Applying human brain image processing methods to honeybee calcium image data
Filed under:
Neuroimaging
Arno Klein (Columbia University), Satrajit Ghosh (MIT), Barrett Klein (University of Konstanz), Lisa Rath (University of Konstanz), Giovanni Galizia (University of Konstanz), Christoph Kleineidam (University of Konstanz)
Methods developed for analyzing human brain fMRI data have great potential for application to brain imaging data of different spatial and temporal scales, different imaging methods, and different species. In this work, we demonstrate a simple analysis of honeybee (Apis mellifera) brain image data using the Python programming language.
To our knowledge, this is the first application of human brain imaging techniques to an invertebrate. These techniques provide advantages when analyzing intra-individual phenomena, and invertebrates such as the honeybee offer the advantage of harboring a simpler, experimentally more accessible nervous system.
Data in invertebrate studies are commonly pooled across multiple specimens based on a segmentation of the neuropil of interest. For many applications, this approach is powerful because physiological measures are based on a population mean. However, traditional methods [1] are limited when insufficient neuroanatomical information prevents a reasonable segmentation. With the proposed method, no a priori segmentation is necessary and the independent intra-individual analysis is more powerful. In our study, we investigated odor information processing in the brains of honeybees while the bees were awake versus while they were asleep [2]. Using functional imaging with fluorescent dyes (calcium imaging), we measured neuronal activity during these two physiological states. We compare the power of an analysis based on the traditional approach of semi-automatic segmentation of functional units with our pixel-based analysis. The open source software will be made available through http://www.mindboggle.info, http://www.nitrc.org, and http://www.github.com.
1. Galizia CG, Szyszka P. 2008. Olfactory coding in the insect brain: molecular receptive ranges, spatial and temporal coding. Entomologia Experimentalis et Applicata. 128:81–92.
2. Kaiser W. 1988. Busy bees need rest, too: behavioural and electromyographical sleep signs in honeybees. J Comp Phys A. 163:565-584.
To our knowledge, this is the first application of human brain imaging techniques to an invertebrate. These techniques provide advantages when analyzing intra-individual phenomena, and invertebrates such as the honeybee offer the advantage of harboring a simpler, experimentally more accessible nervous system.
Data in invertebrate studies are commonly pooled across multiple specimens based on a segmentation of the neuropil of interest. For many applications, this approach is powerful because physiological measures are based on a population mean. However, traditional methods [1] are limited when insufficient neuroanatomical information prevents a reasonable segmentation. With the proposed method, no a priori segmentation is necessary and the independent intra-individual analysis is more powerful. In our study, we investigated odor information processing in the brains of honeybees while the bees were awake versus while they were asleep [2]. Using functional imaging with fluorescent dyes (calcium imaging), we measured neuronal activity during these two physiological states. We compare the power of an analysis based on the traditional approach of semi-automatic segmentation of functional units with our pixel-based analysis. The open source software will be made available through http://www.mindboggle.info, http://www.nitrc.org, and http://www.github.com.
1. Galizia CG, Szyszka P. 2008. Olfactory coding in the insect brain: molecular receptive ranges, spatial and temporal coding. Entomologia Experimentalis et Applicata. 128:81–92.
2. Kaiser W. 1988. Busy bees need rest, too: behavioural and electromyographical sleep signs in honeybees. J Comp Phys A. 163:565-584.
Preferred presentation format:
Poster
Topic:
Neuroimaging
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