Data Analysis

A typical fMRI scanning session lasts 1-2 hr and results in the collection of hundreds of megabytes of data. The theory and practicalities associated with processing that data are complex and evolving. In contrast to functional neuroimaging associated with PET—in which the total amount of data is much smaller, the understanding and agreement about the sources and nature of noise in the data are well established, and there is a consequent widespread agreement about the basic issues in data analysis—the situation with fMRI data is much more complex. The sources of machine-related noise in the raw MR images are relatively well understood. However, the general consensus regarding noise in fMRI data is that the most important sources are physiologically based (in the subject) rather than machine based (from the scanner). There is less agreement about the details and the consequences of modeling these noise sources in terms of the practical consequences for data analysis.

Perhaps even more important, the present (and future) spatial and temporal resolution of fMRI data encourages modeling of brain systems at a level that may substantially exceed that of previous volumetric imaging systems. Some of these advances (e.g., the ability to obtain precise delineation of multiple visual areas in occipital cortex by virtue of their retinotopic regularities) require different kinds of data analysis and different kinds of visualization tools than made sense in the context of systems with poorer spatial resolution. Finally, the ability to image the same subject multiple times, and the associated potential for the collection of many kinds of functional data from that same subject, encourages novel approaches to data analysis.

Data analysis is a critical, time consuming, and sometimes controversial part of fMRI-based experimentation. Although the nature of many of the problems is well defined, the appropriate solutions are not. There is general agreement on how to handle some of the issues associated with data analysis (e.g., algorithms to detect head movement and correct for head movement) but there are no universally agreed on approaches to many other issues (e.g., the appropriate statistical tests to define the detection of neural activation, the best way to compare data across different subjects, and the best way to visualize and report the results of data analysis). There are a host of software tools for data analysis, each having its strengths and weaknesses. Because of the rapid development in all aspects of fMRI-based research, no de facto standard approach to data analysis has emerged. Figs. 3 and 4 indicate some of the procedures that are discussed in more detail in the following sections.

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