Gavriilidis, F. | Search bioRxiv

bioRxiv2020

Measuring the iron content of dopaminergic neurons in substantia nigra with MRI relaxometry

In Parkinsons disease, the depletion of iron-rich dopaminergic neurons in substantia nigras nigrosome 1 precedes first motor symptoms by two decades. Monitoring this neuronal depletion at an early disease stage is needed for diagnosis and treatment monitoring. Magnetic resonance imaging (MRI) is particularly suitable for this task due to its sensitivity to tissue iron. However, the mechanisms of MRI contrast in substantia nigra are not well understood, hindering the development of specific biomarkers. We showed that the dominant contribution to the effective transverse MRI relaxation rate [Formula] in nigrosome 1 originates from iron accumulated in the neuromelanin of dopaminergic neurons. We linked [Formula] quantitatively to the product of cell density and local iron concentration in dopaminergic neurons, combining quantitative 3D iron histology, biophysical modeling, and quantitative MRI on post mortem brain tissue. This knowledge opens an avenue for monitoring neuronal iron and density in vivo and may be applied to detect early neurodegeneration in Parkinsons disease.

neuroscience↗

bioRxiv2018

Deep Learning meets Topology-preserving Active Contours: towards scalable quantitative histology of cortical cytoarchitecture.

Deep learning has thoroughly changed the field of image analysis yielding impressive results whenever enough annotated data can be gathered. While partial annotation can be very fast, manual segmentation of 3D biological structures is tedious and error-prone. Additionally, high-level shape concepts such as topology or boundary smoothness are hard if not impossible to encode in Feedforward Neural Networks. Here we present a modular strategy for the accurate segmentation of neural cell bodies from light-sheet microscopy combining mixed-scale convolutional neural networks and topology-preserving geometric deformable models. We show that the network can be trained efficiently from simple cell centroid annotations, and that the final segmentation provides accurate cell detection and smooth segmentations that do not introduce further cell splitting or merging.

bioinformatics↗