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Regular version of the site

Neuroimaging Methods Group


Group Leader - Alexei Ossadtchi 

The main goal of our group is to develop methods for efficient handling of non-invasively collected magneto- and electroencephalographic data. We work in a close collaboration with other experimental groups of the Center, providing data analysis and experimental design support.

Our ongoing projects include the following:

I. Methods for source analysis and source space connectivity analysis of MEG, EEG data for cognitive and clinical uses
MEG and EEG-based brain-imaging technology allows us to visualize and study neuronal processes non-invasively, with very high temporal and reasonable spatial resolution. Currently, this method is used not only to localize neuronal activity but also to recover the dynamics of the entire neural network comprised of several distant cortical regions subserving the cognitive phenomenon at hands. The accuracy and reproducibility of such inferences crucially depend on the methods and computational approaches used for analysis of the MEG and EEG sensor data. Development of the novel methods and verification of the existing approaches allow us to ensure that the information present in the data is utilized with maximal efficiency. The use of the subject-specific probabilistic models obtained via data fusion form various modalities including fMRI, DTI, Optical tomography, TMS bear a promise of improving the non-invasively achieved resolution and further open "the non-invasive window" into the brain's function.

The clinical portion of this work is done in close collaboration with the Moscow MEG center, Burdenko Institute of Neurosurgery and the Institute for problems of Mechanical Engineering, RAS, and is supported by an RFFI grant.

Selected publications:
  1. Kozunov VV and Ossadtchi A (2015) GALA: group analysis leads to accuracy, a novel approach for solving the inverse problem in exploratory analysis of group MEG recordings. Front. Neuroscience 9 : 107. doi: 10.3389/fnins.2015.00107
  2. D. Altukhov and A. Ossadtchi, GO-PSIICOS: Globally Optimized Power and Shift Independent Imaging of Coherent Sources, Brain connectivity workshop (BCW 2015), Sand Diego, CA, June 2015
  3. A. Ossadtchi, Interaction Space RAP-MUSIC for estimation of transient networks from MEG data, Proceedings of the 19th international conference on biomagnetism, Halifax, Canada, 2014
  4. A. Ossadtchi, R. Greenblatt, Cross-term deprived covariance approach as an extension of DICS for detection of cross-frequency coupling, Proceedings of the 18th international conference on biomagnetism, Paris, France, 2012
  5. A. Ossadtchi, P. Pronko, S. Baillet, M. Pflieger, T. Stroganova, Mutual information spectrum for selection of event-related spatial components. Application to eloquent motor cortex mapping , Frontiers in Neuroinformatics, January, 2014, doi: 10.3389/fninf.2013.00053
  6. R.E. Greenblatt, M.E. Pflieger, A. Ossadtchi, Connectivity measures applied to human brain electrophysiological data, Journal of Neuroscience Methods 207 (2012) 1– 16

II. Real-time EEG:  neurofeedback and neurointerfaces
Neurofeedback is a promising tool for non-pharmacological therapy for depression, ADHD, epilepsy and other psychiatric diseases and syndromes. This technique is also used for peak performance training and in relaxation practices. Within the neurofeedback paradigm a person is put into a closed loop where his/her brain state is presented as a feedback signal via one of the sensory modalities and is used to consciously (or not so consciously) modify the dynamics of his/her brain rythms. In this project we will consider this closed-loop system from the standpoint of control theory (both continuous and discrete) and will focus on developing novel model-based techniques to improve the efficiency and spatial specificity of neurofeedback therapy. This research will pave the way towards a new generation of more efficient and more natural brain-computer interfaces

Selected publications:
  1. R. van Lutterveld, S. D. Houlihan, P. Pal, M. D. Sacchet, C. McFarlane-Blake, P. R. Patel, J. S. Sullivan, Alex Ossadtchi, S. Druker, C. Bauer, J. A. Brewer, Source-space EEG neurofeedback links subjective experience with brain activity during meditation, Neuroimage (under review) 
  2. Okorokova E., Lebedev M., Linderman M. and Ossadtchi A. (2015). A dynamical model improves reconstruction of handwriting from multichannel electromyographic recordings Front. Neurosci. 9 : 389 doi: 10.3389/fnins.2015.00389
  3. S. L. Shishkin, A. A. Fedorova, Y. O. Nuzhdin, I. P. Ganin, A. E. Ossadtchi, B. B. Velichkovskiy, A. Y. Kaplan, B. M. Velichkovsliy. Towards high-speed eye-brain-computer interfaces: Combining the “single stimulus” paradigm and saccades to stimulus location, Vestnik Moskovskogo Universiteta, Seriya 14. Psikhologiya. 2013. N4 
  4. I. Ovod, A. Ossadtchi, A. Pupyshev, A. Fradkov, Forming neurofeedback signal based on the adaptive model of the EEG observed human brain activity, Neurocomputers and applications, February, 2012 (in Russian).

III. Brain-Computer Interface 

We have developed and tested the EEG-based Brain Computer Interface, which can easily classify up to 6 real and imaginary movements: the arms, the legs, the tongue and the resting state. The Interface is based on a well-established principle of the body parts representations in the servomotor cortex, which can help to infer patterns of electrical activity, differentiating various motor states.


Group members 
Alex Ossadtchi, PhD Group leader 
Dmitriy Altukhov (from 11.2015)Ph.D. student
Nikolay Dagaev research trainee
Nikolay Smetaninjunior researcher
Ksenia Volkovaresearch trainee
Eugene KalenkovitchM.Sc. student (2nd year)
Georgiy SapozhnikovM.Sc. student (2nd year)
Egor SelivanovB.Sc. student