Toward Community-Driven Big Open Brain Science: Open Big Data and Tools for Structure, Function, and Genetics.

As acquiring bigger data becomes easier in experimental brain science, computational and statistical brain science must achieve similar advances to fully capitalize on these data. Tackling these problems will benefit from a more explicit and concerted effort to work together. Specifically, brain science can be further democratized by harnessing the power of community-driven tools, which both are built by and benefit from many different people with different backgrounds and expertise. This perspective can be applied across modalities and scales and enables collaborations across previously siloed communities.

Bisected graph matching improves automated pairing of bilaterally homologous neurons from connectomes.

Graph matching algorithms attempt to find the best correspondence between the nodes of two networks. These techniques have been used to match individual neurons in nanoscale connectomes-in particular, to find pairings of neurons across hemispheres. However, since graph matching techniques deal with two isolated networks, they have only utilized the ipsilateral (same hemisphere) subgraphs when performing the matching. Here, we present a modification to a state-of-the-art graph matching algorithm that allows it to solve what we call the bisected graph matching problem. This modification allows us to leverage the connections between the brain hemispheres when predicting neuron pairs. Via simulations and experiments on real connectome datasets, we show that this approach improves matching accuracy when sufficient edge correlation is present between the contralateral (between hemisphere) subgraphs. We also show how matching accuracy can be further improved by combining our approach with previously proposed extensions to graph matching, which utilize edge types and previously known neuron pairings. We expect that our proposed method will improve future endeavors to accurately match neurons across hemispheres in connectomes, and be useful in other applications where the bisected graph matching problem arises.

Generative network modeling reveals quantitative definitions of bilateral symmetry exhibited by a whole insect brain connectome.

Comparing connectomes can help explain how neural connectivity is related to genetics, disease, development, learning, and behavior. However, making statistical inferences about the significance and nature of differences between two networks is an open problem, and such analysis has not been extensively applied to nanoscale connectomes. Here, we investigate this problem via a case study on the bilateral symmetry of a larval Drosophila brain connectome. We translate notions of 'bilateral symmetry' to generative models of the network structure of the left and right hemispheres, allowing us to test and refine our understanding of symmetry. We find significant differences in connection probabilities both across the entire left and right networks and between specific cell types. By rescaling connection probabilities or removing certain edges based on weight, we also present adjusted definitions of bilateral symmetry exhibited by this connectome. This work shows how statistical inferences from networks can inform the study of connectomes, facilitating future comparisons of neural structures.

Optogenetic spinal stimulation promotes new axonal growth and skilled forelimb recovery in rats with sub-chronic cervical spinal cord injury.

Objective.Spinal cord injury (SCI) leads to debilitating sensorimotor deficits that greatly limit quality of life. This work aims to develop a mechanistic understanding of how to best promote functional recovery following SCI. Electrical spinal stimulation is one promising approach that is effective in both animal models and humans with SCI. Optogenetic stimulation is an alternative method of stimulating the spinal cord that allows for cell-type-specific stimulation. The present work investigates the effects of preferentially stimulating neurons within the spinal cord and not glial cells, termed 'neuron-specific' optogenetic spinal stimulation. We examined forelimb recovery, axonal growth, and vasculature after optogenetic or sham stimulation in rats with cervical SCI.Approach.Adult female rats received a moderate cervical hemicontusion followed by the injection of a neuron-specific optogenetic viral vector ipsilateral and caudal to the lesion site. Animals then began rehabilitation on the skilled forelimb reaching task. At four weeks post-injury, rats received a micro-light emitting diode (µLED) implant to optogenetically stimulate the caudal spinal cord. Stimulation began at six weeks post-injury and occurred in conjunction with activities to promote use of the forelimbs. Following six weeks of stimulation, rats were perfused, and tissue stained for GAP-43, laminin, Nissl bodies and myelin. Location of viral transduction and transduced cell types were also assessed.Main Results.Our results demonstrate that neuron-specific optogenetic spinal stimulation significantly enhances recovery of skilled forelimb reaching. We also found significantly more GAP-43 and laminin labeling in the optogenetically stimulated groups indicating stimulation promotes axonal growth and angiogenesis.Significance.These findings indicate that optogenetic stimulation is a robust neuromodulator that could enable future therapies and investigations into the role of specific cell types, pathways, and neuronal populations in supporting recovery after SCI.

The connectome of an insect brain.

Brains contain networks of interconnected neurons and so knowing the network architecture is essential for understanding brain function. We therefore mapped the synaptic-resolution connectome of an entire insect brain (Drosophila larva) with rich behavior, including learning, value computation, and action selection, comprising 3016 neurons and 548,000 synapses. We characterized neuron types, hubs, feedforward and feedback pathways, as well as cross-hemisphere and brain-nerve cord interactions. We found pervasive multisensory and interhemispheric integration, highly recurrent architecture, abundant feedback from descending neurons, and multiple novel circuit motifs. The brain's most recurrent circuits comprised the input and output neurons of the learning center. Some structural features, including multilayer shortcuts and nested recurrent loops, resembled state-of-the-art deep learning architectures. The identified brain architecture provides a basis for future experimental and theoretical studies of neural circuits.

Connectal coding: discovering the structures linking cognitive phenotypes to individual histories.

Cognitive phenotypes characterize our memories, beliefs, skills, and preferences, and arise from our ancestral, developmental, and experiential histories. These histories are written into our brain structure through the building and modification of various brain circuits. Connectal coding, by way of analogy with neural coding, is the art, study, and practice of identifying the network structures that link cognitive phenomena to individual histories. We propose a formal statistical framework for connectal coding and demonstrate its utility in several applications spanning experimental modalities and phylogeny.