Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

intro to neuroimaging > intro to imaging analysis and data L3 > Flashcards

intro to imaging analysis and data L3 Flashcards

1
Q

what are the main neuroimaging data formats used in analysis?

A
  • DICOM
  • Analyse 7.5
  • NiFTI
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

what are the typical preprocessing steps for neuroimaging data?

A
  • correcting for mechanical artefacts
  • poor signal-to-noise ratio
  • slice timing corrections
    -realignment
    -normalisation
    -spatial smoothing
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

what are the 6 degree of freedom (DoF) in rigid body registration?

A

3 translations (X,Y,Z) and 3 rotations

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

describe the characteristics of analyse 7.5 and NifTI formats

A
  • analyse 7.5 only has info about image itself
  • NifTI is an improved version of analyse 7.5 that adds MR parameters to the header like Tr and Te and voxel dimension
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

what is slice-timing correction and why is it necessary?

A
  • it accounts foe the fact that different brain slices are sampled at different time points in the BOLD repsonse
  • this is because we sample the brain in lots of 2d slices rather than getting a 3D image simultaneously
  • without correction the time series data may be mixture of signals from different parts of the brain
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

what do MRI scanners output the data as? and what is it then converted to?

A
  • output in the format of DICOM
    which ahs both image data and metadata( patient details)
  • but since its not suitable for neuroimaging it is converted ti NifTI or Analyse 7.5
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

NifTI vs Analyse 7.5? whats the difference

A

nifti:
- has parameters that analyse 7.5 doesnt have like:
- Tr, Te, voxel size
- can handle 4D
- for more specific parameters

Analyse 7.5:
- has basic information about the image like dimension/orientation
- limited to only to 3D data and thus cant handle time0series data like fMRI

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

define the preprocessing tricks simply:

A
  • realignment = corrects for patient movements
  • normalisation = aligns brain images to a standard template
  • co-registration = aligns functional (fMRI) and structural(anatomical) data from the same subject
  • smoothing = applies filters to reduce noise and enhance signal
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

what is the difference between fMRI and MRI ?

A

fMRI ( function):
- measures the brain activity by detecting changes in blood 02 levels, allowing for the study of functional processes in the brain

MRI (structure):
- detail of anatomical images of brain and body structure

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

explain the concept of interpolation in slice-timing correction

A
  • interpolation replaces a value based on the two adjacent time points and the reference slice
  • ## sync interpolation is more commonly used than linear interpolation to avoid signal distortion
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

describe the process of realignment in fMRI data preprocessing? and what gets involved in terms of RBR( rigid body resgistration)?

A
  • realignment ensures that each voxel samples the signal from the same part of the brain over time by correcting for head movement
  • moves each volume towards the first or average volume using rigid body registration
  • rigid body registration involves translation and rotation along the X,Y,Z axes resulting in 5 DoF
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

what are the challenges associated with head motion in fMRI?

A
  • movement greater than 1 voxel is a problem
  • realignment minimises the mean square error between adjacent brain volumes to correct for motion
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

explain the two steps involved in normalisation

A
  1. Affine (liear) transformation= involves 12 parameters:
    - translation
    - rotation
    - stretches
    - shears
    and makes the brain approx the same shape and size of the template ( Talairach Atlas)
  2. Non linear warping/deformation = this optional step uses DCT ( discrete cosine transform) ro refine the alignement
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

describe the role of DCTs and regularisation in non-linear warping

A
  • show local deformation of the brain images
  • they are flexible so we need to make sure that the adjustments don’t distort the actual shape of the brain
  • regularisation minimises the mean square error between the template and source image ,AKA keeps the changes realistic
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

what is co-registration and why is it used?

A
  • EPI images can lose signal esp in the frontal and temporal regions of brain.
  • to improve normalisation accuracy, co-registration is used
  • co reg aligns functional (EPI ) images and structural images
  • since functional and structural images have different contrast, methods like MSE ( mean square error) are not good in measuring the alignment so instead mutual information or shared entropy is used to maximise the shared info between 2 types of images
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

what are the benefits of spatial smoothing in fMRI?

A
  • Involves convolving the data with a smoothing kernel= Gaussian kernel
  • it increases the SNR, masks anatomical variability, normalises errors/noise
17
Q

what is the matched filter theorem and its relevance to smoothing?

A
  • states that the kernel size should be equal to the size of the effect of interest
18
Q

explain the aim of the fMRI data analysis

A
  • the goal is to separate each voxel into two parts:
    1. predictors = part explained by the experimental conditions
    2. residual noise
19
Q

Describe the mass-univariate approach in fMRI analysis? with the phases

A
  • it tests each voxel independently meaning the analysis is done on a voxel-by-voxel basis
  • has 2 main phases: single subject responses and group level responses
  • the general linear model(GLM) is used as it explains the changes in the voxels signal over time
20
Q

3 conditions for LTI

A
  1. linear scaling= BOLD response should scale directly proportional to input stimulus
  2. shift invarince= timing the response should depend on when stimulus happens
  3. additivity = réponse to lots stimuli should be sum of the response of each stimuli
21
Q

whats is the role of the hemodynamic function (hrs) in fMRI analysis?

A
  • HRF is a function that shows the delayed and dispersed nature of the BOLD response to a stimulus
  • in fMRI the times when stimuli are shown are combined with HRF using convolution to predict what the signal will look like
  • this convolution assumes that the BOLD response is a linear time-invariant system.
22
Q

what are some considerations regarding HRF model?

A
  • the shape of the BOLD response can vary across everyone and brain regions due to factors like vascular differences and ageing
  • HRF is more for block designs
  • variations can be seen using derivates of the canonical HRF such as temporal derivates and spatial derivates.
23
Q

expain the concept of two-level analysis in fMRI

A
  • 2 level analysis involves seeing data at both the individual subject level ( first level) and the group level ( 2nd level)
  • at first level, each subject’s responses are estimated
  • at 2nd level, group level responses are estimated by combining data from lots of subjects
24
Q

how is low frequency drift addressed in fMRI data analysis?

A
  • the BOLD signal shows low freq which can be removed with a HPF ( high pass filter)
  • HPF removes low frequency components of the signal while keeping higher frequency
  • DCT ( discrete cosine transforms) are used to implement the HPF
25
Q

what is the FIR (finite impulse response) model?

A
  • is good for capturing complex or non-standard BOLD responses , thus flexible
  • but with flexibility comes a risk of overfitting to noise , meaning that model might fit random areas in data rather than the true signal
26
Q

formula for regression

A

y= mx+c+error

27
Q

describe the nuisance regressors in fMRI analysis

A
  • are variables that can affect the fMRI signal
  • variables like : realignment parameters ( head motion), breathing, large motion spikes
  • including nuisance regressors in the model help remove sources of noise that are not needed in task
28
Q

explain the importance of considering autocorrelation in fMRI data

A
  • shows where signals at nearby time points are related
  • not doing this can mess with the results and be biased
  • so ReML ( restricted maximum likelihood) is used to correct for autocorrelation in data
  • ignoring these autocorrelations can led to false positives and overestimating the actual relationship of signal.
29
Q

differentiate between fixed effects, random effects and mixed effects models in fMRI group analysis

A
  • fixed= individual differences within each persons brain activity
  • random= differences in people
  • mixed= to make interference the data is combined
30
Q

why is lots of comparison correction important in fMRI analysis?

A
  • fMRI data has lots of tests one for each voxel
  • without correction the false postives results ( Type 1 error) increase
31
Q

contrast FWER( false family wise error rate) and FDR(false discovery rate) correction methods and Bonferroni are all Multiple comparison. corrections define them

A
  • FWER= controls the probability of any false voxel or cluster in the image
  • uses method like random field theory
  • FDR= controls the proportion of false positive results among the findings
  • Bonferroni p>0.05/n , where n is number of independent tests
  • it changes the threshold to fix the error
32
Q

explain the concept of resolution elements in multiple comparison correction

A

-shows the number of independent tests that are taken
- accounts for spatial correction between voxels due to smoothing
- the number of resolution elements can be calculated as R= v/ smoothness , where v is number of tests

33
Q

define spatial correlation

A

refers to the relationship between values at different locations in space, where nearby locations tend to show similar values or patterns

34
Q

describe the difference between voxel-level and cluster level interference in multiple comparison corrections

A
  • at voxel level, we keep only the strong voxels that are above the threshold as defined by Gaussian random field theory (p<0.05) to say they are active
  • at cluster level:
  • groups nearby voxels into clusters
  • default threshold of p<0.001
35
Q

steps for cluster level

A

1 step:
- set a threshold of p<0.001 to form cluster
- note: if p is low ( less than 0.05) means result is real but if high mean result is random and is thereofre consistent witht he null hypotheses ( means no effect or difference))
2nd step:
- only clusters that are large enough are kept
- this menthod is more sensitive ( better) but lose some detail on excact brain location ( spatial specificty is lost)

36
Q

what is the region of interest (ROI) analysis >

A
  • focuses on specific brain region
  • when using a prior hypothesis and predefines ROI multiple comparison corrections across the whole brain isn’t needed
  • ROI analysis can be done by extracting the signal from the ROI and doing statistical analysis or by using a small volume correction which controls peak level correction to the ROI

intro to neuroimaging (6 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2025 Bold Learning Solutions. Terms and Conditions