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Tomer, O.

Publications and source records attributed to Tomer, O..

2 recordsLinked to original sources

Neural Collision Detection: an open source library to study the three-dimensional interactions of neurons and other tree-like structures

AO_SCPLOWBSTRACTC_SCPLOWThe analysis of neuronal structure and its relation to function has become a fundamental pillar in neuroscience since its earliest days, with the underlying premise that morphological properties can modulate neuronal computations. It is often the case that the rich three-dimensional structure of neurons is quantified by tools developed in other fields, such as graph theory and computational geometry; nevertheless, some of the more advanced tools developed in these fields have not yet been made accessible to the neuroscience community. Here we present Neural Collision Detection, a library providing high-level interfaces to collision-detection routines and alpha shape calculations, as well as statistical analysis and visualizations for 3D objects, with the aim to lower the entry gap for neuroscientists into these worlds. Our work here also demonstrates a variety of use cases for the library and exemplary analysis and visualizations that were carried out with it on real neuronal and vascular data.

neuroscience↗

A Framework for Cortical Layer Composition Analysis using Low Resolution T1 MRI Images

The layer composition of the cerebral cortex represents a unique anatomical fingerprint of brain development, function, connectivity and pathology. Historically the cortical layers were investigated solely ex-vivo using histological means, but recent magnetic resonance imaging (MRI) studies suggest that T1 relaxation images can be utilized to separate the layers. Despite technological advancements in the field of high resolution MRI, accurate estimation of whole brain layer composition has remained limited due to partial volume effects, leaving some layers far beyond the image resolution. In this study we offer a simple and accurate method for layer composition analysis, resolving partial volume effects and cortical curvature heterogeneity. We use a low resolution echo planar imaging inversion recovery (EPI IR) MRI scan protocol that provides fast acquisition (~12 minutes) and enables extraction of multiple T1 relaxation time components per voxel, which are assigned to types of brain tissue and utilized to extract the subvoxel composition of each T1 layer. While previous investigation of the layers required the estimation of cortical normals or smoothing of layer widths (similar to VBM), here we developed a sphere-based approach to explore the inner mesoscale architecture of the cortex. Our novel algorithm conducts spatial analysis using volumetric sampling of a system of virtual spheres dispersed throughout the entire cortical space. The methodology offers a robust and powerful framework for quantification and visualization of the layers on the cortical surface, providing a basis for quantitative investigation of their role in cognition, physiology and pathology.

neuroscience↗