A Short History of Statistical Parametric Mapping in Functional Neuroimaging

The history of human brain mapping is actually much shorter than one might have anticipated on entering the field. The first brain activation studies harnessed the ability to take images of brain function, successively within the same scanning session. This was facilitated by the use of short half-life radio tracers using Positron Emission Tomography (PET). These techniques became available in the late 80s and the first reports of human brain activation studies appeared in 1988. Up until this time, regional differences among brain scans had been characterised in terms of hand-drawn regions of interest, reducing hundreds of thousands of voxels to a handful of ROI measurements, with a somewhat imprecise anatomical validity. The idea of making voxel-specific statistical inferences, through the use of statistical parametric maps, emerged from the then young brain mapping community in response to the clear need to make valid inferences about brain responses without knowing where those responses would be expressed. Statistical parametric maps refer to image processes of a statistical parameter. This statistic (usually the T or F statistic) is carefully chosen to relate directly to the effect one is interested in. The ensuing SPM can be thought of as an X-ray of the effects significance. In the absence of any effect the distribution of the statistic conforms to its null distribution thereby enabling classical inferences (through rejection of the null hypothesis) in declaring a regionally specific activation. The first SPM was used to establish functional specialization for colour processing in 1989 (Lueck et al 1989). The methodology and conceptual underpinning of statistical parametric mapping were described in two papers in 1990 and 1991. The first paper (Friston et al 1990) entitled " The Relationship Between Global and Local Changes in PET Scans " presented the formal basis of statistical parametric mapping using both the T & F statistics. This may seem an odd title to introduce statistical parametric mapping but it is interesting to understand its motivation from a historical perspective. Previously, average values from ROIs had been