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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:

Predicting sporadic Alzheimer's disease progression via inherited Alzheimer's disease‐informed machine‐learning
Nicolai Franzmeier, Nikolaos Koutsouleris, Tammie L.S. Benzinger, et al.
Alzheimer s & Dementia (2020) Vol. 16, Iss. 3, pp. 501-511
Open Access | Times Cited: 61

Showing 1-25 of 61 citing articles:

Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease
Dallas P. Veitch, Michael W. Weiner, Paul Aisen, et al.
Alzheimer s & Dementia (2021) Vol. 18, Iss. 4, pp. 824-857
Open Access | Times Cited: 110
Dissecting the clinical heterogeneity of early-onset Alzheimer’s disease
Daniel W. Sirkis, Luke W. Bonham, Taylor P. Johnson, et al.
Molecular Psychiatry (2022) Vol. 27, Iss. 6, pp. 2674-2688
Open Access | Times Cited: 89
Artificial intelligence in brain MRI analysis of Alzheimer’s disease over the past 12 years: A systematic review
Tory O. Frizzell, Margit Glashutter, Careesa C. Liu, et al.
Ageing Research Reviews (2022) Vol. 77, pp. 101614-101614
Closed Access | Times Cited: 85
Tau-PET and in vivo Braak-staging as prognostic markers of future cognitive decline in cognitively normal to demented individuals
Davina Biel, Matthias Brendel, Anna Rubinski, et al.
Alzheimer s Research & Therapy (2021) Vol. 13, Iss. 1
Open Access | Times Cited: 103
Segregation of functional networks is associated with cognitive resilience in Alzheimer’s disease
Michael Ewers, Ying Luan, Lukas Frontzkowski, et al.
Brain (2021) Vol. 144, Iss. 7, pp. 2176-2185
Open Access | Times Cited: 99
Artificial Intelligence for Alzheimer’s Disease: Promise or Challenge?
Carlo Fabrizio, Andrea Termine, Carlo Caltagirone, et al.
Diagnostics (2021) Vol. 11, Iss. 8, pp. 1473-1473
Open Access | Times Cited: 89
Earlier Alzheimer’s disease onset is associated with tau pathology in brain hub regions and facilitated tau spreading
Lukas Frontzkowski, Michael Ewers, Matthias Brendel, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 54
Functional brain networks in the evaluation of patients with neurodegenerative disorders
Matej Perovnik, Tomaž Rus, Katharina A. Schindlbeck, et al.
Nature Reviews Neurology (2022) Vol. 19, Iss. 2, pp. 73-90
Closed Access | Times Cited: 52
Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer’s disease?
Sophia Mirkin, Benedict C. Albensi
Frontiers in Aging Neuroscience (2023) Vol. 15
Open Access | Times Cited: 26
Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data
Ziyi Li, Xiaoqian Jiang, Yizhuo Wang, et al.
Emerging Topics in Life Sciences (2021) Vol. 5, Iss. 6, pp. 765-777
Open Access | Times Cited: 46
The Road to Personalized Medicine in Alzheimer’s Disease: The Use of Artificial Intelligence
Anuschka Silva‐Spínola, Inês Baldeiras, Joel P. Arrais, et al.
Biomedicines (2022) Vol. 10, Iss. 2, pp. 315-315
Open Access | Times Cited: 36
A Long Short-Term Memory Biomarker-Based Prediction Framework for Alzheimer’s Disease
Anza Aqeel, Ali Hassan, Muhammad Attique Khan, et al.
Sensors (2022) Vol. 22, Iss. 4, pp. 1475-1475
Open Access | Times Cited: 36
Consensus on rapid screening for prodromal Alzheimer’s disease in China
Lin Huang, Qinjie Li, Yao Lu, et al.
General Psychiatry (2024) Vol. 37, Iss. 1, pp. e101310-e101310
Open Access | Times Cited: 6
Abnormal regional homogeneity in right caudate as a potential neuroimaging biomarker for mild cognitive impairment: A resting-state fMRI study and support vector machine analysis
Yujun Gao, Xinfu Zhao, Jichao Huang, et al.
Frontiers in Aging Neuroscience (2022) Vol. 14
Open Access | Times Cited: 23
Prediction of Alzheimer’s Disease Progression Based on Magnetic Resonance Imaging
Ying Zhou, Zeyu Song, Xiao Han, et al.
ACS Chemical Neuroscience (2021) Vol. 12, Iss. 22, pp. 4209-4223
Closed Access | Times Cited: 31
Multiclass prediction of different dementia syndromes based on multi-centric volumetric MRI imaging
Leonie Lampe, Hans‐Jürgen Huppertz, Sarah Anderl‐Straub, et al.
NeuroImage Clinical (2023) Vol. 37, pp. 103320-103320
Open Access | Times Cited: 11
Predicting clinical progression trajectories of early Alzheimer's disease patients
Viswanath Devanarayan, Yuanqing Ye, Arnaud Charil, et al.
Alzheimer s & Dementia (2023) Vol. 20, Iss. 3, pp. 1725-1738
Open Access | Times Cited: 11
Predicted natural progression as an Alzheimer's prognostic covariate improves the precision of lecanemab efficacy assessments and clinical trial efficiency
Viswanath Devanarayan, Yuanqing Ye, Liang Zhu, et al.
Alzheimer s & Dementia (2025) Vol. 21, Iss. 3
Open Access
Identification and Evaluation of Multimodal Connectomics in Early Alzheimer’s Dementia
Yu Chen, Yingwei Fan, Xiaoying Tang, et al.
Lecture notes in computer science (2025), pp. 81-93
Closed Access
The future of Alzheimer’s disease risk prediction: a systematic review
Sophia Nazir
Neurological Sciences (2025)
Closed Access
Resting-State Functional Connectivity Disruption as a Pathological Biomarker in Autosomal Dominant Alzheimer Disease
Robert X. Smith, Jeremy F. Strain, Aaron Tanenbaum, et al.
Brain Connectivity (2021) Vol. 11, Iss. 3, pp. 239-249
Open Access | Times Cited: 25
Aβ and tau prions feature in the neuropathogenesis of Down syndrome
Carlo Condello, Alison M. Maxwell, Erika Castillo, et al.
Proceedings of the National Academy of Sciences (2022) Vol. 119, Iss. 46
Open Access | Times Cited: 18
A non-parametric statistical inference framework for Deep Learning in current neuroimaging
C. Jiménez-Mesa, Javier Ramı́rez, John Suckling, et al.
Information Fusion (2022) Vol. 91, pp. 598-611
Open Access | Times Cited: 17
Unsupervised machine learning model to predict cognitive impairment in subcortical ischemic vascular disease
Qi Qin, Junda Qu, Yunsi Yin, et al.
Alzheimer s & Dementia (2023) Vol. 19, Iss. 8, pp. 3327-3338
Closed Access | Times Cited: 10
Gray matters: ViT-GAN framework for identifying schizophrenia biomarkers linking structural MRI and functional network connectivity
Yuda Bi, Anees Abrol, Sihan Jia, et al.
NeuroImage (2024) Vol. 297, pp. 120674-120674
Open Access | Times Cited: 3
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