Statistical parametric mapping of LORETA using high density EEG and individual MRI: Application to mismatch negativities in Schizophrenia

Abstract

We describe a method for the statistical parametric mapping of low resolution electromagnetic tomography (LORETA) using high-density electroencephalography (EEG) and individual magnetic resonance images (MRI) to investigate the characteristics of the mismatch negativity (MMN) generators in schizophrenia. LORETA, using a realistic head model of the boundary element method derived from the individual anatomy, estimated the current density maps from the scalp topography of the 128-channel EEG. From the current density maps that covered the whole cortical gray matter (up to 20,000 points), volumetric current density images were reconstructed. Intensity normalization of the smoothed current density images was used to reduce the confounding effect of subject specific global activity. After transforming each image into a standard stereotaxic space, we carried out statistical parametric mapping of the normalized current density images. We applied this method to the source localization of MMN in schizophrenia. The MMN generators, produced by a deviant tone of 1,200 Hz (5% of 1,600 trials) under the standard tone of 1,000 Hz, 80 dB binaural stimuli with 300 msec of inter-stimulus interval, were measured in 14 right-handed schizophrenic subjects and 14 age-, gender-, and handedness-matched controls. We found that the schizophrenic group exhibited significant current density reductions of MMN in the left superior temporal gyrus and the left inferior parietal gyrus (P < 0. 0005). This study is the first voxel-by-voxel statistical mapping of current density using individual MRI and high-density EEG.

Distinct neural responses to chord violations: A multiple source analysis study

“…The MEG study of Maess et al (2001) and two fMRI studies by Koelsch et al (2005) and Tillmann et al (2003) additional sources in the right inferior frontal gyrus and inferior parietal lobule (Opitz et al, 2002;Park et al, 2002). In our discrete source analysis, the individual and grand average sources of the ERAN were located significantly more anterior than the MMN sources (see Fig.…”

“…The MMN is a fronto-central negative potential peaking at around 150-250 ms, occurring at early stages of auditory processing regardless of attention and hypothesized to be important for survival by detecting unexpected events (Näätänen, 1995;Näätänen et al, 2007). It has main sources in the superior temporal gyrus and sometimes with weak additional sources in the right inferior frontal gyrus (Opitz et al, 2002;Rinne et al, 2005;Tse and Penney, 2008) and the inferior parietal lobule (Park et al, 2002). The MMN is suggested to reflect the automatic formation of brief neural models of sequential regularities in the auditory environment that are typically formed in the course of the experimental session by repeating acoustic or abstract features of one or several sounds (Näätänen et al, 2007;Winkler et al, 1996).…”

Electrophysiological correlates of cognitive inhibition in college students with schizotypal traits

“…We used the statistical parametric mapping toolbox (SPM8) (http:// www.l.ion.ucl.ac.uk/spm/) implemented in Matlab version 7.1 (Mathworks, USA) for the current-density-image analysis. We applied realigning, coregistering, normalizing, and smoothing for the spatial preprocessing of the current-density image [44]. Finally, statistical parametric mapping was applied to the normalized current-density images using SPM8 and the two-sample t-test to statistically compare the current density elicited by the incongruent minus congruent conditions.…”

Cortical network dynamics during source memory retrieval: Current density imaging with individual MRI

“…Because we set the global mean as one, all voxel values subtracted by one were used for the onesample t-test. We provided a detailed description of these normalization processes and the statistical analysis of the current density analysis using LORETA in our previous article [Park et al, 2002].…”

“…Unlike the dSPM method, sLORETA is recommended to use pseudo-statistic values as estimates of brain activity, and to apply in statistical nonparametric mapping for the analysis of experimental designs. Park et al [2002] published a detailed step-wise description of SPM-type statistics for tomographic EEG source images of LORETA. Up to now, there is no standardized method in statistical processing of EEG source images as well as inverse solutions.…”