Lecture 6: Fundamentals of fMRI - Part 2 Preprocessing Flashcards
What is intersubject variability? - (2)
Variability in fMRI data across a set of subjects; it
includes the factors associated with intrasubject variability, along with between-subjects differences in task
performance and physiology.
e.g., one participants’ area of brain may react quicker than same brian in another participan
What is intrasubject variability?
- Variability in the fMRI data from a single subject
associated with thermal, system, and physiological noise, as well as with variability in the pattern of brain activity during task performance.
What does this diagram show? - intersubject and intrasubject variability in hemodynamic response in participants
- Here, hemodynamic responses derived from four different brain regions are plotted for 20 subjects within the same experiment. - (5)
- Although there are substantial differences between
individuals in the timing of their hemodynamic responses, with some peaking earlier and some later than the canonical hemodynamic response (lines), - there is relatively good consistency in the timing of the hemodynamic response across regions within the same individual. (After Handwerker et al., 2004.)
- show general pattern which is good which is needed for analysis - (standard) canovical HRF is used for analysis
- In some areas of the brain, some participants’ responses does not allign too well with standard (canovial )HRF than others - inter subject
- Variation between people and variation in different parts of brain in participant e.g., subject 4 each part of brain has different hemodynamic response
Diagram - providing evidence that within subject is better than between subject + consistency of responses - (5)
- Dave (on left) going in both as experimenter and participant in MRI scanner for a very long period of time and doing tasks being exposed to visual stimuli
- This study wanted to see whether Dave would have the same BOLD signal responses on different occasions
- In A, show patterns of BOLD signal varied over time with voxel signifiance threshold being strict
- In B, when lowered voxel significance threshold, see similar BOLD signal activation across each occasions shown in each task
- The concept is that within a given pp and given brain region, we see consistent responses in hemodynamic response in given task –> fMRI data provides reliable link to underlying neural activity
Due to inter-subject and intra subject variation, within-subject design is needed as… - (2)
- If we did between-subject and looked at different people’s brain we will be comparing very unlike things potentially
- Big advantage statistically of using within subject of seeing how the same brain area of the same participant behaves in different tasksif possible
What is the key characteristics of BOLD signal? - (4)
- Sensitive
- Reliable
- (Approximately) Linear
- Sluggish
The characteristic of BOLD signal being sensitive is that
Visual stimulus as brief as 5ms can produce measureable signal (Yeşilyurt et al., 2008)
The characteristic of BOLD signal being reliable means - (3)
- Responses found across the whole brain (grey matter)
- Consistent within-subject and region
- BUT Varies between subjects and brain regions
The characteristic of BOLD signal being approximately linear means - (2)
- Signals scale with stimulus intensity/neural activity
- Signal from successive stimuli sum together (where stimulus durations > several seconds) - at least if keep stimulus/task changes far enough apart in time
The characteristic of BOLD signal being sluggish means
Response takes around 5s after stimulus onset to peak and up to 15 s to return to baseline.
The implications of characteristic of BOLD signal for experimental design - (5)
- Inter-subject variation means that between subject designs are less favourable and typically avoided unless essential to the research question.
- Within subjects designs are favoured where possible – they make use of the reliability of BOLD signals measured repeatedly in the same part of the same person’s brain.
- The sluggish nature of hemodynamic responses relative to neural activity means that fMRI experiments have a natural tempo.
- Task manipulations occur at a rate designed to elicit large hemodynamic changes over several seconds.
- The (approximate) linearity of the BOLD signal on this timescale means that signals elicited by different tasks can to some extent be “unpicked” and decomposed into different neural activity using linear models (GLM, next week).
To increase ampltidue of BOLD signal is to increase the
scanner field strength (T - Telsa)
As static field strength increase in scanner, the - (2)
raw BOLD signal changes r increases quadratically
Thus 3.0T scanners measure 4x as much of raw signal than 1.5 T
What has Turner and colleagues found of effects of field strength in fMRI - (2)
- Turner and colleagues measured changes in visual cortex activation at 1.5 T and 4.0 T.
- Approximately 2 to
3 times as much signal was recorded at the higher field strength..
Greater field strength has 2 main effects on spatial distribution of activation - (3)
- Improvements in spatial specficity e.g., 7.0T used and identify spatial patterns of activity that reflect ocular dominance columns
- Result in better sensitivty to signal changes from blood vessels
- Increases spatial extent of activation - numbers of voxels within a region
What happens to T1 and T2 when increasing magnetic field strength (T) in fMRI? - (2)
- The parameter T1 increases with field strength (by about 30% from 1.5 T to 3.0 T), and this could reduce the effective signal recovery for short TR values.
- The parameter T2* decreases with increasing field strength,
and this could reduce the time available to acquire a signal.
What is suspectibility artefacts?
Signal losses on T2* dependent images due to magnetic field inhomogenities in regions where air and tissue is adjacent (i.e., around sinuses - nose)
The problem with high-field MRI is that - (4)
- Suspectibility artifacts disorts the uniformity of magnetic field
- Suspectibiltiy artiefactts increase at higher field strengths at air tissue boundaries
- Both images scanned at 1.5 T (A) and 4.0 T (B)
- B shows areas of singal loss in ventral lobe in darker patch and more extensive in 4.0 T than 1.5 T
To minimise the unwanted variability in BOLD signals nearly all fMRI studies incorporate
preprocessing algortihms which compensate for specific sources of variability (e.g., head motion)
What is preprocessing? - (2)
computational processing procedures that are applied to fMRI data that follow image reconstruction but before statistical analyses
Preprocessing steps are intended to reduce variability in the data that is not associated with experimental task to prepare the data for statistical testing
Example of signal to noise - (2)
Signal = freind’s speech
Noise = other sounds that interfere with your ability to hear friend
What is signal in fMRI? - (2)
Meaningful changes in some quantity
For fMRI an important class of signals include changes in intensity associated with BOLD resposne across a series of T2* images
What is noise in fMRI? - (2)
Nonmeaningful changes in some quantity.
There are many sources of noise in fMRI studies, and some changes may be classified as either noise or signal, depending on the goals of the study.
We can improve ability to detect fMRI signal by - (2)
increasining its ampltidue or decreasing its noise
Thus increasing signal-to-noise ratio
What is signal to noise ratio?
relative strength of signal compared with other sources of variability in the data
The quantity measured in fMRI which is MR signal reflects both changes in
- net magnetisation caused by excitation pulse (signal) and fluctuations caused by thermal energy in sample and imaging hardware (noise)
When is raw SNR used?
evaluate the performance of the scanner hardware, and institutions compare such measures of SNR when
deciding which MRI scanner to purchase and when monitoring the quality of an MRI scanner over time.
Contrast of MRI image refers to
physical property to which it is sensitive
For example, an image senstivie to T1 contrast will be… - (2)
- An image sensitive to T 1 contrast will be bright for voxels with short T1 values (like white matter) and dark for voxels with long T1 values (like gray matter or cerebrospinal fluid).
- Thus,T 1 weighted pulse sequences have a good ability to distinguish between gray matter and white matter (high CNR), but only a limited ability to distinguish between cerebrospinal fluid and air (low CNR).
CNR stands for contrast-to-noise ratio which is
magnitude of the intensity differences between quantities divied by variability in their measurements
Diagram of noise in fMRI - (4)
- (A) noise from all sources
- (B) physiological noises due to variation in blood flow and merabolic processes
- (C) noise due to bulk head motion and cardiac and respiration (D)
- (E) and (F) show data collected from fluid-filled ball and can’t escape from noise (F)
What does this diagram show - reproducibility of fMRI activations across sessions in one participant - (4)
- Participant attended 33 fMRI sessions each containingmotor, visual and cognitive tasks
- Experimental procedures were repeated in same manner
- In A, it shows different patterns of activations evoked in different sessions based on voxels passing convential sig criteria of p < 0.05
- In B, analysis showed there was activation differences found between sessions was large
Most labs to prevent head motion is
head restraints (A), (B) bite bar, (C) thermoplastic molds to participants’ head or not shown in image is participate in training sessions with MRI scanner in mock scanner
What does this diagram show? - effects of head motion on fMRI data - (4)
(A) Following a discrete movement of the head, large-intensity transitions exist at tissue boundaries,
including the edges of the brain.
(B,C) The effects of head motion on voxel
intensity. The magnified views show the position of the brain before head motion (B) and after a movement of one voxel to the right (C).
The numeri- cal intensity values for the voxels within the blue square are shown below.
Note that the intensity in a given voxel may change by more than a factor of 5 due solely to head motion.
What does this diagram show? edge effects of head motion in fMRI analysis - (5)
Edge effects of head motion in fMRI analyses. Because the intensity transitions in the brain are greatest at its edges, head motion often results in systematic rings of artifactual activation around the edges of the brain.
Shown are activation maps, in four axial slices, derived from the analysis of a motor task
Here, however, the brain moved forward by two voxels at the beginning of one stimulus onset and remained for-
ward for 4 s.
The result was large changes in signal intensity around the edges in the brain; for example, voxels at the back of the brain changed from high intensity to low intensity as a result of the movement.
Thus, these voxels had a significant decrease in activation (plotted in a blue-to-white color map) due entirely to the
movement, and these movement-related effects dwarf the significant task-related increases in activation (plotted in a red-to-yellow color map) within motor cortex.
What does these diagram show - plots of head mtion over an experimental session - (7)
Plots of head motion over an experimental session.
Shown are plots of translational (A) and rotational (B) head motion from a single fMRI session.
By convention, translational effects comprise movements from left to right (x-axis), forward to back (y-axis), and top to bottom (i.e., in the slice acquisition direction; z- axis).
Rotational effects comprise turns around the x-axis (pitch), around the y-axis (roll), and around the z-axis (yaw).
This experiment consisted of seven runs, each of
410 images, with a TR of 1500 ms.
Large motions between runs are visible as verti-
cal lines on the plot; for example, see the vertical line near image 2050 that reflects an upward through-plane movement.
Note that these are estimated motion values
at each point in time and thus can be influenced by a number of factors besides
head motion itself, as indicated in the text.
Spatial smoothing - (3)
Many sources of noise have a small spatial scale, whereas BOLD signal changes tend to operate on a larger scale.
Spatial smoothing works by “averaging” the signal in each voxel with its neighbours.
Noise tends to cancel out, while signal is boosted.
What does this diagram show? - temporal filtering
Noise from the pulse and breathing produces much higher frequency signal changes than signal changes induced by task changes (here 20s blocks of motor activity alternating with 20s rest)
What does this diagram show?
Other sources of noise can be very low frequency, e.g., “scanner drift” in which MR signal gradually waxes and wanes over many minutes.
Diagram of raw signal to high pass filtered
