An Overview of Functional Connectomics & Resting-State fMRI
Introducing the resting-state fMRI technique through comparison of diffusion and BOLD signal data
I recently asked myself a question:
How do you map parallel, interacting systems of anatomically connected areas with non-invasive techniques?
Your brain, one of the most complex entities in the universe is at work right now. From the ways it allows you to be you, or the connections it forms, it’s working. If we were to want to get a picture of the brain’s state right now, we would think about utilizing Magnetic Resonance Imaging (MRI).
The key question right now though is: how functional (pun intended) are the original MRI’s adaptations?
What is Functional Connectivity?
As connectomics aims to map the structural and functional connections in the brain, we are in need of an efficient way to estimate the function-based connections. This is where functional connectivity comes in. Functional connectivity is the estimation of connections between functional regions in the brain.
Think of functional connectivity as the word counting tool on Google Docs. Rather than counting the structure of your piece of writing (paragraphs), this word counter counts the functionality of the words in a paragraph.
Functional connectivity is still present even if it is indirect (polysypnatic). Function in the brain involves, for instance, the communication between neurons as opposed to structure connectivity like the number of dendrites on a neuron.
Defining: Functional Magnetic Resonance Imaging
Functional Magnetic Resonance Imaging (fMRI) works by detecting the changes in blood oxygenation (more on this later) and flow that is associated with specific neural activity. For example, when a brain area is more active, it consumes more oxygen and to meet this increased demand blood flow increases to that specific area. fMRI can be used to produce activation maps showing which parts of the brain are involved in a particular mental process.
The development of the fMRI technique in the 1990s, introduced by Seiji Ogawa and Ken Kwong is still to this day, a brand-new technique. Some adaptions of the fMRI and their results include positron emission tomography (PET) and near-infrared spectroscopy (NIRS), which use blood flow and oxygen metabolism to look into one’s brain activity. As a brain imaging technique fMRI has many advantages over the previous techniques:
- fMRI is non-invasive and doesn’t involve any radiation.
- This technique has an excellent spatial and temporal resolution.
- fMRI is easy for experimenters to use due to its adaptability to mass amounts of software.
Resting-State Functional MRI (rfMRI)
Resting-state functional MRI (rfMRI) is an fMRI modality that measures spontaneous temporal fluctuations in brain activity. This is key to understand as this measurement intertwines the anatomical portions of the brain to understand the blood oxygen levels. This modality is also attributed to a subject being ‘at rest.’
Essentially, rfMRI is used as an update to previous functional connectivity measurements. Given that functionally connected areas in the brain are connected in terms of time series, rfMRI has the greatest potential in understanding the functionality of the human brain.
To understand neuroimaging techniques, you must first understand the key principles:
Spatial resolution refers to the size of one pixel on the ground. Temporal resolution refers to the how often data of the same area is collected.
Resting-State Networks (RSNs)
Like previously mentioned, temporal resolution patterns at times also visualize the brain at rest. However, that is defined, the brain at rest has specific networks that occur. These networks are called ‘resting-state networks’ or RSNs. Resting-State Networks are functional networks in the brain that are most commonly estimated from rfMRI data.
These RSNs persist even during sleep and under anesthesia. As well as persist across subjects and species. Today, in rfMRI literature it is accepted that RSNs not only reflect networks of brain function but also that the extensive set of functional networks identified in the task can be found in rfMRI data.
Diffusion Tensor Imaging (DTI)
Another non-invasive MRI technique is the diffusion tensor imaging (DTI) method. This method utilizes a key property that water molecules have. This property is directed toward the dogma that water molecules move along white-matter bundles faster than they do against them.
The whole idea behind this method is that of diffusion in neurobiological settings:
Diffusion is the movement of a substance from an area of high concentration to an area of low concentration.
By measuring water diffusion in multiple directions, the location and trajectories of axonal bundles can be estimated and those pathways reconstructed using MRI techniques.
One major strength of the diffusion techniques is that they directly measure the anatomic structure. Nonetheless, one of the reasons that fMRI techniques prevail in the connectomics community is that they directly measure the anatomic structure. Doing so, this technique isn’t able to resolve complex fibre organization in the brain.
One of the most CRUCIAL ideas in fMRI techniques is the BOLD signal. One cannot express how crucial this principle is in the field of neuroimaging, but more specifically connectomics. The BOLD signal is actually an acronym, but for the purposes of making the title look pretty, I didn’t add the periods. In actuality, this acronym would look like this: B.O.L.D. The BOLD signal is the blood oxygenation level-dependent signal that reflects neuronal activation in the brain.
The BOLD signal reflects the changes in deoxyhemoglobin caused by changes in brain blood flow and blood oxygenation. The core idea, as demonstrated above is that neuronal activity has direct links to the deoxyhemoglobin changes presented through the BOLD signal. Now, what are these changes?
Overview: Change in Blood Flow
In the BOLD signal, concentrations of oxyhemoglobin and deoxyhemoglobin are the prime contrast that shows in BOLD signal results. Without going too far into it, if we merely look into the names of these variations we can infer through our knowledge of basic Latin that oxyhemoglobin has oxygen, whilst deoxyhemolgobin does not.
Another key contrast is in the magnetism of the two variations. Oxyhemoglobin has no unpaired electrons and is weakly diamagnetic (prone to magnetic attraction). On the other hand, four unpaired electrons are exposed at each iron centre as oxygen is released to form deoxyhemoglobin, causing the molecule to become highly paramagnetic (opposite of diamagnetic).
The outcome of this contrast is the BOLD signal. No matter which technique, brain areas with more oxyhemoglobin will have a higher signal (and appear brighter) than those containing deoxyhemoglobin. fMRI scans and all of their modalities are influenced by the great differences between these two ideas. These two ideas influence the world’s entire perception of the human brain.
One of the ideas previously mentioned above but not elaborated on is the data imaging techniques seed-to-seed connectivity and seed-to-voxel connectivity.
Seed-based connectivity measurements characterize connectivity patterns with an already determined seed or ROI (region of interest). These measurements are often used when researchers are interested in a particular or many specific regions and their patterns.
On top of this, there are many variations created for the purposes of studying potential connectivity paths like the mSBC, estimating condition-specific connectivity measures (wSBC) and identifying task-related modulations in event-related designs (gPPI).
These maps are very intricate and therefore need a way to connect every seed and ROI. SBC maps are computed as the Fisher-transformed bivariate correlation coefficients between an ROI BOLD time series and each individual voxel BOLD time series:
Essentially, this idea looks at the Regions of Interest (ROI)s and their connection with the BOLD signal mentioned above. But, the added piece, the r(x) we are looking at is the time series of the BOLD signal.
Now that you have understood the idea behind functional connectivity and fMRI + rfMRI techniques, stay tuned for my data visualization of these techniques through Matlab!
- Functional connectivity is the estimation of connections between functional regions in the brain.
- Functional Magnetic Resonance Imaging (fMRI) works by detecting the changes in blood oxygenation.
- Resting-State Networks are functional networks in the brain that are most commonly estimated from rfMRI data.
- The BOLD signal is the blood oxygenation level-dependent signal that reflects neuronal activation in the brain.
- Seed-based connectivity measurements characterize connectivity patterns with an already determined seed or ROI (region of interest).