Leukoaraiosis and Lacunes – A Very Brief Overview

As people age, it is common for their brain white matter to change. These changes often appear as bright white spots on T2-weighted MR scans. These areas or spots of hyperintensity (i.e., white matter hyperintensities {WMH}) are also called leukoaraiosis (LA). Researchers are still investigating the exact nature and pathology of these abnormalities but our understanding of them is increasing. They most often seem to start around the lateral ventricles and spread from there, although it is possible to have punctate WMH throughout the brain white matter (i.e., WMH that are not connected to other regions). WMH on brain MRIs represent rarefaction of the white matter, including swelling, demyelination, and damage, although the exact nature and combination of the white matter changes is not known. These WMH can interfere with normal cognitive functioning, including processing speed, attention, inhibition, as well as global executive functioning (although these claims are still being investigated).

Other damage to white matter includes lacunes, which are little holes in the brain, much like the holes in Swiss cheese. They are caused by mini infarcts, or strokes, or other processes. Most of the time they are due to “silent strokes”, or strokes that are small enough that the person does not have any noticeable stroke symptoms. These lacunes can have similar impact on cognition as WMH. Both WMH and lacunes are related to vascular risk factors, such as hyper- or hypo-tension, diabetes, etc.

MedINRIA MRI Visualization and Processing

I just ran across a site that has a few medical imaging software packages. One of them is MedINRIA.

“MedINRIA aims at providing to clinicians state-of-the-art algorithms dedicated to medical image processing and visualization. Efforts have been made to simplify the user interface, while keeping high-level algorithms. Each application is called a module, and can be loaded dynamically from a single main window. MedINRIA is available for Microsoft Windows XP/Vista, Linux Fedora Core, MacOSX, and is fully multithreaded.”

Link to a description and download.

MedINRIA screenshot

I have not tried the software yet – my MRI analysis software is FSL – but this software looks promising. Plus it runs natively on Windows, Linux (Fedora Core), and Mac OS X (FSL only runs natively in OS X and Linux – it’s a little tricky to run in Windows). Not that running in Windows is necessarily a perk – our preferred MRI processing workstation is a Mac – but many people are using Windows. If I get around to installing the software, I’ll post a review of it later. I’m always looking to user-friendly ways to analyze MRI data. Best of all, like FSL, it is free. It is based, in part, on the open-source and excellent ITK and VTK packages.

MRI Quenching

I learned something new this week. Modern MRI scanners produce high-strength magnetic fields (typically 1.5T up to about 20T – scanners for use with humans max out at about 7T right now {those are very rare though, 1.5T and 3T are more common). To produce these fields the scanners need to have strong electric currents. In order to handle large currents, scanners use superconductors cooled with liquid helium. In cases of serious malfunction or emergency the MRI scanner can be quenched, which releases all of the liquid helium. The helium turns into a gaseous state rapidly and expands to fill the room. The quench will make a loud sound like a jet engine or a pop. If the room is small enough, all of the air can be pushed out as the helium expands and increases the pressure. Most MRI rooms have fail-safe systems that release the helium outside, which prevents the occupants from suffocating.MRI Quench

Image from here.

Diffusion Tensor Imaging and High Angular Resolution Diffusion Imaging

I attended an interesting lecture this week. The professor who spoke talked about Diffusion Tensor Imaging (DTI) as well as about a newer technology they are trying to help develop – High Angular Resolution Diffusion Imaging (HARDI). DTI is based on tensor mathematics and physics. The tensor in DTI is basically a 3×3 matrix (x, y, and z planes) of numbers that represent the diffusion per voxel in the brain. A voxel is a volumetric pixel – a 3D portion of the brain in MR imaging. The highest resolution we can typically get with clinical MR scanners is a cubic mm voxel. So with DTI we have a tensor, a matrix, that describes the diffusion of water molecules within each voxel in the brain. Diffusion in a jar of water or in the ventricles of the brain tends to be fast and spherical. It is less spherical in the gray matter and even less so in the white matter. In fact, the diffusion of water is highly directional in white matter (the myelinated axons of neurons). This means that the water molecules tend to diffuse somewhat parallel to the length of the axon. The movements of these water molecules are picked up by the MR scanner (which is technically “focusing” on the hydrogen atoms in water).
The diffusion per voxel can be quantified by measures of fractional anisotropy (how directional is the movement), Mean Diffusivity (total diffusion within the voxel), and by the eigenvalues of the matrix (basically how far the molecules moved in the direction of the eigenvector).
Back to HARDI. HARDI improves upon DTI by allowing for more directions of the white matter fibers to be separated out than is possible with DTI. There are some areas of the brain where there are a lot of crossing fibers and these areas show up as dark spots on DTI (which looks like a hole in the brain). With HARDI, you can see that the fibers are just more complex than is possible to calculate with DTI.
Both of these methods are useful for measuring the overall integrity (and potentially connectivity) of the white matter in the brain.

The 3D brain

Technology Review has an interesting article about “new” 3D brain imaging software being developed at Thomas Jefferson University Hospital in Philadelphia, PA (I put “new” in quotation marks because there are other similar programs out there; they might not be as polished but some are even open source). Their software fuses MRI, fMRI, and DTI together to create a fairly comprehensive view of the brain: “The fusion of these different images produces a 3-D display that surgeons can manipulate: they can navigate through the images at different orientations, virtually slice the brain in different sections, and zoom in on specific sections.”

The software looks like it is aimed more at neurosurgeons than researchers (i.e., it probably isn’t free like a lot of MRI image processing software). It does produce amazing images (view the images here) and looks like it could be a very useful tool for at least a qualitative approach to brain imaging.

DTI fibers near a tumor

The software is focused a lot on DTI (diffusion tensor imaging) and how the white matter fibers in the brain interact with lesions or tumors. I think that one researcher’s word of caution is important:

“Bruce Fischl, an assistant in neuroscience at Massachusetts General Hospital, says that the idea is ‘interesting’ but cautions that there are a number of levels of ambiguity when talking about connectivity in imaging. ‘Just because you live next to the Mass Pike doesn’t mean that there is an exit,’ he says.”

In other words, don’t get too caught up in the fact that fibers are right by a tumor, they may not really have anything to do with the part of the brain the tumor is most affecting.

In any case, I think that the idea behind this software is amazing. The graphics renderings are impressive (but they are just the pretty pictures – the rendering details may be beneficial in clinical surgery settings but they are not particularly useful in research situations, other than producing nice pictures to go in your publication). This software is very similar to something that I envisioned using a few years ago and I’m glad to see it being developed.

Image credit: Song Lai, Thomas Jefferson University Hospital (borrowed via technologyreview.com)

The basics of MRI

For a simply fabulous introduction to magnetic resonance imaging (MRI) visit Dr. Hornak’s site: http://www.cis.rit.edu/htbooks/mri/

It provides a basic but very in-depth overview of MR imaging, including the statistics and physics behind the images. It’s probably the best freely-available resource about MRI on the web.

A basic introduction to fMRI and MRI

MRI scannerfMRI (functional magnetic resonance imaging) builds on a basic MRI (magnetic resonance imaging) by looking at blood flow. An MRI works because protons, which make up atoms, are affected by magnetic fields. Basically, an MRI aligns a very small proportion of the protons in body tissue (it usually affects hydrogen the most because of hydrogen’s proton and neutron composition; hydrogen is also prevalent in body tissue and so it is easy to affect). Normally the protons in hydrogen are randomly orientated which means their minute magnetic fields are also randomly orientated. When these protons are placed in the vicinity of the strong magnetic field produced by MRI machines, some of them align with the magnetic field of the machine. The machine also produces radio waves that slightly affect the aligned protons. These waves will cause the protons to spin a certain way in response to the radio waves. The radio waves are then turned off and the protons realign themselves to the magnetic field produced by the MRI machine. The machine picks up this re-alignment and a computer processes it to create an image of the brain (or what ever else is scanned). Since protons in different tissues align at different rates, the machine can differentiate between different types of tissue (such as skull and white and gray matter).

An fMRI just builds on the MRI by focusing on the ratio between oxygenated to deoxygenated blood; this is the blood oxygenation level dependent effect (BOLD effect). Basically, an fMRI indirectly measures brain activity by measuring the change in blood levels (specifically hemoglobin as it deoxygenates). An fMRI works because as brains process information blood flows to those areas to help provide the needed oxygen and glucose. The result of this process is a scan of the brain with lighter (or darker) areas where blood is flowing in greater quantity.

One example of how an fMRI was used to test a cognitive neuroscience theory was when Deibert et al. (1999) had subjects close their eyes and try to identify objects only by touch. The researchers discovered through fMRI that the subjects’ visual cortex was activated even though their eyes were closed. There were two different explanations: first the objects were identified and then visual images were created or the visual image was created during the process of identification and thus helped the subjects recognize the objects. However, fMRI alone was not sufficient to support the correct theory. When researchers used transcranial magnetic stimulation (TMS) they discovered that they could interrupt the processing in the occipital lobe and interfere with object recognition. So the combination of fMRI and TMS showed that the visual image formed during tactile exploration is important for object recognition. While fMRI was not sufficient in this case, it was key in uncovering and explaining the theory about how tactile object recognition works in the absence of visual input.

Image courtesy of MacRonin47.