The Origins of Our Cortical Circuits

Brains make sense and perceptions from the signals that flow through them. Can we identify elementary neural “wiring” configurations and interactions that help us to understand how brains accomplish this feat?

Figure 1. A 40 nanometers wide slice of mouse neocortex cut from a one cubic millimeter sample. The sample this slice was taken from was about the size of a grain of sand and contained about one hundred thousand neurons with nearly a billion synapses. From the “ Allen Institute for Brain Science” and “The Quest to Unravel The Connectome“.

Figure 2. A schematic diagram of currently established elementary circuit configurations. The red triangle represents the cell body of a pyramidal cell. The thick trunk reaching up and ending with two thinner branches reaching diagonally is the pyramidal cell’s apical dendrite. The branches reaching diagonally down from the triangle’s two lower corners represent its basal dendrites. An axon leaves the bottom center of the pyramidal cell to various destinations (see text). Two other axons (red) enter the diagram. The axon that enters from right comes from other cortical areas. The axon that enters at left from below carries sensory input from the visual thalamus. Two inhibitory interneurons (blue) complete the circuit. ffexc: feed forward excitation, fbexc: feed back excitation, lexc: lateral excitation, ffinh: feed forward inhibition, fbinh: feedback inhibition. Figure 2(A) from “Neocortical Lamination: Insights from Neuron Types and Evolutionary Precursors” published November 7, 2017 in Frontiers in Neuroanatomy.

Cortical tissue is packed tight with neurons, dendrites, and axons as illustrated in the electron microscope image of a slice of mouse neocortex in Figure 1 above. To better comprehend this tangle, neuroscientists have worked to identify elementary categories of interactions between neurons and the ways those neurons are wired together.

Neurons in cortical tissue can be divided into excitatory and inhibitory cells as in Figure 2 at right (red for excitatory and blue for inhibitory). This schematic diagram shows interactions in the context of elementary connection motifs. The figure is from a recent paper summarizing evidence the mammalian neocortex is an elaboration of a brain area from a common ancestor of mammals, reptiles, and birds thought to have existed about 320 million years ago (“Neocortical Lamination: Insights from Neuron Types and Evolutionary Precursors” published November 7, 2017 in Frontiers in Neuroanatomy).

Research on the evolutionary origin of or our six-layered neocortex may provide us glimpses into why specific neuron types or connection motifs provided selective advantages to our ancestors. Evidence from comparative anatomy and physiology suggests that the common ancestor had a three-layered brain region which was lost in birds, remained three-layered in reptiles, and developed into six-layered neocortex in mammals. Layered brain areas in today’s reptiles (turtles) and mammals (mice) that are thought to have a common origin are highlighted in blue in Figure 3 below.

Figure 3. The blue highlighted brain regions are thought to have common origins between reptiles (turtle at left) and mammals (mouse at right). Figure S1(A) from “Evolution of pallium, hippocampus, and cortical cell types revealed by single-cell transcriptomics in reptiles” published May 25, 2018 in Science.

Single-cell RNA sequencing was used to investigate the evolution of brain regions thought to have common origins (blue in Figure 3) in a study published in Science(“Evolution of pallium, hippocampus, and cortical cell types revealed by single-cell transcriptomics in reptiles” published May 25, 2018). Genetic techniques provide some of the most direct methods available to investigate ancestral relationships among brain tissues. First, scientists pulled sequences of expressed genes from identified neuron types residing in specific regions of turtle and lizard brains. Next they looked in mammalian brains to see where those same gene sequences were expressed and in which neuron types.

Figure 4. A schematic diagram of the six layered mammalian neocortex. The lamination of the basic circuit with elaboration of the excitatory pyramidal neuron into specialized intratelencephalic (IT), pyramidal tract (PT), and corticothalamic (CT) neurons. ffexc: feed forward excitation, fbexc: feed back excitation, lexc: lateral excitation, ffinh: feed forward inhibition, fbinh: feedback inhibition. Figure 2(C) from “Neocortical Lamination: Insights from Neuron Types and Evolutionary Precursors” published November 7, 2017 in Frontiers in Neuroanatomy.

Researchers found inhibitory neurons express similar sets of genes in reptiles and mammals, indicating a common ancestry. In contrast, excitatory neurons differ substantially in gene expression across these two groups. Their results are consistent with Shepherd and Rowe’s idea that three-layer cortex was co-opted and transformed into six-layer cortex through the emergence of new types of excitatory neurons after the ancestral excitatory neurons went through profound changes of gene expression. The basic circuit module seen in the ancestral three-layer cortex in Figure 2 is transformed in the mammalian neocortex into layered versions of the basic circuit with three distinct genetically expressed versions of excitatory pyramidal cells as shown in Figure 4.

In summary, recent research has provided a putative common ancestral circuit, the basic cortical module, and three variants in mammalian neocortex. Studies that compare and contrast signal processing in these four circuits could provide insights into how brains make sense and perceptions from the signals that flow through them.

ChemNetDB: Fifty Years of Rat Brain Connection and Neurotransmitter Data

Nineteen large scale brain regions (listed) partition a 125 node cerebral connectome in a. Connectomes in b through f each match the color of a neurotransmitter listed at lower right. Figure 1 from “A multi scale cerebral neurochemical connector of the rat brain” published July 3, 2017 in PLOS Biology.

Since the 1960s a lot of scientists have worked very hard, and a lot of rats have given their all, to trace brain connections and their neurotransmitters. Fundamental to understanding how the brain works is to know how it is wired and how the signals are transmitted by those wires. Work reported in a new paper “A multi scale cerebral neurochemical connector of the rat brain” (published July 3, 2017 in PLOS Biology) identified 1,560 original research articles with high quality connectivity data from 36,464 rats from the past 50 years that they transformed into multi scale atlas of rat brain connections and their neurotransmitters.

The authors point to three main features ChemNetDB has over other existing databases. First, they claim that ChemNetDB is the most comprehensive rat connectivity database of the last 50 years of research data currently in existence. Second, they used a transparent, consistent, and validated method to integrate neurochemical information with the connectivity data. And third, they used data from animals of a consistent age along with transparent and consistent terminology.

I was very excited on coming across and reading their paper. However, on visiting the site chemnetdb.org I was immediately struck by very limited data access. Perhaps there is a data endpoint or an Application Programming Interface (API). To my knowledge, the best you can currently do on the site is to search a brain area or structure and get a list of the areas connecting with that area or structure along with the connection’s neurotransmitters. Or the reverse: you may search on a neurotransmitter. References associated with the connections are displayed.

The authors may have future plans but no hints have been provided. These data would be valuable as part of the rich set of life sciences linked-data available across the Internet. Their rat connectivity data could be associated with with a rat brain anatomy ontology and a huge and growing number of other relevant ontologies and opened up through SPARQL endpoints. Then there would be a world of possibilities!

Genes, Pathways, and Autistic Spectrum Disorder

Interactions between the top 50 pathways from 659 Autistic Spectrum Disorder (ASD) genes are displayed. Pathways are grouped as Disease Pathways (left column; purple) and Functional Pathways (right column; green). The color of each node inside the groupings represents the p-value of that pathway (p-value bar at lower left). The size of each node represents the number of ASD genes in that pathways. From Figure 2 in “Pathway Network Analysis for Autism Reveal Multisystem Involvement, Major Overlaps with Other Diseases and Convergence upon MAPK and Calcium Signalling” published April 7, 2016 in PLOS One.

Autistic Spectrum Disorder (ASD) is defined behaviorally and has no known biological marker. Does ASD encompass a collection of disorders? Are they necessarily disorders or simply differences that may become disorders under some biological or environmental circumstances? We simply do not know for certain.

Research has shown that hundreds of genes, multitudes of biological pathways, and many systems seem to contribute to ASD. If all of these data are brought together and visualized will it provide a clue to the biological basis of ASD? The recent publication “Pathway Network Analysis for Autism Reveal Multisystem Involvement, Major Overlaps with Other Diseases and Convergence upon MAPK and Calcium Signalling” (April 7, 2016 in PLOS One) attempts this approach with genes and pathways known to be active in ASD patients.

When this study began, 667 genes were thought to contribute to ASD. Of these, 659 genes were in the annotation set the researchers were using to do analysis. They examined the interactions among the pathways associated with these genes and selected the top 50 pathways based on the statistical significance of pathway overlaps. Their results are displayed in the figure above.

Notice that the figure is divided into two columns. At left is the Disease Pathways column (purple). At right is the Functional Pathways column (green). Each node in a pathway grouping has a size and color. The size represents the number of ASD genes in that pathway (larger diameter means more genes). The color of each node represents the p-value of that pathway (p-value bar at lower left; red indicates a very high significance of overlap).

The resulting map shows three “hot” spots that are both large diameter (a lot of ASD genes) and red (very high significance of overlap) under the Neural and Cell Signaling functional pathways and the Cancer disease pathways. Specifically, the following three nodes stand out:

Data showed that MAPK signaling pathway interacts with half of the pathways in the network and is the most interactive pathway in the ASD data. They showed that the calcium signaling pathway is the second most interactive pathway and is associated with the most ASD genes. These two pathway types overlapped with 8 ASD genes (green intersection) known to be important in the process of calcium-PKC-Ras-Raf-MAPK/ERK. From Figure 3 in “Pathway Network Analysis for Autism Reveal Multisystem Involvement, Major Overlaps with Other Diseases and Convergence upon MAPK and Calcium Signalling

1. Neuro-active ligand-receptor interaction

2. Calcium signaling pathway

3. Collection cancer

Neuro-active ligand-receptor interaction is key to communication between each and every nerve cell both in our brains in throughout our body. The calcium signaling pathway is fundamental to each and every cell in our bodies. The genes and pathways were identified in ASD patients because their products and interactions were significantly different than in non-ASD patients. Do those diagnosed with ASD have significantly different metabolisms than the rest us? Do their brain cells communicate differently?

Research based on pulling together existent data is implicitly biased by the experiments that were performed to produce those data. Perhaps researchers were most interested in cell signaling and cancer genes and pathways and that is the reason that these kinds pathways are more common in data repositories. The result could be their prominent association with ASD genes.