Many of the methods and techniques central to neuroscientific discovery rely on assumptions that can limit the interpretation of the data. Philosophers of neuroscience have discussed such assumptions in the use of
functional magnetic resonance imaging (fMRI), dissociation in cognitive
neuropsychology,
single unit recording, and
computational neuroscience. Following are descriptions of many of the current controversies and debates about the methods employed in neuroscience.
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. An 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.
Rejections of fMRI 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 repairman thought experiment. In this thought experiment, a radio repairman opens up a radio and rips out a tube. The radio begins whistling loudly and the radio repairman declares that he must have ripped out the anti-whistling tube. There is no anti-whistling tube in the radio and the radio repairman 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, neuroimagers assume that 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 uses the appearance of brain region activation as proof of the correctness of their cognitive theories.
Reverse inference 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
New York 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 researchers 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. Roskies 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 a 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.
Pure insertion One final assumption 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 processes without affecting the functioning of the rest. For example, to find the reading comprehension area of the brain, researchers might scan participants while they were presented with a word and while they were presented with a non-word (e.g. "Floob"). If the researchers then infer that the resulting difference in brain pattern represents the regions of the brain involved in reading comprehension, they 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.
Resting-state functional-connectivity MRI Recently, researchers have begun 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
blood-oxygen-level-dependent (BOLD) pattern while the subject is at rest, the researchers can see which brain regions co-vary in activation together. Afterward, they can use the patterns of covariance to construct maps of functionally-linked brain areas. 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.
Methodological issues 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 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 only a 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 spatial scale, but
graph theory methods are extremely sensitive to the way nodes are defined. Brain regions can be divided according to their
cellular architecture, according to their
connectivity, or according to
physiological measures. Alternatively, one could take a "theory-neutral" approach, and randomly divide the cortex into partitions with an arbitrary size. As mentioned earlier, there are several approaches to network analysis once the brain regions have been defined. Seed-based analysis begins with an
a priori 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 (ICA) to create spatio-temporal component maps, and the components are sorted into those 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). This places the researcher at risk of cherry-picking regions and connections according to their own preconceived theories. However, graph theory analysis is still considered extremely valuable, as it is the only method that gives
pair-wise relationships between nodes. While ICA may have an advantage in being a fairly principled method, it seems that using both methods will be important to better understanding 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.
Dissociation in cognitive neuropsychology Cognitive neuropsychology studies brain damaged patients and uses the patterns of selective impairment in order to make inferences on the underlying cognitive structure.
Dissociation between cognitive functions is taken to be evidence that these functions are independent. Theorists have identified several key assumptions that are needed to justify these inferences: •
Functional modularity – the mind is organized into functionally separate cognitive modules. •
Anatomical modularity – the brain is organized into functionally separate modules. This assumption is very similar to the assumption of functional localization. These assumptions differ from the assumption of functional modularity, because it is possible to have separable cognitive modules that are implemented by diffuse patterns of brain activation. •
Universality – The basic organization of functional and anatomical modularity is the same for all normal humans. This assumption is needed if we are to make any claim about functional organization based on dissociation that extrapolates from the instance of a case study to the population. •
Transparency /
Subtractivity – the mind does not undergo substantial reorganization following brain damage. It is possible to remove one functional module without significantly altering the overall structure of the system. This assumption is necessary in order to justify using brain damaged patients in order to make inferences about the cognitive architecture of healthy people. There are three principal types of evidence in cognitive neuropsychology: association, single dissociation and double dissociation. Association inferences observe that certain deficits are likely to co-occur. For example, there are many cases who have deficits in both abstract and concrete word comprehension following brain damage. Association studies are considered the weakest form of evidence, because the results could be accounted for by damage to neighboring brain regions and not damage to a single cognitive system. Single Dissociation inferences observe that one cognitive faculty can be spared while another can be damaged following brain damage. This pattern indicates that a) the two tasks employ different cognitive systems b) the two tasks occupy the same system and the damaged task is downstream from the spared task or c) that the spared task requires fewer cognitive resources than the damaged task. The "gold standard" for cognitive neuropsychology is the double dissociation. Double dissociation occurs when brain damage impairs task A in Patient1 but spares task B and brain damage spares task A in Patient 2 but damages task B. It is assumed that one instance of double dissociation is sufficient proof to infer separate cognitive modules in the performance of the tasks. Many theorists criticize cognitive neuropsychology for its dependence on double dissociations. In one widely cited study, Joula and Plunkett used a model connectionist system to demonstrate that double dissociation behavioral patterns can occur through random lesions of a single module. They created a multilayer connectionist system trained to pronounce words. They repeatedly simulated random destruction of nodes and connections in the system and plotted the resulting performance on a
scatter plot. The results showed deficits in irregular noun pronunciation with spared regular verb pronunciation in some cases and deficits in regular verb pronunciation with spared irregular noun pronunciation. These results suggest that a single instance of double dissociation is insufficient to justify inference to multiple systems. Charter offers a theoretical case in which double dissociation logic can be faulty. If two tasks, task A and task B, use almost all of the same systems but differ by one mutually exclusive module apiece, then the selective lesioning of those two modules would seem to indicate that A and B use different systems. Charter uses the example of someone who is allergic to peanuts but not shrimp and someone who is allergic to shrimp and not peanuts. He argues that double dissociation logic leads one to infer that peanuts and shrimp are digested by different systems. John Dunn offers another objection to double dissociation. He claims that it is easy to demonstrate the existence of a true deficit but difficult to show that another function is truly spared. As more data is accumulated, the value of your results will converge on an effect size of zero, but there will always be a positive value greater than zero that has more statistical power than zero. Therefore, it is impossible to be fully confident that a given double dissociation actually exists. On a different note, Alphonso Caramazza has given a principled reason for rejecting the use of group studies in cognitive neuropsychology. Studies of brain damaged patients can either take the form of a single case study, in which an individual's behavior is characterized and used as evidence, or group studies, in which a group of patients displaying the same deficit have their behavior characterized and averaged. In order to justify grouping a set of patient data together, the researcher must know that the group is homogenous, that their behavior is equivalent in every theoretically meaningful way. In brain damaged patients, this can only be accomplished
a posteriori by analyzing the behavior patterns of all the individuals in the group. Thus according to Caramazza, any group study is either the equivalent of a set of single case studies or is theoretically unjustified. Newcombe and Marshall pointed out that there are some cases (they use Geschwind's syndrome as an example) and that group studies might still serve as a useful heuristic in cognitive neuropsychological studies.
Single-unit recordings It is commonly understood in neuroscience that information is encoded in the brain by the firing patterns of neurons. Many of the philosophical questions surrounding the neural code are related to questions about representation and computation that are discussed below. There are other methodological questions including whether neurons represent information through an average firing rate or whether there is information represented by the temporal dynamics. There are similar questions about whether neurons represent information individually or as a population.
Computational neuroscience Many of the philosophical controversies surrounding computational neuroscience involve the role of simulation and modeling as explanation.
Carl Craver has been especially vocal about such interpretations. Jones and Love wrote an especially critical article targeted at
Bayesian behavioral modeling that did not constrain the modeling parameters by psychological or neurological considerations Eric Winsberg has written about the role of computer modeling and simulation in science generally, but his characterization is applicable to computational neuroscience. == Relations between psychological and neuroscientific inquiries ==