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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:

Pattern recognition of magnetic resonance imaging-based gray matter volume measurements classifies bipolar disorder and major depressive disorder
Harry Rubin‐Falcone, Francesca Zanderigo, Binod Thapa-Chhetry, et al.
Journal of Affective Disorders (2017) Vol. 227, pp. 498-505
Open Access | Times Cited: 65

Showing 1-25 of 65 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
Differentiating between bipolar and unipolar depression in functional and structural MRI studies
Kyu‐Man Han, Domenico De Berardis, Michele Fornaro, et al.
Progress in Neuro-Psychopharmacology and Biological Psychiatry (2018) Vol. 91, pp. 20-27
Closed Access | Times Cited: 181
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
Artificial intelligence in psychiatry research, diagnosis, and therapy
Jie Sun, Qunxi Dong, San-Wang Wang, et al.
Asian Journal of Psychiatry (2023) Vol. 87, pp. 103705-103705
Closed Access | Times Cited: 72
CI-GNN: A Granger causality-inspired graph neural network for interpretable brain network-based psychiatric diagnosis
Kaizhong Zheng, Shujian Yu, Badong Chen
Neural Networks (2024) Vol. 172, pp. 106147-106147
Open Access | Times Cited: 23
Using structural MRI to identify bipolar disorders – 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group
Abraham Nunes, Hugo G. Schnack, Christopher R. K. Ching, et al.
Molecular Psychiatry (2018) Vol. 25, Iss. 9, pp. 2130-2143
Open Access | Times Cited: 153
Recent advances in psychoradiology
Lekai Luo, Wanfang You, Melissa P. DelBello, et al.
Physics in Medicine and Biology (2022) Vol. 67, Iss. 23, pp. 23TR01-23TR01
Open Access | Times Cited: 49
Distinguishing between depression in bipolar disorder and unipolar depression using magnetic resonance imaging: a systematic review
J E Siegel, Michele A. Bertocci, Bryan Wu, et al.
Bipolar Disorders (2022) Vol. 24, Iss. 5, pp. 474-498
Closed Access | Times Cited: 39
A Comprehensive Overview of the Role of Visual Cortex Malfunction in Depressive Disorders: Opportunities and Challenges
Fangfang Wu, Qing-Bo Lu, Yan Kong, et al.
Neuroscience Bulletin (2023) Vol. 39, Iss. 9, pp. 1426-1438
Open Access | Times Cited: 32
Artificial intelligence significantly facilitates development in the mental health of college students: a bibliometric analysis
Jing Chen, Dongfeng Yuan, Ruotong Dong, et al.
Frontiers in Psychology (2024) Vol. 15
Open Access | Times Cited: 11
Machine learning for the diagnosis accuracy of bipolar disorder: a systematic review and meta-analysis
Yi Pan, Pushi Wang, Bowen Xue, et al.
Frontiers in Psychiatry (2025) Vol. 15
Open Access | Times Cited: 1
Predicting differential diagnosis between bipolar and unipolar depression with multiple kernel learning on multimodal structural neuroimaging
Benedetta Vai, Lorenzo Parenti, Irene Bollettini, et al.
European Neuropsychopharmacology (2020) Vol. 34, pp. 28-38
Closed Access | Times Cited: 55
Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions
Laurie‐Anne Claude, Josselin Houenou, Édouard Duchesnay, et al.
Bipolar Disorders (2020) Vol. 22, Iss. 4, pp. 334-355
Open Access | Times Cited: 54
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
Shared and specific patterns of dynamic functional connectivity variability of striato-cortical circuitry in unmedicated bipolar and major depressive disorders
Guanmao Chen, Pan Chen, Jiaying Gong, et al.
Psychological Medicine (2020) Vol. 52, Iss. 4, pp. 747-756
Closed Access | Times Cited: 48
Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning
Zhihong Chen, Tao Yan, Erlei Wang, et al.
Computational Intelligence and Neuroscience (2020) Vol. 2020, pp. 1-13
Open Access | Times Cited: 41
Identification of Diagnostic Markers for Major Depressive Disorder Using Machine Learning Methods
Zhao Shu, Zhiwei Bao, Xinyi Zhao, et al.
Frontiers in Neuroscience (2021) Vol. 15
Open Access | Times Cited: 37
Promises and pitfalls of topological data analysis for brain connectivity analysis
Luigi Caputi, Anna Pidnebesna, Jaroslav Hlinka
NeuroImage (2021) Vol. 238, pp. 118245-118245
Open Access | Times Cited: 36
Application of Artificial Intelligence in the MRI Classification Task of Human Brain Neurological and Psychiatric Diseases: A Scoping Review
Zhao Zhang, Guangfei Li, Yong Xu, et al.
Diagnostics (2021) Vol. 11, Iss. 8, pp. 1402-1402
Open Access | Times Cited: 36
Machine learning approaches for prediction of bipolar disorder based on biological, clinical and neuropsychological markers: A systematic review and meta-analysis
F. Colombo, F. Calesella, Mario Gennaro Mazza, et al.
Neuroscience & Biobehavioral Reviews (2022) Vol. 135, pp. 104552-104552
Closed Access | Times Cited: 25
Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions
Thalia Richter, Barak Fishbain, Gal Richter‐Levin, et al.
Journal of Personalized Medicine (2021) Vol. 11, Iss. 10, pp. 957-957
Open Access | Times Cited: 31
Discriminating between bipolar and major depressive disorder using a machine learning approach and resting-state EEG data
Maryam Ravan, Amin Noroozi, M. Margarette Sanchez, et al.
Clinical Neurophysiology (2022) Vol. 146, pp. 30-39
Closed Access | Times Cited: 19
Classification of female MDD patients with and without suicidal ideation using resting-state functional magnetic resonance imaging and machine learning
Morteza Fattahi, Milad Esmaeil-Zadeh, Hamid Soltanian‐Zadeh, et al.
Frontiers in Human Neuroscience (2025) Vol. 18
Open Access
Machine learning based differential diagnosis of schizophrenia, major depression disorder and bipolar disorder using structural magnetic resonance imaging
Peiyu Cao, Runda Li, Yuting Li, et al.
Journal of Affective Disorders (2025)
Closed Access
Support Vector Machine Classification of Obsessive-Compulsive Disorder Based on Whole-Brain Volumetry and Diffusion Tensor Imaging
Cong Zhou, Yuqi Cheng, Liangliang Ping, et al.
Frontiers in Psychiatry (2018) Vol. 9
Open Access | Times Cited: 32
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