Autoradiographic deoxyglucose study of visually activated extrastriate cortex in the primate: three-dimensional reconstruction

Metabolic activity patterns in the monkey visual cortex as revealed by spectral analysis

The metabolic activity pattern of the monkey visual cortex was mapped quantitatively with [14C]-2-deoxyglucose during the performance of a visually guided reaching task. After bandpass filtering of the reconstructed two-dimensional metabolic maps of areas V1 and V2, alternating bands of high and low metabolic activity were apparent in control and experimental hemispheres. The spatial arrangement of active bands was studied with two-dimensional spectral analysis, and bands were found to be more organized in the experimental monkey. In area V1 of the control monkey the spectral amplitude was spread over a wider range of directions and frequencies than in the experimental subject. The finding that layer IV is characterized by more complex spectra than layers I through III suggests the coexistence of more than one active columnar system in the geniculorecipient layer. In area V2, stripes running almost perpendicular to the V1/V2 border were found along with superimposed patches of enhanced metabolic activity. In the experimental hemispheres, the corresponding spectra were extremely sharp yielding a constant periodicity. It is suggested that the well-organized columnar arrangement within areas V1 and V2 of the experimental hemispheres emerges from the diffusely organized background network of activity patterns in the control state.

Objective image alignment for three-dimensional reconstruction of digital autoradiograms

Autoradiography can generate large quantities of information related to brain metabolism, blood flow, transport across the blood-brain barrier, neurotransmitter-receptor binding and other aspects of brain function. Three-dimensional (3D) reconstruction of digitized autoradiograms provides a mechanism for efficient analysis of function, in detail, over the entire brain. 3D reconstructions of the mean and variance can be obtained by superimposing data from similar experiments, leading ultimately to 3D reconstructions of differences with statistical tests of significance. Image registration is essential for reconstruction, and this article reports two independent algorithms for coronal image alignment that have been successfully implemented in computer programs. The first algorithm superimposes the centroids and principal axes of serial images; the extent and direction of the translation and rotation required for each image is obtained from an analysis of the inertia matrix of that image. The second algorithm matches the edges of structure features in serial-adjacent images, from analyses of the cross-correlation function of each pair of adjacent images. The cross-correlation method requires a great deal more computation than the principal axes method, but it can align damaged sections not reliably treated by the principal axes method. The methods are described in detail, and a quantitative assessment of the registration of non-identical images is considered.