The songbird connectome (OSCINE-NET.ORG): structure-function organization beyond the canonical vocal control network

Abstract

Background: Understanding the neural basis of behavior requires insight into how different brain systems coordinate with each other. Existing connectomes for various species have highlighted brain systems essential to various aspects of behavior, yet their application to complex learned behaviors remains limited. Research on vocal learning in songbirds has extensively focused on the vocal control network, though recent work implicates a variety of circuits in contributing to important aspects of vocal behavior. Thus, a more comprehensive understanding of brain-wide connectivity is essential to further assess the totality of circuitry underlying this complex learned behavior.

Results: We present the Oscine Structural Connectome for Investigating NEural NETwork ORGanization (OSCINE-NET.ORG), the first interactive mesoscale connectome for any vocal learner. This comprehensive digital map includes all known connectivity data, covering major brain superstructures and functional networks. Our analysis reveals that the songbird brain exhibits small-world properties, with highly connected communities functionally designated as motor, visual, associative, vocal, social, and auditory. Moreover, there is a small set of significant connections across these communities, including from social and auditory sub-communities to vocal sub-communities, which highlight ethologically relevant facets of vocal learning and production. Notably, the vocal community contains the majority of the canonical vocal control network, as well as a variety of other nodes that are highly interconnected with it, meriting further evaluation for their inclusion in this network. A subset of nodes forms a "rich broker club," highly connected across the brain and forming a small circuit amongst themselves, indicating they may play a key role in information transfer broadly. Collectively, their bidirectional connectivity with multiple communities indicates they may act as liaisons across multiple functional circuits for a variety of complex behaviors.

Conclusions: OSCINE-NET.ORG offers unprecedented access to detailed songbird connectivity data, promoting insight into the neural circuits underlying complex behaviors. This data emphasizes the importance of brain-wide integration in vocal learning, facilitating a potential reevaluation of the canonical vocal control network. Furthermore, we computationally identify a small, previously unidentified circuit-one which may play an impactful role in brain-wide coordination of multiple complex behaviors.

Keywords: Connectome; Graph theory; Network analysis; Oscine; Songbird; Vocal learning.

Fig. 1

Curation and Visualization of the Oscine Structural Connectome for Investigating NEural NETwork ORGanization (OSCINE-NET.ORG). Nodes and connections were trimmed from the present dataset at two exclusion bottlenecks (described in Methods). a The homepage of the anatomical connections map contains a general description panel that introduces the map and provides a link to a continuously updated Google Sheet of all connections, as well as b buttons to focus on nodes from select functional networks or c brain superstructures. d The connections to and from an individual node can be highlighted and navigated by level of connection using the focus buttons. Defined brain regions and widely used subdivisions are displayed as individual nodes (circles), color-coded by superstructure. Nodes are placed approximately within each superstructure, condensed onto a single sagittal plane. Anatomical connections are represented by gray arrows, which can be clicked on to view citation information in the side panel

Fig. 2

Community detection partitions nodes into six highly-interconnected communities. Communities (shaded groups along the diagonal) and their respective sub-communities (smallest squares along the diagonal) are shown. Each row depicts all connections a node sends, while its corresponding column depicts all the connections it receives. Within sub-communities, nodes are in descending order of degree. The nodes making up each community and sub-community are presented in Table 3, organized in the same manner

Fig. 3

Cross-community analysis identifies connectivity structure at the level of sub-communities. A Community-to-community connections are represented by chords, weighted to the number of connections. Communities are color-coded in the same way as Fig. 2, and chords are color-coded by the sending community. B Raw number of sub-community to sub-community connections are shown at each intersection, organized so that each row depicts the number of connections a sub-community sends to each other sub-community, and its corresponding column depicts the connections it receives from each other sub-community. Sub-communities that demonstrate higher connection frequencies than chance are highlighted in pink (p < 0.05). Sub-communities are defined in part by their high connectivity within themselves, so their significance is not highlighted here (see Fig. 2). C Representation of significant connection frequencies between sub-communities is shown in B. Color of connections indicate the origin sub-community of the connection

Fig. 4

The vocal control network is split between vocal sub-communities, which have different community connectivity. In-degree (A) and out-degree (B) of each canonical vocal control network node to all communities, excluding canonical connections diagrammed in C. In- and out-degrees for each community are stacked such that the community with the highest number of connections to a node is lowest within the bar. C Canonical connections of the songbird vocal control network. The two vocal sub-communities, and their member nodes, are color-coded. Non-canonical vocal control network nodes are condensed into a single bubble per community. D In- and out-degree of all nodes in each vocal sub-community. Connections to and from the vocal community are excluded to highlight connections outside the vocal sub-communities. Bars are stacked in the same manner as A and B

Fig. 5

A small number of nodes occupy a distinct connectivity space: the “rich broker club.” A Principal component analysis for dimensionality reduction and k-means clustering parses out one cluster (blue, “Cluster 2”) that is distinctly different from the others. B Comparison of the observed (black solid line) and random mean (blue dotted line) relationships between rich club coefficients and degree. The point at which an observed relationship exits the standard deviation (blue shading) of the random mean marks the degree threshold above which nodes in our network are connected more than what would be expected in a randomly connected network of the same size (48, arrowhead). C Nodes plotted by betweenness centrality and degree separate the same cluster of nodes revealed in A, which we deem the “rich broker club.” See additional files for a table of these and other values for each node (Additional file 3). D Anatomical connections between the members of the rich broker club. Nodes are colored based on community membership and organized by superstructure. In-degree (E) and out-degree (F) of each member of the rich broker club are represented as stacked bar plots grouped by community. Bars are organized with each node's highest community degree value closest to the x-axis, followed by the second-highest, and so on

Fig. 6

The rich broker club connects functional networks necessary for vocal learning. A visual representation of how the rich broker club could act as a collective liaison between functional communities. Represented nodes in each community were selected based on functional relevance for vocal learning and/or high degree. Within communities, all connectivity between selected nodes are shown. Between rich broker club members and selected nodes, only bidirectional connectivity (black lines) is shown. Directionality of all other connections is marked by arrowheads. All connectivity from rich broker nodes can be found at OSCINE-NET.ORG