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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:

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

Showing 26-50 of 163 citing articles:

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis
Zhiyi Chen, Xuerong Liu, Qingwu Yang, et al.
JAMA Network Open (2023) Vol. 6, Iss. 3, pp. e231671-e231671
Open Access | Times Cited: 19
Systematic review and meta-analysis on predictors of prognosis in patients with schizophrenia spectrum disorders: An overview of current evidence and a call for prospective research and open access to datasets
Violet van Dee, Hugo G. Schnack, Wiepke Cahn
Schizophrenia Research (2023) Vol. 254, pp. 133-142
Open Access | Times Cited: 18
Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry
Zhiyi Chen, Bowen Hu, Xuerong Liu, et al.
BMC Medicine (2023) Vol. 21, Iss. 1
Open Access | Times Cited: 17
Translating research findings into clinical practice: a systematic and critical review of neuroimaging-based clinical tools for brain disorders
Cristina Scarpazza, Minji Ha, Lea Baecker, et al.
Translational Psychiatry (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 41
Machine learning applications in tobacco research: a scoping review
Rui Fu, Anasua Kundu, Nicholas Mitsakakis, et al.
Tobacco Control (2021) Vol. 32, Iss. 1, pp. 99-109
Closed Access | Times Cited: 40
Advantages of Machine Learning in Forensic Psychiatric Research—Uncovering the Complexities of Aggressive Behavior in Schizophrenia
Lena Hofmann, Steffen Lau, Johannes Kirchebner
Applied Sciences (2022) Vol. 12, Iss. 2, pp. 819-819
Open Access | Times Cited: 25
The use of artificial intelligence in mental health services in Turkey: What do mental health professionals think?
Mücahit GÜLTEKİN, Meryem ŞAHİN
Cyberpsychology Journal of Psychosocial Research on Cyberspace (2024) Vol. 18, Iss. 1
Open Access | Times Cited: 5
Lack of evidence for predictive utility from resting state fMRI data for individual exposure-based cognitive behavioral therapy outcomes: A machine learning study in two large multi-site samples in anxiety disorders
Kevin Hilbert, Joscha Böhnlein, Charlotte Meinke, et al.
NeuroImage (2024) Vol. 295, pp. 120639-120639
Open Access | Times Cited: 5
Predicting Antidepressant Treatment Response From Cortical Structure on MRI: A Mega‐Analysis From the ENIGMA‐MDD Working Group
Maarten G. Poirot, Daphne E. Boucherie, Matthan W.A. Caan, et al.
Human Brain Mapping (2025) Vol. 46, Iss. 1
Open Access
Prospects for the Use of Machine Learning for Mood Disorders
Ekaterina Mosolova, A. Е. Alfimov, E G Kostyukova, et al.
Digital Diagnostics (2025)
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
Neuroimaging in Psychiatric Disorders: A Bibliometric Analysis of the 100 Most Highly Cited Articles
Bo Gong, Sadiq Naveed, Dawood M. Hafeez, et al.
Journal of Neuroimaging (2018) Vol. 29, Iss. 1, pp. 14-33
Open Access | Times Cited: 43
Automation to optimise physician treatment of individual patients: examples in psychiatry
Michael Bauer, Scott Monteith, John Geddes, et al.
The Lancet Psychiatry (2019) Vol. 6, Iss. 4, pp. 338-349
Closed Access | Times Cited: 38
A machine-learning framework for robust and reliable prediction of short- and long-term treatment response in initially antipsychotic-naïve schizophrenia patients based on multimodal neuropsychiatric data
Karen S. Ambrosen, Martin W. Skjerbæk, Jonathan Foldager, et al.
Translational Psychiatry (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 38
Predicting anxiety from wholebrain activity patterns to emotional faces in young adults: a machine learning approach
Liana Catarina Lima Portugal, Jessica Schrouff, Ricki Stiffler, et al.
NeuroImage Clinical (2019) Vol. 23, pp. 101813-101813
Open Access | Times Cited: 37
Random Forest Classification of Alcohol Use Disorder Using fMRI Functional Connectivity, Neuropsychological Functioning, and Impulsivity Measures
Chella Kamarajan, Babak A. Ardekani, Ashwini K. Pandey, et al.
Brain Sciences (2020) Vol. 10, Iss. 2, pp. 115-115
Open Access | Times Cited: 37
Using machine learning to explain the heterogeneity of schizophrenia. Realizing the promise and avoiding the hype
Neeraj Tandon, Rajiv Tandon
Schizophrenia Research (2019) Vol. 214, pp. 70-75
Closed Access | Times Cited: 36
Automated classification of depression from structural brain measures across two independent community‐based cohorts
Aleks Stolicyn, Mathew A. Harris, Xueyi Shen, et al.
Human Brain Mapping (2020) Vol. 41, Iss. 14, pp. 3922-3937
Open Access | Times Cited: 35
Identification of attention-deficit hyperactivity disorder based on the complexity and symmetricity of pupil diameter
Sou Nobukawa, Aya Shirama, Tetsuya Takahashi, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 29
Artificial intelligence-driven phenotyping of zebrafish psychoactive drug responses
Dmitrii V. Bozhko, Vladislav Myrov, Sofia M. Kolchanova, et al.
Progress in Neuro-Psychopharmacology and Biological Psychiatry (2021) Vol. 112, pp. 110405-110405
Closed Access | Times Cited: 28
Predicting Treatment Response in Schizophrenia With Magnetic Resonance Imaging and Polygenic Risk Score
Meng Wang, Ke Hu, Lingzhong Fan, et al.
Frontiers in Genetics (2022) Vol. 13
Open Access | Times Cited: 22
Can cognition help predict suicide risk in patients with major depressive disorder? A machine learning study
Shuqiong Zheng, Weixiong Zeng, Qianqian Xin, et al.
BMC Psychiatry (2022) Vol. 22, Iss. 1
Open Access | Times Cited: 20
Connectome‐based predictive modelling of cognitive reserve using task‐based functional connectivity
Rory Boyle, Michael Connaughton, Eimear McGlinchey, et al.
European Journal of Neuroscience (2022) Vol. 57, Iss. 3, pp. 490-510
Open Access | Times Cited: 19
Identification of chronic mild traumatic brain injury using resting state functional MRI and machine learning techniques
Faezeh Vedaei, Najmeh Mashhadi, George Zabrecky, et al.
Frontiers in Neuroscience (2023) Vol. 16
Open Access | Times Cited: 12
Utilizing Machine Learning for Early Intervention and Risk Management in the Opioid Overdose Crisis
Andy Man Yeung Tai, Alireza Kazemi, Jane J. Kim, et al.
Wiley Interdisciplinary Reviews Computational Statistics (2025) Vol. 17, Iss. 1
Open Access

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