BME PhD Prospectus Defense - Kunjan Rana

10:15 am on Friday, June 14, 2013
44 Cummington, Room 705
Title: "An integrated toolbox for source-space reconstruction and network analysis within the magnetoencephalography imaging modality: applications to visual search and neurofeedback rehabilitation"

Advisor/Chair: Dr. Lucia M. Vaina, M.D. Ph.D.
2nd Reader: Dr. Matti S. Hämäläinen, Ph.D.
3rd Reader: Dr. Eric D. Kolaczyk, Ph.D.
4th Reader: Dr. Jerome Mertz, Ph.D.
5th Reader: Dr. Prakash Ishwar, Ph.D.

This thesis is concerned with the development of a toolbox for brain connectivity analysis for the magnetoencephalographic imaging modality (MEG). EEG (Electroencephalography) and MEG measure the electrical activity of the cortical surface by measuring the magnetic flux density along an array of sensors above the scalp[1, 2]. Importantly, MEG directly captures the firing of pyramidal cells, organized in axon bundles firing into the cortex. This direct measure of electrical activity allows for the capture of activation at the time scale of cognitive processing[3]. An important component of neuroimaging analyses is the defining of functional regions of interest (ROIs), those areas defined by cortical matter that are coactive when performing a certain function. MEG is particularly suited to functional segregation of these ROIs since it has 1-2 cm accuracy in localization across the cortical surface. In the work we propose to carry out in the PhD thesis we are particularly interested in using this dynamic activation profile for the functional segregation of the brain into functional regions of interest (ROIs) and measure the integration of these regions through functional connectivity analysis [4]. This thesis will fill a critical gap in the MEG analysis methods, by providing a set of computational/analytical methods organized into a toolbox that will bridge the complexity of spatial localization to network characterization in the magnetoencephalographic imaging modality. The overall goal of the proposed research is to develop a rich toolbox around a pipeline for network analysis from the mapping of the magnetic flux sensor signals onto segregated regions of interest to analysis of connections between these source-separated areas. The toolbox will have two major components; (1) an offline stream for computing the inverse solutions and obtaining the ROI set, which will be used for spectral and connectivity analysis, and (2) an online stream for analysis and feedback of realtime MEG data (neurofeedback).
We will validate the usage of this toolbox in psychophysical tasks. The offline component of the toolbox and its pipeline will be used to characterize cortical connectivity in a task of search for a moving object in an optic flow field in two conditions: visual information only, and visual information and a moving auditory cue colocalized and congruent with the moving object (the target) [5]. The online component of the toolbox, will be applied to will investigating activation and connectivity to MT+ (human Middle Temporal area) through a realtime MEG (rt-MEG) paradigm involving feedback of neuronal signals (neurofeedback) to patients with V1+ lesions.