C. Jiménez-Mesa

878 total citations · 1 hit paper
14 papers, 449 citations indexed

About

C. Jiménez-Mesa is a scholar working on Cognitive Neuroscience, Signal Processing and Artificial Intelligence. According to data from OpenAlex, C. Jiménez-Mesa has authored 14 papers receiving a total of 449 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Cognitive Neuroscience, 4 papers in Signal Processing and 4 papers in Artificial Intelligence. Recurrent topics in C. Jiménez-Mesa's work include Functional Brain Connectivity Studies (4 papers), Blind Source Separation Techniques (3 papers) and Neural dynamics and brain function (2 papers). C. Jiménez-Mesa is often cited by papers focused on Functional Brain Connectivity Studies (4 papers), Blind Source Separation Techniques (3 papers) and Neural dynamics and brain function (2 papers). C. Jiménez-Mesa collaborates with scholars based in Spain, United Kingdom and Germany. C. Jiménez-Mesa's co-authors include J. M. Górriz, Javier Ramı́rez, Xiang Yu, Hua Hu, Min Li, Qinghua Zhou, Shuihua Wang‎, Zhengchao Dong, Francisco J. Martínez and Yudong Zhang and has published in prestigious journals such as IEEE Access, Human Brain Mapping and Pharmacological Research.

In The Last Decade

C. Jiménez-Mesa

13 papers receiving 439 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.

Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation

2020 313 citations Yudong Zhang, Zhengchao Dong et al. Information Fusion profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
C. Jiménez-Mesa Spain	9	138	120	101	93	71	14	449
Xujing Yao United Kingdom	8	201 1.5×	165 1.4×	179 1.8×	82 0.9×	95 1.3×	11	617
Yiding Lv China	9	119 0.9×	77 0.6×	101 1.0×	81 0.9×	98 1.4×	13	399
Sang‐Woong Lee South Korea	10	232 1.7×	116 1.0×	212 2.1×	69 0.7×	91 1.3×	19	519
A. Brahim Spain	9	91 0.7×	105 0.9×	104 1.0×	93 1.0×	123 1.7×	16	498
Seda Arslan Tuncer Türkiye	13	148 1.1×	113 0.9×	165 1.6×	118 1.3×	31 0.4×	55	542
Sidra Abbas Pakistan	14	303 2.2×	80 0.7×	99 1.0×	43 0.5×	81 1.1×	29	714
Zhuqing Jiao China	12	84 0.6×	96 0.8×	120 1.2×	164 1.8×	96 1.4×	65	623
Delaram Sadeghi Australia	9	173 1.3×	196 1.6×	72 0.7×	352 3.8×	68 1.0×	11	710
Cheng‐Kai Lu Malaysia	13	93 0.7×	223 1.9×	128 1.3×	83 0.9×	26 0.4×	64	502
Loc Tran United States	10	147 1.1×	139 1.2×	161 1.6×	33 0.4×	129 1.8×	37	498

Countries citing papers authored by C. Jiménez-Mesa

Since Specialization

Citations

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

Fields of papers citing papers by C. Jiménez-Mesa

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by C. Jiménez-Mesa. 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 C. Jiménez-Mesa. The network helps show where C. Jiménez-Mesa may publish in the future.

Co-authorship network of co-authors of C. Jiménez-Mesa

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

All Works

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14 of 14 papers shown

Górriz, J. M., Javier Ramı́rez, F. Segovia, et al.. (2025). Statistical agnostic regression: A machine learning method to validate regression models. Journal of Advanced Research. 80. 503–533. 1 indexed citations

Wagemann, Olivia, Francisco J. Martínez-Murcia, Elisabeth Wlasich, et al.. (2025). Exploratory analysis of the proteomic profile in plasma in adults with Down syndrome in the context of Alzheimer's disease. Alzheimer s & Dementia. 21(3). e70040–e70040. 5 indexed citations

Martínez-Murcia, Francisco J., Juan E. Arco, C. Jiménez-Mesa, et al.. (2024). Bridging Imaging and Clinical Scores in Parkinson’s Progression via Multimodal Self-Supervised Deep Learning. International Journal of Neural Systems. 34(8). 2450043–2450043. 7 indexed citations

Jiménez-Mesa, C., Javier Ramı́rez, Zhenghui Yi, et al.. (2024). Machine learning in small sample neuroimaging studies: Novel measures for schizophrenia analysis. Human Brain Mapping. 45(5). e26555–e26555. 3 indexed citations

Castillo-Barnés, Diego, Francisco J. Martínez-Murcia, C. Jiménez-Mesa, et al.. (2023). Nonlinear Weighting Ensemble Learning Model to Diagnose Parkinson’s Disease Using Multimodal Data. International Journal of Neural Systems. 33(8). 2350041–2350041. 11 indexed citations

Jiménez-Mesa, C., Juan E. Arco, Francisco J. Martínez-Murcia, et al.. (2023). Applications of machine learning and deep learning in SPECT and PET imaging: General overview, challenges and future prospects. Pharmacological Research. 197. 106984–106984. 17 indexed citations

Jiménez-Mesa, C., Juan E. Arco, Meritxell Valentí, et al.. (2023). Using Explainable Artificial Intelligence in the Clock Drawing Test to Reveal the Cognitive Impairment Pattern. International Journal of Neural Systems. 33(4). 2350015–2350015. 16 indexed citations

Jiménez-Mesa, C., Javier Ramı́rez, Carlos González‐García, et al.. (2022). Standardization of Agnostic Learning Techniques in Neuroimaging: a Case Study in EEG. 1–4.

Jiménez-Mesa, C., Javier Ramı́rez, John Suckling, et al.. (2022). A non-parametric statistical inference framework for Deep Learning in current neuroimaging. Information Fusion. 91. 598–611. 16 indexed citations

10.

Górriz, J. M., C. Jiménez-Mesa, F. Segovia, et al.. (2021). Statistical Agnostic Mapping: A framework in neuroimaging based on concentration inequalities. arXiv (Cornell University). 13 indexed citations

11.

Górriz, J. M., C. Jiménez-Mesa, F. Segovia, Javier Ramı́rez, & John Suckling. (2021). A Connection Between Pattern Classification by Machine Learning and Statistical Inference With the General Linear Model. IEEE Journal of Biomedical and Health Informatics. 26(11). 5332–5343. 11 indexed citations

12.

Zhang, Yudong, Zhengchao Dong, Shuihua Wang‎, et al.. (2020). Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation. Information Fusion. 64. 149–187. 313 indexed citations breakdown →

13.

Jiménez-Mesa, C., I. Álvarez, Alberto Martín‐Martín, et al.. (2020). Optimized One vs One Approach in Multiclass Classification for Early Alzheimer’s Disease and Mild Cognitive Impairment Diagnosis. IEEE Access. 8. 96981–96993. 24 indexed citations

14.

Ramı́rez, Javier, Francisco J. Martínez-Murcia, F. Segovia, et al.. (2019). Granger causality-based information fusion applied to electrical measurements from power transformers. Information Fusion. 57. 59–70. 12 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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