Topological analysis of our data establishes that this sexual dimorphism extends to deeper features of CNS organisation. We also discover an unexpected divergence between the sexes at this juvenile developmental stage, with the female CNS having significantly more neurons than that of males. We have deployed the approach to determine with exactitude the number of functional neurons and glia in the entire intact larval Drosophila CNS, revealing fewer neurons and more glial cells than previously predicted. Here, we describe the development of a synergistic pipeline of molecular genetic, imaging, and computational technologies designed to allow high-throughput, precise quantitation with cellular resolution of reporters of gene expression in intact whole tissues with complex cellular constitutions such as the brain. Currently, however, strict quantitation of cell numbers has been achieved only for the nervous system of Caenorhabditis elegans. This pipeline provides three-dimensional (3-D) plots of pairwise and higher-order interactions projected in a brain atlas, a new feature tailor-made for network neuroscience.Įstablishing with precision the quantity and identity of the cell types of the brain is a prerequisite for a detailed compendium of gene and protein expression in the central nervous system (CNS). Moreover, we would like to highlight one part of our notebook dedicated to the realistic visualisation of high order interactions in brain networks. We use an open-source language (Python) and provide an accompanying publicly available Jupyter Notebook that uses the 1000 Functional Connectomes Project dataset.
#CALEB MAZAIKA HOW TO#
We will start by giving a concise (and by no means complete) overview of the field to introduce the two frameworks and then explain how to compute both well-established and newer metrics on resting-state functional magnetic resonance imaging. In this hands-on tutorial, our goal is to provide the computational tools to explore neuroimaging data using these frameworks and to facilitate their accessibility, data visualisation, and comprehension for newcomers to the field.
Recently, topological data analysis has gained more attention as an alternative framework by providing a set of metrics that go beyond pairwise connections and offer improved robustness against noise.
The field of network neuroscience has been trying to tackle these challenges, and graph theory has been one of its essential branches through the investigation of brain networks. With that comes the need for sophisticated tools for analysis.
With the recent advances in technology, researchers can now collect enormous amounts of data from the brain using neuroimaging at different scales and from numerous modalities. The brain is an extraordinarily complex system that facilitates the optimal integration of information from different regions to execute its functions.
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