1 / 31

SENSOR LEVEL ANALYSIS AND SOURCE LOCALISATION in M/EEG

SENSOR LEVEL ANALYSIS AND SOURCE LOCALISATION in M/EEG. METHODS FOR DUMMIES 2013-2014 Mrudul Bhatt & Wenjun Bai. M/EEG SO FAR. Source of Signal Dipoles Preprocessing and Experimental design. Source Reconstruction. Statistical Analysis. 3. Statistical Analysis.

cforrester
Download Presentation

SENSOR LEVEL ANALYSIS AND SOURCE LOCALISATION in M/EEG

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. SENSOR LEVEL ANALYSIS AND SOURCE LOCALISATION in M/EEG • METHODS FOR DUMMIES • 2013-2014 • Mrudul Bhatt & Wenjun Bai

  2. M/EEG SO FAR • Source of Signal • Dipoles • Preprocessing and Experimental design

  3. Source Reconstruction Statistical Analysis 3

  4. Statistical Analysis 1. Sensor level analysis in SPM 2. Scalp vs. Time Images 3. Time-frequency analysis

  5. Data is time varying modulation of EEG/MEG signal amplitude (or frequency specific power) at each electrode or sensor. • Interested in statistical significance of condition specific effects (observed at some peri-stimulus time or at a particular sensor) at sensors • Need to control FWER - the probability of making a false positive over the whole search space - AKA multiple comparisons problem • FWER scales with number of observations • Bonferroni too conservative due to assumption of independence between neighbouring samples • Can circumvent issue if space/time of interest is specified a priori • Average data over pre-specified sensors or time bins of interest - produces one summary statistic per subject per condition • If this is not possible can use topological inference

  6. Topological inference • Based on RFT • RFT provides a way of adjusting p-values for the fact that neighbouring sensors are not independent due to continuity in the original data • Provided data is smooth, RFT correction is more sensitive than a bonferroni correction • This is the method used in SPM

  7. Steps in SPM • Data transformed to image files (NifTI) • Procedurally identical to 1st level analysis in PET or 2nd level in fMRI after this • Analysis assumes one summary statistic image per subject per condition

  8. Creating Summary Statistics: Conversion to images • Data converted to an image by generating a scalp map for each time frame and stacking over peristimulus time • Scalp maps are generated from using the 2D sensor layout (specified in data set) and linear interpolation between sensors (64 pixels each spatial direction suggested) • 3D image files (space x space x time) • If time-window of interest is known in advance we can average over this are and create a 2D spatial image

  9. 9

  10. Time-Frequency Data • In principle can apply topological inference for n dimensions • In SPM 8 its limited to 3 dimensions • If data has time-frequency components it must be reduced from 4D (space x space x time x frequency) to 3/2D • Reduce data by averaging over frequency (3D) or spatial channels (2D time-frequency image) • When averaging over frequency, bandwidth must be specified and a new data set is produced and is exported in the same way images in the time domain are

  11. 11

  12. Smoothing • Smoothing: prior to 2nd level/group analysis -multi dimensional convolution with Gaussian kernel. • Important to accommodate spatial/temporal variability over subjects and ensure images conform to assumptions. Multi-dimensional convolution with Gaussian kernel

  13. EEG analysis steps Epoching D/A conversion Digital filtering Baseline correction Artifact reduction Single trial averaging Re-referencing Grand averaging Plotting, spline and CSD maps Quantification Statistical evaluation 13

  14. EEG/MEG source localization • The purpose of source localization • The hurdle prevent us to accurately localize the source : Inverse Problem A little recap: The advantage of EEG compare to fMRI: Superior Temporal resolution, with the cost of inferior spatial resolution 14

  15. Why it is so challenging? Smearing and distortion 15

  16. Inverse Problem Forward problem (well-posed) Inverse problem (ill-posed) Data Y Current density J 16

  17. Analogy to understand the inverse problem

  18. How We Deal with Inverse Problem • Setting up Assumptions(Constraints) • Two Basic Approaches • A. Discrete Source Analysis • B. Distributed Source Analysis Anatomical constraints Final Product: Reconstructed Source ill-posed inverse problem EEG/MEG Data Functional constraints 18

  19. ConstraintsAssumptions about the nature of the sources Three Types of Constraints: 1. Mathematic Constraints( e.g., minimum norm, maximum smoothness, optimal resolution, temporal independence) 2. Anatomical Constraints (e.g., Normally use the subject’s MRI scan, if not, it is possible to use standardized MRI brain atlas (e.g., MNI) can be be warped to optimally fit the subject's anatomy based on the subject's digitized head shape.) 3. Functional Constraints (e.g., 19

  20. Discrete vs. Distributed Source Model 20

  21. Algorithms associated with each analysis Take Home Message 1: No cure-it-all Approach 21

  22. SPM Pipeline for source localization One kind reminder: Source Localization(source reconstruction) is a computationally intense procedure. If you get “out of Memory” error message, try more powerful computer 22

  23. Step 1: Mesh

  24. MRI – individual head meshes (boundaries of different head compartments) based on the subject’s structural scan Template – SPM’s template head model based on the MNI brain template MRI

  25. Step 2: Coregister Co-register

  26. Step 3: Forward Model

  27. Step 4: Invert (The most crucial)

  28. WHAT DO WE GET

  29. Comparison between fMRI and MEG on Temporal Resolution Take Home Message 2: Source Localization is not perfect, being cautious in drawing any inferences related to location and strengthen of the source

  30. REFERENCES • Tolga Esat Ozkurt-High Temporal Resolution brain Imaging with EEG/MEG Lecture 10: Statistics for M/EEG data • James Kilner and Karl Friston. 2010.Topological Inference for EEG and MEG. Annals of Applied Statistics Vol 4:3 pp 1272-1290 • Vladimir Litvak et al. 2011. EEG and MEG data analysis in SPM 8. Computational Intelligence and Neuroscience Vol 2011 • MFD 2012/13 31

More Related