The Centers for Disease Control and Prevention (CDC) estimates 1 in 59 children is now affected by Autism Spectrum Disorder (ASD), and although early diagnosis is critical to healthy development, most children are not diagnosed until age four.1 As is the case with many neurological disorders, much is unknown about the cause of ASD, and therefore effective treatment options are limited. Several genes have been identified as potential causes, but none of these appear to explain all cases. Therapeutics are often used to manage aspects of ASD such as seizures, mood disorders, increased energy levels, and difficulty focusing.2 Behavioral therapies can also help autistic children learn a variety of skills and promote positive behaviors.2
As translation from animal models to clinical outcomes is a common issue in neurological disorder research, much of the current preclinical ASD research is focused on establishing a quality model of the disorder so future research can use it to successfully identify treatment targets.
Approaches to Studying ASD
ASD is considered a psychiatric disorder and recent research has shown common genetic roots which may lead to shared symptoms with disorders such as Schizophrenia and ADHD. To mimic comparable symptoms in animals, scientists have developed pharmacological and genetic approaches which led to a focus on objective physiological and behavioral parameters. If such parameters are studied simultaneously, the information gathered could offer better insight into disease progression and a successful pharmacological treatment. Parameters of interest often include:
- Hyperactivity, often studied through locomotor activity levels and changes in sleep patterns (EEG & EMG).4
- Reduction in social interaction and increased aggression as seen through altered EEG (increased frequency in gamma bands) and cardiovascular pattern levels.5
- Behavioral impairment and increased arousal state, often studied by correlating physiologic data with video acquisition and/or behavioral analysis.
The following is an abbreviated sample of studies focused on various aspects of ASD in which DSI technology assisted in achieving their research goals.
Replicable in vivo physiological and behavioral phenotypes of the Shank3B null mutant mouse model of autism
The goal of this study was to create a comprehensive and reproducible animal model of ASD, incorporating it’s behavioral and physiological characteristics to improve future preclinical ASD research. Specifically, they wanted to identify EEG and behavioral phenotypes which can be replicated in independent mouse model cohorts. Genetic disturbance of the SHANK3 gene is thought to be responsible for many cases of ASD. This study quantified translational EEG activity in a SHANK3 null mouse model through in vivo EEG measurement and behavioral assays in two cohorts. The team measured and analyzed EEG, temperature, and activity using DSI telemetry and software solutions.6
Sleep/Wake Physiology and Quantitative Electroencephalogram Analysis of the Neuroligin-3 Knockout Rat Model of Autism Spectrum Disorder
Another gene believed to play a role in ASD is Neuroligin-3 (NLGN3). Previous studies of various ASD models and models in the neuroligin family have shown sleep disturbances, but few studies have fully evaluated sleep in ASD models. This study aimed to assess sleep/wake physiology and behavioral phenotypes in a rat model with genetic ablation of NlGN3. The researchers used DSI telemetry and software solutions to measure and analyze EEG, EMG, temperature, and activity data. Their results showed these mice spent less time in non-rapid eye movement (NREM) sleep and more time in rapid eye movement (REM) sleep. In addition, they exhibited elevated theta power during wakefulness and REM, elevated delta power during NREM, and suppressed power of beta and gamma across all states.7
Neuronal glucose transporter isoform 3 deficient mice demonstrate features of autism spectrum disorders
Previous studies have shown patients with ASD experience a reduced uptake of brain glucose, and the resulting abnormal neuronal energy metabolism could be a cause of ASD. This research team wanted to see if GLUT3 (the predominant neuronal isoform fueling energy metabolism) deficiency would result in a comprehensive mouse model of ASD. They created a GLUT3-null mouse and studied its development. Their results show this model displayed characteristics associated with clinical ASD including “abnormal spatial learning and working memory but normal acquisition and retrieval during contextual conditioning, abnormal cognitive flexibility with intact gross motor ability, electroencephalographic seizures, perturbed social behavior with reduced vocalization and stereotypies at low frequency”. The research team used DSI telemetry to study epileptic events through EEG measurements.8
DSI Telemetry offers researchers the ability to collect many physiologic endpoints of interest in ASD studies such as biopotentials (EEG, EMG, EOG), temperature, and activity. In addition, these endpoints can be combined with various pressure measurements for assessing impacts on cardiovascular health. With telemetry, endpoints are collected from conscious, freely-moving animals, offering improved animal welfare and data integrity over restrained or anesthetized methods. DSI telemetry also offers the flexibility to socially house animals for studies interested in social behaviors.
If a less invasive option is preferred, hardwired options are also available to collect continuous measurements of biopotentials. Hardwired solutions allow the use of a tether to monitor up to 12 biopotential channels.
In addition to data acquisition solutions, DSI offers analysis software. Neuroscore™ can be used to efficiently analyze chronic data sets as well as scoring sleep and seizure data.
If you are interested in learning more about DSI’s solutions for ASD and other neuroscience research, download our free whitepaper or schedule a consultation with us to discuss your research goals.
1Austism Speaks. (2018). “CDC increases estimate of autism’s prevalence by 15 percent, to 1 in 59 children”. https://www.autismspeaks.org/science-news/cdc-increases-estimate-autisms-prevalence-15-percent-1-59-children
2Centers for Disease Control and Prevention. (2018). “Treatment for Autism Spectrum Disorder”. https://www.cdc.gov/ncbddd/autism/treatment.html
3Cross-Disorder Group of the Psychiatric Genomics Consortium. (2013). “Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis”. The Lancet. 381(9875); 1371-1379. https://doi.org/10.1016/S0140-6736(12)62129-1
4Ahnaou A, Ver Donck L, Drinkenburg WH. (2014). “Blockade of the metabotropic glutamate (mGluR2) modulates arousal through vigilance states transitions: evidence from sleep-wake EEG in rodents”. Behavioral Brain Research. 270; 56-67. https://doi.org/10.1016/j.bbr.2014.05.003
5Uhlhaas PJ, Singer W. (2010). “Abnormal neural oscillations and synchrony in schizophrenia”. Nature Reviews Neuroscience. 11; 100-113. https://www.nature.com/articles/nrn2774
6Dhamne SC, Silverman JL, Super CE, Lammers SHT, Hameed MQ, Modi ME, Copping NA, Pride MC, Smith DG, Rotenberg A, Crawley JN, Sahin M. (2017). “Replicable in vivo physiological and behavioral phenotypes of the Shank3B null mutant mouse model of autism”. Molecular Autism. 8(26). https://doi.org/10.1186/s13229-017-0142-z
7Thomas AM, Schwartz MD, Saxe MD, Kilduff TS. (2017). “Sleep/Wake Physiology and Quantitative Electroencephalogram Analysis of the Neuroligin-3 Knockout Rat Model of Autism Spectrum Disorder”. Sleep. 40(10). https://doi.org/10.1093/sleep/zsx138
8Zhao Y, Fung C, Shin D, Shin BC, Thamotharan S, Sankar R, Ehninger D, Silva A, Devaskar SU. (2010). “Neuronal glucose transporter isoform 3 deficient mice demonstrate features of autism spectrum disorders”. Molecular Psychiatry. 15; 286-299. https://www.nature.com/articles/mp200951