Maxwell Shinn

2.4k total citations · 2 hit papers
15 papers, 1.1k citations indexed

About

Maxwell Shinn is a scholar working on Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging and Molecular Biology. According to data from OpenAlex, Maxwell Shinn has authored 15 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Cognitive Neuroscience, 5 papers in Radiology, Nuclear Medicine and Imaging and 4 papers in Molecular Biology. Recurrent topics in Maxwell Shinn's work include Neural dynamics and brain function (10 papers), Functional Brain Connectivity Studies (7 papers) and Advanced Neuroimaging Techniques and Applications (4 papers). Maxwell Shinn is often cited by papers focused on Neural dynamics and brain function (10 papers), Functional Brain Connectivity Studies (7 papers) and Advanced Neuroimaging Techniques and Applications (4 papers). Maxwell Shinn collaborates with scholars based in United States, United Kingdom and France. Maxwell Shinn's co-authors include John D. Murray, Alan Anticevic, Edward T. Bullmore, Joshua B. Burt, Markus Helmer, Jakob Seidlitz, František Váša, Petra E. Vértes, Rafael Romero-García and Peter B. Jones and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Nature Communications.

In The Last Decade

Maxwell Shinn

14 papers receiving 1.1k citations

Hit Papers

Morphometric Similarity Networks Detect Microscale Cortic... 2017 2026 2020 2023 2017 2020 50 100 150 200 250
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.

  1. Morphometric Similarity Networks Detect Microscale Cortical Organization and Predict Inter-Individual Cognitive Variation
    2017 284 citations Jakob Seidlitz, František Váša et al. Neuron profile →
  2. Generative modeling of brain maps with spatial autocorrelation
    2020 231 citations Joshua B. Burt, Markus Helmer et al. NeuroImage profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Maxwell Shinn United States 12 835 360 175 173 131 15 1.1k
Lianne H. Scholtens Netherlands 22 1.1k 1.3× 641 1.8× 157 0.9× 114 0.7× 139 1.1× 34 1.4k
František Váša United Kingdom 13 993 1.2× 522 1.4× 171 1.0× 103 0.6× 177 1.4× 24 1.3k
Koen V. Haak Netherlands 19 1.1k 1.3× 339 0.9× 180 1.0× 174 1.0× 85 0.6× 44 1.3k
Aurina Arnatkevičiūtė Australia 18 1.2k 1.4× 431 1.2× 350 2.0× 169 1.0× 196 1.5× 32 1.8k
Longchuan Li United States 23 859 1.0× 710 2.0× 117 0.7× 76 0.4× 72 0.5× 42 1.4k
Joshua Faskowitz United States 21 1.1k 1.3× 437 1.2× 83 0.5× 83 0.5× 226 1.7× 57 1.4k
Siemon C. de Lange Netherlands 12 673 0.8× 335 0.9× 113 0.6× 47 0.3× 125 1.0× 26 940
Ibai Díez United States 22 754 0.9× 225 0.6× 186 1.1× 147 0.8× 171 1.3× 81 1.3k
Golia Shafiei Canada 12 773 0.9× 358 1.0× 56 0.3× 105 0.6× 95 0.7× 24 936

Countries citing papers authored by Maxwell Shinn

Since Specialization
Citations

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

Fields of papers citing papers by Maxwell Shinn

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Maxwell Shinn

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

All Works

15 of 15 papers shown
1.
Dora, Jonas, Maxwell Shinn, Elizabeth C. Neilson, et al.. (2025). How people decide to consume (more) alcohol when feeling stressed. Addiction. 121(2). 370–387.
2.
Shinn, Maxwell, Stephanie Noble, Katrin H. Preller, et al.. (2023). Functional brain networks reflect spatial and temporal autocorrelation. Nature Neuroscience. 26(5). 867–878. 39 indexed citations
3.
Shinn, Maxwell. (2023). Phantom oscillations in principal component analysis. Proceedings of the National Academy of Sciences. 120(48). e2311420120–e2311420120. 21 indexed citations
4.
Shinn, Maxwell, Daeyeol Lee, John D. Murray, & Hyojung Seo. (2022). Transient neuronal suppression for exploitation of new sensory evidence. Nature Communications. 13(1). 23–23. 2 indexed citations
5.
Dipoppa, Mario, Dimitris Nicoloutsopoulos, Maxwell Shinn, et al.. (2022). A transcriptomic axis predicts state modulation of cortical interneurons. Nature. 607(7918). 330–338. 65 indexed citations
6.
Shinn, Maxwell, et al.. (2020). Confluence of Timing and Reward Biases in Perceptual Decision-Making Dynamics. Journal of Neuroscience. 40(38). 7326–7342. 11 indexed citations
7.
Burt, Joshua B., Markus Helmer, Maxwell Shinn, Alan Anticevic, & John D. Murray. (2020). Generative modeling of brain maps with spatial autocorrelation. NeuroImage. 220. 117038–117038. 231 indexed citations breakdown →
8.
Shinn, Maxwell, Norman H. Lam, & John D. Murray. (2020). A flexible framework for simulating and fitting generalized drift-diffusion models. eLife. 9. 67 indexed citations
9.
Nykamp, Duane Q., Daniel Friedman, Maxwell Shinn, et al.. (2017). Mean-field equations for neuronal networks with arbitrary degree distributions. Physical review. E. 95(4). 42323–42323. 11 indexed citations
10.
Romero-García, Rafael, Kirstie Whitaker, František Váša, et al.. (2017). Structural covariance networks are coupled to expression of genes enriched in supragranular layers of the human cortex. NeuroImage. 171. 256–267. 122 indexed citations
11.
Shinn, Maxwell, Rafael Romero-García, Jakob Seidlitz, et al.. (2017). Versatility of nodal affiliation to communities. Scientific Reports. 7(1). 4273–4273. 15 indexed citations
12.
Váša, František, Jakob Seidlitz, Rafael Romero-García, et al.. (2017). Adolescent Tuning of Association Cortex in Human Structural Brain Networks. Cerebral Cortex. 28(1). 281–294. 201 indexed citations
13.
Seidlitz, Jakob, František Váša, Maxwell Shinn, et al.. (2017). Morphometric Similarity Networks Detect Microscale Cortical Organization and Predict Inter-Individual Cognitive Variation. Neuron. 97(1). 231–247.e7. 284 indexed citations breakdown →
14.
Holt, Abbey B., Dan Wilson, Maxwell Shinn, Jeff Moehlis, & Théoden I. Netoff. (2016). Phasic Burst Stimulation: A Closed-Loop Approach to Tuning Deep Brain Stimulation Parameters for Parkinson’s Disease. PLoS Computational Biology. 12(7). e1005011–e1005011. 69 indexed citations
15.
Holt, Abbey B., Maxwell Shinn, & Théoden I. Netoff. (2015). Closed-loop approach to tuning deep brain stimulation parameters for Parkinson's disease. BMC Neuroscience. 16(S1). 2 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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