Elodie André

Mapping track density changes in nigrostriatal and extranigral pathways in Parkinson's disease

In Parkinsons disease (PD) the demonstration of neuropathological disturbances in nigrostriatal and extranigral brain pathways using magnetic resonance imaging remains a challenge. Here, we applied a novel diffusion-weighted imaging approach—track density imaging (TDI). Twenty-seven non-demented Parkinsons patients (mean disease duration: 5 years, mean score on the Hoehn & Yahr scale = 1.5) were compared with 26 elderly controls matched for age, sex, and education level. Track density images were created by sampling each subjects spatially normalized fiber tracks in 1 mm isotropic intervals and counting the fibers that passed through each voxel. Whole-brain voxel-based analysis was performed and significance was assessed with permutation testing. Statistically significant increases in track density were found in the Parkinsons patients, relative to controls. Clusters were distributed in disease-relevant areas including motor, cognitive, and limbic networks. From the lower medulla to the diencephalon and striatum, clusters encompassed the known location of the locus coeruleus and pedunculopontine nucleus in the pons, and from the substantia nigra up to medial aspects of the posterior putamen, bilaterally. The results identified in brainstem and nigrostriatal pathways show a large overlap with the known distribution of neuropathological changes in non-demented PD patients. Our results also support an early involvement of limbic and cognitive networks in Parkinsons disease.

Influence of noise correction on intra- and inter-subject variability of quantitative metrics in diffusion kurtosis imaging

Diffusion kurtosis imaging (DKI) is a promising extension of diffusion tensor imaging, giving new insights into the white matter microstructure and providing new biomarkers. Given the rapidly increasing number of studies, DKI has a potential to establish itself as a valuable tool in brain diagnostics. However, to become a routine procedure, DKI still needs to be improved in terms of robustness, reliability, and reproducibility. As it requires acquisitions at higher diffusion weightings, results are more affected by noise than in diffusion tensor imaging. The lack of standard procedures for post-processing, especially for noise correction, might become a significant obstacle for the use of DKI in clinical routine limiting its application. We considered two noise correction schemes accounting for the noise properties of multichannel phased-array coils, in order to improve the data quality at signal-to-noise ratio (SNR) typical for DKI. The SNR dependence of estimated DKI metrics such as mean kurtosis (MK), mean diffusivity (MD) and fractional anisotropy (FA) is investigated for these noise correction approaches in Monte Carlo simulations and in in vivo human studies. The intra-subject reproducibility is investigated in a single subject study by varying the SNR level and SNR spatial distribution. Then the impact of the noise correction on inter-subject variability is evaluated in a homogeneous sample of 25 healthy volunteers. Results show a strong impact of noise correction on the MK estimate, while the estimation of FA and MD was affected to a lesser extent. Both intra- and inter-subject SNR-related variability of the MK estimate is considerably reduced after correction for the noise bias, providing more accurate and reproducible measures. In this work, we have proposed a straightforward method that improves accuracy of DKI metrics. This should contribute to standardization of DKI applications in clinical studies making valuable inferences in group analysis and longitudinal studies.

A finite-element reciprocity solution for EEG forward modeling with realistic individual head models

We present a finite element modeling (FEM) implementation for solving the forward problem in electroencephalography (EEG). The solution is based on Helmholtzs principle of reciprocity which allows for dramatically reduced computational time when constructing the leadfield matrix. The approach was validated using a 4-shell spherical model and shown to perform comparably with two current state-of-the-art alternatives (OpenMEEG for boundary element modeling and SimBio for finite element modeling). We applied the method to real human brain MRI data and created a model with five tissue types: white matter, gray matter, cerebrospinal fluid, skull, and scalp. By calculating conductivity tensors from diffusion-weighted MR images, we also demonstrate one of the main benefits of FEM: the ability to include anisotropic conductivities within the head model. Root-mean square deviation between the standard leadfield and the leadfield including white-matter anisotropy showed that ignoring the directional conductivity of white matter fiber tracts leads to orientation-specific errors in the forward model. Realistic head models are necessary for precise source localization in individuals. Our approach is fast, accurate, open-source and freely available online.