Anatomical tract tracing methods are the gold standard for estimating the weight of axonal connectivity between a pair of pre-defined brain regions. Large studies, comprising hundreds of experiments, have become feasible by automated methods. However, this comes at the cost of positive-mean noise making it difficult to detect weak connections, which are of particular interest as recent high resolution tract-tracing studies of the macaque have identified many more weak connections, adding up to greater connection density of cortical networks, than previously recognized. We propose a statistical framework that estimates connectivity weights and credibility intervals from multiple tract-tracing experiments. We model the observed signal as a log-normal distribution generated by a combination of tracer fluorescence and positive-mean noise, also accounting for injections into multiple regions. Using anterograde viral tract-tracing data provided by the Allen Institute for Brain Sciences, we estimate the connection density of the mouse intra-hemispheric cortical network to be 73% (95% credibility interval (CI): 71%, 75%); higher than previous estimates (40%). Inter-hemispheric density was estimated to be 59% (95% CI: 54%, 62%). The weakest estimable connections (about 6 orders of magnitude weaker than the strongest connections) are likely to represent only one or a few axons. These extremely weak connections are topologically more random and longer distance than the strongest connections, which are topologically more clustered and shorter distance (spatially clustered). Weak links do not substantially contribute to the global topology of a weighted brain graph, but incrementally increased topological integration of a binary graph. The topology of weak anatomical connections in the mouse brain, rigorously estimable down to the biological limit of a single axon between cortical areas in these data, suggests that they might confer functional advantages for integrative information processing and/or they might represent a stochastic factor in the development of the mouse connectome.
Recently, there has been much interest in the connectome perspective of the brain, which aims to map the entire set of connections or interactions between brain regions, rather than the more traditional focus on individual regions and their connectivity [1,2]. This view may offer new insights both into general rules underlying the pattern of connections of the healthy developing brain, and how these patterns are disturbed in disorders and disease [3–5]. Non-invasive techniques such as functional or diffusion magnetic resonance imaging, electroencephalography and magnetoencephalography allow for measuring these networks at the whole-brain scale. However, these techniques only indirectly measure the actual axonal connectivity between brain regions. Despite major advances in sophisticated statistical and computational methods to process these data, direct interpretation in terms of neurons and axons is infeasible. For this reason, there is great interest in carrying out such analyses in animal model systems where more direct measurements can be made. In mammals, tract tracing is considered the gold standard for assessing axonal connectivity. This invasive technique allows for quantitative measurement of the strength or weight of axonal projections between cortical areas. However, the technique has been challenging to scale up, and historically most tract-tracing studies have necessarily focused on connectivity of a few regions experimentally studied. Meta-analytic collation of multiple primary studies in the literature was used for pioneering graph theoretical analysis of the cat and the macaque connectomes [6]; but there are many technical complications in combining published tract-tracing data, such as inconsistent usage of atlases and methodology across primary studies [7]. Recent years, however, have seen several institutes publish large-scale efforts to comprehensively map axonal connectivity in the mouse [8,9] and the macaque [10] by collecting high resolution images of tracer propagation from a large number of injection experiments coordinated by a standard protocol. Initial analyses have already shown the potential value of these “next generation” tract-tracing datasets for informing our understanding of the organization of the mammalian connectome [11–15] and for identifying a large number of so-called “new found projections” or axonal projections below the limit of resolution of historical methods. The gold standard for assessing connectivity from microscopy images is expert visual inspection and manual demarcation of signal from background noise. This process of expert curation of the images has been used to define axonal connections in a large dataset of tract-tracing experiments in the macaque [10,16]. But expert curation is time-consuming and observer dependent so the Allen Institute for Brain Sciences (AIBS) has taken a more computational approach in analysing their large tract-tracing dataset in the mouse, using automated image analysis algorithms to quantify the amount of signal present in each target region [9]. This algorithmic approach may suffer from decreased sensitivity, i. e. greater risk of failing to detect a true signal when it is present, or decreased specificity, i. e. a greater risk of failing to rule out a true signal when it is absent. Statistically principled methods are currently lacking to estimate connectivity from these datasets. However, the published large datasets contain repeated measurements for many connections. These allow for estimation of variability, an indicator of experimental noise and inter-animal differences, and can thus be used for principled estimation of uncertainty in connectivity estimates. In particular, the mouse tract-tracing data published by the AIBS might benefit from a more formal statistical approach, since the segmentation algorithm used for automated analysis was found to have low specificity or high false positive rate [9], when algorithmic assessment was benchmarked against expert evaluation of a subset of experiments. Moreover, such a method may be used to assess the impact of incomplete data, as even the comprehensive AIBS dataset does not include primary injections into all cortical regions. An open question in systems neuroscience is what percentage of brain regions that could be connected are, in fact, connected. This is fundamentally a question about specificity and sensitivity of detection of connections or links. Recently, it has been claimed that 62% of all possible interregional intrahemispheric connections in macaque cortex exist [10,17] (66% for a fully investigated subset of regions), which is much higher than previous estimates of 15%-45% cortical connection density [18–20], perhaps reflecting the superior sensitivity of next-generation tract-tracing technologies. Biologically, one would expect network density to increase with decreasing brain size, both because in smaller brains long-distance connections are relatively less costly [21], and because smaller brains generally have fewer regions. However, recent estimates of cortical network density are low for mouse [34%-41% (isocortex) [8]; 35. 4%-53. 5% (Table S5 from [9]) ] and rat [45. 1% [13]]. Here, we provide a statistical framework for estimating axonal connectivity in large-scale tract-tracing datasets. First, we explore the reliability of different tract-tracing datasets by assessing the variability of repeated measurements of axonal connectivity in (i) the collated Cocomac macaque dataset (collated macaque) [22]; (ii) the multi-experiment dataset by Markov et al. on macaque (multi-experiment macaque) [10]: and (iii) the multi-experiment dataset by the AIBS on the mouse (multi-experiment mouse) [9]. Second, we propose a statistical model to estimate the mouse brain network from algorithmically segmented data with positive-mean noise and co-injected regions, estimating both connectivity weights and associated credibility intervals. Third, we use this approach to rigorously estimate cortical network density of the mouse brain and to enable graph theoretical analysis of connectome topology over the full range of axonal connection weights.
The connectivity weight can be defined in different ways. All definitions depend on the total volume of signal Oej found in the target region, but differ in how this value is normalized [9,12]. Preferably, our definition of weight would not depend on the total volume Vi, Vj of regions involved. We will therefore estimate the “normalized connection density” nij: the amount of signal expected per unit volume of target region j, after injection of one unit volume of source region i. nij can be estimated from an experiment as O e j V j I e i. However, data on the amount of tracer injected, Iei, are only available for the multi-experiment mouse dataset. Therefore, in analyses comparing the different datasets, we will use the fractional weight fij, the fraction of the total signal measured in target regions that is contained in region j after injection of region i, introduced as the “fraction of labeled neurons” by Markov et al [16]. fij can be estimated from an experiment as Oej/∑k ≠ i Oek. For each of these three datasets, we explored the variability of the measured weight of connectivity between regions. We focus on the continuous variable of weight rather than simply the (binary) presence or absence of connections as the mouse dataset shows an extremely low variability of binary connections (i. e. Oej > 0), due to its high false positive rate. For the mouse data, and the multi-experiment macaque data, we log-transformed the weights, to make them more comparable to the ordinal weights {1,2, 3} assigned to each connection in the collated macaque dataset. We constructed a measure of variability V as follows. First, we selected all pairs of regions for which at least one non-zero value was measured, and computed the mean connection weight mij for each pair. Second, we selected all pairs of regions for which at least two non-zero values were measured, and for each pair calculated the variance vij of the measured values. Last, we defined our measure of variability as the average of these variances, divided by the variance of the means V = E (v ¯) Var (m ¯) (1) This measure represents the variance in repeated measurements of the weight of the same connection scaled by the overall variance measured across all weights in the dataset. Note that this measure is invariant to affine transformations of the connection weights, which is important as the collated macaque labels {1,2, 3} could be arbitrarily scaled to fit with more continuously quantitative measures of connection weight. To examine whether weaker connections are more variable, we compute V0. 5 and V0. 1, defined as above but with the variance taken over the 50% and 10% weakest weights. Finally, we perform sensitivity analyses on this metric to test robustness of results to selection of region pairs and exclusion of zero weights (S1 Appendix). Two probability thresholds exist in the multi-experiment mouse tract-tracing data, which put a lower bound on the weight at which connections can still be identified. These are the noise threshold and the co-injection threshold. The noise threshold is the result of incorrectly identified signal in the absence of a connection. We quantify its region-specific probability density function gj as above. This distribution describes the density values to be expected for region j in the absence of a connection. A connection can only be identified if an injection into the source region generates a density in the target region that is higher than this noise threshold. The density generated depends both on the connection weight μij and the injection volume Iei, as an increase in either will increase the expected density due to this connection: log 10 (I e i 10 μ i j). We thus compute the noise threshold T n x (i, j) as the minimum connection strength μij needed such that the expected density of axonal connectivity of region j for at least one experiment is larger than the xth percentile of the noise distribution g: T n x (i, j) = log 10 (min e 10 g j x I e i) with g j x the xth percentile of gj. The co-injection threshold is due to injections not limited to one region. Such co-injections lead to a threshold very similar to the noise threshold described above. For example, if both region i1 and i2 are injected, a strong connection of i1 to region j would mask the connection from i2 to j. To make this effect visible, we calculate the co-injection threshold Tc (i, j) as the minimum μij needed to ensure that, for at least one experiment, the expected signal due to this connection is larger than the expected signal due to all other connections: T c (i, j) = log 10 (min e ∑ k ≠ i I e k 10 μ k j I e i) (6) We obtain a distribution of Tc (i, j) by computing this statistic for each sample from the MCMC, and obtain the 90% credibility interval (CI) by taking the appropriate percentiles. We estimate the network density function d (x), defined as the percentage of all possible connections that are of at least weight x. We estimate this function separately for intra-hemispheric and for inter-hemispheric connections. To facilitate comparison of the datasets, we here take the weight to be the fractional weight fij introduced before, which can be obtained from our estimated parameters as V i 10 μ i j ∑ k V k 10 μ k j. d (x) gives the network density of the brain network if a cut-off of x is employed, i. e. setting all edges with weight smaller than x to 0. d is a monotonously decreasing function, and limx → ∞ d (−x) is the total network density. As there is no information about the weight of a connection if it is weaker than its associated thresholds, we compute d (x) using only those connections C (x) for which both the median of its noise threshold and of its co-injection threshold are smaller than x: d (x) = 1 | C (x) | ∑ (i, j) ∈ C (x) 1 f i j > x (7) with 1 the indicator function, and |C (x) | the size of the set C (x). d (x) can be calculated for each sample of the posterior from the MCMC run, thus providing the uncertainty in the network density estimates. To obtain a point estimate and CI for the overall network density, we evaluate d (x) at the x such that exactly 50% of connections have a threshold higher than x, i. e. at the point where still half of connections can be measured. This choice balances the competing requirements of obtaining the network density at the finest level, i. e. minimal x, and basing the network density estimate on as many connections as possible, i. e. maximal x. The multi-experiment macaque dataset was expertly curated and, by definition, contains no demonstrable false positives. Furthermore, all injections are into a single region, so there is no co-injection threshold to consider. We thus estimate the fij as above, assuming no noise, obtaining the credibility interval for each connection weight. As only a subset of projecting neurons are counted, there is a threshold to consider; the smallest weight measurable for each experiment is 1/ (number of neurons measured). Therefore, for each level x, we compute d (x) as before from all regions i such that the maximum number of neurons measured for any injection into this region is at least 1/x. Finally, we should note that not all regions were (sufficiently) injected to estimate connectivity, both for mouse (31/43 regions injected) and macaque (29/91 regions injected). This incompleteness may cause a bias, e. g. regions are both more likely to be injected and to have many connections when they are larger. In S1 Appendix we assess the possible bias caused by the preferential injection of larger, more connected regions, by estimating the connectivity of the uninjected regions using regression on simple anatomical properties. To better understand the possible role of the weak but above-threshold connections, we perform two graph theoretical analyses on the estimated mouse connectome. Graph theory is a mathematical discipline that abstractly represents and analyses the mouse brain network as a set of nodes (cortical regions) and edges (axonal connections) between them. First, we separately investigated the set of 5% weakest and 5% strongest above-threshold connections. We mapped these connections in anatomical space and compared their spatial distance distribution, using the Euclidean distance between regional centroids. Second, we estimated the topological metric of global efficiency for the whole network, using both a weighted graph that considers the estimated connection weights, and a binary graph model network where connectivity weights are thresholded so that edges are either absent or present. We investigated how global efficiency changes as edges are deleted below a continuously variable threshold weight. This procedure is very similar to the calculation of the network density d (x) above, except that the metric requires that all connection weights are known. We therefore restrict our analysis to those target regions that are also the source region for at least one experiment. We make the additional assumption that connections are identical for contralateral homologue regions, i. e. if i and k are homologues, and so are j and l, we have μij = μkl. As 31 regions were injected with at least half of total injection volume in the mouse cortex, we retain 62 regions and 62 × 61 connections. For arbitrary threshold x, we can then construct the network whose edge weights eij are given by e i j (x) = { 10 μ i j, if μ i j > x 0, otherwise (8) We define the distance between two nodes as the inverse, i. e. 1 e i j, which is infinite when the weight is zero. A shortest path between any two nodes A, B is then a sequence of nodes n1, …, nk such that n1 = A, nk = B, eni ni+1 > 0, i = 1, …, k − 1 and the length of the path L A B = ∑ i = 1 k - 1 1 e n i n i + 1 is minimal. Global efficiency is defined as the average inverse shortest path length [28] G = 1 N (N - 1) ∑ A ≠ B 1 L A B (9) where N is the number of nodes in the network. We also compute the fractional size of the largest component as a function of x. A component of the network is a subnetwork in which all nodes are directly or indirectly connected by edges. A fully connected network has only one component; in a network without edges each node is a component. The size of the largest connected component is the number of nodes it contains, which is a number between 1 and the total number of nodes in the network; divided by the number of nodes, this is the fractional size of the largest component, which ranges from 1 N to 1.
The number of region pairs for which there were at least 2 non-zero weights was 492 for the multi-experiment mouse dataset; 153 for the multi-experiment macaque dataset; and 300 for the meta-analytically collated macaque dataset. The corresponding weights are shown in Fig 2. For the mouse dataset, variance for a region pair was V = 0. 35 times as large as the total variance in the dataset. The multi-experiment macaque dataset was less variable (V = 0. 13), whereas the collated macaque dataset was more variable, with more within-pair variance than between-pair variance (V = 1. 3). This result remained unchanged when we restricted the region pairs for which the mean was calculated, or when not log-transforming the measured weights (S1 Appendix). Fig 2 further shows that in the mouse dataset, variance is particularly large for weaker connections: this might well be because measured signal is dominated by false positive noise. Computing the same measure as before with variance calculated over the 50% or 10% weakest weights leads to a stark increase: V0. 5 = 0. 54, V0. 1 = 1. 3. This effect was not there for the macaque: V0. 5 = 0. 16, V0. 1 = 0. 12. Fig 5 shows our estimates of overall intra- and inter-hemispheric network density d (x) in the AIBS dataset on the mouse and the Markov et al (2012) dataset on the macaque. To calculate d (x), we only consider those connections whose noise and co-injection threshold is lower than x. We see that overall the mouse has a higher network density than the macaque. We estimate the intrahemispheric network density as 73% (95% CI: 71%, 75%) for mouse and 59% (95% CI: 54%, 62%) for macaque. The interhemispheric network density for mouse was found to be 57% (95% CI: 54%, 59%). Note that our estimates are slightly lower than the sample mean weights [10] because we consider the log-transformed measurements, and E[log (X) ] ≤ log (E[X]) (Jensen’s inequality). We find similar values (e. g. mouse intrahemispheric 71% (95% CI: 67%—75%) ) when we adjust for the 12 uninjected cortical areas (S1 Appendix). From Fig 5 it can be seen that the mouse network density increases slowly as the threshold x is lowered from 0 to -1, sharply and nearly linearly as x decreases from -1 to around -5, and then reaches a plateau. The threshold at which the plateau is reached is close to the estimated lower bound of the fraction of signal due to a single projecting neuron (Fig 5). The apparent decrease in network density around x = −7 is somewhat surprising, as the true network density is a decreasing function of x. However, our estimate of d (x) is based on only those connections that can be measured at this level, i. e. whose median noise threshold and median co-injection threshold are smaller than x. Thus, the estimate of d (x) is based on a decreasing number of connections as x decreases. The remaining connections at x = −7 are to the few target regions that have median noise threshold smaller than -7, which can bias the estimate of d (x). The macaque network density shows a similar first slow and then rapid linear increase, but does not reach a plateau. None of the macaque experiments has enough sensitivity to reach the estimated lower bound of the fraction of signal due to a single projecting neuron (Fig 5), possibly reflecting the small injection volume relative to the volume of the injected regions [10]. The 5% strongest (mean log weight (nij) ∼10−0 mm-3) and the 5% weakest connections (mean log weight (nij) ∼10−5 mm-3) were mapped separately in anatomical space (Fig 6). It is clear by inspection that the strongest connections were more locally and topologically clustered whereas the weakest connections were more random topologically and subtended longer connection distances spatially. The distance distribution is shifted to the right for the weakest connections and the degree distribution has a fatter tail, implying greater probability of high degree hubs, for the strongest connections. Considering the topology of the whole connectome, we used weighted and binary graph models to investigate how global efficiency and the fractional size of the largest connected component (two metrics of network integration) behaved as a function of variable threshold for edge identification. For both analyses, the network becomes fully connected, i. e. the fractional size of the largest connected component becomes 1, at high thresholds (x ∼ 10−3; Fig 7). In the weighted graph analysis, the network reaches maximal efficiency similarly quickly, when only a relatively small proportion of strongly weighted edges have been included in the network. Addition of weak edges, by lowering the threshold, does not materially affect this metric of weighted graph topology. In the binary graph analysis, progressive lowering of the threshold is associated with a more gradual increase in efficiency, with incremental increases even at the lowest thresholds inclusive of the weakest connections.
We have assessed the variability of three large, quantitative tract-tracing datasets on the axonal connectivity of the mammalian cortex. We found lower variability for expertly curated data on the macaque than for algorithmically segmented data on the mouse; and highest variability for the meta-analytically collated dataset on the macaque. We then articulated a statistical framework to estimate the connection weights, and quantify the uncertainty in these estimates. We applied this approach principally to the the multi-experiment mouse dataset provided by the Allen Institute for Brain Science (AIBS) [9] and estimated the mouse connectome (S3 Table), accounting for false positive signals generated by algorithmic segmentation and for co-injection of tracer into more than one source region in a single experiment. Finally, we have explored some of the biological implications of these results. We found that the connection density of the mouse cortex was considerably higher than previously reported (73%) and that the weakest connections, probably representing no more than a few axonal projections, have a relatively random organisation with modest impact on the topology of the mouse cortical connectome. Variability in repeated measurements of the same connection arises as a combination of inter-animal biological variability and measurement errors. We quantified the variability of each dataset as the mean variance of repeated measurements of the weight of the same connection normalized by the variance of all connection weights. Ideally, the former should be small compared to the latter. The multi-experiment macaque data from Markov et al [10] are clearly much less variable than the multi-experiment mouse data from Oh et al [9], with variability values of 0. 13 and 0. 35 respectively. This finding can be partly explained by the inhomogeneity of brain regions. Markov et al. performed repeat injections in the exact same position in the brain region specifically to assess variability, whereas Oh et al. performed injections in variable positions within each source region, specifically in order to increase coverage of cortex by tracer injections. Worryingly, the CoCoMac dataset has variability of 1. 3, indicating that there is greater variance of repeated measurements than of all connection weights. This high value may be attributable to technical limitations in the algorithm employed to map all primary studies to the same anatomical atlas [23]. More fundamentally, it may reflect the inevitably lower reliability of measurements made in different labs using different experimental procedures (such as differing planes of section) to measure nominally identical connections [29]. In any case, it seems clear that the pioneering value of meta-analytic approaches to mammalian cortical connectomics has been surpassed by the much higher reliability of next-generation tract-tracing datasets compiled from multiple experiments conducted according to standard experimental protocols. Our analysis has made clear how the mouse data provided by Oh et al. [9] suffers from at least two sources of noise. Firstly, the segmentation algorithm employed generates many false positive signals. We have quantified the distribution of this segmentation noise, constructing a per-region threshold for the minimum connectivity weight that is sufficient to refute the null hypothesis. Any connection to this region that would result in signal weaker than the threshold cannot be distinguished from positive-mean segmentation noise. The second source of noise in the AIBS datasets arises from co-injections. Among the large number of injection experiments provided by the AIBS—here, 489 experiments each involving a single localised cortical region of anterograde tracer—not all the injections have been constrained to a single anatomical region as pre-defined by an atlas or template. In some experiments, a third or more of total tracer volume will have been injected into one or more spatially adjacent cortical areas. Co-injection of two regions, for example, makes it difficult to disentangle the specific connectivity of each of these two source regions to the same target region. Statistically, this uncertainty about the weight of pair-wise axonal connectivity estimated for co-injected source regions can be represented by a co-injection threshold, which is unique for each pair of regions, and depends on the co-injection pattern of the experiments. Our statistical framework takes into account these two thresholds, and correctly reports high uncertainty when connections are weak relative to the two thresholds. We have found the intra-hemispheric network density, i. e. the fraction of possible pair-wise connections that exists, of the mouse to be 73% (95% CI: 71%, 75%). This estimate appeared robust; adjusting for uninjected regions using a linear model that accounted for region size and neuronal density yielded a very similar density of 71% (95% CI: 67%—75%; S1 Appendix). These estimates of cortical connection density are higher than previous estimates in mouse [34%-41% (isocortex) [8]; 35. 4%-53. 5% [9]], but closer to recent estimates of cortical intra-hemispheric connection density in macaque [62% [10]]. There are two reasons we would a priori expect to find a somewhat higher network density for the mouse than macaque. Firstly, the cost of long-range connections increases at a higher rate for larger brains [21], thus we would expect higher network density for smaller brains. Secondly, the mouse has fewer identified cortical areas, and network density increases with decreasing number of brain areas. Thus, the finding of 73% network density seems to corroborate claims that the mammalian cortical network density has long been under-estimated [17]. As a technical caveat, we should note that inter-areal connections in mouse can be completely embedded in grey matter and that the anterograde tracer employed is sensitive to these axonal projections passing through a cortical area en route to termination in another area, which could potentially lead to an overestimation of the network density in mouse. Nevertheless, it is clear that advances in tract-tracing technology have enabled detection of axonal connectivity over 6 orders of magnitude with just-detectable connections constituting very weak links compared to the nearly million-fold greater connectivity weight of the most strongly weighted connections. The biological meaning of such unprecedentedly weak axonal connectivity is not immediately clear. We computed a simple approximation of the connectivity weight attributable to a single axonal projection, by dividing the total number of neurons in mouse cortex (4 × 106) by the number of cortical regions (43) so that average total tracer signal from any region was attributable to at most 4 × 10 6 43 axonal projections. This single axon threshold corresponds to connection weights ∼10−5. The single axon threshold corresponds reasonably well with the point at which the estimated mouse network density, both intra- and inter-hemispheric, reaches a plateau. This observation is consistent with the log-normal distribution of connection weights [16,30] extending to the scale of a single axonal projection, which would maximize the dynamic range of connection weights hypothesised to be important for brain function [31]. Weak axonal projections, down to the limit of a single axon connecting two cortical areas, should not be discounted as organisationally trivial. In social and other complex networks [32] weak links are well-recognised to serve important integrative functions in social functions, e. g. , mediating information transfer, or gossip, between otherwise isolated cliques. In the macaque brain, it has been consistently argued that weak connections may have functionally important effects on brain dynamics by orchestrating oscillatory coherence of anatomically distributed cortical areas [10,14,16,17,33]. In the mouse connectome, the weakest links were topologically more random than the strongest links, and they traversed greater anatomical distances than the more locally clustered or lattice-like strongest links. These properties are conceptually consistent with the weakest links of the mouse connectome playing a similarly integrative role to weak links in social networks [34]. However, it is difficult to say how functionally important a single axonal projection to a cortical area comprising ∼105 neurons is likely to be in real-life. In a graph theoretical analysis weighted by axonal connectivity over 6 orders of magnitude, the topology of the network is dominated by strongly connected edges. The network is node-connected and has maximal global efficiency at a 10−2 weight threshold, equivalent to a network density of 7. 5%. Further relaxation of the weight threshold does not substantially change the global topological properties established by the most strongly connected edges, consistent with results found in macaque [14]. However, if the network is modelled as a binary graph, effectively equalizing all true connection weights, then we can see theoretically expected incremental increases of network efficiency and integration as progressively weaker edges are added by relaxation of the edge threshold down to the minimum imposed by experimental noise. Future studies may explore this question in more depth by explicitly considering the topological properties of those weak connections that are estimated to exist with high confidence. One way of thinking about these observations is that the weakest links add randomness to mouse connectome topology and integrative capacity to mouse connectome function. Prior results on retrograde fluorescent tract-tracing data on the macaque [10,14,16,33] likewise found that the weaker connections (new found projections) were topologically integrative; but also demonstrated that the new found projections shared an anatomically specific profile of inter-areal connectivity in common with stronger (previously known) connections between cortical areas. The anatomical specificity of new found projections in the macaque cortex suggests that they are not as randomly organised as the weakest connections of the mouse cortex reported here. However, it is important to recognise that new found projections constituted approximately the weakest 36% of all inter-areal connections (on average over areas) in the macaque [33]; whereas we have focused on the weakest 5% of all connections in the mouse. It is plausible that the topological randomness of weak links may increase monotonically as a function of increasing weakness. In other words, the greater randomness of these results on the mouse may not be attributable to inter-species differences but rather to our focus on a smaller subset of more extremely weak links than the relatively larger subset of less weak new found projections. Future comparative studies would be useful to test this prediction more rigorously. Future studies of the mouse connectome might also explore the generative hypothesis that the random topology of the weakest links reflects their formation by stochastic processes of cortical network development. For example, synaptic connectivity of cortical neurons peaks in early post-natal years for mammals and there typically follows a prolonged phase of synaptic pruning or deletion of functionally unimportant, aberrant or over-exuberant synaptic connections and associated axonal projections. Therefore, one possible developmental mechanism for the randomly organised weak links of the adult mouse connectome could be that they reflect what remains after pruning of functionally prioritised connections. A testable prediction of this hypothesis is that the weight of the weakest adult connections should rise and then fall during post-natal connectome development. In conclusion, we have provided a statistical framework to analyse tract tracing data, yielding estimates of connection weights and their associated uncertainty. We have provided these estimates and thresholds for the mouse connectome, such that they may be analysed by other researchers (S3 Table). We have drawn attention to the higher-than-expected maximum cortical connection density of the mouse, attributable to the precision of the measurements allowing resolution of tracer signals in the order of a single axon. These very weak inter-areal axonal projections are not yet completely understood biologically. Like weak links in social networks, they could be topologically integrative and important for global information transfer and/or the weakest (ultimately single axon) projections could be the lucky survivors of a developmental pruning process programmed to entirely eliminate them. It seems likely that functional importance will go with increasing axonal connectivity weight, so the very weakest connections are least likely to be functionally important for inter-areal communication; but the minimum weight needed for functional importance, or the functional threshold on axonal connectivity, is not yet known.
Tract-tracing depends on active axonal transport of tracers between nerve cells, indicating the anatomical connectivity between areas of the brain. Recent advances in tract-tracing technology have enabled reconstruction of the connectome or wiring diagram of mammalian cerebral cortex. Here, we propose a novel statistical model to account for the noise arising from automation of tract-tracing measurements and from injections of tracer into multiple cortical areas simultaneously. On this basis, we find that the strength of anatomical connectivity in the mouse brain varies over six orders of magnitude, with the weakest links between regions approximately representing a few axons. Including all weak links above the statistical noise thresholds, we find that the connection density of the mouse connectome (73%) is greater than previously reported. Many of the complex topological and spatial properties of the mouse brain network emerge on the basis of the strongest axonal projections, whereas the weakest links have a more random organization. We conclude that inter-areal connections mediated by a few axons can be rigorously distinguished from experimental sources of noise in contemporary tract tracing data. Such weak links could support integrated functions of the mouse brain network and/or could represent an element of randomness in its formation.
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Statistical Analysis of Tract-Tracing Experiments Demonstrates a Dense, Complex Cortical Network in the Mouse