Profile

Okito Yamashita,

Department head of CBI in ATR and Team leader of RIKEN-AIP

Okito Yamashita

  • Affiliation
 ATR , Neural Information Analysis Laboratories, Department of Computational Brain Imaging

RIKEN-AIP, Computational Brain Dynamics team (link)
  • Research Interest
 Applications of Bayesian inference to noninvasive brain imaging data such as fMRI, MEG, EEG and NIRS.

Main applications are
– Diffusion optical tomography (2010- )
– MEG/EEG Source Localization Problem  (2001-)
– Brain Computer Interface (2007-)
– fMRI decoding.  (2005-)

Main tools  in statistics are
– Time Series Analysis (AR model., Hidden Markov model, State Space model)
– Sparse Bayesian Learning (Automatic Relevance Determination Priors, Variational Bayesian algorithm).
  • Contact
 ADRESS : 2-2-2, Hikaridai, Seikacho, Sorakugun, Kyoto 619-0288, Japan
TELL  : +81-774-95-1073
FAX    : +81-774-95-1259
E-mail : oyamashi(at)atr.jp
  • Employment History
2016 November  – RIKEN-AIP, Computational Brain Dynamics team (concurrent post)

  • Big data analysis of brain imaging data,  method development for exploring brain imaging-based psychiatric bio-marker
2013 April – ATR, Neural Information Analysis Laboratories, Deparment head

  • Brain network dynamics estimation with the multimodal integration approach
2010 April – 2013 March ATR, Neural Information Analysis Laboratories, Senior Researcher

  • Cross-freuqency coupling of ECoG data (data analysis, method proposal)
  • Diffusion optical tomography (experimental validation, data analysis, toolbox)
2004 Semptember – 2009 March ATR, Computational Neuroscience Laboratories, Researcher

  • Source localization method with MEG/EEG-fMRI mutlimodal integration (method extension, toolbox)
  • EEG/NIRS combined brain machine interface (data analysis)
  • Sparse classification for fMRI decoding and brain machine interface (method proposal, toolbox)
2004 April-August Institute of statistical mathematics,  Part-time researcher

  • dynamic source localization extended to non-stationary case (method proposal)
  • Education
Ph.D. in Statitics, 2004 The Graduate University for Advanced Studies, Department of Statistical Science

  • Application of the statistical timeseries analysis to neuroimaging data
  • Effective connectivity estimation of fMRI   (Granger causality analysis)
  • Dynamic source localization of EEG         (High dimensional Kalman filter)

Title of Ph.D. thesis : Dynamical Inverse Problem and Causality Analysis of fMRI Data [PDF file]
M.A. in Engineering, 2001 The University of Tokyo, Faculty of Engineering, Department of Mathematical Engineering and Information Physics

  • Parameter estimation problem of  non-linear AR model
B.A. in Engineering, 1999 The University of Tokyo, Faculty of Engineering, Department of Mathematical Engineering and Information Physics

  • Observation noise reduction of chaotic dynamical model

This post is also available in: 日本語 (Japanese)