Understanding the rat neurochemical connectome is fundamental for exploring neuronal information processing. By using advanced data mining, supervised machine learning, and network analysis, this study integrates over 5 decades of neuroanatomical investigations into a multiscale, multilayer neurochemical connectome of the rat brain. This neurochemical connectivity database (ChemNetDB) is supported by comprehensive systematically-determined receptor distribution maps. The rat connectome has an onion-type structural organization and shares a number of structural features with mesoscale connectomes of mouse and macaque. Furthermore, we demonstrate that extremal values of graph theoretical measures (e. g. , degree and betweenness) are associated with evolutionary-conserved deep brain structures such as amygdala, bed nucleus of the stria terminalis, dorsal raphe, and lateral hypothalamus, which regulate primitive, yet fundamental functions, such as circadian rhythms, reward, aggression, anxiety, and fear. The ChemNetDB is a freely available resource for systems analysis of motor, sensory, emotional, and cognitive information processing.
Automatic keyword-based search (1,750 keywords) and manual grey search on electronic databases revealed 124,694 abstracts, titles, or both identified as original publications. Out of these, 4,517 studies were relevant for data mining and data from 1,560 original research articles with 36,464 rats were selected for the connectome identification based on the inclusion criteria. A flow diagram of the study selection process is represented in supplementary information (S1 Fig). According to our stringent inclusion and exclusion criteria, these selected studies represent the relevant published outcome of 55 years of neuroanatomical research. All selected studies were performed in outbred rats with no specific genotype or phenotype. Furthermore, animals did not receive any pharmacological treatment or behavioral training. On average, 86. 08% ± 0. 02% of the cases were male animals. The strain of the animals was inverse Gaussian distributed (μ = 0. 25; λ = 1. 24). Thereby, 54. 03% ± 0. 01% and 24. 41% ± 0. 01% of the animals were Sprague-Dawley and Wistar rats, respectively. In total, the systematic literature search identified 281 interconnected nuclei (4,585 axonal projections) of cerebral regions, cerebellum, and components of medulla oblongata. The connectivity matrices associated with this raw database are presented in the supplementary information (S7, S8 and S9 Tables) along with Bregma levels from rostral to caudal. The nomenclature-consistent, brain-wide (cerebral) core of the chemical connectivity database (ChemNetDB) comprises by 188 cortical and subcortical morphologically distinct regions with 3,712 connections. The database references are provided within S1 Text. This database considerably differs from the existing macroscopic connectomes [17,18] of the rat central nervous system. In contrast to ChemNetDB, which is a multiscale connectome database, these databases are often restricted to a single spatial scale. Furthermore, with the exception of a few brain regions such as amygdala, the bed nucleus of stria terminalis (BNST), and cerebral cortex, they are only sparsely connected, and do not provide edge-complete connectivity. Databases lacking the edge-completeness property include brain regions that are either not connected to any other region, or receive only afferents from other regions, or send only efferents to other brain areas. None of these cases are biologically or physically feasible within the central nervous system. Thus, these databases effectively present cerebral subnetworks of the full connectome. In addition, concerns regarding nomenclature and data integration (such as combining data from neonatal and adult animals) and other inconsistencies make these platforms much more difficult to use. ChemNetDB overcomes these issues and shows significant advantages over the existing databases in that (1) it is currently the most comprehensive multiscale database that contains previous databases as subsystems and it is nearly edge-complete; (2) it integrates neurochemical information in a consistent and validated manner; and (3) it is consistent with respect to age of the animals and nomenclature (see S2 Text for more detail). Divisive hierarchical algorithms with edge-completeness constraints further reduced the dimension of the network and integrated the extracted cerebral core into a multilayer cerebral neurochemical connectome of the rat brain. Neurochemical connections within and between brain regions were mapped into a 3-dimensional space using a standardized platform to generate a comprehensive and quantitative database of inter-areal and cell-type−specific projections. The connectome consists of 125 scale-consistent cerebral nuclei and 2,931 multiscale, multichemical connections (Fig 1a). Thereby, 632 bidirectional, 1,642 unidirectional connections, and 25 loops utilize 25 different neurochemical compounds, such as amino acids, monoamines, cannabinoids, - opioids, and several other neurotransmitters. Despite such diversity, the chemical coverage of the network is only 25. 08% and the majority of the connections were treated as binary links. Gamma-aminobutyric acid-ergic (GABAergic) neuronal connections (Fig 1b) constitute the dominant chemical components at the short-range (18. 36% of the chemically denoted connections). In contrast, dopamine (15. 37%; Fig 1c), serotonin (13. 47%; Fig 1d), glutamate (10. 75%; Fig 1e), and enkephalin (7. 76%; Fig 1f) represent the majority of chemical colocalizations of long-range axonal projections. A comprehensive systematic review on receptor distribution according to their mRNA expression levels and receptor binding was used to improve the robustness of these observations by providing spatial colocalization patterns of the transmitter systems within the connectome (S10–S13 Tables). For this purpose data on glutamatergic (mGluR1-7, GluK4-5, GluA1-4, GluN1-2 (A-D) ), monoaminergic (D1-D5,5-HT1-7 (A-C), β1–2, α1-2 (A-D) ), cholinergic (M1-M5, nAChRα (2–6) β (2–3) ), GABAergic (GABAAα (1–6) β (2–3) γ (1–3) δ, GABAB1 (a, b, p), GABAB2), opioid (μ, δ, κ, ORL1), and cannabinoid receptors from 103 (out of 1,843) selected original research articles are qualitatively integrated into brain-wide receptor distribution density maps (S2–S12 Figs). In the following, we provide examples of network analyses of the ChemNetDB. First, we investigated the network properties of the whole-brain connectome at 2 levels of resolution, namely as a coarser 19-node network (see Methods) described by the G19×19 adjacency matrix [12] and then at full resolution, as a 125-node network (G125×125). The G19×19 has a total of 236 directed links and thus a graph density of ρ19=23619×18=0. 69, similar to the interareal networks in the macaque and the mouse [19,20]. The 125-node network G125×125 has 2,906 directed links and a graph density of ρ125=2906125×124=0. 19, significantly lower than the coarse version. Both graphs are relatively small and the degree distributions are noisy (Fig 2a and 2b), hence it is difficult to identify these distributions by simple fitting. However, below we present a model that generates predictions for these distributions (black lines in Fig 2a and 2b). Plotting the degree sequence in Fig 2c and 2d (rank ordering the nodes by their in- and out-degrees), we observe the existence of a small number of high degree nodes (hubs) receiving and/or sending many connections to the rest of the network. The BNST comprises 21 distinct brain areas, which are responsible for integration of limbic information and valence monitoring, processing threat reaction, fear, anxiety, and many other functions, collect 178 in-links, and project 365 out-links to the rest of the connectome. The dorsal raphe nucleus (DRN) has the largest number of outgoing projections (83) and the fourth largest of incoming links (53). This correlates with the fact that the DRN is the largest provider of serotonin innervation to the rest of the brain. Similarly, infra- and prelimbic cortices that comprise prefrontal cortex have high out-going projections (each 61) and a relatively high total degree of 105. This corresponds with the key role of prefrontal cortex in regulating cognitive functions. Moreover, another hub is the lateral hypothalamic area (with 52 in-degree and 60 out-degree), which is responsible for a significant array of functions, such as feeding behavior, wakefulness, thermoregulation, gastrointestinal functions, energy homeostasis, and visceral functions. Supplementary tables show the top list of degrees (S1 and S2 Tables) and betweenness centralities (b. c. s; see Methods) for nodes (S3 and S5 Tables) and edges (S4 and S6 Tables) at both resolution levels, and Fig 2e–2h show the distributions of the betweenness values. According to these lists, DRN has the largest node betweenness, and that, expectedly, correlates with its high in- and out- degrees, a property that holds in general, although with some exceptions, such as the central nucleus of the amygdala (CeA). CeA, with the second highest b. c. , has the largest in-degree, but it is only 19th in the out-degree list. The fact that it plays a key role in information transmission and processing resonates with the observation that it is a principal area for controlling emotional reactions. The distributions of both node (Fig 2c and 2d) and edge betweenness (Fig 2e–2h) show a concentration at large values, suggesting a heterogeneous network structure; in particular, there are 8 edges with exceptionally high b. c. Given that for both macaque and mouse the interareal network is heterogeneous and shows a strong core-periphery structure [19–21], we have analyzed whether the same holds in the rat brain as well. By using a stochastic block modeling [22,23] method (see Methods section), we determined the probabilities of the individual nodes to belong either to the core or periphery (Fig 3a–3c). In Fig 3 the core nodes are colored red while the periphery nodes are blue. It shows a clear separation of the two classes in a similar fashion to the macaque and mouse. For G19×19 the core contains 12 nodes with an internal density of ρcc = 0. 87, a periphery of 7 nodes of internal density ρpp = 0. 24, and the subgraph of edges running between core and periphery nodes of density ρcp = 0. 66. For G125×125 the core has 69 nodes with a high internal density of ρcc = 0. 41, a periphery of 56 nodes of internal density ρpp = 0. 03 and the subgraph of edges running between core and periphery nodes of density ρcp = 0. 12. The list of core areas and the corresponding regions (in the 19-node segmentation) they belong to is shown in S13 Fig. Fig 3e shows the location of core nodes within G125×125, using a force-based layout representation. Note that while this analysis clearly indicates the core-periphery organization of the connectome, it does not reveal its internal structure, in particular, the internal network communities, if any. To explore this aspect, we performed a hierarchical decomposition of the network using the Girvan-Newman algorithm (see Methods), which outputs a hierarchical community dendrogram [24]. The results are shown in Fig 3b and 3d for both resolution levels. They indicate that in contrast with social networks, which have a clustering of communities over several levels, the rat connectome (at either resolution) has an onion-type structural organization [25], in which layers of areas are added on top of the previous ones. On the x axis, we colored the areas according to their core-periphery membership, which correlates well with depth in the dendrogram, with core nodes concentrating towards the inside of the onion structure. While this overall onion structure is a direct consequence of high-network density, the membership of the levels from the innermost to the outer layers is highly specific to the network. Although the densities of G125×125 and its core are not drastically different (0. 19 versus 0. 41), at this resolution such difference implies significant specificity. Specificity can be estimated by computing an upper bound to the probability that an Erdos-Renyi random graph on 125 nodes of density p = 0. 19 will have a core on 69 nodes of internal density ρcc = 0. 41. The computation is shown in the Methods section, giving a very low probability of approximately 10−228, showing that the rat connectome core-periphery structure is, indeed, highly specific. The raphe nuclei, DRN and medial raphe nucleus (MRN), constitute the deepest core of the dendrogram or the heart of the onion structure. These serotonergic systems play an important and generalized role in regulation of sleep-wake states and behavioral arousal. While MRN is mostly involved in stress- and anxiety-related processes [26], the DRN is critically involved in the neuronal regulation of circadian rhythms and sleep [27], which may support the hypothesis that DRN acts as a pacemaker of the network. A prerequisite of controlling a complex biological system is its structural controllability [28–30]. As another example analysis, here we identify the minimal set of driver nodes that could be used to control the system, through searching for a maximum matching in the graph (see definitions in Methods section). A realization of maximum matching is shown in Fig 4a with four driver nodes: subthalamic nucleus (STh), subiculum, CA1, and medial nucleus of amygdala. The interesting finding here is the role of STh in the structural controllability of the rat brain. Numerous deep brain stimulation studies have already shown the remarkable effects of activation of neurons within STh on global circuit dynamics [31] and in the treatment of Parkinson disease and other disorders [32]. This observation suggests an agreement between structural and functional controllability of neuronal networks and attracts our attention towards hippocampal and amygdaloid regions as potential targets for deep brain and other stimulation techniques. While the number of driver nodes (4) is small compared to the total number of 125 nodes of the network, it is still significantly larger than in random networks with similar density. To show this we performed 500 randomizations of the network preserving the degree sequence and obtained an average of 1. 23 driver nodes (SD: 0. 45). Sequentially removing the longest (weakest) edge from the network, the number of driver nodes increases, as expected, exponentially (Fig 4b). However, the number of driver nodes for the original network, is consistently larger than for its degree preserved randomized versions. Thus, the original network structure is such as to allow for more points of control in the network, that is for a larger control diversity, than in a similar random graph with the same degree sequence. Consistent, cortex-wide retrograde tracer injections in several species (macaque [20,33]), mouse and microcebus [19] have shown that the distribution of the lengths of white matter (WM) axons follow an exponential decay, called exponential distance rule (EDR), with a species-dependent decay rate (λ). Naturally, 2 questions arise: Is the rat large-scale connectome also described well by the EDR network model? And (2) Does the agreement with the EDR model break with increasing resolution? The second question could not be answered for the other species, as there is no (nearly) edge-complete database available at higher resolutions for them, and thus ChemNetDB is especially valuable in this regard. By using ChemNetDB, we next attempt to answer both questions by modeling the connectome with the EDR network model at the G19×19 and G125×125 levels, respectively. Network comparison is based on parametric property matching described briefly in the Methods section and in detail in refs [19,20]. Fig 5 shows the results for the G19×19 using a set of commonly used graph measures: number of uni- and bidirectional edges (Fig 5a), the root-mean-square (RMS) of deviations for the 3-motif counts between model and data (Fig 5b, 5f and 5g), the RMS of clique-count deviations between model and data (Fig 5c and 5h), the RMS of the deviation of the eigenvalues of the co-occurrence matrix AAT between model and data networks (Fig 5d and 5i) and finally the clustering coefficients (Fig 5e). These results show that all models generate the same value for the decay rate λ in the range 0. 60 − 0. 65mm−1 consistently, indicating that the EDR network model is a good model for the rat connectome at the large-scale level. Fits generated by and EDR with λ = 0. 6mm−1 are shown in Fig 2a and 2c for the degree distributions and the degree sequence, respectively. Moving to the higher resolution connectome G125×125, the same procedure yields the comparisons shown in Fig 6. The figures now show a different picture: the best λ values determined from parameter matching are varying, from measure to measure, indicating that a single λ-parameter EDR model cannot describe the whole data network at this resolution. The reason lies with the fact that once we subdivide brain regions (as it was done in going from 19 areas to 125), there will be an increasing number of area pairs that are connected by gray matter (GM; nonmyelinated) connections, instead of WM connections. Note that at the G19×19 level, the connections are WM connections, just as for the cortical interareal networks in the mouse and macaque, where the EDR descriptions work well. As shown by experiments presented in Horvát et al. [19], the decay rate is sensitive to the nature of the medium in which the connections are running (WM versus GM). In particular, while local (GM) connections obey an EDR with almost the same decay rate (4. 6 ÷ 4. 9mm−1) in macaque, mouse and rat (see Fig 11B in [19]), for WM connections, the EDR decay rate decreases with increasing brain size (0. 19mm−1 for macaque, 0. 8mm−1 for mouse, and 0. 6mm−1 for rat—this paper). The larger the brain, the smaller the decay rate for nonlocal (WM) connections. The EDR network model is defined via a single decay rate and it works well for all those brain networks for which the connections obey a single parameter EDR. Once the network contains both types of connections (GM and WM), as the rat brain at 125-area resolution, a single decay rate model will not work as shown in Fig 6. To develop a two-parameter EDR model, however, we also need to include information about the location of the GM connections with respect to the WM ones, resulting in a more involved model, which will be the subject of a forthcoming paper.
Most studies in neurobiology rely on a precise understanding of the neuronal connectivity and its neurochemical actors. Yet, investigators commonly face massive data that require enormous resources to be processed for their demands. By utilizing advanced neuroinformatics, our study resolves this problem and integrates over 50 years of neuroanatomy research on rat brains into a consistent multiscale, multilayer neurochemical cerebral connectome. Supervised machine learning was applied to resolve nomenclature issues resulting in an extensive standardized database, which combines the state-of-the-art knowledge of connectomics and neurochemistry. Establishing the neurochemical connectivity database (ChemNetDB) is a novel approach towards topological mapping of the brain that takes the neuroconnectomics well beyond the binary constructs and paves the way for more advanced and accurate investigations of healthy and disease states of rat brains. We have investigated, as examples of analytic studies that could be done on the database, several network measures and their relationships with similar analyses in macaque and the mouse. Analysis of the structural properties of the resulting network reflect and confirm the key functional roles of deep subcortical brain areas such as lateral hypothalamus, BNST, and DRN. These regions are known to be responsible for primitive yet essential and evolutionary conserved functions, such as regulation of sleep and circadian rhythms, reward, anxiety, aggression, and fear. The importance of these brain areas for survival is also associated with an early formation in the developmental stages as reflected in the connectome by their extremal values of various graph connectivity measures such as b. c. and/or node degree. Previous theoretical analysis demonstrated that the optimal core-periphery structures in networks that are stable against both random and targeted failures/attacks (targeting hubs) are onion-like structures [34], however, they have not been observed in real-world networks [25]. Our analysis shows that the onion structure occurs in large-scale, dense brain neuronal networks, where robustness against information transmission failures (of all types) is a critical requirement. The observed onion structure does not contradict the well-known hierarchically modular structure of brain networks. At this large-scale resolution, applying the Girvan-Newman methodology [24], the paths-based network modularity is not revealed due to the high density of the network. Instead, the onion organization is dominant and the community structure appears as core-periphery at this scale. As another example of an analytical study on ChemNetDB, we tested whether the EDR property and the associated network model are consistent with the rat connectome structure, at different levels of resolution. The EDR, expressing economy of wiring has been found to hold in both macaque and mouse for both mesoscale WM connections and local GM connections. While direct measurements of the WM axon length distribution in the rat are currently lacking, the EDR network is highly consistent with the large-scale ChemNetDB connectome, suggesting that the whole-brain WM connectivity is also strongly determined by the EDR. By using maximum matching algorithms, we have identified 4 nontrivial driver nodes of the network. In particular, STh appears as a feasible candidate to be involved in control mechanisms of the brain. However, the actual biological interpretation of driver nodes is not yet clear and the role of the identified brain regions in controlling brain activity requires further investigations and experimental validations. Note that the outcomes of the example network analyses presented here might change with the addition of new data; however, the main observations, such as the strong core-periphery structure and network measures expressed as fractions (including density, the fraction of 3-motifs, etc.), are expected to be robust against changes due to the high density of the networks. ChemNetDB provides the first, whole-brain, large-scale and consistently collated rat connectome database that also includes neurochemical specificity. This will provide researchers with a tool to gain insights into the fundamental relationships between connectome architecture, information processing, and brain function, with potential for advancing preclinical research and clinical applications such as those related to substance abuse and depression.
The mammalian brain consists of a network of chemically diverse, multiscale, and multilayer neuronal wiring patterns that form the physical infrastructure underlying the processing of motoric, sensory, emotional, and cognitive information. Decades of histological track-tracing studies have generated a set of highly valuable yet disorganized data, which is very hard to retrieve in a hypothesis-free manner. We present an open-access database (ChemNetDB) that organizes over 50 years of neuroanatomical track-tracing and neurochemical measurements from 36,464 rats. This neurochemical connectome is terminologically consistent and shares several network invariants with mouse and macaque cortical networks, suggesting that the mammalian brain exhibits universal structural features. Furthermore, several network measures reflect and confirm the key functional roles of deep subcortical brain areas, which are known to be responsible for primitive, yet essential and evolutionary conserved functions related to survival. ChemNetDB provides the first whole-brain, multiscale, and consistently collated rat connectome database. ChemNetDB also includes neurochemical specificity and represents a powerful tool for in vitro, in vivo, and in silico investigations of brain function and disorders.
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A multiscale cerebral neurochemical connectome of the rat brain