Lecture 1 - The basics Flashcards
Types of MRI?
+pros and cons of fMRI
- sMRI = high resolution anatomical scans (T1, T2)
- fMRI = also called T2*-weighted images. Uses BOLD contrast: measures the ratio of oxygenated to deoxygenated haemoglobin (indirect measure).
Pros of fMRI: high temporal resolution (i.e. can be measured over time). Can use them to relate signal changes that vary from second to second.
Cons of fMRI: have lower spatial resolution (i.e. blurrier) than sMRI
What can we use fMRI for?
- LOCALISATION = of a cognitive activity to a distinct brain region
- CONNECTIVITY = ‘traditional mass univariate analysis’ allowed only investigating functional segregation: i.e. specialisation of brain regions for cognitive activity. Now fMRI can be used for functional segregation and functional integration (i.e. functional connectivity = finding statistical patterns)
- PREDICTION = classification of mental disorders, predicting early onset of disease. Using brain activation patterns to predict subjective human experience (e.g. offering objective interpretation of pain)
Brain Mapping. What is SPM?
- Maps = both functional and structural imaging rely on construction of maps of localised signals.
- Statistical Parametric Mapping (SPM) = scientists want to make quantitative inferences, which means statistically comparing image data across conditions or individuals and then showing maps of the statistical results
What are the types of processes that researchers map to local brain regions or networks?
- Effects of experimental manipulations
- Correlations with behaviour, clinical status, or other person-level outcomes
- Correlations with performance or other within-person variables
- Brain area’ correlation with other specific areas
- Brain areas that are part of a group of areas (e.g. cluster or network)
Types of maps - Single Subject Maps
- Data from single individual, scanned repeatedly
- Common in vision science, or primate neuroimaging
- Present in clinical and legal applications
Constructed by comparing data from one condition with another across repeated measurements, which then tests statistical significance in each brain voxel - Another way is by comparing data from an individual with a population of other individuals (if the stats are valid)
- THEY CANNOT TELL US MUCH ABOUT THE BRAIN’S GENERAL ORGANISATION: RESEARCHERS CANNOT USE THEM TO MAKE POPULATION INFERENCES
Types of maps - Group Level Maps
- To make population inferences it is necessary to scan a group of participants and conduct statistical tests to evaluate how well the findings will generalise to new individuals
- They identify brain regions which show consistent effects across individuals
- These maps require statistical procedure which we often call RANDOM EFFECT ANALYSIS because the statistical model treats each participant as a random effect
- This is what is focussed on in this module
What is forward inference?
Df. What brain activity/region is associated with a given experimental condition…
- Gives info about brain functioning
- E.g. visual motion (visual items vs stationary baseline), task difficulty (harder task vs. easier baseline task)
- Goal is to manipulate the environment (experimental conditions) and see how brain activity changes
- Can still lead to inappropriate interpretations
What is reverse inference?
Df. What cognitive processes is occurring given the brain activity
- Basically, reasoning backwards from brain activity
- The hope is to use the brain to infer something about unmeasured internal processes
- This type of inference can be problematic and is usually done unintentionally by the researcher
- Affirming the consequent
- We can calculate reverse inference quantitatively using BAYESIAN STATS (probabalistic framework for inference in neuroscience) in order to do this we need to know probabilities of activation in certain areas during engagement with a particular cognitive task - through databases such as BrainMap.org
Examples of inferences gone right/wrong
- De Quervain et al (2004) = found activation in the caudate nucleus when participants got the chance to punish wrong doers… The FI here is if you punish wrongdoers, you activate the caudate nucleus. The RI here, which was used wrongly by researchers was, if you activate the caudate nucleus, you must enjoy whatever it is you are doing. So if you’re enjoying something, then you’re caudate must be active (this is the case if enjoyment is the ONLY thing that activates the caudate - which is not the case!)
- Poldrack (2006) = examined the reverse inference that activation of broco’s area implies engagement of language function. Basically reviewed the use of reverse inference to come up with the conclusion that in order to learn something about the mind, we need to use reverse inference, however brain mapping approaches only provide forward inferences. We should therefore only calculate reverse inference qualitatively.
Christine Dell-Amore = “bikinis make men see women as objects” - the logic used here was that tools activate area X, women in bikinis activate area X, therefore women in bikinis are viewed as tools - example of affirming the consequent (however correct logic only works only if area X is involved in only one mental process - which is rarely or never true)
How can reverse inference be done?
- Strategy 1 = LEVERAGE NEUROSCIENCE -> strong prior knowledge about psychological processes that do (and don’t) activate a brain area. Assume that activation implies psychological processes
- Strategy 2 = QUANTITATIVE REVERSE INFERENCE -> assess activation of a region across set of tasks. Quantify its sensitivity, specificity and positive and negative predictive values, then use meta-analyses AND/OR Bayesian inferences (both can and should be used together).
Summary of Reverse Inference…
- It is the practice of treating the brain as a marker for something
- When researchers make reverse inferences, they assume a HIGH POSITIVE PREDICTED VALUE (PPV)
- PPV can be calculated, but requires assessment of multiple potentially confusable tasks/states
- For regional brain activity to have a high PPV, it must: (1) respond consistently to the task/state (high sensitivity), (2) respond only to the task/state (high specificity)
What is brain mapping good and not good for?
Good for:
- Making inferences on the presence of activity to either test a theory (using leverage neuroscience) or characterise the pattern of brain responses to a task
- Limiting the false positive rate to a specified level
Not good for:
- Reverse inference: does not provide direct inferences about psychological states; you need clever experimental designs, specialised analyses and meta-analyses
- Estimating effect sizes and predictive accuracy
- Testing assumptions: inference already depends on many assumptions (which you must check yourself)
- Comparing evidence for different theories (cannot confirm theories, only falsify)
Kossyln’s theory about mental imagery…
2 studies that support and one that does not
Pylyshyn (1973 and on): Argued that imagery is “propositional” – a set of logical rules. & The feeling of “seeing” mental images is epiphenomenal.
Kosslyn: Imagery is depictive and analogue. Kosslyn theory is the strictest form of imagery theories who assumed that during mental imagery the geometrical information of remembered objects and scenes are processed in the primary visual cortex.
2 fMRI studies that support Kossyln’s hypothesis:
- One study compared sensorimotor and cognitive tasks and reports a significant increase of activation in the primary visual cortex during imagery tasks. Abbah P et al (1995)
- The other study (using a 4 T scanner) reports that the lateral geniculate nucleus was activated during visual imagery processes in the human brain together with V1 and other activation Chen W et al (1998)
One fMRI study that does not support the theory…
- Knauff M et al (2002) -> In the occipital cortex a slightly increased activity was found only in the visual association cortex (BA 19), whereas the highest activation was observed in the parietal cortex (BA 7 and 40). The results of the study do not support the assumption that the primary visual cortex is involved in visual mental imagery, but rather that a network of spatial subsystems and higher visual areas appears to be involved.
