Tamás Spisák

1.3k total citations
39 papers, 574 citations indexed

About

Tamás Spisák is a scholar working on Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence. According to data from OpenAlex, Tamás Spisák has authored 39 papers receiving a total of 574 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Cognitive Neuroscience, 9 papers in Radiology, Nuclear Medicine and Imaging and 5 papers in Artificial Intelligence. Recurrent topics in Tamás Spisák's work include Functional Brain Connectivity Studies (22 papers), Neural dynamics and brain function (8 papers) and Advanced Neuroimaging Techniques and Applications (8 papers). Tamás Spisák is often cited by papers focused on Functional Brain Connectivity Studies (22 papers), Neural dynamics and brain function (8 papers) and Advanced Neuroimaging Techniques and Applications (8 papers). Tamás Spisák collaborates with scholars based in Hungary, Germany and United Kingdom. Tamás Spisák's co-authors include Zsigmond Tamás Kincses, Ulrike Bingel, Matthias Zunhammer, Miklós Emri, Tobias Schmidt‐Wilcke, Bálint Kincses, István Fekete, Béla Clemens, Katalin Hollódy and András Fogarasi and has published in prestigious journals such as Nature Communications, PLoS ONE and NeuroImage.

In The Last Decade

Tamás Spisák

36 papers receiving 566 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tamás Spisák Hungary 14 349 120 108 89 52 39 574
Shi Tang China 16 499 1.4× 197 1.6× 130 1.2× 68 0.8× 34 0.7× 30 742
Benedikt Sundermann Germany 14 337 1.0× 85 0.7× 136 1.3× 61 0.7× 22 0.4× 35 562
Elisabetta Geda Italy 11 320 0.9× 118 1.0× 75 0.7× 51 0.6× 42 0.8× 16 541
Ali H. Palejwala United States 11 334 1.0× 103 0.9× 178 1.6× 35 0.4× 41 0.8× 28 593
Foucaud Du Boisguéheneuc France 7 510 1.5× 123 1.0× 144 1.3× 31 0.3× 46 0.9× 17 791
Rui Yuan United States 12 340 1.0× 80 0.7× 194 1.8× 49 0.6× 55 1.1× 25 518
Pedro Alves Portugal 9 247 0.7× 101 0.8× 122 1.1× 38 0.4× 64 1.2× 30 604
Christopher J. Starr United States 5 247 0.7× 121 1.0× 47 0.4× 207 2.3× 33 0.6× 6 456
Huaigui Liu China 13 543 1.6× 168 1.4× 230 2.1× 63 0.7× 35 0.7× 28 789

Countries citing papers authored by Tamás Spisák

Since Specialization
Citations

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

Fields of papers citing papers by Tamás Spisák

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tamás Spisák

This figure shows the co-authorship network connecting the top 25 collaborators of Tamás Spisák. A scholar is included among the top collaborators of Tamás Spisák 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 Tamás Spisák. Tamás Spisák 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.
Kincses, Bálint, et al.. (2025). External validation of machine learning models—registered models and adaptive sample splitting. GigaScience. 14. 6 indexed citations
2.
Kotikalapudi, Raviteja, et al.. (2025). On the replicability of diffusion weighted MRI-based brain-behavior models. Communications Biology. 8(1). 1512–1512.
3.
Kotikalapudi, Raviteja, Bálint Kincses, Matthias Zunhammer, et al.. (2023). Brain morphology predicts individual sensitivity to pain: a multicenter machine learning approach. Pain. 164(11). 2516–2527. 11 indexed citations
4.
Schedlowski, Manfred, Harald Engler, Winfried Rief, et al.. (2023). ALIIAS: Anonymization/Pseudonymization with LimeSurvey integration and II-factor Authentication for Scientific research. SoftwareX. 24. 101522–101522. 13 indexed citations
5.
Kotikalapudi, Raviteja, et al.. (2023). Predictive modeling of optimism bias using gray matter cortical thickness. Scientific Reports. 13(1). 302–302.
6.
Pogatzki‐Zahn, Esther, et al.. (2023). Machine learning and artificial intelligence in neuroscience: A primer for researchers. Brain Behavior and Immunity. 115. 470–479. 40 indexed citations
7.
Spisák, Tamás, et al.. (2021). Deciphering the scopolamine challenge rat model by preclinical functional MRI. Scientific Reports. 11(1). 10873–10873. 2 indexed citations
8.
Veréb, Dániel, Bálint Kincses, Tamás Spisák, et al.. (2021). Resting-state functional heterogeneity of the right insula contributes to pain sensitivity. Scientific Reports. 11(1). 22945–22945. 9 indexed citations
9.
Spisák, Tamás, Bálint Kincses, Matthias Zunhammer, et al.. (2020). Pain-free resting-state functional brain connectivity predicts individual pain sensitivity. Nature Communications. 11(1). 187–187. 83 indexed citations
10.
Kincses, Bálint, Tamás Spisák, Péter Faragó, et al.. (2020). Brain MRI Diffusion Encoding Direction Number Affects Tract‐Based Spatial Statistics Results in Multiple Sclerosis. Journal of Neuroimaging. 30(4). 512–522. 4 indexed citations
11.
Opposits, Gábor, Levente Lánczi, Ervin Berényi, et al.. (2020). Effective connectivity differences in motor network during passive movement of paretic and non-paretic ankles in subacute stroke patients. PeerJ. 8. e8942–e8942. 2 indexed citations
12.
13.
Spisák, Tamás, Matthias Zunhammer, Ulrike Bingel, et al.. (2018). Probabilistic TFCE: A generalized combination of cluster size and voxel intensity to increase statistical power. NeuroImage. 185. 12–26. 73 indexed citations
14.
Tóth, Eszter, Nikoletta Szabó, Gergő Csete, et al.. (2017). Gray Matter Atrophy Is Primarily Related to Demyelination of Lesions in Multiple Sclerosis: A Diffusion Tensor Imaging MRI Study. Frontiers in Neuroanatomy. 11. 23–23. 20 indexed citations
15.
Emri, Miklós, Tamás Spisák, Ervin Berényi, et al.. (2016). The Effect of Passive Movement for Paretic Ankle-Foot and Brain Activity in Post-Stroke Patients. European Neurology. 76(3-4). 132–142. 13 indexed citations
16.
Clemens, Béla, Tamás Spisák, Gábor Opposits, et al.. (2016). Increased resting-state EEG functional connectivity in benign childhood epilepsy with centro-temporal spikes. Seizure. 35. 50–55. 21 indexed citations
17.
Opposits, Gábor, Ervin Berényi, László Csiba, et al.. (2016). Population‐Level Correction of Systematic Motion Artifacts in fMRI in Patients with Ischemic Stroke. Journal of Neuroimaging. 27(4). 397–408. 4 indexed citations
18.
Clemens, Béla, Tamás Spisák, Miklós Emri, et al.. (2014). Valproate treatment normalizes EEG functional connectivity in successfully treated idiopathic generalized epilepsy patients. Epilepsy Research. 108(10). 1896–1903. 27 indexed citations
19.
20.
Clemens, Béla, et al.. (2013). Remission of benign epilepsy with rolandic spikes: An EEG-based connectivity study at the onset of the disease and at remission. Epilepsy Research. 106(1-2). 128–135. 8 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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