Computer-assisted visualizations of neural networks: expanding the field of view using seamless confocal montaging

Microscopic analysis of anatomic relationships within the neural networks o f adult and developing tissues often requires sampling large spatial region s of neuronal architecture. To accomplish this, there are two common imagin g approaches: (1) image the entire area at once with low spatial resolution ; or (2) image small sections at higher magnification/resolution and then j oin the sections back together by mosaic reconstruction (photamontaging). L ow magnification imaging is relatively rapid to perform, resulting in a vis ualization that encompasses a large field of view with an extended depth of field. However, for fluorescence microscopy, low magnification visualizati ons are often plagued by poor spatial resolution. High magnification imagin g possesses superior spatial resolution, but it produces an image with limi ted depth of field. When creating a larger field of view, the final image i s also fragmented at the boundaries where multiple images are stitched toge ther. Using confocal microscopy as well as features of common image process ing programs, we outline a new method to transform individual, spatially co ntiguous z-series into a montage with a seamless field of view and an exten ded depth of field. In addition, we show that the manual alignment of image s our method requires does not introduce significant errors into the final image. We illustrate our method for visualizing neural networks using tissu es from the adult gastropod mollusc, Tritonia diomedea, and the developing zebrafish, Danio rerio. (C) 2000 Elsevier Science B.V. All rights reserved.