The Generality o f Experimental Data

The results of all scientific experiments, however well controlled, are influenced by a collection of variables that are usefully grouped together under the heading

context. Abelson (1995) defines context as ’Everything about the experiment

beyond the critical manipulation of the treatment - research team, time, place, subjects, and ancillary aspects of the procedure and materials - becomes context’. Context’ differs between scientific disciplines; I will focus exclusively on its influence on experiments in experimental psychology, and by extension, neuroimaging.

Context can be illustrated by the following example. A researcher wishes to use neuroimaging to investigate differences between Parkinsonian patients and normal controls while performing a simple movement task. Unfortunately for the researcher, the Parkinsonian patient population is based at another neuroimaging institute. For pragmatic reasons, the researcher is forced to assemble her own control group and scan them using her local MRI scanner, and analyse the data en

masse once data from the other population have been collected. Once completed,

the researcher publishes her results as an illustration of the differences between Parkinsonian patients and normal subjects.

This (rather contrived) example usefully illustrates some o f the problems faced in attempting to derive general laws and principles from a limited number of manipulations and subjects. When studying human subjects one usually cannot obtain experimental results from every member of the groups (hypothetical or otherwise) being studied. It is therefore necessary to make a compromise: a

sample of the group (or population) must be taken. To protect against bias,

samples should be random - theoretically, every member o f the population must have an equal chance of being included in the sample. In addition, samples should

be independent - the selection of one unit from the population should have no influence on the selection o f other units.

This is not the case in the above example: logistical problems mean that the researcher has to take whatever Parkinsonian patients she has access to. The opposite of this process would be access to a large number of Parkinsonian patients who are all equally motivated and can come into the lab at any time. To a lesser extent, the normal population that the researcher is using is usually well motivated right-handed Caucasians who respond to financial reward - typically university undergraduates.^ Another problematic issue arising from the Parkinsonian example is the comparison of experimental results from two different research establishments. Differences may therefore arise from different researchers being involved in scanning the subjects (the ‘experimenter effect’; of.

Hicks et al., 1970).

In addition, because neuroimaging is a relatively new science, the slightly different hardware used at both institutes may produce systematic differences where none exist (known in electrical engineering as ‘loading the circuit’). This phenomenon is not unknown in experimental psychology. At the beginning of the century, many psychologists were interested in studying the Féré effect (what is now known as the galvanic skin response [GSR]). However, a number of different factors affect the magnitude of the GSR: electrode type, the concentration of salt used, and the level of stimulating current can all produce effects that are not related to the psychological factors being studied (Plutchik, 1974). Studies that demonstrate a difference in GSR must therefore ensure that the differences cannot be explained by mere differences in data acquisition methods. In neuroimaging there are a similar number of experimental variables that may lead to systematic differences between studies. While the relative contributions of each of these effects to variability can be assessed over time, this requires considerable effort.

* This point is often made by neuroscientists working in the field o f animal experimentation: cognitive neuroscience purports to study human behaviour but can only generalise to college

students. Such examples of ' j ’accuse’ arc somewhat undermined by said researchers’ own reliance

on inbred strains of experimental animal who often bear little resemblance to the species found in the wild.

As demonstrated in Chapter three, when evaluating the effectiveness of a particular form of somatosensory stimulation it is often useful to know how much variation one would expect by chance (or context). In other words, while there may indeed be a ‘mean’ somatopical map, its intrinsic variability may mean that several measurements are necessary to properly characterise it. However, although exceptions exist, it is unusual for a subject to be scanned on more than one occasion and, more often than not, a single fMRI session is assumed to give an accurate representation of a subject’s functional neuroanatomy. If there is high variability in the spatial expression of the evoked neurovascular response or the neuronal response, a single session has little descriptive power.

There are therefore obvious problems with the ‘one subject, one session’ approach to neuroimaging experiments. One session is only a single, discrete ‘snapshot’ of the subject’s brain, and may not epitomise responses to the sensorimotor or cognitive challenge employed. Indeed, differences between sessions are inevitable: for example, the BOLD response is an indirect and semi- qualitative measure of neuronal activity, and the relationship between BOLD contrast and cerebral oxygen metabolism is influenced by a number of physiological factors (e.g. for review see Ogawa et al, 1998). Furthermore, single session results may be influenced by slight variations in the hardware characteristics of the MR scanner, which are not systematic across sessions (e.g. the shim performed to homogenise the Bo field of the scanner; Howseman et a l,

1998). Any differences in subject position within the headcoil on separate scanning sessions may also result in greater variability in voxel signal change due to partial volume effects. In addition to the above, nonspecific physiological effects such as the level of arousal may further influence the neurovascular response to the activation task in question.

These effects are hard to control and may substantially influence single session results, such that the experiment may ultimately say as much about the context under which the data were acquired as the effects of the experimental manipulation itself. Although few researchers would expect a precise replication

of the results if an experiment were repeated, it is currently unclear how generalisable single session results are with fMRI.

This influence of session context on the effects of an experimental manipulation constitutes a session by condition interaction. Although a number o f studies have examined the reproducibility of fMRI across a small number of sessions (Cohen

et a l, 1999; Noll et a l, 1997; Rombouts et a l, 1998; Tegeler et a l, 1999; Yetkin

et a l, 1996), the small sample size of these studies limits their conclusions. My

primary aim in this chapter is to examine how well a single session typifies a subject’s responses, using simple activation paradigms. Just as the significance of within-session experimental effects are assessed by sampling a number of scans for each condition, to assess between-session differences one must sample multiple sessions. If a single session is to be a good exemplar of a subject’s functional neuroanatomy, session by condition interactions must be minimal. Although the experiments in this chapter were primarily concerned with the stability o f the BOLD response in the somatosensory cortices over multiple stimulus presentations, I did not use a somatosensory task as one of my activation paradigms. I instead chose to examine multiple, simple activation paradigms that could be easily implemented in the scanning environment.