A new toolbox for combining magnetoencephalographic source analysis and cytoarchitectonic probabilistic data for anatomical classification of dynamic brain activity

By our bootstraps: Comparing methods for measuring auditory 40 Hz steady-state neural activity

Although the 40 Hz auditory steady-state response (ASSR) is of clinical interest, the construct validity of EEG and MEG measures of 40 Hz ASSR cortical microcircuits is unclear. This study evaluated several MEG and EEG metrics by leveraging findings of (a) an association between the 40 Hz ASSR and age in the left but not right hemisphere, and (b) right- > left-hemisphere differences in the strength of the 40 Hz ASSR. The contention is that, if an analysis method does not demonstrate a left 40 Hz ASSR and age relationship or hemisphere differences, then the obtained measures likely have low validity. Fifty-three adults were presented 500 Hz stimuli modulated at 40 Hz while MEG and EEG were collected. ASSR activity was examined as a function of phase similarity (intertrial coherence) and percent change from baseline (total power). A variety of head models (spherical and realistic) and a variety of dipole source modeling strategies (dipole source localization and dipoles fixed to Heschl's gyri) were compared. Several sensor analysis strategies were also tested. EEG sensor measures failed to detect left 40 Hz ASSR and age associations or hemisphere differences. A comparison of MEG and EEG head-source models showed similarity in the 40 Hz ASSR measures and in estimating age and left 40 Hz ASSR associations, indicating good construct validity across models. Given a goal of measuring the 40 Hz ASSR cortical microcircuits, a source-modeling approach was shown to be superior in measuring this construct versus methods that rely on EEG sensor measures.

Multimodal Imaging Brain Connectivity Analysis (MIBCA) toolbox

Aim. In recent years, connectivity studies using neuroimaging data have increased the understanding of the organization of large-scale structural and functional brain networks. However, data analysis is time consuming as rigorous procedures must be assured, from structuring data and pre-processing to modality specific data procedures. Until now, no single toolbox was able to perform such investigations on truly multimodal image data from beginning to end, including the combination of different connectivity analyses. Thus, we have developed the Multimodal Imaging Brain Connectivity Analysis (MIBCA) toolbox with the goal of diminishing time waste in data processing and to allow an innovative and comprehensive approach to brain connectivity.

Materials and Methods. The MIBCA toolbox is a fully automated all-in-one connectivity toolbox that offers pre-processing, connectivity and graph theoretical analyses of multimodal image data such as diffusion-weighted imaging, functional magnetic resonance imaging (fMRI) and positron emission tomography (PET). It was developed in MATLAB environment and pipelines well-known neuroimaging softwares such as Freesurfer, SPM, FSL, and Diffusion Toolkit. It further implements routines for the construction of structural, functional and effective or combined connectivity matrices, as well as, routines for the extraction and calculation of imaging and graph-theory metrics, the latter using also functions from the Brain Connectivity Toolbox. Finally, the toolbox performs group statistical analysis and enables data visualization in the form of matrices, 3D brain graphs and connectograms. In this paper the MIBCA toolbox is presented by illustrating its capabilities using multimodal image data from a group of 35 healthy subjects (19–73 years old) with volumetric T1-weighted, diffusion tensor imaging, and resting state fMRI data, and 10 subjets with 18F-Altanserin PET data also.

Results. It was observed both a high inter-hemispheric symmetry and an intra-hemispheric modularity associated with structural data, whilst functional data presented lower inter-hemispheric symmetry and a high inter-hemispheric modularity. Furthermore, when testing for differences between two subgroups (<40 and >40 years old adults) we observed a significant reduction in the volume and thickness, and an increase in the mean diffusivity of most of the subcortical/cortical regions.

Conclusion. While bridging the gap between the high numbers of packages and tools widely available for the neuroimaging community in one toolbox, MIBCA also offers different possibilities for combining, analysing and visualising data in novel ways, enabling a better understanding of the human brain.

BA3b and BA1 activate in a serial fashion after median nerve stimulation: direct evidence from combining source analysis of evoked fields and cytoarchitectonic probabilistic maps

This study combines source analysis imaging data for early somatosensory processing and the probabilistic cytoarchitectonic maps (PCMs). Human somatosensory evoked fields (SEFs) were recorded by stimulating left and right median nerves. Filtering the recorded responses in different frequency ranges identified the most responsive frequency band. The short-latency averaged SEFs were analyzed using a single equivalent current dipole (ECD) model and magnetic field tomography (MFT). The identified foci of activity were superimposed with PCMs. Two major components of opposite polarity were prominent around 21 and 31 ms. A weak component around 25 ms was also identified. For the most responsive frequency band (50-150 Hz) ECD and MFT revealed one focal source at the contralateral Brodmann area 3b (BA3b) at the peak of N20. The component ~25 ms was localised in Brodmann area 1 (BA1) in 50-150 Hz. By using ECD, focal generators around 28-30 ms located initially in BA3b and 2 ms later to BA1. MFT also revealed two focal sources - one in BA3b and one in BA1 for these latencies. Our results provide direct evidence that the earliest cortical response after median nerve stimulation is generated within the contralateral BA3b. BA1 activation few milliseconds later indicates a serial mode of somatosensory processing within cytoarchitectonic SI subdivisions. Analysis of non-invasive magnetoencephalography (MEG) data and the use of PCMs allow unambiguous and quantitative (probabilistic) interpretation of cytoarchitectonic identity of activated areas following median nerve stimulation, even with the simple ECD model, but only when the model fits the data extremely well.

[Magnetoencephalography in psychiatry]

Neuropsychiatric disorders usually come with only sublime structural changes. Functional imaging can point at specific disturbances in information processing in neural networks. Besides imaging of receptor and metabolic functions with PET and fMRI, electromagnetic methods such as electroencephalography (EEG) and magnetoencephalography (MEG) offer the possibility for imaging of dynamic dysfunctions. As compared to EEG, MEG has a shorter history and is less common despite offering considerable advantages in temporospatial resolution and sensitivity to detect impaired signal processing and network functioning which renders it particularly interesting for psychiatric applications. Disturbed processing in the auditory and visual domain emerging in schizophrenic, affective and anxiety disorders can be detected with high sensitivity. Moreover, the neuromagnetic baseline activity allows conclusions to be drawn regarding neural network functions. Due to its high sensitivity to single deficits in information processing and to pharmacological effects, MEG will achieve clinical significance in specific areas.

An automatic procedure for the analysis of electric and magnetic mismatch negativity based on anatomical brain mapping

Data processing techniques in electroencephalography (EEG) and magnetoencephalography (MEG) need user interactions. However, particularly in clinical applications, fast and objective data processing is important. Here we present an observer-independent method for EEG and MEG analysis of mismatch negativity (MMN) that allows reliable estimation of source activity based on objective anatomical references. The procedure integrates several steps including artifact rejection, source estimation and statistical analysis. It enables the evaluation of source activity in a fully automatic and unsupervised manner. To test its feasibility we obtained EEG and MEG responses in an auditory oddball paradigm in 12 healthy volunteers. The automatized method of EEG and MEG data analysis estimated source activity. The automatically detected MMN was closely comparable with the results obtained by a user-controlled method based on the dipole fitting. The presented workflow can be performed easily, rapidly, and reliably. This development may open new fields in research and clinical applications of source-based EEG and MEG.