Browsing Multiple Subjects When the Atlas Adaptation Cannot Be Achieved via a Warping Strategy

• Published in: Frontiers in neuroinformatics Vol. 16; p. 803934
• Main Authors: Rivière, Denis, Leprince, Yann, Labra, Nicole, Vindas, Nabil, Foubet, Ophélie, Cagna, Bastien, Loh, Kep Kee, Hopkins, William, Balzeau, Antoine, Mancip, Martial, Lebenberg, Jessica, Cointepas, Yann, Coulon, Olivier, Mangin, Jean-François
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 03.03.2022; Frontiers Media; Frontiers Media S.A
• Subjects: Adaptation; Bioengineering; brain atlas; Brain mapping; Brain research; Browsing; Cognitive Sciences; Coordinate transformations; Deep learning; Engineering Sciences; Functional morphology; Imaging; inter-subject; Learning algorithms; Life Sciences; Machine learning; Medical imaging; Neuroimaging; Neurons and Cognition; Neuroscience; Nomenclature; parcellation atlas; Psychology and behavior; Signal and Image processing; Software; structural approach; Topography; visualization; 3D; brain atlas; visualization; structural approach; inter-subject; parcellation atlas
• DOI: 10.3389/fninf.2022.803934
• ISSN: 1662-5196

Brain mapping studies often need to identify brain structures or functional circuits into a set of individual brains. 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 study 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 study 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, and 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 subject 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 make it a unique viewer for structural atlas browsing.