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Scientific Understanding of Consciousness |
Neural Dynamics Imaging in Transparent Zebrafish
Nature 485, 453–455 (24 May 2012) Monitoring Brain-wide Patterns of Neuronal Activity in Zebrafish Joseph R. Fetcho Department of Neurobiology and Behavior, Cornell University, Ithaca, New York 14853, USA. [paraphrase] Neurobiologists studying brains must deal with massive numbers of neurons — perhaps as many as 100 billion in a human brain — that interact with one another to produce the electrical activity that drives behaviour. Moreover, both the interactions and the activity change constantly as animals learn to modify their behaviour as a result of experience. Researchers have now imaged the activity of individual neurons throughout the brain of zebrafish larvae while the animals interacted with a changing virtual environment and adjusted their behaviour accordingly. Most animals are opaque, so obtaining images of their brain with the resolution needed to see individual neurons (tens of micrometres or less) might seem impossible. But researchers used a small, transparent vertebrate that is amenable to genetic manipulation — the larval zebrafish. To detect the nerve cells that were active at any given moment, the researchers introduced into the animal a gene coding for a fluorescent calcium indicator, which labels brain neurons and becomes brighter as calcium enters electrically active cells. This, combined with advanced optical methods (such as two-photon microscopy), allowed the researchers to see the active neurons light up anywhere in the brain. The authors set out to study a key problem in neuroscience: how do patterns of neuronal activity change throughout the brain as an animal modifies its behaviour when it learns something new? Fish adjust how strongly they activate their muscles by using visual feedback about how quickly the world is passing by as an indication of the speed at which they are moving. To facilitate imaging while fish learned to make these adjustments, Ahrens et al. immobilized zebrafish larvae by injecting them with a paralysing agent, and put them into a simulated virtual environment. Using computer monitors, the researchers controlled the rate at which the virtual world seemed to move past when the fish tried to move. The virtual scenes changed according to the animals' intended movements, which were inferred by the detection of electrical signals from nerves connected to the muscles that would normally produce the movements. By changing how fast the world seemed to move past when the paralysed animals attempted to activate their muscles, the authors tricked the fish into thinking that their muscles were suddenly weaker or stronger than normal. The fish learned to compensate for the changes by altering the strength of muscle activation to allow them to control their movements in the changing virtual environment. The authors found that, even in the relatively simple brain of the larval zebrafish, the neurons that showed increased or decreased activity during learning were widespread. As expected, some of these cells were located in regions that receive information from the eyes as well as in others involved in producing the movements. Other brain areas that changed activity included the cerebellum and the inferior olive, which are crucial for complex control of movements, and the pallium in the front of the brain, a higher-level processing region. Of note, these changes in activity patterns, although extensive, are probably an underestimate of the extent of the changes that occur during learning — although the researchers' approach is the most powerful available, it is prone to missing small changes in the activity of individual neurons. Nonetheless, Ahrens and colleagues' work faces head-on the problem of the complex ties between the brain and behaviour. Although behaviour is known to emerge from the activity of, and the interactions among, neurons across many brain regions, neurobiologists typically focus their research on just one of these regions at a time because of the technical difficulties associated with brain-wide studies at the cellular level. Mapping activity patterns at the single-cell level throughout a brain while the animal is learning is a major step towards a more integrated view of how neuronal circuits, in their entirety, drive behaviour. The authors' study is a remarkable achievement, but documenting the patterns of activity everywhere in the brain is just the beginning of the task ahead. Many important details about the neurons whose activity changed during Ahrens and colleagues' experiments are unknown. We need not only a broad picture of the activity at the cellular level as provided by the authors' work, but also information about whether the neurons excite or inhibit other cells and how they connect to other neurons in the brain to form the circuits that drive behaviour. This is the focus of 'connectomics', an effort to map all of the connections in the brain. Once neuronal activity, and both structural and functional connectivity, are mapped, models of the way in which circuits throughout the brain produce and modify behaviour must be formulated and tested. A powerful technique for this purpose is optogenetics, which allows neurons to be turned on and off with light during behaviour to test the contributions of different sets of cells — an approach that is especially applicable to a transparent animal. Therefore, all of the necessary tools for mapping activity and connectivity, and for testing neuronal function on a large scale, are now at hand and promise to provide increasingly complete pictures of behaviour-generating circuits throughout the brain. The larval zebrafish will probably remain at the forefront of these efforts because of the powerful combination of optical, genetic and behavioural approaches that can be used to study it. These larval fish show most of the behaviours typical of adult vertebrates, although some aspects of their behaviour are not as highly refined as that of adult fish or mammals. Most importantly, in no other vertebrate animal model is it possible to accomplish cellular-level-resolution imaging of the entire brain. The tools to map connectivity and perturb neuronal activity are also easily applied to this small, transparent fish, making it the best hope among vertebrate models for revealing how neurons throughout the brain interact to produce behaviour. With the help of a tiny fish, a long-standing, but distant, goal of neuroscience is finally moving into view. [end of paraphrase]
Nature 485, 471–477 (24 May 2012) Brain-wide neuronal dynamics during motor adaptation in zebrafish Department of Molecular and Cellular Biology, Harvard University, 16 Divinity Avenue, Cambridge, Massachusetts 02138, USA Misha B. Ahrens, Jennifer M. Li, Drew N. Robson, Alexander F. Schier, Florian Engert & Ruben Portugues Computational and Biological Learning Laboratory, Department of Engineering, Cambridge University, Trumpington Street, Cambridge CB2 1PZ, UK Misha B. Ahrens Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Avenida Brasília, Doca de Pedrouços, 1400-038 Lisboa, Portugal Michael B. Orger [paraphrase] A fundamental question in neuroscience is how entire neural circuits generate behaviour and adapt it to changes in sensory feedback. Here we use two-photon calcium imaging to record the activity of large populations of neurons at the cellular level, throughout the brain of larval zebrafish expressing a genetically encoded calcium sensor, while the paralysed animals interact fictively with a virtual environment and rapidly adapt their motor output to changes in visual feedback. We decompose the network dynamics involved in adaptive locomotion into four types of neuronal response properties, and provide anatomical maps of the corresponding sites. A subset of these signals occurred during behavioural adjustments and are candidates for the functional elements that drive motor learning. Lesions to the inferior olive indicate a specific functional role for olivocerebellar circuitry in adaptive locomotion. This study enables the analysis of brain-wide dynamics at single-cell resolution during behaviour. The generation of motor output and the influence of sensory input on future motor programs engage neural activity in many neurons across multiple brain regions. However, past measurements of neural activity during behaviour have been hampered by the inability to monitor exhaustively all neurons in the brain of a behaving animal. Although it is possible to record activity from behaving animals, the large size and opacity of the vertebrate brain constrains experimenters to focus on small fractions of the total number of neurons. Here we develop a preparation in which neuronal activity can be monitored anywhere in the brain using two-photon calcium imaging in paralysed larval zebrafish that interact with a virtual environment and adjust their behaviour to changes in visual feedback. When visual feedback following a motor command does not meet expectation, animals can learn to adapt the strength of subsequent motor commands. In the past this has been studied in controlled laboratory settings by perturbing visual feedback in the context of insect flight, the vestibulo-ocular reflex and reaching movements. Here we study adaptive control of locomotion in larval zebrafish. This animal swims in discrete swim bouts during which the visual environment moves relative to its retina. One hypothesis is that this optic flow is used as a measure of displacement and serves to tune the strength of future motor commands to the desired travel distance. Such sensorimotor recalibration is particularly important during the optomotor response, in which animals move in the direction of motion of the visual surround—thereby stabilizing their location in the presence of, for example, water flow—a response that occurs in many animal species. If motor output is not correctly calibrated to visual feedback, a fish may systematically overshoot or undershoot the desired travel distance, instead of stabilizing its location. Sensorimotor recalibration is necessary for accurate locomotion because the rate of optic flow following a motor command is affected by temperature-dependent changes in muscle strength, the viscosity of the water and the distance of objects from the retina. To examine neural dynamics across brain areas that drive sensorimotor recalibration, we developed a system to study neural activity at cellular resolution, using two-photon microscopy, anywhere in the brain during closed-loop optomotor behaviour in larval zebrafish. These animals have a small and transparent brain that is readily accessible for optogenetic recording and stimulation, electrophysiology and single-cell ablation. To remove motion artefacts, we developed a swim simulator for completely paralysed larvae. Motor commands, or ‘fictive swims’, are recorded at the motor neuron level and translated, in real time, into visual feedback that resembles the optic flow of freely swimming fish. This constitutes a fictively driven virtual-reality setup. Simultaneously, a two-photon microscope scanning over a transgenic fish expressing GCaMP2 in almost all neurons allows activity to be monitored throughout the brain at single-neuron resolution. As the experimenter is in complete control of the visual feedback, this allowed us to study neural dynamics during visually guided motor adaptation throughout the brain at the cellular level. The ability to monitor neural activity at single-cell resolution throughout the whole brain of a behaving animal creates new opportunities for studying circuit function during behaviour. The demonstration that paralysed larval zebrafish interact readily with a virtual environment, and the remarkable finding that these animals still showed short-term forms of motor learning in the fictive virtual-reality setup, provided an exciting opportunity to study the circuit dynamics occurring during this behaviour. Here we identified neural populations activated during specific phases of adaptive locomotion that span multiple areas of the larval zebrafish brain. Both the inferior olive and the cerebellum contained many neurons correlating with adaptive motor control. In mammals, cerebellar circuits play an important role in motor control and in fish the cerebellum has been shown to be involved in the selection of motor programs. Furthermore, the structure of olivocerebellar circuitry in zebrafish is remarkably similar to that of mammals and the transient gain-down activity observed in the inferior olive and cerebellum may represent error signals driving motor learning mechanisms. To test whether the inferior olive is necessary for motor adaptation, we next lesioned it with an infrared laser. Post lesion, the power of swim bouts in the high- and low-gain settings became statistically indistinguishable. Although damage to passing axons cannot be ruled out, similar lesions in the dorsal anterior hindbrain did not affect motor adaptation (P < 0.001 pre lesion; P = 0.01 post lesion). The optomotor response was still intact. These results indicate that the inferior olive is necessary for successful adaptation of motor programs to external feedback gain. One possibility is that an error signal is computed in the inferior olive through subtractive interaction of an efference copy of motor output (for example, through inhibitory connections from premotor circuits) and visual feedback from a swim bout, which then activates appropriate circuits in the cerebellum via climbing fibres. Cerebellar activity may drive changes in motor programs through the deep cerebellum, which in mammals projects to premotor circuits. Although synaptic plasticity underlies much of motor learning, it is only one of several candidate mechanisms for the behaviour observed here. An alternative idea is that sustained firing rates of neuronal populations, perhaps subsets of the motor and motor-off populations, implement and maintain the different levels of locomotor drive over prolonged periods; for example, through attractor states. The function of the observed adaptive sensorimotor behaviour may be multifold. On long timescales, development of muscle and body shape will require continuous adjustment of sensorimotor control. On medium timescales, fluctuating body mass due to eating, and fluctuating viscosity of the water require some adaptation of swimming behaviour. In addition, temperature fluctuations cause changes in muscle efficacy that must be counterbalanced by adjustments of locomotor drive. On short timescales, the relationship between the speed of optic flow following a swim bout depends on the distance to the optical surround (objects farther away induce smaller optic flow), requiring recalibration of the sensorimotor loop on the timescale of seconds. In our experiments, we observed adaptation occurring on such a timescale. Human motor control faces many similar challenges and is subject to continuous recalibration to cope with changing conditions (for example, leg injury, walking on a slippery floor or carrying a heavy bag). Thus, the current study of brain-wide activity during adaptive locomotion is an important step towards understanding entire circuits in the precise context for which they evolved: the flexible control of behaviour in changing environments. [end of paraphrase] |