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The King's Indian is a hugely popular opening at all levels of chess. Rather than attempting to secure early equality, Black is fighting for the initiative from the very first moves. White is allowed to build up an early central advantage but Black relies on the middlegame, hoping that the central installations that White has constructed will become unwieldy and vulnerable to a devastating counterattack. In many variations, White pursues material or strategic gains but in return Black has tactical and attacking opportunities. The King's Indian Defence appeals to players who arrive at the board prepared for a fight.
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Repetitive action, resistance to environmental change and fine motor disruptions are hallmarks of autism spectrum disorder (ASD) and other neurodevelopmental disorders, and vary considerably from individual to individual. In animal models, conventional behavioral phenotyping captures such fine-scale variations incompletely. Here we observed male and female C57BL/6J mice to methodically catalog adaptive movement over multiple days and examined two rodent models of developmental disorders against this dynamic baseline. We then investigated the behavioral consequences of a cerebellum-specific deletion in Tsc1 protein and a whole-brain knockout in Cntnap2 protein in mice. Both of these mutations are found in clinical conditions and have been associated with ASD.
We used advances in computer vision and deep learning, namely a generalized form of high-dimensional statistical analysis, to develop a framework for characterizing mouse movement on multiple timescales using a single popular behavioral assay, the open-field test. The pipeline takes virtual markers from pose estimation to find behavior clusters and generate wavelet signatures of behavior classes. We measured spatial and temporal habituation to a new environment across minutes and days, different types of self-grooming, locomotion and gait.
Our automated pipeline for deep phenotyping successfully captures model-specific deviations in adaptation and movement as well as differences in the detailed structure of behavioral dynamics. The reported deficits indicate that deep phenotyping constitutes a robust set of ASD symptoms that may be considered for implementation in clinical settings as quantitative diagnosis criteria.
The highly variable phenotypes associated with neurodevelopmental disorders such as autism spectrum disorder (ASD) present a challenge to classification. Diagnosis of ASD draws heavily on the identification of distinctive actions. Arising in early life, this disorder is defined by abnormal social interactions and communication, stereotyped repetitive behaviors and restricted interests [1, 2]. The intensity and manifestation of these traits can vary greatly between individuals. In addition to cognitive and social variation, persons on the autism spectrum also express variable degrees of deficit in movement and sensory response [3,4,5]. The entire range of variation is currently classified for treatment purposes as a single broad entity, ASD [6, 7].
Traditionally, mouse behavioral testing consists of discrete measurements such as location in a multi-arm maze or a three-chamber test apparatus [10, 11]. Such characterization omits both finer details of movement and higher-order complex behavioral motifs . These details can now be extracted efficiently using modern computational methods in machine vision. Recent advances in automated tracking allow deep phenotyping of movement consisting of simultaneous tracking of body-centric joint and body part positions, \(x-y\) position in an arena and task performance, thereby providing a multi-level view of behavior [13,14,15,16]. This allows the measurement of movement and cognitive/social features from a single dataset [17, 18]. We developed a method using such a system for semi-supervised behavioral classification in individual mice during spontaneous activity. Our method describes long-term structure and fine-scale kinematic details in repeated recordings. Body part identification is consistent within individual mice over multiple sessions in the open-field arena. From these detailed features, we used posture dynamical clustering [19,20,21,22] to quantify the entire repertoire of movement.
We used 11 high-confidence virtual markers to represent posture. The posture of an animal over a recording is now represented as an \(11 \times 11 \times n\) array where n is the number of frames in a given movie. To simplify this matrix, we further remove all indices where \(i \le j\), as these are always self-distances and equal to 0 (when \(i=j\)), or repetitions of the same distances (when \(i
Because of the unbalanced nature of our data, where extremely specific yet common behaviors like locomotion and a particular speed might overwhelm an automated clustering algorithm, we first used importance sampling to design a representative behavioral sample set, like we did with postures for training the DNN. We sampled movies from each experimental condition and created a dataset that encompassed all of the types of movements that were found in our recordings. This was particularly important for labeling uncommon behaviors that may not have appeared in many movies but were still behaviorally interesting.
We categorized each of the 100 clusters into one of eight behavioral classes (Additional file 1: Fig. S1 and Fig. 2a). This manual curation step revealed that time spent in the open field was composed of commonly studied behaviors such as locomotion and grooming, but also less distinct movements made for a large fraction of the time including spatial exploration and ambling. We defined the classes fast exploration and slow exploration to characterize periods that are usually unaccounted for in traditional measurement paradigms. Fast exploration, for example, included quick turns and sniffs that are characteristic of alertness or anxiety, whereas slow exploration included head sweeps and extensions during calm periods.
The grooming class could be further divided into several modes of grooming: body grooming, face/paw grooming/licking, small quick movements, foot scratching and a mixture mode to account for time spent readjusting between modes. To validate grooming modes, human annotators were presented with movie segments that included grooming based on the algorithm and then were asked to annotate videos frame by frame (Additional file 3: Fig. S3d). A comparison indicated that the algorithm emphasized shorter timescales and identified more mixed bouts of grooming than human annotators (Additional file 3: Fig. S3e). The total number of episodes spent grooming was normalized to 1 to obtain a relative usage of these grooming modes (Fig. 4b). Despite differences in total grooming frequency between C57BL/6J males and females, their relative grooming-mode frequencies were similar and showed the same trend over time. Across days, body grooming increased in relative frequency (repeated-measures ANOVA, \(F(1,77) = 8.62\), \(p = 0.02\)) while face grooming and quick grooming movements became less frequent (repeated-measures ANOVA, \(F(1,77) = 8.74\), \(p = 0.02\) and \(F(1,77) = 6.96\), \(p = 0.048\)).
The two types of neurodevelopmental mutants displayed differences in both the walk-to-trot transition speed and the phase difference between opposite-limb (FL/HR and FR/HL pairs) movements. Cntnap2 KO mice showed a similar transition speed of about 0.1 m/s as their littermates. In contrast, L7-Tsc1 mutants used the walking gait much more and transitioned to the trotting gait at a much higher velocity of \(\sim 0.2\) m/s. The phase difference during trotting was larger for both neurodevelopmental mouse models. Cntnap2 KO had a phase difference at 0.2 m/s of 0.7 rad compared to a difference of 0.5 rad for their littermates. L7-Tsc1 mutants had an even larger difference of 1.0 rad at 0.2 m/s compared to 0.5 rad for their littermates.
Animal behavior is high-dimensional. Changes in movement, transitions between states and adaptive change are most easily observed by different metrics on multiple timescales. Our method for semi-supervised behavioral classification allows exploration of all three types of dynamics from a single video recording. Such deep phenotyping can capture rich behavioral readouts that illuminate the effects of genetic and experimental perturbations in naturalistic environments.
Previous methods for analyzing freely moving behavior have been limited to restricted feature sets. Observations from freely moving animals have focused on simple measures, such as spatial location, using footprint measurement and centroid tracking. Detailed limb movements have been observed in constrained environments such as a corridor or treadmill to model the relationship between gait and postural features [47, 48], but this approach does not capture movement in a more natural context. Our approach allows all these types of measurements to be made at once in an automated manner.
The simplicity of the open field allows rapid characterization without need to train mouse or experimenter. This level of automation and standardization may contribute to differences with past work. For example, we found that L7-Tsc1 mutant mice showed reduced grooming compared with wild types, yet past studies report increased grooming. We found that grooming included many sub-states that involved different movements. Differences in how human scorers classify such states may lead to laboratory-to-laboratory variability in quantification. Additional sources of variation include conditions that facilitate grooming, such as spraying mice with water or including bedding material in the chamber. Conversely, our automated methods incorporated a temporal window for clustering, so that singular quick grooming movements might have been missed. Further study is needed to reconcile these approaches.
The ability to simultaneously characterize movement kinematics and larger-scale features of behavior, even without a task condition, has particular relevance to the study of autism. Movement disruptions appear in 80% of children with autism [43, 44], despite the fact that movement is not considered part of the classic triad of defining symptoms. Injury to the cerebellum at birth leads to a sharply increased likelihood of autism, raising the possibility that the same structure that regulates smooth movement may also play a key role in driving the development of higher-level behavioral capacity ). In this way, movement and cognitive maturation may use shared neural substrates. Our analysis of Cntnap2 KO and L7-Tsc1 mutant mice reveals a shared disruption to specific parameters of gait arising from different genetic perturbations, one brain-wide and one cerebellar. We find that each model exemplifies different features: hyperactivity in one case, and failure to adapt in the other. In the future it may be possible to use alterations in movement to aid in the classification of autistic individuals. In addition, early life identification of motor symptoms may allow rapid risk stratification for early intervention. 041b061a72