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Brain mapping studies often need to identify brain structures or functional circuits into a set of individual brain. To this end, multiple atlases have been published to represent such structures, based on different modalities, subject sets, and techniques. The mainstream approach to exploit these atlases consists in spatially deforming each individual data onto a given atlas using dense deformation fields, which supposes the existence of a continuous mapping between atlases and individuals. However this continuity is not always verified, and this “iconic” approach has limits. We present in this paper an alternative, complementary, “structural” approach, which consists in extracting structures from the individual data, and comparing them without deformation. A “structural atlas” is thus a collection of annotated individual data with a common structure nomenclature. It may be used to characterize structure shape variability across individuals or species, or to train machine learning systems. This paper exhibits Anatomist, a powerful structural 3D visualization software dedicated to building, exploring, and editing structural atlases involving a large number of subjects. It has been developed primarily to decipher the cortical folding variability: cortical sulci vary enormously in both size and shape, some may be missing, or have various topologies, which makes iconic approaches inefficient to study them. We therefore had to build structural atlases for cortical sulci, and use them to train sulci identification algorithms. Anatomist can display multiple subjects data in multiple views, supports all kinds of neuroimaging data including compound structural object graphs, handles arbitrary coordinate transformation chains between data, and has multiple display features. It is designed as a programming library in both C++ and Python languages, and may be extended or used to build dedicated custom applications. Its generic design makes all the display and structural aspects used to explore the variability of the cortical folding pattern work in other applications, for instance to browse axonal fiber bundles, deep nuclei, functional activations, or other kinds of cortical parcellations. Multimodal, multi-individual, or inter-species display is supported, and adaptations to large scale screen walls have been developed. These very original features makes it a unique viewer for structural atlas browsing.

Superficial white matter (SWM) has been less studied than long-range connections despite being of interest to clinical research, andfew tractography parcellation methods have been adapted to SWM. Here, we propose an efficient geometry-based parcellation method (GeoLab) that allows high-performance segmentation of hundreds of short white matter bundles from a subject. This method has been designed for the SWM atlas of EBRAINS European infrastructure, which is composed of 657 bundles. The atlas projection relies on the precomputed statistics of six bundle-specific geometrical properties of atlas streamlines. In the spirit of RecoBundles, a global and local streamline-based registration (SBR) is used to align the subject to the atlas space. Then, the streamlines are labeled taking into account the six geometrical parameters describing the similarity to the streamlines in the model bundle. Compared to other state-of-the-art methods, GeoLab allows the extraction of more bundles with a higher number of streamlines.

We present a robust method, as well as a new metric, for the comparison of permittivity models in terahertz timedomain spectroscopy (THz-TDS). In this work, we perform an extensive noise analysis of a THz-TDS system, we remove and model the unwanted deterministic noises and implement them into our fitting process. This is done using our open-source software, Fit@TDS, available at : https://github.com/THzbiophotonics/Fit-TDS. This work is the first step towards the derivation of uncertainties, and therefore the use of error bars. We hope that this will lead to performing analytical analysis with THz-TDS, as results obtained from different setups will be comparable. Finally, we apply this protocol to the study of a \\(\\alpha\\)-lactose monohydrate pellet in order to give more insight on the molecular dynamics behind the absorption peaks. The comparison with simulation results is made easier thanks to the probabilities derived from the metric.