FMRI
Many fMRI studies rely heavily on the assumption of "localization of function"(same as functional specialization). Localization of function means that many cognitive functions can be localized to specific brain regions. A good example of functional localization comes from studies of the motor cortex. There seem to be different groups of cells in the motor cortex responsible for controlling different groups of muscles. Many philosophers of neuroscience criticize fMRI for relying too heavily on this assumption. Michael Anderson points out that subtraction method fMRI misses a lot of brain information that is important to the cognitive processes. Subtraction fMRI only shows the differences between the task activation and the control activation, but many of the brain areas activated in the control are obviously important for the task as well.
Some philosophers entirely reject any notion of localization of function and thus believe fMRI studies to be profoundly misguided. These philosophers maintain that brain processing acts holistically, that large sections of the brain are involved in processing most cognitive tasks (see holism in neurology and the modularity section below). One way to understand their objection to the idea of localization of function is the radio repair man thought experiment. In this thought experiment, a radio repair man opens up a radio and rips out a tube. The radio begins whistling loudly and the radio repair man declares that he must have ripped out the anti-whistling tube. There is not really any anti-whistling tube in the radio and it is obvious that the radio repair man has confounded function with effect. This criticism was originally targeted at the logic used by neuropsychological brain lesion experiments, but the criticism is still applicable to neuroimaging. These considerations are similar to Van Orden's and Paap's criticism of circularity in neuroimaging logic. According to them, neuoimagers assume that there their theory of cognitive component parcellation is correct and that these components divide cleanly into feed-forward modules. These assumptions are necessary to justify their inference of brain localization. The logic is circular if the researcher then use the appearance of brain region activation as proof of the correctness of their cognitive theories.
A different problematic methodological assumption within fMRI research is the use of reverse inference A reverse inference is when the activation of a brain region is used to infer the presence of a given cognitive process. Poldrack points out that the strength of this inference depends critically on the likelihood that a given task employs a given cognitive process and the likelihood of that pattern of brain activation given that cognitive process. In other words, the strength of reverse inference is based upon the selectivity of the task used as well as the selectivity of the brain region activation. A 2011 article published in the NY times has been heavily criticized for misusing reverse inference. In the study, participants were shown pictures of their iPhones and the researchers measured activation of the insula. The researches took insula activation as evidence of feelings of love and concluded that people loved their iPhones. Critics were quick to point out that the insula is not a very selective piece of cortex, and therefore not amenable to reverse inference.
The Neuropsychologist Max Coltheart took the problems with reverse inference a step further and challenged neuroimagers to give one instance in which neuroimaging had informed psychological theory Coltheart takes the burden of proof to be an instance where the brain imaging data is consistent with one theory but inconsistent with another theory. Rooskies maintains that Coltheart's ultra cognitive position makes his challenge unwinnable. Since Coltheart maintains that the implementation of a cognitive state has no bearing on the function of that cognitive state, then it is impossible to find neuroimaging data that will be able to comment on psychological theories in the way Coltheart demands. Neuroimaging data will always be relegated to the lower level of implementation and be unable to selectively determine one or another cognitive theory. In an 2006 article, Richard Henson suggests that forward inference can be used to infer dissociation of function at the psychological level. He suggests that these kinds of inferences can be made when there is crossing activations between two task types in two brain regions and there is no change in activation in a mutual control region.
One final assumption worth mentioning is the assumption of pure insertion in fMRI. The assumption of pure insertion is the assumption that a single cognitive process can be inserted into another set of cognitive process without effecting the functioning of the rest. For example, if you wanted to find the reading comprehension area of the brain, you might scan participants while they were presented with a word and while they were presented with a non-word (e.g. "Floob"). If you infer that the resulting difference in brain pattern represents the regions of the brain involved in reading comprehension, you have assumed that these changes are not reflective of changes in task difficulty or differential recruitment between tasks. The term pure insertion was coined by Donders as a criticism of reaction time methods.
Recently, researchers have began using a new functional imaging technique called resting state functional connectivity MRI. Subjects' brains are scanned while the subject sits idly in the scanner. By looking at the natural fluctuations in the bold pattern while the subject is at rest, the researchers can see which brain regions co-vary in activation together. They can use the patterns of covariance to construct maps of functionally linked brain areas. It is worth noting that the name "functional connectivity" is somewhat misleading since the data only indicates co-variation. Still, this is a powerful method for studying large networks throughout the brain. There are a couple of important methodological issues that need to be addressed. Firstly, there are many different possible brain mappings that could be used to define the brain regions for the network. The results could vary significantly depending on the brain region chosen. Secondly, what mathematical techniques are best about to characterize these brain regions?
The brain regions of interest are somewhat constrained by the size of the voxels. Rs-fcMRI uses voxels that are few millimeters cubed so the brain regions will have to be defined on a larger scale. Two of the statistical methods that are commonly applied to network analysis can work on the single voxel spacial scale, but graph theory methods are extremely sensitive to the way nodes are defined. Brains regions can be divided according to their cellular architectural, according to their connectivity, or according to physiological measures. Alternatively, you could take a theory neutral approach and randomly divide the cortex into partitions of the size of your choosing. As mentioned earlier, there are several approaches to network analysis once the your brain regions have been defined. Seed based analysis begins with an a prior defined seed region and finds all of the regions that are functionally connected to that region. Wig et al. caution that the resulting network structure will not give any information concerning the inter-connectivity of the identified regions or the relations of those regions to regions other than the seed region. Another approach is to use independent component analysis to create spatio-temporal component maps and the components are sorted by components that carry information of interest and those that are caused by noise. Wigs et al. once again warns that inference of functional brain region communities is difficult under ICA. ICA also has the issue of imposing orthogonality on the data. Graph theory uses a matrix to characterize covariance between regions which is then transformed into a network map. The problem with graph theory analysis is that network mapping is heavily influenced by a priori brain region and connectivity (nodes and edges), thus the researcher is at risk for cherry picking regions and connections according to their own theories. However, graph theory analysis is extremely valuable since it is the only method that gives pair-wise relationships between nodes. ICA has the added advantage of being a fairly principled method. It seems that using both methods will be important in uncovering the network connectivity of the brain. Mumford et al. hoped to avoid these issues and use a principled approach that could determine pair-wise relationships using a statistical technique adopted from analysis of gene co-expression networks.
Read more about this topic: Neurophilosophy, The Indirectness of Studies of Mind and Brain