Karten,  H. J.

Mitra,  P. P.

Rosa,  M. G.

Cold Spring Harbor Laboratory Institutional Repository

E-Mail karger@karger.com Highlights and Perspectives on Evolutionary Neuroscience  Brain Behav Evol  DOI: 10.1159/000350241  Panoptic Neuroanatomy: Digital Microscopy of Whole Brains andBrain-Wide Circuit Mapping  Partha P. Mitra a    Marcello G.P. Rosa c    Harvey J. Karten b   a  Cold Spring Harbor Laboratory,  Cold Spring Harbor, N.Y. , and  b  University of California San Diego,  La Jolla, Calif. , USA;  c  Department of Physiology and Monash Vision Group, Monash University,  Clayton, Vic. , Australia vidual neuron or action potential; relative-ly few electrodes (compared to the total number of neurons) can be put into the brain at a time due to mechanical/physio-logical constraints, and the live brain of any reasonably sized adult vertebrate can be probed with optical methods only over short distances because of optical scatter in live-brain tissue. There is, however, one level of analysis at which studying the entire nervous sys-tem as a unit is possible: neuroanatomy. There is, in principle, no limitation to studying the entire ex vivo brain, while still being able to access microscopic detail down to the level of individual proteins or organelles. Addressing the integration problem in neuroscience is a primary rea-son for such whole-brain, multi-scale neu-roanatomy. Just as genomes have provided an integrative framework for cellular/mo-lecular biology, so can whole-brain ana-tomical datasets provide a unifying infor-mational framework for neuroscience. Neuroanatomy has often been referred to in a derogatory sense as ‘largely descrip-tive’. In contrast, digitized whole-brain da-tasets promise an era of quantitative analy-sis, while also providing a geometrical/spa-tial informational framework addressing the problem of discordant nomenclature [Bohland et al., 2009a] that hinders the in- Philosophers have pointed to the preva-lence of the ‘mereological fallacy’ in con-temporary neuroscience (attributing the properties of the whole to a part, a.k.a. the blind men and the elephant fallacy) [Ben-nett and Hacker, 2003]. Attention is often focused on the details of individual brain systems. Specific sensory or motor path-ways are well studied and, increasingly, modulatory systems are as well (reward, sleep). However, integrative study of the whole nervous system is rare, and the knowledge remains fragmented. Overarch-ing models, in the tradition of Skinner or Pavlov, are also oversimplified and there-fore unable to grapple with the full com-plexity of brains and behaviors. In addition to focusing on brain subsystems, there is an increasing focus on specific model organ-isms, which makes this problem worse [Manger et al., 2008]. A basic reason for the difficulty of inte-grative study is instrumental. Despite tech-nical advances, we do not have the means of simultaneously measuring all relevant variables (e.g. the state or activity of all neu-rons, including subcompartments). This is unlikely to change, due to basic physics/physiology limitations. Whole-brain imag-ing techniques (PET, fMRI and MEG/EEG) have fundamental spatial and tempo-ral resolutions far removed from the indi- Published online: June 14, 2013  Prof. Partha P. Mitra  Cold Spring Harbor Laboratory  1 Bungtown Road Cold Spring Harbor, NY 11724 (USA)  E-Mail mitra   @   cshl.edu  © 2013 S. Karger AG, Basel0006–8977/13/0000–0000$38.00/0  www.karger.com/bbe tegration of knowledge. Another rationale, also in parallel to genomic analysis, is un-covering the mechanistic basis of neuro-logical disorders. Brain-wide digital neuro-anatomy will produce reference datasets for brain circuits and cytoarchitecture that can be compared to the corresponding da-tasets for animal models of brain disorders. This will shed light on the largely unsolved problem of which neural circuit changes characterize neuropsychiatric disorders, and provide a valuable intermediate phe-notype for genetic analysis. Perhaps the most important scientific rationale for the new effort is the study of brain evolution. Comparative genomic analysis has fundamentally advanced our understanding of evolution; similarly, brain-wide digital datasets for circuits and cytoarchitecture could resolve long-stand-ing controversies in brain evolution, such as the homologies between avian and mammalian brains [Jarvis et al., 2005]. With the addition of whole-brain develop-mental datasets in many species, we foresee a new era of quantitative ‘evo-devo’ style analysis in neuroscience, which also prom-ises to counteract the increasingly narrow focus on a few model organisms. The last decade has seen rapid develop-ments in whole-brain digital neuroanato-my. Amongst the first such datasets were Downloaded by: Cold Spring Harbor Laboratory143.48.6.5 - 6/28/2013 3:53:22 PM Mitra  /Rosa  /Karten   Brain Behav EvolDOI: 10.1159/0003502412Edward G. Jones’ rich inventory of com-parative neuroanatomy at high resolution [Jones et al., 2011] (http://www.brain-maps.org) and the Allen Gene Expression Atlas of mouse brain [Lein et al., 2007]. Cir-cuit mapping is inherently more complex than mapping gene expression, as connec-tivity information can grow with the square of the number of nodes. Complete map-ping of the local microcircuitry in small portions of the neuropil is possible using electron microscopy, but this method is presently not scalable to whole brains ex-cept in very small brains (such as in flies or worms). Here, we confine our discussions to light-microscopy-based data [Helm-staedter and Mitra, 2012]. Recently, a proposal was made for whole-brain circuit mapping at a ‘meso-scopic scale’ of analysis, using tracer-based neuroanatomical tractography and light microscopy [Bohland et al., 2009b]. In this ‘grid-based’ approach that resembles ge-nomic sequence assembly using a shotgun method, a number of injection locations are chosen to cover the entire brain, each loca-tion receiving a tracer injection in individ-ual mice. The resulting datasets are subse-quently coregistered to a common spatial framework to provide a brain-wide connec-tivity atlas. Three projects following this ba-sic proposal are currently underway at the Cold Spring Harbor Laboratory (http://mouse.brainarchitecture.org), Allen Insti-tute for Brain Sciences (http://brain-map.org) and University of California, Los An-geles (http://mouseconnectome.org). While employing the same overall grid-based strategy, the projects have significant differ-ences in the tracers, genetic constructs and imaging strategies used that make the data-sets complementary. In the Mouse Brain Architecture Project, a total of four differ-ent tracer types, two anterograde and two retrograde, are injected at 262 grid loca-tions distributed over the left hemisphere of an adult male C57BL/6 mouse, with just a single tracer type and injection location per mouse. Preliminary datasets are available from the project website (http://mouse.brainarchitecture.org). Also available are auxiliary datasets employing a variety of cy-toarchitectonic markers. While the mouse has become the most widely studied model species due to the wide availability of transgenetic constructs and pharmaceutical research, studies of other species are essential to enable com-parative and evolutionary analysis. Just as in genomics, it is imperative that we also have whole-brain neuroanatomy projects characterizing the circuits and cytoarchi-tecture of other species, covering major taxa on the phylogenetic tree. Efforts inthis direction have begun, pioneered by the late E.G. Jones [Jones et al., 2011] (http://brain-maps.org), but require significantly more support. Gene expression maps for the human brain are now available (http://brain-map.org). A project to map gene ex-pression in the avian brain is underway (http://zebrafinchatlas.org) and whole-brain cytoarchitectonic datasets for the Ze-bra Finch are available (http://zebrafinch.brainarchitecture.org). A digital whole-brain circuit-mapping project in the com-mon marmoset monkey  (Callithrix jac-chus) is also in progress (http://marmoset.brainarchitecture.org), enabled by a high-quality collection of digitized histological materials (http://marmoset-brain.org). Marmosets are ideal nonhuman primate species for whole-brain digital neuroanat-omy [Paxinos et al., 2012], and may pro-vide a much-needed model for under-standing the development of brain circuits involved in cognition, given their short de-velopmental cycle and existing technology for generating stable transgenic lines [Sa-saki et al., 2009]. Whole-brain neuroanatomy and circuit mapping are consequent to three major technological advances: (1)  The exponentially decreasing costs of storage and computation. Digitizing a mouse brain at a resolution of approxi-mately 1 μm generates about a terabyte worth of data (a human brain at the same resolution is a petabyte of data). Twenty years ago, a terabyte of disk storage would have cost close to USD 1 million. At present it costs less than USD 100. (2)  Increasing automation, with digital slide-scanning microscopes or serial block-face sectioning microscopes being em-ployed to acquire the primary image data. These, as well as automated on-slide histo-chemistry and cover-slipping machines de-veloped for use in clinical histopathology are serendipitously enabling whole-brain neuroanatomy by automating repetitive la-bor-intensive tasks. (3)  Molecular biology methods allow definition of specific cell types for analysis, through the insertion of transgenes into neurons, using transgenic animals, intrace-rebral injection of viral vectors carrying the transgenes or fluorescent reporters into the brain, or a combination of the two. These fluorescent reporters provide bright intrin-sic labeling, obviating the complexities of histochemical processing.  In addition to the experimental acquisi-tion, digital analysis and computational in-tegration of the data is equally important. The challenges include detection of cells and processes, coregistration of sections from a single brain or brains across indi-viduals, statistical characterization of cyto-architecture and stereological consider-ations, quantification and analysis of indi-vidual variations from brain to brain for the large, anisotropic data volumes. All of these have to be done on petabyte scale data, requiring ‘big data’ computational in-frastructure. In addition, the web infra-structure required for making these data available through the internet is substan-tial. Progress has begun on all these fronts, and we can expect these computational is-sues to increasingly take a central role as the field advances. Given the current lacuna in our knowl-edge about the complete circuit diagram of any vertebrate brain, the need for mapping out brain circuitry at an anatomical level may be obvious. Nevertheless, skeptics will point out that without further ‘functional’ characterization (in terms of the activity of the neural network, the traditional subject of neurophysiological investigations), even having the whole brain circuit diagram will not be adequate. Often cited in this context is the example of the nervous system of the roundworm  Caenorhabditis elegans , which has been available for decades from analy-sis of electron microscopy image data, but arguably has not led to an ‘understanding’ of how this worm’s brain works. In addi-tion, it is pointed out that the brain is plas-tic, responding to experience, raising ques-tions about a static circuit diagram as well as bringing up the important issue of indi-vidual variability. However, ‘function’ can be layered on to the underlying informational scaffold-ing of the circuit geometry and topology. This can be done using the same ex vivo techniques that permit detailed whole-brain analysis by adding information about neurotransmitters and receptors through the usage of immunohistochemistry or transgene labels. Physiological methods can be combined with whole-brain neuro-anatomical methods by first performing electrophysiological studies or optogenetic fMRI to map neuronal activity (though ata coarser resolution) and then registering these functional data to digital templates generated from neuroanatomical analyses.Downloaded by: Cold Spring Harbor Laboratory143.48.6.5 - 6/28/2013 3:53:22 PM Neuroanatomy in the Digital Age Brain Behav EvolDOI: 10.1159/0003502413 Individuals belonging to the same spe-cies are expected to share a common, ge-netically determined, species-typical neu-roanatomical substrate, which is modu-lated by effects of phenotypic or adult plasticity. In fact, this is part of the ratio-nale for meso-scale circuit mapping: it is at this level, familiar with the brain atlases of classical neuroanatomy, that one ex-pects a species-typical circuit diagram that is robust in the face of individual variation and (normal) environmental perturba-tions. Digital whole-brain datasets will permit both the determination of this un-derlying, ‘reference’ circuitry and an un-precedented quantitative study of the in-dividual variations around the species-typical reference. Similar concerns were also raised in the early days of whole-genome sequencing, with gene transcription or translation play-ing the roles of neurophysiological activity. In that case, whole-genome analysis of RNA transcription and regulation (as in the ENCODE project) has naturally fol-lowed the initial whole-genome sequenc-ing efforts. Once whole-brain neuroanato-my provides an informational reference framework, whole-brain physiological analysis will naturally follow. Coupled with high-throughput behavioral analysis, this will enable us to address the ‘blind men and the elephant’ problem in neuroscience, of excessive focus on narrow aspects of the nervous system. Such analysis is already underway for specific brain circuits; the generalization to entire brains is a logical next step. This new era of computationally en-abled, brain-wide neuroanatomy at light microscope resolution, a ‘pan-optic neu-roanatomy’ of entire brains, will help us bring the same integrative approach to the study of the nervous system as the nervous system itself brings to the world, binding the multifaceted and complex array of ac-tivities of neurons into one experiential whole.  References  Bennett MR, Hacker PMS (2003): Philosophical Foundations of Neuroscience. Malden, Black-well.  Bohland JW, Bokil H, Allen CB, Mitra PP (2009a): The brain atlas concordance problem: quan-titative comparison of anatomical parcella-tions. PLoS One 4:e7200.  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Panoptic Neuroanatomy: Digital Microscopy of Whole Brains and Brain-Wide Circuit Mapping

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