Background and purpose: Histopathologic analysis of experimental brain damage has traditionally been performed by measuring areas of infarction and/or selective neuronal alterations on a section-by-section basis in individual animals. For series containing multiple replicate animals, quantitation of tissue injury is typically performed at similar coronal levels throughout an experimental group. A means of facilitating pictorial group comparisons of these histopathologic alterations between different series of replicate studies is highly desirable.
Methods: We introduce a newly designed approach to achieve this goal, based on a linear affine transformation that is used to map corresponding sections at the same anatomic level into a common template to yield a frequency distribution map depicting the aggregate data set. We have applied this approach to compare the histopathologic features of two models of middle cerebral artery (MCA) occlusion in rats: (1) photothrombotically induced permanent distal MCA occlusion in spontaneously hypertensive rats (SHR) and (2) temporary MCA occlusion by intraluminal suture in Wistar rats.
Results: The brains of SHR rats with permanent distal MCA occlusion showed a high frequency of infarction involving the dorsolateral and lateral portions of the ipsilateral neocortex, whereas Wistar rats with 90-minute MCA suture occlusion showed a zone of infarction largely concentrated in the dorsolateral portion of the ipsilateral caudoputamen. Infarct frequency distributions for the two animal groups were compared statistically at three corresponding anatomic levels by Fisher's exact test; the resulting statistical parametric maps are shown.
Conclusions: With the use of frequency distribution maps, the pattern of trends within a group can be observed coronally or three-dimensionally. One can directly access data as to numbers of rats with infarction for any point on the map. Studies performed under different experimental conditions can also be compared with one another by means of the generated data sets.