Link Tejavibulya

425 total citations · 1 hit paper
14 papers, 143 citations indexed

About

Link Tejavibulya is a scholar working on Cognitive Neuroscience, Experimental and Cognitive Psychology and Health, Toxicology and Mutagenesis. According to data from OpenAlex, Link Tejavibulya has authored 14 papers receiving a total of 143 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Cognitive Neuroscience, 4 papers in Experimental and Cognitive Psychology and 3 papers in Health, Toxicology and Mutagenesis. Recurrent topics in Link Tejavibulya's work include Functional Brain Connectivity Studies (11 papers), Mental Health Research Topics (4 papers) and Advanced Neuroimaging Techniques and Applications (3 papers). Link Tejavibulya is often cited by papers focused on Functional Brain Connectivity Studies (11 papers), Mental Health Research Topics (4 papers) and Advanced Neuroimaging Techniques and Applications (3 papers). Link Tejavibulya collaborates with scholars based in United States, Netherlands and Switzerland. Link Tejavibulya's co-authors include Dustin Scheinost, Stephanie Noble, Matthew Rosenblatt, Rongtao Jiang, Max Rolison, Siyuan Gao, Hannah Peterson, Abigail S. Greene, Qinghao Liang and Javid Dadashkarimi and has published in prestigious journals such as Nature Communications, NeuroImage and Scientific Reports.

In The Last Decade

Link Tejavibulya

11 papers receiving 139 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Data leakage inflates prediction performance in connectome-based machine learning models

2024 47 citations Matthew Rosenblatt, Link Tejavibulya et al. Nature Communications profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Link Tejavibulya United States	6	87	28	24	18	15	14	143
Vishnu Bashyam United States	5	71 0.8×	65 2.3×	16 0.7×	37 2.1×	21 1.4×	9	188
Javid Dadashkarimi United States	7	126 1.4×	36 1.3×	55 2.3×	39 2.2×	33 2.2×	17	212
Jessica Dafflon United Kingdom	8	122 1.4×	54 1.9×	26 1.1×	27 1.5×	29 1.9×	10	216
Phoebe Imms United States	7	97 1.1×	72 2.6×	11 0.5×	6 0.3×	16 1.1×	16	175
Johanna Bayer Australia	6	159 1.8×	98 3.5×	48 2.0×	25 1.4×	34 2.3×	7	284
David Sabbagh France	5	146 1.7×	25 0.9×	11 0.5×	19 1.1×	17 1.1×	7	182
Gajendra J. Katuwal United States	5	152 1.7×	26 0.9×	11 0.5×	9 0.5×	28 1.9×	7	185
Gustav Mårtensson Sweden	9	88 1.0×	37 1.3×	13 0.5×	15 0.8×	81 5.4×	14	189
Pedro F. da Costa United Kingdom	6	106 1.2×	50 1.8×	12 0.5×	26 1.4×	35 2.3×	11	178
Bonhwang Koo United States	3	58 0.7×	22 0.8×	28 1.2×	5 0.3×	13 0.9×	5	119

Countries citing papers authored by Link Tejavibulya

Since Specialization

Citations

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

Fields of papers citing papers by Link Tejavibulya

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Link Tejavibulya

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

All Works

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14 of 14 papers shown

Tejavibulya, Link, et al.. (2025). Brain handedness associations depend on how and when handedness is measured. Scientific Reports. 15(1). 9674–9674.

Adkinson, Brendan, Matthew Rosenblatt, Javid Dadashkarimi, et al.. (2024). Brain-phenotype predictions of language and executive function can survive across diverse real-world data: Dataset shifts in developmental populations. Developmental Cognitive Neuroscience. 70. 101464–101464.

Rosenblatt, Matthew, et al.. (2024). Edge-centric network control on the human brain structural network. Imaging Neuroscience. 2.

Rosenblatt, Matthew, Link Tejavibulya, Rongtao Jiang, Stephanie Noble, & Dustin Scheinost. (2024). Data leakage inflates prediction performance in connectome-based machine learning models. Nature Communications. 15(1). 1829–1829. 47 indexed citations breakdown →

Rosenblatt, Matthew, Link Tejavibulya, Brendan Adkinson, et al.. (2024). Power and reproducibility in the external validation of brain-phenotype predictions. Nature Human Behaviour. 8(10). 2018–2033. 3 indexed citations

Rapuano, Kristina M., Link Tejavibulya, Anfei Li, et al.. (2023). Heightened sensitivity to high-calorie foods in children at risk for obesity: insights from behavior, neuroimaging, and genetics. Brain Imaging and Behavior. 17(5). 461–470. 3 indexed citations

Dadashkarimi, Javid, Amin Karbasi, Qinghao Liang, et al.. (2023). Cross Atlas Remapping via Optimal Transport (CAROT): Creating connectomes for different atlases when raw data is not available. Medical Image Analysis. 88. 102864–102864. 6 indexed citations

Tejavibulya, Link, Max Rolison, Siyuan Gao, et al.. (2022). Predicting the future of neuroimaging predictive models in mental health. Molecular Psychiatry. 27(8). 3129–3137. 29 indexed citations

Horien, Corey, Dorothea L. Floris, Abigail S. Greene, et al.. (2022). Functional Connectome–Based Predictive Modeling in Autism. Biological Psychiatry. 92(8). 626–642. 21 indexed citations

10.

Horien, Corey, Kangjoo Lee, Margaret L. Westwater, et al.. (2022). A protocol for working with open-source neuroimaging datasets. STAR Protocols. 3(1). 101077–101077. 3 indexed citations

11.

Aydın, Begüm, Michael Sierk, Mireia Moreno‐Estellés, et al.. (2022). Foxa2 and Pet1 Direct and Indirect Synergy Drive Serotonergic Neuronal Differentiation. Frontiers in Neuroscience. 16. 903881–903881. 1 indexed citations

12.

Noble, Stephanie, et al.. (2022). Leveraging edge-centric networks complements existing network-level inference for functional connectomes. NeuroImage. 264. 119742–119742. 6 indexed citations

13.

Tejavibulya, Link, Hannah Peterson, Abigail S. Greene, et al.. (2022). Large-scale differences in functional organization of left- and right-handed individuals using whole-brain, data-driven analysis of connectivity. NeuroImage. 252. 119040–119040. 23 indexed citations

14.

Dufford, Alexander J., et al.. (2022). Predicting Transdiagnostic Social Impairments in Childhood Using Connectome-Based Predictive Modeling. Biological Psychiatry. 91(9). S87–S87. 1 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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