Amy Kuceyeski

4.2k total citations · 1 hit paper
86 papers, 2.3k citations indexed

About

Amy Kuceyeski is a scholar working on Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging and Pathology and Forensic Medicine. According to data from OpenAlex, Amy Kuceyeski has authored 86 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 50 papers in Cognitive Neuroscience, 45 papers in Radiology, Nuclear Medicine and Imaging and 18 papers in Pathology and Forensic Medicine. Recurrent topics in Amy Kuceyeski's work include Functional Brain Connectivity Studies (44 papers), Advanced Neuroimaging Techniques and Applications (39 papers) and Multiple Sclerosis Research Studies (18 papers). Amy Kuceyeski is often cited by papers focused on Functional Brain Connectivity Studies (44 papers), Advanced Neuroimaging Techniques and Applications (39 papers) and Multiple Sclerosis Research Studies (18 papers). Amy Kuceyeski collaborates with scholars based in United States, Canada and Australia. Amy Kuceyeski's co-authors include Ashish Raj, Michael W. Weiner, Keith Jamison, Mert R. Sabuncu, Norman Relkin, Eve LoCastro, Susan A. Gauthier, Meenakshi Khosla, Jun Maruta and Abhishek Jaywant and has published in prestigious journals such as Nature Communications, Neuron and SHILAP Revista de lepidopterología.

In The Last Decade

Amy Kuceyeski

82 papers receiving 2.2k 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.

A Network Diffusion Model of Disease Progression in Dementia

2012 485 citations Amy Kuceyeski et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Amy Kuceyeski United States	25	1.3k	880	345	284	276	86	2.3k
Ashish Raj United States	32	1.6k 1.2×	1.4k 1.6×	601 1.7×	166 0.6×	445 1.6×	113	3.0k
Natalie M. Zahr United States	28	867 0.7×	1.0k 1.2×	219 0.6×	227 0.8×	229 0.8×	95	2.9k
Ernesto Sanz‐Arigita Netherlands	27	2.4k 1.9×	1.2k 1.3×	431 1.2×	234 0.8×	157 0.6×	42	3.6k
Rüdiger Ilg Germany	17	945 0.7×	577 0.7×	125 0.4×	362 1.3×	127 0.5×	31	2.1k
Philipp G. Sämann Germany	31	1.7k 1.3×	600 0.7×	140 0.4×	140 0.5×	145 0.5×	71	3.0k
Dominique Sappey‐Marinier France	29	546 0.4×	1.1k 1.3×	198 0.6×	382 1.3×	357 1.3×	83	2.4k
Anna Prinster Italy	23	609 0.5×	532 0.6×	261 0.8×	294 1.0×	154 0.6×	60	2.0k
Stefan Förster Germany	34	811 0.6×	1.2k 1.4×	629 1.8×	118 0.4×	364 1.3×	121	3.4k
Silvester Czanner United Kingdom	9	1.4k 1.1×	1.3k 1.5×	308 0.9×	110 0.4×	127 0.5×	32	2.9k
Alessandro Daducci Italy	26	1.0k 0.8×	2.0k 2.2×	132 0.4×	228 0.8×	127 0.5×	115	2.8k

Countries citing papers authored by Amy Kuceyeski

Since Specialization

Citations

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

Fields of papers citing papers by Amy Kuceyeski

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Amy Kuceyeski

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

All Works

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20 of 20 papers shown

Singleton, S. Parker, Christopher Timmermann, Andrea I. Luppi, et al.. (2025). Network control energy reductions under DMT relate to serotonin receptors, signal diversity, and subjective experience. Communications Biology. 8(1). 631–631.

Arora, Jaya, Keith Jamison, Amy Kuceyeski, et al.. (2025). Depression Symptoms Associated With Clinical Symptoms, Disability, and Functional Connectivity After Traumatic Brain Injury. Journal of Neuropsychiatry. 38(1). 93–98.

Hojjati, Seyed Hani, Kewei Chen, Gloria Chiang, et al.. (2024). Utilizing structural MRI and unsupervised clustering to differentiate schizophrenia and Alzheimer's disease in late-onset psychosis. Behavioural Brain Research. 480. 115386–115386.

Zhao, Qingyu, Kate B. Nooner, Susan F. Tapert, et al.. (2024). The Transition From Homogeneous to Heterogeneous Machine Learning in Neuropsychiatric Research. SHILAP Revista de lepidopterología. 5(1). 100397–100397. 2 indexed citations

Singleton, S. Parker, Julie B. Wang, Michael C. Mithoefer, et al.. (2023). Altered brain activity and functional connectivity after MDMA-assisted therapy for post-traumatic stress disorder. Frontiers in Psychiatry. 13. 947622–947622. 21 indexed citations

Su, Chang, et al.. (2023). Graph‐matching distance between individuals' functional connectomes varies with relatedness, age, and cognitive score. Human Brain Mapping. 44(9). 3541–3554. 1 indexed citations

Jamison, Keith, et al.. (2023). The sequence of regional structural disconnectivity due to multiple sclerosis lesions. Brain Communications. 5(6). fcad332–fcad332. 9 indexed citations

Jamison, Keith, Abhishek Jaywant, Arindam RoyChoudhury, et al.. (2023). Comparisons of electrophysiological markers of impaired executive attention after traumatic brain injury and in healthy aging. NeuroImage. 274. 120126–120126. 3 indexed citations

Jamison, Keith, et al.. (2022). Larger lesion volume in people with multiple sclerosis is associated with increased transition energies between brain states and decreased entropy of brain activity. Network Neuroscience. 7(2). 539–556. 8 indexed citations

10.

Fuchs, Tom, Amy Kuceyeski, Xian Li, et al.. (2022). Functional alteration due to structural damage is network dependent: insight from multiple sclerosis. Cerebral Cortex. 33(10). 6090–6102. 2 indexed citations

11.

Jamison, Keith, et al.. (2022). Personalized visual encoding model construction with small data. Communications Biology. 5(1). 1382–1382. 9 indexed citations

12.

Jamison, Keith, Thanh D. Nguyen, Ulrike W. Kaunzner, et al.. (2021). Structural disconnectivity from paramagnetic rim lesions is related to disability in multiple sclerosis. Brain and Behavior. 11(10). e2353–e2353. 18 indexed citations

13.

Khosla, Meenakshi, Gia H. Ngo, Keith Jamison, Amy Kuceyeski, & Mert R. Sabuncu. (2021). Cortical response to naturalistic stimuli is largely predictable with deep neural networks. Science Advances. 7(22). 25 indexed citations

14.

Jamison, Keith, et al.. (2021). Estimated connectivity networks outperform observed connectivity networks when classifying people with multiple sclerosis into disability groups. NeuroImage Clinical. 32. 102827–102827. 8 indexed citations

15.

Edwards, Dylan J., Aaron D. Boes, Douglas Labar, et al.. (2020). Machine Learning Methods Predict Individual Upper-Limb Motor Impairment Following Therapy in Chronic Stroke. Neurorehabilitation and neural repair. 34(5). 428–439. 50 indexed citations

16.

Fuchs, Tom, Stefano Ziccardi, Ralph H. B. Benedict, et al.. (2020). Functional Connectivity and Structural Disruption in the Default‐Mode Network Predicts Cognitive Rehabilitation Outcomes in Multiple Sclerosis. Journal of Neuroimaging. 30(4). 523–530. 19 indexed citations

17.

Khosla, Meenakshi, Keith Jamison, Amy Kuceyeski, & Mert R. Sabuncu. (2019). Ensemble learning with 3D convolutional neural networks for functional connectome-based prediction. NeuroImage. 199. 651–662. 78 indexed citations

18.

Kang, Yeona, David J. Schlyer, Ulrike W. Kaunzner, et al.. (2018). Comparison of two different methods of image analysis for the assessment of microglial activation in patients with multiple sclerosis using (R)-[N-methyl-carbon-11]PK11195. PLoS ONE. 13(8). e0201289–e0201289. 7 indexed citations

19.

Fuchs, Tom, Ralph H. B. Benedict, Niels Bergsland, et al.. (2018). Impact of Focal White Matter Damage on Localized Subcortical Gray Matter Atrophy in Multiple Sclerosis: A 5-Year Study. American Journal of Neuroradiology. 39(8). 1480–1486. 18 indexed citations

20.

Yao, Yihao, Thanh D. Nguyen, Sneha Pandya, et al.. (2017). Combining Quantitative Susceptibility Mapping with Automatic Zero Reference (QSM0) and Myelin Water Fraction Imaging to Quantify Iron-Related Myelin Damage in Chronic Active MS Lesions. American Journal of Neuroradiology. 39(2). 303–310. 37 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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