Ventral hippocampal projections to the medial prefrontal cortex regulate social memory

Inputs from the ventral hippocampus (vHIP) to the medial prefrontal cortex (mPFC) are implicated in several neuropsychiatric disorders. Here, we show that the vHIP-mPFC projection is hyperactive in the Mecp2 knockout mouse model of the autism spectrum disorder Rett syndrome, which has deficits in social memory. Long-term excitation of mPFC-projecting vHIP neurons in wild-type mice impaired social memory, whereas their long-term inhibition in Rett mice rescued social memory deficits. The extent of social memory improvement was negatively correlated with vHIP-evoked responses in mPFC slices, on a mouse-per-mouse basis. Acute manipulations of the vHIP-mPFC projection affected social memory in a region and behavior selective manner, suggesting that proper vHIP-mPFC signaling is necessary to recall social memories. In addition, we identified an altered pattern of vHIP innervation of mPFC neurons, and increased synaptic strength of vHIP inputs onto layer five pyramidal neurons as contributing factors of aberrant vHIP-mPFC signaling in Rett mice.

A Brief History of Simulation Neuroscience

Our knowledge of the brain has evolved over millennia in philosophical, experimental and theoretical phases. We suggest that the next phase is simulation neuroscience. The main drivers of simulation neuroscience are big data generated at multiple levels of brain organization and the need to integrate these data to trace the causal chain of interactions within and across all these levels. Simulation neuroscience is currently the only methodology for systematically approaching the multiscale brain. In this review, we attempt to reconstruct the deep historical paths leading to simulation neuroscience, from the first observations of the nerve cell to modern efforts to digitally reconstruct and simulate the brain. Neuroscience began with the identification of the neuron as the fundamental unit of brain structure and function and has evolved towards understanding the role of each cell type in the brain, how brain cells are connected to each other, and how the seemingly infinite networks they form give rise to the vast diversity of brain functions. Neuronal mapping is evolving from subjective descriptions of cell types towards objective classes, subclasses and types. Connectivity mapping is evolving from loose topographic maps between brain regions towards dense anatomical and physiological maps of connections between individual genetically distinct neurons. Functional mapping is evolving from psychological and behavioral stereotypes towards a map of behaviors emerging from structural and functional connectomes. We show how industrialization of neuroscience and the resulting large disconnected datasets are generating demand for integrative neuroscience, how the scale of neuronal and connectivity maps is driving digital atlasing and digital reconstruction to piece together the multiple levels of brain organization, and how the complexity of the interactions between molecules, neurons, microcircuits and brain regions is driving brain simulation to understand the interactions in the multiscale brain.

Threshold-Based Ordering of Sequential Actions during Drosophila Courtship

Goal-directed animal behaviors are typically composed of sequences of motor actions whose order and timing are critical for a successful outcome. Although numerous theoretical models for sequential action generation have been proposed, few have been supported by the identification of control neurons sufficient to elicit a sequence. Here, we identify a pair of descending neurons that coordinate a stereotyped sequence of engagement actions during Drosophila melanogaster male courtship behavior. These actions are initiated sequentially but persist cumulatively, a feature not explained by existing models of sequential behaviors. We find evidence consistent with a ramp-to-threshold mechanism, in which increasing neuronal activity elicits each action independently at successively higher activity thresholds.

p53-Sensitive Epileptic Behavior and Inflammation in Ft1 Hypomorphic Mice

Epilepsy is a complex clinical condition characterized by repeated spontaneous seizures. Seizures have been linked to multiple drivers including DNA damage accumulation. Investigation of epilepsy physiopathology in humans imposes ethical and practical limitations, for this reason model systems are mostly preferred. Among animal models, mouse mutants are particularly valuable since they allow conjoint behavioral, organismal, and genetic analyses. Along with this, since aging has been associated with higher frequency of seizures, prematurely aging mice, simulating human progeroid diseases, offer a further useful modeling element as they recapitulate aging over a short time-window. Here we report on a mouse mutant with progeroid traits that displays repeated spontaneous seizures. Mutant mice were produced by reducing the expression of the gene Ft1 (AKTIP in humans). In vitro, AKTIP/Ft1 depletion causes telomere aberrations, DNA damage, and cell senescence. AKTIP/Ft1 interacts with lamins, which control nuclear architecture and DNA function. Premature aging defects of Ft1 mutant mice include skeletal alterations and lipodystrophy. The epileptic behavior of Ft1 mutant animals was age and sex linked. Seizures were observed in 18 mutant mice (23.6% of aged ≥ 21 weeks), at an average frequency of 2.33 events/mouse. Time distribution of seizures indicated non-random enrichment of seizures over the follow-up period, with 75% of seizures happening in consecutive weeks. The analysis of epileptic brains did not reveal overt brain morphological alterations or severe neurodegeneration, however, Ft1 reduction induced expression of the inflammatory markers IL-6 and TGF-β. Importantly, Ft1 mutant mice with concomitant genetic reduction of the guardian of the genome, p53, showed no seizures or inflammatory marker activation, implicating the DNA damage response into these phenotypes. This work adds insights into the connection among DNA damage, brain function, and aging. In addition, it further underscores the importance of model organisms for studying specific phenotypes, along with permitting the analysis of genetic interactions at the organismal level.

Kinematic analysis of bimanual movements during food handling by head-fixed rats

Bimanual coordination, in which both hands work together to achieve a goal, is crucial for the basic needs of life, such as gathering and feeding. Such coordinated motor skill is highly developed in primates, where it has been most extensively studied. Rodents also exhibit remarkable dexterity and coordination of forelimbs during food handling and consumption. However, rodents have been less commonly used in the study of bimanual coordination because of limited quantitative measuring techniques. In this article we describe a high-resolution tracking system that enables kinematic analysis of rat forelimb movement. The system is used to quantify forelimb movements bilaterally in head-fixed rats during food handling and consumption. Forelimb movements occurring naturally during feeding were encoded as continuous three-dimensional trajectories. The trajectories were then automatically segmented and analyzed, using a novel algorithm, according to the laterality of movement speed or the asymmetry of movement direction across the forelimbs. Bilateral forelimb movements were frequently observed during spontaneous food handling. Both symmetry and asymmetry in movement direction were frequently observed, with symmetric bilateral movements quantitatively more common. The proposed method overcomes a limitation in the precise quantification of bimanual coordination in rodents. This enables the use of powerful rodent-based research tools such as optogenetics and chemogenetics in the further investigation of neural mechanisms of bimanual coordination. NEW & NOTEWORTHY We describe a new method for quantifying and classifying three-dimensional, bilateral forelimb trajectories in head-fixed rats. The method overcomes limits on quantifying bimanual coordination in rats. When applied to kinematic analysis of food handling behavior, continuous forelimb trajectories were automatically segmented and classified. Bilateral forelimb movements were observed more frequently than unilateral movements during spontaneous food handling. Both symmetry and asymmetry in movement direction were frequently observed. However, symmetric bilateral forelimb movements were more common.

Automated monitoring of mouse feeding and body weight for continuous health assessment

Routine health assessment of laboratory rodents can be improved using automated home cage monitoring. Continuous, non-stressful, objective assessment of rodents unaware that they are being watched, including during their active dark period, reveals behavioural and physiological changes otherwise invisible to human caretakers. We developed an automated feeder that tracks feed intake, body weight, and physical appearance of individual radio frequency identification-tagged mice in social home cages. Here, we experimentally induce illness via lipopolysaccharide challenge and show that this automated tracking apparatus reveals sickness behaviour (reduced food intake) as early as 2-4 hours after lipopolysaccharide injection, whereas human observers conducting routine health checks fail to detect a significant difference between sick mice and saline-injected controls. Continuous automated monitoring additionally reveals pronounced circadian rhythms in both feed intake and body weight. Automated home cage monitoring is a non-invasive, reliable mode of health surveillance allowing caretakers to more efficiently detect and respond to early signs of illness in laboratory rodent populations.

Functional Imaging and Optogenetics in Drosophila

Understanding how activity patterns in specific neural circuits coordinate an animal’s behavior remains a key area of neuroscience research. Genetic tools and a brain of tractable complexity make Drosophila a premier model organism for these studies. Here, we review the wealth of reagents available to map and manipulate neuronal activity with light.

Automated measurement of upper thermal limits in small aquatic animals

We present a method for automating the measurement of upper thermal limits in small aquatic organisms. Upper thermal limits are frequently defined by the cessation of movement at high temperature, with measurement being performed by manual observation. Consequently, estimates of upper thermal limits may be subject to error and bias, both within and among observers. Our method utilises video-based tracking software to record the movement of individuals when exposed to high, lethal temperatures. We develop an algorithm in the R computing language that can objectively identify the loss of locomotory function from tracking data. Using independent experimental data, we validate our approach by demonstrating the expected response in upper thermal limits to acclimation temperature.

DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other animals are often marked with reflective markers to assist with computer-based tracking, but markers are intrusive, and the number and location of the markers must be determined a priori. Here we present an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results with minimal training data. We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. Remarkably, even when only a small number of frames are labeled (~200), the algorithm achieves excellent tracking performance on test frames that is comparable to human accuracy.

Target tracking and 3D trajectory acquisition of cabbage butterfly (P. rapae) based on the KCF-BS algorithm

Insect behaviour is an important research topic in plant protection. To study insect behaviour accurately, it is necessary to observe and record their flight trajectory quantitatively and precisely in three dimensions (3D). The goal of this research was to analyse frames extracted from videos using Kernelized Correlation Filters (KCF) and Background Subtraction (BS) (KCF-BS) to plot the 3D trajectory of cabbage butterfly (P. rapae). Considering the experimental environment with a wind tunnel, a quadrature binocular vision insect video capture system was designed and applied in this study. The KCF-BS algorithm was used to track the butterfly in video frames and obtain coordinates of the target centroid in two videos. Finally the 3D trajectory was calculated according to the matching relationship in the corresponding frames of two angles in the video. To verify the validity of the KCF-BS algorithm, Compressive Tracking (CT) and Spatio-Temporal Context Learning (STC) algorithms were performed. The results revealed that the KCF-BS tracking algorithm performed more favourably than CT and STC in terms of accuracy and robustness.

High-resolution behavioral mapping of electric fishes in Amazonian habitats

The study of animal behavior has been revolutionized by sophisticated methodologies that identify and track individuals in video recordings. Video recording of behavior, however, is challenging for many species and habitats including fishes that live in turbid water. Here we present a methodology for identifying and localizing weakly electric fishes on the centimeter scale with subsecond temporal resolution based solely on the electric signals generated by each individual. These signals are recorded with a grid of electrodes and analyzed using a two-part algorithm that identifies the signals from each individual fish and then estimates the position and orientation of each fish using Bayesian inference. Interestingly, because this system involves eavesdropping on electrocommunication signals, it permits monitoring of complex social and physical interactions in the wild. This approach has potential for large-scale non-invasive monitoring of aquatic habitats in the Amazon basin and other tropical freshwater systems.

Measuring behavior across scales

The need for high-throughput, precise, and meaningful methods for measuring behavior has been amplified by our recent successes in measuring and manipulating neural circuitry. The largest challenges associated with moving in this direction, however, are not technical but are instead conceptual: what numbers should one put on the movements an animal is performing (or not performing)? In this review, I will describe how theoretical and data analytical ideas are interfacing with recently-developed computational and experimental methodologies to answer these questions across a variety of contexts, length scales, and time scales. I will attempt to highlight commonalities between approaches and areas where further advances are necessary to place behavior on the same quantitative footing as other scientific fields.

Social Immunity: Emergence and Evolution of Colony-Level Disease Protection

Social insect colonies have evolved many collectively performed adaptations that reduce the impact of infectious disease and that are expected to maximize their fitness. This colony-level protection is termed social immunity, and it enhances the health and survival of the colony. In this review, we address how social immunity emerges from its mechanistic components to produce colony-level disease avoidance, resistance, and tolerance. To understand the evolutionary causes and consequences of social immunity, we highlight the need for studies that evaluate the effects of social immunity on colony fitness. We discuss the roles that host life history and ecology have on predicted eco-evolutionary dynamics, which differ among the social insect lineages. Throughout the review, we highlight current gaps in our knowledge and promising avenues for future research, which we hope will bring us closer to an integrated understanding of socio-eco-evo-immunology.

Automated tracking to measure behavioural changes in pigs for health and welfare monitoring

Since animals express their internal state through behaviour, changes in said behaviour may be used to detect early signs of problems, such as in animal health. Continuous observation of livestock by farm staff is impractical in a commercial setting to the degree required to detect behavioural changes relevant for early intervention. An automated monitoring system is developed; it automatically tracks pig movement with depth video cameras, and automatically measures standing, feeding, drinking, and locomotor activities from 3D trajectories. Predictions of standing, feeding, and drinking were validated, but not locomotor activities. An artificial, disruptive challenge; i.e., introduction of a novel object, is used to cause reproducible behavioural changes to enable development of a system to detect the changes automatically. Validation of the automated monitoring system with the controlled challenge study provides a reproducible framework for further development of robust early warning systems for pigs. The automated system is practical in commercial settings because it provides continuous monitoring of multiple behaviours, with metrics of behaviours that may be considered more intuitive and have diagnostic validity. The method has the potential to transform how livestock are monitored, directly impact their health and welfare, and address issues in livestock farming, such as antimicrobial use.

A computer vision for animal ecology

A central goal of animal ecology is to observe species in the natural world. The cost and challenge of data collection often limit the breadth and scope of ecological study. Ecologists often use image capture to bolster data collection in time and space. However, the ability to process these images remains a bottleneck. Computer vision can greatly increase the efficiency, repeatability and accuracy of image review. Computer vision uses image features, such as colour, shape and texture to infer image content. I provide a brief primer on ecological computer vision to outline its goals, tools and applications to animal ecology. I reviewed 187 existing applications of computer vision and divided articles into ecological description, counting and identity tasks. I discuss recommendations for enhancing the collaboration between ecologists and computer scientists and highlight areas for future growth of automated image analysis.

Open questions: Tackling Darwin's "instincts": the genetic basis of behavioral evolution

All of us have marveled at the remarkable diversity of animal behaviors in nature.

None of us has much idea of how these have evolved.