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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:

A wavelet-based technique to predict treatment outcome for Major Depressive Disorder
Wajid Mumtaz, Likun Xia, Mohd Azhar Mohd Yasin, et al.
PLoS ONE (2017) Vol. 12, Iss. 2, pp. e0171409-e0171409
Open Access | Times Cited: 171

Showing 1-25 of 171 citing articles:

Applications of machine learning algorithms to predict therapeutic outcomes in depression: A meta-analysis and systematic review
Yena Lee, Renee‐Marie Ragguett, Rodrigo B. Mansur, et al.
Journal of Affective Disorders (2018) Vol. 241, pp. 519-532
Closed Access | Times Cited: 348
The promise of machine learning in predicting treatment outcomes in psychiatry
Adam M. Chekroud, Julia Bondar, Jaime Delgadillo, et al.
World Psychiatry (2021) Vol. 20, Iss. 2, pp. 154-170
Open Access | Times Cited: 321
Making Individual Prognoses in Psychiatry Using Neuroimaging and Machine Learning
Ronald J. Janssen, Janaı́na Mourão-Miranda, Hugo G. Schnack
Biological Psychiatry Cognitive Neuroscience and Neuroimaging (2018) Vol. 3, Iss. 9, pp. 798-808
Open Access | Times Cited: 163
Electroencephalographic Biomarkers for Treatment Response Prediction in Major Depressive Illness: A Meta-Analysis
Alik S. Widge, Mustafa Taha Bilge, Rebecca E. Montana, et al.
American Journal of Psychiatry (2018) Vol. 176, Iss. 1, pp. 44-56
Open Access | Times Cited: 162
Deep learning and machine learning in psychiatry: a survey of current progress in depression detection, diagnosis and treatment
Matthew Squires, Xiaohui Tao, Soman Elangovan, et al.
Brain Informatics (2023) Vol. 10, Iss. 1
Open Access | Times Cited: 46
Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach
Abdolkarim Saeedi, Maryam Saeedi, Arash Maghsoudi, et al.
Cognitive Neurodynamics (2020) Vol. 15, Iss. 2, pp. 239-252
Open Access | Times Cited: 126
Classification of Depression Patients and Normal Subjects Based on Electroencephalogram (EEG) Signal Using Alpha Power and Theta Asymmetry
Shalini Mahato, Sanchita Paul
Journal of Medical Systems (2019) Vol. 44, Iss. 1
Closed Access | Times Cited: 109
Detection of major depressive disorder using linear and non-linear features from EEG signals
Shalini Mahato, Sanchita Paul
Microsystem Technologies (2018) Vol. 25, Iss. 3, pp. 1065-1076
Closed Access | Times Cited: 103
A major depressive disorder classification framework based on EEG signals using statistical, spectral, wavelet, functional connectivity, and nonlinear analysis
Reza Akbari Movahed, Gila Pirzad Jahromi, Shima Shahyad, et al.
Journal of Neuroscience Methods (2021) Vol. 358, pp. 109209-109209
Closed Access | Times Cited: 99
Prediction of rTMS treatment response in major depressive disorder using machine learning techniques and nonlinear features of EEG signal
Fatemeh Hasanzadeh, Maryam Mohebbi, Reza Rostami
Journal of Affective Disorders (2019) Vol. 256, pp. 132-142
Closed Access | Times Cited: 90
EEG Signal denoising using hybrid approach of Variational Mode Decomposition and wavelets for depression
Chamandeep Kaur, Amandeep Bisht, Preeti Singh, et al.
Biomedical Signal Processing and Control (2020) Vol. 65, pp. 102337-102337
Closed Access | Times Cited: 78
Machine Learning Approaches for MDD Detection and Emotion Decoding Using EEG Signals
Lijuan Duan, Huifeng Duan, Yuanhua Qiao, et al.
Frontiers in Human Neuroscience (2020) Vol. 14
Open Access | Times Cited: 72
Decision support system for major depression detection using spectrogram and convolution neural network with EEG signals
Hui Wen Loh, Chui Ping Ooi, Emrah Aydemir, et al.
Expert Systems (2021) Vol. 39, Iss. 3
Closed Access | Times Cited: 72
Detection of Depression and Scaling of Severity Using Six Channel EEG Data
Shalini Mahato, Nishant Goyal, Daya Ram, et al.
Journal of Medical Systems (2020) Vol. 44, Iss. 7
Closed Access | Times Cited: 70
Major depressive disorder assessment via enhanced k-nearest neighbor method and EEG signals
Maryam Saeedi, Abdolkarim Saeedi, Arash Maghsoudi
Physical and Engineering Sciences in Medicine (2020) Vol. 43, Iss. 3, pp. 1007-1018
Closed Access | Times Cited: 70
Predicting treatment response using EEG in major depressive disorder: A machine-learning meta-analysis
Devon Watts, Rafaela Fernandes Pulice, J.P. Reilly, et al.
Translational Psychiatry (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 60
Resting-State EEG Signal for Major Depressive Disorder Detection: A Systematic Validation on a Large and Diverse Dataset
Chien‐Te Wu, Hao-Chuan Huang, Shiuan Huang, et al.
Biosensors (2021) Vol. 11, Iss. 12, pp. 499-499
Open Access | Times Cited: 58
Automated Diagnosis of Major Depressive Disorder Using Brain Effective Connectivity and 3D Convolutional Neural Network
Danish M. Khan, Norashikin Yahya, Nidal Kamel, et al.
IEEE Access (2021) Vol. 9, pp. 8835-8846
Open Access | Times Cited: 55
Prediction of drug response in major depressive disorder using ensemble of transfer learning with convolutional neural network based on EEG
Mohsen Sadat Shahabi, Ahmad Shalbaf, Arash Maghsoudi
Journal of Applied Biomedicine (2021) Vol. 41, Iss. 3, pp. 946-959
Closed Access | Times Cited: 55
Development of Wavelet Coherence EEG as a Biomarker for Diagnosis of Major Depressive Disorder
Danish M. Khan, Komal Masroor, Muhammad Fahim Mohd Jailani, et al.
IEEE Sensors Journal (2022) Vol. 22, Iss. 5, pp. 4315-4325
Closed Access | Times Cited: 39
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
Automated major depressive disorder diagnosis using a dual-input deep learning model and image generation from EEG signals
Ahmad Afzali, Ali Khaleghi, Boshra Hatef, et al.
Waves in Random and Complex Media (2023), pp. 1-16
Closed Access | Times Cited: 22
Electroencephalography signals-based sparse networks integration using a fuzzy ensemble technique for depression detection
Surbhi Soni, Ayan Seal, Sraban Kumar Mohanty, et al.
Biomedical Signal Processing and Control (2023) Vol. 85, pp. 104873-104873
Closed Access | Times Cited: 22
TSF-MDD: A Deep Learning Approach for Electroencephalography-Based Diagnosis of Major Depressive Disorder with Temporal–Spatial–Frequency Feature Fusion
Wei Gan, R. P. Zhao, Yujie Ma, et al.
Bioengineering (2025) Vol. 12, Iss. 2, pp. 95-95
Open Access | Times Cited: 1
Leveraging Machine Learning Approaches for Predicting Antidepressant Treatment Response Using Electroencephalography (EEG) and Clinical Data
Natalia Jaworska, Sara de la Salle, Mohamed Hamza Ibrahim, et al.
Frontiers in Psychiatry (2019) Vol. 9
Open Access | Times Cited: 74
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