Connectome History

8/10/2019 Connectome History

1/12

Review

Connectomic approaches before the connectome

Marco Catani a,, Michel Thiebaut de Schotten a,b, David Slater c, Flavio Dell'Acqua c,d

a Natbrainlab, King's College London, Institute of Psychiatry, Department of Forensic and Neurodevelopmental Sciences, London SE5 8AF, UKb UMR_S 975, CNRS UMR 7225, Centre de Recherche de l'Institut du Cerveau et de la Moelle pinire, Groupe Hospitalier Piti-Salptrire, 75013 Paris, Francec Natbrainlab, King's College London, Institute of Psychiatry, Department of Neuroimaging, London SE5 8AF, UKd NIHR Biomedical Research Centre for Mental Health at South London and Maudsley NHS Foundation Trust and Institute of Psychiatry, King's College London, UK

a b s t r a c ta r t i c l e i n f o

Article history:

Accepted 20 May 2013Available online xxxx

Keywords:

ConnectomeConnectionDiffusionTractographyHodologyNetworks

Connectome is a term with a short history but a long past. Since the origins of neuroscience the concept of amap of neural connectionshas been a constant inspiring idea for those who believed the brain as the organof intellect. A myriad ofproto-connectome maps have been produced throughout the centuries, each onereecting the theory and method of investigation that prevailed at the time. Even contemporary denitionsof the connectome rest upon the formulation of a neuronal theory that has been proposed over a hundredyears ago. So, what is new? In this article we attempt to trace the development of certain anatomical andphysiological concepts at the origins of modern denitions of the connectome. We argue that compared toprevious attempts current connectomic approaches benet from a wealth of imaging methods that in partcould justify the enthusiasm fornally succeeding in achieving the goal. One of the unique advantages of con-temporary approaches is the possibility of using quantitative methods to dene measures of connectivitywhere structure, function and behaviour are integrated and correlated. We also argue that many contempo-rary maps are inaccurate surrogates of the true anatomy and a comprehensive connectome of the humanbrain remains a far distant point in the history to come.

2013 Elsevier Inc. All rights reserved.

Contents

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0Early proto-connectome maps (Table 1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0Modern cartography (Table 2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0Contemporary and future connectomes between macroscopic metaphors and microscopic myths . . . . . . . . . . . . . . . . . . . . . . . . . 0Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0

Introduction

The pattern of scientic investigation cannot be understood inisolation: it must be set against the background of wider trends inthe sciences, methodological advancements, and general culture ofthe time (Clarke and Jacyna, 1987). Social, biological, and technologicalnetwork models dominate contemporary approaches to complexity(Egerstedt,2011). Telecommunications, socialnetworks, transportationlogistics, molecular interactions, and metabolic pathways are just someexamples in which network analysis is used to described complexdynamics (Sporns, 2011; Strogatz, 2001).

Thesame approachhas been proposedto describe the complexity ofthe nervous system. Here, interactions between 86 billion of neuronscould be dened using nodes, hubs, connections and their propertiesquantiedin terms ofefciency, centrality, global and local integration,etc. (Bullmore and Sporns, 2009). There is also a general perceptionthat network analysis could bring us closer to a true understandingof the real working of the human brain and its disorders. Tworecent multicentre research projects testify to the interest andcommitment of the international scientic community to this endeav-our. The Human Connectome Project (http://www.humanconnectome.org) is a $40 million NIH funded study to map the human connectomein 1200 healthy subjects using large scale functional and structural im-aging. The Human Brain Project (http://www.humanbrainproject.eu ) isone of the agship projects of the European Commission that is likely toreceive funding in the region of 1 billion for the simulation of the

NeuroImage xxx (2013) xxxxxx

Corresponding author at: Natbrainlab, PO50 Department of Forensic and Neurodevel-opmental Sciences, Institute of Psychiatry, King's College London, London SE5 8AF, UK.

E-mail address:[email protected](M. Catani).

YNIMG-10548; No. of pages: 12; 4C: 5, 6, 8

1053-8119/$ see front matter 2013 Elsevier Inc. All rights reserved.

http://dx.doi.org/10.1016/j.neuroimage.2013.05.109

Contents lists available at SciVerse ScienceDirect

NeuroImage

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / y n i m g

Please cite this article as: Catani, M., et al., Connectomic approaches before the connectome, NeuroImage (2013), http://dx.doi.org/10.1016/j.neuroimage.2013.05.109
http://www.humanconnectome.org/http://www.humanconnectome.org/http://www.humanbrainproject.eu/http://dx.doi.org/10.1016/j.neuroimage.2013.05.109http://dx.doi.org/10.1016/j.neuroimage.2013.05.109http://dx.doi.org/10.1016/j.neuroimage.2013.05.109mailto:[email protected]://dx.doi.org/10.1016/j.neuroimage.2013.05.109http://www.sciencedirect.com/science/journal/10538119http://dx.doi.org/10.1016/j.neuroimage.2013.05.109http://dx.doi.org/10.1016/j.neuroimage.2013.05.109http://dx.doi.org/10.1016/j.neuroimage.2013.05.109http://dx.doi.org/10.1016/j.neuroimage.2013.05.109http://www.sciencedirect.com/science/journal/10538119http://dx.doi.org/10.1016/j.neuroimage.2013.05.109mailto:[email protected]://dx.doi.org/10.1016/j.neuroimage.2013.05.109http://www.humanbrainproject.eu/http://www.humanconnectome.org/http://www.humanconnectome.org/


2/12

entirehuman brain connectivity at a neuronallevel andemulation of itscomputational capabilities. With the recent announcement of PresidentObama of a budget of possibly $100 million a year forthe Brain Researchthrough Advancing Innovative Neurotechnologies (BRAIN) Initiative,thiseld of research will see an even greater expansion.

This unprecedented support is in large part due to the developmentof new methods to image networks in the living human brain and thecomputational capability of processing and storing large amounts of

data. The connectome approach, although new in its overarching con-ception, represents the culmination of converging lines of research,each of which have developed over the course of many centuries(Tables 1 and 2). In this paperwe attempt to trace timelines of those an-atomical (both at the macro- and microscopical scale), physiologicaland methodological advances that helped to generate brain maps andshape their evolution throughout history. Tables 1 and 2 describesome of the pivotal discoveries in neurosciences from the eld of mi-croscopy, electrophysiology/computational sciences, and anatomy/neuroimaging. These timelines are not intended to be exhaustivebut rather highlight only a number of key discoveries and develop-ments that have some relevance to contemporary connectomicapproaches. Our aim is to give credit to pioneers and put currentconnectome projects into a wider context. The gures reproducedin the article are examples of historical brain maps that can beconsidered as possible forerunners of contemporary connectomes.In the nal part we try to take advantage of our historical survey

to identify those steps needed to ll the gap between currentconnectome maps and the real underlying anatomy of the humanbrain.

Early proto-connectome maps (Table 1)

A connectome is dened by its nodes and connections, whose anato-my and function have been varyingly dened throughout history,

according to the predominant theoretical constructsand thelatest meth-odological advancements available at the time. For a long time the mostpopular representations of the brain and its functions consisted ofdiagrams depicting a variable number of intercommunicating ventricu-lar cells (Fig. 1). The ventricular theory was the direct result of the useof brain dissections as originally proposed by Herophilus of Alexandriain the fourth century BC and two centuries later by Galen (Clarke andO'Malley, 1996). In these maps nodes correspond to ventricular reser-voirs or cells with specialised functions. These chambers communicatethrough a system of interventricular foramina and to peripheral organsby means of hollow nerves. In these proto-connectome maps, essentialelements of contemporary connectome descriptions can be found. Im-plicit to the ventricular theory was the anatomical differentiation andfunctional specialisation of ventricular cells communicating through anetwork system. Further, brain functions were seen as emerging fromthe dynamic and regulated exchange between hierarchically organisedventricular cells.

Table 1

Milestones in the history of neuroscience from Renaissance to the end of 19th century.

1590 Hans and Zacharias Janssen invent themicroscope

1666Marcello Malpighi observes the cortex anddescribes globules and fibres

1674

MICROSCOPY

1833-8Christian Ehrenberg, Gabriel Valentin and

Jan Purkinje describe single populations ofneurons

1873 Camillo Golgi discovers the black reaction

1839 Theodor Schwann proposes the cell theoryfor all living organisms

1887theory

1891 Heinrich Waldeyer-Hartz uses the term neuron toindicate the functional unit of the nervous system

ELECTROPHYSIOLOGY/COMPUTATIONAL

1649motor mechanism for involuntary muscle contraction

1660 Jan Swammerdam discovers that the mechanicalstimulation of nerves produces muscle contractions

1713Isaac Newton suggests the electrical nature ofnerve signal propagation

1791 Luigi Galvani publishes his work on animal electricity anddescribes nerves as pathways that conduct electricity

1803 Giovanni Aldini, applies electrical currents to mammalianbrains to trigger motor responses

1848Emile du Bois-Reymond discovers the action potential(negative Schwankung)

1868

Herman von Helmholtz measures the propagationspeed of the nerve impulse

Julius Bernstein obtains direct recording of the actionpotential and its kinetics

NEUROANATOMY/NEUROIMAGING

1586Arcangelo Piccolomini, distinguishes the medulla (i.e.white matter) and the cerebrum (i.e. grey matter)

1840Jules Baillarger describes the cortical layersand compares them to a Galvanic pile

1664Thomas Willis speculates on the sensory and motornature of ascending and descending projectionpathways

1684centrum ovale

1809-12Johann Reil describes several association tracts,including the uncinate, arcuate, inferior longitudinalfasciculus and cingulum

1819-26 Karl Burdach extends Reil's work and giveslatin names to association tracts

1870-85Theodor Meynert formulates the associationisttheory of brain function

1874of higher cognitive functions

1870 Gustav Fritsch and Julius Hitzig use electricity to localizemotor regions

1875 Richard Caton records electrical activity from exposedrabbit and mouse brains

1862

1860 Otto Deiters describes axonal and dendriticprocesses

1850degeneration of axons

1886 Vittorio Marchi develops a technique to tracedegenerating axons over long distances

1897 Charles Sherrington coins the term synapse

1852in the cortex

1867 Theodor Meynert describes interlobarvariations in the cortical layering

1843-45Wilhelm Griesinger and Thomas Laycock develop theconcept of psychic reflex for higher cognitive functions

1786 Felix Vicq dAzyr, describes commissural andassociative pathways

1810localises cerebral functions in the cerebral cortex

1857 Franois Lauret and Louis Gratiolet propose the lobardivision of the brain

1861

mathematical term graph1878

1855Bartolomeo Panizza discovers the visual centre in theoccipital lobe

1830sCarlo Matteucci measures an electrical voltage acrossthe cell membrane

1825 Jean-Baptiste Bouillaud demonstrates that speechis localised in the anterior regions of the brain

Paul Flechsig obtains myelogenetic maps of the humanbrain and distinguishes primary from association areas

1896

1736Leonhard Euler gives a mathematical formulation of the

1850

Antonius van Leewenhoek provides the firstdetailed description of nerve fibres

Augustus Waller describes the Wallerian

Rudolph Kolliker identifies fibres and cells

Carl Weigert develops the first myelin stain

Santiago Ramn y Cajal proposes the neuron

Rene Descartes describes the reflex as a sensory-

Knegsberg Bridge from which originates the modern graph

James Sylvester introduces for the first time the

Viessens separates commissural fibres of thecorpus callosum from projection fibres of the

Joseph Gall identifies different cerebral gyri and

Paul Broca identifies an area for speech production

Carl Wernicke puts forward the first nerwork model

theory

2 M. Catani et al. / NeuroImage xxx (2013) xxxxxx

http://dx.doi.org/10.1016/j.neuroimage.2013.05.109http://dx.doi.org/10.1016/j.neuroimage.2013.05.109http://dx.doi.org/10.1016/j.neuroimage.2013.05.109http://dx.doi.org/10.1016/j.neuroimage.2013.05.109


3/12

The ventricular model had a long lasting inuence despite the fact thatobvious experimental evidence of its fallacy emerged during the Renais-sance. Leonardo da Vinci, for example, obtained a wax cast of the ventri-cles that was clearly against the classical representation of theventricular system (Pevsner, 2002). Similarly, Vesalius showed that theventricular anatomy described by Galen was questionable and its doc-trine did not t with the evidence from dissections (Vesalius, 1543).From the sixteenth century the ventricular theory co-existed with anew spirit in science based on the experimental method (Galilei, 1638)and the renewed belief of an intimate relationship between anatomyand function (Catani, 2007; Catani and Thiebaut de Schotten, 2012).

Post-mortem dissection became the primary method of investigation ofthe nervous system and this led to important anatomical discoveries,among them the distinction between the cerebrum (i.e. grey matteror cortex) and the medulla (i.e. white matter) by Arcangelo Piccolominiin1586:

I call the cerebrum [grey matter] that whole ashen-colored body,

darkening from white, which very closely encompasses the medulla.

The medulla is the whole of the white and more solid body, which is

concealed within the ashen-colored one. Thus the cerebrum differs and

is distinguished from the medulla by color, because the cerebrum is

ashen-coloredbut the medulla is white;in consistency, because the cere-

brum is softer and the medulla a littleharder and more compact; in loca-

tion, because the medulla is in the middle of the cerebrum which wholly

covers it over; also the ashen-colored body is distinguished from the

white by certain lines. The cerebrum commences everywhere by convo-

lutions and extends as far as the corpus callosum...

This distinction has direct relevance to modern connectomicswhere hubs are located in the grey matter and connections in thewhite matter. This is particularly true for those approaches that useneuroimaging and electrophysiological methods to map whole brainnetworks at the macroscopic level.

Thestudy of thewhite matter anatomy expanded in theseventeenthcentury when many scientists recognised that white matter contains-bres whose trajectories could be followed and described if specimens

were carefully prepared using all the necessary precautions (Steno,1669):

bres must be disposed in the most artful manner, since all the

diversity of our sensations and movements depend upon them. We ad-

mire the contrivance of thebres in each muscle, and ought still more

to admire their disposition in the brain, where conned in a very small

space, eachexecute theirparticular ofces without confusion or disorder.

From these anatomical studies a new view of the white matteremerged; no more a homogenous support structure around the ven-tricles, but rather a complex medium composed of tubular lamentsfor the passage ofuid between central cells and peripheral nerves(Descartes, 1662). These connecting laments were found to origi-

nate from the cortex (Malpighi, 1666), specialise in motor and

Table 2

Milestones in the history of neuroscience in the 20th century.

1927

Walter Dandy introduces ventriculography

Egas Moniz develops cerebral angiography

1935Joseph Klinger describes a new procedure toperform blunt dissections of white matter tracts

Walle Nauta and Paul Gygax develop a newstaining method for degenerating axons

1951

Intra-axonal tract tracing compounds aredeveloped (e.g. horseradish peroxidaseand radiolabeled amino acids )

1960-70

1977

1979

complete connectomme of the C. elegans(302neurons)

1986

Ernst Ruska develops the electron microscope1938

1952 Alan Hodgkin and Andrew Huxley publish a mathematicalmodel for nerve excitation

1959 David Hubel and Torsten Wiesel describe oriented

1969-71David Marr and James Albus develop a neurobiologicaland computational theory of cerebellar function

1943mathematical model of a neural network

1946 Donald Hebb proposes a theory for synaptic plasticity andlearning process

1946Felix Block and Edward Purcell independentlydescribe the NMR phenomenon for liquid and solid

1907

1927

1918

MICROSCOPY ELECTROPHYSIOLOGY/COMPUTATIONAL NEUROANATOMY/NEUROIMAGING

automatic form of learning1903

1937sensory homunculus in man

1905

Korbinian Brodmann produces cyto-architectonic maps of the brain

1909

Oskar and Cecile Vogt work on myelo-architectonic maps of the human frontal lobe

1910

Constantin Economo and Georg Koskinaspublish the most comprehensivecytoarchitectonic maps

1925

1901Joseph Dejerine describes the topographic

1986 Jay McClelland and David Rumelhart apply parallel distributedprocessing theories to cognitive psychology and cognitive neuroscience

Eduardo Macagno, uses a serial electronmicroscopy to map an isolated neuron in the First Clinical MRI scanner

1950sscanners are developed

1973

1970s

1980

Jean Talairach and Pierre Tournoux publish the1988

Seiji Ogawa describes the BOLD effect1990

Denis Le Bihan applies Diffusion MRI to theliving human brain

1985

1960s David Cohen develops MEG

Julius Bernstein advances the hypothesis that the actionpotential results from a change in the permeability of theaxonal membrane to ions

1902

Peter Basser develops diffusion tensor imaging1994

First in-vivo human diffusion tractography

reconstructions

1999

Gabriella Ugolini introduces the use of virusesas transneuronal tracers1987

and visualize patterns of interpersonal relationships1933

1995Rabies virus are used to study polysynapticneural networks

1942staining.

1989Tim Berners-Lee develops a new hypertext system thatruns across the internet, the world wide web

1998Duncan Watts and Steven Strogatz present amathematical model to describe small world networks

1959 Mountcastle and Powell identify the columnar organization

of the cortex

Alfred Campbell publishes the first map of thebrain divided into 17 cortical fields

Albert Coons proposes immunoflurescence

Hans Kuypers uses fluorescent axonal tracers

water flea

Electron microscopy is used to describe the first

Ivan Pavlov discovers the conditioned reflex as an

Luis Lapicque publishes a model of integrate-and-fire

neurons suggesting a threshold for firing

Hans Berger records the first human EEG

Jacob Moreno presents the first sociogram as a tool to study

Wilder Penfield and Edwin Boldrev describe the motor and

Warren McCulloch and Walter Pitts propose the first

receptive fields in the cat's primary visual contex

distribution of fibres within the internal capsule

The first Positron Emission Tomography

Godfrey Hounsfield produces Computerised Tomography

Paul Lauterbur publishes the first NMR image

first atlas in a common space of reference

3M. Catani et al. / NeuroImage xxx (2013) xxxxxx



4/12

sensory functions (Willis, 1664), and coordinate a wide range of be-haviours, from simple reex responses to cognition (Descartes,

1664). Some of the maps of this period had little anatomical accuracy.The gure produced by Descartes to illustrate the complexity of whitematter connections, for example, was pictorial in its representation ofa philosophical concept rather than a faithful portrait of the real

anatomy (Fig. 2, left)(Descartes, 1664). Others produced images thathad the primary objective of displaying true anatomical ndings(Fig. 2, right)(Vieussens, 1685). By now the concept of connectivitywas implicit in the idea ofbres and nerves.

Originally prompted by the ventricular theory, the study of thewhite matter pathways remained linked for a long time to a hydraulicphysiology of the brain. After all if, according to the ventricular theory,cogitation emerges from the ow and passage ofuid through hollow

bres, an exact description of these pathways could reveal the mecha-nisms of brain function. With the progressive accumulation of new an-atomical ndings the ventricular theory evolved, with a shift fromcentral ventricular specialisation of function to a compartmentalisedsystem of individual gyri containing animal spirits (Willis, 1664):

But a no less important reason and necessity for the twistings [gyri] in

the brain arises from the distribution of the animal spirits. Since for

the various act of imagination and memory the animal spirits must

be moved back and forth repeatedly within certain distinct limits

and through the same tracts or pathways, therefore numerous folds

and convolutions of the brain are required for these various arrange-

ments of the animal spirits; that is, the appearance of perceptible

things are stored in them, just as in various storerooms and ware-

houses, and at given times can be called forth from them. Hence these

folds or convolutions are far more numerous and larger in man thanin any other animal because of the variety and number of acts of the

higher faculties

At the beginning of the nineteenth century brain maps of white mat-ter connections became gradually more rened with differentiation oftracts into callosal, projections and association pathways. Individual as-sociation tracts were also identied and their anatomy described in de-tail (Burdach, 1822; Reil, 1809, 1812). The convergence of anatomicaladvancements with the progressive corticalisation of brain functionsculminated in Gall and Spurzheim's organology theory (Fig. 3). They be-lieved thatthe brain is theorgan ofthe mindand itself ismadeup of mul-tiple organs, tobe identied with theconvolutions(Galland Spurzheim,1810):

The convolutions, as far as they constitute an organ, receive their-bers from different regionsThesebres orbre bundles have a con-

stant and uniform direction, different however in each region; they

form their own expansions and their own convolutions; they develop

Fig. 2.The beginning of the modern study of white matter connections based on methods for bre dissection. Left)Descartes' (1664)representation of the intricate system of whitematter passages in the human brain had little anatomical correspondence. Right) Vieussens (1684) used post-mortem dissections to identify white matter tracts and was the rst to

separate the centrum ovale composed of projection bres from the commissural bres of the corpus callosum, in this gure partially removed in the midline.

Fig. 1. The ventricular system represented in a drawing dating from about 1310(reproduced fromClarke and Dewhurst, 1972). The ve cells are named according totheir specialisation of function: the most anterior cells are the sensus communis(e.g.common sense) and the ymaginatio(e.g. visual sense) connected to the eyes throughthe optic nerves. Behind are the cell estimativa and the cell cogitativa, the latterconnected to a fth cell, the vis memorativalocated below the vermisof the cerebel-lum. Cognitive processes result from the passage ofspiritsfrom one cell to the other.Also note the hierarchical arrangement of the cells with the visual sense acting as amajor hub connected to three other cells and the eye, while the other cells are onlyconnected to either one or two cells.




5/12

at different stages of life; their number varies greatly in different

kinds of animal each organ is independent and acts by itself by

the virtue of its own powers and it contains directly within itself theproximate cause of the phenomena which it offers.

But their view, although very modern in some respects, fell into dis-repute mainly for two reasons. First, organology failed to embrace newideas on the physiology of the nervous system based on experimentalwork of Galvani and Aldini (see below). Second, organology lostscientic credibility when Gall and Spurzheim later suggested thatmental faculties are localised in well-developed cortical organs andconcluded that larger organs leave greater impressions on the skull.With their new Phrenological theory they hoped to ascertaining theseveral intellectual and moral dispositions of man and animal, by theconguration of their heads(Gall and Spurzheim, 1810). Despite thefast spreading of phrenological ideas and their large diffusion acrossthe continents, others continue to work on alternative models.

In particular the rst half of the nineteenth century saw theemergence of a new paradigm: animal electricity (Galvani, 1791). Thissparked debates in Italy and Europe and propelled a newseries of exper-iments that signposted the origin of modern electrophysiology. The newparadigm attracted illustrious gures from other elds and promotedcross-fertilisation between anatomy and physics. The layering of thecortex, for example, was seen as an electrical generatorof the human

brain (Baillarger, 1840) and current was applied to the brain toelicit movement (Aldini, 1803). New recording methods were also

introduced to study the characteristics of the action potential in themuscles (Bois-Reymond, 1848; Matteucci, 1830) and peripheralnerve (Bernstein, 1868). By the mid-nineteenth century, the conceptof spinal reex was already established. This was extended from thespine to the brain (Griesinger, 1843; Laycock, 1845) and, with theexperimental work ofIvan Pavlov (1903), conditioned reex becamea basic aspect of physiological psychology.

The second half of the nineteenth century witnessed also the explo-sion of advanced methods for microscopy. In 1839 Schwann proposedthe cell theory for all living organisms and many microscopists begunto describe newcells in thebrain (Schwann, 1839). Klliker dividedhis-tological features of the cortex into myelo- (i.e. the pattern of distribu-tion of corticalbres) and cytoarchitectonic (i.e. the pattern of cellulardistribution in the cortex) (Klliker, 1859) and Meynert observedinterregional variations of the cortical layering (Meynert, 1868).Methods forbre staining advanced even at a faster pace.The introduc-tion, for example, of methods for myelin staining by Carl Weigert andVittorio Marchi, and the development of the precision microtome forthe study of serial sections improved the visualisation of small bresin the normal and pathological brains (Bentivoglio and Mazzarello,2010). Bernhard von Gudden perfected an experimental method inanimals whereby he could produce secondary degeneration and atrophy

Fig. 4. Cortical centers and connections in the late nineteenth century dened usingclinico-anatomical correlation and post-mortem dissections. Left) Cerebral centresin the humanbrain dedicated to motor, somatosensory and language functions as displayed in one of the most popular neuroanatomy textbooks of the time (Testut, 1897): I) writing centre ofExner; II) Broca's centre for speech; III) motor centre, lower limb; IV) motor centre, upper limb; V) motor centre, face and tongue; VIVII) Dejerine's centre for reading; and VIII)Wernicke's acoustic centre for verbal comprehension. Blue and purple areas are zones of the association centres according toFlechsig (1896). Right) Dejerine's representation of thewhite matter tracts of the human brain responsible for language and reading (1895). Note that at that time the exact correspondence between centres and cortical projections was

not well established.

Fig. 3.Organology and phrenology according toGall and Spurzheim (1810). Left) Lateral view of a human brain where different groups of gyri are indicated with progressive num-bering according to their functional specialisation (Organology). Right) Protuberances on the skull that according to the phrenological theory resulted from the progressive expan-sion of the underlying gyri. Please note the correspondence of the numbers between the two gures.




6/12

of the nerve nuclei and their connections by removing the peripheralsense organs, such as the eyes, ears, or various cranial nerves (vonGudden, 1870). Constantin von Monakow an assistant of Gudden, contin-ued the work of his mentor on the thalamic and motor projection bres(von Monakow, 1897). Following extirpationof circumscribed areas of

the cortex he was able to follow retrogradely the degenerating tract tothe thalamic nuclei and other subcortical nuclei. Von Monakow wasalso therst to draw attention to the effects of a lesion on other distantregions of the brain, a mechanism that he called diaschisis (vonMonakow, 1914). Paul Emile Flechsig studied the developing pathwayswith his myelogenetic method consisting of staining myelin in brainsof foetuses or newborns and mapping a chronological maturational

sequence of the white matter pathways in the human brain. Consideringthat the projection tracts are among the rst to myelinate, he was ableto trace the origin and course of the corticospinal tract and describe fortherst time asymmetry in its crossing (i.e. decussation) at the level ofthe medulla (Flechsig, 1876). For many a true understanding of the ner-

vous system was possible only through a precise depiction of its connec-tions (DejerineandDejerine-Klumpke, 1895, 1901; vonBechterew, 1900).

At the same time clinicians begun to apply their neuroanatomical in-sights to patients (Catani et al., 2012a). The clinicalanatomical correla-tion method replaced phrenology and became a popular investigationto infer possible functional correlates of cortical areas (Bouillaud, 1825;Broca, 1861). Disorders of the nervous system were explained in terms

Fig. 5.Microscopy applied to the study of microcircuits of the hippocampus. Left) Hippocampal histology according to Camillo Golgi, inventor of the reazione nera(i.e. black reac-tion) method in 1873. Right) Hippocampal histology according toSantiago Ramon y Cajal (1911).Cajal also used the black reaction and introduced new concepts derived fromhis anatomical observations, including the directionality of impulse propagation (here indicated by the arrows).

Fig. 6.Cortical myelogenetic maps and white matter projections according to Flechsig (1896).Left) Flechsig identied different areas according to their degree of myelination atbirth. Primordial areas are already myelinated at birth and have some correspondence with primary sensory and motor areas (densely dotted red areas). Tertiary associationareas myelinate after birth and correspond to large regions of the frontal, parietal, temporal and occipital lobes. Sparsely red-dotted areas show intermediate degrees of myelination

at birth. Right) Flechsig was able to reconstruct the trajectories of the main projection bres from his myelogenetic maps.




7/12

of either cortical damage or disconnection syndromes (Fig. 4)(Dejerineand Dejerine-Klumpke, 1895, 1901). Behind these maps was the ideathat brain should be understood as a whole, a system of integrated andinterconnected areas. This approach required a good degree of approxi-mation, often resulting in a trivialised transposition of complex anatom-ical details into simplied diagrams (Cataniand ffytche, 2005; CataniandMesulam, 2008).

Modern cartography (Table 2)

By the end of the nineteenth century two lines of investigationemerged from theeldof microscopy. Therst importantdevelopmentwas the detailed description of microcircuits (Fig. 5) (Golgi, 1873; Cajal,1893). Ramon y Cajal championed thisapproach and introduced impor-tant concepts in neurosciences, including a set of fundamental biologi-cal laws of neuronal organisation. He described, for example, how theshape of axons and dendrites are constrained by biophysicalparameterssuch as cytoplasmic volume, space and conduction time. He also intro-duced the law of dynamic polarisation that identies dendrites and

cell body as themain receiving structures of theaction potential andax-onal terminations as the main output. The existence of these laws en-abled him to formulate the neuron doctrine and to infer directionalityin signal ow between neurons (Cajal, 1911).

The second line of investigation attempted to produce whole-brainmaps based on the study of interregional variations of the cyto- andmyeloarchitecture of the cortex. The development of new stainingmethods combined with painstaking observations of large numbers ofbrain slices led to important landmark discoveries, including the identi-cation of primary and associative areas of the brain (Fig. 6) (Flechsig,

1896), delineation of maturational trajectories of the long white matterprojection tracts (Flechsig, 1896) and improved localisation of functionsand associated symptoms (Campbell, 1905; vonEconomo and Koskinas,1925). However, cartographers differed among them in the ultimateobjective of their work and disputed on whether it could be possibleto understand brain function from their cortical maps. Two schools ofthought emerged.

For many, cortical maps were primarily anatomical divisions withoutany inference on function. Surprisingly, among them was KorbinianBrodmann who was clearly disdainful of any functional localisation inspecic cortical areas (Brodmann, 1909):

In reality there is only one psychic centre: the brain as a whole with

all its organs activated for every complex psychic event, either all to-

gether or most at the same time, and so widespread over the different

parts of the cortical surface that one can never justify any separate

specially differentiated psychiccentres within this whole.

Paradoxically, in contemporary cognitive neurosciences, Brodmann'smaps have become thelingua francaof cortical localisation numbers

used universally as shorthand for a precise cortical locus and, in somecortical elds, a precise functional role (Fig. 7). Brodmann's approachto clinico-anatomical correlation was shared by many other inuentialgures of his time and had long lasting consequences, especially in theeld of psychology. Those who insisted on approaching the brain func-tion from an anatomical point of view were disparagingly referred to asthediagram makersand localisation theory were given very little im-portance (Head, 1926; Lashley, 1950).

The second approach was based on the assumption that corticaldivisions derived from cytoarchitectonic or myeloarchitectonic could

Fig. 7. Early cytoarchitectonic maps of the human brain. Left) Campbell's division of the cortex in 17 elds according to the interregional differences in cortical cyto- andmyeloarchitecture (1905). Right) Brodmann's maps according to purely cytoarchitectonic criteria (1909). Note that while Campbell believed that its areas had some functionalcorrespondence (hence terms like visuo-sensoryand visuo-psychic), Brodmann rejected any correlation between individual areas and functional localisation.

Fig. 8.Cortical divisions of the human brain and corresponding clinical syndromes according to Economo and Koskinas (1925). Left) Distribution of the ve principal types of neo-cortex in lateral surface of the human brain: 1. agranular, 2. frontal, 3. parietal, 4. granular, 5. polar. Right) Corresponding clinical syndromes (in German). Please note the attempt to

localise not only neurological syndromes but also psychiatric conditions (e.g. depressivevs expansivemental disorders in the frontal lobe).




8/12

reveal important functional divisions. This approach, pioneered byBaillarger, Meynert, Wernicke and many others, had as ultimate goalthe establishment of solid scientic bases for an anatomically-basedclassication of psychiatric disorders (Baillarger, 1840; Meynert, 1868;Wernicke, 1906). The work of these psychiatrists had a great inu-ence on future generations. In Germany Paul Flechsig developed amethod for staining myelinated bres and was able to distinguishareas according to the order of myelination during the perinatal

development (Fig. 6). In England, Alfred Campbell combined cyto-and myeloarchitectonic observations with histological studies ofpatients with various disorders to produce a map of distinctive corti-cal elds in the brain ofHomo sapiens and other primate species(Fig.7) (Campbell, 1905). Campbell's monograph was a monumentalachievement for several reasons, the most important being the empha-sis given to function. Campbell's project went beyond cytoarchitectoniccartography, attempting to integrate clinical, anatomical, and physio-logical evidence to provide a guide to function (ffytche and Catani,2005). Indeed, Campbell's 17 cortical elds are labelled not by numbersbut by function (e.g. visuo-sensory, audito-psychic, olfactory, pre-central motor, and post-central somatosensory).

The era of cortical mapping culminated with the work of Cecile andOskarVogtand Economo and Koskinas. By studyingthe variation of cor-tical myeloarchitectonic, the Vogt's tandem identied more than 200areas, many of which represent subdivisions of the cytoarchitectonicareas of Brodmann (Vogt and Vogt, 1926). Despite working four-handedly on the project and relying on the help of other assistants,they never nished their monumental project and their anatomicalendeavourhasremained incompleteformostof thetemporaland occipitalcortex (Nieuwenhuys,2013). In 1925 Economo andKoskinassucceeded inpublishing a prodigious atlas containing the cytoarchitectonic analysis of107 cortical areas, for each of which quantitative measurements wererecorded for variations in cortical thickness and volume, form, size,

number of cells, their density, grouping in stripes and layers (Fig. 8).The atlas is a monumental work, which, despite being considered bymany the denitive text on cortical cartography, never met the favourof the scientic community. This is probably in part due to its encyclo-paedic proportions, the lack of clear boundaries between some of thesmallest areas and possiblythe general feeling against the crazy pavingschool of cortical research, which peaked shortly after (Le Gros Clark,1952).

By the mid-twentieth century the combination of histologicalmethods with neurophysiological techniques applied to the animalbrain provided cortical architecture with a precise functional meaning.This approach, as exemplied in the work ofMountcastle and Powell(1959)on the somatosensory cortex of the monkey and ofHubel andWiesel (1962)on the visual cortex of the cat, was based on the use ofsingle- or multi-unit recording, axonal tracing by means of microlesionor dye injection and combined with the cytoarchitectural description ofsections later cut from the same brain. Key principles of cellular organi-sation and neuronal physiology were discovered, such as columnar or-ganisation of the cortex and oriented receptor elds. At the same timethe use of disconnection procedures in the monkey, combined with be-havioural studies, intracortical recording, and axonal tracing revitaliseda network approach to brain functions (Fig. 9)(Mishkin, 1966). Newmethods became available for the identication of single axons andtheir exact cortical projection and termination (Fink and Heimer,1967; Nauta and Gygax, 1951). By the end of 1960s novel powerfultracers were developed based on the active transport of proteins andother elementsalong the axonalbres.These methods require injectionof tract tracers into a predetermined corticalor subcorticalregion of thenervous system. Once injected, the tracers enter the neuron and aretransported from the body of the neuron to its terminations (i.e. anter-ograde direction) or in the opposite direction (i.e. retrograde direction)(Morecraft et al., 2009). Tracer compounds would differ for their ability

Fig. 9.Connectivity maps based on animal studies. One of the advantages of animal studies is the ability to use data obtained with different methods and directly test network-basedmodels of brain functions using experimental approaches (e.g. disconnection lesions).Lowerleft) Leslie Ungerleiderand MortimerMishkin usedanimalelectrophysiology, axonal tracingandlesionstudiesto formulate a dual streammodel of visual processing (lower left) (Mishkinet al.,1983). Right) Fellemanand VanEssen's(1991) projectomeof the visual system show-ing thehierarchical connectivityof the visual areas. One of the limitationsof these approaches is the inability to quantifythe strength of connectivitybetween different areas. Thismeansthat the arrows and lines in the diagrams could be representative of single axons or large bundles. Also these maps have been transposed to humans without a direct anatomical veri-cation. Bailey and von Bonin (1951), for example (upper left) applied their ndings from the monkey directly to the human brain without experimental verication. The use of

tractography could help to identify equivalence and between species differences in the anatomy of these tracts (Thiebaut de Schotten et al., 2011, 2012).




9/12

to follow a predominantly anterograde or retrograde direction althoughmost classical tracers are bidirectional (Glover et al., 1986; Kuypers etal., 1977; Mesulam, 1982). The eld evolved further with methodsthat combined different tracers (e.g. several conventional compoundsor conventional with viral transneuronal tracers) and multiple sites ofinjection (Morecraft et al., 2009). These methods led to a greaterunderstanding of the main features of many cortico-cortical andcortico-subcortical pathways, such as feedback and forward organisa-

tion, hierarchical arrangement, and parallel organisation (Fellemanand Van Essen, 1991; Mesulam, 1998). They alsoprovided solid founda-tions for computational approaches to brain function. One limitation ofthese approaches to connectivity is the general assumption that nd-ings from animals can be directly translated to humans. This viewmay be valid for sensory and motor functions but there is some doubtthat it may hold true for other aspects of cognition such as language.

Post-mortem methods for cortical mapping of the human brain havealso evolved signicantly in the last two decades. Karl Zilles and collab-orators, for example, have developed a number of methods for auto-matic cytoarchitectonic and receptor mapping of post-mortem humanbrains (Zilles and Amunts, 2009). The advantage of these maps is thatthey are provided within a standard template of reference and can beused to guide, complement and integrate other in vivo imagingmethods (Caspers et al., this issue).

In contemporary neuroscience neuroimagingmethodshaveinaugu-rated a new era in the study of functional and anatomical connectivityin the living human brain. Although many methods are still in develop-ment and their use is limited, especially in the clinical settings, someprinciples of brain function are emerging from their application tolarge population of subjects. PET and fMRI studies for example, showedthe existence of a default mode networkconsisting a group of medialand lateral regions that is active during the resting state, a conditionin which the majority of the subjects engage in an introspective,self-directed stream of thought (i.e. similar to daydreaming) ( Raichleet al., 2001; Raichle and Snyder, 2007). A synchronous deactivation ofthedefault network areas is observed in the transition between the rest-ing state and the execution of goal directed tasks, including workingmemory, focusing attention to sensorially driven activities, understand-

ingother people's intention (mentalising or theory of mind), prospectivethinking (envisioning the future) and memory for personal events(autobiographic memory) (Raichle and Snyder, 2007). Alteration of thedefault network activation has been reported in functional imagingstudies of patients with neuropsychiatric disorders, such as autism andschizophrenia (Broyd et al., 2009).

Another signicant contribution from neuroimaging is the possibil-ity of measuring cortical thickness in healthy subjects and patientswith neurological and psychiatric disorders. The use of this method onpatients with progressive loss of language due to a primary neurode-generative disorder (i.e. Primary Progressive Aphasia), for example,has signicantly contributed to expand the classical model of languagenetworks based on stroke studies to regions of the anterior temporallobe and medial frontal cortex (Rogalski et al., 2011). Similarly

tractography studies based on diffusion tensor imaging and sphericaldeconvolution are revealing the existence of novel tracts underlyingfrontal lobe functions (Catani et al., 2012b) and language (Cataniet al., 2005; Catani et al., in press). A fundamental contribution of con-temporary neuroimaging is related to the description of interindividualdifferences in brain connectivity and the possibility of quantifyingparameters that give indirect measurements of the functional and ana-tomical strength of connections between regions (Catani et al., 2007;Thiebaut de Schotten et al., 2011). The above examples are indicativeof the far reaching potential of neuroimaging methods and their abilityto give answers to questions that were not possible to address before.With the introduction of the concept of a brain connectomethe eldmoved a step farther (Sporns et al., 2005; Hagmann et al., 2007). Herethe ambition is to dene the overall structural and functional brain ar-

chitecture to understand how anatomical networks inuence neuronal

dynamics (Sporns, 2013). A popular approach uses network analysisframeworks based on graph theory (Fornito et al., 2013; Hagmann etal., 2010). This special issue is entirely dedicated to neuroimagingmethods for mapping the connectome and we refer to other papersfor an in depth illustration of the advantages and limitations of thisapproach.

However, it is important to bear in mind that neuroimaging is onlyone of many methods for mapping the connectome and an important

distinction should be made between those approaches that adoptneuroimaging for mapping whole brain networks and other methodsthat characterise the most detailed features of microconnections usingadvanced microscopy technology (Lichtman and Denk, 2011; Seung,2012; Sporns, 2011). Clearly for the two approaches the sufx -omerefers to different concepts, one related to the totality of the brain (i.e.the connectomeas a map of the entire brain connections), the otherto the totality of all its constituents (the connectomeas the most de-tailed description of the elements that form neuronal connections).Hence, the rst approach provides a global overview (whole-brain) ofthe principal brain networks at a macroscopic level (i.e. large-scale con-nections or bundles), the second aspires to the most detailed descriptionof local networks at a micro- and nano-scale (e.g. single axons and den-drites). While the rst approach can only be achieved through data re-duction and oversimplication (i.e. connectome as a metaphor), thesecond may never realise for the entire brain (i.e. connectome as amyth). In the last paragraph we argue that the two approaches couldconverge at the mesoscale level, at least for post-mortem studies in thenear future and perhaps in vivo in the long term.

Contemporary and future connectomes between macroscopic

metaphors and microscopic myths

Current approaches to brain mapping result from the coalescenceof fast paced advancements in computing (data processing and stor-age, software development, etc.), quantitative and statistical neuro-psychological testing, MRI capability (higher resolutions, plethora ofsequences for structural and functional imaging) and computationaltheories (Lichtman and Denk, 2011; Seung, 2012; Sporns, 2011;

Dell'Acqua and Catani 2012). In post-mortem brains the use of auto-mated histological analysis combined with transmitter receptordistribution and microarray proling is beginning to delineate anew landscape of human cartography where multiple information isavailable for each area (e.g. cytoarchitectonic, receptor and geneexpression) (Hawrylycz et al., 2012; Zilles and Amunts, 2009). Thisinformation can be applied to in vivo imaging using atlas-basedapproaches and correlated with differences in functional activity, dif-fusion connectivity and behaviour. This could lead for the rst time tomultimodal brain maps that dene interindividual variability amongthe general population and help in understanding not only neuralmechanisms of normal cognition, but also identify vulnerable connec-tivity patterns in those at risk for mental illness, and predict treat-ment response and recovery after injury (Bullmore and Sporns,

2009; Jbabdi et al., 2007; Stephan et al., 2009).Nevertheless, contemporary approaches based on brain imaging are

not without limitations. One risk of contemporary connectome maps isto divert from the real functional anatomy of the brain and to follow in-stead a path of their own as it happened before in other disciplines. Theeld of articial neural networks, for example, just after McCulloch andPitts proposed the rst mathematical model for a neural network in1943, quickly evolved into statistical and pattern recognition tools farremoved from the action of complex neurophysiological mechanismssubserving their biological counterparts (Rosenblatt, 1958). In thecase of the connectome there are several factors that may contributeto a similar outcome. Mapping methods, for example, are typicallybased on data acquired at lowresolution (a fewmillimeters) with severaldistortions due to high background noise and eld dishomogeneity.

Signal derived from the MRI scans reects an average information,




10/12

which derives from thecombinationof complex biologicalfeatures of theunderlying tissue, thus making any interpretation of the resultsunspecic and often speculative. Further the number of passages relatedto the data processing introduces artifacts and distortions that aredifcult to distinguish from the real structural and functional anatomy.This is particularly true for connectomes based on diffusion imagingwhere the limitations of both the tensor model and more advancedmethods (e.g. Diffusion Spectrum Imaging and High Angular Resolution

Diffusion Imaging) (Dell'Acqua et al., 2010; Dell'Acqua et al., in press-a;Descoteaux et al., 2009; Tournier et al., 2007; Wedeen et al., 2012) mayproduce connectivity maps that do not reect the real underlying anato-my (Cataniet al., 2012c; Hagmann et al., 2010; Jones et al., 2013; Wedeenet al., 2012).

With diffusion datasets acquired at higher spatial resolution (cur-rently ~1mm) some of these limitations may reduce. In post-mortemsamples even higher resolutions can be obtained (Kleinnijenhuis etal., 2012; Leuze et al., 2013; McNab et al., 2009) with the advantageof direct validation with histology of tractography reconstructions(Takahashi et al., 2013; Dell'Acqua et al., in press-b).

Another risk is that the language used today in contemporary map-ping and analysis techniques can be ambiguous at times as it uses a ter-minology that alludes to anatomical properties when in fact it reectsonly features of the derived maps or graphs. Thus, a distance betweentwo nodes in a network described using graph theory is not equivalentto the axonal length between the neurons that constitute those nodesbut is the minimum number of steps required to connect the twonodes, regardless of their physical distances (Bullmore and Sporns,2009; Fornito et al., 2013). Similarly, the average length of streamlinesin tractographyis not equivalent to the averagelength of the connectingtracts. Moreover, many apparently functional properties of the neuro-imaging networks (e.g. connectivity, synchrony, etc.) are mathematicaland statistical concepts rather thanphysiological. Therefore, concludingthat the brains of certain groups of patients have reduced connectivitybecause of the reduced fMRI co-activation or shorter length of theirtractography streamlines calculated from DTI scans could be incorrect.

Today we are certainly in a better position to effectively integratedifferent neuroimaging modalities and complement structural and

functional ndings. There is, however, an inevitable tension in contem-porary mapmaking between thedesire to cram in more and more infor-mation and the need to keep things clear. This happened before in thehistory of neuroscience and history tends to repeat itself. Our currentconnectomes too often reect a distorted image of the real architectureof thebrain andwe tend to see in our connectomespatterns of neuronalorganisation that may just represent a biased view of the real anatomy.Here again validation is paramount (Mesulam, 2012) perhaps not onlyusing post-mortem approaches but also in vivo studies in animalmodels (Lee et al., 2012).

At the microscopic level the use of advanced imaging methodsbased on uorescent protein staining, electron microscopy andsuper-resolution light microscopy provides more condence for theinterpretation of the connectome results (Lichtman and Sanes,

2008; Denk et al., 2012). Here the problem is of a different natureand mainly related to its gargantuan scale. The realisation of the com-plete connectome of the 302 neurons and 5000 synapses that formthe nervous system of theCaenorhabditis elegans(White et al., 1986)was certainly an encouraging result that gave hope for similar achieve-ments in larger brains. Completing the connectome of the Drosophilacould also represent a signicant leap forward in the phylogeny scale.When we deal with the human brain the problem is related not onlyto the 86 billion neurons and 100 trillion synapses that form our net-works but to the ever changing anatomy of its constituents. Wheneverwe engage in an action or thought we reshape our connectome byforming new synapses and pruning neuronal dendritic trees. In otherwords we wake upwith one connectome and goto bed with a differentone. Even reading this paper could have an impact on the anatomy of

the reader's connectome. It is this dynamic nature of our connectomes

that holds the key to the real working of the brain and only aconnectome map of the brain in action will capture the anatomical,electrophysiological and computational elements of those networksthat characterise human cognition and behaviour. Although this mayseem a distantpoint, at the time of thesubmission of this paper a reportpublished in Nature Methods reawakened our optimism. Researchers atthe Howard Hughes Medical Institute's Janelia Farm Research Campusin Ashburn,Virginia have been able to recordactivity across a whole lar-

val

sh brain, detecting 80% of its 100,000 neurons (Ahrens and Keller,2013). The imaging system relies on a genetically engineered zebrashwhose neurons make a protein that uoresces in response to uctua-tions in the concentration of calcium ions, which occur when nervecells re. A system composed of a microscope and detectors records ac-tivityfrom the full brain. The neurons arevisible thanksto the transpar-ency of almost the entire non-neuronal tissue of the zebrash.Potentially this system could show the dynamics throughout thenervous system while the zebrash engages in different behavioursand during learning paradigms. Certainly the journey from here to ap-plication of similar methods in thehuman brain (perhaps with high res-olution functional diffusion imaging) is a long one in the history tocome. In the meantime setting up combined imaging and post-mortem histology studies of the white matter, perhaps using cutting-edge methods for network analysis at the axonal level (e.g. PolarisedLight Imaging or Clarity) (Axer et al., 2011; Chung et al., 2013) couldhelp us to validate our current structural methods and move more se-cure steps on the steep ascent of the connectome science.

Acknowledgments

We would like to thank Richard Joules, Stefano Sandrone and theother members of the NatBrainLab (http://www.natbrainlab.com)for their helpful advice on the manuscript. This work was supportedby Guy's and St Thomas Charity and the NIHR Biomedical ResearchCentre for Mental Health at the South London and Maudsley NHSFoundation Trust.

References

Ahrens, M.B., Keller, P.J., 2013. Whole-brain functional imaging at cellular resolutionusing light-sheet microscopy. Nat. Methods 10, 413420.

Aldini, G., 1803.An Account of the Late Improvements in Galvanism : To which isAdded, an Appendix, Containing Experiments on the Body of a Malefactor.Cuthell & Martin.

Axer, M., Amunts, K., Grssel, D., Palm, C., Dammers, J., Axer, H., Pietrzyk, U., Zilles, K.,2011.A novel approach to the human connectome: ultra-high resolution mappingofber tracts in the brain. Neuroimage 54, 10911101.

Bailey, P., von Bonin, G., 1951.The Isocortex of Man. University of Illinois Press, Urbana.Baillarger, J.G.F., 1840. Recherches sur la structure de la couche corticale des

circonvolutions du cerveau. J.-B. Baillire.von Bechterew, W., 1900. Les voies de conduction du cerveau et de la moelle. Storck

and Doin.Bentivoglio, M., Mazzarello, P., 2010.The anatomical foundations of clinical neurology.

In: Finger, S., Boller, F., Tyler, K.L. (Eds.), Handbook of Clinical Neurology, vol. 95,pp. 149168 (3rd series).

Bernstein, J., 1868. Ueber den zeitlichen Verlauf der negativen Schwankung desNervenstroms. Pugers Arch. 1, 173207.

Bois-Reymond, E.H.D., 1848.Untersuchungen ber thierische elektricitt. G. Reimer.Bouillaud, J., 1825.Trait clinique et physiologique de l'encphalite. J.B. Baillire, Paris.Broca, P., 1861. Nouvelle observation d'aphmie produite par une lesion de la partie

postrieure des deuxime et troisime circonvolutions frontales. Bull. Soc. Anat.Paris 36, 398407.

Brodmann, K., 1909.Vergleichende Localisationslehre der Grosshirnrinde in ihrenPrinzipien dargestellt auf Grund des Zellenbaues. Barth, Leipzig.

Broyd, S.J., Demanuele, C., Debener, S., Helps, S.K., James, C.J., Sonuga-Barke, E.J.S., 2009.Default-mode brain dysfunction in mental disorders: a systematic review. Neurosci.Biobehav. Rev. 33, 279296.

Bullmore, E., Sporns, O., 2009.Complex brain networks: graph theoretical analysis ofstructural and functional systems. Nat. Rev. Neurosci. 10, 186198.

Burdach, K., 1822.Vom Baue und Leben des Gehirns. In der Dyk'schen Buchhandlung,Leipzig.

Cajal, S.R. y, 1893. Manual de histologa normal y tcnica microgrca. Libreria dePascual Aguilar.

Cajal, S., 1911.Histologie du Systeme nerveux. A. Maloine, Paris.Campbell, A.W., 1905. Histological Studies on the Localisation of Cerebral Function.

University Press.


http://www.natbrainlab.com/http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0405http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0405http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0405http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0405http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0010http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0010http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0010http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0010http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0010http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0010http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0010http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0015http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0015http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0015http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0015http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0015http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0015http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0020http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0410http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0410http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0415http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0415http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0420http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0420http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0420http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0420http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0420http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0030http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0030http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0030http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0030http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0030http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0030http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0035http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0040http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0045http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0045http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0045http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0045http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0045http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0050http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0050http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9000http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9000http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9000http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9000http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0055http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0055http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0055http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0055http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0060http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0060http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0425http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0425http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0425http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0425http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0065http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0070http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0070http://dx.doi.org/10.1016/j.neuroimage.2013.05.109http://dx.doi.org/10.1016/j.neuroimage.2013.05.109http://dx.doi.org/10.1016/j.neuroimage.2013.05.109http://dx.doi.org/10.1016/j.neuroimage.2013.05.109http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0070http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0070http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0065http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0425http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0425http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0060http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0060http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0055http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0055http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9000http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9000http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0050http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0050http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0045http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0045http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0045http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0040http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0035http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0030http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0030http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0420http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0420http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0420http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0415http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0415http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0410http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0410http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0020http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0015http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0015http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0010http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0010http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0010http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0405http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0405http://www.natbrainlab.com/


11/12

Caspers, S., Eickhoff, S.B., Zilles, K., Amunts, K., 2013. Microstructural grey matterparcellation and its relevance for connectome analyses. Neuroimage (S1053-8119(13)00319-4) (this issue).

Catani, M., 2007.From hodology to function. Brain 130, 602605.Catani, M., ffytche, D.H., 2005. The rises and falls of disconnection syndromes. Brain

128, 22242239.Catani, M., Mesulam, M.M., 2008.The arcuate fasciculus and the disconnection theme

in language and aphasia: history and current state. Cortex 44, 953 961.Catani, M., Thiebaut de Schotten, M., 2012.Atlas of Human Brain Connections. Oxford

University Press, Oxford.Catani, M., Allin, P.G.A., Husain, M., Pugliese, L., Mesulam, M.M., Murray, R.M., Jones,

D.K., 2007.Symmetries in human brain pathways predict verbal recall. Proc. Natl.Acad. Sci. U. S. A. 104, 1716317168.Catani, M., Jones, D.K., ffytche, D.H., 2005.Perisylvian language networks of the human

brain. Ann. Neurol. 57, 816.Catani, M., Dell'acqua, F., Bizzi, A., Forkel, S.J., Williams, S.C., Simmons, A., Murphy, D.G.,

Thiebaut de Schotten, M., 2012a.Beyond cortical localization in clinico-anatomicalcorrelation. Cortex 48, 12621287.

Catani, M., Dell'acqua, F., Vergani, F., Malik, F., Hodge, H., Roy, P., Valabregue, R.,Thiebaut de Schotten, M., 2012b. Short frontal lobe connections of the humanbrain. Cortex 48, 273291.

Catani, M., Bodi, I., Dell'Acqua, F., 2012c.Comment on The geometric structure of thebrain ber pathways. Science 337, 1605.

Catani, M., Mesulam, M., Jakobsen, E., et al., 2013. A novel frontal pathway underliesverbal uency in primary progressive aphasia. Brain (in press).

Chung, K., Wallace, J., Kim, S.Y., Kalyanasundaram, S., Andalman, A.S., Davidson, T.J., etal., 2013.Structural and molecular interrogation of intact biological systems. Na-ture 497 (7449), 332337.

Clarke, E., Dewhurst, K., 1972. An Illustrated History of Brain Function. SandfordPublications.

Clarke, E., Jacyna, L.S., 1987. Nineteenth-century Origins of Neuroscientic Concepts.Diane Pub Co.

Clarke, E., O'Malley, C.D., 1996. The Human Brain and Spinal Cord: A Historical StudyIllustrated by Writings from Antiquity to the 20th Century. Norman Publishing.

Djerine, J.J., Dejerine-Klumpke, A., 1895.Anatomie des centres nerveux, vol. 1. Rueff etCie, Paris.

Djerine, J.J., Dejerine-Klumpke, A., 1901.Anatomie des centres nerveux, vol. 2. Rueff etCie, Paris.

Dell'Acqua, F., Catani, M., 2012. Structural human brain networks: hot topics in diffu-sion tractography. Curr. Opin. Neurol. 25, 375383.

Dell'Acqua, F., Scifo, P., Rizzo, G., Catani, M., Simmons, A., Scotti, G., Fazio, F., 2010.Amodied damped Richardson-Lucy algorithm to reduce isotropic background ef-fects in spherical deconvolution. Neuroimage 49, 14461458.

Dell'Acqua, F., Simmons, A., Williams, S.C., Catani, M., 2013a. Can spherical deconvolutionprovide more information than ber orientations? Hindrance modulated orienta-tional anisotropy, a true-tract specic index to characterize white matter diffusion.Hum. Brain Mapp.http://dx.doi.org/10.1002/hbm.22080(in press) (Apr 5, Electronicpublication ahead of print).

Dell'Acqua, F., Bodi, I., Slater, D., Catani, M., Modo, M., 2013b. MR diffusion-based his-tology and micro-tractography reveal mesoscale features of the human cerebel-lum. Cerebellum.http://dx.doi.org/10.1007/s12311-013-0503-x(in press).

Denk, W., Briggman, K.L., Helmstaedter, M., 2012.Structural neurobiology: missing linkto a mechanistic understanding of neural computation. Nat. Rev. Neurosci. 13,351358.

Descartes, R., 1664.L'Homme de Ren Descartes et un Trait de la formation du ftus.Charles Angot, Paris.

Descartes, R., 1662.De homine. apud Franciscum Moyardum & Petrum Leffen .Descoteaux, M., Deriche, R., Knsche, T.R., Anwander, A., 2009.Deterministic and prob-

abilistic tractography based on complex bre orientation distributions. IEEE Trans.Med. Imaging 28, 269286.

Economo, C.F., Koskinas, G.N., 1925.Die Cytoarchitektonik der Hirnrinde des erwachsenenMenschen. J. Springer.

Egerstedt, M., 2011.Complex networks: degrees of control. Nature 473, 158159.Felleman, D.J., Van Essen, D.C., 1991.Distributed hierarchical processing in the primate

cerebral cortex. Cereb. Cortex 1, 147.ffytche, D.H., Catani, M., 2005.Beyond localization: from hodology to function. Philos.

Trans. R. Soc. Lond. B. Biol. Sci. 360, 767779.

Fink, R.P., Heimer, L., 1967. Two methods for selective silver impregnation of degeneratingaxons andtheir synaptic endingsin thecentral nervoussystem. Brain Res. 4, 369374.Flechsig, P.E., 1876.Die Leitungsbahnen im Gehirn und Rckenmark des Menschen auf

Grund entwicklungsgeschichtlicher Untersuchungen. Engelmann, Leipzig.Flechsig, P.E., 1896.Gehirn und seele. Verlag von Veit & Comp, Leipzig.Fornito, A., Zalesky, A., Breakspear, M., 2013.Graph analysis of the human connectome:

promise, progress, and pitfalls. Neuroimage (this issue).Galilei, G., 1638.Discorsi e dimostrazioni matematiche: intorno a due nuove scienze,

attenenti alla mecanica e i movimenti locali Con una appendice del centro digravit d'alcuni solidi. Appresso gli Elsevirii.

Gall, F., Spurzheim, G., 1810.Anatomie et physiologie du systme nerveux en gnral etsur celui du cerveau en particulier. Schoell, Paris.

Galvani, L., 1791.De viribus electricitatis in motu musculari: Commentarius.Glover, J.C., Petursdottir, G., Jansen, J.K., 1986.Fluorescent dextran-amines used as axonal

tracers in thenervous systemof thechickenembryo.J. Neurosci.Methods 18,243254.Golgi, C., 1873.Sulla struttura della sostanza grigia del cervello. Gazz. Med. Ital. (Lomb.)

33, 244246.Griesinger, W., 1843.ber psychische Reexaktionen. Mit einem Blick auf das Wesen

der psychischen Krankheiten. Arch. Physiol. Heilk. 2, 76113.

Gudden, B.A., 1870.Experimentaluntersuchungen bei das peripherischer und centraleNervensystem. Arch. Psychiatr. Nervenkr. 2, 693723.

Hagmann,P., et al.,2007.Mappinghuman whole-brain structural networkswith diffusionMRI. PLoS One 2 (7), e597.

Hagmann, P., et al., 2010. MR connectomics: principles and challenges. J. Neurosci.Methods 194, 3445.

Hawrylycz, M.J., Lein, E.S., Guillozet-Bongaarts, A.L., Shen, E.H., Ng, L., Miller, J.A., et al.,2012.An anatomically comprehensive atlas of the adult human brain transcriptome.Nature 489, 391399.

Head, H., 1926.Aphasia and Kindred Disorders of Speech. The University Press.Hubel, D.H., Wiesel, T.N., 1962.Receptive elds, binocular interaction and functional

architecture in the cat's visual cortex. J. Physiol. (Lond.) 160, 106 154.Jbabdi, S., Woolrich, M.W., Andersson, J.L., Behrens, T.E., et al., 2007.A Bayesian frame-work for global tractography. Neuroimage 37 (1), 116129.

Jone s, D.K. , Kns che, T.R., Turne r, R., 2013. White matter integrity, ber count,and other fallacies: the do's and don'ts of diffusion MRI. Neuroimage 73,239254.

Kleinnijenhuis, M., Zerbi, V., Ksters, B., Slump, C.H., Barth, M., van Cappellen vanWalsum, A.M., 2012.Layer-specic diffusion weighted imaging in human primaryvisual cortex in vitro. Cortex (doi:pii: S0010-9452(12)00334-6) (this issue).

Klliker, A., 1859.Handbuch der Gewebelehre des Menschen. Wilhelm Engelmann .Kuypers, H.G., Catsman-Berrevoets, C.E., Padt, R.E., 1977.Retrograde anoxal transport

ofuorescent substances in the rat's forebrain. Neurosci. Lett. 6 (23), 127133.Lashley, K.S., 1950.In search of the engram. Symp. Soc. Exp. Biol. 4, 454482.Laycock, T., 1845.On the reex function of the brain. BFMR 19, 298311.Le Gros Clark, W.E., 1952. A note on cortical cyto-architectonics. Brain 75, 96104.Lee, H.J., Yoo, S.J., Lee, S., Song, H.J., Huh, M.I., Jin, S.U., Lee, K.Y., Lee, J., Cho, J.H., Chang,

Y., 2012.Functional activity mapping of rat auditory pathway after intratympanicmanganese administration. Neuroimage 60, 10461054.

Leuze, C.W., Anwander, A., Bazin, P.L., Dhital, B., Stber, C., Reimann, K., Geyer, S., Turner,

R., 2013.Layer-specic intracortical connectivity revealed with diffusion MRI. Cereb.Cortex (in press).

Lichtman, J.W., Denk, W., 2011.The big and the small: challenges of imaging the brain'scircuits. Science 334, 618623.

Lichtman, J.W., Sanes, J.R., 2008. Ome sweet ome: what can the genome tell us aboutthe connectome? Curr. Opin. Neurobiol. 18, 346353.

Malpighi, M., 1666.De cerebri cortice. Montius, Bologna.Matteucci, C., 1830. Sulla contrazione provata dagli animali all'aprirsi del circolo

elettrico in che trovansi. Casali, Forl, Italy.McCulloch, W.S., Pitts, W.H., 1943.A logical calculus of the ideas immanent in nervous

activity. Bull. Math. Biophys. 5, 115133.Mcnab, J.A., Jbabdi, S., Deoni, S.C.L., Douaud, G., Behrens, T.E.J., Miller, K.L., 2009. High

resolution diffusion-weighted imaging in xed human brain using diffusion-weighted steady state free precession. Neuroimage 46, 775785.

Mesulam, M., 1982.Tracing Neural Connections with Horseradish Peroxidase. Wiley,New York.

Mesulam, M., 1998.From sensation to cognition. Brain 121, 10131052.Mesulam, M., 2012.The evolving landscape of human cortical connectivity: facts and

inferences. Neuroimage 62, 21822189.Meynert, T., 1868.Neue Untersuchungen ber den Bau der Grosshirnrinde und seine

rtlichen. Verschiedenheiten, Vortrag.Mishkin, M., 1966.Visual mechanisms beyond the striate cortex. In: Russel, R. (Ed.),

Frontiers in Physiological Psychology. Academic Press, New York.Mishkin, M., Ungerleider, L.G., Macko, K.A., 1983. Object vision and spatial vision: two

cortical pathways. Trends Neurosci. 6, 414417.Monakow, C., 1897.Gehirnpathologie. A. Hlder, Vienna.Monakow, C., 1914.Die Lokalisation im Grosshirn und der Abbau der Funktion durch

kortikale Herde. JF Bergmann, Wiesbaden.Morecraft, R.J., Ugolini, G., Lanciego, J.L., Wouterlood, F.G., Pandya, D.N., 2009.Classic and

contemporary neural tract tracing techniques. In: Johansen-Berg, H., Behrens, T.E.J.(Eds.), Diffusion MRI. Elsevier, pp. 273308.

Mountcastle, V.B., Powell, T.P., 1959. Neural mechanisms subserving cutaneous sensi-bility, with special reference to the role of afferent inhibition in sensory perceptionand discrimination. Bull. Johns Hopkins Hosp. 105, 201232.

Nauta, W.J., Gygax, P.A., 1951.Silver impregnation of degenerating axon terminals in thecentral nervous system: (1) technic. (2) Chemical notes. Stain Technol. 26 (1), 511.

Nieuwenhuys, R., 2013.The myeloarchitectonic studies on the human cerebral cortex

of the Vogt-Vogt school, and their signicance for the interpretation of functionalneuroimaging data. Brain Struct. Funct. 218, 303352.Pavlov, I.D., 1903.The experimental psychology and psychopathology of animals. The

14th International Medical Congress, Madrid, Spain.Pevsner, J., 2002.Leonardo da Vinci's contributions to neuroscience. Trends Neurosci.

25, 217220.Piccolomini, A., 1586.Anatomicae Praelectiones, explicantes miricam corporis humani

fabricam. Bonfadini.Raichle, M.E., Snyder, A.Z., 2007. A default mode of brain function: a brief history of an

evolving idea. Neuroimage 37, 10831090 (discussion 10971099).Raichle,M.E.,MacLeod, A.M., Snyder,A.Z., Powers,W.J.,Gusnard, D.A., Shulman,G.L.,2001.

A default mode of brain function. Proc. Natl. Acad. Sci. U. S . A. 98, 676682.Reil,J.C., 1809.Die Sylvische Grube oder das Thal, das gestreifte groe Hirnganglium,

dessen Kapsel und die Seitentheile des groen Gehirns. Arch. Physiol. 9,195208.

Reil, J.C., 1812.Die vrdere Commissur im groen Gehirn. Arch. Physiol. 11, 89100.Rogalski, E., Cobia, D., Harrison, T.M., Wieneke, C., Thompson, C.K., Weintraub, S.,

Mesulam, M.-M., 2011.Anatomy of language impairments in Primary ProgressiveAphasia. J. Neurosci. 31, 33443350.


http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0075http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0075http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0075http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0080http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0080http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0080http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0095http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0095http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0095http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0095http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0100http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0100http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0100http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0100http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0085http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0085http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0090http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0090http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0090http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0090http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9005http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9005http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9005http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9005http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9010http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9010http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9010http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9010http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9015http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9015http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9015http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9015http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9020http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9020http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9020http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9020http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9020http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9020http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9020http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9020http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0430http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0430http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0430http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0430http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0105http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0105http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0105http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0105http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0110http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0110http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0115http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0115http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0115http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0115http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0120http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0120http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0130http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0130http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0435http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0435http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0125http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0125http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0125http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0125http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9230http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9230http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9230http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9230http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9230http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9230http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9230http://dx.doi.org/10.1002/hbm.22080http://dx.doi.org/10.1007/s12311-013-0503-xhttp://refhub.elsevier.com/S1053-8119(13)00612-5/rf9340http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9340http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9340http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9340http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9340http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0445http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0445http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0445http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0445http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0450http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0140http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0140http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0140http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0140http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0140http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0140http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0140http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0560http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0560http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0150http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0150http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0150http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0155http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0155http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0155http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0155http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9370http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9370http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9370http://refhub.elsevier.com/S1053-8119(13)00612-5/rf9370http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0170http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0170http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0170http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0170http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0160http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0160http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0165http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0455http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0455http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0460http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0460http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0460http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0460http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0460http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0175http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0175http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0465http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0180http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0180http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0180http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0180http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0185http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0185http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0185http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0185http://refhub.elsevier.com/S1053-8119(13)00612-5/rf0470http://refhub.elsevier.com/S1053-8

Documents

Connectome History