Automatic skull segmentation from MR images for realistic volume conductor models of the head: Assessment of the state-of-the-art.

Anatomically realistic volume conductor models of the human head are important for accurate forward modeling of the electric field during transcranial brain stimulation (TBS), electro- (EEG) and magnetoencephalography (MEG). In particular, the skull compartment exerts a strong influence on the field distribution due to its low conductivity, suggesting the need to represent its geometry accurately. However, automatic skull reconstruction from structural magnetic resonance (MR) images is difficult, as compact bone has a very low signal in magnetic resonance imaging (MRI). Here, we evaluate three methods for skull segmentation, namely FSL BET2, the unified segmentation routine of SPM12 with extended spatial tissue priors, and the skullfinder tool of BrainSuite. To our knowledge, this study is the first to rigorously assess the accuracy of these state-of-the-art tools by comparison with CT-based skull segmentations on a group of ten subjects. We demonstrate several key factors that improve the segmentation quality, including the use of multi-contrast MRI data, the optimization of the MR sequences and the adaptation of the parameters of the segmentation methods. We conclude that FSL and SPM12 achieve better skull segmentations than BrainSuite. The former methods obtain reasonable results for the upper part of the skull when a combination of T1- and T2-weighted images is used as input. The SPM12-based results can be improved slightly further by means of simple morphological operations to fix local defects. In contrast to FSL BET2, the SPM12-based segmentation with extended spatial tissue priors and the BrainSuite-based segmentation provide coarse reconstructions of the vertebrae, enabling the construction of volume conductor models that include the neck. We exemplarily demonstrate that the extended models enable a more accurate estimation of the electric field distribution during transcranial direct current stimulation (tDCS) for montages that involve extraencephalic electrodes. The methods provided by FSL and SPM12 are integrated into pipelines for the automatic generation of realistic head models based on tetrahedral meshes, which are distributed as part of the open-source software package SimNIBS for field calculations for transcranial brain stimulation.

Neuroprotective and memory enhancing properties of a dual agonist of the FGF receptor and NCAM.

The fibroblast growth factor receptor (FGFR) plays a vital role in the development of the nervous system regulating a multitude of cellular processes. One of the interaction partners of the FGFR is the neural cell adhesion molecule (NCAM), which is known to play an important role in neuronal development, regeneration and synaptic plasticity. Thus, simultaneous activation of FGFR- and NCAM-mediated signaling pathways may be expected to affect processes underlying neurodegenerative diseases. We here report the identification of a peptide compound, Enreptin, capable of interacting with both FGFR and NCAM. We demonstrate that this dual specificity agonist induces phosphorylation of FGFR and differentiation and survival of primary neurons in vitro, and that these effects are inhibited by abrogation of both NCAM and FGFR signaling pathways. Furthermore, Enreptin crosses the blood-brain barrier after subcutaneous administration, enhances long-term memory in normal mice and ameliorates memory deficit in mice with induced brain inflammation. Moreover, Enreptin reduces cognitive impairment and neuronal death induced by Aß25-35 in a rat model of Alzheimer's disease, and reduces the mortality rate and clinical signs of experimental autoimmune encephalomyelitis in rats. Thus, Enreptin is an attractive candidate for the treatment of neurological diseases.

Accurate anatomical head segmentations: a data set for biomedical simulations.

Detailed computational anatomical models of the entire head are needed for accurate in silico modeling in a variety of transcranial stimulation applications. Models from different subjects help to understand and account for population variability. To this end, we have developed a new library of head models of 20 individuals, segmented from co-aligned multi-modal medical image data. The acquired image modalities allow to accurately model tissues with different material properties, such as electrical conductivity or spatially varying acoustic properties. The usefulness of the models is illustrated for two example applications.