Sergey Plis

8.9k citations

118 papers · 5.6k indexed · 4 hit papers · h-index 26

Impact in

Cognitive Neuroscience top 0.5%
- Functional Brain Connectivity Studies
- Neural dynamics and brain function
- EEG and Brain-Computer Interfaces
Radiology, Nuclear Medicine and Imaging top 0.5%
- Advanced Neuroimaging Techniques and Applications
- Advanced MRI Techniques and Applications

Papers in ⓘ

Cognitive Neuroscience 86
- Functional Brain Connectivity Studies 79
- Neural dynamics and brain function 35
- EEG and Brain-Computer Interfaces 25
Radiology, Nuclear Medicine and Imaging 37
- Advanced Neuroimaging Techniques and Applications 27
- Advanced MRI Techniques and Applications 19

Co-authors: Vince D. Calhoun (93 shared papers)Elena A. Allen (6 shared papers)Tom Eichele (5 shared papers)Eswar Damaraju (14 shared papers)Erik B. Erhardt (4 shared papers)Jing Sui (10 shared papers)Mohammad R. Arbabshirani (5 shared papers)Alvaro Ulloa (4 shared papers)
Journals: NeuroImage (14 papers)Frontiers in Neuroscience (5 papers)Frontiers in Neuroinformatics (5 papers)Human Brain Mapping (4 papers)Scientific Reports (3 papers)
Partner nations: United States China Norway

In The Last Decade

Sergey Plis

114 papers receiving 5.5k citations

Hit Papers

align trajectories log scale

Automated Interpretation of Clinical Electroencephalograms Using Artificial Intelligence 2023 · 84 citations

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if any of the following hold:

it has ≥500 total citations;
it reaches ≥1.5× the top-1% citation threshold for papers in the same subfield and year (the threshold is the minimum needed to enter the top 1%, not the average within it);
it reaches the top citation threshold in at least one of its specific research topics.

2023 JAMA Neurology
2016 NeuroImage
2016 Molecular Pharmaceutics
2012 Cerebral Cortex

Peers

Countries citing papers authored by Sergey Plis

Since Specialization

Citations

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

Fields of papers citing papers by Sergey Plis

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Sergey Plis, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Sergey Plis Line = papers co-authored together Sergey Plis links everyone, so they are left out of the graph.

All Works

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20 of 20 papers shown

Showing the 20 most-cited of 118 papers — load more, or switch the sort, to bring in the rest.

#	Work
1	Tracking Whole-Brain Connectivity Dynamics in the Resting State Cerebral Cortex ·Elena A. Allen, Eswar Damaraju, Sergey Plis, Erik B. Erhardt, Tom Eichele, Vince D. Calhoun Hit paper breakdown →	2012	2171
2	Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls NeuroImage ·Mohammad R. Arbabshirani, Sergey Plis, Jing Sui, Vince D. Calhoun Hit paper breakdown →	2016	588
3	Deep learning for neuroimaging: a validation study Frontiers in Neuroscience ·Sergey Plis, Devon Hjelm, Ruslan Salakhutdinov, Elena A. Allen, H. Jeremy Bockholt, Jeffrey D. Long, Hans J. Johnson, Jane S. Paulsen, Jessica A. Turner, Vince D. Calhoun	2014	392
4	Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data Molecular Pharmaceutics ·Alexander Aliper, Sergey Plis, Artem V. Artemov, Alvaro Ulloa, Polina Mamoshina, Alex Zhavoronkov Hit paper breakdown →	2016	363
5	Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning Nature Communications ·Anees Abrol, Zening Fu, Mustafa S. Salman, Rogers F. Silva, Yuhui Du, Sergey Plis, Vince D. Calhoun	2021	125
6	Analysis of Multimodal Neuroimaging Data IEEE Reviews in Biomedical Engineering ·Felix Bießmann, Sergey Plis, Frank C. Meinecke, Tom Eichele, Klaus‐Robert Müller	2011	114
7	Deep residual learning for neuroimaging: An application to predict progression to Alzheimer’s disease Journal of Neuroscience Methods ·Anees Abrol, Manish Bhattarai, Alex Fedorov, Yuhui Du, Sergey Plis, Vince D. Calhoun	2020	114
8	Restricted Boltzmann machines for neuroimaging: An application in identifying intrinsic networks NeuroImage ·R Devon Hjelm, Vince D. Calhoun, Ruslan Salakhutdinov, Elena A. Allen, Tülay Adalı, Sergey Plis	2014	105
9	Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks NeuroImage ·Hojin Jang, Sergey Plis, Vince D. Calhoun, Jong‐Hwan Lee	2016	86
10	Automated Interpretation of Clinical Electroencephalograms Using Artificial Intelligence JAMA Neurology ·Jesper Tveit, Harald Aurlien, Sergey Plis, Vince D. Calhoun, William O. Tatum, Donald L. Schomer, Vibeke Arntsen, F. M. Cox, Firas Fahoum, William B. Gallentine, Elena Gardella, Cecil D. Hahn, Aatif M. Husain, Sudha Kilaru Kessler, Mustafa Aykut Kural, Fábio A. Nascimento, Hatice Tankişi, Line Bédos Ulvin, Richard Wennberg, Sándor Beniczky Hit paper breakdown →	2023	84
11	Impact of autocorrelation on functional connectivity NeuroImage ·Mohammad R. Arbabshirani, Eswar Damaraju, Ronald Phlypo, Sergey Plis, Elena A. Allen, Sai Ma, Daniel H. Mathalon, Adrian Preda, Jatin G. Vaidya, Tülay Adalı, Vince D. Calhoun	2014	77
12	Modular Organization of Functional Network Connectivity in Healthy Controls and Patients with Schizophrenia during the Resting State Frontiers in Systems Neuroscience ·Qingbao Yu, Sergey Plis, Erik B. Erhardt, Elena A. Allen, Jing Sui, Kent A. Kiehl, Godfrey D. Pearlson, Vince D. Calhoun	2012	74
13	Group-level component analyses of EEG: validation and evaluation Frontiers in Neuroscience ·René J. Huster, Sergey Plis, Vince D. Calhoun	2015	74
14	Independent Component Analysis for Brain fMRI Does Indeed Select for Maximal Independence PLoS ONE ·Vince D. Calhoun, Vamsi K. Potluru, Ronald Phlypo, Rogers F. Silva, Barak A. Pearlmutter, Arvind Caprihan, Sergey Plis, Tülay Adalı	2013	65
15	Disrupted correlation between low frequency power and connectivity strength of resting state brain networks in schizophrenia Schizophrenia Research ·Qingbao Yu, Jing Sui, Jingyu Liu, Sergey Plis, Kent A. Kiehl, Godfrey D. Pearlson, Vince D. Calhoun	2012	64
16	COINSTAC: A Privacy Enabled Model and Prototype for Leveraging and Processing Decentralized Brain Imaging Data Frontiers in Neuroscience ·Sergey Plis, Anand D. Sarwate, Dylan Wood, Christopher Dieringer, Drew Landis, Cory Reed, Sandeep Panta, Jessica A. Turner, Jody M. Shoemaker, Kim W. Carter, Paul M. Thompson, Kent E. Hutchison, Vince D. Calhoun	2016	57
17	Deep Learning in Neuroimaging: Promises and challenges IEEE Signal Processing Magazine ·Weizheng Yan, Gang Qu, Wenxing Hu, Anees Abrol, Biao Cai, Chen Qiao, Sergey Plis, Yu‐Ping Wang, Jing Sui, Vince D. Calhoun	2022	47
18	Quantitative EEG Biomarkers for Mild Traumatic Brain Injury Journal of Clinical Neurophysiology ·Jeffrey D. Lewine, Sergey Plis, Alvaro Ulloa, Christopher S. Williams, Mark C. Spitz, John Foley, Kim Paulson, John L. Davis, Nitin Bangera, Travis Snyder, Lindell K. Weaver	2019	45
19	Reading the (functional) writing on the (structural) wall: Multimodal fusion of brain structure and function via a deep neural network based translation approach reveals novel impairments in schizophrenia NeuroImage ·Sergey Plis, Md. Faijul Amin, Adam M. Chekroud, Devon Hjelm, Eswar Damaraju, Hyo Jong Lee, Juan Bustillo, Kyunghyun Cho, Godfrey D. Pearlson, Vince D. Calhoun	2018	43
20	Spatiotemporal Bayesian inference dipole analysis for MEG neuroimaging data NeuroImage ·Sung Chan Jun, John George, E. Juliana Paré‐Blagoev, Sergey Plis, Doug Ranken, David Schmidt, C. C. Wood	2005	42

About Sergey Plis

Sergey Plis is a scholar working on Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Signal Processing and Experimental and Cognitive Psychology, having authored 118 papers that have together received 5.6k indexed citations. Recurring topics across this work include Functional Brain Connectivity Studies (79 papers), Neural dynamics and brain function (35 papers), Advanced Neuroimaging Techniques and Applications (27 papers), EEG and Brain-Computer Interfaces (25 papers), Blind Source Separation Techniques (25 papers), Advanced MRI Techniques and Applications (19 papers), Privacy-Preserving Technologies in Data (10 papers) and Mental Health Research Topics (7 papers). The work is most often cited by research in Cognitive Neuroscience (4.1k citations), Radiology, Nuclear Medicine and Imaging (1.8k citations), Health Informatics (87 citations), Experimental and Cognitive Psychology (692 citations) and Computational Mathematics (26 citations). Sergey Plis has collaborated with scholars based in United States, China and Norway. Frequent co-authors include Vince D. Calhoun, Elena A. Allen, Tom Eichele, Eswar Damaraju, Erik B. Erhardt, Jing Sui, Mohammad R. Arbabshirani, Alvaro Ulloa, Jessica A. Turner and Alex Zhavoronkov. Their work appears in journals such as NeuroImage, Frontiers in Neuroscience, Frontiers in Neuroinformatics, Human Brain Mapping and Scientific Reports.

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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