Tao, Soman Elangovan, et al.
Computer Methods and Programs in Biomedicine (2023) Vol. 242, pp. 107771-107771
Open Access | Times Cited: 5
Next-generation cognitive assessment: Combining functional brain imaging, system perturbations and novel equipment interfaces
Peter A. Hall, Amer M. Burhan, James C. MacKillop, et al.
Brain Research Bulletin (2023) Vol. 204, pp. 110797-110797
Open Access | Times Cited: 4
Predicting Sustainable Crop Yields: Deep Learning and Explainable AI Tools
Ivan Malashin, В С Тынченко, Andrei Gantimurov, et al.
Sustainability (2024) Vol. 16, Iss. 21, pp. 9437-9437
Open Access | Times Cited: 1
Effectiveness of Repetitive Transcranial Magnetic Stimulation (rTMS) on DLPFC for Enhancing Cognitive Function in Healthy Adults: A Review
Mostafa Asgarinejad, Marzieh Saviz, Seyyed Mostafa Sadjadi, et al.
(2023)
Closed Access | Times Cited: 3
SIFT-based Approach for Dorsal Hand Veins Images Recognition
D. P. Egorov, Pavel V. Mizinov, O. V. Kravchenko, et al.
(2024), pp. 1-4
Closed Access
Electroencephalography oscillations can predict the cortical response following theta burst stimulation
Guiyuan Cai, Jiayue Xu, Qian Ding, et al.
Brain Research Bulletin (2024) Vol. 208, pp. 110902-110902
Open Access
The effect of repetitive transcranial magnetic stimulation on the Hamilton Depression Rating Scale-17 criterion in patients with major depressive disorder without psychotic features: a systematic review and meta-analysis of intervention studies
Elham Hassanzadeh, Ghobad Moradi, Modabber Arasteh, et al.
BMC Psychology (2024) Vol. 12, Iss. 1
Open Access
Resting-state EEG biomarkers of accelerated intermittent theta burst stimulation treatment for depression: a pilot study
Guo Ling, Zhuo Zhang, Xiao Tan, et al.
(2024), pp. 1-4
Closed Access
Enhancing the accuracy of electroencephalogram-based emotion recognition through Long Short-Term Memory recurrent deep neural networks
Mohammad Reza Yousefi, Amin Dehghani, Hamid Taghaavifar
Frontiers in Human Neuroscience (2023) Vol. 17
Open Access | Times Cited: 1
Bayesian networks for feature selection and patient screening for depressive symptomatology: Practical applications of machine learning models (Preprint)
Eduardo Maekawa, Eoin Martino Grua, Carina Akemi Nakamura, et al.
(2023)
Closed Access
Baseline resting EEG measures differentiate rTMS treatment responders and non-responders
Pakin Kaewpijit, Paul B. Fitzgerald, Kate E. Hoy, et al.
medRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access
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