scholarly journals Deep Phenotyping Reveals Movement Phenotypes in Mouse Neurodevelopmental Models

2021 ◽  
Author(s):  
Ugne Klibaite ◽  
Mikhail Kislin ◽  
Jessica L. Verpeut ◽  
Xiaoting Sun ◽  
Joshua W. Shaevitz ◽  
...  
Keyword(s):  
Developmental Disorders ◽  
Autism Spectrum ◽  
Multiple Time Scales ◽  
Fine Motor ◽  
Multiple Time ◽  
Wild Type ◽  
Behavioral Dynamics ◽  
Diagnosis Criteria ◽  
Clinical Conditions ◽  
Deep Phenotyping

Abstract Background: 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 aimed at investigating behavioral consequences of a cerebellum-specific deletion in Tsc1 protein and a whole-brain knockout in Cntnap2 protein in mice, both mutations are found in the clinical conditions and have been associated with ASD. 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. Methods: Here, we use advances in computer vision and deep learning, a generalized form of high-dimensional statistical analysis, to develop a framework for characterizing mouse movement on multiple time scales 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. Results: Both Cntnap2 knockout and L7-Tsc1 mutants showed forelimb lag during gait. L7-Tsc1 mutants showed complex defects in multi-day adaptation, lacking the tendency of wild-type mice to spend progressively more time in corners of the arena. In L7-Tsc1 mutant mice, failure-to-adapt took the form of maintained ambling, turning, and locomotion, and an overall decrease in grooming. Adaptation in Cntnap2 knockout mice more broadly resembled that of wild-type. L7-Tsc1 mutant and Cntnap2 knockout mouse models showed different patterns of behavioral state occupancy. Limitations: Genetic risk factors for autism are numerous, and here we tested only two. Our pipeline was only applied to conditions of free behavior. Testing under task or social conditions would reveal more information about behavioral dynamics and variability. Conclusions: 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 a quantitative diagnosis criteria.

scholarly journals Deep Behavioral Phenotyping of Mouse Autism Models using Open-Field Behavior

2021 ◽  
Author(s):  
Ugne Klibaite ◽  
Mikhail Kislin ◽  
Jessica L. Verpeut ◽  
Xiaoting Sun ◽  
Joshua W. Shaevitz ◽  
...  
Keyword(s):  
Open Field ◽  
Phenotypic Diversity ◽  
Specific Model ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Wild Type ◽  
Open Field Behavior ◽  
Behavioral Phenotyping ◽  
Behavioral Dynamics ◽  
Deep Phenotyping

AbstractAutism is noted for both its genotypic and phenotypic diversity. Repetitive action, resistance to environmental change, and motor disruptions vary from individual to individual. In animal models, conventional behavioral phenotyping captures such fine-scale variations incompletely. Here we use advances in computer vision and deep learning to develop a framework for characterizing mouse behavior on multiple time scales using a single popular behavioral assay, the open field test. We observed male and female C57BL/6J mice to develop a dynamic baseline of adaptive behavior over multiple days. We then examined two rodent models of autism, a cerebellum-specific model, L7-Tsc1, and a whole-brain knockout model, Cntnap2. Both Cntnap2 knockout and L7-Tsc1 mutants showed forelimb lag during gait. L7-Tsc1 mutants showed complex defects in multi-day adaptation, lacking the tendency of wild-type mice to spend progressively more time in corners of the arena. In L7-Tsc1 mutant mice, failure-to-adapt took the form of maintained ambling, turning, and locomotion, and an overall decrease in grooming. Adaptation in Cntnap2 knockout mice more broadly resembled that of wild-type. L7-Tsc1 mutant and Cntnap2 knockout mouse models showed different patterns of behavioral state occupancy. 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.

scholarly journals Major motor and gait deficits with sexual dimorphism in a Shank3 mutant mouse model

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Emmanuel Matas ◽  
Alexandre Maisterrena ◽  
Mathieu Thabault ◽  
Eric Balado ◽  
Maureen Francheteau ◽  
...  
Keyword(s):  
Animal Models ◽  
Stereotyped Behavior ◽  
Autism Spectrum ◽  
Special Focus ◽  
Clinical Settings ◽  
Novelty Preference ◽  
Protein Levels ◽  
Diagnosis Criteria ◽  
Clinical Conditions ◽  
Post Hoc

Abstract Background Contrasting findings were reported in several animal models with a Shank3 mutation used to induce various autism spectrum disorder (ASD) symptoms. Here, we aimed at investigating behavioral, cellular, and molecular consequences of a C-terminal (frameshift in exon 21) deletion in Shank3 protein in mice, a mutation that is also found in clinical conditions and which results in loss of major isoforms of Shank3. A special focus was made on cerebellar related parameters. Methods All three genotypes were analyzed [wild type (WT), heterozygote (Shank3+/ΔC) and homozygote (Shank3 ΔC/ΔC)] and males and females were separated into two distinct groups. Motor and social behavior, gait, Purkinje cells (PC) and glutamatergic protein levels were determined. Behavioral and cellular procedures used here were previously validated using two environmental animal models of ASD. ANOVA and post-hoc analysis were used for statistical analysis. Results Shank3 ΔC/ΔC mice showed significant impairments in social novelty preference, stereotyped behavior and gait. These were accompanied by a decreased number of PC in restricted cerebellar sub-regions and decreased cerebellar expression of mGluR5. Females Shank3 ΔC/ΔC were less affected by the mutation than males. Shank3+/ΔC mice showed impairments only in social novelty preference, grooming, and decreased mGluR5 expression and that were to a much lesser extent than in Shank3 ΔC/ΔC mice. Limitations As Shank3 mutation is a haploinsufficiency, it is of interest to emphasize that Shank3+/ΔC mice showed only mild to no deficiencies compared to Shank3 ΔC/ΔC. Conclusions Our findings indicate that several behavioral, cellular, and molecular parameters are affected in this animal model. The reported deficits are more pronounced in males than in females. Additionally, male Shank3 ΔC/ΔC mice show more pronounced alterations than Shank3+/ΔC. Together with our previous findings in two environmental animal models of ASD, our studies indicate that gait dysfunction constitutes a robust set of motor ASD symptoms that may be considered for implementation in clinical settings as an early and quantitative diagnosis criteria.

Altered EEG Variability on Different Time Scales in Participants with Autism Spectrum Disorder

2021 ◽  
Author(s):  
Lukas Hecker ◽  
Mareike Wilson ◽  
Ludger Tebartz van Elst ◽  
Jürgen Kornmeier
Keyword(s):  
Autism Spectrum Disorder ◽  
Time Scales ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Neural Noise ◽  
Scientific Attention ◽  
Neural Variability ◽  
Different Time Scales

Abstract Background: One of the great challenges in psychiatry is finding reliable biomarkers that may allow for more accurate diagnosis and treatment of patients. In this context the topic of neural variability received scientific attention in recent years. Altered neural variability was found in different cohorts of patients with autism spectrum disorder (ASD) using both functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). These findings lead to the neural noise hypothesis of ASD. The number of studies focusing on neural variability is, however, yet small and the reported effects are controversial and poorly understood. Methods: In the present study we compared different temporal and structural aspects of variability in visually evoked EEG activity in a cohort of 16 adult participants with Asperger Syndrome (AS) and 19 matched neurotypical (NT) controls. Participants performed a visual oddball task using fine and coarse checkerboard stimuli. Results: We investigated various measures of neural variability and found effects on multiple time scales. (1) As opposed to some of the previous studies, we found reduced inter-trial variability in the AS group compared to NT. (2) This effect builds up over the entire course of a 5-minute experiment and (3) seems to be based on smaller variability of neural background activity in patients compared to NTs. Limitations: The present study is exploratory in nature with a hypothesis generating character. Further studies with a new and larger set of participants are thus mandatory to verify or falsify our findings. Conclusion: The here reported variability effects come with considerably large effect sizes, making them promising candidates for potentially reliable biomarkers in psychiatric diagnostics. The observed pattern of universality across different time scales and stimulation conditions indicates trade like effects. The inconsistency of our findings with previous reports from the literature, on the other hand, rather points towards state-like effects, specific to the current stimulus material and/or experimental paradigm.

SOME REMARKS ON THE MULTIPLE TIME SCALES OF DIFFUSION IN MINERALS AND MELTS

2018 ◽  
Author(s):  
Yan Liang ◽  
◽  
Daniele J. Cherniak ◽  
Chenguang Sun
Keyword(s):  
Time Scales ◽  
Multiple Time Scales ◽  
Multiple Time

scholarly journals Quantitative Checklist for Autism in Toddlers (Q-CHAT). A population screening study with follow-up: the case for multiple time-point screening for autism

2021 ◽  
Vol 5 (1) ◽  
pp. e000700
Author(s):  
Carrie Allison ◽  
Fiona E Matthews ◽  
Liliana Ruta ◽  
Greg Pasco ◽  
Renee Soufer ◽  
...  
Keyword(s):  
Phase 1 ◽  
Autism Spectrum ◽  
Population Screening ◽  
Diagnostic Assessment ◽  
Test Accuracy ◽  
Multiple Time ◽  
Phase 2 ◽  
Time Points ◽  
Screening Study

ObjectiveThis is a prospective population screening study for autism in toddlers aged 18–30 months old using the Quantitative Checklist for Autism in Toddlers (Q-CHAT), with follow-up at age 4.DesignObservational study.SettingLuton, Bedfordshire and Cambridgeshire in the UK.Participants13 070 toddlers registered on the Child Health Surveillance Database between March 2008 and April 2009, with follow-up at age 4; 3770 (29%) were screened for autism at 18–30 months using the Q-CHAT and the Childhood Autism Spectrum Test (CAST) at follow-up at age 4.InterventionsA stratified sample across the Q-CHAT score distribution was invited for diagnostic assessment (phase 1). The 4-year follow-up included the CAST and the Checklist for Referral (CFR). All with CAST ≥15, phase 1 diagnostic assessment or with developmental concerns on the CFR were invited for diagnostic assessment (phase 2). Standardised diagnostic assessment at both time-points was conducted to establish the test accuracy of the Q-CHAT.Main outcome measuresConsensus diagnostic outcome at phase 1 and phase 2.ResultsAt phase 1, 3770 Q-CHATs were returned (29% response) and 121 undertook diagnostic assessment, of whom 11 met the criteria for autism. All 11 screened positive on the Q-CHAT. The positive predictive value (PPV) at a cut-point of 39 was 17% (95% CI 8% to 31%). At phase 2, 2005 of 3472 CASTs and CFRs were returned (58% response). 159 underwent diagnostic assessment, including 82 assessed in phase 1. All children meeting the criteria for autism identified via the Q-CHAT at phase 1 also met the criteria at phase 2. The PPV was 28% (95% CI 15% to 46%) after phase 1 and phase 2.ConclusionsThe Q-CHAT can be used at 18–30 months to identify autism and enable accelerated referral for diagnostic assessment. The low PPV suggests that for every true positive there would, however, be ~4–5 false positives. At follow-up, new cases were identified, illustrating the need for continued surveillance and rescreening at multiple time-points using developmentally sensitive instruments. Not all children who later receive a diagnosis of autism are detectable during the toddler period.

scholarly journals Early Diagnostics and Early Intervention in Neurodevelopmental Disorders—Age-Dependent Challenges and Opportunities

2021 ◽  
Vol 10 (4) ◽  
pp. 861
Author(s):  
Mijna Hadders-Algra
Keyword(s):  
Early Intervention ◽  
High Risk ◽  
Early Detection ◽  
Developmental Disorders ◽  
Familial Risk ◽  
Brain Structures ◽  
Autism Spectrum ◽  
First Year ◽  
Early Diagnostics ◽  
Challenges And Opportunities

This review discusses early diagnostics and early intervention in developmental disorders in the light of brain development. The best instruments for early detection of cerebral palsy (CP) with or without intellectual disability are neonatal magnetic resonance imaging, general movements assessment at 2–4 months and from 2–4 months onwards, the Hammersmith Infant Neurological Examination and Standardized Infant NeuroDevelopmental Assessment. Early detection of autism spectrum disorders (ASD) is difficult; its first signs emerge at the end of the first year. Prediction with the Modified Checklist for Autism in Toddlers and Infant Toddler Checklist is possible to some extent and improves during the second year, especially in children at familial risk of ASD. Thus, prediction improves substantially when transient brain structures have been replaced by permanent circuitries. At around 3 months the cortical subplate has dissolved in primary motor and sensory cortices; around 12 months the cortical subplate in prefrontal and parieto-temporal cortices and cerebellar external granular layer have disappeared. This review stresses that families are pivotal in early intervention. It summarizes evidence on the effectiveness of early intervention in medically fragile neonates, infants at low to moderate risk, infants with or at high risk of CP and with or at high risk of ASD.

Home-based music therapy for children with developmental disorders during the COVID-19 pandemic

2021 ◽  
pp. 1357633X2098121
Author(s):  
Sarah Bompard ◽  
Tommaso Liuzzi ◽  
Susanna Staccioli ◽  
Fiammetta D’Arienzo ◽  
Sahereh Khosravi ◽  
...  
Keyword(s):  
Sleep Quality ◽  
Music Therapy ◽  
Developmental Disorders ◽  
Developmental Process ◽  
Short Form ◽  
Stress Index ◽  
Parental Distress ◽  
Home Based ◽  
Clinical Conditions ◽  
Children’S Sleep

Introduction During the COVID-19 pandemic, children with neurodevelopmental disabilities could not attend their usual rehabilitation therapies, with a consequent reduced support of developmental process and risk of worsening of their clinical conditions. Methods We prospectively enrolled 14 children with developmental delay, who had already tried a personalised music therapy (Euterpe method). We included them in a 12-day programme of home-based music therapy. The children and their parents were investigated using the Sleep Disturbance Scale for Children and the Parent Stress Index-Short Form. Results Fourteen children started the intervention, while only 12 children completed all the planned home sessions and assessments. We observed a significant improvement in children’s sleep quality and a reduction of parental distress. Discussion The significant improvements in parental distress and sleep quality must be considered important achievements for the quality of life of a child and their family. Home-based music therapy can provide a feasible approach to improving sleep and parent’s stress for children with developmental disorders.

scholarly journals Odor response adaptation in Drosophila—a continuous individualization process

2021 ◽  
Vol 383 (1) ◽  
pp. 143-148
Author(s):  
Shadi Jafari ◽  
Mattias Alenius
Keyword(s):  
Sensory Neurons ◽  
Olfactory System ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Olfactory Perception ◽  
Adaptation Mechanisms ◽  
Metabolic States ◽  
The Central Nervous System ◽  
Olfactory Adaptation ◽  
Central Adaptation

AbstractOlfactory perception is very individualized in humans and also in Drosophila. The process that individualize olfaction is adaptation that across multiple time scales and mechanisms shape perception and olfactory-guided behaviors. Olfactory adaptation occurs both in the central nervous system and in the periphery. Central adaptation occurs at the level of the circuits that process olfactory inputs from the periphery where it can integrate inputs from other senses, metabolic states, and stress. We will here focus on the periphery and how the fast, slow, and persistent (lifelong) adaptation mechanisms in the olfactory sensory neurons individualize the Drosophila olfactory system.

scholarly journals Health-Related Quality of Life in Children with Developmental Disorders

2021 ◽  
Author(s):  
Marina M. Schoemaker ◽  
Suzanne Houwen
Keyword(s):  
Quality Of Life ◽  
Developmental Disorders ◽  
Developmental Coordination Disorder ◽  
Autism Spectrum ◽  
Future Research ◽  
Health Related Quality ◽  
Related Quality ◽  
Health Related ◽  
Lower Hrqol

Abstract Purpose of Review (1) To give an overview of what is currently known about health-related quality of life (HRQoL) in three common and co-occurring developmental disorders: attention deficit hyperactivity disorder (ADHD), autism spectrum disorders (ASD), and developmental coordination disorder (DCD), and (2) to provide directions for future research. Recent Findings HRQoL is compromised in all three developmental disorders, affecting various domains of HRQoL. However, some domains are more affected than others depending on the nature of the core deficits of the disorder. Overall, parents’ rate HRQoL of their children lower than the children themselves. Children with ASD and ADHD with co-occurring disorders have lower HRQoL compared to those with singular disorders. Future studies in DCD are needed to investigate the effect of co-occurring disorder in this population. Summary Children with developmental disorders have lower HRQoL than typically developing children. Future research should focus on the effects of co-occurring disorders on HRQoL and on protective factors that may increase HRQoL. HRQoL should be a part of clinical assessment, as it reveals the areas in life children are struggling with that could be targeted during intervention.

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