BigBrain: An Ultrahigh-Resolution 3D Human Brain Model

Abstract

Reference brains are indispensable tools in human brain mapping, enabling integration of multimodal data into an anatomically realistic standard space. Available reference brains, however, are restricted to the macroscopic scale and do not provide information on the functionally important microscopic dimension. We created an ultrahigh-resolution three-dimensional (3D) model of a human brain at nearly cellular resolution of 20 micrometers, based on the reconstruction of 7404 histological sections. "BigBrain" is a free, publicly available tool that provides considerable neuroanatomical insight into the human brain, thereby allowing the extraction of microscopic data for modeling and simulation. BigBrain enables testing of hypotheses on optimal path lengths between interconnected cortical regions or on spatial organization of genetic patterning, redefining the traditional neuroanatomy maps such as those of Brodmann and von Economo.

Measuring individual morphological relationship of cortical regions

“…Firstly, local cortical measures other than gray matter volume can be used, such as the cortical thickness or surface area. The approach could also be applied to measuring the human brain at the cellular level, such as in the only ultrahigh-resolution brain model developed thus far, BigBrain (Amunts et al, 2013). We refer to a study under this framework as a Distribution Based Inter-regional Relation (DBIR) study.…”

The human brain project: neuroscience perspectives and German contributions

“…Cell body-stained sections of human brains are the basis for 3D reconstructed brain models such as the BigBrain [2]. In addition, serial sections with cellular markers are examined, allowing the analysis of the number and distribution of neurons and glial cells in different brain regions (collaboration with Javier DeFelipe (SP1), Universidad Politécnica de Madrid; [6]).…”

“…The JuBrain Atlas contains cytoarchitectonic probability maps of cortical and subcortical areas; those information can, inter alia, be used for a reliable localization of findings from fMRI studies [1,20]. However, in order to better account for the different scales of brain organization and represent the different data in a common reference space, the BigBrain was developed [2]. Due to its resolution of 20 microns in any direction in space, it is even possible to map data for the localization of single neurons or microcircuits according to their location in the cortex.…”

“…Those insights into the brain provide simulation, and give computer scientists the opportunity to develop a new generation of computers and software that are inspired by the functional principles of the brain. HPC opens up new avenues for neuroscientists to develop virtual brain models, such as the Big-BRAIN [2] model, which connects the macroscopic with the microscopic organization level for the first time in a reference system. In such models, data from the genetic, molecular, and cellular levels up to cognitive systems could be combined together for a subsequent analysis at different scales.…”

Registration of in-vivo to ex-vivo MRI of surgically resected specimens: A pipeline for histology to in-vivo registration

“…The majority of these studies focused on primates (Dauguet et al, 2007;Malandain et al, 2004;Breen et al, 2005;Ceritoglu et al, 2010;Choe et al, 2011) or rodents (Jacobs et al, 1999;Humm et al, 2003;Meyer et al, 2006;Lebenberg et al, 2010;Yang et al, 2012;Liu et al, 2012). The few studies that registered human brain MRIto histology were performed on wholebrain (Schormann et al, 1995;Kim et al, 2000;Singh et al, 2008), or single hemisphere (Yelnik et al, 2007;Osechinskiy and Kruggel, 2011) post-mortem serially sectioned data (Amunts et al, 2013) created a 3D model of single subject's brain using post-mortem histological sections reconstructed at 20 m isotropic resolution and registered it to a T1 average atlas created from 24 subjects. Eriksson et al (2005) reported registering histology of neocortical specimens from anterior temporal lobectomies to in-vivo MRI; however, their approach only involved visually selecting the closest coronal MRI slice for each histology slide, and did not attempt to find a dense correspondence between each histology slide and its corresponding MRI slice.…”