How Much Do We Know About Our Neural Interactions?

You Haven’t Heard of Connectomics?

Connectomics is the study of the human connectome. The human connectome? Essentially the human connectome is a 3D structure that shows all of the neural pathway connections in the brain. The brain is made up of 100 trillion connections. That’s insane. To give you some perspective, the universe is only made up of 100 billion stars.

Functional Connectomics

Functional connectomics is a subset of connectomics focusing on the collective set of functional connections in the brain. A functional connectome has the purpose of measuring the connectivity between particular brain regions.

Anatomical Connectivity vs. Functional Connectivity

Breakthroughs in Connectomics

It terms of connectomes, we’ve only fully mapped the connectome of the roundworm C. elegans and Google’s working on developing a high resolution connectome of the fly brain.

Fly Brain Connectome

How’re We Measuring This Data?

Functional connectomics is measured at a macroscopic scale providing researchers with millimetre resolution through the use of non-invasive neuroimaging methods.

The Power of fMRI

Not only can fMRI help diagnose diseases in the brain, but it might also enable doctors to get inside our mental processes to determine what we’re thinking and feeling.

MRI vs. fMRI

fMRI scans use the same basic principles of atomic physics as MRI scans, but MRI scans image anatomical structure whereas fMRI images metabloic function. Thus, the images generated by MRI scans are like three dimensional pictures of anatomical structure.

PET, MRI, fMRI

BOLD Contrast Imaging

fMRI uses Blood-oxygen-level-dependent imaging, or BOLD-contrast imaging to observe different areas of the brain or other organs, which are found to be active at any given time. BOLD contrast derives from variations in the magnetic susceptibility of blood due to variations in the concentration of deoxyhemoglobin.

Analyzing Functional Connectivity

There’s a few ways we’re currently using fMRI and other scans to analyze functional connectivity in the brain.

1. Seed-Voxel Correlation Mapping

Seed-voxel correlation mapping is the simplest technique for studying functional connectivity. The correlation coefficient between the fMRI signal at different times and measurements of the activation in a region is calculated for each voxel in the brain. This data is then displayed as a parametric image.

2. Structural Equation Modelling

Structural equation modelling (SEM) essentially takes a step beyond calculating correlations. SEM is calculated via a maximum likeliehood procedure which adjusts the parameters of the model until the predicted correlations match the correlations in the data as closely as possible. The connection strengths are used to compare experimental groups or conditions.

3. Psychophysiological Interaction

A psychophysiological interaction (PPI) technique used to determine a stimulus or context dependent change in the influence of one brain region on another. A PPI uses a linear regression model to predicte influence on a region’s data, such as stimulus related changes. They allow for a whole brain search for voxels that exhibit response to a particular influencing region.

4. Principal Components Analysis

Principal components analysis (PCA) decomposes imaging data into a series of components. PCA is applied where the data is repeated fMRI scans during an experimental run. The spatial variation in the signal is represented by an image called the eigenimage and the temporal variation is represented by the eigenvector. The eigenimages capture features of the data that are expressed in multiple regions of functional connectivity.

5. Partial Leas Squares

Partial least squares (PLS) is mathematically very similar to PCA, as it uses a correlation matrix. But instead of looking at the covariance of the data, PLS examines the correlations of image data values with some other variables. PLS produces images and associated vectors of scores for each image to understand connectivity.

6. Multivariate Autoregressive Models

Multivariate autoregressive models (MAR models) incorporate the cross-covariances between regions and understand the temporal relationships between different scans to produce conclusions about the predominant directions of influence between regions as well as their strength.

Key Takeways

  • Connectomics is the study of all of the neural pathway structures in the brain.
  • Functional connectomics focuses on understanding the connectivity between specific brain regions.
  • fMRI is the most commonly used neuroimaging technique using Blood-oxygen-level-dependent imaging to observe activiy different areas of the brain.
  • There are six common ways to analyze functional connectivity, through seed-voxel correlation mapping, structural equation modelling, psychophysiological interaction, principal components analysis, partial least squares, and multivariate autoregressive models.

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