Computer-assisted visualizations of neural networks: expanding the field of view using seamless confocal montaging
- Beck, J.C., Murray, J.A., Willows, A.O.D., and Cooper,M.S.
- Journal of Neuroscience Methods 98(2): 155-163 (Journal)
- Registered Authors
- Beck, James, Cooper, Mark S.
- Tritonia; Aplysia; zebrafish; sensorin; immunocytochemistry; photomosaic; photomontage; adobe photoshop
- MeSH Terms
- Image Processing, Computer-Assisted/methods*
- Microscopy, Confocal/methods*
- Nerve Net/growth & development
- Nerve Net/physiology*
- Spinal Cord/cytology
- gamma-Aminobutyric Acid/analysis
- 10880829 Full text @ J. Neurosci. Methods
Beck, J.C., Murray, J.A., Willows, A.O.D., and Cooper,M.S. (2000) Computer-assisted visualizations of neural networks: expanding the field of view using seamless confocal montaging. Journal of Neuroscience Methods. 98(2):155-163.
Microscopic analysis of anatomic relationships within the neural networks of adult and developing tissues often requires sampling large spatial regions of neuronal architecture. To accomplish this, there are two common imaging approaches: (1) image the entire area at once with low spatial resolution; or (2) image small sections at higher magnification/resolution and then join the sections back together by mosaic reconstruction (photomontaging). Low magnification imaging is relatively rapid to perform, resulting in a visualization that encompasses a large field of view with an extended depth of field. However, for fluorescence microscopy, low magnification visualizations are often plagued by poor spatial resolution. High magnification imaging possesses superior spatial resolution, but it produces an image with limited depth of field. When creating a larger field of view, the final image is also fragmented at the boundaries where multiple images are stitched together. Using confocal microscopy as well as features of common image processing programs, we outline a new method to transform individual, spatially contiguous z-series into a montage with a seamless field of view and an extended depth of field. In addition, we show that the manual alignment of images our method requires does not introduce significant errors into the final image. We illustrate our method for visualizing neural networks using tissues from the adult gastropod mollusc, Tritonia diomedea, and the developing zebrafish, Danio rerio.
Genes / Markers
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Engineered Foreign Genes