In-vivo cortical thickness estimation from high-resolution T1w MRI scans in healthy and mucopolysaccharidosis affected dogs

Automatic brain extraction and brain tissues segmentation on multi-contrast animal MRI

For many neuroscience applications, brain extraction in MRI images is the first pre-processing step of a quantification pipeline. Once the brain is extracted, further post-processing calculations become faster, more specific and easier to implement and interpret. It is the case, for example, of functional MRI brain studies, or relaxation time mappings and brain tissues classifications to characterise brain pathologies. Existing brain extraction tools are mostly adapted to work on the human anatomy, this gives poor results when applied to animal brain images. We have developed an atlas-based Veterinary Images Brain Extraction (VIBE) algorithm that encompasses a pre-processing step to adapt the atlas to the patient's image, and a subsequent registration step. We show that the brain extraction is achieved with excellent results in terms of Dice and Jaccard metrics. The algorithm is automatic, with no need to adapt the parameters in a broad range of situations: we successfully tested multiple MRI contrasts (T1-weighted, T2-weighted, T2-weighted FLAIR), all the acquisition planes (sagittal, dorsal, transverse), different animal species (dogs and cats) and canine cranial conformations (brachycephalic, mesocephalic, dolichocephalic). VIBE can be successfully extended to other animal species, provided that an atlas for that specific species exists. We show also how brain extraction, as a preliminary step, can help to segment brain tissues with a K-Means clustering algorithm.

Neuroanatomy of the grey seal brain: bringing pinnipeds into the neurobiological study of vocal learning

Comparative animal studies of complex behavioural traits, and their neurobiological underpinnings, can increase our understanding of their evolution, including in humans. Vocal learning, a potential precursor to human speech, is one such trait. Mammalian vocal learning is under-studied: most research has either focused on vocal learning in songbirds or its absence in non-human primates. Here, we focus on a highly promising model species for the neurobiology of vocal learning: grey seals (Halichoerus grypus). We provide a neuroanatomical atlas (based on dissected brain slices and magnetic resonance images), a labelled MRI template, a three-dimensional model with volumetric measurements of brain regions, and histological cortical stainings. Four main features of the grey seal brain stand out: (i) it is relatively big and highly convoluted; (ii) it hosts a relatively large temporal lobe and cerebellum; (iii) the cortex is similar to that of humans in thickness and shows the expected six-layered mammalian structure; (iv) there is expression of FoxP2 present in deeper layers of the cortex; FoxP2 is a gene involved in motor learning, vocal learning, and spoken language. Our results could facilitate future studies targeting the neural and genetic underpinnings of mammalian vocal learning, thus bridging the research gap from songbirds to humans and non-human primates. Our findings are relevant not only to vocal learning research but also to the study of mammalian neurobiology and cognition more in general. This article is part of the theme issue 'Vocal learning in animals and humans'.