H.-P. Müller

Preservation of diffusion tensor properties during spatial normalization by use of tensor imaging and fibre tracking on a normal brain database

White matter connectivity in the human brain can be mapped by diffusion tensor magnetic resonance imaging (DTI). After reconstruction, the diffusion tensors, the diffusion amplitude and the diffusion direction can be displayed on a morphological background. Consequently, diffusion tensor fibre tracking can be applied as a non-invasive in vivo technique for the delineation and quantification of specific white matter pathways. The aim of this study was to show that normalization to the Montreal Neurological Institute (MNI) stereotaxic standard space preserves specific diffusion features. Therefore, techniques for tensor imaging and fibre tracking were applied to the normalized brains as well as to the group averaged brain data. A normalization step of individual data was included by registration to a scanner- and sequence-specific DTI template data set which was created from a normal database transformed to MNI space. The algorithms were tested and validated for a group of 13 healthy controls.

New methods in fMRI analysis

Hierarchical cluster analysis improves signal-to-noise ratio compared to standard techniques. In this work we present a new approach for improving the SNR in fMRI. The special implementation of the voxel-based motion-correction algorithms allows for very fast and precise correction of head movements in the frame of the selected model. The hierarchical cluster analysis has proven to be a useful tool for the increase of the SNR of the functional results. As these tools and established deterministic and statistical functional analysis tools are implemented in one software platform (FAMIS), direct comparison between the results becomes feasible. As each analysis result can be obtained within seconds and the user can switch between the display for the different results, direct comparison between the methods can be performed. Deterministic analysis leads to an activation concept that is independent from the noise level, and vice versa the statistical analysis leads to an activation concept where the activation is intended as deviation from noise and in such way the activation becomes related to the noise level. The deterministic methods exclude true positive results with increasing noise, and the statistical methods include false positive results with increasing noise. Nevertheless, the decision regarding which of the methods should be the method of choice for fMRI analysis is not the subject of this work.

Stability effects on results of diffusion tensor imaging analysis by reduction of the number of gradient directions due to motion artifacts: an application to presymptomatic Huntington's disease.

In diffusion tensor imaging (DTI), an improvement in the signal-to-noise ratio (SNR) of the fractional anisotropy (FA) maps can be obtained when the number of recorded gradient directions (GD) is increased. Vice versa, elimination of motion-corrupted or noisy GD leads to a more accurate characterization of the diffusion tensor. We previously suggest a slice-wise method for artifact detection in FA maps. This current study applies this approach to a cohort of 18 premanifest Huntington’s disease (pHD) subjects and 23 controls. By 2-D voxelwise statistical comparison of original FA-maps and FA-maps with a reduced number of GD, the effect of eliminating GD that were affected by motion was demonstrated. We present an evaluation metric that allows to test if the computed FA-maps (with a reduced number of GD) still reflect a “true” FA-map, as defined by simulations in the control sample. Furthermore, we investigated if omitting data volumes affected by motion in the pHD cohort could lead to an increased SNR in the resulting FA-maps. A high agreement between original FA maps (with all GD) and corrected FA maps (i.e. without GD corrupted by motion) were observed even for numbers of eliminated GD up to 13. Even in one data set in which 46 GD had to be eliminated, the results showed a moderate agreement.

Motor network structure and function are associated with motor performance in Huntington's disease.

In Huntington’s disease, the relationship of brain structure, brain function and clinical measures remains incompletely understood. We asked how sensory-motor network brain structure and neural activity relate to each other and to motor performance. Thirty-four early stage HD and 32 age- and sex-matched healthy control participants underwent structural magnetic resonance imaging (MRI), diffusion tensor, and intrinsic functional connectivity MRI. Diffusivity patterns were assessed in the cortico-spinal tract and the thalamus–somatosensory cortex tract. For the motor network connectivity analyses the dominant M1 motor cortex region and for the basal ganglia-thalamic network the thalamus were used as seeds. Region to region structural and functional connectivity was examined between thalamus and somatosensory cortex. Fractional anisotropy (FA) was higher in HD than controls in the basal ganglia, and lower in the external and internal capsule, in the thalamus, and in subcortical white matter. Between-group axial and radial diffusivity differences were more prominent than differences in FA, and correlated with motor performance. Within the motor network, the insula was less connected in HD than in controls, with the degree of connection correlating with motor scores. The basal ganglia-thalamic network’s connectivity differed in the insula and basal ganglia. Tract specific white matter diffusivity and functional connectivity were not correlated. In HD sensory-motor white matter organization and functional connectivity in a motor network were independently associated with motor performance. The lack of tract-specific association of structure and function suggests that functional adaptation to structural loss differs between participants.

Using independent component analysis for noise reduction of magnetocardiographic data in case of exercise with an ergometer

In 1992, Brockmeier et al. showed that there is a strong difference in magnetocardiography (MCG)-detected field distribution generated by the heart at rest and under stress. To study the possible clinical applications of this finding, it is convenient to avoid pharmacological stress and to perform stress MCG (SMCG) using conventional physical stress with an ergometer. When using a non-magnetic ergometer, the MCG recordings under physical stress are more noisy due to the unavoidable movement artefacts from the patient and from the residual artefacts of the ergometer. To remove these artefacts a denoising was performed using independent component analysis (ICA) in a new implementation. This work shows that with ICA in this special implementation it is becoming feasible to extract heart signals from SMCG data recorded during ergometer exercise.

Open magnetic and electric graphic analysis

In this article, a novel analysis technique, open magnetic and electric graphic analysis (OMEGA), is described and is applied to combine MEG and fMRI measurements in a motor task. The study was intended to demonstrate how, within OMEGA, the localization of brain activation can be complemented by integrated analysis of human multimodal functional (MEG, fMRI) and anatomical (MRI) measurements. The OMEGA software provides an analysis platform for user-independent, fast, and reproducible multimodal analysis in one single software environment. The implementation of OMEGA allows the analyst to receive comprehensive MEG/fMRI results in a matter of minutes after the measurements have been completed. With OMEGA, the clinical researcher gets comprehensive information in a quick and standardized approach about the sites and the time course of neurological activation, which is useful for clinical applications and diagnostics.

On the variability of QRS time-duration in magnetocardiographic recordings.

High resolution electrocardiography (HRECG) recordings have already shown an increased beat-to-beat microvariability of the QRS duration of the terminal QRS in patients with a history of ventricular tachycardia (VT). The purpose of this study is to detect QRS-duration microvariability with magnetocardiographic (MCG) recordings in normals, patients with coronary heart disease (CHD), patients with a history of myocardial infarction (MI), and VT patients. QRS microvariability is calculated as the variance of time-shifts of single beats respectively to the average of all beats. The average over all channels of the MCG is performed. QRS microvariability was evaluated from 55-channel MCG in 15 normal persons, in 12 patients with CHD, in 13 patients with MI, and in 10 patients with VT. We found a significantly higher microvariability in patients with MI compared to normals. The highest microvariability was found in VT patients.

Analysis of the ST-segment in terms of principal components: application on multichannel magnetocardiographic recordings.

Parameterization of the ST-segment is used as a tool for risk stratification for patients to suffer from ventricular tachycardia. This parameterization is performed in terms of Principal Component Analysis (PCA) applied on multichannel magnetocardiographic (MCG) recordings. 55-channel MCG was recorded from 14 normal persons, 10 patients with CHD, 14 patients with MI, and six patients with VT. We found a significantly (p < 0.05) lower PCA-score in patients with MI compared to normals. The lowest PCA-score was found in VT patients. Significant differences can be found between VT patients and normals and also between VT patients and CHD patients.

FV8. Sequential pathology spread in amyotrophic lateral sclerosis: In-vivo evidence from a systematic meta-analysis of structural brain data

Background The use of in vivo diffusion tensor imaging (DTI) has substantially improved the understanding of the in vivo cerebral neuropathology of the neurodegenerative disorder amyotrophic lateral sclerosis (ALS) ( Filippi et al., 2015 ) as a rapidly progressive neurodegenerative disease with a well-defined neuroanatomical propagation pattern according to the Braak stages ( [Muller et al., 2016] , [Kassubek et al., in press] ). Objective Investigation of DTI-based in vivo ALS metadata for the validation of the proposed sequential disease spread in ALS. Methods A meta-analysis on whole-brain based DTI analysis in ALS patients compared with controls using MEDLINE database was performed. Fifty-five studies, including 1319 ALS patients and 1183 controls, were enrolled. Results The data analysis demonstrated a characteristic pattern of significant white matter alterations in regions that are known to become sequentially involved. In particular, the hypothesis-guided computation revealed the highest prevalence of significant DTI based alterations in regions associated with stage I followed by stage II and III while the prevalence was demonstrated to be low in regions associated with stage IV. Conclusion The DTI-based meta-analysis results were in line with present findings of white matter alterations in ALS and support the model of the corticofugal disease spread in four stages and put further in vivo evidence to the proposed ALS staging scheme.

FV7. Tract of interest-based DTI analysis in upper and lower motor neuron disease variants of ALS

Background It is still a topic of research if specific clinical presentations of motoneuron disease (MND) patients should be classified as amyotrophic lateral sclerosis (ALS) variants, i.e. primary lateral sclerosis (PLS) with predominant upper motor neuron on the one hand and pure lower motor neuron disease (LMND) on the other hand. Objective markers for ALS are to be investigated in these patients. Objective The aim of the study was to investigate white matter damage by a hypothesis-guided tract-of-interest-based approach in patients with classical ALS, LMND, and PLS in contrast to healthy controls in order to challenge the hypothesis that brain structural changes according to the neuropathologically defined ALS affectation pattern ( Kassubek et al., 2014 ) can be observed. LMND data were pooled from previous studies at two different study sites (Ulm, Germany and Milano, Italy). Methods DTI-based white matter integrity mapping was performed by voxelwise statistical comparison and by a tractwise analysis of fractional anisotropy (FA) maps according to the ALS-staging pattern for 65 LMND patients (clinically differentiated in fast and slow progressors), 42 PLS patients, and 101 ALS patients with a ’classical’ phenotype vs 92 matched controls to identify white matter structural alterations. Results The analysis of predefined tracts-of-interest according to the neuropathological spreading pattern demonstrated regional characteristic alteration patterns (FA reductions) fast LMND progressors and in ALS compared to controls. ( Muller et al., 2018 ) The degree of involvement correlated with the clinical phenotype. PLS also presented substantial tract involvement in the tract-of-interest-based approach. Conclusion In the tract-specific analysis according to the ALS-staging pattern, upper (PLS) as well as lower motor neuron disease (LMND) pathology showed alterations of ALS-related corticoefferent fibers. The neuroimaging results confirm the clinical approach to these phenotypes as ALS variants, in accordance with the latest revision of the El Escorial criteria for ALS, in favour of the consequence to treat these patients like ALS and also to include them into clinical trials of ALS.