Cortical activity emerges in region-specific patterns during early brain development.

The development of precise neural circuits in the brain requires spontaneous patterns of neural activity prior to functional maturation. In the rodent cerebral cortex, patchwork and wave patterns of activity develop in somatosensory and visual regions, respectively, and are present at birth. However, whether such activity patterns occur in noneutherian mammals, as well as when and how they arise during development, remain open questions relevant for understanding brain formation in health and disease. Since the onset of patterned cortical activity is challenging to study prenatally in eutherians, here we offer an approach in a minimally invasive manner using marsupial dunnarts, whose cortex forms postnatally. We discovered similar patchwork and travelling waves in the dunnart somatosensory and visual cortices at stage 27 (equivalent to newborn mice) and examined earlier stages of development to determine the onset of these patterns and how they first emerge. We observed that these patterns of activity emerge in a region-specific and sequential manner, becoming evident as early as stage 24 in somatosensory and stage 25 in visual cortices (equivalent to embryonic day 16 and 17, respectively, in mice), as cortical layers establish and thalamic axons innervate the cortex. In addition to sculpting synaptic connections of existing circuits, evolutionarily conserved patterns of neural activity could therefore help regulate other early events in cortical development.

Cortical activity emerges in region-specific patterns during early brain development.

The development of precise neural circuits in the brain requires spontaneous patterns of neural activity prior to functional maturation. In the rodent cerebral cortex patchwork and wave patterns of activity develop in somatosensory and visual regions, respectively, and are present at birth. However, whether such activity patterns occur in non-eutherian mammals, as well as when and how they arise during development remain open questions relevant to understand brain formation in health and disease. Since the onset of patterned cortical activity is challenging to study prenatally in eutherians, here we offer a new approach in a minimally invasive manner using marsupial dunnarts, whose cortex forms postnatally. We discovered similar patchwork and travelling waves in the dunnart somatosensory and visual cortices at stage 27 (equivalent to newborn mice), and examined progressively earlier stages of development to determine their onset and how they first emerge. We observed that these patterns of activity emerge in a region-specific and sequential manner, becoming evident as early as stage 24 in somatosensory and stage 25 in visual cortices (equivalent to embryonic day 16 and 17, respectively, in mice), as cortical layers establish and thalamic axons innervate the cortex. In addition to sculpting synaptic connections of existing circuits, evolutionarily conserved patterns of neural activity could therefore help regulate early events in cortical development. Significance Statement: Region-specific patterns of neural activity are present at birth in rodents and are thought to refine synaptic connections during critical periods of cerebral cortex development. Marsupials are born much more immature than rodents, allowing the investigation of how these patterns arise in vivo. We discovered that cortical activity patterns are remarkably similar in marsupial dunnarts and rodents, and that they emerge very early, before cortical neurogenesis is complete. Moreover, they arise from the outset in different patterns specific to somatosensory and visual areas (i.e., patchworks and waves) indicating they may also play evolutionarily conserved roles in cortical regionalization during development.

fmr1 Mutation Alters the Early Development of Sensory Coding and Hunting and Social Behaviors in Larval Zebrafish.

Autism spectrum disorders (ASDs) are developmental in origin; however, little is known about how they affect the early development of behavior and sensory coding. The most common inherited form of autism is Fragile X syndrome (FXS), caused by a mutation in FMR1 Mutation of fmr1 in zebrafish causes anxiety-like behavior, hyperactivity, and hypersensitivity in auditory and visual processing. Here, we show that zebrafish fmr1-/- mutant larvae of either sex also display changes in hunting behavior, tectal coding, and social interaction. During hunting, they were less successful at catching prey and displayed altered behavioral sequences. In the tectum, representations of prey-like stimuli were more diffuse and had higher dimensionality. In a social behavioral assay, they spent more time observing a conspecific but responded more slowly to social cues. However, when given a choice of rearing environment fmr1-/- larvae preferred one with reduced visual stimulation, and rearing them in this environment reduced genotype-specific effects on tectal excitability. Together, these results shed new light on how fmr1-/- changes the early development of neural systems and behavior in a vertebrate.SIGNIFICANCE STATEMENT Autism spectrum disorders (ASDs) are caused by changes in early neural development. Animal models of ASDs offer the opportunity to study these developmental processes in greater detail than in humans. Here, we found that a zebrafish mutant for a gene which in humans causes one type of ASD showed early alterations in hunting behavior, social behavior, and how visual stimuli are represented in the brain. However, we also found that mutant fish preferred reduced visual stimulation, and rearing them in this environment reduced alterations in neural activity patterns. These results suggest interesting new directions for using zebrafish as a model to study the development of brain and behavior in ASDs, and how the impact of ASDs could potentially be reduced.

Not so spontaneous: Multi-dimensional representations of behaviors and context in sensory areas.

Sensory areas are spontaneously active in the absence of sensory stimuli. This spontaneous activity has long been studied; however, its functional role remains largely unknown. Recent advances in technology, allowing large-scale neural recordings in the awake and behaving animal, have transformed our understanding of spontaneous activity. Studies using these recordings have discovered high-dimensional spontaneous activity patterns, correlation between spontaneous activity and behavior, and dissimilarity between spontaneous and sensory-driven activity patterns. These findings are supported by evidence from developing animals, where a transition toward these characteristics is observed as the circuit matures, as well as by evidence from mature animals across species. These newly revealed characteristics call for the formulation of a new role for spontaneous activity in neural sensory computation.

Spontaneous and evoked activity patterns diverge over development.

The immature brain is highly spontaneously active. Over development this activity must be integrated with emerging patterns of stimulus-evoked activity, but little is known about how this occurs. Here we investigated this question by recording spontaneous and evoked neural activity in the larval zebrafish tectum from 4 to 15 days post-fertilisation. Correlations within spontaneous and evoked activity epochs were comparable over development, and their neural assemblies refined in similar ways. However, both the similarity between evoked and spontaneous assemblies, and also the geometric distance between spontaneous and evoked patterns, decreased over development. At all stages of development, evoked activity was of higher dimension than spontaneous activity. Thus, spontaneous and evoked activity do not converge over development in this system, and these results do not support the hypothesis that spontaneous activity evolves to form a Bayesian prior for evoked activity.

Model-based decoupling of evoked and spontaneous neural activity in calcium imaging data.

The pattern of neural activity evoked by a stimulus can be substantially affected by ongoing spontaneous activity. Separating these two types of activity is particularly important for calcium imaging data given the slow temporal dynamics of calcium indicators. Here we present a statistical model that decouples stimulus-driven activity from low dimensional spontaneous activity in this case. The model identifies hidden factors giving rise to spontaneous activity while jointly estimating stimulus tuning properties that account for the confounding effects that these factors introduce. By applying our model to data from zebrafish optic tectum and mouse visual cortex, we obtain quantitative measurements of the extent that neurons in each case are driven by evoked activity, spontaneous activity, and their interaction. By not averaging away potentially important information encoded in spontaneous activity, this broadly applicable model brings new insight into population-level neural activity within single trials.

Behavioral Signatures of a Developing Neural Code.

During early life, neural codes must develop to appropriately transform sensory inputs into behavioral outputs. Here, we demonstrate a link between the maturity of neural coding in the visual brain and developmental changes in visually guided behavior. In zebrafish larvae, we show that visually driven hunting behavior improves from 4 to 15 days post-fertilization, becoming faster and more accurate. During the same period, population activity in parts of the optic tectum refines, improving decoding and information transmission for particular spatial positions. Remarkably, individual differences in decoding can predict each fish's hunting success. Together, these results help reveal how the neural codes required for a natural behavior emerge during development.

Correction to: Detecting neural assemblies in calcium imaging data.

Upon publication of the original article, [1], the authors noticed that the first authors' affiliation contained an error.

Detecting neural assemblies in calcium imaging data.

BACKGROUND: Activity in populations of neurons often takes the form of assemblies, where specific groups of neurons tend to activate at the same time. However, in calcium imaging data, reliably identifying these assemblies is a challenging problem, and the relative performance of different assembly-detection algorithms is unknown. RESULTS: To test the performance of several recently proposed assembly-detection algorithms, we first generated large surrogate datasets of calcium imaging data with predefined assembly structures and characterised the ability of the algorithms to recover known assemblies. The algorithms we tested are based on independent component analysis (ICA), principal component analysis (Promax), similarity analysis (CORE), singular value decomposition (SVD), graph theory (SGC), and frequent item set mining (FIM-X). When applied to the simulated data and tested against parameters such as array size, number of assemblies, assembly size and overlap, and signal strength, the SGC and ICA algorithms and a modified form of the Promax algorithm performed well, while PCA-Promax and FIM-X did less well, for instance, showing a strong dependence on the size of the neural array. Notably, we identified additional analyses that can improve their importance. Next, we applied the same algorithms to a dataset of activity in the zebrafish optic tectum evoked by simple visual stimuli, and found that the SGC algorithm recovered assemblies closest to the averaged responses. CONCLUSIONS: Our findings suggest that the neural assemblies recovered from calcium imaging data can vary considerably with the choice of algorithm, but that some algorithms reliably perform better than others. This suggests that previous results using these algorithms may need to be reevaluated in this light.

Emergence of spontaneous assembly activity in developing neural networks without afferent input.

Spontaneous activity is a fundamental characteristic of the developing nervous system. Intriguingly, it often takes the form of multiple structured assemblies of neurons. Such assemblies can form even in the absence of afferent input, for instance in the zebrafish optic tectum after bilateral enucleation early in life. While the development of neural assemblies based on structured afferent input has been theoretically well-studied, it is less clear how they could arise in systems without afferent input. Here we show that a recurrent network of binary threshold neurons with initially random weights can form neural assemblies based on a simple Hebbian learning rule. Over development the network becomes increasingly modular while being driven by initially unstructured spontaneous activity, leading to the emergence of neural assemblies. Surprisingly, the set of neurons making up each assembly then continues to evolve, despite the number of assemblies remaining roughly constant. In the mature network assembly activity builds over several timesteps before the activation of the full assembly, as recently observed in calcium-imaging experiments. Our results show that Hebbian learning is sufficient to explain the emergence of highly structured patterns of neural activity in the absence of structured input.

Principles of Functional Circuit Connectivity: Insights From Spontaneous Activity in the Zebrafish Optic Tectum.

The brain is continuously active, even in the absence of external stimulation. In the optic tectum of the zebrafish larva, this spontaneous activity is spatially organized and reflects the circuit's functional connectivity. The structure of the spontaneous activity displayed patterns associated with aspects of the larva's preferences when engaging in complex visuo-motor behaviors, suggesting that the tectal circuit is adapted for the circuit's functional role in detecting visual cues and generating adequate motor behaviors. Further studies in sensory deprived larvae suggest that the basic structure of the functional connectivity patterns emerges even in the absence of retinal inputs, but that its fine structure is affected by visual experience.

Code Under Construction: Neural Coding Over Development.

Developing animals must begin to interact with the world before their neural development is complete. This means they must build neural codes appropriate for turning sensory inputs into motor outputs adaptively as their neural hardware matures. We review some recent progress in the understanding of the relationship between neural coding and neural circuit development. We focus particularly on neural coding in the context of topographic maps and spontaneous activity, as well as receptive field and circuit development, drawing on examples from both mammalian visual cortex and fish optic tectum. Overall we suggest that neural coding strategies during development may be highly dynamic.

Spontaneous Activity in the Zebrafish Tectum Reorganizes over Development and Is Influenced by Visual Experience.

Spontaneous patterns of activity in the developing visual system may play an important role in shaping the brain for function. During the period 4-9 dpf (days post-fertilization), larval zebrafish learn to hunt prey, a behavior that is critically dependent on the optic tectum. However, how spontaneous activity develops in the tectum over this period and the effect of visual experience are unknown. Here we performed two-photon calcium imaging of GCaMP6s zebrafish larvae at all days from 4 to 9 dpf. Using recently developed graph theoretic techniques, we found significant changes in both single-cell and population activity characteristics over development. In particular, we identified days 5-6 as a critical moment in the reorganization of the underlying functional network. Altering visual experience early in development altered the statistics of tectal activity, and dark rearing also caused a long-lasting deficit in the ability to capture prey. Thus, tectal development is shaped by both intrinsic factors and visual experience.

Limitations of Neural Map Topography for Decoding Spatial Information.

UNLABELLED: Topographic maps are common throughout the nervous system, yet their functional role is still unclear. In particular, whether they are necessary for decoding sensory stimuli is unknown. Here we examined this question by recording population activity at the cellular level from the larval zebrafish tectum in response to visual stimuli at three closely spaced locations in the visual field. Due to map imprecision, nearby stimulus locations produced intermingled tectal responses, and decoding based on map topography yielded an accuracy of only 64%. In contrast, maximum likelihood decoding of stimulus location based on the statistics of the evoked activity, while ignoring any information about the locations of neurons in the map, yielded an accuracy close to 100%. A simple computational model of the zebrafish visual system reproduced these results. Although topography is a useful initial decoding strategy, we suggest it may be replaced by better methods following visual experience. SIGNIFICANCE STATEMENT: A very common feature of brain wiring is that neighboring points on a sensory surface (eg, the retina) are connected to neighboring points in the brain. It is often assumed that this "topography" of wiring is essential for decoding sensory stimuli. However, here we show in the developing zebrafish that topographic decoding performs very poorly compared with methods that do not rely on topography. This suggests that, although wiring topography could provide a starting point for decoding at a very early stage in development, it may be replaced by more accurate methods as the animal gains experience of the world.

Balanced interhemispheric cortical activity is required for correct targeting of the corpus callosum.

Bilateral integration of sensory and associative brain processing is achieved by precise connections between homologous regions in the two hemispheres via the corpus callosum. These connections form postnatally, and unilateral deprivation of sensory or spontaneous cortical activity during a critical period severely disrupts callosal wiring. However, little is known about how this early activity affects precise circuit formation. Here, using in utero electroporation of reporter genes, optogenetic constructs, and direct disruption of activity in callosal neurons combined with whisker ablations, we show that balanced interhemispheric activity, and not simply intact cortical activity in either hemisphere, is required for functional callosal targeting. Moreover, bilateral ablation of whiskers in symmetric or asymmetric configurations shows that spatially symmetric interhemispheric activity is required for appropriate callosal targeting. Our findings reveal a principle governing axon targeting, where spatially balanced activity between regions is required to establish their appropriate connectivity.

EEG generator--a model of potentials in a volume conductor.

EEG generator-a model of potentials in a volume conductor. The potential recorded over the cortex electro-corticogram (ECoG) or over the scalp [electroencephalograph (EEG)] derives from the activity of many sources known as "EEG generators." The recorded amplitude is basically a function of the unitary potential of a generator and the statistical relationship between different EEG generators in the recorded population. In this study, we first suggest a new definition of the EEG generator. We use the theory of potentials in a volume conductor and model the contribution of a single synapse activated to the surface potential. We then model the contribution of the generator to the surface potential. Once the generator and its contribution are well defined, we can quantitatively assess the degree of synchronization among generators. The measures obtained by the model for a real life scenario of a group of generators organized in a specific statistical way were consistent with the expected values that were reported experimentally. The study sheds new light on macroscopic modeling approaches which make use of mean soma membrane potential. We showed major contribution of activity of superficial apical synapses to the ECoG signal recorded relative to lower somatic or basal synapses activity.

Resolving the dynamics of EEG generators by multichannel recordings.

The voltage recorded over the cortex (ECoG) or over the scalp (EEG) is generated by currents derived from many sources called "generators". Different patterns and amplitudes are observed in aroused, sleepy, epileptic or other brain states. Differences in amplitude are generally attributed to differences in synchrony among generators. The degree of EEG synchrony is measured by the correlation between electrodes placed over different cortical regions. We present a new way to quantitatively assess the degree of synchronization of these generators via multichannel recordings. We illustrate how situations where there are several groups of generators with different inter-group and intra-group synchronies can be analyzed. Finally, we present a way to identify the organization of groups exhibiting topographic organization. Although the model presented here is highly simplified, several methods are based on averaging activity over increasingly larger areas. These types of measurements may be applied as well to EEG and ECoG recordings.