Nicholas J. Ashton

31.7k total citations · 11 hit papers
348 papers, 12.2k citations indexed

About

Nicholas J. Ashton is a scholar working on Physiology, Psychiatry and Mental health and Molecular Biology. According to data from OpenAlex, Nicholas J. Ashton has authored 348 papers receiving a total of 12.2k indexed citations (citations by other indexed papers that have themselves been cited), including 172 papers in Physiology, 141 papers in Psychiatry and Mental health and 65 papers in Molecular Biology. Recurrent topics in Nicholas J. Ashton's work include Alzheimer's disease research and treatments (160 papers), Dementia and Cognitive Impairment Research (136 papers) and Neuroinflammation and Neurodegeneration Mechanisms (26 papers). Nicholas J. Ashton is often cited by papers focused on Alzheimer's disease research and treatments (160 papers), Dementia and Cognitive Impairment Research (136 papers) and Neuroinflammation and Neurodegeneration Mechanisms (26 papers). Nicholas J. Ashton collaborates with scholars based in United Kingdom, Sweden and United States. Nicholas J. Ashton's co-authors include Kaj Blennow, Henrik Zetterberg, Thomas K. Karikari, Juan Lantero‐Rodriguez, Michael Schöll, Manoucher Shakib, Oskar Hansson, Joel Simrén, Niklas Mattsson and Pedro Rosa‐Neto and has published in prestigious journals such as Nature, The Lancet and Nature Communications.

In The Last Decade

Nicholas J. Ashton

321 papers receiving 11.4k citations

Hit Papers

Plasma p-tau231: a new bi... 2020 2026 2022 2024 2021 2021 2022 2020 2022 100 200 300
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. Plasma p-tau231: a new biomarker for incipient Alzheimer’s disease pathology
    2021 390 citations Nicholas J. Ashton, Thomas K. Karikari et al. profile →
  2. Plasma GFAP is an early marker of amyloid-β but not tau pathology in Alzheimer’s disease
    2021 310 citations Shorena Janelidze, Ruben Smith et al. profile →
  3. Head-to-head comparison of 10 plasma phospho-tau assays in prodromal Alzheimer’s disease
    2022 242 citations Nicholas J. Ashton, Henrik Zetterberg et al. profile →
  4. Diagnostic performance and prediction of clinical progression of plasma phospho-tau181 in the Alzheimer’s Disease Neuroimaging Initiative
    2020 218 citations Thomas K. Karikari, Nicholas J. Ashton et al. Molecular Psychiatry profile →
  5. Blood phospho-tau in Alzheimer disease: analysis, interpretation, and clinical utility
    2022 170 citations Thomas K. Karikari, Nicholas J. Ashton et al. profile →
  6. Prediction of Longitudinal Cognitive Decline in Preclinical Alzheimer Disease Using Plasma Biomarkers
    2023 150 citations Nicholas J. Ashton, Henrik Zetterberg et al. profile →
  7. Comparison of Plasma Phosphorylated Tau Species With Amyloid and Tau Positron Emission Tomography, Neurodegeneration, Vascular Pathology, and Cognitive Outcomes
    2021 146 citations Andreas Jeromin, Nicholas J. Ashton et al. profile →
  8. Plasma biomarkers for Alzheimer’s Disease in relation to neuropathology and cognitive change
    2022 134 citations Nicholas J. Ashton, Kaj Blennow et al. profile →
  9. Brain-derived tau: a novel blood-based biomarker for Alzheimer’s disease-type neurodegeneration
    2022 134 citations Przemysław R. Kac, Nicholas J. Ashton et al. profile →
  10. A two-step workflow based on plasma p-tau217 to screen for amyloid β positivity with further confirmatory testing only in uncertain cases
    2023 101 citations Nicholas J. Ashton, Kaj Blennow et al. profile →
  11. Specific associations between plasma biomarkers and postmortem amyloid plaque and tau tangle loads
    2023 92 citations Nicholas J. Ashton, Henrik Zetterberg et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Nicholas J. Ashton United Kingdom 63 4.8k 3.7k 2.6k 2.2k 2.0k 348 12.2k
E. Tessa Hedley‐Whyte United States 53 3.5k 0.7× 1.4k 0.4× 3.2k 1.2× 3.9k 1.7× 390 0.2× 194 13.2k
Anne H. Cross United States 71 1.8k 0.4× 1.6k 0.4× 2.6k 1.0× 3.3k 1.5× 516 0.3× 253 20.3k
Ronald L. Hamilton United States 61 3.3k 0.7× 1.9k 0.5× 3.9k 1.5× 4.4k 2.0× 169 0.1× 200 13.3k
Robert E. Ferrell United States 73 3.7k 0.8× 1.0k 0.3× 6.7k 2.6× 750 0.3× 1.2k 0.6× 384 20.7k
Joseph E. Parisi United States 62 1.5k 0.3× 1.7k 0.4× 2.5k 1.0× 6.6k 2.9× 287 0.1× 180 16.0k
Leonard T. Kurland United States 77 2.1k 0.4× 6.0k 1.6× 2.0k 0.8× 5.6k 2.5× 422 0.2× 238 22.7k
Margaret M. Esiri United Kingdom 79 5.6k 1.2× 2.9k 0.8× 4.7k 1.8× 3.6k 1.6× 190 0.1× 288 21.2k
George C. Ebers Canada 77 912 0.2× 2.2k 0.6× 4.4k 1.7× 7.5k 3.4× 427 0.2× 357 31.9k
Paul O’Connor Canada 52 614 0.1× 1.6k 0.4× 3.4k 1.3× 6.9k 3.1× 794 0.4× 152 25.1k
Gavin Giovannoni United Kingdom 79 1.5k 0.3× 1.4k 0.4× 4.3k 1.7× 7.2k 3.2× 280 0.1× 678 26.2k

Countries citing papers authored by Nicholas J. Ashton

Since Specialization
Citations

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

Fields of papers citing papers by Nicholas J. Ashton

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nicholas J. Ashton. 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 Nicholas J. Ashton. The network helps show where Nicholas J. Ashton may publish in the future.

Co-authorship network of co-authors of Nicholas J. Ashton

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

All Works

20 of 20 papers shown
1.
García‐Ayllón, María‐Salud, Virginia Quaresima, Viviana Cristillo, et al.. (2025). Increased Cerebrospinal Fluid Angiotensin-Converting Enzyme 2 Fragments as a Read-Out of Brain Infection in Patients With COVID-19 Encephalopathy. The Journal of Infectious Diseases. 231(5). e929–e940. 3 indexed citations
2.
Simonsen, Anja Hviid, Nicholas J. Ashton, Hanna Huber, et al.. (2025). Short-term variability of Alzheimer’s disease plasma biomarkers in a mixed memory clinic cohort. Alzheimer s Research & Therapy. 17(1). 26–26. 5 indexed citations
3.
Peretti, Débora Elisa, Max Scheffler, Nicholas J. Ashton, et al.. (2025). Comparison of two preprocessing methods for 18F-Flortaucipir PET quantification in Alzheimer’s disease. European Journal of Nuclear Medicine and Molecular Imaging. 53(1). 480–490.
4.
Chong, Joyce R., Saima Hilal, Boon Yeow Tan, et al.. (2025). Clinical utility of plasma p‐tau217 in identifying abnormal brain amyloid burden in an Asian cohort with high prevalence of concomitant cerebrovascular disease. Alzheimer s & Dementia. 21(2). e14502–e14502. 5 indexed citations
5.
Hagman, Göran, Anna Rosenberg, Nicholas J. Ashton, et al.. (2025). Enhancing diagnostic precision in Alzheimer's disease: Impact of comorbidities on blood biomarkers for clinical integration. Alzheimer s & Dementia. 21(12). e70931–e70931. 1 indexed citations
6.
Durcan, Rory, Amanda Heslegrave, Peter Swann, et al.. (2025). Novel blood‐based proteomic signatures across multiple neurodegenerative diseases. Alzheimer s & Dementia. 21(3). e70116–e70116. 3 indexed citations
7.
Chiotis, Konstantinos, Marco Bucci, Irina Savitcheva, et al.. (2024). Disentangling relationships between Alzheimer's disease plasma biomarkers and established biomarkers in patients of tertiary memory clinics. EBioMedicine. 112. 105504–105504. 3 indexed citations
8.
Smith, Tyler B., Nicholas J. Ashton, Marguerite L. Monogue, et al.. (2024). Alternating magnetic fields (AMF) and linezolid reduce Staphylococcus aureus biofilm in a large animal implant model. Journal of Infection. 89(5). 106271–106271. 2 indexed citations
9.
Arslan, Burak, Henrik Zetterberg, & Nicholas J. Ashton. (2024). Blood-based biomarkers in Alzheimer’s disease – moving towards a new era of diagnostics. Clinical Chemistry and Laboratory Medicine (CCLM). 62(6). 1063–1069. 30 indexed citations
10.
Blennow, Kaj, et al.. (2024). CSF profiling of impaired Blood‐brain barrier individuals: novel putative biomarkers discovery. Alzheimer s & Dementia. 20(S2). 1 indexed citations
11.
Chong, Joyce R., Yuek Ling Chai, Saima Hilal, et al.. (2024). Association of plasma GFAP with elevated brain amyloid is dependent on severity of white matter lesions in an Asian cognitively impaired cohort. Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring. 16(2). e12576–e12576. 7 indexed citations
12.
Bilgel, Murat, Yang An, Keenan A. Walker, et al.. (2023). Longitudinal changes in Alzheimer's‐related plasma biomarkers and brain amyloid. Alzheimer s & Dementia. 19(10). 4335–4345. 40 indexed citations
13.
Egroo, Maxime Van, Joost M. Riphagen, Nicholas J. Ashton, et al.. (2023). Ultra-high field imaging, plasma markers and autopsy data uncover a specific rostral locus coeruleus vulnerability to hyperphosphorylated tau. Molecular Psychiatry. 28(6). 2412–2422. 23 indexed citations
14.
Altomare, Daniele, Federica Ribaldi, Szymon Tomczyk, et al.. (2023). Plasma biomarkers for Alzheimer’s disease: a field-test in a memory clinic. Journal of Neurology Neurosurgery & Psychiatry. 94(6). 420–427. 39 indexed citations
15.
Chiotis, Konstantinos, Charlotte Johansson, Elena Rodriguez‐Vieitez, et al.. (2023). Tracking reactive astrocytes in autosomal dominant Alzheimer disease with plasma GFAP and multi‐modal PET imaging. Alzheimer s & Dementia. 19(S2). 1 indexed citations
16.
Chong, Joyce R., Saima Hilal, Nicholas J. Ashton, et al.. (2023). Brain atrophy and white matter hyperintensities are independently associated with plasma neurofilament light chain in an Asian cohort of cognitively impaired patients with concomitant cerebral small vessel disease. Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring. 15(1). e12396–e12396. 22 indexed citations
17.
Moscoso, Alexis, Thomas K. Karikari, Michel J. Grothe, et al.. (2022). CSF biomarkers and plasma p‐tau181 as predictors of longitudinal tau accumulation: Implications for clinical trial design. Alzheimer s & Dementia. 18(12). 2614–2626. 38 indexed citations
18.
Ashton, Nicholas J., Kaj Blennow, Henrik Zetterberg, et al.. (2021). Ultra-Early Differential Diagnosis of Acute Cerebral Ischemia and Hemorrhagic Stroke by Measuring the Prehospital Release Rate of GFAP. Clinical Chemistry. 67(10). 1361–1372. 31 indexed citations
19.
Tanaka, Tomotaka, Joyce R. Chong, Ying‐Hwey Nai, et al.. (2020). Head‐to‐head comparison of amplified plasmonic exosome Aβ42 platform and single‐molecule array immunoassay in a memory clinic cohort. European Journal of Neurology. 28(5). 1479–1489. 15 indexed citations
20.
Simrén, Joel, Nicholas J. Ashton, Kaj Blennow, & Henrik Zetterberg. (2019). An update on fluid biomarkers for neurodegenerative diseases: recent success and challenges ahead. Current Opinion in Neurobiology. 61. 29–39. 67 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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