English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Using parcellation information in linear EEG/MEG source reconstruction

Fuchs, M., Knösche, T. R., & Maess, B. (2017). Using parcellation information in linear EEG/MEG source reconstruction. Poster presented at International Conference on Basic and Clinical Multimodal Imaging (BaCI 2017), Bern, Switzerland.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/21.11116/0000-0004-C1D4-2 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-C1D5-1
Genre: Poster

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Fuchs, Mirco1, Author              
Knösche, Thomas R.1, Author              
Maess, Burkhard1, Author              
Affiliations:
1Methods and Development Unit - MEG and Cortical Networks, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_2205650              

Content

show
hide
Free keywords: -
 Abstract: The bioelectromagnetic inverse problem cannot be solved based on EEG/MEG data alone and requires additional assumptions. In linear reconstruction methods, spatial smoothness is often used as an additional constraint. This is equivalent to the prior assumption of a particular source covariance structure. Recent publications (Knösche et al., NeuroImage 2013) have suggested altering this spatial correlation structure such that it reflects available knowledge on the functio-anatomical organization of the brain. In particular, it is possible to derive borders between different brain areas from various types of brain images. This allows assuming that sources located within the same area exhibit similar activity and sources in different areas are mutually uncorrelated. We present a technique based on the well-known LORETA method (Pascual-Marqui et al., Int. J. Psychophysiol. 1994), which is capable of incorporating such function-anatomical priors. We show that our method embodies the intended prior knowledge in the prior source covariance in an unbiased way. We present Monte-Carlo simulations, which provide a systematic evaluation of how the prior knowledge influences the estimate of different linear inverse procedures. The study answers questions like “What happens if the course of boundaries is uncertain?”, “What if our knowledge on functional areas is limited to certain cortical regions?” and “Can prior knowledge improve source localization?”. Besides presenting answers to these questions we demonstrate our method to localize auditory N100 activity from experimental EEG/MEG data. The results clearly suggest that spatially informed linear inverse methods provide very plausible reconstruction results.

Details

show
hide
Language(s):
 Dates: 2017-08-31
 Publication Status: Not specified
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: -
 Degree: -

Event

show
hide
Title: International Conference on Basic and Clinical Multimodal Imaging (BaCI 2017)
Place of Event: Bern, Switzerland
Start-/End Date: 2017-08-29 - 2017-09-02

Legal Case

show

Project information

show

Source

show