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David M Senseman

Publications and source records attributed to David M Senseman.

2 recordsLinked to original sources

High-speed VSD imaging of visually evoked cortical waves: decomposition into intra- and intercortical wave motions.

In the pond turtle, Pseudemys scripta elegans, visually evoked cortical waves propagate at different velocities within the primary visual area compared with waves that pass into the secondary visual area. In an effort to separate intra- and intercortical wave motions, movies of visually evoked cortical waves recorded by high-speed voltage-sensitive dye (VSD) imaging were subjected to Karhunen-Loéve (KL) decomposition. This procedure decomposes the VSD movies into a series of basis images that capture different spatial patterns of coherent activity. Most of the energy of the compound wave motion (>95%) was captured by the three largest basis images, M(1,1), M(1,2), and M(2,1). Based on visual comparison with maps of wave front latency, KL basis image M(1,2) appears to capture the spread of depolarization within the primary visual area, whereas KL basis image M(2,1) appears to capture the spread of depolarization from the primary into the secondary visual area. The contribution of different basis images to the intra- and intercortical wave motions was tested by reconstructing the response using different combinations of KL basis images. Only KL basis images M(1,1) and M(1,2) were needed to reconstruct intracortical wave motion, while basis images M(1,1) and M(2,1) were needed to reconstruct intercortical wave motion. It was also found that the direction and speed of wave propagation could be deduced by visual inspection of the basis image projections on to the original data set. The relative advantage of KL decomposition for the analysis of complex wave motions captured by VSD imaging is discussed.

Animals↗

Extracting wave structure from biological data with application to responses in turtle visual cortex.

Waves have long been thought to be a fundamental mechanism for communicating information within a medium and are widely observed in biological systems. However, a quantitative analysis of biological waves is confounded by the variability and complexity of the response. This paper proposes a robust technique for extracting wave structure from experimental data by calculating "wave subspaces" from the KL decomposition of the data set. If a wave subspace contains a substantial portion of the data set energy during a particular time interval, one can deduce the structure of the wave and potentially isolate its information content. This paper uses the wave subspace technique to extract and compare wave structure in data from three different preparations of the turtle visual cortex. The paper demonstrates that wave subspace caricatures from the three cortical preparations have qualitative similarities. In the numerical model, where information about the underlying dynamics is available, wave subspace landmarks are related to activation and changes in behavior of other dynamic variables besides membrane potential.

Animals↗