The promise and perils of causal circuit manipulations

Functional interrogation of neural circuits with virally transmitted optogenetic tools

The vertebrate brain comprises a plethora of cell types connected by intertwined pathways. Optogenetics enriches the neuroscientific tool set for disentangling these neuronal circuits in a manner which exceeds the spatio-temporal precision of previously existing techniques. Technically, optogenetics can be divided in three types of optical and genetic combinations: (1) it is primarily understood as the manipulation of the activity of genetically modified cells (typically neurons) with light, i.e. optical actuators. (2) A second combination refers to visualizing the activity of genetically modified cells (again typically neurons), i.e. optical sensors. (3) A completely different interpretation of optogenetics refers to the light activated expression of a genetically induced construct. Here, we focus on the first two types of optogenetics, i.e. the optical actuators and sensors in an attempt to give an overview into the topic. We first cover methods to express opsins into neurons and introduce strategies of targeting specific neuronal populations in different animal species. We then summarize combinations of optogenetics with behavioral read out and neuronal imaging. Finally, we give an overview of the current state-of-the-art and an outlook on future perspectives.

Estimation of Current and Future Physiological States in Insular Cortex

Interoception, the sense of internal bodily signals, is essential for physiological homeostasis, cognition, and emotions. While human insular cortex (InsCtx) is implicated in interoception, the cellular and circuit mechanisms remain unclear. We imaged mouse InsCtx neurons during two physiological deficiency states: hunger and thirst. InsCtx ongoing activity patterns reliably tracked the gradual return to homeostasis but not changes in behavior. Accordingly, while artificial induction of hunger or thirst in sated mice via activation of specific hypothalamic neurons (AgRP or SFOGLUT) restored cue-evoked food- or water-seeking, InsCtx ongoing activity continued to reflect physiological satiety. During natural hunger or thirst, food or water cues rapidly and transiently shifted InsCtx population activity to the future satiety-related pattern. During artificial hunger or thirst, food or water cues further shifted activity beyond the current satiety-related pattern. Together with circuit-mapping experiments, these findings suggest that InsCtx integrates visceral-sensory signals of current physiological state with hypothalamus-gated amygdala inputs that signal upcoming ingestion of food or water to compute a prediction of future physiological state.

Mechanisms underlying gain modulation in the cortex

Cortical gain regulation allows neurons to respond adaptively to changing inputs. Neural gain is modulated by internal and external influences, including attentional and arousal states, motor activity and neuromodulatory input. These influences converge to a common set of mechanisms for gain modulation, including GABAergic inhibition, synaptically driven fluctuations in membrane potential, changes in cellular conductance and changes in other biophysical neural properties. Recent work has identified GABAergic interneurons as targets of neuromodulatory input and mediators of state-dependent gain modulation. Here, we review the engagement and effects of gain modulation in the cortex. We highlight key recent findings that link phenomenological observations of gain modulation to underlying cellular and circuit-level mechanisms. Finally, we place these cellular and circuit interactions in the larger context of their impact on perception and cognition.

Task-Dependent Changes in the Large-Scale Dynamics and Necessity of Cortical Regions

Neural activity throughout the cortex is correlated with perceptual decisions, but inactivation studies suggest that only a small number of areas are necessary for these behaviors. Here we show that the number of required cortical areas and their dynamics vary across related tasks with different cognitive computations. In a visually guided virtual T-maze task, bilateral inactivation of only a few dorsal cortical regions impaired performance. In contrast, in tasks requiring evidence accumulation and/or post-stimulus memory, performance was impaired by inactivation of widespread cortical areas with diverse patterns of behavioral deficits across areas and tasks. Wide-field imaging revealed widespread ramps of Ca2+ activity during the accumulation and visually guided tasks. Additionally, during accumulation, different regions had more diverse activity profiles, leading to reduced inter-area correlations. Using a modular recurrent neural network model trained to perform analogous tasks, we argue that differences in computational strategies alone could explain these findings.

What would a synthetic connectome look like?

A major challenge of contemporary neuroscience is to unravel the structure of the connectome, the ensemble of neural connections that link between different functional units of the brain, and to reveal how this structure relates to brain function. This thriving area of research largely follows the general tradition in biology of reverse-engineering, which consists of first observing and characterizing a biological system or process, and then deconstructing it into its fundamental building blocks in order to infer its modes of operation. However, a complementary form of biology has emerged, synthetic biology, which emphasizes construction-based forward-engineering. The synthetic biology approach comprises the assembly of new biological systems out of elementary biological parts. The rationale is that the act of building a system can be a powerful method for gaining deep understanding of how that system works. As the fields of connectomics and synthetic biology are independently growing, I propose to consider the benefits of combining the two, to create synthetic connectomics, a new form of neuroscience and a new form of synthetic biology. The goal of synthetic connectomics would be to artificially design and construct the connectomes of live behaving organisms. Synthetic connectomics could serve as a unifying platform for unraveling the complexities of brain operation and perhaps also for generating new forms of artificial life, and, in general, could provide a valuable opportunity for empirically exploring theoretical predictions about network function. What would a synthetic connectome look like? What purposes would it serve? How could it be constructed? This review delineates the novel notion of a synthetic connectome and aims to lay out the initial steps towards its implementation, contemplating its impact on science and society.

Hot topic in optogenetics: new implications of in vivo tissue heating

In this issue of Nature Neuroscience, Owen et al. show that widely used optogenetic light delivery can heat brain tissue and produce changes in neural activity and behavior in the absence of opsins. How will this finding influence experimental design in the optical age of neuroscience?

What Caused What? A Quantitative Account of Actual Causation Using Dynamical Causal Networks

Actual causation is concerned with the question: "What caused what?" Consider a transition between two states within a system of interacting elements, such as an artificial neural network, or a biological brain circuit. Which combination of synapses caused the neuron to fire? Which image features caused the classifier to misinterpret the picture? Even detailed knowledge of the system's causal network, its elements, their states, connectivity, and dynamics does not automatically provide a straightforward answer to the "what caused what?" question. Counterfactual accounts of actual causation, based on graphical models paired with system interventions, have demonstrated initial success in addressing specific problem cases, in line with intuitive causal judgments. Here, we start from a set of basic requirements for causation (realization, composition, information, integration, and exclusion) and develop a rigorous, quantitative account of actual causation, that is generally applicable to discrete dynamical systems. We present a formal framework to evaluate these causal requirements based on system interventions and partitions, which considers all counterfactuals of a state transition. This framework is used to provide a complete causal account of the transition by identifying and quantifying the strength of all actual causes and effects linking the two consecutive system states. Finally, we examine several exemplary cases and paradoxes of causation and show that they can be illuminated by the proposed framework for quantifying actual causation.

A virtual burrow assay for head-fixed mice measures habituation, discrimination, exploration and avoidance without training

We have designed an assay that measures approach and avoidance behaviors in head-fixed mice at millisecond timescale, is compatible with standard electrophysiological and optical methods for measuring neuronal activity, and requires no training. The Virtual Burrow Assay simulates a scenario in which a mouse, poised at the threshold of its burrow, evaluates whether to exit the enclosure or to retreat inside. The assay provides a sensitive readout of habituation, discrimination and exploration, as well as avoidance of both conditioned and innately aversive cues.

Memory systems 2018 - Towards a new paradigm

The multiple memory systems theory (MMS) postulates that the brain stores information based on the independent and parallel activity of a number of modules, each with distinct properties, dynamics, and neural basis. Much of the evidence for this theory comes from dissociation studies indicating that damage to restricted brain areas cause selective types of memory deficits. MMS has been the prevalent paradigm in memory research for more than thirty years, even as it has been adjusted several times to accommodate new data. However, recent empirical results indicating that the memory systems are not always dissociable constitute a challenge to fundamental tenets of the current theory because they suggest that representations formed by individual memory systems can contribute to more than one type of memory-driven behavioral strategy. This problem can be addressed by applying a dynamic network perspective to memory architecture. According to this view, memory networks can reconfigure or transiently couple in response to environmental demands. Within this context, the neural network underlying a specific memory system can act as an independent unit or as an integrated component of a higher order meta-network. This dynamic network model proposes a way in which empirical evidence that challenges the idea of distinct memory systems can be incorporated within a modular memory architecture. The model also provides a framework to account for the complex interactions among memory systems demonstrated at the behavioral level. Advances in the study of dynamic networks can generate new ideas to experimentally manipulate and control memory in basic or clinical research.

Automated home cage training of mice in a hold-still center-out reach task

An obstacle to understanding neural mechanisms of movement is the complex, distributed nature of the mammalian motor system. Here we present a novel behavioral paradigm for high-throughput dissection of neural circuits underlying mouse forelimb control. Custom touch-sensing joysticks were used to quantify mouse forelimb trajectories with micron-millisecond spatiotemporal resolution. Joysticks were integrated into computer-controlled, rack-mountable home cages, enabling batches of mice to be trained in parallel. Closed loop behavioral analysis enabled online control of reward delivery for automated training. We used this system to show that mice can learn, with no human handling, a direction-specific hold-still center-out reach task in which a mouse first held its right forepaw still before reaching out to learned spatial targets. Stabilogram diffusion analysis of submillimeter-scale micromovements produced during the hold demonstrate that an active control process, akin to upright balance, was implemented to maintain forepaw stability. Trajectory decomposition methods, previously used in primates, were used to segment hundreds of thousands of forelimb trajectories into millions of constituent kinematic primitives. This system enables rapid dissection of neural circuits for controlling motion primitives from which forelimb sequences are built. NEW & NOTEWORTHY A novel joystick design resolves mouse forelimb kinematics with micron-millisecond precision. Home cage training is used to train mice in a hold-still center-out reach task. Analytical methods, previously used in primates, are used to decompose mouse forelimb trajectories into kinematic primitives.

More than Just a "Motor": Recent Surprises from the Frontal Cortex

Motor and premotor cortices are crucial for the control of movements. However, we still know little about how these areas contribute to higher-order motor control, such as deciding which movements to make and when to make them. Here we focus on rodent studies and review recent findings, which suggest that-in addition to motor control-neurons in motor cortices play a role in sensory integration, behavioral strategizing, working memory, and decision-making. We suggest that these seemingly disparate functions may subserve an evolutionarily conserved role in sensorimotor cognition and that further study of rodent motor cortices could make a major contribution to our understanding of the evolution and function of the mammalian frontal cortex.