K. A. Smitha

887 total citations · 1 hit paper
8 papers, 609 citations indexed

About

K. A. Smitha is a scholar working on Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging and Genetics. According to data from OpenAlex, K. A. Smitha has authored 8 papers receiving a total of 609 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Cognitive Neuroscience, 6 papers in Radiology, Nuclear Medicine and Imaging and 1 paper in Genetics. Recurrent topics in K. A. Smitha's work include Functional Brain Connectivity Studies (5 papers), Advanced MRI Techniques and Applications (4 papers) and Neural dynamics and brain function (2 papers). K. A. Smitha is often cited by papers focused on Functional Brain Connectivity Studies (5 papers), Advanced MRI Techniques and Applications (4 papers) and Neural dynamics and brain function (2 papers). K. A. Smitha collaborates with scholars based in India and United States. K. A. Smitha's co-authors include Chandrasekharan Kesavadas, P Rajesh, Bejoy Thomas, TR Kapilamoorthy, PN Sylaja, Amod Gupta, Ramapurath S. Jayasree, P.S. Sarma, Suresh E. Joel and Ashalatha Radhakrishnan and has published in prestigious journals such as Physics in Medicine and Biology, American Journal of Neuroradiology and British Journal of Radiology.

In The Last Decade

K. A. Smitha

8 papers receiving 604 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.

Resting state fMRI: A review on methods in resting state connectivity analysis and resting state networks

2017 451 citations K. A. Smitha, P Rajesh et al. The Neuroradiology Journal profile →

Peers

K. A. Smitha

CN

RNMI

PMH

ECP

NEURO

P Rajesh India

Lixue Xu China

Baoquan Min China

Ali H. Palejwala United States

Ann S. Choe United States

Sophia Stoecklein Germany

João Jorge Switzerland

Letizia Squarcina Italy

Nianming Zuo China

P Rajesh India

K. A. Smitha

446 ×1.0

CN

229 ×0.9

RNMI

111 ×1.2

PMH

72 ×1.1

ECP

40 ×0.7

NEURO

Citations per year, relative to K. A. Smitha K. A. Smitha (= 1×) peers P Rajesh

Countries citing papers authored by K. A. Smitha

Since Specialization

Citations

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

Fields of papers citing papers by K. A. Smitha

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of K. A. Smitha

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

All Works

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8 of 8 papers shown

1.

Smitha, K. A., et al.. (2020). Identifying Resting-State Functional Connectivity Changes in the Motor Cortex Using fNIRS During Recovery from Stroke. Brain Topography. 33(6). 710–719. 58 indexed citations

2.

Smitha, K. A., P Rajesh, Bejoy Thomas, et al.. (2019). Resting fMRI as an alternative for task-based fMRI for language lateralization in temporal lobe epilepsy patients: a study using independent component analysis. Neuroradiology. 61(7). 803–810. 8 indexed citations

3.

Smitha, K. A., et al.. (2018). Multiband fMRI as a plausible, time-saving technique for resting-state data acquisition: Study on functional connectivity mapping using graph theoretical measures. Magnetic Resonance Imaging. 53. 1–6. 9 indexed citations

4.

Smitha, K. A., et al.. (2017). Resting state fMRI: A review on methods in resting state connectivity analysis and resting state networks. The Neuroradiology Journal. 30(4). 305–317. 451 indexed citations breakdown →

5.

Smitha, K. A., et al.. (2017). Resting-State Seed-Based Analysis: An Alternative to Task-Based Language fMRI and Its Laterality Index. American Journal of Neuroradiology. 38(6). 1187–1192. 23 indexed citations

6.

Smitha, K. A., et al.. (2017). Functional near-infrared spectroscopy is in moderate accordance with functional MRI in determining lateralisation of frontal language areas. The Neuroradiology Journal. 31(2). 133–141. 15 indexed citations

7.

Smitha, K. A., Amod Gupta, & Ramapurath S. Jayasree. (2015). Relative percentage signal intensity recovery of perfusion metrics—an efficient tool for differentiating grades of glioma. British Journal of Radiology. 88(1052). 20140784–20140784. 15 indexed citations

8.

Smitha, K. A., Amod Gupta, & Ramapurath S. Jayasree. (2015). Fractal analysis: fractal dimension and lacunarity from MR images for differentiating the grades of glioma. Physics in Medicine and Biology. 60(17). 6937–6947. 30 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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