Chapters 6 & 7

 

*          Focus on location in the brain, and ability to actually recognize and label views of the brain (q’s 9-12, but make sure you know what these look like!)

 

 

 

1) Front of the brain- Anterior/ rostral              

2) Back of the brain - Posterior/ caudal

3) Top of the brain- Superior/ dorsal,

4) Bottom of the brain -Inferior/ ventral,

5) Close to the midline- Medial

6) Close to the sides- Lateral

 

 

 

Sagital: A slice from caudal to rostral  

Coronal: from dorsal to ventral orthogonal to sagital

Axial: Horizontal plane

 

 

 

Occipital Lobe - The posterior lobe of the brain that deals with visual processing

Temporal Lobe - The lobe on the ventral surface of the cerebellum that deals with auditory and viual processing, language, memory, and other functions

Parietal Lobe - The posterior-dorsal lobe that is important for spatial processing, cognitive processing, and many other functions.

Frontal Lobe - The most anterior lobe of the cerebellum, which deals with executive processing, motor control, memory, and other functions

Limbic Lobe-  Related to emotional processing and olfactory.

 

 

 

 

*Provide a coherent explanation of the steps relating neuronal activity relates to BOLD signal. Include the following:

What influences blood flow? (Ch6, q18)

Neuronal activation causes the surrounding arterioles to dilate. This dilation can be as large as a 33% increase that was found in rats by Ngai, Winn, and colleagues. The authors had a sensory stimuli and measured the arteriole dilation increases as a function of neuronal activity. They found that not only did the local arterioles dilate and increase blood velocity, but arterioles upstream from the region they were inspecting dilated as well. This is bad news for the spatial resolution of MRI when using hemodynamic blood response as an indicator of the spatial indicator of neural activity. This experiment showed that a relatively large region around an active neuron has increased blood flow, therefore limiting the spatial resolution that can be achieved using an MRI scanner.

What magnetic properties is the BOLD sensitive to? (Ch7, q1)

Oxygenated hemoglobin is diamagnetic which means it has no unpaired elections and zero magnetic moment; deoxygenated hemoglobin is paramagnetic which means it has both unpaired electrons and a significant magnetic moment.  Bold is sensitive to deoxygenated hemoglobin.

What does BOLD stand for? (Ch7, q 6)

   Blood-oxygenation-level dependent

What causes BOLD contrast? (Ch7, q7)

BOLD contrast is due to the difference in signal on T2 weighted images as a function of the amount of deoxygenated hemoglobin. Neuronal activity causes increased metabolic demands and thus, increased oxygen consumption. This increases the amount of deoxygenated hemoglobin, given a constant blood flow. The second mechanism is that increased blood flow in the absence of increased metabolic demand would decrease the amount of deoxygenated hemoglobin.  Thus, the difference we see is a qualitative measure of the amount of deoxygenated hemoglobin. 

What influences spatial resolution of BOLD? (Ch7, q9)

Oxygenated blood is distributed to more of the brain than just the active parts

Why might BOLD be thought of as a quirk? (Ch7, q 12)

There must be an uncoupling of oxygen supply and oxygen consumption for BOLD contrast to be useful for functional neuroimaging because fMRI is based on the detection at the macroscopic level of changes in the microscopic magnetic fields surrounding red blood cells. Going from oxygenated to deoxygenated changes its magnetic properties and thus, is the reason we can see contrast.

What special type of BOLD effect might have better spatial resolution, but has yet to be clearly seen (Ch7, q13)

               Initial dip = initial increase in deoxyhemoglobin. The initial dip is hard to demonstrate conclusively because it isn’t frequently observed as it is rare to have high-field (>4T) MR scanners for functional studies. 

 

 

 

Chapter 9 focus on following:

 

Define ‘signal’ and ‘noise’ (q1)

Signal is the meaningful changes in some quantity.  In fMRI, an important class of signals includes changes in intensity associated with the BOLD response across a series of T2* images.  On the other hand, noise is the non-meaningful changes in quantity.  In fMRI, there are many sources of noise and some can be classified as either noise or signal, depending on the study.

 

 

Describe different types of signal to noise (q2)

 

Raw signal-to-noise-ratio is the ratio between MR signal intensity and the intensity of the thermal noise that is measured outside the sample. When designing an MR machine, engineers strive to minimize this ratio to create an optimally functioning MR unit. Contrast-to-noise-ration is the magnitude of the intensity difference between different quantities divided by the variability in their measurements. CNR is used to identify differences between tissues. Depending on the resolution of the CNR, the ratio between different tissue samples can be found. When CNR increases, the variability increases, but the difference between tissues is more greater. Functional signal-to-noise-ratio is the ratio between the intensity of a signal associated with changes in brain function and the variability in the data due to all sources of noise. CNR depends upon the intensity difference between voxels, whereas fSNR depends upon the intensity difference with a voxel, or group of voxels over time. fSNR is a main concern in fMRI studies because it shows the difference over time of a given region.

 

What are common forms of physiological noise (q7)

 

Physiological noise is commonly attributed to muscles contracting with each breath and heartbeat of the patient.  Also, blood pulses through arteries and veins in the subject.  The metabolic demands on neurons that drive chemical reactions needed to sustain life can create this physiological noise.  Also, the subject shifting position or swallowing can create this.  However, cardiac activity is the main form of physiological noise.

 

How does the balance between different types of noise change with field strength (thermal and physiological) (q15)

 

Thermal noise scales linearly with the field strength, so a 2.0-T scanner measures 2 times as much thermal noise as 1.5-T scanner.  When one divides the quadratic increase in signal by the linear increase in noise, find that the raw SNR only increases linearly with the field strength.  While thermal noise increases linearly with increasing field strength, physiological noise increases quadratically with the field strength.  So, as field strength increases from 1.5-T to 3.0-T, raw signal will quadruple, thermal noise will double, and physiological noise will quadruple.  Physiological noise may become dominant and the improvement of functional SNR with increasing field strength may be considerably less than linear.  This implies that increased functional SNR has an increased spatial extent of activation. 

 

Chapter 10 focus on the following

What is preprocessing vs. experimental analysis? (q1)

 

Preprocessing is the computational procedures that are applied to fMRI data following image reconstruction but before statistical analysis. Preprocessing steps are intended to reduce variability in the data that is not associated with the experimental task and to prepare the data for statistical testing. Experimental analysis is the analysis done after preprocessing steps are taken to reduce variability in the data such as head movement and other artifacts in the image.  

 

What is the first rule of quality assurance? (q2)

 

The first rule is to examine your data. Many common artifacts are readily visible in the raw images even under cursory examination. You may do this as a time-series movie. This method of viewing the data may seem crude, but the human visual system is well equipped at picking up change over time, and with the time-series movie of MR data, one would be able to notice any glitches and big errors.

 

Why do we do slice timing corrections? (q4)

 

Each slice is acquired at a different time point within the TR (repetition time). Through small plane motions, as when a voxel moves from slice 12 to slice 13, the timing of activity will be off by one half of the TR value at affected time points. Thus slice timing correction first for interleaved sequences with long TR's will minimize timing errors.

 

How do researchers correct for head motion (q9)

 

For motion correction, successive image volumes in the time series are coregistered to a single reference volume. Because the brain is the same in every image of the time series, a rigid-body transformation is used. Rigid body transformation assumes that the size and shape of the two objects to be coregistered are identical and superimposes them onto each other through translations and rotations. Spatial interpolation, the estimation of intensity of an image at a special location that was not originally sampled using data from nearby locations, would also be done. This is done so that you can estimate the values that would have been obtained had there been no head movement.    

 

Why do coregistration? (q11)

 

Coregestering allows us to map our functional data onto high-resolution and high-contrast structural images from the same subject. We would want to do this for 2 reasons. The first occurs when the two types of images were acquired at different locations, either because different slices were wanted for each or because the subject moved slightly between their acquisitions. A second reason for function-structural coregistration is image distortion.

 

Why do normalization? (q13)

 

Spatial normalization allows us to compensate for shape differences of the brain by mathematically stretching, squeezing, and warping each brain so that it is the same as every other brain. This allows combination of data across individuals. 

 

What are the problems with normalization? (q16)

 

Normalization is based upon gross morphological features of brains and these features vary among individuals. Furthermore, these gross features do not necessarily indicate functional divisions between brain areas. Even when using cytoarchitecture instead of gross anatomy, boundaries between cytoarchitectonically distinct regions are highly variable.

 

Why smooth? (q 17)

 

Because all subjects’ brain differ from one another in shape and size, and potentially functional organization, areas of activity are rarely represented in exactly the same voxels. Instead, combining data across many subjects distributes activity across a range of voxels and improves SNR. Smoothing not only increases the signal to noise ratio of each voxel, it also reduces the number of resolution elements that are assumed to be independent and used for correction of multiple testing.  Spatial smoothing and signal averaging both serve to increase the SNR. Spatial smoothing, however, combines data from multiple voxels, instead of data from multiple trials, in order to get a more robust SNR.

 

      Explain the problem of multiple comparisons in brain imaging (q18)

 

 

 

The multiple comparison problem is the increase in the number of false-positive results with increasing number of statistical tests. It is of particular consequence for voxelwise fMRI analyses, which may have many thousands of statistical tests. In short, if you set the significance at alph < .05, then there should be more than 5000 voxels active due to chance alone because a typical functional imaging volume has more than 100,000 voxels. If you don’t correct for multiple comparison, you will get random significance.

 

 

 

 

 

What is temporal filtering? Why do low pass filtering? Why do high pass filtering? (p21)

 

Temporal filtering allows one to selectively attenuate frequencies around a certain range while keeping intact other frequencies. This is useful when one wants to eliminate certain variabilies such as heart rates. Typical heart rates during an fMRI experiment vary, but are often between 1 to 1.5 Hz. For comparison, a typical experimental design might present alternating blocks of 12s of task and 12 s of rest, for a total presentation rate of .04 Hz. One can use a low pass filter to exclude frequencies above .2 Hz to remove physiological oscillations such as heart rate.