Diego Sona

1.6k total citations
67 papers, 878 citations indexed

About

Diego Sona is a scholar working on Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence. According to data from OpenAlex, Diego Sona has authored 67 papers receiving a total of 878 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Cognitive Neuroscience, 20 papers in Radiology, Nuclear Medicine and Imaging and 19 papers in Artificial Intelligence. Recurrent topics in Diego Sona's work include Neural dynamics and brain function (18 papers), Functional Brain Connectivity Studies (17 papers) and Advanced Neuroimaging Techniques and Applications (15 papers). Diego Sona is often cited by papers focused on Neural dynamics and brain function (18 papers), Functional Brain Connectivity Studies (17 papers) and Advanced Neuroimaging Techniques and Applications (15 papers). Diego Sona collaborates with scholars based in Italy, Switzerland and United States. Diego Sona's co-authors include Vittorio Murino, Paolo Avesani, Luca Giancardo, Francesco Papaleo, Alessandro Sperduti, Huiping Huang, Sara Sannino, Francesca Managò, Diego Scheggia and Luca Dodero and has published in prestigious journals such as PLoS ONE, NeuroImage and Neurology.

In The Last Decade

Diego Sona

62 papers receiving 844 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Diego Sona Italy 15 239 214 161 152 124 67 878
Yevgeniy B. Sirotin United States 16 684 2.9× 64 0.3× 105 0.7× 120 0.8× 221 1.8× 30 1.1k
Shuo Wang United States 22 970 4.1× 82 0.4× 137 0.9× 337 2.2× 88 0.7× 94 1.5k
Yael Adini Israel 12 645 2.7× 68 0.3× 52 0.3× 709 4.7× 78 0.6× 20 1.4k
Carsten Allefeld Germany 20 1.1k 4.5× 95 0.4× 116 0.7× 40 0.3× 71 0.6× 37 1.3k
Dongchuan Yu China 16 499 2.1× 62 0.3× 68 0.4× 27 0.2× 73 0.6× 54 981
Junichiro Yoshimoto Japan 17 453 1.9× 193 0.9× 57 0.4× 35 0.2× 238 1.9× 60 1.2k
Vicente L. Malave United States 7 1.2k 5.0× 285 1.3× 209 1.3× 137 0.9× 43 0.3× 7 1.6k
Kristofer E. Bouchard United States 17 813 3.4× 110 0.5× 73 0.5× 19 0.1× 331 2.7× 53 1.3k
Michael A. Casey United States 27 603 2.5× 281 1.3× 89 0.6× 1.0k 6.8× 188 1.5× 82 2.4k

Countries citing papers authored by Diego Sona

Since Specialization
Citations

This map shows the geographic impact of Diego Sona'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 Diego Sona with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Diego Sona more than expected).

Fields of papers citing papers by Diego Sona

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Diego Sona. 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 Diego Sona. The network helps show where Diego Sona may publish in the future.

Co-authorship network of co-authors of Diego Sona

This figure shows the co-authorship network connecting the top 25 collaborators of Diego Sona. A scholar is included among the top collaborators of Diego Sona 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 Diego Sona. Diego Sona 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.
Tessadori, Jacopo, Ilaria Boscolo Galazzo, Silvia Francesca Storti, et al.. (2025). Linking dynamic connectivity states to cognitive decline and anatomical changes in Alzheimer’s disease. NeuroImage. 320. 121448–121448.
2.
3.
Valsasina, Paola, Jacopo Tessadori, Massimo Filippi, et al.. (2023). Discovering functional connectivity features characterizing multiple sclerosis phenotypes using explainable artificial intelligence. Human Brain Mapping. 44(6). 2294–2306. 4 indexed citations
4.
Bartolucci, Maurizio, E. Cisbani, Sandra Doria, et al.. (2023). UNet and MobileNet CNN-based model observers for CT protocol optimization: comparative performance evaluation by means of phantom CT images. Journal of Medical Imaging. 10(S1). S11904–S11904. 6 indexed citations
5.
Crimi, Alessandro, Luca Dodero, Fabio Sambataro, Vittorio Murino, & Diego Sona. (2021). Structurally constrained effective brain connectivity. NeuroImage. 239. 118288–118288. 19 indexed citations
6.
Volpi, Riccardo, Alessandro Maccione, Stefano Di Marco, et al.. (2020). Modeling a population of retinal ganglion cells with restricted Boltzmann machines. Scientific Reports. 10(1). 16549–16549. 15 indexed citations
7.
Crimi, Alessandro, Luca Giancardo, Fabio Sambataro, et al.. (2019). MultiLink Analysis: Brain Network Comparison via Sparse Connectivity Analysis. Scientific Reports. 9(1). 65–65. 9 indexed citations
8.
Aslani, Shahab, Michael Dayan, Loredana Storelli, et al.. (2019). Multi-branch convolutional neural network for multiple sclerosis lesion segmentation. NeuroImage. 196. 1–15. 114 indexed citations
9.
Dayan, Michael, et al.. (2019). Investigating the Impact of Genetic Background on Brain Dynamic Functional Connectivity Through Machine Learning: A Twins Study. CINECA IRIS Institutial Research Information System (University of Genoa). 19. 1–4. 1 indexed citations
10.
Sona, Diego, et al.. (2018). A novel unsupervised analysis of electrophysiological signals reveals new sleep substages in mice. PLoS Biology. 16(5). e2003663–e2003663. 10 indexed citations
11.
Hilgen, Gerrit, Alessandro Maccione, Luca Berdondini, et al.. (2017). Unsupervised Spike Sorting for Large-Scale, High-Density Multielectrode Arrays. Cell Reports. 18(10). 2521–2532. 84 indexed citations
12.
Crimi, Alessandro, Luca Dodero, Vittorio Murino, & Diego Sona. (2017). Case-control discrimination through effective brain connectivity. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 970–973. 13 indexed citations
13.
Dodero, Luca, Sebastiano Vascon, Vittorio Murino, et al.. (2015). Automated multi-subject fiber clustering of mouse brain using dominant sets. Frontiers in Neuroinformatics. 8. 87–87. 10 indexed citations
14.
Avesani, Paolo, et al.. (2015). Non-parametric temporal modeling of the hemodynamic response function via a liquid state machine. Neural Networks. 70. 61–73. 3 indexed citations
15.
Murino, Vittorio, et al.. (2014). Bridging the gap in connectomic studies: A particle filtering framework for estimating structural connectivity at network scale. Medical Image Analysis. 21(1). 1–14. 2 indexed citations
16.
Micheli, Alessio, Diego Sona, & Alessandro Sperduti. (2004). A Note on Formal Determination of Context in Contextual Recursive Cascade Correlation Networks. UnipiEprints Open Archive (Università di Pisa). 1 indexed citations
17.
Sona, Diego, et al.. (2004). A Baseline Approach for the Automatic Hierarchical Organization of Learning Resources. Unitn Eprints Research (Università Degli Studi di Trento). 2004(1). 432–437. 1 indexed citations
18.
Micheli, Alessio, Diego Sona, & Alessandro Sperduti. (2004). Contextual Processing of Structured Data by Recursive Cascade Correlation. IEEE Transactions on Neural Networks. 15(6). 1396–1410. 39 indexed citations
19.
Börger, Egon & Diego Sona. (2001). A Neural Abstract Machine. Zenodo (CERN European Organization for Nuclear Research). 4 indexed citations
20.
Sona, Diego, Alessandro Sperduti, & Antonina Starita. (1996). A Constructive Learning Algorithm for Discriminant Tangent Models. Research Padua Archive (University of Padua). 9. 786–792. 6 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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