Comparing histological data from different brains: Sources of error and strategies for minimizing them

Abstract

The recent development of brain atlases with computer graphics templates, and of huge databases of neurohistochemical data on the internet, has forced a systematic re-examination of errors associated with comparing histological features between adjacent sections of the same brain, between brains treated in the same way, and between brains from groups treated in different ways. The long-term goal is to compare as accurately as possible a broad array of data from experimental brains within the framework of reference atlases. Main sources of error, each of which ideally should be measured and minimized, include intrinsic biological variation, linear and nonlinear distortion of histological sections, plane of section differences between each brain, section alignment problems, and sampling errors. These variables are discussed, along with approaches to error estimation and minimization in terms of a specific example—the distribution of neuroendocrine neurons in the rat paraventricular nucleus. Based on the strategy developed here, the main conclusion is that the best long-term solution is a high-resolution 3D computer graphics model of the brain that can be sliced in any plane and used as the framework for quantitative neuroanatomy, databases, knowledge management systems, and structure-function modeling. However, any approach to the automatic annotation of neuroanatomical data—relating its spatial distribution to a reference atlas—should deal systematically with these sources of error, which reduce localization reliability.

Comprehensive neurocognitive endophenotyping strategies for mouse models of genetic disorders

“…, Simmons and Swanson, 2009; Swanson, 1995 for a consideration of these challenges; cf. , Wenzel et al , 2010; Willemsen et al , 2003 for a specific example of seemingly contradictory findings in the same mouse model explained by subtle methodological differences).…”

Thinning, movement, and volume loss of residual cortical tissue occurs after stroke in the adult rat as identified by histological and magnetic resonance imaging analysis

“…This presents a problem however, as the histological preparation of neural tissue is known to introduce a number of morphological anomalies. Tissue perfusion, removal, staining, and dehydration can lead to artificial tissue shrinkage, nicks in surface brain areas, and changes in tissue shape and location (Garman, 1990;Overgaard and Meden, 2000;Sadowski et al, 1995;Simmons and Swanson, 2009;Skoglund et al, 1996). It is possible that any cortical thinning, movement, volume, or density loss observed after stroke in the adult rat could be exaggerated by histological preparation.…”

Comparison of the spatial distribution of seven types of neuroendocrine neurons in the rat paraventricular nucleus: Toward a global 3D model

“…During the course of this relatively high-resolution physical mapping analysis, methods were developed for minimizing error when comparing histological data from different animals and transferring this data to a reference atlas (Simmons and Swanson, 2009), and a highresolution 3D computer graphics PVH model was created from a serial series of reference atlas histological sections (Simmons and Swanson, 2008). The next important technical advances need to be made: greatly speeding up by automation the time required for histological processing, data extraction, and data comparison so that appropriate statistical methods can be developed and applied to the types of analysis presented here.…”