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Biological Psychology > Brain Imaging (CD) > Flashcards

Brain Imaging (CD) Flashcards

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1
Q

BRAIN IMAGINING TECHNIQUES

A 
STRUCTURAL
- CT
- MRI
FUNCTIONAL
- PET
- fMRI
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2
Q

CT

A
  • moderately invasive via X-rays (ionising radiation)
  • inexpensive
  • widely available
  • low spatial resolution (useful clinically NOT scientifically)
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3
Q

MRI

A
  • non-invasive/innocuous via RF fields (radio frequency) to acquire images
  • extremely high spatial resolution (1mm^3)
  • no1 structural brain imaging choice in neuro research
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4
Q

PET

A
  • moderately invasive via radioactivity
  • measures indirect metabolic correlates of neural activity (blood flow/glucose metabolism); can also measure direct synaptic transmission (ie. via labelling receptors)
  • high spatial resolution; extremely low temporal resolution
  • extremely expensive
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5
Q

fMRI (FUNCTIONAL MAGNETIC RESONANCE IMAGING)

A
  • non-invasive/innocuous
  • measures indirect metabolic correlates of neural activity (blood flow/oxygen consumption)
  • high spatial resolution (3mm^3)
  • low temporal precision as measures slow processes
  • moderately expensive
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6
Q

MRI SCANNERS

A
  • originally designed for structural imaging; functional recently
  • incredibly strong magnet (1.5-7 tesla)
  • 1.5 tesla = 15k gauss
  • Earth’s magnetic field strength = 0.5 gauss
  • no metal included
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7
Q

BOLD (BLOOD OXYGEN LEVEL DEPENDENT) SIGNAL

A
  • active neurons = blood flow to brain to provide oxygen to fuel the cells
  • hemoglobin (iron-containing oxygen transporting blood protein) difs in response to magnetic fields depending on if it has bound oxygen molecule
  • MRI scanner (giant magnet) detects these small changes in magnetic field
  • fMRI = measures magnetic properties of oxygenated VS deoxygenated blood (brains don’t just “light up”)
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8
Q

FMRI EXPERIMENTAL DESIGN

A
  1. Design task to be used in scanner.
  2. Collect data.
  3. Pre-process data.
  4. Analyse data.
  5. Interpret results.
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9
Q

FED 1: THE BASELINE

A
  • BOLD signal = arbitrary; no stable baseline
  • most important aspect of any fMRI exp = provide both experimental/baseline condition
    KANWISHER et al (1997)
  • good baseline = difs from exp condition only via process of interest (ie. face processing)
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10
Q

FED 1: BLOCK VS EVENT-RELATED DESIGNS

A
  • block design = grouping trials together
  • event design = dif condition trials randomly intermixed; occur close together in time (allow more complex/novel exps)
  • BOLD signal = slow (peaks 4.5s post stimulus onset; takes 16s to return to baseline)
  • all fMRI exps originally employed block designs (long periods of alternating task/baseline performance)
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11
Q

FED 1: BLOCK DESIGN LIMITS

A

HIGHLY PREDICTABLE STIMULI OCCURANCE
- subjects know what’s coming; may alter strategies accordingly (not always pro)
INFLEXIBLE FOR COMPLEX TASKS
- “oddball” stimuli impact OR stimuli/events occurring uncontrollably?
ECOLOGICAL VALIDITY
- does blocking trials change targeted psych process?
CAN’T SEPERATE TRIALS VIA PERFORMANCE
- (ie. to look at activation associated w/correct/incorrect response)

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12
Q

FED 1: EVENT DESIGN POSITIVES

A

FLEXIBILITY/RANDOMISATION
- eliminate block predictability
- avoid practice effects
POST HOC SORTING
- (ie. correct/aware/remembered/fast VS incorrect/unaware/forgotten/slow RTs)
CAN LOOK AT NOVELY/PRIMING
- (ie. P300)
CAN LOOK AT TEMPORAL DYNAMICS OF RESPONSE
- dissociation of motion artifacts via activation
- dissociate components of delay tasks
- mental chronometry

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13
Q

FED 2: COLLECTING DATA

A
  • 2-3s to collect single volume
  • to ref each point in brain in 3D space we divide image into cubes/voxels
  • typical voxel = 3x3x3mm (refer w/x, y, z coordinates)
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14
Q

FED 3: PRE-PROCESSING STEPS

A
  • correcting for non-task related variability in exp data (getting rid of “noise”)
    1. HIGH PASS FILTERING
    2. MOTION CORRELATION
    3. SLICE TIME CORRELATION
    4. COREGISTRATION
    5. NORMALISATION
    6. SPATIAL SMOOTHING
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15
Q

FED 3: HIGH PASS FILTERING

A
  1. HIGH PASS FILTERING
    - remove low frequency oscillations ie. scanner drift that introduce data noise
    - standard low pass filter = approx 120s
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16
Q

FED 3: NORMALISATION

A
  1. NORMALISATION
    - MNI (Montreal Neurological Institute) Space = combo of 352 MRI scans on normal controls
    - done to compare activation across subjects for group analyses
17
Q

FED 3: SPATIAL SMOOTHING

A
  1. SPATIAL SMOOTHING
    - application of Gaussian kernal
    - why? as neurons don’t fire in isolation; if one fires, close ones tend to too
    - smoothing attempts remodelling of data according to neuron property
18
Q

FED 4: ANALYSIS

A
  • multiple regression determines effect of many IVs/conditions on single DV/brain activation
  • for each voxel we use multiple regressions to estimate how closely BOLD signal correlates w/time-course of each condition
  • finally perform contrast (simple t-test comparing beta condition 1 beta values to condition 2 beta values)
19
Q

FED 4: T-TEST CALC

A
  • calc of t-statistic p/130k brain voxels = “in which regions is activation greater in c2>c1?”
  • raw t-map indicates t-test magnitude via colour scale (yellow = > t-values)
  • applied threshold defines STATSIG acceptance in activation between 2 conditions
  • essentially arbitrary (as in any stat procedure)
20
Q

FED 4: THRESHOLD EFFECT ON BRAIN ACTIVATION

A
  • where is the line drawn?

- standard alpha level in psych research = p<0.05 (ie. we accept 5% false positive rate)

21
Q

FED 4: MULTIPLE COMPARISON CORRECTION

A
  • brain imaged divided to 130k voxels = 130k individual t-tests
  • type 1/false positive error chance ^ w/each performed (5%)
  • w/100 independent t-tests we have 99.4% T1 chance
  • 130k tests + p<0.05 = STATSIG voxels guarantee simply via chance
  • v important to correct for multiple comparisons (adjust alpha level/p-value)
22
Q

FED 4: WHOLE BRAIN ANALYSIS

A
  • examining effects on voxel by whole brain voxel basis
    POSITIVES
  • requires no prior hypotheses about involved areas
  • includes entire brain
    LIMITS
  • can lose spatial resolution w/inter-subject averaging
  • can produce meaningless “laundry list of areas” difficult to interpret
  • depends highly on stats/selected threshold
  • multiple comparison problems
23
Q

FED 4: ROI (REGIONS OF INTEREST) ANALYSIS

A
  • restrict out analysis to particular brain region
    POSITIVES
  • hypothesis driven; avoids meaningless lists of activated regions
  • avoids multiple comparisons problem; data summarised in single number p/subject reflecting mean activation across ROI voxels
  • simple; exportable data treated as any; no special software for further analysis
  • generalisable; easily comparable data across studies (ie. meta analysis)
    LIMITS
  • easy to miss things elsewhere in brain
  • not always simple how to define ROIs
24
Q

FMRI LIMITS

A
  • correlative data so can’t say region activated during task/function = essential for it; need converging TMS/neuro-psych evidence for stronger causation inferences)
  • low temporal fMRI resolution; need converging EEG/TMS evidence for finer grained temporal info
25
Q

FMRI PROCESS

A
  1. High pass filtering.
  2. Slice time correction.
  3. Motion correction.
  4. Coregistration.
  5. Normalisation.
  6. Smoothing.
  7. 1st level data analysis.
  8. 2nd level data analysis.
  9. Thresholding.
  10. Overlay on anatomical template.
  11. Data interpretation.

Biological Psychology (21 decks)

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