Big data in nanoscale connectomics, and the greed for training labels

RoboEM: automated 3D flight tracing for synaptic-resolution connectomics

Mapping neuronal networks from three-dimensional electron microscopy (3D-EM) data still poses substantial reconstruction challenges, in particular for thin axons. Currently available automated image segmentation methods require manual proofreading for many types of connectomic analysis. Here we introduce RoboEM, an artificial intelligence-based self-steering 3D 'flight' system trained to navigate along neurites using only 3D-EM data as input. Applied to 3D-EM data from mouse and human cortex, RoboEM substantially improves automated state-of-the-art segmentations and can replace manual proofreading for more complex connectomic analysis problems, yielding computational annotation cost for cortical connectomes about 400-fold lower than the cost of manual error correction.

D-LMBmap: a fully automated deep-learning pipeline for whole-brain profiling of neural circuitry

Recent proliferation and integration of tissue-clearing methods and light-sheet fluorescence microscopy has created new opportunities to achieve mesoscale three-dimensional whole-brain connectivity mapping with exceptionally high throughput. With the rapid generation of large, high-quality imaging datasets, downstream analysis is becoming the major technical bottleneck for mesoscale connectomics. Current computational solutions are labor intensive with limited applications because of the exhaustive manual annotation and heavily customized training. Meanwhile, whole-brain data analysis always requires combining multiple packages and secondary development by users. To address these challenges, we developed D-LMBmap, an end-to-end package providing an integrated workflow containing three modules based on deep-learning algorithms for whole-brain connectivity mapping: axon segmentation, brain region segmentation and whole-brain registration. D-LMBmap does not require manual annotation for axon segmentation and achieves quantitative analysis of whole-brain projectome in a single workflow with superior accuracy for multiple cell types in all of the modalities tested.

High-contrast en bloc staining of mouse whole-brain and human brain samples for EM-based connectomics

Connectomes of human cortical gray matter require high-contrast homogeneously stained samples sized at least 2 mm on a side, and a mouse whole-brain connectome requires samples sized at least 5-10 mm on a side. Here we report en bloc staining and embedding protocols for these and other applications, removing a key obstacle for connectomic analyses at the mammalian whole-brain level.

Bayesian reconstruction of memories stored in neural networks from their connectivity

The advent of comprehensive synaptic wiring diagrams of large neural circuits has created the field of connectomics and given rise to a number of open research questions. One such question is whether it is possible to reconstruct the information stored in a recurrent network of neurons, given its synaptic connectivity matrix. Here, we address this question by determining when solving such an inference problem is theoretically possible in specific attractor network models and by providing a practical algorithm to do so. The algorithm builds on ideas from statistical physics to perform approximate Bayesian inference and is amenable to exact analysis. We study its performance on three different models, compare the algorithm to standard algorithms such as PCA, and explore the limitations of reconstructing stored patterns from synaptic connectivity.

Dendritic Spine Density Scales with Microtubule Number in Rat Hippocampal Dendrites

Microtubules deliver essential resources to and from synapses. Three-dimensional reconstructions in rat hippocampus reveal a sampling bias regarding spine density that needs to be controlled for dendrite caliber and resource delivery based on microtubule number. The strength of this relationship varies across dendritic arbors, as illustrated for area CA1 and dentate gyrus. In both regions, proximal dendrites had more microtubules than distal dendrites. For CA1 pyramidal cells, spine density was greater on thicker than thinner dendrites in stratum radiatum, or on the more uniformly thin terminal dendrites in stratum lacunosum moleculare. In contrast, spine density was constant across the cone shaped arbor of tapering dendrites from dentate granule cells. These differences suggest that thicker dendrites supply microtubules to subsequent dendritic branches and local dendritic spines, whereas microtubules in thinner dendrites need only provide resources to local spines. Most microtubules ran parallel to dendrite length and associated with long, presumably stable mitochondria, which occasionally branched into lateral dendritic branches. Short, presumably mobile, mitochondria were tethered to microtubules that bent and appeared to direct them into a thin lateral branch. Prior work showed that dendritic segments with the same number of microtubules had elevated resources in subregions of their dendritic shafts where spine synapses had enlarged, and spine clusters had formed. Thus, additional microtubules were not required for redistribution of resources locally to growing spines or synapses. These results provide new understanding about the potential for microtubules to regulate resource delivery to and from dendritic branches and locally among dendritic spines.

Comparative 2D and 3D Ultrastructural Analyses of Dendritic Spines from CA1 Pyramidal Neurons in the Mouse Hippocampus

Three-dimensional (3D) reconstruction from electron microscopy (EM) datasets is a widely used tool that has improved our knowledge of synapse ultrastructure and organization in the brain. Rearrangements of synapse structure following maturation and in synaptic plasticity have been broadly described and, in many cases, the defective architecture of the synapse has been associated to functional impairments. It is therefore important, when studying brain connectivity, to map these rearrangements with the highest accuracy possible, considering the affordability of the different EM approaches to provide solid and reliable data about the structure of such a small complex. The aim of this work is to compare quantitative data from two dimensional (2D) and 3D EM of mouse hippocampal CA1 (apical dendrites), to define whether the results from the two approaches are consistent. We examined asymmetric excitatory synapses focusing on post synaptic density and dendritic spine area and volume as well as spine density, and we compared the results obtained with the two methods. The consistency between the 2D and 3D results questions the need—for many applications—of using volumetric datasets (costly and time consuming in terms of both acquisition and analysis), with respect to the more accessible measurements from 2D EM projections.

A Picture Worth a Thousand Molecules-Integrative Technologies for Mapping Subcellular Molecular Organization and Plasticity in Developing Circuits

A key challenge in developmental neuroscience is identifying the local regulatory mechanisms that control neurite and synaptic refinement over large brain volumes. Innovative molecular techniques and high-resolution imaging tools are beginning to reshape our view of how local protein translation in subcellular compartments drives axonal, dendritic, and synaptic development and plasticity. Here we review recent progress in three areas of neurite and synaptic study in situ-compartment-specific transcriptomics/translatomics, targeted proteomics, and super-resolution imaging analysis of synaptic organization and development. We discuss synergies between sequencing and imaging techniques for the discovery and validation of local molecular signaling mechanisms regulating synaptic development, plasticity, and maintenance in circuits.

Generative and discriminative model-based approaches to microscopic image restoration and segmentation

Image processing is one of the most important applications of recent machine learning (ML) technologies. Convolutional neural networks (CNNs), a popular deep learning-based ML architecture, have been developed for image processing applications. However, the application of ML to microscopic images is limited as microscopic images are often 3D/4D, that is, the image sizes can be very large, and the images may suffer from serious noise generated due to optics. In this review, three types of feature reconstruction applications to microscopic images are discussed, which fully utilize the recent advancements in ML technologies. First, multi-frame super-resolution is introduced, based on the formulation of statistical generative model-based techniques such as Bayesian inference. Second, data-driven image restoration is introduced, based on supervised discriminative model-based ML technique. In this application, CNNs are demonstrated to exhibit preferable restoration performance. Third, image segmentation based on data-driven CNNs is introduced. Image segmentation has become immensely popular in object segmentation based on electron microscopy (EM); therefore, we focus on EM image processing.