Daniel A. Llano

4.5k total citations
120 papers, 2.9k citations indexed

About

Daniel A. Llano is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Sensory Systems. According to data from OpenAlex, Daniel A. Llano has authored 120 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 60 papers in Cognitive Neuroscience, 40 papers in Cellular and Molecular Neuroscience and 29 papers in Sensory Systems. Recurrent topics in Daniel A. Llano's work include Neural dynamics and brain function (42 papers), Neuroscience and Neuropharmacology Research (27 papers) and Hearing, Cochlea, Tinnitus, Genetics (26 papers). Daniel A. Llano is often cited by papers focused on Neural dynamics and brain function (42 papers), Neuroscience and Neuropharmacology Research (27 papers) and Hearing, Cochlea, Tinnitus, Genetics (26 papers). Daniel A. Llano collaborates with scholars based in United States, France and United Kingdom. Daniel A. Llano's co-authors include S. Murray Sherman, Brian Theyel, Viswanath Devanarayan, Donald M. Caspary, Alexandria M.H. Lesicko, Kevin A. Stebbings, Kieran P. Normoyle, Kevin S. Jackson, Matthew F. Sharrock and Jeremy G. Turner and has published in prestigious journals such as Journal of Neuroscience, Nature Neuroscience and PLoS ONE.

In The Last Decade

Daniel A. Llano

115 papers receiving 2.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel A. Llano United States 29 1.3k 688 577 429 365 120 2.9k
Xiaowei Chen China 30 1.4k 1.0× 1.6k 2.3× 303 0.5× 598 1.4× 817 2.2× 153 4.0k
Urs Ribary Canada 29 3.9k 3.0× 1.5k 2.1× 457 0.8× 401 0.9× 444 1.2× 86 6.1k
Mario Džemidžić United States 34 2.2k 1.6× 676 1.0× 237 0.4× 172 0.4× 146 0.4× 119 3.6k
Guangyu Zhou United States 28 784 0.6× 250 0.4× 372 0.6× 174 0.4× 366 1.0× 66 2.2k
Fuqiang Xu China 38 1.5k 1.1× 2.2k 3.1× 1.3k 2.3× 590 1.4× 1.3k 3.6× 180 5.3k
Yong Tang China 33 626 0.5× 867 1.3× 182 0.3× 857 2.0× 740 2.0× 204 4.1k
John Zempel United States 22 3.9k 2.9× 1.0k 1.5× 212 0.4× 223 0.5× 464 1.3× 48 5.3k
Jadwiga Rogowska United States 28 833 0.6× 551 0.8× 111 0.2× 168 0.4× 372 1.0× 52 3.1k
Daniel S. Barth United States 41 3.3k 2.5× 1.7k 2.4× 487 0.8× 135 0.3× 402 1.1× 89 4.5k
Sara N. Burke United States 28 1.7k 1.3× 1.4k 2.0× 161 0.3× 570 1.3× 499 1.4× 84 3.2k

Countries citing papers authored by Daniel A. Llano

Since Specialization
Citations

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

Fields of papers citing papers by Daniel A. Llano

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel A. Llano. 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 Daniel A. Llano. The network helps show where Daniel A. Llano may publish in the future.

Co-authorship network of co-authors of Daniel A. Llano

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel A. Llano. A scholar is included among the top collaborators of Daniel A. Llano 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 Daniel A. Llano. Daniel A. Llano 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.
Wang, Yike, Matthew R. Lowerison, Zhijie Dong, et al.. (2025). Combined Nanodrops Imaging and Ultrasound Localization Microscopy for Detecting Intracerebral Hemorrhage. Ultrasound in Medicine & Biology. 51(4). 707–714. 1 indexed citations
2.
Llano, Daniel A., et al.. (2025). Multiple ischemic strokes in a patient treated with Lecanemab: a case report. Neurocase. 31(6). 295–300.
3.
Devanarayan, Viswanath, T. Doherty, Arnaud Charil, et al.. (2024). Plasma pTau217 predicts continuous brain amyloid levels in preclinical and early Alzheimer's disease. Alzheimer s & Dementia. 20(8). 5617–5628. 21 indexed citations
4.
Devanarayan, Viswanath, Daniel A. Llano, Yan Hu, et al.. (2024). Plasma pTau181 enhances the prediction of future clinical decline in amyloid‐positive mild cognitive impairment. Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring. 16(3). e12621–e12621. 3 indexed citations
5.
6.
Cai, Rui, et al.. (2023). Increased pyramidal and VIP neuronal excitability in rat primary auditory cortex directly correlates with tinnitus behaviour. The Journal of Physiology. 601(12). 2493–2511. 9 indexed citations
7.
Llano, Daniel A., et al.. (2023). Highly branched and complementary distributions of layer 5 and layer 6 auditory corticofugal axons in mouse. Cerebral Cortex. 33(16). 9566–9582. 7 indexed citations
8.
You, Qi, Joshua D. Trzasko, Matthew R. Lowerison, et al.. (2022). Curvelet Transform-Based Sparsity Promoting Algorithm for Fast Ultrasound Localization Microscopy. IEEE Transactions on Medical Imaging. 41(9). 2385–2398. 13 indexed citations
9.
Llano, Daniel A., et al.. (2022). CSF peptides from VGF and other markers enhance prediction of MCI to AD progression using the ATN framework. Neurobiology of Aging. 121. 15–27. 11 indexed citations
10.
Macías, Silvio & Daniel A. Llano. (2022). Descending projections to the auditory midbrain: evolutionary considerations. Journal of Comparative Physiology A. 209(1). 131–143. 3 indexed citations
11.
Llano, Daniel A., et al.. (2021). A novel dynamic network imaging analysis method reveals aging-related fragmentation of cortical networks in mouse. Network Neuroscience. 5(2). 569–590. 3 indexed citations
12.
Llano, Daniel A., et al.. (2021). Top-Down Inference in the Auditory System: Potential Roles for Corticofugal Projections. Frontiers in Neural Circuits. 14. 615259–615259. 33 indexed citations
13.
Lesicko, Alexandria M.H., et al.. (2020). Circuit Mechanisms Underlying the Segregation and Integration of Parallel Processing Streams in the Inferior Colliculus. Journal of Neuroscience. 40(33). 6328–6344. 21 indexed citations
14.
Marini, Francesco, et al.. (2020). Investigating EEG theta and alpha oscillations as measures of value-directed strategic processing in cognitively normal younger and older adults. Behavioural Brain Research. 391. 112702–112702. 11 indexed citations
15.
Llano, Daniel A., et al.. (2019). VGF in Cerebrospinal Fluid Combined With Conventional Biomarkers Enhances Prediction of Conversion From MCI to AD. Alzheimer Disease & Associated Disorders. 33(4). 307–314. 27 indexed citations
16.
Ibrahim, Baher A., et al.. (2018). A novel mutual information estimator to measure spike train correlations in a model thalamocortical network. Journal of Neurophysiology. 120(6). 2730–2744. 10 indexed citations
17.
Slater, Bernard J., et al.. (2018). Thalamocortical and Intracortical Inputs Differentiate Layer-Specific Mouse Auditory Corticocollicular Neurons. Journal of Neuroscience. 39(2). 256–270. 20 indexed citations
18.
Forbes, Angus G., et al.. (2016). SwordPlots: Exploring neuron behavior within dynamic communities of brain networks. 2016(16). 1–13. 2 indexed citations
19.
Stebbings, Kevin A., et al.. (2015). Stretch induced hyperexcitability of mice callosal pathway. Frontiers in Cellular Neuroscience. 9. 292–292. 11 indexed citations
20.
Luo, Feng, Nathan R. Rustay, Terese Seifert, et al.. (2010). Magnetic Resonance Imaging Detection and Time Course of Cerebral Microhemorrhages during Passive Immunotherapy in Living Amyloid Precursor Protein Transgenic Mice. Journal of Pharmacology and Experimental Therapeutics. 335(3). 580–588. 22 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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