Functional Neuronal Topography: A Statistical Approach to Micro Mapping Neuronal Location

Abstract

In order to understand the relationship between neuronal organization and behavior, precise methods that identify and quantify functional cellular ensembles are required. This is especially true in the quest to understand the mechanisms of memory. Brain structures involved in memory formation and storage, as well as the molecular determinates of memory are well-known, however, the microanatomy of functional neuronal networks remain largely unidentified. We developed a novel approach to statistically map molecular markers in neuronal networks through quantitative topographic measurement. Brain nuclei and their subdivisions are well-defined – our approach allows for the identification of new functional micro-regions within established subdivisions. A set of analytic methods relevant for measurement of discrete neuronal data across a diverse range of brain subdivisions are presented. We provide a methodology for the measurement and quantitative comparison of functional micro-neural network activity based on immunohistochemical markers matched across individual brains using micro-binning and heat mapping within brain sub-nuclei. These techniques were applied to the measurement of different memory traces, allowing for greater understanding of the functional encoding within sub-nuclei and its behavior mediated change. These approaches can be used to understand other functional and behavioral questions, including sub-circuit organization, normal memory function and the complexities of pathology. Precise micro-mapping of functional neuronal topography provides essential data to decode network activity underlying behavior.

Disruption of amygdalaTsc2in adolescence leads to changed prelimbic cellular activity and generalized fear responses at adulthood in rats

“…Three EGFP(+) BLA sections were taken from each rat injected with Vir1_Tsc2 or Vir_ctrl. In addition, three tissue sections from the prefrontal cortex were chosen depending on their anteroposterior (AP) coordinates (for Methods, see Bergstrom et al 2011;Jacques et al 2018) from AP 2.8 to AP 2.6 mm relative to Bregma in order to maximize the mPFC cellular activity analysis through the antero-posteriority axis. Briefly, sections containing prelimbic (PL) and infralimbic (IL) cortices at 3 different AP were chosen based on the shape and length of the corpus callosum, measured as the Ferret Length (Jacques et al 2018).…”

“…In addition, three tissue sections from the prefrontal cortex were chosen depending on their anteroposterior (AP) coordinates (for Methods, see Bergstrom et al 2011;Jacques et al 2018) from AP 2.8 to AP 2.6 mm relative to Bregma in order to maximize the mPFC cellular activity analysis through the antero-posteriority axis. Briefly, sections containing prelimbic (PL) and infralimbic (IL) cortices at 3 different AP were chosen based on the shape and length of the corpus callosum, measured as the Ferret Length (Jacques et al 2018). For each AP, the Ferret length had to be statistically not different between subjects and between experimental conditions, but it had to be statistically different between APs.…”

“…Typical landmarks in mPFC were the sizes and shapes of the caudate putamen and corpus callosum. For BLA, the lateral ventricle size, external capsule fiber aspect or optical fiber size were relevant anatomical anchors to isolate proper structure contours (for further methods, see (Bergstrom et al 2011;Jacques et al 2018)); from Paxinos andWatson, 2009 (Paxinos andWatson 2009).…”

“…Methods to create density heat maps have previously been detailed (Jacques et al 2018). We followed this method with slight modifications as we used the Fiji software to calibrate counting areas, draw the structures contours and manually count c-Fos(+) cells.…”

“…Briefly, once all the c-Fos(+) neurons were counted in the calibrated and contoured counting area, their (x,y) coordinates were binned and transformed into matrices to measure the exact localization and density of c-Fos(+) cells. As previously shown (Jacques et al 2018), the number of bins can be calculated based on experimenter parameters or using existing formulas. Here, we calculated bins density with the square root of the total number of cells in each condition.…”