Sunbin Song

1.0k total citations
18 papers, 701 citations indexed

About

Sunbin Song is a scholar working on Cognitive Neuroscience, Social Psychology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Sunbin Song has authored 18 papers receiving a total of 701 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Cognitive Neuroscience, 5 papers in Social Psychology and 4 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Sunbin Song's work include Motor Control and Adaptation (7 papers), Neuroscience and Music Perception (6 papers) and Action Observation and Synchronization (5 papers). Sunbin Song is often cited by papers focused on Motor Control and Adaptation (7 papers), Neuroscience and Music Perception (6 papers) and Action Observation and Synchronization (5 papers). Sunbin Song collaborates with scholars based in United States, United Kingdom and Netherlands. Sunbin Song's co-authors include Darlene V. Howard, James H. Howard, Leonardo G. Cohen, Marco Sandrini, Adam Steel, Stephen J. Gotts, Nikhil Sharma, Ethan R. Buch, Leonardo G. Cohen and Brynn E. Marks and has published in prestigious journals such as Nature Communications, Journal of Neuroscience and PLoS ONE.

In The Last Decade

Sunbin Song

18 papers receiving 689 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Sunbin Song United States	14	600	142	131	126	101	18	701
Valentina Sulpizio Italy	19	778 1.3×	99 0.7×	90 0.7×	98 0.8×	127 1.3×	43	916
Heng‐Ru May Tan United Kingdom	11	526 0.9×	67 0.5×	130 1.0×	72 0.6×	55 0.5×	11	656
Arnaud Boutin France	13	484 0.8×	107 0.8×	133 1.0×	106 0.8×	45 0.4×	27	596
M-Marsel Mesulam United States	9	719 1.2×	107 0.8×	70 0.5×	181 1.4×	52 0.5×	12	867
Catherine L. Elsinger United States	10	612 1.0×	117 0.8×	128 1.0×	80 0.6×	92 0.9×	10	764
Elliot Freeman United Kingdom	17	1.2k 2.1×	140 1.0×	126 1.0×	337 2.7×	117 1.2×	32	1.4k
M. M. Quallo United Kingdom	8	435 0.7×	90 0.6×	315 2.4×	63 0.5×	82 0.8×	8	598
Oliver Zafiris Germany	9	969 1.6×	106 0.7×	202 1.5×	311 2.5×	100 1.0×	12	1.2k
Choi Deblieck United States	11	449 0.7×	71 0.5×	188 1.4×	237 1.9×	182 1.8×	27	703
Philip D. Nixon United Kingdom	9	521 0.9×	76 0.5×	105 0.8×	52 0.4×	151 1.5×	9	610

Countries citing papers authored by Sunbin Song

Since Specialization

Citations

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

Fields of papers citing papers by Sunbin Song

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sunbin Song

This figure shows the co-authorship network connecting the top 25 collaborators of Sunbin Song. A scholar is included among the top collaborators of Sunbin Song 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 Sunbin Song. Sunbin Song is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

18 of 18 papers shown

Motamedi, Gholam K., Sunbin Song, Mihriye Mete, et al.. (2022). Transcranial Alternating Current Stimulation (tACS) as a Treatment for Insomnia. Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques. 50(3). 446–449. 18 indexed citations

Vis, Jill B. De, Sunbin Song, Marie Luby, et al.. (2019). Identifying perfusion deficits on CT perfusion images using temporal similarity perfusion (TSP) mapping. European Radiology. 29(8). 4198–4206. 1 indexed citations

Song, Sunbin, Reinoud P.H. Bokkers, Marie Luby, et al.. (2017). Temporal similarity perfusion mapping: A standardized and model-free method for detecting perfusion deficits in stroke. PLoS ONE. 12(10). e0185552–e0185552. 10 indexed citations

Song, Sunbin, Stephen J. Gotts, Eran Dayan, & Leonardo G. Cohen. (2015). Practice Structure Improves Unconscious Transitional Memories by Increasing Synchrony in a Premotor Network. Journal of Cognitive Neuroscience. 27(8). 1503–1512. 18 indexed citations

Steel, Adam, Sunbin Song, Devin Bageac, et al.. (2015). Shifts in connectivity during procedural learning after motor cortex stimulation: A combined transcranial magnetic stimulation/functional magnetic resonance imaging study. Cortex. 74. 134–148. 44 indexed citations

Song, Sunbin, Lúcia Garrido, Zoltán Nagy, et al.. (2015). Local but not long-range microstructural differences of the ventral temporal cortex in developmental prosopagnosia. Neuropsychologia. 78. 195–206. 57 indexed citations

Song, Sunbin & Leonardo G. Cohen. (2014). Practice and sleep form different aspects of skill. Nature Communications. 5(1). 3407–3407. 33 indexed citations

Song, Sunbin, et al.. (2014). Crossmodal encoding of motor sequence memories. Psychological Research. 79(2). 318–326. 6 indexed citations

Song, Sunbin & Leonardo G. Cohen. (2014). Conscious recall of different aspects of skill memory. Frontiers in Behavioral Neuroscience. 8. 233–233. 5 indexed citations

10.

Song, Sunbin & Leonardo G. Cohen. (2014). Impact of conscious intent on chunking during motor learning. Learning & Memory. 21(9). 449–451. 17 indexed citations

11.

Song, Sunbin, Nikhil Sharma, Ethan R. Buch, & Leonardo G. Cohen. (2011). White Matter Microstructural Correlates of Superior Long-term Skill Gained Implicitly under Randomized Practice. Cerebral Cortex. 22(7). 1671–1677. 33 indexed citations

12.

Song, Sunbin, Marco Sandrini, & Leonardo G. Cohen. (2011). Modifying somatosensory processing with non-invasive brain stimulation. Restorative Neurology and Neuroscience. 29(6). 427–437. 42 indexed citations

13.

Song, Sunbin. (2008). Consciousness and the consolidation of motor learning. Behavioural Brain Research. 196(2). 180–186. 63 indexed citations

14.

Song, Sunbin, Brynn E. Marks, James H. Howard, & Darlene V. Howard. (2008). Evidence for parallel explicit and implicit sequence learning systems in older adults. Behavioural Brain Research. 196(2). 328–332. 26 indexed citations

15.

Song, Sunbin, James H. Howard, & Darlene V. Howard. (2008). Perceptual sequence learning in a serial reaction time task. Experimental Brain Research. 189(2). 145–158. 63 indexed citations

16.

Song, Sunbin, James H. Howard, & Darlene V. Howard. (2007). Sleep Does Not Benefit Probabilistic Motor Sequence Learning. Journal of Neuroscience. 27(46). 12475–12483. 147 indexed citations

17.

Song, Sunbin, James H. Howard, & Darlene V. Howard. (2007). Implicit probabilistic sequence learning is independent of explicit awareness. Learning & Memory. 14(3). 167–176. 91 indexed citations

18.

Chen, Hui, Fredrik H. Nyström, Lily Dong, et al.. (2001). Insulin Stimulates Increased Catalytic Activity of Phosphoinositide-Dependent Kinase-1 by a Phosphorylation-Dependent Mechanism. Biochemistry. 40(39). 11851–11859. 27 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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