A method for modeling and visualizing the three-dimensional organization of neuron populations from replicated data: Properties, implementation and illustration

Understanding how the architecture of neuronal populations contributes to brain function requires three-dimensional representations and analyses. Neuroanatomical techniques are available to locate neurons in animal brains. Repeating an experiment in different individuals yields a collection of point patterns from which common organization principles are generally difficult to extract. We recently addressed the problem of generating statistical density maps to integrate replicated point pattern data into meaningful, interpretable representations. Applications to different neuroanatomical systems illustrated the ability of our method to reveal organization rules that cannot be perceived directly on raw data. To make the method practicable for further applications, the aim of the present paper is to establish general guidelines for appropriate parameter tuning, valid result interpretation as well as efficient implementation. Accordingly, we characterize the method by analyzing the role of its main parameter, by reporting results on its statistical properties and by demonstrating its robustness, using both simulated and real neuroanatomical data.

We investigate a method based on statistical intensity maps for modeling and visualizing neuronal populations from replicated data.Figure optionsDownload high-quality image (107 K)Download as PowerPoint slideHighlights
► Statistical representations of neuron populations are computed from replicated data.
► We use unbiaised, consistent, asymptotically efficient, robust intensity estimation.
► A rank parameter controls smoothing and acts as a threshold on neuron cluster size.
► Statistical intensity maps reveal organization rules not visible on individual data.