Alfonso Nieto-Castanon
Alfonso Nieto-Castanon | |
|---|---|
 Alfonso Nieto-Castanon in 2016 | |
| Born | September 1972 |
| Alma mater | Universidad de Valladolid, Boston University |
| Known for | functional neuroimaging, subject-specific ROIs, connectome, CONN |
| Scientific career | |
| Fields | Computational neuroscience |
| Institutions | Boston University, Massachusetts Institute of Technology |
| Doctoral advisor | Frank H. Guenther |
Alfonso Nieto-Castanon (born September 1972) is a Spanish computational neuroscientist and one of the world's leading developers of computational neuroimaging analysis tools and techniques. He is a senior research scientist, visiting researcher at the Boston University College of Health and Rehabilitation Sciences,[1] and research affiliate at MIT McGovern Institute for Brain Research.[2] His research focuses on the understanding and characterization of human brain dynamics underlying mental function[3].
Early life and Education
Nieto-Castanon was born in Spain in 1972. He was part of the first Spanish team to participate in the International Physics Olympiad in 1990. He went to college at the Universidad de Valladolid from 1991 to 1995 and earned a B.S./M.S. in Telecommunications Engineering. In 1998 he pursued graduate studies in Boston University Cognitive and Neural Systems Department and was awarded a research training fellowship from Fundación Séneca/Cedetel, and a graduate research fellowship from Boston University. He received a Ph.D. in Computational Neuroscience in 2004.[4]
Contributions to science
In some of his early work Nieto-Castanon helped develop novel methods for region of interest (ROI) analyses of fMRI data,[5] with a focus on multivariate techniques and the use of subject-specific ROIs, where regions of interest are defined differently for each person based on common anatomical or functional landmarks.[6][7] Subject-specific ROIs allowed researchers to probe the limits of the functional localization hypotheses common in neuroimaging, and better understand the spatial and functional specificity of different brain areas.
Nieto-Castanon also developed multiple influential mathematical and computational techniques for functional connectivity analyses,[8] with a special emphasis on the robust estimation of functional connectivity measures in the presence of subject-motion and physiological noise sources.[9] In 2011 he developed CONN to integrate and facilitate best practices in functional connectivity studies[10]. CONN included a combination of novel methods such as multivariate connectivity analyses and dynamic connectivity estimation, together with multiple well known techniques such as psycho-physiological interactions, graph analyses, or independent component analyses. His software has been since widely adopted in the field and it has become a reference in functional connectivity studies with over 900 citations in 2021 alone[11]
Nieto-Castanon has given numerous courses and lectures worldwide[12][13][14][15][16] and his work has been cited in over 8000 refereed journal articles to date.[17]
International competitions
Beyond his research, Nieto-Castanon is also recognized for his numerous prizes in international programming and data-analysis competitions. Using Matlab as his primary programming language, Nieto-Castanon won in 2009 and again in 2011 the Color Bridge and Vines MathWorks collaborative-programming competitions.[18][19] He also won in 2011 the Microsoft/Chalearn Kinect gesture challenge competition,[20] obtained second place at the Marinexplore and Cornell University Whale Detection challenge,[21] won in 2013 Genentech's Flu Forecasting competition,[22] and placed second in MathWorks 2014 Packing Santa's Sleigh algorithm competition.[23] In 2013 Nieto-Castanon was ranked as the third best data-scientist in Kaggle,[24] and he has been ranked as the best Matlab programmer in MathWorks Cody games continuously between 2013 to 2019.[25]
References
- ^ [1][2]bu.edu
- ^ mit.edu
- ^ alfnie.com
- ^ Nieto-Castanon, A. (2004). An investigation of articulatory-acoustic relationships in speech production. Boston University
- ^ Nieto-Castanon, A., Ghosh, S.S., Tourville, J.A., and Guenther, F.H. (2003). Region-of-interest based analysis of functional imaging data.NeuroImage, 19:1303-1316 PMID: 1294868
- ^ Nieto-Castañón, A., Fedorenko, E., (2012). Subject-specific functional localizers increase sensitivity and functional resolution of multi-subject analyses. Neuroimage , 63(3):1646-1669 PMCID:PMC3477490
- ^ Fedorenko, E., Hsieh, P.-J., Nieto-Castañon, A., Whitfield-Gabrieli, S., Kanwisher, N. (2010). New method for fMRI investigations of language: Defining ROIs functionally in individual subjects. Journal of Neurophysiology. 104:1177-1194. PMCID: PMC2934923
- ^ Nieto-Castanon, A. (2020). Handbook of functional connectivity Magnetic Resonance Imaging methods in CONN. Hilbert Press.
- ^ Chai, X.J., Nieto-Castañón, A., Öngür, D., Whitfield-Gabrieli, S. (2012). Anticorrelations in resting state networks without global signal regression. Neuroimage 59(2):1420-1428. PMCID: PMC3230748
- ^ Whitfield-Gabrieli, S., & Nieto-Castanon, A. (2012). Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain connectivity, 2(3), 125-141.
- ^ Google Scholar
- ^ Harvard/MGH
- ^ Neurometrika
- ^ Harvard/MGH
- ^ University of Cincinnati & CHMC
- ^ BCBL
- ^ Google Scholar
- ^ Matlab contest
- ^ Developers TI
- ^ Kaggle Microsoft Kinect
- ^ Kaggle Marinexplore
- ^ Kaggle Genentech
- ^ MathWorks blog
- ^ Kaggle alfnie
- ^ MathWorks blog
