Keith Smith

5.1k total citations
36 papers, 306 citations indexed

About

Keith Smith is a scholar working on Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging and Statistical and Nonlinear Physics. According to data from OpenAlex, Keith Smith has authored 36 papers receiving a total of 306 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Cognitive Neuroscience, 10 papers in Radiology, Nuclear Medicine and Imaging and 5 papers in Statistical and Nonlinear Physics. Recurrent topics in Keith Smith's work include Functional Brain Connectivity Studies (17 papers), Neural dynamics and brain function (11 papers) and Advanced Neuroimaging Techniques and Applications (8 papers). Keith Smith is often cited by papers focused on Functional Brain Connectivity Studies (17 papers), Neural dynamics and brain function (11 papers) and Advanced Neuroimaging Techniques and Applications (8 papers). Keith Smith collaborates with scholars based in United Kingdom, United States and China. Keith Smith's co-authors include Javier Escudero, Daniel Abásolo, Ron L. Evans, Simon R. Cox, Mark E. Bastin, Feng Dong, Chao Tan, Hamed Azami, Cathie Sudlow and Colin R. Buchanan and has published in prestigious journals such as PLoS ONE, NeuroImage and Scientific Reports.

In The Last Decade

Keith Smith

34 papers receiving 299 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Keith Smith United Kingdom 10 128 46 39 28 27 36 306
Siyi Tang United States 8 202 1.6× 87 1.9× 18 0.5× 52 1.9× 14 0.5× 20 381
Mei Ying Boon Australia 14 218 1.7× 210 4.6× 29 0.7× 10 0.4× 19 0.7× 53 730
Pamela K. Douglas United States 13 222 1.7× 137 3.0× 24 0.6× 17 0.6× 20 0.7× 22 594
Jenna Reinen United States 11 227 1.8× 57 1.2× 44 1.1× 14 0.5× 10 0.4× 20 414
Thomas Iype India 14 126 1.0× 11 0.2× 36 0.9× 39 1.4× 13 0.5× 60 549
Roberto Romanello Italy 8 201 1.6× 20 0.4× 26 0.7× 25 0.9× 36 1.3× 11 529
Murad Megjhani United States 14 164 1.3× 51 1.1× 33 0.8× 57 2.0× 37 1.4× 40 762
Marc-André Schulz Germany 7 136 1.1× 62 1.3× 18 0.5× 6 0.2× 9 0.3× 11 298
Grace Huckins United States 1 233 1.8× 83 1.8× 18 0.5× 30 1.1× 13 0.5× 2 442
Jakub Kopál Canada 9 108 0.8× 38 0.8× 6 0.2× 33 1.2× 46 1.7× 29 236

Countries citing papers authored by Keith Smith

Since Specialization
Citations

This map shows the geographic impact of Keith Smith's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Keith Smith with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Keith Smith more than expected).

Fields of papers citing papers by Keith Smith

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Keith Smith. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Keith Smith. The network helps show where Keith Smith may publish in the future.

Co-authorship network of co-authors of Keith Smith

This figure shows the co-authorship network connecting the top 25 collaborators of Keith Smith. A scholar is included among the top collaborators of Keith Smith based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Keith Smith. Keith Smith is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Buchanan, Colin R., Ian J. Deary, Elliot M. Tucker–Drob, et al.. (2025). Relative Strength Variability Measures for Brain Structural Connectomes and Their Relationship With Cognitive Functioning. Human Brain Mapping. 46(11). e70314–e70314.
2.
Moshfeghi, Yashar, et al.. (2024). FAST functional connectivity implicates P300 connectivity in working memory deficits in Alzheimer’s disease. Network Neuroscience. 8(4). 1467–1490. 1 indexed citations
3.
Smith, Keith, et al.. (2024). A statistical mechanics investigation of unfolded protein response across organisms. Scientific Reports. 14(1). 27658–27658. 2 indexed citations
4.
Shen, Xueyi, Aleks Stolicyn, Mark J. Adams, et al.. (2024). A comprehensive hierarchical comparison of structural connectomes in Major Depressive Disorder cases v. controls in two large population samples. Psychological Medicine. 54(10). 2515–2526.
5.
Stolicyn, Aleks, Xueyi Shen, Mark J. Adams, et al.. (2023). Classification accuracy of structural and functional connectomes across different depressive phenotypes. Imaging Neuroscience. 2. 1 indexed citations
6.
Stolicyn, Aleks, Colin R. Buchanan, Elliot M. Tucker–Drob, et al.. (2022). Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes. Human Brain Mapping. 44(5). 1913–1933. 16 indexed citations
7.
Smith, Keith, et al.. (2022). Robust Assessment of EEG Connectivity Patterns in Mild Cognitive Impairment and Alzheimer's Disease. PubMed. 1. 924811–924811. 2 indexed citations
8.
Smith, Keith, John M. Starr, Javier Escudero, Agustín Ibáñez, & Mario A. Parra. (2022). Abnormal Functional Hierarchies of EEG Networks in Familial and Sporadic Prodromal Alzheimer's Disease During Visual Short-Term Memory Binding. PubMed. 1. 883968–883968. 2 indexed citations
9.
Shen, Xueyi, Aleks Stolicyn, Laura de Nooij, et al.. (2021). Spectral clustering based on structural magnetic resonance imaging and its relationship with major depressive disorder and cognitive ability. European Journal of Neuroscience. 54(6). 6281–6303. 5 indexed citations
10.
Klein, Brennan, et al.. (2021). A computational exploration of resilience and evolvability of protein–protein interaction networks. Communications Biology. 4(1). 1352–1352. 13 indexed citations
11.
Zhang, Huayu, Amy Ferguson, Teng Zhang, et al.. (2021). Benchmarking network-based gene prioritization methods for cerebral small vessel disease. Briefings in Bioinformatics. 22(5). 16 indexed citations
12.
Smith, Keith. (2021). Explaining the emergence of complex networks through log-normal fitness in a Euclidean node similarity space. Scientific Reports. 11(1). 1976–1976. 5 indexed citations
13.
Blesa, Manuel, Paola Galdi, Simon R. Cox, et al.. (2020). Hierarchical Complexity of the Macro-Scale Neonatal Brain. Cerebral Cortex. 31(4). 2071–2084. 20 indexed citations
14.
Smith, Keith, Mark E. Bastin, Simon R. Cox, et al.. (2019). Hierarchical complexity of the adult human structural connectome. NeuroImage. 191. 205–215. 16 indexed citations
15.
Tan, Chao, et al.. (2018). Gas-water two-phase flow pattern recognition based on ERT and ultrasound Doppler. 1–6. 6 indexed citations
16.
López, José David, et al.. (2018). Phenotyping Ex-Combatants From EEG Scalp Connectivity. IEEE Access. 6. 55090–55098. 5 indexed citations
17.
Smith, Keith, Daniel Abásolo, & Javier Escudero. (2017). Accounting for the complex hierarchical topology of EEG phase-based functional connectivity in network binarisation. PLoS ONE. 12(10). e0186164–e0186164. 31 indexed citations
18.
Smith, Keith & Javier Escudero. (2016). The complex hierarchical topology of EEG functional connectivity. Journal of Neuroscience Methods. 276. 1–12. 18 indexed citations
19.
Azami, Hamed, Keith Smith, Alberto Fernández, & Javier Escudero. (2015). Evaluation of resting-state magnetoencephalogram complexity in Alzheimer's disease with multivariate multiscale permutation and sample entropies. PubMed. 2015. 7422–7425. 10 indexed citations
20.
Evans, Ron L., et al.. (1986). Cognitive Telephone Group Therapy with Physically Disabled Elderly Persons. The Gerontologist. 26(1). 8–11. 41 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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