The journal of neuroscience : the official journal of the Society for Neuroscience.

The suprachiasmatic nucleus (SCN) of the hypothalamus is remarkable. Despite numbering only about 10,000 neurons on each side of the third ventricle, the SCN is our principal circadian clock, directing the daily cycles of behaviour and physiology that set the tempo of our lives. When this nucleus is isolated in organotypic culture, its autonomous timing mechanism can persist indefinitely, with precision and robustness. The discovery of the cell-autonomous transcriptional and post-translational feedback loops that drive circadian activity in the SCN provided a powerful exemplar of the genetic specification of complex mammalian behaviours. However, the analysis of circadian time-keeping is moving beyond single cells. Technical and conceptual advances, including intersectional genetics, multidimensional imaging and network theory, are beginning to uncover the circuit-level mechanisms and emergent properties that make the SCN a uniquely precise and robust clock. However, much remains unknown about the SCN, not least the intrinsic properties of SCN neurons, its circuit topology and the neuronal computations that these circuits support. Moreover, the convention that the SCN is a neuronal clock has been overturned by the discovery that astrocytes are an integral part of the timepiece. As a test bed for examining the relationships between genes, cells and circuits in sculpting complex behaviours, the SCN continues to offer powerful lessons and opportunities for contemporary neuroscience.

Generation of circadian rhythms in the suprachiasmatic nucleus.

Network science of biological systems at different scales: A review

Tumor evolution: Linear, branching, neutral or punctuated?☆

The brain is characterized by heterogeneous patterns of structural connections supporting unparalleled feats of cognition and a wide range of behaviours. New non-invasive imaging techniques now allow comprehensive mapping of these patterns. However, a fundamental challenge remains to understand how the brain’s structural wiring supports cognitive processes, with major implications for personalized mental health treatments. Here, we review recent efforts to meet this challenge, drawing on physics intuitions, models and theories, spanning the domains of statistical mechanics, information theory, dynamical systems and control. We first describe the organizing principles of brain network architecture instantiated in structural wiring under constraints of spatial embedding and energy minimization. We then survey models of brain network function that stipulate how neural activity propagates along structural connections. Finally, we discuss perturbative experiments and models for brain network control; these use the physics of signal transmission along structural connections to infer intrinsic control processes that support goal-directed behaviour and to inform stimulation-based therapies for neurological and psychiatric disease. Throughout, we highlight open questions that invite the creative efforts of pioneering physicists. The brain is the quintessential complex system, boasting incredible feats of cognition and supporting a wide range of behaviours. Physics has much to offer in the quest to distil the brain’s complexity to a number of cogent organizing principles.

The physics of brain network structure, function and control

Connectomics has focused primarily on the mapping of synaptic links in the brain; yet it is well established that extrasynaptic volume transmission, especially via monoamines and neuropeptides, is also critical to brain function and occurs primarily outside the synaptic connectome. We have mapped the putative monoamine connections, as well as a subset of neuropeptide connections, in C. elegans based on new and published gene expression data. The monoamine and neuropeptide networks exhibit distinct topological properties, with the monoamine network displaying a highly disassortative star-like structure with a rich-club of interconnected broadcasting hubs, and the neuropeptide network showing a more recurrent, highly clustered topology. Despite the low degree of overlap between the extrasynaptic (or wireless) and synaptic (or wired) connectomes, we find highly significant multilink motifs of interaction, pinpointing locations in the network where aminergic and neuropeptide signalling modulate synaptic activity. Thus, the C. elegans connectome can be mapped as a multiplex network with synaptic, gap junction, and neuromodulator layers representing alternative modes of interaction between neurons. This provides a new topological plan for understanding how aminergic and peptidergic modulation of behaviour is achieved by specific motifs and loci of integration between hard-wired synaptic or junctional circuits and extrasynaptic signals wirelessly broadcast from a small number of modulatory neurons.

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The Multilayer Connectome of Caenorhabditis elegans.

Brains contain networks of interconnected neurons, so knowing the network architecture is essential for understanding brain function. We therefore mapped the synaptic-resolution connectome of an insect brain (Drosophila larva) with rich behavior, including learning, value-computation, and action-selection, comprising 3,013 neurons and 544,000 synapses. We characterized neuron-types, hubs, feedforward and feedback pathways, and 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 powerful machine learning architectures. The identified brain architecture provides a basis for future experimental and theoretical studies of neural circuits. One-Sentence Summary We generated a synaptic-resolution brain connectome and characterized its connection types, neuron types, and circuit motifs.

The connectome of an insect brain

The pattern of synaptic connections among neurons defines the circuit structure, which constrains the computations that a circuit can perform. The strength of synaptic connections is costly to measure yet important for accurate circuit modeling. It has been shown that synaptic surface area correlates with synaptic strength, yet in the emerging field of connectomics, most studies rely instead on the counts of synaptic contacts between two neurons. Here we quantified the relationship between synaptic count and synaptic area as measured from volume electron microscopy of the larval Drosophila central nervous system. We found that the total synaptic surface area, summed across all synaptic contacts from one presynaptic neuron to a postsynaptic one, can be accurately predicted solely from the number of synaptic contacts, for a variety of neurotransmitters. Our findings support the use of synaptic counts for approximating synaptic strength when modeling neural circuits.

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Christopher L. Barnes

Papers

The Multilayer Connectome of Caenorhabditis elegans.

The connectome of an insect brain

Synaptic counts approximate synaptic contact area in Drosophila