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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:

A comprehensive analysis of resting state fMRI measures to classify individual patients with Alzheimer's disease
Frank de Vos, Marisa Koini, Tijn M. Schouten, et al.
NeuroImage (2017) Vol. 167, pp. 62-72
Open Access | Times Cited: 184

Showing 1-25 of 184 citing articles:

Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging
Yuhui Du, Zening Fu, Vince D. Calhoun
Frontiers in Neuroscience (2018) Vol. 12
Open Access | Times Cited: 303
Altered static and dynamic functional network connectivity in Alzheimer's disease and subcortical ischemic vascular disease: shared and specific brain connectivity abnormalities
Zening Fu, Arvind Caprihan, Jiayu Chen, et al.
Human Brain Mapping (2019) Vol. 40, Iss. 11, pp. 3203-3221
Open Access | Times Cited: 152
Diagnostic power of resting‐state fMRI for detection of network connectivity in Alzheimer's disease and mild cognitive impairment: A systematic review
Buhari Ibrahim, Subapriya Suppiah, Normala Ibrahim, et al.
Human Brain Mapping (2021) Vol. 42, Iss. 9, pp. 2941-2968
Open Access | Times Cited: 144
High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection
Frank Emmert‐Streib, Matthias Dehmer
Machine Learning and Knowledge Extraction (2019) Vol. 1, Iss. 1, pp. 359-383
Open Access | Times Cited: 130
Resting State Dynamic Functional Connectivity in Neurodegenerative Conditions: A Review of Magnetic Resonance Imaging Findings
Massimo Filippi, Edoardo Gioele Spinelli, Camilla Cividini, et al.
Frontiers in Neuroscience (2019) Vol. 13
Open Access | Times Cited: 100
Group ICA for identifying biomarkers in schizophrenia: ‘Adaptive’ networks via spatially constrained ICA show more sensitivity to group differences than spatio-temporal regression
Mustafa S. Salman, Yuhui Du, Dongdong Lin, et al.
NeuroImage Clinical (2019) Vol. 22, pp. 101747-101747
Open Access | Times Cited: 95
Abnormal dynamic functional connectivity in Alzheimer’s disease
Yue Gu, Ying Lin, Liangliang Huang, et al.
CNS Neuroscience & Therapeutics (2020) Vol. 26, Iss. 9, pp. 962-971
Open Access | Times Cited: 94
Local dynamic spontaneous brain activity changes in first-episode, treatment-naïve patients with major depressive disorder and their associated gene expression profiles
Kaizhong Xue, Sixiang Liang, Bingbing Yang, et al.
Psychological Medicine (2020) Vol. 52, Iss. 11, pp. 2052-2061
Closed Access | Times Cited: 90
Abnormal dynamic functional connectivity is linked to recovery after acute ischemic stroke
Anna K. Bonkhoff, Markus D. Schirmer, Martin Bretzner, et al.
Human Brain Mapping (2021) Vol. 42, Iss. 7, pp. 2278-2291
Open Access | Times Cited: 61
Alzheimer’s Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield and Amygdala Volume of Structural MRI
Uttam Khatri, Goo‐Rak Kwon
Frontiers in Aging Neuroscience (2022) Vol. 14
Open Access | Times Cited: 53
Machine learning and artificial intelligence in neuroscience: A primer for researchers
Fakhirah Badrulhisham, Esther Pogatzki‐Zahn, Daniel Segelcke, et al.
Brain Behavior and Immunity (2023) Vol. 115, pp. 470-479
Open Access | Times Cited: 37
Predicting Alzheimer’s Disease Using Deep Neuro-Functional Networks with Resting-State fMRI
S. Sambath Kumar, Nandhini Malaiyappan, Rajakumar Ramalingam, et al.
Electronics (2023) Vol. 12, Iss. 4, pp. 1031-1031
Open Access | Times Cited: 24
Whole-brain mapping of effective connectivity by fMRI with cortex-wide patterned optogenetics
Seonghoon Kim, Hyun Seok Moon, Thanh Tan Vo, et al.
Neuron (2023) Vol. 111, Iss. 11, pp. 1732-1747.e6
Open Access | Times Cited: 23
Spatiotemporal feature extraction and classification of Alzheimer’s disease using deep learning 3D-CNN for fMRI data
Harshit Parmar, Brian Nutter, L. Rodney Long, et al.
Journal of Medical Imaging (2020) Vol. 7, Iss. 05
Open Access | Times Cited: 62
Network‐targeted approach and postoperative resting‐state functional magnetic resonance imaging are associated with seizure outcome
Varina L. Boerwinkle, Emilio Cediel, Lucia Mirea, et al.
Annals of Neurology (2019) Vol. 86, Iss. 3, pp. 344-356
Open Access | Times Cited: 61
Classification of Unmedicated Bipolar Disorder Using Whole-Brain Functional Activity and Connectivity: A Radiomics Analysis
Ying Wang, Kai Sun, Zhenyu Liu, et al.
Cerebral Cortex (2019) Vol. 30, Iss. 3, pp. 1117-1128
Open Access | Times Cited: 57
Personalized brain models identify neurotransmitter receptor changes in Alzheimer's disease
Ahmed Faraz Khan, Quadri Adewale, Tobias R. Baumeister, et al.
Brain (2021) Vol. 145, Iss. 5, pp. 1785-1804
Open Access | Times Cited: 41
Tai Chi enhances cognitive training effects on delaying cognitive decline in mild cognitive impairment
Binyin Li, Huidong Tang, Guiying He, et al.
Alzheimer s & Dementia (2022) Vol. 19, Iss. 1, pp. 136-149
Closed Access | Times Cited: 30
Eyes-Open and Eyes-Closed Resting States With Opposite Brain Activity in Sensorimotor and Occipital Regions: Multidimensional Evidences From Machine Learning Perspective
Wei Jie, Tong Chen, Chuandong Li, et al.
Frontiers in Human Neuroscience (2018) Vol. 12
Open Access | Times Cited: 56
Diagnosis of early Alzheimer’s disease based on dynamic high order networks
Baiying Lei, Shuangzhi Yu, Xin Zhao, et al.
Brain Imaging and Behavior (2020) Vol. 15, Iss. 1, pp. 276-287
Closed Access | Times Cited: 45
Classification and Graphical Analysis of Alzheimer’s Disease and Its Prodromal Stage Using Multimodal Features From Structural, Diffusion, and Functional Neuroimaging Data and the APOE Genotype
Yubraj Gupta, Ji‐In Kim, Byeong C. Kim, et al.
Frontiers in Aging Neuroscience (2020) Vol. 12
Open Access | Times Cited: 39
Bridging Scales in Alzheimer's Disease: Biological Framework for Brain Simulation With The Virtual Brain
Leon Stefanovski, J. Meier, Roopa Pai, et al.
Frontiers in Neuroinformatics (2021) Vol. 15
Open Access | Times Cited: 36
Use of machine learning method on automatic classification of motor subtype of Parkinson's disease based on multilevel indices of rs-fMRI
Huize Pang, Ziyang Yu, Hongmei Yu, et al.
Parkinsonism & Related Disorders (2021) Vol. 90, pp. 65-72
Closed Access | Times Cited: 35
ENIGMA’s simple seven: Recommendations to enhance the reproducibility of resting-state fMRI in traumatic brain injury
Karen Caeyenberghs, Phoebe Imms, Andrei Irimia, et al.
NeuroImage Clinical (2024) Vol. 42, pp. 103585-103585
Open Access | Times Cited: 5
Differentiation between multiple sclerosis and neuromyelitis optic spectrum disorders with multilevel fMRI features: A machine learning analysis
Xiao Liang, Qingwen Zeng, Yanyan Zhu, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
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