Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Prediction of clinical depression scores and detection of changes in whole-brain using resting-state functional MRI data with partial least squares regression
Kosuke Yoshida, Yu Shimizu, Junichiro Yoshimoto, et al.
PLoS ONE (2017) Vol. 12, Iss. 7, pp. e0179638-e0179638
Open Access | Times Cited: 86

Showing 1-25 of 86 citing articles:

Machine learning in major depression: From classification to treatment outcome prediction
Shuang Gao, Vince D. Calhoun, Jing Sui
CNS Neuroscience & Therapeutics (2018) Vol. 24, Iss. 11, pp. 1037-1052
Open Access | Times Cited: 294
Neuroimaging-based Individualized Prediction of Cognition and Behavior for Mental Disorders and Health: Methods and Promises
Jing Sui, Rongtao Jiang, Juan Bustillo, et al.
Biological Psychiatry (2020) Vol. 88, Iss. 11, pp. 818-828
Open Access | Times Cited: 268
Towards a brain‐based predictome of mental illness
Barnaly Rashid, Vince D. Calhoun
Human Brain Mapping (2020) Vol. 41, Iss. 12, pp. 3468-3535
Open Access | Times Cited: 149
Machine learning studies on major brain diseases: 5-year trends of 2014–2018
Koji Sakai, Kei Yamada
Japanese Journal of Radiology (2018) Vol. 37, Iss. 1, pp. 34-72
Closed Access | Times Cited: 114
An insight into diagnosis of depression using machine learning techniques: a systematic review
Sweta Bhadra, Chandan Jyoti Kumar
Current Medical Research and Opinion (2022) Vol. 38, Iss. 5, pp. 749-771
Closed Access | Times Cited: 53
A Clinical-Oriented Non-Severe Depression Diagnosis Method Based on Cognitive Behavior of Emotional Conflict
Mi Li, Jinyu Zhang, Jie Song, et al.
IEEE Transactions on Computational Social Systems (2022) Vol. 10, Iss. 1, pp. 131-141
Closed Access | Times Cited: 52
Aberrant resting-state co-activation network dynamics in major depressive disorder
Ziqi An, Kai Tang, Yuanyao Xie, et al.
Translational Psychiatry (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 14
Anhedonia in chronic pain and prescription opioid misuse
Eric L. Garland, Martin Trøstheim, Marie Eikemo, et al.
Psychological Medicine (2019) Vol. 50, Iss. 12, pp. 1977-1988
Open Access | Times Cited: 64
Quantitative Identification of Major Depression Based on Resting-State Dynamic Functional Connectivity: A Machine Learning Approach
Baoyu Yan, Xiaopan Xu, Mengwan Liu, et al.
Frontiers in Neuroscience (2020) Vol. 14
Open Access | Times Cited: 61
Prediction of Depression Severity Scores Based on Functional Connectivity and Complexity of the EEG Signal
Yousef Mohammadi, Mohammad Hassan Moradi
Clinical EEG and Neuroscience (2020) Vol. 52, Iss. 1, pp. 52-60
Closed Access | Times Cited: 58
The use of machine learning and deep learning algorithms in functional magnetic resonance imaging—A systematic review
Mamoon Rashid, Harjeet Singh, Vishal Goyal
Expert Systems (2020) Vol. 37, Iss. 6
Closed Access | Times Cited: 51
Unsupervised cross-domain functional MRI adaptation for automated major depressive disorder identification
Yuqi Fang, Mingliang Wang, Guy G. Potter, et al.
Medical Image Analysis (2022) Vol. 84, pp. 102707-102707
Open Access | Times Cited: 33
A deep learning approach for mental health quality prediction using functional network connectivity and assessment data
Meenu Ajith, Dawn M. Aycock, Erin B. Tone, et al.
Brain Imaging and Behavior (2024) Vol. 18, Iss. 3, pp. 630-645
Closed Access | Times Cited: 6
Ensemble graph neural network model for classification of major depressive disorder using whole-brain functional connectivity
V. Sujitha, Mikhail Votinov, Lisa Wagels, et al.
Frontiers in Psychiatry (2023) Vol. 14
Open Access | Times Cited: 15
Partial Least Squares Regression Performs Well in MRI-Based Individualized Estimations
Chen Chen, Xuyu Cao, Lixia Tian
Frontiers in Neuroscience (2019) Vol. 13
Open Access | Times Cited: 36
A Model of Normality Inspired Deep Learning Framework for Depression Relapse Prediction Using Audiovisual Data
Alice Othmani, Assaad Oussama Zeghina, Muhammad Muzammel
Computer Methods and Programs in Biomedicine (2022) Vol. 226, pp. 107132-107132
Open Access | Times Cited: 21
Higher amplitudes of visual networks are associated with trait- but not state-depression
Wei Zhang, Rosie Dutt, Daphne Lew, et al.
Psychological Medicine (2025), pp. 1-12
Open Access
Using effective connectivity-based predictive modeling to predict MDD scale scores from multisite rs-fMRI data
Peishan Dai, Zhuang He, Jialin Luo, et al.
Journal of Neuroscience Methods (2025) Vol. 417, pp. 110406-110406
Closed Access
Prediction of recurrent venous thromboembolism using a spatiotemporal phenomenological model and artificial neural network
Mohamad Al Bannoud, Tiago Dias Martins, Silmara Aparecida de Lima Montalvão, et al.
Neural Computing and Applications (2025)
Closed Access
Multiparametric MRI Can Detect Enhanced Myelination in the Ex Vivo Gli1−/− Mouse Brain
Choong H. Lee, Mara Holloman, James L. Salzer, et al.
NMR in Biomedicine (2025) Vol. 38, Iss. 5
Closed Access
Predicting depression severity using effective and functional brain connectivity of the electroencephalography signals
Naif H. Alotaibi, Dalal M. Bakheet
Computers in Biology and Medicine (2025) Vol. 190, pp. 110045-110045
Closed Access
Identifying resting‐state effective connectivity abnormalities in drug‐naïve major depressive disorder diagnosis via graph convolutional networks
Eunji Jun, Kyoung‐Sae Na, Wooyoung Kang, et al.
Human Brain Mapping (2020) Vol. 41, Iss. 17, pp. 4997-5014
Open Access | Times Cited: 30
Enhancing Multi-Center Generalization of Machine Learning-Based Depression Diagnosis From Resting-State fMRI
T. Nakano, Masahiro Takamura, Naho Ichikawa, et al.
Frontiers in Psychiatry (2020) Vol. 11
Open Access | Times Cited: 28
Effectiveness of artificial intelligence in detecting and managing depressive disorders: Systematic review
Yoonseo Park, Sewon Park, Munjae Lee
Journal of Affective Disorders (2024) Vol. 361, pp. 445-456
Closed Access | Times Cited: 3
New Graph-Blind Convolutional Network for Brain Connectome Data Analysis
Yanfu Zhang, Heng Huang
Lecture notes in computer science (2019), pp. 669-681
Closed Access | Times Cited: 25
Page 1 - Next Page

Scroll to top