Behavioral and Neuroanatomical Phenotypes in Mouse Models of Autism

Ellegood, Jacob; Crawley, Jacqueline N.

doi:10.1007/s13311-015-0360-z

Cited by 118 publications

(108 citation statements)

References 176 publications

(214 reference statements)

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“…We show that tracking analysis alone was incapable of detecting differences in the frequency of social interactions between control C57BL/6N mice and BTBR mice, a previously reported autism model (1,(37)(38)(39). Application of the pose estimator, by contrast, detected a significant difference between strains, as did the automated behavior classifier.…”

Section: Discussionmentioning

confidence: 77%

Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning

Hong

Kennedy

Burgos-Artizzu

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

279

234

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A lack of automated, quantitative, and accurate assessment of social behaviors in mammalian animal models has limited progress toward understanding mechanisms underlying social interactions and their disorders such as autism. Here we present a new integrated hardware and software system that combines video tracking, depth sensing, and machine learning for automatic detection and quantification of social behaviors involving close and dynamic interactions between two mice of different coat colors in their home cage. We designed a hardware setup that integrates traditional video cameras with a depth camera, developed computer vision tools to extract the body "pose" of individual animals in a social context, and used a supervised learning algorithm to classify several well-described social behaviors. We validated the robustness of the automated classifiers in various experimental settings and used them to examine how genetic background, such as that of Black and Tan Brachyury (BTBR) mice (a previously reported autism model), influences social behavior. Our integrated approach allows for rapid, automated measurement of social behaviors across diverse experimental designs and also affords the ability to develop new, objective behavioral metrics.

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Section: Discussionmentioning

confidence: 77%

Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning

Hong

Kennedy

Burgos-Artizzu

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

279

234

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“…The global and regional alterations remain unchanged in age-specific subgroups, with the caveat of a significant decrease in power (Supplemental Figures S2 and S4A). The developmental onset of global and regional differences in 16p11.2 CNV carriers remains unknown, but the insula, striatum, and thalamus are also altered in a 7-day-old 16p11.2 deletion mouse model (54,55), suggesting an early developmental effect. However, specific anatomical effects are difficult to interpret between humans and mouse models.…”

Section: Limitationsmentioning

confidence: 99%

Quantifying the Effects of 16p11.2 Copy Number Variants on Brain Structure: A Multisite Genetic-First Study

Martin

Rodríguez-Herreros

Nielsen

et al. 2018

Biological Psychiatry

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BACKGROUND: 16p11.2 breakpoint 4 to 5 copy number variants (CNVs) increase the risk for developing autism spectrum disorder, schizophrenia, and language and cognitive impairment. In this multisite study, we aimed to quantify the effect of 16p11.2 CNVs on brain structure. METHODS: Using voxel-and surface-based brain morphometric methods, we analyzed structural magnetic resonance imaging collected at seven sites from 78 individuals with a deletion, 71 individuals with a duplication, and 212 individuals without a CNV. RESULTS: Beyond the 16p11.2-related mirror effect on global brain morphometry, we observe regional mirror differences in the insula (deletion . control . duplication). Other regions are preferentially affected by either the deletion or the duplication: the calcarine cortex and transverse temporal gyrus (deletion . control; Cohen's d . 1), the superior and middle temporal gyri (deletion , control; Cohen's d , 21), and the caudate and hippocampus (control . duplication; 20.5 . Cohen's d . 21). Measures of cognition, language, and social responsiveness and the presence of psychiatric diagnoses do not influence these results. CONCLUSIONS: The global and regional effects on brain morphometry due to 16p11.2 CNVs generalize across site, computational method, age, and sex. Effect sizes on neuroimaging and cognitive traits are comparable. Findings partially overlap with results of meta-analyses performed across psychiatric disorders. However, the lack of correlation between morphometric and clinical measures suggests that CNV-associated brain changes contribute to clinical manifestations but require additional factors for the development of the disorder. These findings highlight the power of genetic risk factors as a complement to studying groups defined by behavioral criteria. Autism spectrum disorder (ASD) and related neurodevelopmental disorders are defined behaviorally and characterized by a significant clinical and etiologic heterogeneity. As a consequence, investigating ASD under the assumption of an underlying homogeneous condition has resulted in controversial findings in the field of neuroimaging (1). Increased brain growth early in development (2-4) and alterations of many regional brain volumes (5) have been implicated in ASD, but results have proven difficult to replicate (1,(6)(7)(8).To mitigate some of these issues, cohorts of individuals with shared genetic risk factors have been assembled to minimize the noise introduced by etiologic and biological heterogeneity (9). Such a "genetic-first" study design provides the opportunity to investigate a given neurodevelopmental risk (and associated mechanism) shared by individuals who carry the same genetic etiology irrespective of the psychiatric diagnosis.Copy number variants (CNVs) at the 16p11.2 (breakpoints 4-5, 29.6-30.2 Mb-hg19) (10) are among the most frequent risk factors for neurodevelopmental and psychiatric conditions.

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“…This effort has been accompanied by the development of ASD-relevant behavioral phenotyping assays, primarily targeted at social, communication, and repetitive behaviors (Silverman et al, 2010a; Wöhr and Scattoni, 2013; Kas et al, 2014; Homberg et al, 2016). Interestingly, many—but not all—models showed autism-like traits, with manifestations ranging from repetitive behaviors to reduced social communication (ultrasonic vocalizations) and social interest (reviewed in Ellegood and Crawley, 2015). However, despite the widespread application and high face validity of ASD behavioral phenotyping, the significance and translational relevance of mouse behavioral alterations to human ASD remain debated (Wöhr and Scattoni, 2013) and should be extrapolated with caution.…”

Section: Bridging the Gap: Functional Connectivity Mapping In Mouse Amentioning

confidence: 99%

Can Mouse Imaging Studies Bring Order to Autism Connectivity Chaos?

Liska

Gozzi

2016

Front. Neurosci.

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Functional Magnetic Resonance Imaging (fMRI) has consistently highlighted impaired or aberrant functional connectivity across brain regions of autism spectrum disorder (ASD) patients. However, the manifestation and neural substrates of these alterations are highly heterogeneous and often conflicting. Moreover, their neurobiological underpinnings and etiopathological significance remain largely unknown. A deeper understanding of the complex pathophysiological cascade leading to aberrant connectivity in ASD can greatly benefit from the use of model organisms where individual pathophysiological or phenotypic components of ASD can be recreated and investigated via approaches that are either off limits or confounded by clinical heterogeneity. Despite some obvious limitations in reliably modeling the full phenotypic spectrum of a complex developmental disorder like ASD, mouse models have played a central role in advancing our basic mechanistic and molecular understanding of this syndrome. Recent progress in mouse brain connectivity mapping via resting-state fMRI (rsfMRI) offers the opportunity to generate and test mechanistic hypotheses about the elusive origin and significance of connectional aberrations observed in autism. Here we discuss recent progress toward this goal, and illustrate initial examples of how the approach can be employed to establish causal links between ASD-related mutations, developmental processes, and brain connectional architecture. As the spectrum of genetic and pathophysiological components of ASD modeled in the mouse is rapidly expanding, the use of rsfMRI can advance our mechanistic understanding of the origin and significance of the connectional alterations associated with autism, and their heterogeneous expression across patient cohorts.

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Behavioral and Neuroanatomical Phenotypes in Mouse Models of Autism

Cited by 118 publications

References 176 publications

Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning

Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning

Quantifying the Effects of 16p11.2 Copy Number Variants on Brain Structure: A Multisite Genetic-First Study

Can Mouse Imaging Studies Bring Order to Autism Connectivity Chaos?

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