Melissa Lamar

8.8k citations
245 papers · 4.9k · h-index 39

Impact in

Papers in

Melissa Lamar

228 papers receiving 4.9k citations

Peers

Melissa Lamar
Comparison fields: 5 of 155
  • Psychiatry and Mental health 2.2k
  • Cognitive Neuroscience 1.8k
  • Neuropsychology and Physiological Psychology 141
  • Neurology 527
  • Biological Psychiatry 117
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Angela L. Jefferson United States
Daniela Mapelli Italy
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Citations per field
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Citations per year

Countries citing papers authored by Melissa Lamar

Since Specialization
Citations

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

Fields of papers citing papers by Melissa Lamar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Melissa Lamar, 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 Melissa Lamar Line = papers co-authored together Melissa Lamar links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 245 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2013175
2 2010155
3 2018127
4 2015118
5 2016114
6 2003109
7 2011107
8 200395
9 200993
10 199790
11 201888
12 201384
13 201183
14 200280
15 201879
16 201377
17 200072
18 201970
19 202067
20 201960

About Melissa Lamar

Melissa Lamar is a scholar working on Psychiatry and Mental health, Cognitive Neuroscience, Physiology, Radiology, Nuclear Medicine and Imaging and Cardiology and Cardiovascular Medicine, having authored 245 papers that have together received 4.9k indexed citations. Recurring topics across this work include Dementia and Cognitive Impairment Research (119 papers), Functional Brain Connectivity Studies (38 papers), Health disparities and outcomes (27 papers), Advanced Neuroimaging Techniques and Applications (26 papers), Neurobiology of Language and Bilingualism (23 papers), Alzheimer's disease research and treatments (21 papers), Neural and Behavioral Psychology Studies (15 papers) and Cerebrovascular and Carotid Artery Diseases (14 papers). The work is most often cited by research in Psychiatry and Mental health (2.2k citations), Cognitive Neuroscience (1.8k citations), Neuropsychology and Physiological Psychology (141 citations), Neurology (527 citations) and Biological Psychiatry (117 citations). Melissa Lamar has collaborated with scholars based in United States, United Kingdom and Israel. Frequent co-authors include David J. Libon, Susan M. Resnick, Olusola Ajilore, Catherine C. Price, Lisa L. Barnes, Tanvi Bhatt, Tania Giovannetti, Shaolin Yang, David A. Bennett and Julie A. Schneider. Their work appears in journals such as Alzheimer s & Dementia, Journal of Alzheimer s Disease, Neuropsychology, Journal of the International Neuropsychological Society and American Journal of Geriatric Psychiatry.

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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