Fig. 2: Decomposition, feature extraction and graph annotation.

a, The input data (mesh and synapses) required for the NEURD workflow. b, The reconstructed meshes are pre-processed through a number of steps including decimation, glia and nuclei removal, soma detection and skeletonization. Mesh features are projected back onto the skeleton and spines are detected. c, Decomposition graph object composed of two neurons merged together. The decomposition compresses the skeleton, mesh and synapse annotations of a non-branching segment into a single node in a graph, with directed edges to the downstream segments connected at a branch point. The soma is the singular root node of this tree. d, NEURD automates computation of features at multiple levels. Node (non-branching segment)-level features include basic mesh characteristics (for example, diameter of the neural process or number of synapses per skeletal length). Subgraph features capture relationships between adjacent nodes such as branching angle or width differences. Graph features capture characteristics of the entire neuron and are computed by weighted average or sum of node features, or by counting subgraph motifs. Postsyn, postsyaptic region. e, The final product is a cleaned and annotated decomposition object with a single soma that can be fed into a variety of downstream analyses. f, NEURD supports a variety of operations and manipulations on the decomposition objects. Multi-soma splitting is performed with heuristic rules. The entire decomposition graph is classified as excitatory or inhibitory and one subgraph is identified as the axon. Automated proofreading is performed to remove probable merge errors (see Fig. 3). A set of heuristic rules is implemented to label neural compartments, followed by a finer-scale cell-type classification using graph neural networks (GNNs) (Supplementary Fig. 12). PCA, principal components analysis.