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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:

Application of machine learning to structural connectome to predict symptom reduction in depressed adolescents with cognitive behavioral therapy (CBT)
Olga Tymofiyeva, Justin P. Yuan, Chiung‐Yu Huang, et al.
NeuroImage Clinical (2019) Vol. 23, pp. 101914-101914
Open Access | Times Cited: 31

Showing 1-25 of 31 citing articles:

Technology Use for Adolescent Health and Wellness
Ana Radović, Sherif M. Badawy
PEDIATRICS (2020) Vol. 145, Iss. Supplement_2, pp. S186-S194
Open Access | Times Cited: 104
Can we predict who will benefit from cognitive-behavioural therapy? A systematic review and meta-analysis of machine learning studies
Sandra Vieira, Xinyi Liang, Raquel Guiomar, et al.
Clinical Psychology Review (2022) Vol. 97, pp. 102193-102193
Open Access | Times Cited: 49
Which client with generalized anxiety disorder benefits from a mindfulness ecological momentary intervention versus a self-monitoring app? Developing a multivariable machine learning predictive model
Nur Hani Zainal, Michelle G. Newman
Journal of Anxiety Disorders (2024) Vol. 102, pp. 102825-102825
Open Access | Times Cited: 8
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: 4
Translational Connectomics: overview of machine learning in macroscale Connectomics for clinical insights
J. Deepa Anbarasi, Radha Kumari, Malvika Ganesh, et al.
BMC Neurology (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 4
Graph Neural Network Learning on the Pediatric Structural Connectome
Anand Srinivasan, Rajikha Raja, John O. Glass, et al.
Tomography (2025) Vol. 11, Iss. 2, pp. 14-14
Open Access
Predicting responses of neuromodulation and psychotherapies for major depressive disorder: A coordinate-based meta-analysis of functional magnetic resonance imaging studies
Yunlin Mao, Linlin Fan, Chunliang Feng, et al.
Neuroscience & Biobehavioral Reviews (2025), pp. 106120-106120
Closed Access
Test–Retest Reliability of Graph Theoretic Metrics in Adolescent Brains
Justin P. Yuan, Eva Henje, Trevor Flynn, et al.
Brain Connectivity (2018) Vol. 9, Iss. 2, pp. 144-154
Open Access | Times Cited: 24
Overall goal of Cognitive-Behavioral Therapy in Major Psychiatric Disorders and Suicidality
Gianluca Serafini, Alessandra Costanza, Andrea Aguglia, et al.
Medical Clinics of North America (2022) Vol. 107, Iss. 1, pp. 143-167
Closed Access | Times Cited: 13
Aberrant single-subject morphological brain networks in first-episode, treatment-naive adolescents with major depressive disorder
Xiaofan Qiu, Junle Li, Fen Pan, et al.
Deleted Journal (2023) Vol. 3
Open Access | Times Cited: 8
The default mode network is associated with changes in internalizing and externalizing problems differently in adolescent boys and girls
Yoon Ji Lee, Rajpreet Chahal, Ian H. Gotlib
Development and Psychopathology (2023) Vol. 36, Iss. 2, pp. 834-843
Open Access | Times Cited: 7
A lateralized model of the pain-depression dyad
Anne Margarette S. Maallo, Eric A. Moulton, Christine B. Sieberg, et al.
Neuroscience & Biobehavioral Reviews (2021) Vol. 127, pp. 876-883
Open Access | Times Cited: 13
Development of a model to predict psychotherapy response for depression among Veterans
Hannah N. Ziobrowski, Ruifeng Cui, Eric L. Ross, et al.
Psychological Medicine (2022) Vol. 53, Iss. 8, pp. 3591-3600
Open Access | Times Cited: 8
The Effects of Cognitive-Behavioral Therapy (CBT) in Well-Being and Perceived Stress in Adolescents with Low Academic Performance During the COVID-19 Pandemic
Nasim Shahrokhian, Saeid Hassanzadeh, Hadi Hashemi Razini, et al.
International journal of Sport Studies for Health (2022) Vol. 4, Iss. 2
Open Access | Times Cited: 7
Biomarkers associated with treatment outcome in young people with depression: A systematic review
Anna Zierotin, Valeria Mondelli, Zuzanna Zajkowska
Neuroscience Applied (2024) Vol. 3, pp. 104082-104082
Open Access | Times Cited: 1
Personalized Diagnosis and Treatment for Neuroimaging in Depressive Disorders
Jongha Lee, SuHyuk Chi, Moon‐Soo Lee
Journal of Personalized Medicine (2022) Vol. 12, Iss. 9, pp. 1403-1403
Open Access | Times Cited: 6
Classification of multiple sclerosis women with voiding dysfunction using machine learning: Is functional connectivity or structural connectivity a better predictor?
Khue Tran, Betsy Salazar, Timothy B. Boone, et al.
BJUI Compass (2023) Vol. 4, Iss. 3, pp. 277-284
Open Access | Times Cited: 2
Trends, Technology, and Implementation of Digital Counseling in a Human Mental Health
Agus Aan Jiwa Permana, Rukmi Sari Hartati, Made Sudarma, et al.
Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) (2023) Vol. 12, Iss. 1, pp. 135-145
Open Access | Times Cited: 1
Machine learning in connectomics: from representation learning to model fitting
Sofia Ira Ktena, Aristeidis Sotiras, Enzo Ferrante
Elsevier eBooks (2023), pp. 267-287
Closed Access | Times Cited: 1
Weighting the structural connectome: Exploring its impact on network properties and predicting cognitive performance in the human brain
Hila Gast, Yaniv Assaf
Network Neuroscience (2023) Vol. 8, Iss. 1, pp. 119-137
Open Access | Times Cited: 1
Artificial Intelligence and Large Language Models in Mental Healthcare

(2024), pp. 195-215
Closed Access
The Human Connectome
James C. Harris, Joseph T. Coyle
Oxford University Press eBooks (2024), pp. 202-225
Closed Access
Artificial intelligence role in advancement of human brain connectome studies
Dorsa Shekouh, Helia Sadat Kaboli, Mohammadreza Ghaffarzadeh-Esfahani, et al.
Frontiers in Neuroinformatics (2024) Vol. 18
Open Access
Brain structural connectomic topology predicts medication response in youth with bipolar disorder: A randomized clinical trial
Lei Du, Kun Qin, Wenbin Li, et al.
Journal of Affective Disorders (2024) Vol. 371, pp. 324-332
Closed Access
MRI Insights Into Adolescent Neurocircuitry—A Vision for the Future
Olga Tymofiyeva, Vivian X. Zhou, Chuan Mei Lee, et al.
Frontiers in Human Neuroscience (2020) Vol. 14
Open Access | Times Cited: 2
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