Comparing Activation States

Fundamental to the understanding of fMRI as a tool for representing the localization of brain function is the idea that a single image, in isolation, conveys little if any useful information. Rather, it is the comparison of multiple images that are collected during different states of neural activity that supplies interpretable data. Note that this statement is not true for structural MR images. A single structural image conveys a great deal of useful information because data about change is not sought (except on a much longer timescale, as in developmental and longitudinal studies of brain structure). In contrast, functional imaging data are almost exclusively about changes in neuronal activity.

One might ask, Why isn't a single image, collected during rest, a useful definition of the "resting,"

"neutral," or "idling" state of the brain? In some ways, a single image might be interpretable this way. Indeed, some variants of PET can be used to yield a single snapshot of the metabolic state of the brain. However, the variation in local activity in the brain during "rest" is not very meaningful—the demonstration that one portion of the brain is more active during rest than another has limited value. On the other hand, the demonstration that a particular manipulation (of the stimulus or task requirements for the subject) causes a localized change in neuronal activity is far more useful.

The art of fMRI experimental design lies largely in the creation of tasks that accurately probe the cognitive function of interest. One natural way to design an experiment in functional neuroimaging is to create two tasks that are identical except for one minor difference. This is the basis of the classic subtraction method originally delineated by Donders and widely used in cognitive research. Such experimental designs are sometimes called "tight" task comparisons. The difference between experimental conditions in a tight task comparison is either in the stimulus alone (while keeping the response task of the subject fixed) or in the response task alone (while keeping the stimulus fixed).

Such an approach is particularly useful for testing specific hypotheses about the activation pattern in a single brain region.

However, there are practical and theoretical reasons for including experimental conditions that are more broadly different from the main conditions of interest. Frequently, this is accomplished via the use of a low-level control task, such as simple visual fixation or rest. This has sometimes been called a ''loose'' task comparison. It is particularly useful for seeing the simultaneous activation of many areas of the brain. The loose task comparison not only provides an internal check for the integrity of the data collected (because it typically includes robust activations of no direct experimental interest, but the absence of which could indicate a problem with the subject, the machine, or the data analysis) but also serves as an important point of reference for observed differences within the tight task comparison. For instance, a difference between two conditions in a tight task comparison could reflect either an increase in activity in one condition or a decrease in activity in the other. The addition of a loose task comparison provides a means of disambiguating such a situation by providing a baseline against which the two tight task conditions can be compared.

More generally, it is essential to have at least two conditions to be compared, but the power of fMRI-based experiments to test interesting theories is greatly enhanced by the presence of more conditions in the design. Sometimes these multiple conditions are qualitatively different (as indicated previously), but increasingly subtle experiments are being done that make use of quantitative (parametric) variation in the experimental conditions. In general, when attempting to model and understand the networks of the brain, all types of experimental sampling are needed.

Finally, the critical importance (and occasional irrelevance) of behavioral measures must be discussed. It might seem obvious that obtaining observable behavioral responses could only be a good thing in functional neuroimaging research. Certainly most investigators try to have an observable behavioral measure for their tasks when possible. (In ''imagination'' studies, such as imagining visual images or imagining performance of a motor task, it is sometimes impossible to have an observable behavioral response measure, but even in the context of something as ''unobservable'' as mental rotation, investigators have sometimes found ways to obtain associated reaction times and accuracy measures.) On the other hand, at least one prominent psychologist has argued against the necessity of behavioral response measures, suggesting that the imaging data are sufficient and that adding irrelevant behavioral tasks will only confuse the issue by eliciting neural activity unrelated to the particular cognitive task of interest. Also, several researchers have commented that, independent of anything else, it is good to have a behavioral task associated with the imaging study because it will help keep the subject awake in the scanner.

Each of these observations has merit, but there are more important uses for behavioral response measures in most studies, and for some studies they are critical. Specifically, a number of studies have made the analysis of the imaging data depend crucially on the observed behavioral responses. Examples from the study of memory and from the study of the effects of cocaine on brain activity are described in Section I.

Understanding And Treating Autism

Understanding And Treating Autism

Whenever a doctor informs the parents that their child is suffering with Autism, the first & foremost question that is thrown over him is - How did it happen? How did my child get this disease? Well, there is no definite answer to what are the exact causes of Autism.

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