Ubiquitous lognormal distribution of neuron densities in mammalian cerebral cortex

A complex network-based approach to detect and investigate connectome motifs in the larval Drosophila

Analyzing the connectome of an organism allows us to understand how different areas of its brain communicate with each other and how the structure of the brain is related to its function. Thanks to new technological advances, the connectome of increasingly complex organisms has been reconstructed in recent years. Drosophila melanogaster is currently the most complex organism whose complete connectome is known, both structurally and functionally. In this paper, we aim to contribute to the study of the Drosophila structural connectome by proposing an ad hoc approach for the discovery of network motifs that may be present in it. Unlike previous approaches, which focused on parts of the connectome of complex organisms or the entire connectome of very simple organisms, our approach operates at the whole-brain scale for the most complex organism whose complete connectome is currently known. Furthermore, while previous works have focused on extending existing motif extraction approaches to the connectome case, our approach proposes a motif concept specifically designed for the connectome of an organism. This allows us to find very complex motifs while abstracting them into a few simple types that take into account the brain regions to which the neurons involved belong.

A novel approach to cytoarchitectonics: developing an objective framework for the morphological analysis of the cerebral cortex

Introduction

The cytoarchitectonic boundaries between cortical regions and layers are usually defined by the presence or absence of certain cell types. However, these cell types are often not clearly defined and determining the exact boundaries of regions and layers can be challenging. Therefore, in our research, we attempted to define cortical regions and layers based on clear quantitative criteria.

Methods

We performed immunofluorescent anti-NeuN labelling on five adult human brains in three cortical regions-Brodmann areas (BA) 9, 14r, and 24. We reconstructed the cell bodies of 90,723 NeuN-positive cells and analyzed their morphometric characteristics by cortical region and layer. We used a supervised neural network prediction algorithm to classify the reconstructions into morphological cell types. We used the results of the prediction algorithm to determine the proportions of different cell types in BA9, BA14r and BA24.

Results

Our analysis revealed that the cytoarchitectonic descriptions of BA9, BA14r and BA24 were reflected in the morphometric measures and cell classifications obtained by the prediction algorithm. BA9 was characterized by the abundance of large pyramidal cells in layer III, BA14r was characterized by relatively smaller and more elongated cells compared to BA9, and BA24 was characterized by the presence of extremely elongated cells in layer V as well as relatively higher proportions of irregularly shaped cells.

Discussion

The results of the prediction model agreed well with the qualitative expected cytoarchitectonic descriptions. This suggests that supervised machine learning could aid in defining the morphological characteristics of the cerebral cortex.

Preconfigured architecture of the developing mouse brain

In the adult brain, structural and functional parameters, such as synaptic sizes and neuronal firing rates, follow right-skewed and heavy-tailed distributions. While this organization is thought to have significant implications, its development is still largely unknown. Here, we address this knowledge gap by investigating a large-scale dataset recorded from the prefrontal cortex and the olfactory bulb of mice aged 4-60 postnatal days. We show that firing rates and spike train interactions have a largely stable distribution shape throughout the first 60 postnatal days and that the prefrontal cortex displays a functional small-world architecture. Moreover, early brain activity exhibits an oligarchical organization, where high-firing neurons have hub-like properties. In a neural network model, we show that analogously right-skewed and heavy-tailed synaptic parameters are instrumental to consistently recapitulate the experimental data. Thus, functional and structural parameters in the developing brain are already extremely distributed, suggesting that this organization is preconfigured and not experience dependent.

Advances in cellular resolution microscopy for brain imaging in rats

Rats are used in neuroscience research because of their physiological similarities with humans and accessibility as model organisms, trainability, and behavioral repertoire. In particular, rats perform a wide range of sophisticated social, cognitive, motor, and learning behaviors within the contexts of both naturalistic and laboratory environments. Further progress in neuroscience can be facilitated by using advanced imaging methods to measure the complex neural and physiological processes during behavior in rats. However, compared with the mouse, the rat nervous system offers a set of challenges, such as larger brain size, decreased neuron density, and difficulty with head restraint. Here, we review recent advances in in vivo imaging techniques in rats with a special focus on open-source solutions for calcium imaging. Finally, we provide suggestions for both users and developers of in vivo imaging systems for rats.