ARTICLE

doi:10.1038/nature13186

A mesoscale connectome of themouse brain
Seung Wook Oh1*, Julie A. Harris1*, Lydia Ng1*, Brent Winslow1, Nicholas Cain1, Stefan Mihalas1, Quanxin Wang1, Chris Lau1,
Leonard Kuan1, Alex M. Henry1, Marty T. Mortrud1, Benjamin Ouellette1, Thuc Nghi Nguyen1, Staci A. Sorensen1,
Clifford R. Slaughterbeck1, Wayne Wakeman1, Yang Li1, David Feng1, Anh Ho1, Eric Nicholas1, Karla E. Hirokawa1, Phillip Bohn1,
Kevin M. Joines1, Hanchuan Peng1, Michael J. Hawrylycz1, John W. Phillips1, John G. Hohmann1, Paul Wohnoutka1,
Charles R. Gerfen2, Christof Koch1, Amy Bernard1, Chinh Dang1, Allan R. Jones1 & Hongkui Zeng1

Comprehensive knowledge of the brain’s wiring diagram is fundamental for understanding how the nervous systemprocesses information at both local and global scales. However, with the singular exception of the C. elegans microscaleconnectome, there are no complete connectivity data sets in other species. Here we report a brain-wide, cellular-level,mesoscale connectome for the mouse. The Allen Mouse Brain Connectivity Atlas uses enhanced green fluorescent protein(EGFP)-expressing adeno-associated viral vectors to trace axonal projections from defined regions and cell types, andhigh-throughput serial two-photon tomography to image the EGFP-labelled axons throughout the brain. This systematicand standardized approach allows spatial registration of individual experiments into a common three dimensional (3D)
reference space, resulting in a whole-brain connectivity matrix. A computational model yields insights into connectional
strength distribution, symmetry and other network properties. Virtual tractography illustrates 3D topography amonginterconnected regions. Cortico-thalamic pathway analysis demonstrates segregation and integration of parallel
pathways. The Allen Mouse Brain Connectivity Atlas is a freely available, foundational resource for structural andfunctional investigations into the neural circuits that support behavioural and cognitive processes in health and disease.
A central principle of neuroscience is that the nervous system is a network of diverse types of neurons and supporting cells communicatingwith each other mainly through synaptic connections. This overall brainarchitecture is thought to be composed of four systems—motor, sensory, behavioural state and cognitive—with parallel, distributed and/or
hierarchical sub-networks within each system and similarly complex,
integrative interconnections between different systems1. Specific groupsof neurons with diverse anatomical and physiological properties populate each node of these sub- and supra-networks, and form extraordinarily intricate connections with other neurons located near and far.
Neuronal connectivity forms the structural foundation underlying neuralfunction, and bridges genotypes and behavioural phenotypes2,3. Connectivity patterns also reflect the evolutionary conservation and divergencein brain organization and function across species, as well as both thecommonality among individuals within a given species and the uniqueness of each individual brain.
Despite the fundamental importance of neuronal connectivity, ourknowledge of it remains remarkably incomplete. C. elegans is the only
species for which an essentially complete wiring diagram of its 302 neurons has been obtained through electron microscopy4. Histological tract
tracing studies in a wide range of animal species has generated a richbody of knowledge that forms the foundation of our current understanding of brain architecture, such as the powerful idea of multi-hierarchicalprocessing in sensory cortical systems5. However, much of these data
are qualitative, incomplete, variable, scattered and difficult to retrieve.
Thus, our knowledge of whole-brain connectivity is fragmented, without a cohesive and comprehensive understanding in any single vertebrate animal species (see for example the BAMS database for the rat
brain6). With recent advances in both computing power and optical imaging techniques, it is now feasible to systematically map connectivity

throughout the entire brain. A salient example of this is the ongoing
effort in mapping connections in the Drosophila brain7,8.
The connectome9 refers to a comprehensive description of neuronalconnections, for example, the wiring diagram of the entire brain. Given
the enormous range of connectivity in the mammalian brain and therelative inaccessibility of the human brain, such descriptions can exist
at multiple levels: macro-, meso- or microscale. At the macroscale, longrange, region-to-region connections can be inferred from imaging whitematter fibre tracts through diffusion tensor imaging (DTI) in the living
brain10. However, this is far from cellular-level resolution, given the sizeof single volume elements (voxels .1 mm3). At the microscale, connectivity is described at the level of individual synapses, for example,
through electron microscopic reconstruction at the nanometer scale4,11–15.
At present, the enormous time and resources required for this approachmakes it best suited for relatively small volumes of tissue (,1 mm3). At
the mesoscale, both long-range and local connections can be describedusing a sampling approach with various neuroanatomical tracers thatenable whole-brain mapping in a reasonable time frame across many
animals. In addition, cell-type-specific mesoscale projects have the potential to dramatically enhance our understanding of the brain’s organization and function because cell types are fundamental cellular units
often conserved across species16,17.
Here we present a mesoscale connectome of the adult mouse brain,
The Allen Mouse Brain Connectivity Atlas. Axonal projections fromregions throughout the brain are mapped into a common 3D space using
a standardized platform to generate a comprehensive and quantitative
database of inter-areal and cell-type-specific projections. This Connectivity Atlas has all the desired features summarized in a mesoscale connectome position essay18: brain-wide coverage, validated and versatile
experimental techniques, a single standardized data format, a quantifiable1

Allen Institute for Brain Science, Seattle, Washington 98103, USA. 2Laboratory of Systems Neuroscience, National Institute of Mental Health, Bethesda, Maryland 20892, USA.
*These authors contributed equally to this work.
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RESEARCH ARTICLE
and integrated neuroinformatics resource and an open-access public
online database.

Creating the Allen Mouse Brain Connectivity Atlas
A standardized data generation and processing platform was established (Fig. 1a, see Methods). Recombinant adeno-associated virus (AAV),
serotype 1, expressing EGFP optimally was chosen as the anterograde
tracer to map axonal projections19,20. We also confirmed that AAV wasat least as efficient as, and more specific than, the classic anterograde
tracer biotinylated dextran amine (BDA) (Extended Data Fig. 1), as
described separately21.
EGFP-labelled axonal projections were systematically imaged using
the TissueCyte 1000 serial two-photon (STP) tomography system22, which
couples high-speed two-photon microscopy with automated vibratome
sectioning of an entire mouse brain. High x–y resolution (0.35 mm) 2D
images in the coronal plane were obtained at a z-sampling interval of
100-mm across the entire brain during a continuous 18.5 h scanning
period, resulting in 140 serial sections (a ,750 gigabyte (GB) data set)for each brain (Extended Data Fig. 2a and Supplementary Video 1).
Owing to its block-face imaging nature, STP tomography essentiallyeliminates tissue distortions that occur in conventional section-based
histological methods and provides a series of highly conformed, inherently pre-aligned images amenable to precise 3D mapping.
Image series were processed in an informatics pipeline with a seriesof modules (see Methods). The injection site location of each brain was
manually drawn and annotated using the Allen Reference Atlas23 andother reference data sets when appropriate. Stringent quality control criteria were applied, discarding ,25% of all scanned brains due to insufficient quality in labelling or imaging. Each image set was registeredinto a 3D Allen Reference Atlas model in two steps (Fig. 1b, upper
panels). First, a registration template was created by averaging many
image sets, and every image stack was aligned to this average template
brain. This process was repeated for multiple rounds, first globally (affine
registration) and then locally (deformable registration), each round generating a better average template and more precise alignment of individual brains. The final average template brain, averaged from 1,231aData QC and injection siteannotation

Pre-pipeline activities
Cre-independent AAV tracer
pSyn

EGFP

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brains, shows remarkably clear anatomical features and boundaries.
Second, the average template brain was aligned with the 3D reference
model, again using local alignment (Supplementary Video 2).
We developed a signal detection approach and applied it to each section
to segment GFP signals from background (Fig. 1b, lower panels). Signals
within injection site polygons were computed separately from the restof the brain. The segmented pixel counts were gridded into 100 3 100
3 100 mm3 voxels to create an isotropic 3D summary of the projection
data. These voxels were used for data analysis, real-time data and correlative searches, and visualization of projection relationships in the
Brain Explorer.
Meaningful informatics data quantification and comparison relieson the mapping precision of the raw data sets into the 3D reference
framework. We investigated registration variability in two ways. First,
we selected 10 widely distributed anatomical fiducial points to compare
variability among 30 randomly selected brains (Extended Data Fig. 2b).
We found a high degree of concordance among individual brains, withmedian variation , 49 mm in each dimension between each brain andthe average template brain, which is comparable to the median interrater variation of , 39 mm. The median difference is , 71 mm between
each brain and the Reference Atlas. Second, we compared manual andinformatics annotations of the injection sites from all Phase I (see below)
brains. The informatics-derived assignment of injection site structures
had . 75% voxel-level concordance with manual expert annotation foralmost all injection sites (Extended Data Fig. 2c). These analyses confirmed the relatively high fidelity of co-registration of raw image datawith the Allen Reference Atlas. The remaining difference is mainly due
to the imperfect alignment between the average template brain and the
Nissl-section-based Reference Atlas (Supplementary Video 2).

Mapping axonal projections in the whole mouse brain
The connectivity mapping was carried out in two phases. In Phase I (regional projection mapping), axonal projections from 295 non-overlapping
anatomical regions, defined from the Allen Reference Atlas ontology
and tiling the entire brain space (Supplementary Table 1), were characterized in wild-type mice with a pan-neuronal AAV vector expressing
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Figure 1 | Creation of the Connectivity Atlas. a, The data generation andprocessing pipeline. QC, quality control. b, The two main steps of informatics
data processing: registration of each image series to a 3D template (upper
panels) and segmentation of fluorescent signal from background (lower
panels). c, Distribution of injection sites across the brain. The volume of theinjection was calculated and represented as a sphere. Locations of all these
injection spheres are superimposed together (left panel). Mean injection
volumes ( 6 s.e.m.) across major brain subdivisions are shown (right panel, see
Extended Data Fig. 3).

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ARTICLE RESEARCHEGFP under the human synapsin I promoter (AAV2/1.pSynI.EGFP.
WPRE.bGH, Fig. 1a). In Phase II (Cre driver based projection mapping),
axonal projections from genetically defined neuronal populations arecharacterized in Cre driver mouse lines with a Cre-dependent AAV(AAV2/1.pCAG.FLEX.EGFP.WPRE.bGH, Fig. 1a). We only report hereon the completed Phase I study, which includes 469 image sets with
injection sites covering nearly the entire brain (Fig. 1c, Extended Data
Fig. 3 and Supplementary Video 3). Only 18 intended structures werecompletely missed due to redundancy or injection difficulty (Supplementary Table 1).
We examined multiple projection data sets in detail and found thatthey were complete in capturing all known projection target sites throughout the brain, sensitive in detecting thin axon fibres, and consistent inquality to allow qualitative and quantitative comparisons. As an example,
7 representative isocortical injections (Fig. 2) reveal distinct projection
patterns in the striatum, thalamus, zona incerta, midbrain, pons andmedulla. To compare the brain-wide spatial distribution of projections
between cortical source regions, we placed each isocortical injection experiment into one of 9 broad functional groups: frontal, motor, anterior
cingulate, somatosensory, auditory, retrosplenial, visual, ventral and associational areas (Extended Data Fig. 4). The average percentages of total
projection signals into 12 major brain subdivisions showed disproportionately large projections within the isocortex, as well as distinct subcortical distributions.

but our techniques may be used at deeper levels in future studies. The
projection signal strength between each source and target was definedas the total volume of segmented pixels in the target (summed acrossall voxels within each target), normalized by the injection site volume(total segmented pixels within the manually drawn injection area).
The majority of the 469 Phase I image sets are single injections into
spatially distinct regions, but a subset of these are repeated injections
into the same regions. To assess the consistency of projection patterns
across different animals and the reliability of using a single experiment
to define connections from any particular region, we compared brainwide connectivity strengths in 12 sets of duplicate injections (Extended
Data Fig. 5). Each pair was highly correlated across a range of projection strengths. Differences between any two points were on average only
a half order of magnitude (within one standard deviation). In primate
cortex, single tracer injections were also found to reliably predict meanvalues obtained from repeated injections into the same source24.
The AAV tracer expresses cytoplasmic EGFP, which labels all processes of the infected neuron, including axons and synaptic terminals.
Signals associated with the major fibre tracts of the brain, marked in
the Allen Reference Atlas, were removed before the informatics quantification. However, there are also areas (for example, striatum) whereaxons pass through without making synapses. Although passing fibres
can generally be distinguished from terminal zones by visual inspection of morphology in the 2D images (axons in terminal zones ramify
and contain synaptic boutons, see Extended Data Fig. 6), it is difficult
to confidently make this distinction algorithmically. We comparedresults of terminal labelling using Synaptophysin-EGFP-expressing AAV
with the cytoplasmic EGFP AAV (Extended Data Fig. 6). Outside ofmajor fibre tracts, there was high correspondence between synaptic
EGFP and cytoplasmic EGFP signals in target regions. Nonetheless, it
should be noted that the connectivity matrix contains passing fibre
signals within grey matter, the nature of which should be manually
examined in 2D section images.
This connectivity matrix (Fig. 3) has several striking features. First,
connectivity strengths span a greater than 105-fold range across thebrain (Extended Data Fig. 7), suggesting that quantitative descriptions

Brain-wide connectivity matrix
After segmentation and registration, we derived quantitative values fromsegmented signals in each of the ,500,000 voxels contained within eachbrain. We constructed a brain-wide, inter-areal, weighted connectivity
matrix using the entire Phase I experimental data set (Fig. 3, see Supplementary Table 2 for the underlying values). The Allen Reference
Atlas contains 863 grey-matter structures at the highest level of the
ontology tree (Supplementary Table 1). We focused our analyses on thechosen 295 structures, which are at a mid-ontology level corresponding best with the approximate size of the tracer infection areas (forexample, isocortical areas are not subdivided by layers in this matrix),
ACAd

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Figure 2 | Whole brain projection patterns fromseven representative cortical regions. One
coronal section at the centre of each injection site isshown in the top row (see Supplementary Table 1
for the full name of each region). In the second row,
3D thumbnails of signal density projected onto asagittal view of the brain reveal differences in brainwide projection patterns. The bottom 6 rows show
examples of EGFP-labelled axons in representative
subcortical regions.

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Target: left hemisphere (contralateral)
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Figure 3 | Adult mouse brain connectivity matrix. Each row shows thequantitative projection signals from one of the 469 injected brains to each of the
295 non-overlapping target regions (in columns) in the right (ipsilateral) andleft (contralateral) hemispheres. Both source and target regions are displayed in
ontological order. The colour map indicates log10-transformed projection

strength (raw values in Supplementary Table 2). All values less than 1023.5 areshown as blue to minimize false positives due to minor tissue and segmentation
artefacts and all values greater than 1020.5 are shown as red to reduce thedominant effect of projection signals in certain disproportionately large regions(for example, striatum).

of connectivity must be considered for understanding neural network
properties24. Second, there are prevalent bilateral projections to corresponding ipsilateral and contralateral target sites, with ipsilateral projections generally stronger than contralateral ones (total normalized
projection volumes from all experiments are 4.3:1 between ipsilateraland contralateral hemispheres). Third, of all possible connections, strong
connections are found in only a small fraction. Whereas 63% ipsilateraland 51% contralateral targets have projection strength values above theminimal true positive level of 1024 (which has a potential false positive
rate of 27%, Extended Data Fig. 7), only 21% ipsilateral and 9% contralateral targets have projection strength values above the intermediatelevel of 1022.

The 469 experiments allowed us to compute the mutual connections
among 213 regions. The best-fit model (Fig. 4a, see Supplementary
Table 3 for the underlying values) results from a bounded optimization
followed by a linear regression to determine connection coefficients,
assigning statistical confidence (P values) to each connection in thematrix. Based on the bounded optimization, the number of non-zero
entries provides an upper bound estimate for sparsity: 36% for theentire brain and 52% for cortico-cortical connections. Using confidence values for each non-zero connection, the lower bound on sparsity
is 13% for the entire brain and 32% for cortico-cortical connections.
Connection strengths spanned ,105-fold range, and negatively correlated with the distance between connected regions (Supplementary
Notes and Supplementary Table 4). Based on the Akaike information
criterion (AIC), among hypothesized connection strength distributions(lognormal, normal, exponential, inverse Gaussian) the brain-wide data
are best fit by a lognormal distribution (Fig. 4b, red lines). However, thelog-transformed connection strengths failed to pass the Shapiro-Wilk
test for normality (ipsilateral: P 5 0.039; contralateral: P 5 0.023), andamong Gaussian mixture models, a two-component one provided thebest fit (Fig. 4b, green lines). For cortico-cortical connections, both intraand inter-hemispheric distributions are well fit by lognormals (ipsilateral:P 5 0.23; contralateral: P 5 0.21) individually (Fig. 4b), but they are
different enough that when combined the distribution is no longer lognormal (P 5 0.0019). This extends previously reported findings that corticocortical connections follow a lognormal distribution in the primate24,25
and mouse cortex26 to the entire mouse brain. These observations combined indicate that connections might be lognormally distributed withina region, yet vary systematically with statistics unique to the region.
Previous studies on connectivity considered global organizational
principles from a graph-theory perspective26–28. We transformed ourAn inter-region connectivity model
Infected neurons in injection sites often span several brain areas. To
better describe the mutual connection strengths between ontologicallydefined regions rather than injection sites, we constructed inter-region
connectivity matrices via a computational model (see Supplementary
Notes for a detailed description), using segmented projection volumes(Fig. 3) to define connection strengths. Two basic modelling assumptions were used. The first, regional homogeneity, assumes that projections
between source X and target Y regions are homogeneously distributed,
so that infection of a subarea of the region is representative of the entire
region. This allows the value of WX,Y , a regional connectivity measure,
to be inferred from data that can at best only sample the source region.
The second assumption, projection additivity, assumes that the projection density of multiple source regions sum linearly to produce projection density in a target region. This allows relative contributions of different
sources to be determined for a target region, assuming at least partiallyindependent injections.
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ARTICLE RESEARCH

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Figure 4 | A computational model of inter-regional connection strengths.
a, The inter-region connectivity matrix, with connection strengths represented
in colours and statistical confidence depicted as an overlaid opacity. Notethat in this matrix, the sources (rows) are regions, whereas for the matrix ofFig. 3, the sources are injection sites. b, Both whole-brain and cortico-corticalconnections can be fit by one-component lognormal distributions (red lines).
However, the log distributions of whole-brain connection strengths are best

fit by a two-component Gaussian mixture model (green lines). c, Nodedegree and clustering coefficient distributions for a binarized version of thelinear model network, compared against Erdos-Renyi, Watts-Strogatz andBarabasi-Albert networks with matched graph statistics. d, Comparison of
the correlation coefficients of normalized connection density between areas,
defined as the common source for projections to other regions (left) and as
the common target of projections from other regions (right).

weighted, directed, connectivity matrix (Fig. 4a) to binary directed andbinary undirected data sets. Network analyses (Fig. 4c, see also Supplementary Notes) reveal that the mouse brain has a higher meanclustering coefficient (which gives the ratio of existing over possible
connections), 0.42, than expected by a random network29,30 with identicalsparseness, 0.12. Random graphs with matched node degree distribution
show a similar drop in clustering coefficient to 0.16. A ‘small-world’
network model31 approximates the clustering coefficient distribution
after being fit to its mean; however, its node degree distribution poorly
matches the data. Here, a better fit is achieved with a scale-free network32; however, neither model simultaneously fits both distributions.
Next, we analysed similarity in connection patterns between different regions. Similarity is characterized by two measures: correlations
between outgoing projections originating in two areas and correlations
between incoming projections ending in these two areas. Figure 4d depicts
heat maps of correlation coefficients between the same regions of thelinear model (Fig. 4a) depicted across the rows (that is, as a common
source for other regions), and down the columns (that is, as a common
target from other regions). The number of strong correlations is larger
than expected by chance, suggesting a tendency of regions to organize
into clusters to allow for strong indirect connectivity.

(Extended Data Fig. 8a, b). Such grouping effectively divides the cortex
into 21 predominantly non-overlapping spatial zones as shown in aflat-map cortex representation (Fig. 5a) defined by similar output projections. To effectively visualize different projection patterns in a common 3D space, voxel densities from 21 selected injections, one (centrally
located) from each cluster, were overlaid to create ‘dotograms’ (Fig. 5b,c and Extended Data Fig. 8c), demonstrating that projections fromdifferent cortical regions divide up striatum and thalamus into distinct
domains.
Average inter-group distances (Extended Data Fig. 9a–d) were used
to quantify the degree to which inter-group spatial relationships within
the cortex are preserved in target domains. Distance matrices for both
ipsilateral and contralateral cortical targets were highly correlated withthe distance matrix of injection sites, as were ipsilateral striatal and thalamic distance matrices. Weaker correlations were observed in contralateral striatum and thalamus. The computed distance matrices showthat the spatial relationship between injection sites is recapitulated in
the projections to striatum and thalamus, with some transformation ofscale and rotation.
This highly synchronized topography can be determined via virtualtractography. Real tractography (following single axons) cannot be done
because of the discrete 100-mm sampling between sections. Instead, fromevery voxel we computed a path back to the injection site by finding theshortest density-weighted distance through the voxels. The 3D tractography paths were plotted for both cortical hemispheres (Fig. 5d andExtended Data Fig. 9e), ipsilateral striatum and thalamus (Fig. 5e, f).
The tractography shows that the paths themselves also retain the same
spatial organization. In particular for the thalamus, anterior groups passthrough fibre tracts in the striatum, narrowing through the globus pallidus,

The cortico-striatal-thalamic network
Different cortical areas project to different domains of striatum andthalamus with some degree of topography33–35. We used 80 isocorticalinjection experiments to examine this. Spearman’s rank correlation
coefficient of segmented projection volumes of all voxels across the entire
brain was computed between every pair of experiments, and hierarchical clustering led to 21 distinct groups, each containing 1 to 10 injections

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before spreading throughout the thalamus (Extended Data Fig. 9f). Posterior groups (RSP, VIS) bypass the striatum but retain a strict topography following the medial/lateral axis (Fig. 5f).
Although the striatum is a cellularly homogeneous structure thatcan be subdivided into distinct domains selectively targeted by corticaland other inputs36 (Fig. 5e), the thalamus is highly heterogeneous, composed of up to 50 discrete nuclei37, receiving and relaying diverse sensory,
motor, behavioural state and cognitive information in parallel pathways
to and from the isocortex. We constructed a comprehensive wiring diagram between major, functionally distinct cortical regions and thalamic
nuclei in the ipsilateral hemisphere (Fig. 6 and Extended Data Fig. 10),by combining the quantitative connectivity matrix (Fig. 3) with the linear
model (Fig. 4a), manual proof-checking in the raw image sets, and crossreferencing published literature (83 publications, mostly from rat data,
see Extended Data Fig. 10). This wiring diagram demonstrates specificpoint-to-point interconnections between corresponding clusters that
divide the cortico-thalamic system into six functional pathways: visual,
somatosensory, auditory, motor, limbic and prefrontal. We also observed
cross-talk between these pathways, mediated by specific associational
cortical areas and integrative thalamic nuclei.
The specific observations from our data are mostly consistent (witha few additions) with extensive previous studies in rats and the fewer
number of studies in mice (Extended Data Fig. 10) as well as with studies
in other mammalian species37–39, providing a comprehensive and unifying view of mouse cortico-thalamic connections for the first time.
Much work is still needed to obtain a full picture of connectivity in thecortico-thalamic system, including intra-cortical and intra-thalamic connections, their relationships with the interconnections between cortex

AUDd
AUDv
VISl

Figure 5 | Topography of corticostriatal and cortico-thalamic
projections. a, Cortical domains in
the cortex flat-map. Each circle
represents one of 80 cortical injection
experiments, whose location is
obtained via multidimensional
scaling from 3D to allow
visualization of all the sites in one 2D
plane. The size of the circle is
proportional to the injection volume.
Clustered groups from Extended
Data Fig. 8b are systematicallycolour-coded. The selected injections
for b are marked with a black outline.
b, For co-visualization, voxel
densities from the 21 selected
injections from a are overlaid as‘dotograms’ at 8 coronal levels for
ipsilateral hemisphere. For the
dotogram, one circle, whose size is
proportional to the projection
strength, is drawn for each injectionin each voxel; the circles are sorted sothat the largest is at the back and thesmallest at the front, and are partially
offset as a spiral. c, Enlarged viewof the dotogram from the area
outlined by a white box in b. d, 3D
tractography paths in both cortical
hemispheres. e, A medial view of
3D tractography paths into theipsilateral caudoputamen. Voxel
starting points are represented as
filled circles and injection site end
points as open circles. f, A top-downview of 3D tractography paths into
the ipsilateral thalamus.

and thalamus, and the exquisite cortical laminar specificity of the originating and terminating zones of many of these connections5,38,40.

Discussion
The standardized projection data set and the informatics framework
built around it provide a brain-wide, detailed and quantitative connectivity map that is the most comprehensive, to date, in any vertebrate
species. The high-throughput whole-brain mapping approach is remarkably consistent across animals, with an average correlation of 0.90 across
12 duplicate sets of mice (Extended Data Fig. 5). Informatics processing of the data set, such as co-registration and voxelization, helps withdirect comparison between any image series, and systematic modellingand computational analyses of the entire network. Furthermore, theentire data set preserves the 3D spatial relationship of different domains,
pathways and topography (Fig. 5). Thus, our connectivity atlas lays thegroundwork for large-scale analyses of global neural networks, as well
as networks within and between different neural systems.
As an initial analysis of this large-scale data set, we present an examination of both general principles of whole brain architecture and specific properties of cortical connections. We found that projections within
the ipsilateral hemisphere and to the corresponding locations in the contralateral hemisphere are remarkably similar across the brain (Figs 3
and 4; Pearson’s r 5 0.595), with the contralateral connection strengths
significantly weaker than ipsilateral ones. The mouse brain shows defining features of both small-world and scale-free networks, that is, itclusters and has hubs; but neither of these models in isolation can fully
explain it. Interestingly, the connection strengths at both cortico-corticaland whole-brain levels show lognormal distributions, that is, long-tailed

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ARTICLE RESEARCH
Figure 6 | A wiring diagram ofconnections between major corticalregions and thalamic nuclei. Upperand lower panels show projections
from cortex to thalamus or fromthalamus to cortex, respectively(ipsilateral projections only). Colour
coding of different cortical regions
and their corresponding thalamic
nuclei is similar to the flat-map
cortex in Fig. 5a. Thickness of thearrows indicates projection strength,
which is shown in three levels as
in Extended Data Fig. 10 andcorresponds roughly to the red,orange and yellow colours in the raw
connectivity matrix (Fig. 3). LGv
and PF do not have significant
projections to cortex. The reticular
nucleus of the thalamus (RT) (thedashed box) is placed in between
cortex and thalamus to illustrate its
special role as a relay nucleus which
all cortico-thalamic and thalamocortical projections pass through andmake collateral projections into.
The asterisks indicate that corticothalamic and thalamo-corticalprojections in the gustatory/visceralpathway are between GU/VISC
cortical areas and VPMpc/VPLpc
nuclei (instead of VPM/VPL). See
Supplementary Table 1 for the fullname of each region.

CortexRT**

ThalamusCortexRT**

Thalamusdistributions with small numbers of strong connections and large numbers of weak connections. In connections among isocortex, striatum andthalamus, clustering analysis and virtual tractography recapitulate anatomical parcellation and topography of functional domains and projection pathways (Fig. 5). The extensive reciprocal connections between
isocortex and thalamus (Fig. 6) further illustrate general principles of
network segregation and integration.
Our Connectivity Atlas represents a first systematic step towards
the full understanding of the complex connectivity in the mammalianbrain. Through the process, limitations of the current approach andopportunities for future improvement can be identified. On the technical side, any potential new connections identified in the Phase I dataset (which does not yet have extensive redundancy in regional coverage)
will need to be confirmed with more data. Also, we cannot exclude thepossibility that the AAV tracer we chose to use (with the specific promoter and serotype) may not be completely unbiased in labelling all neuronal types. The connectivity matrix has been shown to contain false
positive signals (Extended Data Fig. 7), mainly due to tissue and imaging artefacts and injection tract contaminations. The connectivity matrix
based on cytoplasmic EGFP labelling does not distinguish passing fibres
from terminal zones, and examination of raw images is needed to help
with such distinction, using features such as ramification of axon fibres,and boutons or enlargements in axons. The Atlas could also be enhancedin the future with more systematic mapping using synaptic-terminalspecific viral tracers as shown (Extended Data Fig. 6). Regarding signal
quantification, we chose to use projection volume (sum of segmented
pixel counts) over projection fluorescence intensity (sum of segmented
pixel intensity), because we found the former more reliable and lessvariable across different brains (even after normalization). However,the use of projection volume will probably underestimate the strengthof dense projections. Thus, the true range of projection strengths may
go beyond the 105-fold reported here. Finally, we observed that thealignment between the average template brain and our existing Reference
Atlas model (which was drawn upon Nissl sections) is not perfect, whichleads to a degree of registration imprecision that could affect the accuracy of the quantitative connectivity matrices. Our work shows the needto generate a new reference model based on a realistic 3D brain, such as
the average template brain presented here. Our data set can also help
this by adding connectivity information to improve anatomical delineations previously defined solely by cyto- and chemoarchitecture.
Beyond the above technical issues, identities of the postsynaptic neurons at the receiving end of the mapped connections are not labelled
and therefore unknown. Microscale, synaptic-level details are missing,and electrical connections through gap junctions are not revealed. Moreover, our mesoscale connectome provides a static, structural connectivity map, which is necessary but insufficient for understanding function.
Moving from here to functional connectivity and circuit dynamics ina living brain will require fundamentally different approaches41. One
important aspect concerns the types of synapses present in each connectional path, as determined by their neurotransmitter contents and their
physiological properties. Anatomical connection strength (for example,
numbers of axon fibres and boutons) needs to be combined with physiological connection properties (for example, excitatory vs inhibitory
types of synapses, fast vs slow neurotransmission, and the specific strengthand plasticity of each synapse) to yield a true functional connection strength.
With the goal of bridging structural connectivity and circuit function41, we have taken a genetic approach, using AAV viral tracers that
express EGFP in either a pan-neuronal or cell-type-specific manner.
The same neural networks mapped here can be further investigated bysimilar viral vectors expressing tools for activity monitoring (for example,
genetically encoded calcium indicators) and activity manipulation (forexample, channelrhodopsins)16. Furthermore, our ongoing efforts of Credriver dependent tracing will allow more specific connectivity mappingfrom discrete areas and specific functional cell types. Such cell-typespecific connectivity mapping is perhaps the greatest advantage of thegenetic tracing approach, allowing dissection of differential projection
patterns from different neuronal types that are often intermingled in
the same region. The genetic tracing approach can be further extended
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RESEARCH ARTICLE
into identification of inter-connected pre- and postsynaptic cell types
and individual cells, using approaches such as retrograde or anterograde trans-synaptic tracing42–44. Our approach can also be applied
to animal models of human brain diseases and the connectivity data
generated here can be instructive to human connectome studies, which
will help to further our understanding of human brain connectivity
and its involvement in brain disorders.

METHODS SUMMARY
C57BL/6J male mice at age P56 were injected with EGFP-expressing AAV using
iontophoresis by the stereotaxic method. The brains were scanned using the STP
tomography systems. Images were subject to data quality control and all the injection sites were manually annotated. All the image sets were co-registered into the
3D reference space. EGFP-positive signals were segmented from background, andbinned at voxel levels for quantitative analyses. The raw connectivity data are servedwith various navigation tools on the web through the Allen Institute’s data portal.
See the full Methods section for detailed descriptions.
Online Content Any additional Methods, Extended Data display items and Source
Data are available in the online version of the paper; references unique to these
sections appear only in the online paper.
Received 22 July 2013; accepted 27 February 2014.
Published online 2 April 2014.
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.

21.

Swanson, L. W. Brain Architecture: Understanding the Basic Plan 331 (Oxford Univ.
Press, 2012).
Seung, S. Connectome: How The Brain’s Wiring Makes Us Who We Are 359(Houghton Mifflin Harcourt, 2012).
Sporns, O. Networks of the Brain 412 (The MIT Press, 2011).
White, J. G., Southgate, E., Thomson, J. N. & Brenner, S. The structure of thenervous system of the nematode Caenorhabditis elegans. Phil. Trans. R. Soc. Lond. B
314, 1–340 (1986).
Felleman, D. J. & Van Essen, D. C. Distributed hierarchical processing in the
primate cerebral cortex. Cereb. Cortex 1, 1–47 (1991).
Bota, M., Dong, H. W. & Swanson, L. W. Combining collation and annotation efforts
toward completion of the rat and mouse connectomes in BAMS. Front.
Neuroinform. 6, 2 (2012).
Chiang, A. S. et al. Three-dimensional reconstruction of brain-wide wiring
networks in Drosophila at single-cell resolution. Curr. Biol. 21, 1–11 (2011).
Jenett, A. et al. A GAL4-driver line resource for Drosophila neurobiology. Cell Rep. 2,
991–1001 (2012).
Sporns, O., Tononi, G. & Kotter, R. The human connectome: A structuraldescription of the human brain. PLoS Comput. Biol. 1, e42 (2005).
Van Essen, D. C. et al. The WU-Minn Human Connectome Project: an overview.
NeuroImage 80, 62–79 (2013).
Bock, D. D. et al. Network anatomy and in vivo physiology of visual cortical neurons.
Nature 471, 177–182 (2011).
Briggman, K. L., Helmstaedter, M. & Denk, W. Wiring specificity in the directionselectivity circuit of the retina. Nature 471, 183–188 (2011).
Chklovskii, D. B., Vitaladevuni, S. & Scheffer, L. K. Semi-automated reconstructionof neural circuits using electron microscopy. Curr. Opin. Neurobiol. 20, 667–675(2010).
Helmstaedter, M. et al. Connectomic reconstruction of the inner plexiform layer inthe mouse retina. Nature 500, 168–174 (2013).
Takemura, S. Y. et al. A visual motion detection circuit suggested by Drosophilaconnectomics. Nature 500, 175–181 (2013).
Huang, Z. J. & Zeng, H. Genetic approaches to neural circuits in the mouse. Annu.
Rev. Neurosci. 36, 183–215 (2013).
Masland, R. H. The neuronal organization of the retina. Neuron 76, 266–280(2012).
Bohland, J. W. et al. A proposal for a coordinated effort for the determination ofbrainwide neuroanatomical connectivity in model organisms at a mesoscopic
scale. PLoS Comput. Biol. 5, e1000334 (2009).
Chamberlin, N. L., Du, B., de Lacalle, S. & Saper, C. B. Recombinant adenoassociated virus vector: use for transgene expression and anterograde tract
tracing in the CNS. Brain Res. 793, 169–175 (1998).
Harris, J. A., Oh, S. W. & Zeng, H. Adeno-associated viral vectors for anterograde
axonal tracing with fluorescent proteins in nontransgenic and Cre driver mice.
Curr. Protoc. Neurosci. 59, 1.20.1–1.20. 18 http://dx.doi.org/10.1002/
0471142301.ns0120s59 (2012).
Wang, Q. et al. Systematic comparison of adeno-associated virus and biotinylated
dextran amine reveals equivalent sensitivity between tracers and novel projection
targets in the mouse brain. J. Comp. Neurol. http://dx.doi.org/10.1002/
cne.23567 (Feb 22, 2014).

22. Ragan, T. et al. Serial two-photon tomography for automated ex vivo mouse brain
imaging. Nature Methods 9, 255–258 (2012).
23. Dong, H. W. The Allen Reference Atlas: a Digital Color Brain Atlas of the C57BL/6J
Male Mouse (John Wiley & Sons, 2008).
24. Markov, N. T. et al. A weighted and directed interareal connectivity matrix for
macaque cerebral cortex. Cereb. Cortex 24, 17–36 (2014).
25. Ercsey-Ravasz, M. et al. A predictive network model of cerebral corticalconnectivity based on a distance rule. Neuron 80, 184–197 (2013).
26. Wang, Q., Sporns, O. & Burkhalter, A. Network analysis of corticocorticalconnections reveals ventral and dorsal processing streams in mouse visual cortex.
J. Neurosci. 32, 4386–4399 (2012).
27. Bullmore, E. & Sporns, O. Complex brain networks: graph theoretical analysis of
structural and functional systems. Nature Rev. Neurosci. 10, 186–198 (2009).
28. Schmitt, O. et al. The intrinsic connectome of the rat amygdala. Front. Neural
Circuits 6, 81 (2012).
29. Erdős, P. & Rényi, A. On the evolution of random graphs. Publ. Math. Inst. Hung.
Acad. Sci. 5, 17–61 (1960).
30. Bollobás, B. Random Graphs (Cambridge Univ. Press, 2001).
31. Watts, D. J. & Strogatz, S. H. Collective dynamics of ‘small-world’ networks. Nature
393, 440–442 (1998).
32. Barabási, A. L. & Albert, R. Emergence of scaling in random networks. Science 286,
509–512 (1999).
33. Groenewegen, H. J. & Berendse, H. W. The specificity of the ‘nonspecific’ midline
and intralaminar thalamic nuclei. Trends Neurosci. 17, 52–57 (1994).
34. Joel, D. & Weiner, I. The organization of the basal ganglia-thalamocortical circuits:
open interconnected rather than closed segregated. Neuroscience 63, 363–379(1994).
35. Mailly, P., Aliane, V., Groenewegen, H. J., Haber, S. N. & Deniau, J. M. The rat
prefrontostriatal system analyzed in 3D: evidence for multiple interacting
functional units. J. Neurosci. 33, 5718–5727 (2013).
36. Gerfen, C. R. in The Rat Nervous System (ed. Paxinos, G.) 455–508 (Elsevier
Academic Press, 2004).
37. Jones, E. G. The Thalamus (Cambridge Univ. Press, 2007).
38. Jones, E. G. Viewpoint: the core and matrix of thalamic organization. Neuroscience85, 331–345 (1998).
39. Scannell, J. W., Burns, G. A., Hilgetag, C. C., O’Neil, M. A. & Young, M. P. The
connectional organization of the cortico-thalamic system of the cat. Cereb. Cortex
9, 277–299 (1999).
40. Barbas, H. & Rempel-Clower, N. Cortical structure predicts the pattern ofcorticocortical connections. Cereb. Cortex 7, 635–646 (1997).
41. Bargmann, C. I. & Marder, E. From the connectome to brain function. Nature
Methods 10, 483–490 (2013).
42. Lo, L. & Anderson, D. J. A. Cre-dependent, anterograde transsynaptic viral tracer
for mapping output pathways of genetically marked neurons. Neuron 72,
938–950 (2011).
43. Wall, N. R., Wickersham, I. R., Cetin, A., De La Parra, M. & Callaway, E. M.
Monosynaptic circuit tracing in vivo through Cre-dependent targeting andcomplementation of modified rabies virus. Proc. Natl Acad. Sci. USA 107,
21848–21853 (2010).
44. Wickersham, I. R. et al. Monosynaptic restriction of transsynaptic tracing fromsingle, genetically targeted neurons. Neuron 53, 639–647 (2007).
Supplementary Information is available in the online version of the paper.
Acknowledgements We wish to thank the Allen Mouse Brain Connectivity Atlas
Advisory Council members, D. Anderson, E. M. Callaway, K. Svoboda, J. L. R. Rubenstein,
C. B. Saper and M. P. Stryker for their insightful advice. We thank T. Ragan for providing
invaluable support and advice in the development and customization of the TissueCyte
1000 systems. We are grateful for the technical support of the many staff members in
the Allen Institute who are not part of the authorship of this paper. This work was fundedby the Allen Institute for Brain Science. The authors wish to thank the Allen Institutefounders, P. G. Allen and J. Allen, for their vision, encouragement and support.
Author Contributions H.Z., S.W.O., J.A.H. and L.N. contributed significantly to overall
project design. S.W.O. and H.Z. did initial proof-of-principle studies. J.A.H., M.T.M., B.O.,
P.B., T.N.N., K.E.H. and S.A.S. conducted tracer injections and histological processing.
B.W., C.R.S., E.N., A.H., P.W. and A.B. carried out imaging system establishment,
maintenance, and imaging activities. L.N., C.L., L.K., W.W., Y.L., D.F. and H.P. conducted
informatics data processing and online database development. A.M.H., K.M.J. and Q.W.
conducted image quality control and data annotation. N.C., S.M. and C.K. performed
computational modelling. H.Z., S.W.O., A.R.J., C.D., C.K., A.B., J.G.H., J.W.P. and M.J.H.
performed managerial roles. H.Z., J.A.H., L.N., S.M., N.C., Q.W., S.W.O., C.R.G. and C.K.
were main contributors to data analysis and manuscript writing, with input from otherco-authors.
Author Information The Allen Mouse Brain Connectivity Atlas is accessible at (http://
connectivity.brain-map.org). All AAV viral tracers are available at Penn Vector Core, andAAV viral vector DNA constructs have been deposited at the plasmid repository
Addgene. Reprints and permissions information is available at www.nature.com/
reprints. The authors declare no competing financial interests. Readers are welcome tocomment on the online version of the paper. Correspondence and requests formaterials should be addressed to H.Z. (hongkuiz@alleninstitute.org).

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ARTICLE RESEARCHMETHODSAll experimental procedures related to the use of mice were approved by theInstitutional Animal Care and Use Committee of the Allen Institute for Brain
Science, in accordance with NIH guidelines.
Outline of the Connectivity Atlas data generation and processing pipeline. A
standardized data generation and processing platform was established (Fig. 1a).
Viral tracers were validated and experimental conditions were established throughpre-pipeline activities. C57BL/6J mice at age P56 were injected with viral tracers
using iontophoresis by the stereotaxic method. Six STP tomography systems generated high-resolution images from up to 6 brains per day. Images were subject todata quality control and all the injection sites annotated. A stack of qualified 2D
images then underwent a series of informatics processes. The image data and informatics products as well as all metadata are stored, retrieved and maintained using
our laboratory information management system (LIMS). The generated connectivity data are served with various navigation tools on the web through the Allen
Institute’s data portal along with other supporting data sets.
Stereotaxic injections of AAV using iontophoresis. Recombinant adeno-associatedvirus (AAV) expressing EGFP was chosen as the anterograde tracer to map axonal
projections because of several advantages over conventional neuroanatomical tracers19.
First, AAV mediates robust fluorescent labelling of the soma and processes of infected
neurons, which can be coupled with direct imaging methods for high-throughput
production without additional histochemical staining steps. Second, compared toconventional tracers which often have mixed anterograde and retrograde transport, retrograde labelling with AAV (except for certain serotypes) is generally negligible.
In this study, retrogradely infected cells were seen only rarely. Notable exceptions
are found in specific circuits that might have certain types of strong presynaptic
inputs, such as the entorhinal cortex projection to hippocampal subregions dentate
gyrus and CA1, where retrogradely infected input cells can be brightly labelled.
Finally, perhaps the greatest advantage of using AAV over conventional tracers isthe flexible molecular strategies that can be used to introduce various transgenes
and to label specific neuronal populations by combining cell-type-selective Cre
driver mice with AAV vectors harbouring a Cre-dependent expression cassette.
To optimize the tracing approach for the large-scale atlas data generation, we
tested various AAV constructs, serotypes and injection methods. We selected
AAV vectors that express EGFP at the highest levels. We found that AAV serotype
1 produces the most robust and uniform neuronal tropism and that iontophoretic
delivery of AAV gives rise to the most consistent and confined viral infection volume.
Thus, the entire atlas data generation was standardized with the use of AAV
serotype 1 and iontophoresis for stereotaxic injections20.
Stereotaxic coordinates were chosen for each target area based on The Mouse
Brain in Stereotaxic Coordinates45. For the majority of target sites, the anterior/posterior (AP) coordinates are referenced from Bregma, the medial/lateral (ML)coordinates are distance from midline at Bregma, and the dorsal/ventral (DV)coordinates are measured from the pial surface of the brain. For several of themost caudal medullary nuclei (for example, gracile nucleus and spinal nucleus of
the trigeminal, caudal part), the calamus (at the floor of the fourth ventricle) isused as a registration point instead of Bregma. For many cortical areas, injections
were made at two depths to label neurons throughout all six cortical layers and/or
at an angle to infect neurons along the same cortical column. For laterally locatedcortical areas (for example, orbital area, medial part; prelimbic area; agranular insulararea), the injections were made at two adjacent ML coordinates for the same reason,
since the pipette angle required for injection along the cortical column is nearly
90u, beyond our technical limit. The stereotaxic coordinates used for generating
data are listed under the Documentation tab in the data portal.
Adult male C57BL/6J mice (stock no. 00064, The Jackson Laboratory, Bar Harbour,ME) were used for AAV tracer (AAV2/1.pSynI.EGFP.WPRE.bGH, Penn Vector
Core, Philadelphia, PA) injections at P56 6 2 postnatal days. Mice were anaesthetized with 5% isoflurane and placed into a stereotaxic frame (model no. 1900, David
Kopf Instruments, Tujunga, CA). For all injections using Bregma as a registration
point, an incision was made to expose the skull and Bregma and Lambda landmarks were visualized using a stereomicroscope. A hole overlying the targeted area
was made by first thinning the skull using a fine drill burr until only a thin layer ofbone remained. A microprobe and fine forceps were used to peel away this final
layer of bone to reveal the brain surface. For targeting caudal nuclei in the medulla,
ketamine-anaesthetized mice were placed in the stereotaxic frame with the nose
pointed downward at a 45–60 degree angle. An incision was made in the skin at thebase of the skull and muscles were bluntly dissected to reveal the posterior atlantooccipital membrane overlying the surface of the medulla. A needle was used topuncture the membrane and the calamus was visualized.
All mice received one unilateral injection into a single target region in the righthemisphere. Glass pipettes had inner tip diameters of 10–20 mm. The majority of
injections were done using iontophoresis with 3 mA at 7 s ‘on’ and 7 s ‘off’ cycles

for 5 min total. These settings resulted in infection areas of approximately 400–
1,000 mm in diameter, depending on target region. Reducing the current strength
to 1 mA decreased the area of infected neurons, and was used when 3 mA currents
produced infection areas larger than ,700 mm. Mice quickly recovered after surgery and survived for 21 days before euthanasia. Injection sites ranged from 0.002
to 1.359 mm3 in volume, with an average size of 0.24 mm3 across all 469 data sets.
Serial two-photon tomography. Mice were perfused with 4% paraformaldehyde(PFA). Brains were dissected and post-fixed in 4% PFA at room temperature for
3–6 h and then overnight at 4 uC. Brains were then rinsed briefly with PBS andstored in PBS with 0.1% sodium azide before proceeding to the next step. Agarose
was used to embed the brain in a semisolid matrix for serial imaging. After removing residual moisture on the surface with a Kimwipe, the brain was placed in a 4.5%
oxidized agarose solution made by stirring 10 mM NaIO4 in agarose, transferredthrough phosphate buffer and embedded in a grid-lined embedding mould tostandardize its placement in an aligned coordinate space. The agarose block wasthen left at room temperature for 20 min to allow solidification. Covalent interactions between brain tissue and agarose were promoted by placing the solidified
block in 0.5% sodium borohydride in 0.5 M sodium borate buffer (pH 9.0) overnight at 4 uC. The agarose block was then mounted on a 1 3 3 glass slide using
Loctite 404 glue and prepared immediately for serial imaging.
Image acquisition was accomplished through serial two-photon (STP) tomography22 using six TissueCyte 1000 systems (TissueVision, Cambridge, MA) coupledwith Mai Tai HP DeepSee lasers (Spectra Physics, Santa Clara, CA). The mounted
specimen was fixed through a magnet to the metal plate in the centre of the cutting
bath filled with degassed, room-temperature PBS with 0.1% sodium azide. A new
blade was used for each brain on the vibratome and aligned to be parallel to theleading edge of the specimen block. Brains were imaged from the caudal end. We
optimized the imaging conditions for both high-throughput data acquisition anddetection of single axon fibres throughout the brain with high resolution andmaximal sensitivity. The specimen was illuminated with 925 nm wavelength lightthrough a Zeiss 320 water immersion objective (NA 5 1.0), with 250 mW light
power at objective. The two-photon images for red, green and blue channels weretaken at 75 mm below the cutting surface. This depth was found optimal as it is deep
enough to avoid any major groove on the cutting surface caused by vibratome sectioning but shallow enough to retain sufficient photons for high contrast images. In
order to scan a full tissue section, individual tile images were acquired, and theentire stage was moved between each tile. After an entire section was imaged, the x
and y stages moved the specimen to the vibratome, which cut a 100-mm section,and returned the specimen to the objective for imaging of the next plane. The bladevibrated at 60 Hz and the stage moved towards the blade at 0.5 mm per sec during
cutting. Images from 140 sections were collected to cover the full range of mousebrain. It takes about 18.5 h to image a brain at an x,y resolution of ,0.35 mm per
pixel, amounting to ,750 GB worth of images per brain. Upon completion ofimaging, sections were retrieved from the cutting bath and stored in PBS with 0.1%
sodium azide at 4 uC.
Image data processing. The informatics data pipeline (IDP)46 manages the processing and organization of the image and quantified data for analysis and display
in the web application. The two key algorithms are signal detection and image
registration.
The signal detection algorithm was applied to each image to segment positive
fluorescent signals from background. Image intensity was first rescaled by squareroot transform to remove second-order effects followed by histogram matching at
the midpoint to a template profile. Median filtering and large kernel low pass filter
was then applied to remove noise. Signal detection on the processed image wasbased on a combination of adaptive edge/line detection and morphological processing. High-threshold edge information was combined with spatial distanceconditioned low-threshold edge results to form candidate signal object sets. The
candidate objects were then filtered based on their morphological attributes suchas length and area using connected component labelling. In addition, high intensity pixels near the detected objects were included into the signal pixel set. In a
post-segmentation step, detected objects near hyper-intense artefacts occurringin multiple channels were removed. It should be noted that passing fibres andterminals are not distinguished. The output is a full resolution mask that classifies
each 0.35 mm 3 0.35 mm pixel as either signal or background. Isotropic 3D summary of each brain is constructed by dividing each image into 100 mm 3 100 mm
grid voxels. Total signal is computed for each voxel by summing the number ofsignal-positive pixels in that voxel.
The highly aligned nature from section to section throughout a single brain allowed
us to simply stack the section images together to form a coherent reconstructed 3Dvolume. Each image stack was then registered to the 3D Allen Reference Atlas model.
To avoid possible bias introduced by using a single specimen as template and toincrease the convergence rate of the registration algorithm, a registration template
was created by iteratively averaging 1,231 registered and resliced brain specimens.

©2014 Macmillan Publishers Limited. All rights reserved

RESEARCH ARTICLE
A global affine (linear) registration to the template was first performed using a combination of image moments and maximizing normalized mutual information between the red channel of the image stack and the template using a multi-resolution
gradient descent optimization. A B-spline based deformable registration was thenapplied using a coarse-to-fine strategy through four resolution levels with decreasing smoothness constraints. In each generation, a new template was created using
the previous generation results to further improve registration convergence. The
template was then deformably registered to the 3D reference model by maximizing
the mutual information of large structure annotation and the template intensity.
Segmentation and registration results are combined to quantify signal for each
100 mm 3 100 mm 3 100 mm voxel in the reference space and for each structure in
the ontology by combining voxels from the same structure in the 3D reference
model. To generate the raw connectivity matrix (Fig. 3), the projection signal wasquantified by summing the number of segmented pixels in every voxel, andscaling this value to a mm3 volume. The voxel values within each of the 469
injection sites (source) or each of the 295 target sites in either hemisphere werebinned and summed based on which structure the voxel belongs to. The target
structure values presented across the columns are normalized by each experiment’s
injection volume to allow comparison between injections. Fluorescent signals withineach injection area were excluded from projection signal calculation.
The informatics data processing supports key features in the web application,
including an interactive projection summary graph for each specimen, an image
synchronization feature to browse images from multiple injections, referenceatlases and other data set in a coordinated way, and on-the-fly search services tosearch for a specimen with specific projection profiles. Further details of the pipeline
processing and web application features are described under the documentation
tab in the data portal.
Quality control, injection site annotation and polygon drawing. A rigorous
manual quality control protocol was established which includes identification of
the injection structure(s) according to the Allen Reference Atlas ontology, delineation of the injection site location and decisions on failing an experiment due to
production issues affecting specimen and image quality. Severe artefacts such as
missing tissue or sections, poor orientation, edge cutoff, tessellation and low signal
strength lead to elimination of the entire image series. In some cases, the quality
control process extended to identification and masking of areas of high intensity/
high frequency artefacts and areas of signal dropout. This information is used indownstream search and analysis to reduce false positive and false negative returns.
For each passed image series, the anatomical location(s) of injection site wasannotated based on the Allen Reference Atlas23 and The Mouse Brain in Stereotaxic
Coordinates45. If an injection has hit multiple structures, the structure containing
the majority of the tracer is named as the primary injection structure, and any otherstructures containing tracer-infected neurons are considered secondary injection
structures. Polygons were manually drawn overlaying the cell bodies of infected
neurons for each passed injection with an electronic region of interest for ease of
injection site location by the end user, and further informatics processing. Afterdata registration into the 3D reference space, injection sites were also annotatedcomputationally. In most cases, results obtained from manual and informatics annotations are the same. The manually derived primary and secondary injection structures are provided as search entries for the Atlas, while the computationally derived
sites are available on the projection summary page for each experiment.
45. Paxinos, G. & Franklin, K. B. J. The Mouse Brain in Stereotaxic Coordinates(Academic Press, 2001).
46. Dang, C. et al. The Allen Brain Atlas: Delivering Neuroscience to the Web on a
Genome Wide Scale. Lecture Notes in Computer Science 4544, 17–26 (2007).
47. Alloway, K. D., Olson, M. L. & Smith, J. B. Contralateral corticothalamic projections
from MI whisker cortex: potential route for modulating hemispheric interactions.
J. Comp. Neurol. 510, 100–116 (2008).
48. Aronoff, R. et al. Long-range connectivity of mouse primary somatosensory barrel
cortex. Eur. J. Neurosci. 31, 2221–2233 (2010).
49. Azuma, M. & Chiba, T. Afferent projections of the infralimbic cortex (area 25) inrats: a WGA-HRP study. Kaibogaku Zasshi 71, 523–540 (1996).
50. Beckstead, R. M. An autoradiographic examination of corticocortical andsubcortical projections of the mediodorsal-projection (prefrontal) cortex in the rat.
J. Comp. Neurol. 184, 43–62 (1979).
51. Benzinger, H. & Massopust, L. C. Brain stem projections from cortical area 18 in
the albino rat. Exp. Brain Res. 50, 1–8 (1983).
52. Berendse, H. W. & Groenewegen, H. J. Restricted cortical termination fields of the
midline and intralaminar thalamic nuclei in the rat. Neuroscience 42, 73–102(1991).
53. Bokor, H., Acsady, L. & Deschenes, M. Vibrissal responses of thalamic cells thatproject to the septal columns of the barrel cortex and to the second
somatosensory area. J. Neurosci. 28, 5169–5177 (2008).
54. Burwell, R. D. & Amaral, D. G. Perirhinal and postrhinal cortices of the rat:
interconnectivity and connections with the entorhinal cortex. J. Comp. Neurol. 391,
293–321 (1998).

55. Cechetto, D. F. & Saper, C. B. Evidence for a viscerotopic sensory representation in
the cortex and thalamus in the rat. J. Comp. Neurol. 262, 27–45 (1987).
56. Conte, W. L., Kamishina, H., Corwin, J. V. & Reep, R. L. Topography in the projectionsof lateral posterior thalamus with cingulate and medial agranular cortex inrelation to circuitry for directed attention and neglect. Brain Res. 1240, 87–95(2008).
57. Cornwall, J. & Phillipson, O. T. Afferent projections to the parafascicular thalamic
nucleus of the rat, as shown by the retrograde transport of wheat germ agglutinin.
Brain Res. Bull. 20, 139–150 (1988).
58. Deacon, T. W., Eichenbaum, H., Rosenberg, P. & Eckmann, K. W. Afferent
connections of the perirhinal cortex in the rat. J. Comp. Neurol. 220, 168–190(1983).
59. Desbois, C. & Villanueva, L. The organization of lateral ventromedial thalamic
connections in the rat: a link for the distribution of nociceptive signals to
widespread cortical regions. Neuroscience 102, 885–898 (2001).
60. Donaldson, L., Hand, P. J. & Morrison, A. R. Cortical-thalamic relationships in therat. Exp. Neurol. 47, 448–458 (1975).
61. Féger, J., Bevan, M. & Crossman, A. R. The projections from the parafascicularthalamic nucleus to the subthalamic nucleus and the striatum arise fromseparate neuronal populations: a comparison with the corticostriatal andcorticosubthalamic efferents in a retrograde fluorescent double-labelling study.
Neuroscience 60, 125–132 (1994).
62. Graziano, A., Liu, X. B., Murray, K. D. & Jones, E. G. Vesicular glutamate transporters
define two sets of glutamatergic afferents to the somatosensory thalamus and two
thalamocortical projections in the mouse. J. Comp. Neurol. 507, 1258–1276(2008).
63. Guldin, W. O. & Markowitsch, H. J. Cortical and thalamic afferent connections of
the insular and adjacent cortex of the rat. J. Comp. Neurol. 215, 135–153 (1983).
64. Haque, T. et al. Thalamic afferent and efferent connectivity to cerebral corticalareas with direct projections to identified subgroups of trigeminal
premotoneurons in the rat. Brain Res. 1346, 69–82 (2010).
65. Hoogland, P. V., Welker, E. & Van der Loos, H. Organization of the projections frombarrel cortex to thalamus in mice studied with Phaseolus vulgaris-leucoagglutinin
and HRP. Exp. Brain Res. 68, 73–87 (1987).
66. Hoover, W. B. & Vertes, R. P. Anatomical analysis of afferent projections to themedial prefrontal cortex in the rat. Brain Struct. Funct. 212, 149–179 (2007).
67. Hoover, W. B. & Vertes, R. P. Projections of the medial orbital and ventral orbital
cortex in the rat. J. Comp. Neurol. 519, 3766–3801 (2011).
68. Hsu, D. T. & Price, J. L. Midline and intralaminar thalamic connections with theorbital and medial prefrontal networks in macaque monkeys. J. Comp. Neurol.
504, 89–111 (2007).
69. Hsu, D. T. & Price, J. L. Paraventricular thalamic nucleus: subcortical connections
and innervation by serotonin, orexin, and corticotropin-releasing hormone in
macaque monkeys. J. Comp. Neurol. 512, 825–848 (2009).
70. Hurley, K. M., Herbert, H., Moga, M. M. & Saper, C. B. Efferent projections of theinfralimbic cortex of the rat. J. Comp. Neurol. 308, 249–276 (1991).
71. Kaitz, S. S. & Robertson, R. T. Thalamic connections with limbic cortex. II.
Corticothalamic projections. J. Comp. Neurol. 195, 527–545 (1981).
72. Kamishina, H. et al. Cortical connections of the rat lateral posterior thalamic
nucleus. Brain Res. 1264, 39–56 (2009).
73. Kealy, J. & Commins, S. The rat perirhinal cortex: A review of anatomy, physiology,
plasticity, and function. Prog. Neurobiol. 93, 522–548 (2011).
74. Killackey, H. P. & Sherman, S. M. Corticothalamic projections from the rat primarysomatosensory cortex. J. Neurosci. 23, 7381–7384 (2003).
75. Kolmac, C. I., Power, B. D. & Mitrofanis, J. Dorsal thalamic connections of theventral lateral geniculate nucleus of rats. J. Neurocytol. 29, 31–41 (2000).
76. Krettek, J. E. & Price, J. L. The cortical projections of the mediodorsal nucleus andadjacent thalamic nuclei in the rat. J. Comp. Neurol. 171, 157–191 (1977).
77. Kuramoto, E. et al. Two types of thalamocortical projections from the motor
thalamic nuclei of the rat: a single neuron-tracing study using viral vectors. Cereb.
Cortex 19, 2065–2077 (2009).
78. Li, S. & Kirouac, G. J. Projections from the paraventricular nucleus of the thalamus
to the forebrain, with special emphasis on the extended amygdala. J. Comp.
Neurol. 506, 263–287 (2008).
79. Li, S. & Kirouac, G. J. Sources of inputs to the anterior and posterior aspects of theparaventricular nucleus of the thalamus. Brain Struct. Funct. 217, 257–273(2012).
80. Liao, C. C., Chen, R. F., Lai, W. S., Lin, R. C. & Yen, C. T. Distribution of large terminal
inputs from the primary and secondary somatosensory cortices to the dorsal
thalamus in the rodent. J. Comp. Neurol. 518, 2592–2611 (2010).
81. Linke, R. & Schwegler, H. Convergent and complementary projections of thecaudal paralaminar thalamic nuclei to rat temporal and insular cortex. Cereb.
Cortex 10, 753–771 (2000).
82. Llano, D. A. & Sherman, S. M. Evidence for nonreciprocal organization of themouse auditory thalamocortical-corticothalamic projection systems. J. Comp.
Neurol. 507, 1209–1227 (2008).
83. Lu, S. M. & Lin, R. C. Thalamic afferents of the rat barrel cortex: a light- andelectron-microscopic study using Phaseolus vulgaris leucoagglutinin as ananterograde tracer. Somatosens. Mot. Res. 10, 1–16 (1993).
84. Mason, R. & Groos, G. A. Cortico-recipient and tecto-recipient visual zones in the
rat’s lateral posterior (pulvinar) nucleus: an anatomical study. Neurosci. Lett. 25,
107–112 (1981).
85. McIntyre, D. C., Kelly, M. E. & Staines, W. A. Efferent projections of the anterior
perirhinal cortex in the rat. J. Comp. Neurol. 369, 302–318 (1996).
86. McKenna, J. T. & Vertes, R. P. Afferent projections to nucleus reuniens of thethalamus. J. Comp. Neurol. 480, 115–142 (2004).

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ARTICLE RESEARCH
87. Miller, M. W. & Vogt, B. A. Direct connections of rat visual cortex with sensory,
motor, and association cortices. J. Comp. Neurol. 226, 184–202 (1984).
88. Mitchell, B. D. & Cauller, L. J. Corticocortical and thalamocortical projections tolayer I of the frontal neocortex in rats. Brain Res. 921, 68–77 (2001).
89. Monconduit, L., Bourgeais, L., Bernard, J. F., Le Bars, D. & Villanueva, L.
Ventromedial thalamic neurons convey nociceptive signals from the whole bodysurface to the dorsolateral neocortex. J. Neurosci. 19, 9063–9072 (1999).
90. Nakashima, M. et al. An anterograde and retrograde tract-tracing study on the
projections from the thalamic gustatory area in the rat: distribution of neurons
projecting to the insular cortex and amygdaloid complex. Neurosci. Res. 36,
297–309 (2000).
91. Ohno, S. et al. A morphological analysis of thalamocortical axon fibers of rat
posterior thalamic nuclei: a single neuron tracing study with viral vectors. Cereb.
Cortex 22, 2840–2857 (2012).
92. Porter, L. L. & White, E. L. Afferent and efferent pathways of the vibrissal region ofprimary motor cortex in the mouse. J. Comp. Neurol. 214, 279–289 (1983).
93. Reep, R. L., Chandler, H. C., King, V. & Corwin, J. V. Rat posterior parietal cortex:
topography of corticocortical and thalamic connections. Exp. Brain Res. 100,
67–84 (1994).
94. Reep, R. L. & Corwin, J. V. Topographic organization of the striatal and thalamic
connections of rat medial agranular cortex. Brain Res. 841, 43–52 (1999).
95. Reep, R. L., Corwin, J. V. & King, V. Neuronal connections of orbital cortex in rats:
topography of cortical and thalamic afferents. Exp. Brain Res. 111, 215–232(1996).
96. Reep, R. L. & Winans, S. S. Efferent connections of dorsal and ventral agranularinsular cortex in the hamster, Mesocricetus auratus. Neuroscience 7, 2609–2635(1982).
97. Robertson, R. T. & Kaitz, S. S. Thalamic connections with limbic cortex. I.
Thalamocortical projections. J. Comp. Neurol. 195, 501–525 (1981).
98. Roger, M. & Arnault, P. Anatomical study of the connections of the primary
auditory area in the rat. J. Comp. Neurol. 287, 339–356 (1989).
99. Samuelsen, C. L., Gardner, M. P. & Fontanini, A. Thalamic contribution to corticalprocessing of taste and expectation. J. Neurosci. 33, 1815–1827 (2013).
100. Sanderson, K. J., Dreher, B. & Gayer, N. Prosencephalic connections of striate andextrastriate areas of rat visual cortex. Exp. Brain Res. 85, 324–334 (1991).
101. Schobber, W. Efferent and afferent connections of the nucleus lateralis posterior
thalami (‘‘pulvinar’’) in the albino rats. Z. Mikrosk. Anat. Forsch. 95, 827–844(1981).
102. Sherman, S. M. Thalamic relays and cortical functioning. Prog. Brain Res. 149,
107–126 (2005).
103. Shi, C. J. & Cassell, M. D. Cortical, thalamic, and amygdaloid projections of rat
temporal cortex. J. Comp. Neurol. 382, 153–175 (1997).
104. Shi, C. J. & Cassell, M. D. Cortical, thalamic, and amygdaloid connections of theanterior and posterior insular cortices. J. Comp. Neurol. 399, 440–468 (1998).
105. Shibata, H. Efferent projections from the anterior thalamic nuclei to the cingulate
cortex in the rat. J. Comp. Neurol. 330, 533–542 (1993).
106. Shibata, H. & Honda, Y. Thalamocortical projections of the anterodorsal thalamic
nucleus in the rabbit. J. Comp. Neurol. 520, 2647–2656 (2012).
107. Shibata, H. & Naito, J. Organization of anterior cingulate and frontal corticalprojections to the anterior and laterodorsal thalamic nuclei in the rat. Brain Res.
1059, 93–103 (2005).
108. Shipley, M. T. & Geinisman, Y. Anatomical evidence for convergence of olfactory,
gustatory, and visceral afferent pathways in mouse cerebral cortex. Brain Res. Bull.
12, 221–226 (1984).

109. Simmons, P. A., Lemmon, V. & Pearlman, A. L. Afferent and efferent connections of
the striate and extrastriate visual cortex of the normal and reeler mouse. J. Comp.
Neurol. 211, 295–308 (1982).
110. Takagishi, M. & Chiba, T. Efferent projections of the infralimbic (area 25) region of
the medial prefrontal cortex in the rat: an anterograde tracer PHA-L study. Brain
Res. 566, 26–39 (1991).
111. Van der Werf, Y. D., Witter, M. P. & Groenewegen, H. J. The intralaminar and midline
nuclei of the thalamus. Anatomical and functional evidence for participation inprocesses of arousal and awareness. Brain Res. Brain Res. Rev. 39, 107–140(2002).
112. van Groen, T., Kadish, I. & Wyss, J. M. Efferent connections of the anteromedial
nucleus of the thalamus of the rat. Brain Res. Brain Res. Rev. 30, 1–26 (1999).
113. van Groen, T. & Wyss, J. M. Connections of the retrosplenial granular a cortex in therat. J. Comp. Neurol. 300, 593–606 (1990).
114. Van Groen, T. & Wyss, J. M. Projections from the anterodorsal and anteroventral
nucleus of the thalamus to the limbic cortex in the rat. J. Comp. Neurol. 358,
584–604 (1995).
115. Van Groen, T. & Wyss, J. M. Connections of the retrosplenial granular b cortex in
the rat. J. Comp. Neurol. 463, 249–263 (2003).
116. Vaudano, E., Legg, C. R. & Glickstein, M. Afferent and efferent connections of
temporal association cortex in the rat: a horseradish peroxidase study. Eur. J.
Neurosci. 3, 317–330 (1991).
117. Vertes, R. P. Analysis of projections from the medial prefrontal cortex to thethalamus in the rat, with emphasis on nucleus reuniens. J. Comp. Neurol. 442,
163–187 (2002).
118. Vertes, R. P. Differential projections of the infralimbic and prelimbic cortex in therat. Synapse 51, 32–58 (2004).
119. Vertes, R. P., Hoover, W. B., Do Valle, A. C., Sherman, A. & Rodriguez, J. J. Efferent
projections of reuniens and rhomboid nuclei of the thalamus in the rat. J. Comp.
Neurol. 499, 768–796 (2006).
120. Vertes, R. P., Hoover, W. B. & Rodriguez, J. J. Projections of the central medial
nucleus of the thalamus in the rat: node in cortical, striatal and limbic forebrain
circuitry. Neuroscience 219, 120–136 (2012).
121. Vogt, B. A. & Miller, M. W. Cortical connections between rat cingulate cortex andvisual, motor, and postsubicular cortices. J. Comp. Neurol. 216, 192–210 (1983).
122. Wang, Y. & Kurata, K. Quantitative analyses of thalamic and cortical origins ofneurons projecting to the rostral and caudal forelimb motor areas in the cerebral
cortex of rats. Brain Res. 781, 137–147 (1998).
123. Welker, E., Hoogland, P. V. & Van der Loos, H. Organization of feedback andfeedforward projections of the barrel cortex: a PHA-L study in the mouse. Exp.
Brain Res. 73, 411–435 (1988).
124. Wimmer, V. C., Bruno, R. M., de Kock, C. P., Kuner, T. & Sakmann, B. Dimensions ofa projection column and architecture of VPM and POm axons in rat vibrissal
cortex. Cereb. Cortex 20, 2265–2276 (2010).
125. Wright, N. F., Vann, S. D., Erichsen, J. T., O’Mara, S. M. & Aggleton, J. P. Segregationof parallel inputs to the anteromedial and anteroventral thalamic nuclei of the rat.
J. Comp. Neurol. 521, 2966–2986 (2013).
126. Yasui, Y., Itoh, K., Sugimoto, T., Kaneko, T. & Mizuno, N. Thalamocortical andthalamo-amygdaloid projections from the parvicellular division of theposteromedial ventral nucleus in the cat. J. Comp. Neurol. 257, 253–268 (1987).
127. Zhang, Z. W. & Deschenes, M. Projections to layer VI of the posteromedial barrel
field in the rat: a reappraisal of the role of corticothalamic pathways. Cereb. Cortex
8, 428–436 (1998).

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Extended Data Figure 1 | AAV/BDA tracer comparison, using a primary
motor cortex (MOp) injection as an example. The cortical and subcorticalprojections from MOp injection are labelled similarly with the AAV tracer(green) and conventional tracer BDA (red). a, Injection sites of AAV and BDA
are mostly overlapping (yellow), with a blue DAPI counterstain. b, Confocal
image taken from the box in a shows BDA tracer uptake in individual neurons
at the injection site. b’, The same box in a shows AAV infection of individual
neurons. b’’, Overlay of b and b’ shows the presence of both tracers in the same
region and their colocalization in some neurons (yellow). c–f, Examples ofcortical projections in the contralateral primary motor cortex, ipsilateral
primary somatosensory cortex, agranular insular area (dorsal part), andperirhinal cortex labelled with red, green, or yellow. g–n, Examples ofsubcortical projections in the ipsilateral ventral posterolateral nucleus of thethalamus and posterior nucleus of the thalamus, superior colliculus, pontine

grey, caudate putamen, zona incerta and subthalamic nucleus, midbrain
reticular nucleus, parabrachial nucleus, and contralateral bed nucleus of theanterior commissure. Scale bars are 1,000 mm (a); 100 mm in (b, b’ and b’’); and258 mm (c–n). Approximately 18 brain regions were selected throughout thebrain to represent broad anatomical areas and diverse cell types (3 cortical and
15 subcortical structures). AAV and BDA were co-injected into each selected
brain region in wild-type mice using a sequential injection method developed totarget virtually the same anatomical region. For most cases, the anatomicalarea(s) of tracer uptake are well matched. We found the long-range projections
from all studied regions with both tracers. Their patterns were similar between
the two tracers in mostly overlapped injection cases. There were more
retrogradely labelled neurons with BDA than AAV, although a few retrogradeneurons were observed in all studied regions with both tracers. BDA was clearly
uptaken by passing fibres in some injections but AAV was not.

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Extended Data Figure 2 | Consistency among individual STP tomography
image sets. a, High-resolution images of 140 serial sections of a single brain are
shown as an example (injection into the primary visual area). The injection siteand major projection targets can be easily observed in this ‘contact-sheet’ view.
b, Registration variability study. A set of ten 3D fiducial points of interest(POIs) were manually identified on the Nissl 3D reference space, the averagetemplate brain, and in 30 randomly selected individual image sets by threeraters independently. The POIs were selected such that they span the brain
and can be easily and repeatedly identified in 3D. Each POI (p) from eachexperiment was then projected into 3D reference space (p’) using the transformparameters computed by the Alignment module. Statistics were gathered on thetarget registration error between p’ and its ‘gold standard’ correspondence(computed as the mean of the labelling of 3 raters) in the Nissl (pNissl) andtemplate (ptemplate) volumes. In summary, for all POIs in template space, theobserved median variation in each direction are 28 mm for left-right, 35 mm for
inferior-superior, and 49 mm for anterior-posterior. In Nissl space, theseobserved median variations are 42, 71 and 60 mm, respectively. The registration
variations in different directions are shown here. For each POI, the green dot
shows the position in template space (ptemplate) and the red dot shows the
position in Nissl space (pNissl). The small yellow bar shows the medianvariation (among 30 image sets) away from ptemplate in each direction. POIs:
AP: area postrema, midline; MM: medial mammillary nucleus, midline; cc1:
corpus callosum, midline; cc2: corpus callosum, midline; acoL, acoR: anterior
commisure, olfactory limb; arbL, arbR: arbor vitae; DGsgL, DGsgR: dentate
gyrus, granule cell layer. c, Percent agreement between computationallyassigned injection site voxels and manually assigned injection structures. Eachhistogram data point corresponds to a single injection, and 100% indicates thatevery injection site voxel was computationally assigned to a structure included
on the manually annotated injection structures list. Voxels which werecomputationallyassigned to fibre tracts or ventricles are excluded from thecomputation. Neither fibre tracts nor ventricles were incorporated into the manual
annotation process; their exclusion allows for a more commensurate comparison.

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Extended Data Figure 3 | Distribution of injection sites across the brain.
a, Locations of injection spheres within 12 major brain subdivisions are shown
as a projection onto the mouse brain in sagittal views. n 5 108 isocortex, 23
olfactory areas (OLF), 42 hippocampal formation (HPF), 8 cortical subplate(CTXsp), 38 striatum (STR), 9 pallidum (PAL), 57 thalamus (TH), 47
hypothalamus (HY), 50 midbrain (MB), 21 pons (P), 45 medulla (MY) and 21
cerebellum (CB). b, Frequency histogram for the injection site volumes of all
469 data sets is shown. c, The Allen Reference Ontology was collapsed into 295
non-overlapping, unique, anatomical structures for analyses, distributed acrossmajor brain subdivisions as shown (black bars). For most structures, a single
injection was sufficient to infect the majority of neurons in that region. For

larger structures (for example, primary motor cortex), multiple injections weremade into several, spatially separate locations. The majority of these 295
regions have at least one injection targeted to that structure as either the
primary or secondary injection site (white bars); only 18 structures are notcovered at all (grey bars, for details see Supplementary Table 1). These missed
structures (minimal to no infected cells in either the primary or secondary
injection sites) were either very small (for example, nucleus y in the medulla),
purposefully left out due to the presence of other injections under the samelarge parent structure (for example, four of the cerebellar cortex lobules), or
technically challenging to reach via stereotaxic injection (for example,
suprachiasmatic nucleus).

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Extended Data Figure 4 | Distribution of whole brain projections fromdifferent cortical source areas. Pie charts show the percentage of total
projection volume across all voxels outside of the injection site distributed in
the 12 major brain subdivisions from both hemispheres. Each pie chart
represents the average of 4 to 27 cortical injections grouped by the broad
regions listed. A pie chart key of the volume distribution (number of voxels per
structure/total number of voxels per brain) of these 12 subdivisions is at thebottom right for comparison. The largest projection signal from each corticalinjection is found within isocortex (range of 45.4–69.8% of projection signals

depending on source region, with an average of 59% for all cortical injections),
although the isocortex accounts for only 30.2% of total brain volume.
Differences in the subcortical distribution of relative signal strength between
cortical sources were also observed. For example, within the striatum (lightblue), the percentage of total signal is low from auditory, retrosplenial andvisual areas (6.2%, 3.4% and 7.6%, respectively), but much greater from frontal,
motor, cingulate and somatosensory areas (16.5%, 27.7%, 20.3% and 17.6%,
respectively).

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Extended Data Figure 5 | Variability of brain-wide projection signal
strength. To examine animal-to-animal variability in projection patterns,
12 sources with two spatially overlapping injection experiments were identified
from the full data set of tracer injections shown in Fig. 3. a, Rows show
segmented projection volumes normalized to the injection volume(log10-transformed) in the 295 ipsilateral target regions for each of 2 individual
overlapping tracer injections per source region indicated (above and below
solid black line). The colour map is as shown in Fig. 3. b, Maximal density
projections of whole brain signals from each of the two spatially overlapping
injection experiments per source region visibly demonstrate consistency ofbrain-wide connections. Scatter plots of all ipsilateral and contralateral target
structure values above a minimum threshold in both members of the pair(log10 5 23.5; non-blue values from a) show significant correlations between
each pair of injections across a four orders of magnitude range of projection

strengths. Values in the scatter plots are Pearson’s correlation coefficients (r).
Note that in some cases (for example, PTLp) axon pathways appear to belabelled in only one of the pair. This could be due to random differences in the
proportion of corticospinal projecting neurons labelled in a particular injection
within the same source area. Signal in large annotated white matter tracts
are computationally removed from the connectivity matrix, and thus notincluded in the scatter plots. c, Detected fluorescent signals from each of two
injections into the same location of primary somatosensory cortex registeredand overlaid with the average template brain (grey). Lower 2 rows, 2D raw
images from each injection experiment at different anterior-posterior levels.
The centres of these injection sites are in the far left panel and their major
targets are in the right panels. See Supplementary Table 1 for the corresponding
full name and acronym for each region.

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Extended Data Figure 6 | Cytoplasmic EGFP and synaptophysin-EGFP
AAV tracer comparison, using primary motor cortex (MOp) injections as
an example. a–f, Two-photon images showing an example of the labellingobtained using the Phase I virus for whole-brain projection mapping, which
consists of a human Synapsin I promoter driving expression of EGFP. g–l, To
compare cytoplasmic labelling of projections and identification of terminal
regions with a presynaptic reporter virus, we made a construct with the same
hSynapsin promoter driving expression of a triple reporter cassette (AAV2/
1.pSynI.nls-hrGFP-T2A-tagRFP-T2A-sypGFP.WPRE.bGH): a nuclear
localization signal attached to humanized Renilla GFP (nls-hrGFP), a T2A
sequence followed by cytoplasmic tagRFP, a second T2A sequence, and thesynaptophysin-EGFP fusion (sypGFP). Owing to the two-photon imaging
wavelength used (925 nm), the tagRFP (red) signal is weak to non-existent inthese images. a, g, An image at the centre of the infected area after injection into
the same region of MOp with the Phase I cytoplasmic viral tracer (a) andthe nuclear and synaptic reporter viral tracer (g). Examples of viral labelling in
the caudoputamen (b, h), somatosensory cortex (c, i), thalamus (d, j), pontine
grey (e, k) and inferior olivary complex in the medulla (f, l) are shown forboth tracers. The cytoplasmic viral tracer labels axons, revealing densebranching patterns in presumed terminal zones. The presynaptic reporter
virus predominantly shows a punctate pattern of labelling consistent with
presynaptic protein expression patterns, and indicative of terminal zones.

Punctate or diffuse labelling was observed with the synaptic reporter virus innearly all MOp target regions manually and computationally identified using
the cytoplasmic reporter, including those with both large and small signals.
Fluorescent signal originating in fibres outside of large white matter tracts areincluded in the signal quantification and matrix shown in Fig. 3, but signals
from these large annotated fibre tracts are computationally removed.
m–p, High-resolution images of terminal zones in the somatosensory cortex(m) and thalamus (o) identified using the cytoplasmic viral tracer show axon
ramification and punctate structures consistent with bouton labelling,similar to the synaptic reporter in the corresponding regions (n, p).
q–r’, To validate the presynaptic expression of the sypGFP fusion protein,sections including and adjacent to the thalamic region shown in j were collected
after two-photon imaging and immunostained with antibodies against GFP(chicken polyclonal 1:500, Aves Labs, Inc. #GFP-1020) and synapsin I(rabbit polyclonal, 1:200, Millipore, #AB1543P) or GFP (rabbit polyclonal,
Life Technologies, #A-11122) and SV2 (mouse monoclonal, DSHB,
SV2-supernatant) presynaptic proteins. q, r, Confocal images at a single planeshow punctate labelling indicative of presynaptic boutons for both sypGFP andsynapsin or SV2. Many puncta were colocalized (yellow arrows show selectexamples in q’ and r’), although quantification was not reliable due to the very
high density of presynaptic labelling by anti-Synapsin and anti-SV2.

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RESEARCH ARTICLE

Extended Data Figure 7 | Distribution of log10-transformed normalized
projection volumes from the entire matrix presented in Fig. 3. The values(left-hand y axis, black bars) were number of target regions and derived
from Supplementary Table 2. The entire range of the normalized projection
volumes in this matrix was between log10 5 214 and log10 5 1.5, and it peaked
between log10 5 23.5 and log10 5 23.0. A manual analysis of true positive andtrue negative signals from 20 randomly chosen injection experiments,
representing the range of injection sizes, was used to estimate the false positive
rate at different threshold levels, shown on the right-hand y axis (grey circles).
True positive values predominantly fall within the range of log10 5 24 to 1.5.
For example, at a threshold of log10 5 24, the false positive rate was 27%,
dropping to 14.5% at log10 5 23.5. False positives were almost exclusively due
to small segmentation artefacts in areas without actual fluorescently labelledaxon fibres.

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ARTICLE RESEARCH

Extended Data Figure 8 | Cortical domains identified by clustering analysis
of projection patterns. Eighty injections were used in the analysis for which
the injection site is strictly within the isocortex and injection volume.0.07 mm3. a, Scatter plot of the voxel densities (excluding injection sites) of
the whole brains from two nearby anterior cingulate injections (ACAd andACAv) shows a strong correlation between the two (Spearman’s r 5 0.82),
whereas that of two distant injections (ACAd and SSp-m) shows little
correlation (r 5 20.03). b, Hierarchical clustering of the projection patternbased on Spearman’s rank correlation coefficient of voxel density over theentire brain. The pseudo-F statistics measures the coherence of clusters and is
the ratio of mean sum of squares between groups to the mean sum of squares
within group. Peaks in the pseudo-F statistics (for example, at n 5 3, 8 and 21
clusters) are indicators of greater cluster separation. For n 5 21 clusters, asystematic colour-code is given to each cluster to provide a visual guide to theircortical location (Fig. 5a), the numbers in parentheses indicate the number of
injections in each group. c, Voxel densities from the 21 selected injections from
Fig. 5a are overlaid as ‘dotograms’ at 8 coronal levels for the contralateralhemisphere.

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RESEARCH ARTICLE

Extended Data Figure 9 | Topography of cortico-striatal and corticothalamic projections. Average inter-group distance was used to quantify the
degree of which inter-group spatial relationship within the cortex is preservedin target domains. a, Inter-group injection distances were obtained bycomputing the 3D Euclidean distances between injection site voxels of two
experiments, one from each group, and then averaging over all injection pairs.
For visualization, the distances are embedded into a 2D plane using
multidimensional scaling to create a group-level injection flat-map. b, Intergroup projection distances were obtained by computing the 3D Euclidean
distance between a pair of voxels in a target domain weighted by the product ofvoxel density of two injections, one from each group. The distances are thenaveraged over all voxel pairs in the target domain and injection pairs between
groups. c, Inter-group projection distance matrices for each target domain

visualized as false-coloured heatmaps. The black columns and rows incontralateral caudoputamen and thalamus are due to four missing structures.
d, Inter-group projection distances are embedded into a 2D plane using multidimensional scaling to visualize the spatial relationship between groups. e, 3D
tractography paths from decimated (every other indices in each dimension)
voxels in both cortical hemispheres. Voxels belonging to the medial corticalgroups have been omitted to reveal a reconstructed corpus callosum showing
parallel crossings with a conserved spatial configuration. f, A top-down view of
3D tractography paths into the ipsilateral thalamus for all voxels excluding the
RSP/VIS groups showing axonal projections passing through fibre tracts inthe striatum, narrowing through the globus pallidus, before spreading
throughout the thalamus.

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ARTICLE RESEARCH

Extended Data Figure 10 | A matrix of major connections between
functionally distinct cortical regions and thalamic nuclei, corresponding
to Fig. 6. Upper and lower panels show projections from cortex (source) tothalamus (target) or from thalamus (source) to cortex (target), respectively(ipsilateral projections only). The label ‘pc’ indicates that cortico-thalamic andthalamo-cortical projections in the gustatory/visceral pathway are between
GU/VISC cortical areas and VPMpc/VPLpc nuclei. The number of pluses
denotes relative connectivity strength and corresponds to the thickness ofarrows in Fig. 6. All the connections described here were found in our data set.
Connections labelled in red are previously known, whereas those labelled inblack are not previously described in the rodent literature to our knowledge.
There are also cases in which a connection was described in the literature but is
excluded here because we could not find solid evidence in our data set tosupport it. All references that we have used to compare with our data arelisted33,39,47–127. Specifically, the cortico-thalamic system can be divided into six
functional pathways: visual, somatosensory, auditory, motor, limbic andprefrontal. The visual pathway is composed of primary and associational visual
cortical areas (VISp, VISam, VISal/l, and TEa) and thalamic nuclei LGd, LGv,and LP100,103,109, with LGd and VISp playing primary roles in processing
incoming visual sensory information, visual associational areas involved inhigher-order information processing and LP potentially modulating thefunction of all visual cortical areas (thus similar to the pulvinar in primates).
LGv does not project back to cortex. Similarly, the somatosensory pathway iscomposed of primary and secondary somatosensory cortical areas (SSp andSSs) and thalamic nuclei VPM, VPL and PO48,74, with SSp and VPM/VPL
playing primary roles in processing incoming somatosensory information, SSs
in higher-order information processing and PO modulating the function of all
somatosensory cortical areas. The gustatory and visceral pathway (involving
GU/VISC cortical areas and VPMpc/VPLpc nuclei)55,108 and the auditory
pathway (involving primary and secondary AUD areas and different MG

nuclei)82,98 also have similar organizations, although our current data do nothave sufficient resolution to resolve fine details. The motor pathway iscomposed of primary and secondary motor cortical areas (MOp and MOs) andthe VAL nucleus92,94. The limbic pathway (which is closely integrated with the
hippocampal formation system not discussed here) is composed of theretrosplenial (RSP) and anterior cingulate (ACA) cortical areas and thalamic
nuclei AV, AD and LD107,114. The prefrontal pathway, which is considered toplay major roles in cognitive and executive functions, is composed of themedial, orbital and lateral prefrontal cortical areas (including PL, ILA, ORB andAI) and many of the medial, midline, and intralaminar nuclei of the thalamus(including MD, VM, AM, PVT, CM, RH, RE and PF)111. The reticular nucleus(RT) is unique in that it is a relay nucleus for all these pathways, receiving
collaterals from both cortico-thalamic and thalamo-cortical projections
although itself only projecting within the thalamus. Between these pathways,
we have observed cross-talks, mediated by specific associational cortical areas
and thalamic nuclei that may be considered to play integrative functions.
For example, the anterior cingulate cortex (ACA) appears to bridge theprefrontal and the limbic pathways, interconnecting extensively with both. The
posterior parietal cortex (PTLp) and the LD nucleus may relay information
between the visual and the limbic pathways. PTLp, while hardly receiving
any inputs from the thalamus, projects strongly to both LP and LD. On theother hand, LD, while projecting quite exclusively to the limbic cortical areas,
receives strong projections from all visual cortical areas. There is also extensive
cross-talk between the motor pathway and the prefrontal pathway, withboth MOp and MOs receiving strong inputs from VM and sending strong
projections to MD, and additionally with MOs projecting widely into many
medial, midline and intralaminar nuclei. Finally, the thalamic nuclei PO, VM,
CM, RH and RE all send out widely distributed, albeit weak, projections tomany cortical areas in different pathways, thus potentially capable ofmodulating activities in large cortical fields.

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