Cam‐CAN

2.0k total citations · 1 hit paper
15 papers, 1.1k citations indexed

About

Cam‐CAN is a scholar working on Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging and Psychiatry and Mental health. According to data from OpenAlex, Cam‐CAN has authored 15 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Cognitive Neuroscience, 5 papers in Radiology, Nuclear Medicine and Imaging and 3 papers in Psychiatry and Mental health. Recurrent topics in Cam‐CAN's work include Functional Brain Connectivity Studies (12 papers), Neural dynamics and brain function (7 papers) and Advanced Neuroimaging Techniques and Applications (4 papers). Cam‐CAN is often cited by papers focused on Functional Brain Connectivity Studies (12 papers), Neural dynamics and brain function (7 papers) and Advanced Neuroimaging Techniques and Applications (4 papers). Cam‐CAN collaborates with scholars based in United Kingdom, Netherlands and Canada. Cam‐CAN's co-authors include Richard N. Henson, Linda Geerligs, Lorraine K. Tyler, Jason R. Taylor, Meredith A. Shafto, Nitin Williams, Kamen A. Tsvetanov, Rhodri Cusack, Mikail Rubinov and Marie Dixon and has published in prestigious journals such as Journal of Neuroscience, NeuroImage and Neuropsychologia.

In The Last Decade

Cam‐CAN

15 papers receiving 1.1k 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.

The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: Structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample

2015 396 citations Jason R. Taylor, Nitin Williams et al. NeuroImage profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Cam‐CAN United Kingdom	11	940	362	166	116	56	15	1.1k
Caio Seguin Australia	15	965 1.0×	520 1.4×	176 1.1×	98 0.8×	28 0.5×	33	1.3k
Nanbo Sun United States	9	798 0.8×	357 1.0×	236 1.4×	139 1.2×	28 0.5×	11	965
Jeffrey J. Berg United States	9	1.3k 1.4×	499 1.4×	237 1.4×	80 0.7×	37 0.7×	10	1.4k
Mark Fiecas United States	19	570 0.6×	246 0.7×	190 1.1×	141 1.2×	37 0.7×	76	1.1k
Andrew M. Michael United States	20	1.3k 1.4×	549 1.5×	234 1.4×	222 1.9×	48 0.9×	43	1.6k
Barnaly Rashid United States	13	941 1.0×	409 1.1×	262 1.6×	153 1.3×	51 0.9×	26	1.1k
Ameera X. Patel United Kingdom	13	923 1.0×	375 1.0×	203 1.2×	98 0.8×	61 1.1×	20	1.2k
Ashley N. Nielsen United States	13	812 0.9×	248 0.7×	204 1.2×	105 0.9×	35 0.6×	24	979
Graham L. Baum United States	13	1.3k 1.4×	686 1.9×	289 1.7×	159 1.4×	54 1.0×	21	1.6k
Shile Qi United States	19	795 0.8×	282 0.8×	249 1.5×	242 2.1×	46 0.8×	58	1.2k

Countries citing papers authored by Cam‐CAN

Since Specialization

Citations

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

Fields of papers citing papers by Cam‐CAN

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Cam‐CAN. 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 Cam‐CAN. The network helps show where Cam‐CAN may publish in the future.

Co-authorship network of co-authors of Cam‐CAN

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

All Works

Sort: Min cites: Since: Top N: Style:

15 of 15 papers shown

Geerligs, Linda, et al.. (2023). Lifespan differences in visual short-term memory load-modulated functional connectivity. NeuroImage. 270. 119982–119982. 5 indexed citations

Knights, Ethan, et al.. (2023). Does functional system segregation mediate the effects of lifestyle on cognition in older adults?. Neurobiology of Aging. 134. 126–134. 4 indexed citations

Tyler, Lorraine K., et al.. (2023). Cognition’s dependence on functional network integrity with age is conditional on structural network integrity. Neurobiology of Aging. 129. 195–208. 7 indexed citations

Tyler, Lorraine K., et al.. (2022). Cerebral blood flow predicts multiple demand network activity and fluid intelligence across the adult lifespan. Neurobiology of Aging. 121. 1–14. 10 indexed citations

Henson, Richard N., et al.. (2020). Characterising group-level brain connectivity: A framework using Bayesian exponential random graph models. NeuroImage. 225. 117480–117480. 16 indexed citations

Shafto, Meredith A., Richard N. Henson, Fiona E. Matthews, et al.. (2019). Cognitive Diversity in a Healthy Aging Cohort: Cross-Domain Cognition in the Cam-CAN Project. Journal of Aging and Health. 32(9). 1029–1041. 14 indexed citations

White, Simon R., et al.. (2017). Assessing dynamic functional connectivity in heterogeneous samples. NeuroImage. 157. 635–647. 17 indexed citations

Geerligs, Linda, Kamen A. Tsvetanov, Cam‐CAN, & Richard N. Henson. (2017). Challenges in measuring individual differences in functional connectivity using fMRI: The case of healthy aging. Human Brain Mapping. 38(8). 4125–4156. 120 indexed citations

Phillips, Holly N., Alejandro O. Blenkmann, Laura E. Hughes, et al.. (2016). Convergent evidence for hierarchical prediction networks from human electrocorticography and magnetoencephalography. Cortex. 82. 192–205. 52 indexed citations

10.

Kievit, Rogier, Simon W. Davis, John D. Griffiths, et al.. (2016). A watershed model of individual differences in fluid intelligence. Neuropsychologia. 91. 186–198. 86 indexed citations

11.

Geerligs, Linda, Cam‐CAN, & Richard N. Henson. (2016). Functional connectivity and structural covariance between regions of interest can be measured more accurately using multivariate distance correlation. NeuroImage. 135. 16–31. 84 indexed citations

12.

Geerligs, Linda, Mikail Rubinov, Cam‐CAN, & Richard N. Henson. (2015). State and Trait Components of Functional Connectivity: Individual Differences Vary with Mental State. Journal of Neuroscience. 35(41). 13949–13961. 181 indexed citations

13.

Taylor, Jason R., Nitin Williams, Rhodri Cusack, et al.. (2015). The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: Structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample. NeuroImage. 144(Pt B). 262–269. 396 indexed citations breakdown →

14.

Tsvetanov, Kamen A., Richard N. Henson, Lorraine K. Tyler, et al.. (2015). The effect of ageing on fMRI: Correction for the confounding effects of vascular reactivity evaluated by joint fMRI and MEG in 335 adults. Human Brain Mapping. 36(6). 2248–2269. 134 indexed citations

15.

Green, Emma, Meredith A. Shafto, Fiona E. Matthews, Cam‐CAN, & Simon R. White. (2015). Adult Lifespan Cognitive Variability in the Cross-Sectional Cam-CAN Cohort. International Journal of Environmental Research and Public Health. 12(12). 15516–15530. 3 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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