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Neuroimaging 2 > Applications in Research > Flashcards

Applications in Research Flashcards

1
Q

define cognition

A

the mental action or process of acquiring knowledge and understanding through thought, experience and the senses

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2
Q

why is it important to study cognition?

A

it may be impaired in a number of contexts, which can be distressing for patients

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3
Q

give the overall hierarchy of cognitive processes and functions

A

see notes

(idea that cognitive mechanisms build on one another to give a more sophisticated function)

*the complete list of cognitive domains is big

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4
Q

why is cognition so hard to measure

A

it’s very abstract/theoretical. Firstly, trying to measure something doesn’t mean you are actually measuring it, we are often using surrogate methods. In addition to this the mind itself is a highly theoretical/abstract concept, hence difficult to define

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5
Q

what are 2 common cognitive tests?

A

1) trail making
2) letter fluency

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6
Q

what does trail making measure?

A

processing speed and aspects of executive function

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7
Q

what does letter fluency measure?

A

semantic abilities, primarily word generation

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8
Q

outline trail making

A
  • first round, subject given piece of paper with numbers or letters scattered randomly and the subject must start at beginning and trace in ascending order, not moving pen from paper. The time to do this is recorded.
  • second round, subject given another piece of paper with letters AND numbers, the participant must again trace these in ascending order but alternating between letters and numbers. Also timed.

time taken to do pt 1 subtracted from pt 2 - gives measure of high cognitive function controlling for motor speed

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9
Q

outline letter fluency

A

participant given a letter of alphabet and in time limit must name as many words starting with this aloud. No proper nouns, no modifying works they’ve already used. correct words counted up.

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10
Q

what is normal distribution/ gaussian distribution/ bell-shaped curve/ parametric sample?

A

where there’s a clear trend for the value of most points to cluster around a central mean with increasing rarity moving away either side

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11
Q

in normal distribution/bell-curve do data need to follow a perfect bell-shape to be determined as normally distributed?

A

no

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12
Q

why is distribution important

A

if variables form a normal distribution, there are a number of mathematical assumptions we can make which determines how we analyse them statistically

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13
Q

what does distribution largely boil down to (at its simplest)

A

if the mean value is a fair way of presenting the average

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14
Q

draw +ve skew, symmetrical and -ve skew distribution charts labelling mean, median and mode for each

A

see notes

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15
Q

what is variance?

A

the average difference of all individual data points from the mean (quantifies how closely data is clustered around the mean)

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16
Q

what is standard deviation (SD)

A

variance is a version f our data after we square everything, so SD uses square root to re-convert variance back in proportion with data

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17
Q

what is the relationship between mean and SD in normal distribution?

A

SD holds a consistent mathematical relationship with mean
as we travel away a number of SDs from mean, % data points included in range increase by a set amount

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18
Q

draw a diagram of normal distribution, mean and SD

A

see notes

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19
Q

more samples (N)=

A

more confidence in findings

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20
Q

what is the letter of significance?

A

p

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21
Q

what is the p-value

A

a number between 0 and 1, essentially a % probability that the data you have observed has happened by chance

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22
Q

what is the p-value convention?

A

if something is p<0.05, there’s less than a 5% probability it happened by chance, it is statistically significant and we therefore can reject the null hypothesis

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23
Q

what are the 2 common statistical designs?

A

1) compare value between groups or before/after - looking for difference (groupwise tests)
2) see how one value changes depending on another (correlation)

24
Q

if we are comparing a score between 2 groups with parametric data what stats test is used?

A

independent sample t-test

25
Q

with a t-test, what do we do once we get a t-value?

A

convert to p-value using degrees of freedom table (DoF N-1)

26
Q

if we are comparing before and after scores for the same group with parametric data, which stats test are we using?

A

paired samples t-test

27
Q

what is the parametric correlational states test?

A

Pearson’s correlation

28
Q

how does a Pearon’s correlation work?

A

we calculate covariance between 2 variables (x and y) to get an r- value between -1 and +1
-1 - perfect negative correlation
0- no correlation
+1 = perfect positive correlation

29
Q

what is the non-parametric equivalent of independent t-test?

A

Mann-Whitney-U

30
Q

what is the non-parametric equivalent of paired t-test?

A

Wilcoxon Signed-rank

31
Q

what is the non-parametric equivalent of Pearson correlation?

A

Spearman rank correlation

32
Q

what is the main difference between parametric and non-parametric tests?

A

non-parametric first rank the data, whereas parametric doesn’t

33
Q

overview multivariate models

A

used when looking at how multiple variables can effect an outcome at once (e.g. the affect of age, brain volume and years of education on cognitive performance)
a single multivariate/multiple regression model is built to test this- this is not the same as separately running single variable analysis and putting together

34
Q

multiple comparisons problem?

A

the more times we run a test, as is the case in multiple comparisons, the more likely we are to get a significant finding by random chance (like the more you roll a dice more likely to roll a 3)

35
Q

what corrects for this multiple comparisons problem?

A

Bonferroni Correction divides 0.05 by the number of comparisons we’re running to get a new threshold e.g. 0.05/20 runs

36
Q

what is experimental control?

A

removing the effect of confounding variables (e.g. testing an active drug against placebo to control for placebo effect)

37
Q

what are mixed study desgisn

A

looking at at least 2 groups in at least 2 conditions e.g. comparing control and placebo (between groups) and baseline and f/u (within groups)

38
Q

what are 2 examples of advanced fMRI designs?

A
  • real-time fMRI
  • multivariate analysis
39
Q

when is it important to think about how you’re going to statistically analyse data?

A

when you’re designing your study

40
Q

what are the 2 overall types of fMRI experimental designs?

A

task-based fMRI and task free fMRI

41
Q

what are the 2 designs within task-based fMRI?

A

block design and event-related design

42
Q

what’s the difference between block design and event related

A

event related is jittered- varying interval lengths between stimuli
block design has stimuli generally for longer periods (longer blocks)
event related allows you to look at more stimuli, but block is more robust for fewer stimuli types

43
Q

what 2 control factors are very important to appropriately choose in fMRI study design?

A

the choice of control condition and the choice of control group

44
Q

what is task-free fMRI

A

aka resting state
participants lay still and told to not think about anything in particular, results in introspection/abstract cognition

45
Q

what have task-free/resting state designs shown?

A

that at rest, the brain is still active in coordinated ways often with same regions (networks) activating together hence important in functional connectivity analysis

46
Q

outline region-based functional connectivity study design

A

usually a region of interest is picked out and activity calculated over time frame and look to see which other areas have activity correlated with ROI time course

47
Q

what is an important factor to remember when undertaking correlational studies

A

that correlational is not equal to causation and may result in spurious correlation (factors shown to be significantly related with no causation)

48
Q

why is fMRI likely to show spurious correlation

A

because large numbers of voxels are being looked at so there is a high chance that 2 or more voxels may be shown to activate along same time frame even if they have no actual functional connectivity

49
Q

advantages of MRI:

A
  • versatile- structural AND functional
  • simple statistic tests
  • in vivo
  • good for longitudinal studies
  • important in translational neuroscience (cognitive research with clinical applications)
  • safe
50
Q

limitations of MRI

A
  • devoid of biological info- surrogate measure
  • also low temporal resolution
  • counterindications e.g. metal
51
Q

what 4 categories are most MRI studies one of:

A

Structural MRI:
1) brain volume
2) cortical thickness
Functional MRI
3) task-based
4) task-free/resting state

52
Q

what is real time fMRI

A

more advanced fMRI technique allowing direct activity feedback, often to subject with rapid analysis and transfer of data within a few seconds of data collection

53
Q

give 2 applications of real-time fMRI feedback

A
  • novel brain-machine interface
  • coupling conscious experience with brain activation
  • planning neurosurgical interventions
  • neural feedback
54
Q

give 2 applications of using BOLD signal from brain to feedback to control localised neural activation

A
  • non-pharmacological therapy
  • overcomes limitation of other feedback methods
  • directly targeting specific brain areas
55
Q

outline research which has used this direct fMRI feedback

A

one study used real-time feedback relating to blood flow in right anterior insula (RAI)
using a block-design of increasing and decreasing blood flow using tasks such as asking participants to remember arousing memories (increase BF) or count backward from 100 in 3s (reduce BF) showed that participants were able to control activity in these regions using feedback and tasks showing only activation changes in RAI in fMRI

Neuroimaging 2 (7 decks)

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