Maxim Sharaev

584 total citations
38 papers, 277 citations indexed

About

Maxim Sharaev is a scholar working on Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence. According to data from OpenAlex, Maxim Sharaev has authored 38 papers receiving a total of 277 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Cognitive Neuroscience, 11 papers in Radiology, Nuclear Medicine and Imaging and 6 papers in Artificial Intelligence. Recurrent topics in Maxim Sharaev's work include Functional Brain Connectivity Studies (18 papers), Neural dynamics and brain function (10 papers) and Advanced MRI Techniques and Applications (7 papers). Maxim Sharaev is often cited by papers focused on Functional Brain Connectivity Studies (18 papers), Neural dynamics and brain function (10 papers) and Advanced MRI Techniques and Applications (7 papers). Maxim Sharaev collaborates with scholars based in Russia, Germany and Canada. Maxim Sharaev's co-authors include Vadim Ushakov, Boris M. Velichkovsky, Marie Arsalidou, Evgeny Burnaev, Sagana Vijayarajah, Alexander Bernstein, Alexey Artemov, Olga Martynova, Olga Sysoeva and Galina Portnova and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Information Sciences.

In The Last Decade

Maxim Sharaev

36 papers receiving 273 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Maxim Sharaev Russia 9 199 55 40 28 22 38 277
Béla Weiss Hungary 12 255 1.3× 40 0.7× 30 0.8× 19 0.7× 26 1.2× 30 341
Petra Hermann Hungary 11 208 1.0× 71 1.3× 52 1.3× 14 0.5× 15 0.7× 24 297
Min Lü China 10 251 1.3× 181 3.3× 45 1.1× 25 0.9× 18 0.8× 23 432
Jessica Samogin Belgium 10 280 1.4× 28 0.5× 16 0.4× 19 0.7× 11 0.5× 19 333
Giuseppe Lisi Japan 10 416 2.1× 66 1.2× 81 2.0× 14 0.5× 20 0.9× 18 480
Bradley Caron United States 8 278 1.4× 117 2.1× 23 0.6× 14 0.5× 31 1.4× 12 398
Vincent Bazinet Canada 5 221 1.1× 90 1.6× 33 0.8× 10 0.4× 11 0.5× 10 297
Karolina Finc Poland 8 308 1.5× 54 1.0× 78 1.9× 39 1.4× 10 0.5× 12 373
Trenton A. Jerde United States 9 394 2.0× 46 0.8× 49 1.2× 43 1.5× 12 0.5× 15 429
Elizabeth Bock Canada 7 384 1.9× 63 1.1× 25 0.6× 12 0.4× 10 0.5× 11 447

Countries citing papers authored by Maxim Sharaev

Since Specialization
Citations

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

Fields of papers citing papers by Maxim Sharaev

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Maxim Sharaev

This figure shows the co-authorship network connecting the top 25 collaborators of Maxim Sharaev. A scholar is included among the top collaborators of Maxim Sharaev 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 Maxim Sharaev. Maxim Sharaev 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.
Bouzid, Amal, Jean Armengaud, Balázs Sarkadi, et al.. (2025). Translational biomarkers of hypoxic brain injury uncovered in CSF secreting human choroid plexus organoids. Fluids and Barriers of the CNS. 22(1). 117–117.
2.
Карпов, О Э, et al.. (2024). Knowledge-informed randomized machine learning and data fusion for anomaly areas detection in multimodal 3D images. Information Sciences. 686. 121354–121354. 2 indexed citations
3.
Illarionova, Svetlana, Rifat Hamoudi, Alexander Bernstein, et al.. (2024). A hierarchical algorithm with randomized learning for robust tissue segmentation and classification in digital pathology. Information Sciences. 686. 121358–121358. 1 indexed citations
4.
Efimova, Olga, Nikolay A. Anikanov, Maxim Sharaev, et al.. (2024). White matter lipidome alterations in the schizophrenia brain. SHILAP Revista de lepidopterología. 10(1). 123–123. 2 indexed citations
5.
Va, Isakov, et al.. (2023). Spectrum of food diversity of megapolis’ inhabitants obtained by the analysis of buying activity. Problems of Nutrition. 92(5). 60–69. 1 indexed citations
6.
Artemov, Alexey, et al.. (2023). Identification of Diagnostic Schizophrenia Biomarkers Based on the Assessment of Immune and Systemic Inflammation Parameters Using Machine Learning Modeling. Sovremennye tehnologii v medicine. 15(6). 5–5. 1 indexed citations
7.
Hamoudi, Rifat, Hamid Alhaj, Bashar Issa, et al.. (2023). Autoencoders with deformable convolutions for latent representation of EEG spectrograms in classification tasks. 39–39. 1 indexed citations
8.
Bouzid, Amal, Hamid Alhaj, Almira Kustubayeva, et al.. (2023). Integrative bioinformatics and artificial intelligence analyses of transcriptomics data identified genes associated with major depressive disorders including NRG1. Neurobiology of Stress. 26. 100555–100555. 8 indexed citations
9.
Va, Isakov, et al.. (2023). Food diversity analysis based on data of food purchasing in supermarket chain. Problems of Nutrition. 92(3). 62–68.
10.
Pronin, Igor, et al.. (2022). Machine learning for resting state fMRI-based preoperative mapping: comparison with task-based fMRI and direct cortical stimulation. Burdenko s Journal of Neurosurgery. 86(4). 25–25. 1 indexed citations
11.
Martynova, Olga, et al.. (2021). A Toolbox and Crowdsourcing Platform for Automatic Labeling of Independent Components in Electroencephalography. Frontiers in Neuroinformatics. 15. 720229–720229. 17 indexed citations
12.
Sharaev, Maxim, et al.. (2021). A machine learning investigation of factors that contribute to predicting cognitive performance: Difficulty level, reaction time and eye-movements. Decision Support Systems. 155. 113713–113713. 17 indexed citations
13.
Burnaev, Evgeny, et al.. (2020). Data-driven models and computational tools for neurolinguistics: a language technology perspective. 21(1). 15–52. 1 indexed citations
14.
Arsalidou, Marie, Sagana Vijayarajah, & Maxim Sharaev. (2020). Basal ganglia lateralization in different types of reward. Brain Imaging and Behavior. 14(6). 2618–2646. 29 indexed citations
15.
Sharaev, Maxim, et al.. (2019). O-21 Separation of major depression patients from healthy controls using machine learning approach to resting-state EEG. Clinical Neurophysiology. 130(7). e28–e28. 1 indexed citations
16.
Velichkovsky, Boris M., et al.. (2018). Consciousness in a multilevel architecture: Evidence from the right side of the brain. Consciousness and Cognition. 64. 227–239. 18 indexed citations
17.
18.
Velichkovsky, Boris M., et al.. (2017). In search of the “I”: Neuropsychology of lateralized thinking meets Dynamic Causal Modeling. Psychology in Russia State of Art. 10(3). 7–27. 11 indexed citations
19.
Sharaev, Maxim, et al.. (2016). Effective Connectivity within the Default Mode Network: Dynamic Causal Modeling of Resting-State fMRI Data. Frontiers in Human Neuroscience. 10. 14–14. 65 indexed citations
20.
Ushakov, Vadim, et al.. (2016). Dynamic Causal Modeling of Hippocampal Links within the Human Default Mode Network: Lateralization and Computational Stability of Effective Connections. Frontiers in Human Neuroscience. 10. 528–528. 29 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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