Chapter 2
2.1 Introduction
In this chapter we are going to describe about the terms Machine Learning, MRI, fMRI, Region of
Interest (ROI), and Occipital Lobe and their functionalities.
2.2 Machine Learning
Machine Learning is one of the branches of Artificial Intelligence. It is about how we can make
computers learn like we do. In definition, machine learning is the field of study that gives
computers the ability to learn without being explicitly programmed [1]. The field of Machine
Learning develops algorithms that discover knowledge from specific data and experience, based
on sound statistical and computational principles.
2.3 MRI
MRI stands for Magnetic Resonance Imaging. It is a process of creating images from the manipulation of
hydrogen atoms in magnetic fields [1]. An MRI machine uses a magnetic field and radio waves to create
detailed images of the body. MRI is also called as Nuclear Magnetic Resonance Imaging (NMRI) or
Magnetic Resonance Tomography (MRT).
MRI was invented by Dr. Raymond Damadian, an Armenian-American physician, scientist and professor
at the Downstate Medical Centre State University of New York (SUNY). In 1971 in the journal named
“Science”, he reported that tumours and normal tissue can be distinguished in vivo by Nuclear Magnetic
Resonance (NMR).
2.3.1 Why it is done
Magnetic resonance imaging (MRI) is done for many reasons. It is used to find problems such as tumors,
bleeding, injury, blood vessel diseases, or infection. MRI also may be done to provide more information
about a problem seen on an X-ray, ultrasound scan, or CT scan [3]. Contrast material may be used during
MRI to show abnormal tissue more clearly. An MRI scan can be done for the:
Head: MRI can look at the brain for tumors, an aneurysm, bleeding in the brain, nerve injury, and other
problems, such as damage caused by a stroke. MRI can also find problems of the eyes and optic nerves,
and the ears and auditory nerves.
Chest: MRI of the chest can look at the heart, the valves, and coronary blood vessels. It can show if the
heart or lungs are damaged. MRI of the chest may also be used to look for breast or lung cancer.
Blood vessels: Using MRI to look at blood vessels and the flow of blood through them is called magnetic
resonance angiography (MRA). It can find problems of the arteries and veins, such as an aneurysm, a
blocked blood vessel, or the torn lining of a blood vessel (dissection). Sometimes contrast material is used
to see the blood vessels more clearly.
Abdomen and pelvis: MRI can find problems in the organs and structures in the belly, such as
the liver, gallbladder, pancreas, kidneys, and bladder. It is used to find tumors, bleeding, infection, and
blockage. In women, it can look at the uterus and ovaries. In men, it looks at the prostate.
Spine: MRI can check the discs and nerves of the spine for conditions such as spinal stenosis, disc bulges,
and spinal tumors.
2.3.2 How MRI Works
Inside the structure of an MRI machine there are powerful magnetic coils that create an intense
magnetic field where the patient is situated [figure]. This magnetic field causes the protons of
some atoms in the patient's body---notably hydrogen atoms---to line up along the magnetic field.
(Hydrogen atoms are ideal for medical imaging because the human body is predominately made
up of water, which is composed of two hydrogen atoms and one oxygen atom.) The machine
then sends a series of bursts of radio frequency (RF) that causes only the hydrogen atoms to
become excited. These excited hydrogen atoms move out of alignment with the magnetic field
the machine has produced. As the hydrogen atoms then attempt to return to alignment with the
magnetic field, they shed the excess energy they picked up from the RF. The machine detects
and records that energy. In some cases, the patient may swallow or receive an injection of a
contrast agent (typically gadolidium) before undergoing an MRI scan. A contrast agent helps to
create a MRI image with better resolution by making portions of the body where the contrast
agent collects appear brighter in the image.
2.4 fMRI
fMRI stands for functional Magnetic Resonance Imaging. It is a technique for measuring brain
activity. It works by detecting the changes in blood oxygenation and flow that occur in response
to neural activity – when a brain area is more active it consumes more oxygen and to meet this
increased demand blood flow increases to the active area. fMRI can be used to produce
activation maps showing which parts of the brain are involved in a particular mental process.
As a brain imaging technique fMRI has several significant advantages:
1. It is non-invasive and doesn’t involve radiation, making it safe for the subject.
2. It has excellent spatial and good temporal resolution.
3. It is easy for the experimenter to use.
Japanese researcher Seiji Ogawa is credited as the father of modern functional brain imaging.
He determined that the changes in blood oxygen levels cause its magnetic resonance
imaging properties to change, allowing a map of blood, and hence, functional, activity in the
brain to be created. This map reflected which neurons of the brain responded with
electrochemical signals to mental processes. He was the first scientist who demonstrated that the
functional brain imaging is depended on the oxygenation status of the blood, the BOLD effect.
The technique was therefore called Blood-oxygen-level dependent or BOLD contrast. Functional
MRI (fMRI) has been used to map the visual, auditory and sensory regions and moving toward
higher brain functions such as cognitive functions in the brain.
2.4.1 How fMRI works
Before discussing about how fMRI works it is necessary to know about the term voxel.
A voxel is a unit of graphic information that defines a point in three dimensional spaces. Since
a pixel (picture element) defines a point in two dimensional spaces with its x and y coordinates ,
a third z coordinate is needed. In 3-D space, each of the coordinates is defined in terms of its
position, color, and density. Think of a cube where any point on an outer side is expressed with
an x, y coordinate and the third, z coordinate defines a location into the cube from that side, its
density, and its color. With this information and 3-D rendering software, a two-dimensional view
from various angles of an image can be obtained and viewed at our computer.
The cylindrical tube of an MRI scanner houses a very powerful electro-magnet. A typical
research scanner has a field strength of 3 teslas (T), about 50,000 times greater than the Earth’s
field. The magnetic field inside the scanner affects the magnetic nuclei of atoms. Normally
atomic nuclei are randomly oriented but under the influence of a magnetic field the nuclei
become aligned with the direction of the field. The stronger the field, the greater the degree of
alignment. When pointing in the same direction, the tiny magnetic signals from individual nuclei
add up coherently resulting in a signal that is large enough to measure. In fMRI it is the magnetic
signal from hydrogen nuclei in water (H2O) that is detected.
The key to MRI is that the signal from hydrogen nuclei varies in strength depending on the
surroundings. This provides a means of discriminating between gray matter, white matter and
cerebral spinal fluid in structural images of the brain.
Oxygen is delivered to neurons by hemoglobin in capillary red blood cells. When neuronal
activity increases there is an increased demand for oxygen and the local response is an increase
in blood flow to regions of increased neural activity.
Hemoglobin is diamagnetic when oxygenated but paramagnetic when deoxygenated. This
difference in magnetic properties leads to small differences in the MR signal of blood depending
on the degree of oxygenation. Since blood oxygenation varies according to the levels of neural
activity these differences can be used to detect brain activity. This form of MRI is known as
blood oxygenation level dependent (BOLD) imaging.
The direction of oxygenation changes with increased activity. One might expect blood
oxygenation to decrease with activation, but the reality is a little more complex. There is a
momentary decrease in blood oxygenation immediately after neural activity increases, known as
the “initial dip” in the hemodynamic response. This is followed by a period where the blood flow
increases, not just to a level where oxygen demand is met, but overcompensating for the
increased demand. This means the blood oxygenation actually increases following neural
activation. The blood flow peaks after around 6 seconds and then falls back to baseline, often
accompanied by a “post-stimulus undershoot”.
The image shown at below is the result of the simplest kind of fMRI experiment. While lying in
the MRI scanner the subject watched a screen which alternated between showing a visual
stimulus and being dark every 30 second. Meanwhile the MRI scanner tracked the signal
throughout the brain. In brain areas responding to the visual stimulus one would expect the signal
to go up and down as the stimulus is turned on and off, albeit blurred slightly by the delay in the
blood flow response.
Researchers look at activity on a scan in voxels — or volume pixels, the smallest distinguishable
box-shaped part of a three-dimensional image. The activity in a voxel is defined as how closely
the time-course of the signal from that voxel matches the expected time-course. Voxels whose
signal corresponds tightly are given a high activation score, voxels showing no correlation have a
low score and voxels showing the opposite (deactivation) are given a negative score. These can
then be translated into activation maps.
2.4.2 Software packages for fMRI analysis
Today, there are a number of comprehensive software packages for fMRI data analysis, each of
which has a loyal following. (See Table 1.1).
Package
SPM
Developers
University College
London
Platform
Open-source
Licensing
MATLAB
FSL
UNIX
Open source
AFNI
Oxford
University
NIMH
UNIX
Open source
Brain Voyager
Brain Innovation
Mac OS X,
Voyager Windows,
Linux
Commercial
(closed-source)
Table 1.1
2.4.2.1 SPM
SPM (Statistical Parametric Mapping) was the first widely used and openly distributed software
package for fMRI analysis. It is developed by Karl Friston and colleagues in the lab then known
as the Functional Imaging Lab (or FIL) at University College London, it started in the early
1990s as a program for analysis of PET data and was then adapted in the mid-1990s for analysis
of fMRI data. It remains the most popular software package for fMRI analysis. SPM is built in
MATLAB, which makes it accessible on a very broad range of computer platforms.
2.4.2.2 FSL
FSL (FMRIB Software Library) was created by Stephen Smith and colleagues at Oxford
University, and first released in 2000. FSL has gained substantial popularity in recent years, due
to its implementation of a number of cutting-edge techniques. FSL includes an increasingly
powerful visualization tool called FSLView, which includes the ability to overlay a number of
probabilistic atlases and to view time series as a movie.
2.4.2.3 AFNI
AFNI (Analysis of Functional Neuro Images) was created by Robert Cox and his colleagues, first at the
Medical College of Wisconsin and then at the National Institutes of Mental Health. AFNI was developed
during the very early days of fMRI and has retained a loyal following. Its primary strength is in its very
powerful and flexible visualization abilities, including the ability to integrate visualization of volumes and
cortical surfaces using the SUMA toolbox. AFNI’s statistical modeling and inference tools have
historically been less sophisticated than those available in SPM and FSL.
2.5 Occipital Lobe
The occipital lobes are the center of our visual perception system. In the brain,
the occipital lobe is located in the back of the head. It is actually two lobes, with a right and left
side corresponding to right/left brain hemispheres. The occipital lobe is responsible for
controlling most vision and visual processing. Thus, while a person may not see out the back of
his or her head, they see from it and this area of the brain interprets the images taken in by the
eyes.
The occipital lobe is located behind the parietal lobe, and it sits on top of the membrane that
separates it from the cerebellum, which is below it. As mentioned, its function tends to be most
associated with vision. Not only does it allows us to see and identify objects, but it also does
things like see colors and interpret that each color is different. Occipital function is involved
early in life as infants begin to see. As children receive visual stimuli, it gets sent to
the occipital lobe and is ultimately interpreted. More acute vision and ability to understand
images occurs as children grow.
The work of the occipital lobe not only helps with visual recognition but can also help
understand and differentiate between shapes. Things like basic geometric understanding of
shapes would be impossible without this brain area. The brain would lack the capacity to
distinguish between objects like circle/square or triangle/pentagon. Moreover, trying to interpret
an alphabet could be challenging with this processing section, since differences in letters often
come down to slight changes in shape.
2.6 Region of Interest (ROI)
A Region of Interest (ROI) is an area of an image that is graphically selected from a window
displaying that image, in which anyone wants to focus his image analysis. This area can be used
to focus further processing.
2.6.1 Region of Interest in fMRI
ROIs can be defined either in terms of structural or functional features. Structural ROIs are
generally defined based on macro anatomy, such as gyral anatomy. In many cases, the best
practice is to define such ROIs for each subject based on their own anatomy, since there can be
substantial variability between individuals in macroscopic anatomy. Recent developments in
automated anatomical labeling offer the promise of highly reliable labeling of cortical and
subcortical structures in individual anatomical images with a minimum of manual intervention
(Fischl et al.,2004), though it will remain important to confirm these results against the actual
anatomy. One common practice that requires extreme caution is the use of ROIs based on singlesubject anatomical atlases, such as the AAL atlas (Tzourio-Mazoyer et al., 2002) or the Talairach
atlas (Talairach and Tournoux, 1988); because of the inability of spatial normalization to
perfectly match brains across individuals, there will be substantial lack of overlap between any
group of subjects and these atlases (Nieto-Castanonet al., 2003). If it is necessary to use atlasbased ROIs (i.e. ROIs not derived from one's own subjects) then the best practice is to use ROIs
based on probabilistic atlases of macrosopic anatomy (Hammers et al., 2003; Shattuck et
al., 2006) or probabilistic atlases of Brodmann's areas which are available as part of the SPM
Anatomy Toolbox (Eickhoff et al., 2006).
Functional ROIs are generally based on analysis of data from the same individual. One common
approach is to use a separate ‘localizer’ scan to identify voxels in a particular anatomical region
that show a particular response (e.g. voxels in the fusiform gyrus that are more responsive to
faces than other objects); these voxels are then explored to examine their response to some other
manipulation. Alternatively, functional ROIs can be created using orthogonal contrasts in a
factorial design (Friston et al.,2006). Exploratory ROIs are often created by placing small
spheres at local maxima in the statistical map; this provides a set of ROIs that span the clusters of
interest. Because the goal of exploratory ROIs is not statistical control, it is also acceptable to
place ROIs in anatomical areas of interest (using one's best judgment about the placement),
particularly for examining null results; however, it is again critical to note that although this kind
of analysis can be useful for exploration it must not be used for inference since it is heavily
biased.
One additional way that ROIs can be created is based on previous studies. Although one can take
the stereotactic coordinates from an activation in single study and place an ROI at that location,
it is better practice to derive ROIs from meta-analyses of the domain or task of interest. There are
now well-established methods for meta-analysis of functional imaging studies (e.g. Turkeltaub et
al., 2002; Wager and Smith, 2003), and these methods can be used to generate ROIs that will be
less sensitive to noise than those based on single-study activations.
The colored region in the fMRI image (below) is the ROI and the axis are also mentioned.
Figure : ROI in fMRI
Source: http://www.nature.com/mp/journal/v13/n3/fig_tab/4002058f3.html
2.6.2 Coordinates of ROI
The standard space coordinates are: X=−30 to−54,Y=−45 to−70 and Z=−30 to −4. This region is
sometimes referred to as the “visual word form area” (Dehaene et al., 2005; McCandliss et al.,
2003), although the term is misleading as it suggests a functional specificity which is not present
(Price and Devlin, 2003, 2004). The lateral OTC mask encompassed lateral posterior fusiform
gyrus, posterior OTS and lateral parts of posterior ITG —areas consistently activated by visual
objects and collectively known as the “lateral occipital complex” (Grill-Spector et al., 1999;
Malach et al., 1995). The standard space coordinates are X=−33 to−56,Y=−67 to−89 and Z=−20
to +4. Within each mask, only voxels with at least a 20% chance of being grey matter were
included based on an automatic tissue segmentation algorithm (Zhang et al., 2001).
2.6.3 Tools for ROI analysis
There are a number of tools available for ROI analysis. Several common software packages (e.g.
SPM, AFNI, BrainVoyager) include tools for ROI analysis. In addition, the MarsBar tool for
SPM and the FSL ROI Toolbox provide tools for performing ROI analyses on data from these
packages as well.
References:
1. Computer Pioneer Award Winner Arthur Samuel [1901-1990], 1959.
2. The Cardiac Atlas, Auckland MRI research group, National Heart Foundation Of New Zealand,
atlas.scmer.org/mri.html
3. http://www.webmd.com/a-to-z-guides/magnetic-resonance-imaging-mri