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:

Predicting escitalopram treatment response from pre-treatment and early response resting state fMRI in a multi-site sample: A CAN-BIND-1 report
Jacqueline K. Harris, Stefanie Hassel, Andrew D. Davis, et al.
NeuroImage Clinical (2022) Vol. 35, pp. 103120-103120
Open Access | Times Cited: 15

Showing 15 citing articles:

AI-assisted prediction of differential response to antidepressant classes using electronic health records
Yi-han Sheu, Colin Magdamo, Matthew Miller, et al.
npj Digital Medicine (2023) Vol. 6, Iss. 1
Open Access | Times Cited: 45

Predicting treatment outcome based on resting-state functional connectivity in internalizing mental disorders: A systematic review and meta-analysis
Charlotte Meinke, Ulrike Lueken, Henrik Walter, et al.
Neuroscience & Biobehavioral Reviews (2024) Vol. 160, pp. 105640-105640
Open Access | Times Cited: 4

Breakthroughs and challenges for generating brain network-based biomarkers of treatment response in depression
Sapolnach Prompiengchai, Katharine Dunlop
Neuropsychopharmacology (2024) Vol. 50, Iss. 1, pp. 230-245
Closed Access | Times Cited: 4

Generalizability of Treatment Outcome Prediction Across Antidepressant Treatment Trials in Depression
Peter Zhukovsky, Madhukar H. Trivedi, Myrna M. Weissman, et al.
JAMA Network Open (2025) Vol. 8, Iss. 3, pp. e251310-e251310
Open Access

Predicting Antidepressant Treatment Response From Cortical Structure on MRI: A Mega‐Analysis From the ENIGMAMDD Working Group
Maarten G. Poirot, Daphne E. Boucherie, Matthan W.A. Caan, et al.
Human Brain Mapping (2025) Vol. 46, Iss. 1
Open Access

Predicting future depressive episodes from resting-state fMRI with generative embedding
Herman Galioulline, Stefan Frässle, Samuel J. Harrison, et al.
NeuroImage (2023) Vol. 273, pp. 119986-119986
Open Access | Times Cited: 7

Twenty-five years of research on resting-state fMRI of major depressive disorder: A bibliometric analysis of hotspots, nodes, bursts, and trends
Linhan Fu, Mengjing Cai, Yao Zhao, et al.
Heliyon (2024) Vol. 10, Iss. 13, pp. e33833-e33833
Open Access | Times Cited: 2

Individual-Level Prediction of Exposure Therapy Outcome Using Structural and Functional MRI Data in Spider Phobia: A Machine-Learning Study
Alice V. Chavanne, Charlotte Meinke, Till Langhammer, et al.
Depression and Anxiety (2023) Vol. 2023, pp. 1-11
Open Access | Times Cited: 4

Antidepressant Treatment Response Prediction with Early Assessment of Functional Near-Infrared Spectroscopy and Micro-RNA
Lok Hua Lee, Cyrus S. H. Ho, Yee Ling Chan, et al.
IEEE Journal of Translational Engineering in Health and Medicine (2024) Vol. 13, pp. 9-22
Open Access

Functional Neuroimaging Biomarkers
Sydney Singleterry, Damek Homiack, Olusola Ajilore
Biomarkers in Neuropsychiatry (2023), pp. 65-80
Closed Access

Predicting Future Depressive Episodes from Resting-State fMRI with Generative Embedding
Herman Galioulline, Stefan Frässle, Sam Harrison, et al.
medRxiv (Cold Spring Harbor Laboratory) (2022)
Open Access

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