Aspirational Goal: Develop a range of targeted treatments and interventions that optimize function and abilities across the lifespan to achieve meaningful outcomes and maximize quality of life for people on the autism spectrum.
The evolution of this Aspirational Goal reflects the progression of priorities in the autism community. Over the past several years, the IACC’s focus has shifted from "preventing disabilities" (2009 IACC Strategic Plan), to encouraging "building adaptive skills" (2013 IACC Strategic Plan Update), and now emphasizes the construction of lifespan approaches and utilization of more meaningful treatment outcomes for individuals living with ASD and their families. This change also underscores the shifting landscape of treatment opportunities driven by exciting discoveries from cognitive neuroscience, which reveal breathtaking developmental reorganizations of brain function in adolescence and young adulthood,1,2,3 adding new possibilities for intervention and learning across the lifespan.
Since the 2013 IACC Strategic Plan Update, there has been an explosion of behavioral intervention studies and advancements in intervention science, including continued progress in the development and evaluation of multiple intervention types. Key advances include improvements in community implementation of effective interventions, greater numbers of fully powered randomized trials, comparative efficacy studies, and implementation science studies that consider child outcomes as well as best implementation practices. Additionally, the diversity of study participants has improved, as researchers more often strive to include underserved families as well as populations previously excluded or overlooked in ASD research, such as girls and minimally verbal children.
There has also been much progress in brain-behavior measures as predictors of outcomes of interventions, as well as the development of adaptive interventions, recognizing that sequential and multiple interventions are often required to improve child outcomes. Finally, technology has been used more frequently, as a tool within an intervention (such as iPads for communication and storyboarding), to deliver interventions using telehealth methods, and to collect data in real time that can be used to guide intervention and gauge treatment response.
The next generation of more precise, personalized treatments and interventions will be developed with the benefit of the knowledge gained from neuroscience and genetics research on the systems biology of ASD. Researchers are now utilizing the latest discoveries and tools from these fields to develop and evaluate genetically targeted pharmacology, neuroimaging-guided direct brain stimulation, combination drug (or brain stimulation) and behavioral treatments, and intervention approaches that match the needs of individuals with ASD. Great progress has been made, and will continue to be made, by prioritizing the understanding of the brain basis of ASD and biological mechanism(s) underlying a given therapeutic approach.
Intervention and Treatment Types
The autism community continues to emphasize the importance of establishing evidence-based practices in interventions.4 Evidence-based practice is grounded on the premise that there are interventions that have evidence of their positive and strong effects for individuals with ASD, and that practitioners (e.g., psychologists, psychiatrists, speech pathologists, teachers) should therefore prioritize their use while working with families. When strong evidence for an intervention or treatment to address a specific goal or outcome does not exist, the practitioner should try the intervention with the most evidence, although the empirical efficacy may fall below an established standard. Clinical and/or professional expertise plays a major role in selecting an intervention or practice to address a specific goal or more generalized outcomes and is especially useful for adapting the intervention for the individual with ASD when needed.
Looking forward, advances in neuroscience and genetics that provide knowledge of the biobehavioral mechanisms of treatment efficacy (summarized in Chapter 2 of this report) support a new principle to guide evidence-based practice: Preference should be given to those treatments and interventions for which there is a current or emerging understanding of the biobehavioral mechanism(s) of action. This will facilitate highly innovative, randomized, experimental therapeutics trials in human participants. Such trials will improve our understanding of the developmental mechanisms underlying ASD risk and resiliency, thereby enabling the development of novel treatments and intervention strategies.
Behavioral interventions fall into two broad classes: focused intervention practices and comprehensive treatment models (CTMs). Focused intervention practices are instructional or therapeutic approaches applied to an individual’s goals (e.g., making social initiations to peers, reducing self-injury), designed to produce outcomes related specifically to the goal, and are implemented over a relatively short period of time until an individual meets his or her specific goal. Meanwhile, CTMs address broader outcomes (e.g., increases in cognitive abilities, adaptive behavior, social and communication skills). CTMs consist of many focused intervention practices organized around a conceptual framework, are documented through treatment protocols, and exist over a more extended time period. Examples include the Lovaas Model5 and the Early Start Denver Model (ESDM).6
Practitioners use these two classes of interventions/ treatments in different ways. They may select multiple focused intervention practices to build individualized programs for children, youth, and adults with ASD, or they may fully adopt a comprehensive treatment program in which the focused interventions and their use are already prescribed.7 Although several CTMs have been shown to be efficacious, they may be implemented less often by practitioners than focused intervention practices.8,9 The National Standards Project and the National Professional Development Center on ASD (NPDC) have conducted critical and rigorous reviews of the intervention research literature and identified sets of focused intervention practices that have evidence of efficacy.10
The NPDC work specifically focused on practices that could be implemented in school and/or community settings. Similarly, deBruin and colleagues conducted a meta-analysis of school-based interventions in high schools, finding evidence of efficacy for many of the same focused intervention practices (i.e., antecedent-, video-, and consequent-based interventions).11 Other reviews have documented the efficacy of 1) school-based, focused interventions on challenging behavior, 2) the use of peer-networks to foster social engagement, 3) social skills training, and 4) academic interventions.12,13,14,15,16,17 Research on the efficacy of several behavioral interventions continues today, including the Lovaas Model, ESDM, JASPER (Joint Attention, Symbolic Play, Engagement, and Regulation), LEAP (Learning Experiences and Alternative Program for Preschoolers and Their Parents), PRT (Pivotal Response Treatment), First Words Project, DIR/Floortime (Developmental, Individual-Difference, Relationship-Based), EMT (Enhanced Milieu Teaching), and STAR (Strategies for Teaching based on Autism Research).5,6,18,19,20,21,22,23,24,25,26
It can take over a decade and a half for evidence-based interventions to become widely implemented in the community when developed in the laboratory. Thus, researchers are increasingly developing and testing interventions in school-based settings, with the added goal of sustaining the intervention beyond the study period. Two recent studies demonstrate that similar outcomes can be obtained in the community and the lab.27,28 Both of these studies implemented JASPER aimed at improving core impairments in social communication, and noted sustainability of the intervention over a short-term follow-up. As a whole, these and other findings highlight the effectiveness of teacher-implemented interventions in school settings on improving one of the core features of ASD and pave the way for more school-based intervention research.
As diagnostic advances have made it possible to identify children with ASD at earlier ages, researchers have tested a number of parent-mediated interventions in order to meet the need for interventions that can be implemented as early as possible. Most of these are labeled Naturalistic Developmental Behavioral Interventions (NDBIs), a newly vetted grouping of early interventions based on applied behavior analysis (ABA).29 Several recent studies have yielded significant improvements over earlier studies by comparing the experimental treatment to an active control group involving parent education but no hands-on coaching, versus comparing an experimental treatment to treatment as usual.30,31,32
One conclusion of these recently completed studies is that active hands-on parent coaching for social communication outcomes is more effective than parent education models where the same information is provided without active coaching. This conclusion is further supported by another recent study of toddlers at risk for ASD, finding that initial gains in parent responsiveness did not sustain to the follow-up, speaking to the need for longer-term, more intense, or more hands-on intervention.33 A recent parent-mediated intervention study based on the DIR/ Floortime intervention approach suggests efficacy of this model for improving parent and child outcomes.22 Researchers have also studied the benefits of parent group interventions, where groups of parents are coached to deploy interventions. In a recent study of the PRT approach, researchers found that parent group interventions yielded significant parent and child benefit.34 While more cost effective than 1:1 therapy sessions, more research is needed to determine the generalizability and sustainability of parent group interventions to foster meaningful improvement in child behaviors, communication, and functioning.
Altogether, the foregoing studies add to the positive outcomes attained through parent-mediated interventions but raise issues about meaningful outcomes (i.e., spontaneous versus prompted outcomes) and the specific "active ingredients" – or essential components – of treatment (i.e., hands-on coaching, dose, approach). In the future, researchers will need to better understand for whom an intervention works best, and why an intervention provides benefit. Understanding the mechanisms behind effective behavioral interventions helps researchers to identify the essential components of an intervention, making it possible to develop a repertoire of components that can be combined in various ways to customize treatment. Two recent studies suggest that parent synchronization (attuning of the parent’s behavior to the child’s attention) and mirrored pacing (following the child’s lead) are important components or active ingredients of parent-mediated interventions.35,36
Behavioral Interventions in Understudied Populations
Developing interventions for minimally verbal children has been very challenging. A recent study tested whether JASPER combined with a behavioral language intervention with or without an augmentative and alternative communication (AAC) device facilitated greater spoken language over 6 months.32 This study took an adaptive treatment approach, adjusting the treatment midway through the study based on an individual’s progress. The results of this study suggest important implications about the treatment approach and timing of providing an AAC device in treating minimally verbal children with ASD.
For instance, the approach focused on developmental pre-requisites to spoken language, including joint attention, joint engagement, and play along with systematic modeling and prompting for spoken language. The researchers utilized a developmental, child-directed approach with strong naturalistic reinforcement strategies. Adults were contingently responsive to child attempts at communication and provided expansion of language through models that matched the child’s communicative intent. This may have provided the combination of supports needed for minimally verbal children with ASD to successfully increase their spoken communication. Earlier access to speech-generating devices along with naturalistic behavioral interventions at the start of treatment may be most beneficial to minimally verbal children. This is an area that demands much greater research attention.
Girls with ASD are another understudied and underserved group. Recent studies find subtle but important developmental differences between preschool-aged boys and girls with ASD.37 Studies of older children find girls with ASD who have lower IQs also have more impairing symptoms of ASD than boys. Girls with higher IQs report better friendships and social skills and fewer repetitive behaviors than boys.38 School playground observations of girls with ASD find they are overlooked and neglected by their classmates in more subtle ways, whereas boys with ASD are often overtly rejected.39 In part, these differences between girls and boys with ASD are due to the ability of girls to camouflage their interaction difficulties.40 These findings suggest that gender should be included as a tailoring variable when individualizing interventions for children with ASD.
Groundbreaking brain imaging and genetics studies have revealed important differences between males and females in the brain and genetic mechanisms underlying autism. For instance, genomic studies have provided tantalizing evidence for a "Female Protective Effect" (FPE) hypothesis in ASD,41 such that a greater amalgamation (more and/or more intense) of risk factors is necessary in females versus males to lead to autism. To illustrate, deleterious copy number variations (CNVs) are three times more likely in autistic females than in males.42 Furthermore, recent gene expression work from postmortem brain samples demonstrates that autism risk genes, rather than being sexually dimorphic themselves, interact with pathways and cell types that themselves are sexually dimorphic.43
Looking ahead, as it is becoming increasingly clear that females and males with ASD differ in terms of causes, developmental profiles, and symptom profiles, we need to understand how they respond differently to treatment approaches. At present, there are no adequately powered studies that focus on sex differences in behavioral and/ or neural-systems-level treatment response. Such studies should be a major priority for the research community.
Discoveries of neuroplasticity in the adult brain have opened new opportunities to consider autism interventions for use in adulthood. Very few studies of behavioral interventions have been performed in adolescents or adults with ASD, and most of these have focused on training adults to read social cues.44 Executive and social brain networks exhibit the greatest rates of functional maturation during adolescence, establishing adolescence and young adulthood as a sensitive period for socio-emotional and self-control development.45 These new findings suggest that the period from adolescence into young adulthood may offer an important new window of opportunity for individuals with ASD, their families, scientists, and clinicians to design novel approaches for improved outcomes and superior quality of life.
In contrast to the many behavioral intervention options available, only two drugs, risperidone and aripiprazole, currently have Food and Drug Administration (FDA) indication for use in ASD, specifically for the symptom of irritability. There are no approved treatments for the core symptoms of ASD, which include social communication difficulties and restricted, repetitive patterns of behavior, interests, or activities. Although clinical trials of pharmacological interventions for core symptoms of ASD are now underway, they will require several years for completion, analysis, and reporting; thus, there are few published findings to date. Advances in genetics and neurobiology have led to an increase in the number of clinical trials testing medical treatments for ASD. While the majority of such trials are testing pharmacological treatments, neurostimulation (discussed separately below) is also gaining momentum as a modality to alter brain activity and neuronal connectivity, as is the development of approaches based on stem cell technologies.
There has been an abundance of open-label, single-center drug trials that report effectiveness in small samples. Unfortunately, many of these results were not replicated when tested in subsequent larger, randomized, placebo-controlled trials. Many of the drug trials in ASD exclude individuals with intellectual disability and very young children due to ethical and/or practical challenges. However, a mechanism-based intervention intended to improve core symptoms of ASD may be more effective if administered relatively early in life and may be most effective in those most severely affected. Thus, it is crucial that such individuals are included in upcoming trials. This will require researchers to carefully consider how interventions can be adapted to accommodate children or individuals with intellectual disability, and to identify age- and ability-appropriate outcomes and outcome measures. Additionally, researchers must ensure that parents and families are well-informed and actively engaged through all stages of the trial.
Many different genes may contribute to the susceptibility of developing ASD. This heterogeneity of underlying causal mechanisms makes it challenging to identify convergent molecular pathways and brain circuits involved in all individuals with ASD, although there has been recent progress. One promising target is oxytocin, a neuropeptide involved in social cognition that has been investigated in a number of ASD studies.46,47,48 However, its molecular properties pose challenges for potential therapeutic use; thus, further work is needed to determine the best doses and compare methods of delivery. Moreover, given the variation of oxytocin’s effects based on behavioral context, studies aimed at understanding how oxytocin might enhance responses to evidence-based behavioral interventions are recommended.49
Other randomized, placebo-controlled treatment trials have targeted additional mechanisms proposed to contribute to the pathophysiology of ASD, with varying successes.50,51,52,53,54 N-acetylcysteine (NAC), an antioxidant treatment, was well-tolerated and had the expected effect of modulating oxidative stress markers, but had no impact on social impairment in youth with ASD.50 D-cycloserine, a partial agonist of the N-methyl-D-aspartate (NMDA) glutamate receptor, was tested in combination with social skills training. No difference was found in the drug treatment group compared with placebo.51 The serotonin partial agonist buspirone was used to target core symptoms during the developmental period of low serotonin synthesis capacity in young children with ASD. Low-, but not high-, dose buspirone showed significant improvement in a measure of restricted and repetitive behaviors.52 Finally, a double-blind clinical trial using the diuretic bumetanide that reduces intracellular chloride, thereby augmenting GABAergic inhibition, showed that bumetanide significantly reduced clinical symptoms of ASD in children 3-11 years old and was well-tolerated.53 Furthermore, bumetanide combined with a behavioral intervention resulted in a better outcome in children with ASD than a behavioral intervention alone.54 Larger trials are needed to validate these initial findings. Moreover, given the importance of context and a developmental perspective on ASD, it will be very important to conduct well-powered studies that combine pharmacological treatments with evidence-based practices including behavioral approaches, cognitive behavioral therapy, and social skills training. This issue is discussed in detail below.
A number of treatment trials are targeting the associated or co-occurring conditions of autism. One of the most prominent is anxiety, which affects at least 40% of individuals with ASD.55 Treatments range from cognitive behav-ioral therapy (CBT)56,57 to medications.58,59,60,61 While there have been some notable successes, there is also substantial variation in outcome. The variation in success may be due, in part, to the difficulty in assessing anxiety symptoms in autism and to the inappropriate stratification of appropriate subjects. When individuals with and without anxiety are grouped together as the "experimental" group, it is often difficult to attain a high enough level of response to consider the trial a success. This is an issue that relates to all treatment trials of the co-occurring conditions. And, additional research efforts must be directed to adequately subdividing individuals with autism into more homogeneous subgroups that have common symptom profiles.
Given that the rates of diagnosed ASD cases are rising and that there are no effective drugs to treat its core symptoms, it is imperative to further develop pharmacological treatments. Involvement of private industry will be crucial to help address this unmet need, including in industry-academic collaborations. Important private partners include the pharmaceutical industry, as well as those in software, electronics, and robotics development. As much as possible, multi-site, longer-duration, placebo-controlled studies should be prioritized in order to produce more reproducible results, such that private industry will take on the challenges of conducting large Phase III registration studies.
Direct Brain Stimulation
Transcranial magnetic stimulation (TMS) is a potentially promising method for identifying neural mechanisms and treating aspects of altered brain function in ASD.62 TMS can offer a non-invasive tool to study aspects of the altered physiology underlying ASD. Treatment strategies involve using TMS to modulate brain plasticity and network activity.63,64,65 In particular, repetitive TMS (rTMS) can alter brain excitability and network activity beyond the duration of a stimulation session or treatment study, and is being examined as a treatment that could potentially reduce both core and associated ASD symptoms.66
Recent studies have investigated whether neuromodulation via rTMS or transcranial direct current stimuli (tDCS) can induce neurophysiological and clinical benefits in individuals with ASD.67,68,69,70,71 Preliminary results suggest that TMS might be of therapeutic value for improving core and associated symptoms of ASD. However, work on brain stimulation as a therapeutic technique for ASD is still in the very earliest stages of development. There remain many unanswered questions and potential barriers for widespread application of these techniques. In particular, the mechanisms by which these techniques might improve brain and behavioral function in ASD are not yet known. Most studies to date have been based on small samples, employed open-label designs, and provided mixed results; some people with autism appear to benefit while others do not. There is a need for well-controlled, randomized trials with adequate sample sizes to better understand whether brain stimulation is efficacious and safe and whether there are subgroups of individuals with ASD that might benefit from treatments based on brain stimulation. In particular, one major barrier to the widespread application of brain stimulation in treating ASD is the potential to cause epileptic seizure activity, especially in people who are already at risk for developing seizures. Epilepsy is a potentially devastating condition that is much more common among individuals with ASD, especially those people with ASD and lower intellectual abilities.
Stem cell technology has greatly advanced our understanding of typical and atypical neurobiological processes, thereby offering new opportunities for treating neurodevelopmental disorders including autism. Increasing evidence suggests that the pathophysiology of autism may involve neuroinflammation, at least in a subgroup of cases.72 Immune pathology in individuals with ASD is evident in overexpression of immune-related gene networks in postmortem brain tissue,73 presence of maternal antibodies to fetal brain tissue,74 atypical levels of proinflammatory cytokines (IL-6, TNF-α) in the cerebral spinal fluid,75 and excessive microglial activation leading to aberrant neural connectivity pathways.76,77 Stem cell therapies have been shown to modulate immune activity and facilitate neural connectivity and are being tested in autism populations.
Preclinical models have shown that umbilical cord blood contains effector cells that, through paracrine signaling, can alter brain connectivity and suppress inflammation.78,79 Infusions of stem cells in mouse models of autism have resulted in improvements in autism-like symptoms.80,81 In humans, infusions of autologous cord blood cells have been shown to be safe and beneficial in patients with cerebral palsy and other acquired brain injuries.82,83,84 A Phase I, open-label trial assessed the safety and feasibility of a single intravenous infusion of autologous umbilical cord blood in 25 children with ASD, 2-6 years of age.85 Assessment of adverse events across a 12-month period suggested that the treatment was safe and well-tolerated. Significant improvements in children's behavior were observed on parent-report measures of social communication skills and autism symptoms, clinician ratings of overall autism symptom severity and degree of improvement, standardized measures of expressive vocabulary, and objective eye-tracking measures of children's attention to social stimuli, indicating that these measures may be useful endpoints in future studies. Behavioral improvements were observed during the first 6 months after infusion and were greater in children with higher baseline nonverbal intellectual ability. Double-blind, placebo-controlled studies of the efficacy of umbilical cord blood for improving autism symptoms are currently underway.
As with brain stimulation, this research is only just beginning, and there are many hurdles to overcome and unanswered questions to address before the field will know whether stem cell techniques can provide safe and useful treatments for ASD. This will be an important area of investigation to monitor as researchers work to replicate and expand these initial, encouraging findings.
Targeting Specific Biological Mechanisms
The prospect of precision medicine in ASD, i.e., specific, targeted treatments developed after gaining a better understanding of specific disease pathophysiology, is a tantalizing one. Genetically defined disorders such as Rett syndrome (RTT), Fragile X syndrome (FXS) and tuberous sclerosis complex (TSC) provide a unique opportunity to develop mechanism-based treatments for ASD. Thanks to basic science discoveries describing the molecular pathogenesis of these disorders, researchers have begun efforts to evaluate treatments targeting specific proteins in the implicated biological pathways.86 In future work, biomarkers should be incorporated in order to help detect objective improvements in response to treatment and to identify optimal developmental periods to apply the treatment trials.
The most common cause of classic RTT is a de novo mutation in the X-linked gene MECP2 (methyl-CpG-binding protein 2). MECP2 mutations are inherited in X-linked dominant fashion, and females are almost exclusively affected. In light of the basic science discoveries in the pathogenesis of RTT,87 researchers have proposed multiple routes to treatment for the disorder based on knowledge of MECP2 function. These strategies are designed to address either the underlying gene defect or downstream pathways implicated in the disorder. Clinical trials are underway evaluating the use of two different NMDA receptor antagonists, dextromethorphan and ketamine, to improve outcomes like epilepsy in RTT.88 Among neurotrophic factor effectors downstream of MECP2, IGF-1 has been studied.89 Double-blind, placebo-controlled trials of rhIGF-1 and NNZ-2566 (a synthetic version of the terminal tripeptide fragment of IGF-1) have recently finished and are being prepared for publication. The cholesterol pathway has recently been identified as being involved in RTT,90 and lovastatin is currently under investigation in an open-label trial for females with RTT. Finally, directly or indirectly manipulating faulty copies of the MECP2 gene, transcript, or protein is an appealing approach for treating RTT. Read-through strategies as well as gene transfer approaches using adeno-associated viral vectors are being actively pursued.
FXS is an X-linked, trinucleotide repeat expansion disorder involving the FMR1 (fragile X mental retardation 1) gene. This is a leading single-gene cause of ASD. The FMRP protein encoded by this gene regulates protein synthesis in neurons. Advances in our understanding of the pathophysiology of FXS have led to the development of numerous targeted trials. The most prominent theory of FXS, the metabotropic glutamate receptor (mGluR) theory, posits that many symptoms of FXS are due to exaggerated responses to activation of mGluRs. The prediction of this model was that reduced activation of mGluR would remedy the symptoms of FXS. However, recent clinical trials (Phase II and III) with two different mGlu5 inhibitors (basimglurant and mavoglurant) showed no therapeutic benefit in FXS patients for reasons that are as yet unclear.91 Driven by lessons learned from previous trials of mGluR antagonists, investigators are planning a multicenter placebo-controlled trial of mavoglurant for children with FXS ages 2 ½ to 6 years of age. This trial will examine outcome measures, including language, for all participants with a parent-implemented language intervention provided to all participants and psychopharmacologic intervention provided only to some.92
Approximately 50% of the patients affected with TSC also develop ASD, and 90% will have seizures sometime in their life. Importantly, many patients with TSC will be diagnosed with this disease very early in life, usually in the newborn period, due to the presence of heart tumors. This provides the unique opportunity to investigate the development of ASD in this high-risk group during the first year of life. A recent study shows that an abnormal electroencephalography (EEG) signature has 100% positive predictive value for clinical seizures, 2-3 months prior to the onset of these seizures.93 These data have led to the initiation of a "prevention" trial in the high-risk infants with TSC using the anti-seizure medication vigabatrin. TSC patients have hyperactivation of the mTOR pathway, which controls neuronal protein synthesis similar to FMRP. The hypothesis that overactive mTOR signaling in TSC may be amenable to mTOR inhibitors has led to trials involving the use of this class of medications in patients with TSC. A large Phase III trial demonstrated that adjunctive mTOR inhibitor treatment was effective for refractory focal epilepsy in TSC patients.94 Whether mTOR inhibitors will also be effective in improving neurodevelopmental symptoms including ASD is not yet known. A Phase II trial was recently completed; results are pending.
Taken together, advances in the study and treatment of RTT, FXS, and TSC have laid the groundwork for similar mechanism-based treatment trials in genetic disorders associated with ASD. Translating successes from animal studies have not been straightforward to date. Intellectual disability commonly affecting individuals with these neurogenetic disorders is an additional obstacle in study design in this field. Next steps will need to include biomarkers to help detect objective improvements in response to treatment and to identify optimal developmental periods to apply the treatment trials.
Digital-based technology interventions for individuals with ASD have continued to increase in accessibility, breadth, and depth of use. Scientific evidence for the effectiveness of technology-based or technology-enhanced interventions has increased, with a larger number of randomized controlled trials (RCTs) appearing in recent years that highlight the breadth of technology applications in ASD research as well as their increasing rigor.95 In the field of robotics, recent work has highlighted potential advantages of robots over human agents for accelerating several aspects of intervention research. Yet a number of challenges and gaps have been highlighted, which are also shared by speech-generating devices, virtual reality, video games and computer-assisted instruction, mobile applications, and telemedicine.96,97,98 It will be essential for future studies to address these challenges, as the development of interventions using digital technologies offers new opportunities to accelerate research progress. Furthermore, the proliferation of technology-based platforms purporting to help individuals with ASD points to a need for new, efficient, and scalable methods and infrastructure for evaluating technology-based interventions. Technology-based interventions have tremendous potential to benefit individuals on the autism spectrum in many ways, including by helping them improve social and communication skills and gain greater independence, all of which can improve the overall quality of life.
Outcome Measures and Biomarkers
Over the past few decades, significant progress has been made in the development of new behavioral interventions and identification of novel drug targets aimed at reducing core and associated ASD symptoms and improving quality of life across the lifespan. A major challenge in determining whether new treatment approaches are efficacious has been the measurement of treatment response. Measurement of treatment response is particularly complex in ASD due to the heterogeneity resulting from an individual’s symptom profile, sex, cognitive and language abilities, and development level. Moreover, many existing assessment measures were developed for screening and diagnosis and are not sensitive to assessing change in symptoms over time.
Considerable effort has been directed toward evaluating which existing measures are suitable for clinical trials and for developing quantitative, objective, and sensitive measures of treatment response. Increasingly, the input of key stakeholders, including caregivers and persons on the autism spectrum, is solicited to ensure that outcome measures reflect the priorities and needs of persons for which the treatments are being developed. Several reviews and consensus statements have been published that have evaluated the appropriateness of existing parent report and observational measures for clinical trials, including measures of social communication, anxiety, and repetitive behaviors.99,100,101,102 Studies validating observational measures of ASD symptom severity based on the Autism Diagnostic Observation Schedule (ADOS) have also been published,103 and a brief observational assessment of social communication change has also been recently developed.104
Biomarkers of treatment success are needed, as are "stratification" biomarkers for matching people to the best treatment for them at the best time. For example, while oxytocin (OXT) has been a promising target for treating core social communication symptoms in ASD, trials have not produced consistently positive or negative results. This is widely thought to be due to genetic and phenotypic heterogeneity among trial participants who nonetheless all receive the same diagnostic label of ASD. A recent study demonstrated the potential for pretreatment blood levels of OXT to serve as a stratification biomarker. Individuals with the lowest pretreatment blood OXT concentrations benefited the most from intranasal OXT administration.105 Until it becomes possible to biologically measure treatment response, negative results from pharmacological and behavioral interventions will be difficult to interpret, and positive results may not definitively indicate the requisite dose or duration of treatment. Predictive biomarkers (those that help to match individuals to particular treatments) will help to create more precise treatments and help individuals with ASD and their families to avoid wasted time and resources.
Initial efforts have focused on developing measures that are linked indirectly or directly to underlying neural circuitry, which can offer insight regarding whether the treatment is influencing specific aspects of neural circuitry, inform researchers of the neural mechanisms that might underlie the treatment effects, and predict treatment response. These measures include eye tracking, electrophysiological responses, and magnetic resonance imaging, among others. Such measures can also serve as an early efficacy signal that can detect response to treatment before changes in more distal measures such as language and social abilities are evident. Early efficacy markers can be used to identify which individuals are most likely to benefit from a given treatment and/or in adaptive study designs to indicate early in the trial whether modifications in the treatment (e.g., dose) should be made.
Eye Tracking (ET)
ET has great potential for acting as an early indicator of treatment efficacy by tracking changes in social attention.106 While applications of ET to clinical trials and interventions are still relatively new, results have been encouraging and suggest that ET can be used as a method for measuring response across a wide range of treatments. Promising future directions for developing ET as a marker of change include: furthering data-driven, computational, and machine learning approaches towards subtyping and stratification within the autism spectrum and for improved discrimination between individuals with ASD and controls; the design of ET batteries with the express goal of treatment measurement; the adaptation and advancement of ET metrics in technology-driven/ technology-interactive interventions, such as virtual reality, robotics, and simulators, as well as in novel adaptive paradigms designed to change gaze strategies; and advancement of methodological considerations including the promotion of big data studies, facilitation of replication, and increasing adherence to more rigorous and universal technical and methodological standards.
Recent studies suggest that EEG, a non-invasive measure that can record patterns of brain activity throughout the lifespan, offers promise as a metric of treatment response related to neural circuitry.107 Children and adults with ASD have distinct electrophysiological signatures, offering the possibility of using such measures to detect treatment response. Furthermore, distinct EEG signatures have been found among genetic subtypes of individuals with ASD and related disorders, and these signatures could be used in future clinical trials to test drugs targeted to individuals with ASD associated with specific genetic syndromes. In future work, prior to these measures being useful as potential biomarkers, it will be important to demonstrate their ability to reliably predict a signature of dysfunction at the individual subject level, as opposed to group averaged data.
Magnetic Resonance Imaging (MRI)
MRI techniques, including functional MRI (fMRI) and Diffusion Tensor Imaging (DTI), have provided a wealth of information regarding the neurobiological underpinnings of ASD. Specifically, task-based fMRI studies have pointed to atypical social-brain functioning and activation in ASD, while resting-state functional MRI and DTI have pointed to deficiencies in integrative social information processing as indicated by white matter atypicalities and diminished long-range connectivity.108,109 Despite the potential for brain imaging techniques to elucidate mechanisms underlying behavioral treatment response, few studies have directly used it for treatment monitoring or prediction of treatment efficacy. However, this appears to be rapidly changing, with several recent studies expanding on earlier work.110,111 Considerable progress has also been made recently in regards to the use of brain imaging techniques for understanding in vivo pharmacological neural action in individuals with ASD. Altogether, these advancements are beginning to provide the context for expanding the scope and applicability of brain imaging techniques for monitoring treatment across the lifespan, including before the signs of ASD are overtly apparent. Given the many successes yielded from the application of MRI methods to the development of biomarkers in ASD and related fields, considerable opportunity exists for further research and development in this area.
Advances in Developing Measures of Treatment Response
Digital technologies, such as mobile devices, provide another approach for developing quantitative, objective, and sensitive measures of treatment response.112 These tools provide opportunities to study biomarkers in combination with self-report data, often in more naturalistic contexts such as the home. The ability of technology-based systems, such as mobile applications, wearables, and internet resources, to automatically record and generate data will increasingly provide richer, denser, and more meaningful information to researchers. Novel analytic methods, such as machine learning and computer vision analysis, can provide new insights into patterns of behavior. Although early in their development and application to ASD populations, such measures have the advantages of being scalable, objective, and feasible. Thus, studies that explore their utility as a method of treatment monitoring should be pursued. Additional emphasis should also be placed on transforming these signals into useful forms to maximally aid and personalize ongoing, real-world treatment of issues faced by individuals with ASD. As the understanding of these data streams matures, new methods and systems will need to be created to harness the power of this data and to manage the massive flows of information reaching data consumers.
Recently, a number of substantial investments have been made to support large, collaborative efforts aimed at validating biomarkers and outcome measures for use in ASD clinical trials. These consortia involve public-private partnerships among academia, advocacy and other nonprofit organizations, government, and industry, with a goal of de-risking investments into pharmacological ASD trials and optimizing the success of such trials. These projects are examining a wide range of potential biomarkers and their relationships with observational and caregiver-report measures of behavior in large samples of individuals with ASD versus typical development over time. Furthermore, regular communication, data sharing agreements, and shared measures across the existing consortia will increase the scientific utility of these investments. One example is ABC-CT (Autism Biomarkers Consortium for Clinical Trials), a National Institutes of Health (NIH)-, Foundation for the NIH-, and Simons Foundation-funded consortium of sites that aims to develop, validate, and disseminate objective measures of social function and communication for ASD with the ultimate goal of advancing these measures as markers and predictors of treatment response.
In sum, multiple laboratories are conducting studies to develop better ways of measuring treatment response. Continued investment in such studies will ensure that, as new behavioral and medical treatments are developed, we will have the capability of testing their efficacy. Such investments will also be essential for developing improved methods for identifying subgroups that are responsive to specific treatments and identifying neural mechanisms underlying treatment response.
Innovative Combinations Of Therapeutic Modalities
There are now tremendous opportunities for combining therapeutic modalities in ways that allow for positive impacts from the amalgamation that are greater than the sum of the parts. One example would be the combination of psychopharmacology and behavioral treatments. The core impairments of ASD (e.g., social communication) have not been responsive to drug therapies yet. But, the possibility of combining drugs with behavioral interventions still holds promise for improvement in these core areas. A few of these studies are in progress, but none have been reported during this review period.
Advancement of new or reconceptualization of existing treatments into modular therapies (where therapies are organized into therapeutic modules that can be combined and reused in flexible arrangements) can provide finer granularity and more tractable opportunities for understanding change in individuals. This is an area of great need and can especially help address co-occurring conditions, such as anxiety, aggression, and depression. Similarly, adaptive interventions, which incorporate more flexible study designs, can make more efficient use of existing clinical, research, and participant resources, providing more information to researchers and potentially greater benefit to participants. To encourage adoption, investment in study design methodology research (including dissemination of methods and development of trial design resources) will be of significant value.
Accelerating Research and Increasing Access to Evidence-Based Interventions
While research in interventions for individuals with autism has shown consistent growth and advancement, opportunities exist for accelerating the pace of research. First, high-quality intervention studies are expensive to conduct and require substantial specialized expertise to oversee. Additional investment in human and research infrastructure is likely to yield compounding gains in autism intervention research progress. Creating and sustaining networks of institutions, investigators, clinicians, and families committed to shared, large-scale implementation of interventions or experimental research will combat fundamental heterogeneity issues in ASD research, leading to more reproducible and robust scientific findings. These networks can be leveraged to promote testing of novel interventions, exploration of unique scientific perspectives, and commitment to a culture of non-exclusive innovation transcending traditional boundaries. Additional investment should focus on bridging gaps between scientific evidence and clinical and/or community applications of interventions.
Additional opportunities may emerge from standardization of reporting and protocols so as to facilitate aggregation or comparison of clinical trial data at meta-analytic levels. Examination of evidence at a higher analytical level may provide more comprehensive information about treatment effectiveness when clinical uncertainty is matched with appropriate variation along key implementation parameters. Similarly, sharing data at finer level of detail may additionally facilitate data mining investigations that may help to identify more streamlined assessment or nuanced precursors and predictors of treatment response.
Further resources should be directed towards promoting the development of applied scientific tools, including more robust statistical methods, data mining techniques, basic science methods, laboratory techniques, and optimized pipelines for discovery. Additional resources could also be spent at the tail end of intervention science, on the wider dissemination of implementable discoveries. Examples would include encouraging Phase II transitions to Phase III trials, identifying appropriate industry partnerships to foster larger-scale intervention implementation, and in vivo studies of ongoing new intervention integration efforts. Incorporation of business and operations perspectives into autism research infrastructure development may help to optimize intervention deployment efficiency, enabling more studies to be conducted in a sustainable fashion. By focusing on practical barriers to ultimate treatment deployment (including insurance, provider adoption willingness, and marginal expenses), a more robust, efficient, and complete pipeline from idea to effective individual treatment can be realized.
Inclusion and Empowerment of Stakeholders in Intervention Research
Empowering individuals with ASD and their families to act as active directors in the research process can also accelerate scientific progress. Development of tools to help stakeholders manage and maintain research, educational, behavioral, and clinical records could help them better advocate for participation in studies most relevant to their needs or most aligned with their personal goals. With a focus on usability and controlled data sharing, such tools could become interfaces by which information could be bi-directionally shared with researchers and relevant providers, reducing redundancy in information requests, streamlining study deployments, and reducing participant burden.
Currently, several incarnations of such systems have been developed, including Microsoft HealthVault and Apple HealthKit. However, efforts towards tailoring interfaces, cross-platform interoperability, and common standards must be pursued so as to best meet the specific needs of the autism community, to prevent data from becoming unnecessarily locked to proprietary platforms or formats, and to better enable data exchange. Creation of user-friendly research registries that promote awareness of relevant ongoing intervention studies or technologies, that can be personalized by user preferences (including constraints on geography, participation characteristics, and study facets), that are updated regularly and managed in a sustainable fashion, and that facilitate connections between legitimate researchers and qualified research participants (with appropriate governance of privacy and participant rights) would further enable stakeholders to direct their research agenda. Adaptation of stakeholder-held records, including genomic information, for the purposes of creating an interface that would facilitate recruitment of participants with extremely specific characteristics (e.g., pharmacological trials targeting specific gene mutations) may be critical for appropriately powering highly targeted studies and for providing stakeholders access to the most tailored and innovative science.113 Throughout the research process, the involvement and feedback from the autism community should be emphasized so as to provide continuous context for research endeavors.
Much more attention has recently been given to quality of life outcomes for addressing the needs of individuals with ASD, including: academic success, autonomy and self-sufficiency, financial stability, health and well-being, inclusion, independent living, meaningful employment with fair wages, pursuit of dreams, recreation and leisure, respect and dignity, safety, self-identity and acceptance, social connections, and subjective well-being. Using such outcomes allows professionals, parents, and individuals to develop intervention plans that will allow a person with ASD to advance daily in each of the quality of life indicators. Measuring such outcomes can occur both in the short- and long-term and can be developed based on the needs of the individual in terms of their level of skills, functioning, and ability. When such indicators are maximized, the individual will be able to fully live a life maximizing long-term success.
While there have been multiple, important advances in the field of autism interventions and treatments, there is still much progress to be made. Researchers must continue to develop new treatments as well as adapt existing treatments for diverse settings and populations, including males and females, individuals with co-occurring conditions and varying levels of ability across multiple domains, individuals across the lifespan, and those in settings or communities that are under-resourced or underserved. Moving forward, there are several important issues to consider. First, it will be important to leverage advances in our understanding of the neuroscience and neurobiological mechanisms underlying all therapeutic approaches. Second, researchers need to consider designs and recruitment strategies that allow for testing ways to maximize effectiveness and precise matching of treatment plans to individual needs and neurobehavioral profiles by combining therapeutic approaches. More robust, standardized outcome measures should be developed, including adaptive measures, predictive measures, biologically based metrics, measures that address heterogeneity, and measures of practical outcomes and quality of life that will help better target therapies to individual needs and goals. It will also be important to study combination therapies that mimic how therapies may be delivered in real-world settings, and that offer the opportunity to provide greater benefits than any individual therapy alone. To realize the goal of developing the next generation of ASD therapies, funders will need to devote significant investment to building and enhancing the research pipeline to train of the next generation of multidisciplinary intervention scientists. Finally, it will be essential to provide more tools to practitioners through translation of research to community-based practice and to deploy effective, novel dissemination strategies.
OBJECTIVE 1: Develop and improve pharmacological and medical interventions to address both core symptoms and co-occurring conditions in ASD.
- Advance the study and treatment of genetic syndromes related to ASD, including RTT, FXS, TSC, and utilize the groundwork provided by investigations of these disorders to develop similar mechanism-based, genetically targeted pharmacology treatment trials for ASD.
- Explore innovative treatment modalities and combination therapies.
- Develop therapies to address challenges across the spectrum and across the lifespan.
- Investigate treatment response, including how females with ASD respond differently to treatment approaches, with a focus on the use of cognitive neuroscience tools to examine alternative mechanisms of change underlying symptom change.
- Develop biomarkers that can help inform decisions about the most appropriate interventions for particular individuals from across the autism spectrum and provide objective, early assessments of treatment response, prior to overt symptom change.
OBJECTIVE 2: Create and improve psychosocial, developmental, and naturalistic interventions for the core symptoms and co-occurring conditions in ASD.
- Support research to ensure that interventions include the whole autism spectrum and diverse populations, including females, minimally verbal individuals, intellectually disabled individuals, adults, and individuals in under-resourced and underserved communities.
- Leverage the neuroscience of neuroplasticity of the adolescent and adult brain to develop psychosocial interventions targeting these age groups, meeting their specific needs, offering a path toward continued development of life skills, and enhancing quality of life.
- Define the "active ingredients" of successful therapeutic approaches as a basis for future innovation and tailoring of interventions to particular populations or settings.
- Explore combination therapies.
- Develop outcome measures that include biomarkers of treatment success, measures of improvement across multiple domains, and improvements in quality of life.
OBJECTIVE 3: Maximize the potential for technologies and development of technology-based interventions to improve the lives of people on the autism spectrum.
- Develop tools allowing individuals with ASD to track and direct their own treatment.
- Develop technology-based interventions that help people with ASD improve their social and communication skills, increase their independence, and in many other ways help improve the quality of their lives.
- Develop interventions for minimally verbal children and those with intellectual delay, with a focus on the use of technology to augment communication (for minimally verbal children) as well as adaptive, individualized treatment approaches for both groups of underserved children.
- Increase access to interventions by developing technology-based treatments that can be deployed outside of primary care or clinical settings.
1. Paus T, Keshavan M, Giedd JN. Why do many psychiatric disorders emerge during adolescence? Nat Rev Neurosci. 2008 Dec;9(12):947-57. [PMID: 19002191]
2. Blakemore SJ. Imaging brain development: the adolescent brain. Neuroimage. 2012 Jun;61(2):397- 406. [PMID: 22178817]
3. Gogtay N, Giedd JN, Lusk L, Hayashi KM, Greenstein D, Vaituzis AC, Nugent TF 3rd, Herman DH, Clasen LS, Toga AW, Rapoport JL, Thompson PM. Dynamic mapping of human cortical development during childhood through early adulthood. Proc Natl Acad Sci U S A. 2004 May 25;101(21):8174-9. [PMID: 15148381]
4. McGrew JH, Ruble LA, Smith IM. Autism Spectrum Disorder and Evidence-Based Practice in Psychology. Clinical Psychology Science and Practice. 2016;23(3):239-255. http://doi.org/10.1111/cpsp.12160
5. Lovaas OI. Behavioral treatment and normal educational and intellectual functioning in young autistic children. J Consult Clin Psychol. 1987 Feb;55(1):3-9. [PMID: 3571656]
6. Rogers SJ, Dawson G. Early Start Denver Model for young children with autism: promoting language, learning, and engagement. New York: Guilford Press; 2010. xvii, 297 pp.
7. Odom SL, Boyd BA, Hall LJ, Hume KA. Comprehensive Treatment Models For Children And Youth With Autism Spectrum Disorders. In: Handbook of autism and pervasive developmental disorders. Fourth edition. Fred R. Volkmar, Rhea Paul, Sally J. Rogers, and Kevin A. Pelphrey, editors. Hoboken, New Jersey: John Wiley & Sons, Inc.; 2014. p. 770-87.
8. Kasari C. Update on behavioral interventions for autism and developmental disabilities. Curr Opin Neurol. 2015 Apr;28(2):124-9. [PMID: 25695136]
9. Bernstein A, Chorpita BF, Daleiden EL, Ebesutani CK, Rosenblatt A. Building an evidence-informed service array: Considering evidence-based programs as well as their practice elements. J Consult Clin Psychol. 2015 Dec;83(6):1085-96. [PMID: 26030761]
10. Wong C, Odom SL, Hume KA, Cox AW, Fettig A, Kucharczyk S, Brock ME, Plavnick JB, Fleury VP, Schultz TR. Evidence-Based Practices for Children, Youth, and Young Adults with Autism Spectrum Disorder: A Comprehensive Review. J Autism Dev Disord. 2015 Jul;45(7):1951-66. [PMID: 25578338]
11. de Bruin CL, Deppeler JM, Moore DW, Diamond NT. Public School–Based Interventions for Adolescents and Young Adults With an Autism Spectrum Disorder. Review of Educational Research. 2013;83(4):521-50. https://doi.org/10.3102/0034654313498621
12. Whalon KJ, Conroy MA, Martinez JR, Werch BL. School-based peer-related social competence interventions for children with autism spectrum disorder: a meta-analysis and descriptive review of single case research design studies. J Autism Dev Disord. 2015 Jun;45(6):1513-31. [PMID: 25641004]
13. Asmus JM, Carter EW, Moss CK, Biggs EE, Bolt DM, Born TL, Bottema-Beutel K, Brock ME, Cattey GN, Cooney M, Fesperman ES, Hochman JM, Huber HB, Lequia JL, Lyons GL, Vincent LB, Weir K. Efficacy and Social Validity of Peer Network Interventions for High School Students With Severe Disabilities. Am J Intellect Dev Disabil. 2017 Mar;122(2):118-137. [PMID: 28257242]
14. Laugeson EA, Ellingsen R, Sanderson J, Tucci L, Bates S. The ABC's of teaching social skills to adolescents with autism spectrum disorder in the classroom: the UCLA PEERS (®) Program. J Autism Dev Disord. 2014 Sep;44(9):2244-56. [PMID: 24715256]
15. Stichter JP, Herzog MJ, Owens SA, Malugen E. Manualization, feasibility, and effectiveness of the school-based Social Competence Intervention for Adolescents (SCI-A). Psychology in the Schools. 2016;53(6):583-600. http://dx.doi.org/10.1002/pits.21928
16. Fleury VP, Hedges S, Hume K, Browder DM, Thompson JL, Fallin K, El Zein F, Reutebuch CK, Vaughn S. Addressing the Academic Needs of Adolescents With Autism Spectrum Disorder in Secondary Education. Remedial and Special Education. 2014;35(2):68-79. https://doi.org/10.1177/0741932513518823
17. Keen D, Webster A, Ridley G. How well are children with autism spectrum disorder doing academically at school? An overview of the literature. Autism. 2016 Apr;20(3):276-94. [PMID: 25948598]
18. Goods KS, Ishijima E, Chang YC, Kasari C. Preschool based JASPER intervention in minimally verbal children with autism: pilot RCT. J Autism Dev Disord. 2013 May;43(5):1050-6. [PMID: 22965298]
19. Strain PS, Bovey EH. Randomized, Controlled Trial of the LEAP Model of Early Intervention for Young Children With Autism Spectrum Disorders. Topics in Early Childhood Special Education. 2011;31(3):133-54. https://doi.org/10.1177/0271121411408740
20. Koegel RL, Koegel LK. Pivotal response treatments for autism: communication, social, & academic development. Baltimore: Paul H. Brookes; 2006. xiv, 296 pp.
21. Stronach S, Wetherby AM. Examining restricted and repetitive behaviors in young children with autism spectrum disorder during two observational contexts. Autism. 2014 Feb;18(2):127-36. [PMID: 23175750]
22. Pajareya K, Nopmaneejumruslers K. A pilot randomized controlled trial of DIR/Floortime™ parent training intervention for pre-school children with autistic spectrum disorders. Autism. 2011 Sep;15(5):563-77. [PMID: 21690083]
23. Kaiser AP, Roberts MY. Parent-implemented enhanced milieu teaching with preschool children who have intellectual disabilities. J Speech Lang Hear Res. 2013;56(1):295-309. [PMID: 22744141]
24. Roberts MY, Kaiser AP. Early intervention for toddlers with language delays: a randomized controlled trial. Pediatrics. 2015;135(4):686-93. [PMID: 25733749]
25. Arick JR, Loos L, Falco R, Krug DA. STAR Program Manual: Strategies for Teaching Based on Autism Research: PRO-ED, Incorporated; 2004.
26. Weitlauf AS, McPheeters ML, Peters B, Sathe N, Travis R, Aiello R, Williamson E, Veenstra-VanderWeele J, Krishnaswami S, Jerome R, Warren Z. Therapies for Children With Autism Spectrum Disorder: Behavioral Interventions Update [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US); 2014 Aug. [PMID: 25210724]
27. Chang YC, Shire SY, Shih W, Gelfand C, Kasari C. Preschool Deployment of Evidence-Based Social Communication Intervention: JASPER in the Classroom. J Autism Dev Disord. 2016 Jun;46(6):2211-23. [PMID: 26936161]
28. Shire SY, Chang YC, Shih W, Bracaglia S, Kodjoe M, Kasari C. Hybrid implementation model of community-partnered early intervention for toddlers with autism: a randomized trial. J Child Psychol Psychiatry. 2017 May;58(5):612-622. [PMID: 27966784]
29. Schreibman L, Dawson G, Stahmer AC, Landa R, Rogers SJ, McGee GG, Kasari C, Ingersoll B, Kaiser AP, Bruinsma Y, McNerney E, Wetherby A, Halladay A. Naturalistic Developmental Behavioral Interventions: Empirically Validated Treatments for Autism Spectrum Disorder. J Autism Dev Disord. 2015 Aug;45(8):2411-28. [PMID: 25737021]
30. Wetherby AM, Guthrie W, Woods J, Schatschneider C, Holland RD, Morgan L, Lord C. Parent-Implemented Social Intervention for Toddlers With Autism: An RCT. Pediatrics. 2014. http://doi.org/10.1542/peds.2014-0757
31. Kasari C, Gulsrud A, Paparella T, Hellemann G, Berry K. Randomized comparative efficacy study of parent-mediated interventions for toddlers with autism. J Consult Clin Psychol. 2015 Jun;83(3):554-63. [PMID: 25822242]
32. Kasari C, Kaiser A, Goods K, Nietfeld J, Mathy P, Landa R, Murphy S, Almirall D. Communication interventions for minimally verbal children with autism: a sequential multiple assignment randomized trial. J Am Acad Child Adolesc Psychiatry. 2014 Jun;53(6):635-46. [PMID: 24839882]
33. Siller M, Hutman T, Sigman M. A parent-mediated intervention to increase responsive parental behaviors and child communication in children with ASD: a randomized clinical trial. J Autism Dev Disord. 2013 Mar;43(3):540-55. [PMID: 22825926]
34. Hardan AY, Gengoux GW, Berquist KL, Libove RA, Ardel CM, Phillips J, Frazier TW, Minjarez MB. A randomized controlled trial of Pivotal Response Treatment Group for parents of children with autism. J Child Psychol Psychiatry. 2015 Aug;56(8):884-92. [PMID: 25346345]
35. Pickles A, Anderson DK, Lord C. Heterogeneity and plasticity in the development of language: a 17-year follow-up of children referred early for possible autism. J Child Psychol Psychiatry. 2014 Dec;55(12):1354-62. [PMID: 24889883]
36. Gulsrud AC, Hellemann G, Shire S, Kasari C. Isolating active ingredients in a parent-mediated social communication intervention for toddlers with autism spectrum disorder. J Child Psychol Psychiatry. 2016 May;57(5):606-13. [PMID: 26525461]
37. Harrop C, Shire S, Gulsrud A, Chang YC, Ishijima E, Lawton K, Kasari C. Does gender influence core deficits in ASD? An investigation into social-communication and play of girls and boys with ASD. J Autism Dev Disord. 2015 Mar;45(3):766-77. [PMID: 25217088]
38. Frazier TW, Ratliff KR, Gruber C, Zhang Y, Law PA, Constantino JN. Confirmatory factor analytic structure and measurement invariance of quantitative autistic traits measured by the social responsiveness scale-2. Autism. 2014 Jan;18(1):31-44. [PMID: 24019124]
39. Kasari C, Dean M, Kretzmann M, Shih W, Orlich F, Whitney R, Landa R, Lord C, King B. Children with autism spectrum disorder and social skills groups at school: a randomized trial comparing intervention approach and peer composition. J Child Psychol Psychiatry. 2016 Feb;57(2):171-9. [PMID: 26391889]
40. Dean M, Harwood R, Kasari C. The art of camouflage: Gender differences in the social behaviors of girls and boys with autism spectrum disorder. Autism. 2016 Nov 29. [PMID: 27899709]
41. Robinson EB, Lichtenstein P, Anckarsater H, Happe F, Ronald A. Examining and interpreting the female protective effect against autistic behavior. Proc Natl Acad Sci U S A. 2013;110(13):5258-62. [PMID: 23431162]
42. Jacquemont S, Coe BP, Hersch M, Duyzend MH, Krumm N, Bergmann S, Beckmann JS, Rosenfeld JA, Eichler EE. A higher mutational burden in females supports a "female protective model" in neurodevelopmental disorders. Am J Hum Genet. 2014;94(3):415-25. [PMID: 24581740]
43. Werling DM, Parikshak NN, Geschwind DH. Gene expression in human brain implicates sexually dimorphic pathways in autism spectrum disorders. Nat Commun. 2016;7:10717. [PMID: 26892004]
44. Bishop-Fitzpatrick L, Minshew NJ, Eack SM. A Systematic Review of Psychosocial Interventions for Adults with Autism Spectrum Disorders. In: Adolescents and Adults with Autism Spectrum Disorders. Fred R. Volkmar, Brian Reichow, James C. McPartland, editors. New York, NY: Springer New York; 2014. p. 315-327.
45. Blakemore SJ, Choudhury S. Development of the adolescent brain: implications for executive function and social cognition. J Child Psychol Psychiatry. 2006 Mar-Apr;47(3-4):296-312. [PMID: 16492261]
46. Yatawara CJ, Einfeld SL, Hickie IB, Davenport TA, Guastella AJ. The effect of oxytocin nasal spray on social interaction deficits observed in young children with autism: a randomized clinical crossover trial. Mol Psychiatry. 2016 Sep;21(9):1225-31. [PMID: 26503762]
47. Gordon I, Vander Wyk BC, Bennett RH, Cordeaux C, Lucas MV, Eilbott JA, Zagoory-Sharon O, Leckman JF, Feldman R, Pelphrey KA. Oxytocin enhances brain function in children with autism. Proc Natl Acad Sci U S A. 2013 Dec 24;110(52):20953-8. [PMID: 24297883]
48. Watanabe T, Abe O, Kuwabara H, Yahata N, Takano Y, Iwashiro N, Natsubori T, Aoki Y, Takao H, Kawakubo Y, Kamio Y, Kato N, Miyashita Y, Kasai K, Yamasue H. Mitigation of sociocommunicational deficits of autism through oxytocin-induced recovery of medial prefrontal activity: a randomized trial. JAMA Psychiatry. 2014 Feb;71(2):166-75. [PMID: 24352377]
49. Bartz JA, Zaki J, Bolger N, Ochsner KN. Social effects of oxytocin in humans: context and person matter. Trends Cogn Sci. 2011 Jul;15(7):301-9. [PMID: 21696997]
50. Wink LK, Adams R, Wang Z, Klaunig JE, Plawecki MH, Posey DJ, McDougle CJ, Erickson CA. A randomized placebo-controlled pilot study of N-acetylcysteine in youth with autism spectrum disorder. Mol Autism. 2016 Apr 21;7:26. [PMID: 27103982]
51. Minshawi NF, Wink LK, Shaffer R, Plawecki MH, Posey DJ, Liu H, Hurwitz S, McDougle CJ, Swiezy NB, Erickson CA. A randomized, placebo-controlled trial of D-cycloserine for the enhancement of social skills training in autism spectrum disorders. Mol Autism. 2016 Jan 14;7:2. [PMID: 26770664]
52. Chugani DC, Chugani HT, Wiznitzer M, Parikh S, Evans PA, Hansen RL, Nass R, Janisse JJ, Dixon-Thomas P, Behen M, Rothermel R, Parker JS, Kumar A, Muzik O, Edwards DJ, Hirtz D; Autism Center of Excellence Network. Efficacy of Low-Dose Buspirone for Restricted and Repetitive Behavior in Young Children with Autism Spectrum Disorder: A Randomized Trial. J Pediatr. 2016 Mar;170:45-53.e1-4. [PMID: 26746121]
53. Lemonnier E, Degrez C, Phelep M, Tyzio R, Josse F, Grandgeorge M, Hadjikhani N, Ben-Ari Y. A randomised controlled trial of bumetanide in the treatment of autism in children. Transl Psychiatry. 2012 Dec 11;2:e202. [PMID: 23233021]
54. Du L, Shan L, Wang B, Li H, Xu Z, Staal WG, Jia F. A Pilot Study on the Combination of Applied Behavior Analysis and Bumetanide Treatment for Children with Autism. J Child Adolesc Psychopharmacol. 2015 Sep;25(7):585-8. [PMID: 26258842]
55. Sukhodolsky DG, Scahill L, Gadow KD, Arnold LE, Aman MG, McDougle CJ, McCracken JT, Tierney E, Williams White S, Lecavalier L, Vitiello B. Parent-rated anxiety symptoms in children with pervasive developmental disorders: frequency and association with core autism symptoms and cognitive functioning. J Abnorm Child Psychol. 2008;36(1):117-28. [PMID: 17674186]
56. Wood JJ, Ehrenreich-May J, Alessandri M, Fujii C, Renno P, Laugeson E, Piacentini JC, De Nadai AS, Arnold E, Lewin AB, Murphy TK, Storch EA. Cognitive behavioral therapy for early adolescents with autism spectrum disorders and clinical anxiety: a randomized, controlled trial. Behav Ther. 2015;46(1):7-19. [PMID: 25526831]
57. Sukhodolsky DG, Bloch MH, Panza KE, Reichow B. Cognitive-behavioral therapy for anxiety in children with high-functioning autism: a meta-analysis. Pediatrics. 2013;132(5):e1341-50. [PMID: 24167175]
58. Walkup JT, Albano AM, Piacentini J, Birmaher B, Compton SN, Sherrill JT, Ginsburg GS, Rynn MA, McCracken J, Waslick B, Iyengar S, March JS, Kendall PC. Cognitive behavioral therapy, sertraline, or a combination in childhood anxiety. N Engl J Med. 2008;359(26):2753-66. [PMID: 18974308]
59. Buitelaar JK, van der Gaag RJ, van der Hoeven J. Buspirone in the management of anxiety and irritability in children with pervasive developmental disorders: results of an open-label study. J Clin Psychiatry. 1998;59(2):56-9. [PMID: 9501886]
60. Namerow L, Thomas P, Bostic JQ, Prince J, Monuteaux MC. Use of citalopram in pervasive developmental disorders. J Dev Behav Pediatr. 2003;24(2):104-8. [PMID: 12692455]
61. Martin A, Koenig K, Anderson GM, Scahill L. Low-dose fluvoxamine treatment of children and adolescents with pervasive developmental disorders: a prospective, open-label study. J Autism Dev Disord. 2003;33(1):77-85. [PMID: 12708582]
62. Oberman LM, Enticott PG, Casanova MF, Rotenberg A, Pascual-Leone A, McCracken JT; TMS in ASD Consensus Group. Transcranial magnetic stimulation in autism spectrum disorder: Challenges, promise, and roadmap for future research. Autism Res. 2016 Feb;9(2):184-203. [PMID: 26536383]
63. Shafi MM, Brandon Westover M, Oberman L, Cash SS, Pascual-Leone A. Modulation of EEG functional connectivity networks in subjects undergoing repetitive transcranial magnetic stimulation. Brain Topogr. 2014 Jan;27(1):172-91. [PMID: 23471637]
64. Oberman L, Pascual-Leone A. Changes in plasticity across the lifespan: cause of disease and target for intervention. Prog Brain Res. 2013;207:91-120. [PMID: 24309252]
65. Fox MD, Buckner RL, Liu H, Chakravarty MM, Lozano AM, Pascual-Leone A. Resting-state networks link invasive and noninvasive brain stimulation across diverse psychiatric and neurological diseases. Proc Natl Acad Sci U S A. 2014 Oct 14;111(41):E4367-75. [PMID: 25267639]
66. Kobayashi M, Pascual-Leone A. Transcranial magnetic stimulation in neurology. Lancet Neurol. 2003 Mar;2(3):145-56. [PMID: 12849236]
67. Casanova MF, Baruth JM, El-Baz A, Tasman A, Sears L, Sokhadze E. Repetitive Transcranial Magnetic Stimulation (rTMS) Modulates Event-Related Potential (ERP) Indices of Attention in Autism. Transl Neurosci. 2012 Jun 1;3(2):170-180. [PMID: 24683490]
68. Enticott PG, Fitzgibbon BM, Kennedy HA, Arnold SL, Elliot D, Peachey A, Zangen A, Fitzgerald PB. A double-blind, randomized trial of deep repetitive transcranial magnetic stimulation (rTMS) for autism spectrum disorder. Brain Stimul. 2014 Mar-Apr;7(2):206-11. [PMID: 24280031]
69. Panerai S, Tasca D, Lanuzza B, Trubia G, Ferri R, Musso S, Alagona G, Di Guardo G, Barone C, Gaglione MP, Elia M. Effects of repetitive transcranial magnetic stimulation in performing eye-hand integration tasks: four preliminary studies with children showing low-functioning autism. Autism. 2014 Aug;18(6):638-50. [PMID: 24113340]
70. Amatachaya A, Auvichayapat N, Patjanasoontorn N, Suphakunpinyo C, Ngernyam N, Aree-Uea B, Keeratitanont K, Auvichayapat P. Effect of anodal transcranial direct current stimulation on autism: a randomized double-blind crossover trial. Behav Neurol. 2014;2014:173073. [PMID: 25530675]
71. van Steenburgh JJ, Varvaris M, Schretlen DJ, Vannorsdall TD, Gordon B. Balanced bifrontal transcranial direct current stimulation enhances working memory in adults with high-functioning autism: a sham-controlled crossover study. Mol Autism. 2017;8:40. [PMID: 28775825]
72. Young AM, Chakrabarti B, Roberts D, Lai MC, Suckling J, Baron-Cohen S. From molecules to neural morphology: understanding neuroinflammation in autism spectrum condition. Mol Autism. 2016;7:9. [PMID: 26793298]
73. Voineagu I, Wang X, Johnston P, Lowe JK, Tian Y, Horvath S, Mill J, Cantor RM, Blencowe BJ, Geschwind DH. Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature. 2011;474(7351):380-4. [PMID: 21614001]
74. Braunschweig D, Krakowiak P, Duncanson P, Boyce R, Hansen RL, Ashwood P, Hertz-Picciotto I, Pessah IN, Van de Water J. Autism-specific maternal autoantibodies recognize critical proteins in developing brain. Transl Psychiatry. 2013;3:e277. [PMID: 23838888]
75. Vargas DL, Nascimbene C, Krishnan C, Zimmerman AW, Pardo CA. Neuroglial activation and neuroinflammation in the brain of patients with autism. Ann Neurol. 2005;57(1):67-81. [PMID: 15546155]
76. Suzuki K, Sugihara G, Ouchi Y, Nakamura K, Futatsubashi M, Takebayashi K, Yoshihara Y, Omata K, Matsumoto K, Tsuchiya KJ, Iwata Y, Tsujii M, Sugiyama T, Mori N. Microglial activation in young adults with autism spectrum disorder. JAMA Psychiatry. 2013;70(1):49-58. [PMID: 23404112]
77. Morgan JT, Chana G, Pardo CA, Achim C, Semendeferi K, Buckwalter J, Courchesne E, Everall IP. Microglial activation and increased microglial density observed in the dorsolateral prefrontal cortex in autism. Biol Psychiatry. 2010;68(4):368-76. [PMID: 20674603]
78. Bachstetter AD, Pabon MM, Cole MJ, Hudson CE, Sanberg PR, Willing AE, Bickford PC, Gemma C. Peripheral injection of human umbilical cord blood stimulates neurogenesis in the aged rat brain. BMC Neurosci. 2008;9:22. [PMID: 18275610]
79. Shahaduzzaman M, Golden JE, Green S, Gronda AE, Adrien E, Ahmed A, Sanberg PR, Bickford PC, Gemma C, Willing AE. A single administration of human umbilical cord blood T cells produces long-lasting effects in the aging hippocampus. Age (Dordr). 2013;35(6):2071-87. [PMID: 23263793]
80. Ha S, Park H, Mahmood U, Ra JC, Suh YH, Chang KA. Human adipose-derived stem cells ameliorate repetitive behavior, social deficit and anxiety in a VPA-induced autism mouse model. Behav Brain Res. 2017;317:479-84. [PMID: 27717813]
81. Segal-Gavish H, Karvat G, Barak N, Barzilay R, Ganz J, Edry L, Aharony I, Offen D, Kimchi T. Mesenchymal Stem Cell Transplantation Promotes Neurogenesis and Ameliorates Autism Related Behaviors in BTBR Mice. Autism Res. 2016;9(1):17-32. [PMID: 26257137]
82. Sun J, Allison J, McLaughlin C, Sledge L, Waters-Pick B, Wease S, Kurtzberg J. Differences in quality between privately and publicly banked umbilical cord blood units: a pilot study of autologous cord blood infusion in children with acquired neurologic disorders. Transfusion. 2010;50(9):1980-7. [PMID: 20546200]
83. Cotten CM, Murtha AP, Goldberg RN, Grotegut CA, Smith PB, Goldstein RF, Fisher KA, Gustafson KE, Waters-Pick B, Swamy GK, Rattray B, Tan S, Kurtzberg J. Feasibility of autologous cord blood cells for infants with hypoxic-ischemic encephalopathy. J Pediatr. 2014;164(5):973-9 e1. [PMID: 24388332]
84. Sun JM, Grant GA, McLaughlin C, Allison J, Fitzgerald A, Waters-Pick B, Kurtzberg J. Repeated autologous umbilical cord blood infusions are feasible and had no acute safety issues in young babies with congenital hydrocephalus. Pediatr Res. [PMID: 26331765]
85. Dawson G, Sun JM, Davlantis KS, Murias M, Franz L, Troy J, Simmons R, Sabatos-DeVito M, Durham R, Kurtzberg J. Autologous Cord Blood Infusions Are Safe and Feasible in Young Children with Autism Spectrum Disorder: Results of a Single-Center Phase I Open-Label Trial. Stem Cells Transl Med. 2017;6(5):1332-9. [PMID: 28378499]
86. Sztainberg Y, Zoghbi HY. Lessons learned from studying syndromic autism spectrum disorders. Nat Neurosci. 2016 Oct 26;19(11):1408-1417. [PMID: 27786181]
87. Katz DM, Bird A, Coenraads M, Gray SJ, Menon DU, Philpot BD, Tarquinio DC. Rett Syndrome: Crossing the Threshold to Clinical Translation. Trends Neurosci. 2016;39(2):100-13. [PMID: 26830113]
88. Katz DM, Menniti FS, Mather RJ. N-Methyl-D-Aspartate Receptors, Ketamine, and Rett Syndrome: Something Special on the Road to Treatments? Biol Psychiatry. 2016;79(9):710-2. [PMID: 27079494]
89. Khwaja OS, Ho E, Barnes KV, O'Leary HM, Pereira LM, Finkelstein Y, Nelson CA, 3rd, Vogel-Farley V, DeGregorio G, Holm IA, Khatwa U, Kapur K, Alexander ME, Finnegan DM, Cantwell NG, Walco AC, Rappaport L, Gregas M, Fichorova RN, Shannon MW, Sur M, Kaufmann WE. Safety, pharmacokinetics, and preliminary assessment of efficacy of mecasermin (recombinant human IGF-1) for the treatment of Rett syndrome. Proc Natl Acad Sci U S A. 2014;111(12):4596-601. [PMID: 24623853]
90. Buchovecky CM, Turley SD, Brown HM, Kyle SM, McDonald JG, Liu B, Pieper AA, Huang W, Katz DM, Russell DW, Shendure J, Justice MJ. A suppressor screen in Mecp2 mutant mice implicates cholesterol metabolism in Rett syndrome. Nat Genet. 2013;45(9):1013-20. [PMID: 23892605]
91. Berry-Kravis E, Des Portes V, Hagerman R, Jacquemont S, Charles P, Visootsak J, Brinkman M, Rerat K, Koumaras B, Zhu L, Barth GM, Jaecklin T, Apostol G, von Raison F. Mavoglurant in fragile X syndrome: Results of two randomized, double-blind, placebo-controlled trials. Sci Transl Med. 2016;8(321):321ra5. [PMID: 26764156]
92. Ligsay A, Hagerman RJ. Review of targeted treatments in fragile X syndrome. Intractable Rare Dis Res. 2016;5(3):158-67. [PMID: 27672538]
93. Wu JY, Peters JM, Goyal M, Krueger D, Sahin M, Northrup H, Au KS, Cutter G, Bebin EM. Clinical Electroencephalographic Biomarker for Impending Epilepsy in Asymptomatic Tuberous Sclerosis Complex Infants. Pediatr Neurol. 2016;54:29-34. [PMID: 26498039]
94. French JA, Lawson JA, Yapici Z, Ikeda H, Polster T, Nabbout R, Curatolo P, de Vries PJ, Dlugos DJ, Berkowitz N, Voi M, Peyrard S, Pelov D, Franz DN. Adjunctive everolimus therapy for treatment-resistant focal-onset seizures associated with tuberous sclerosis (EXIST-3): a phase 3, randomised, double-blind, placebo-controlled study. Lancet. 2016;388(10056):2153-63. [PMID: 27613521]
95. Grynszpan O, Weiss PL, Perez-Diaz F, Gal E. Innovative technology-based interventions for autism spectrum disorders: a meta-analysis. Autism. 2014 May;18(4):346-61. [PMID: 24092843]
96. Begum M, Serna RW, Kontak D, Allspaw J, Kuczynski J, Yanco HA, Suarez J. Measuring the Efficacy of Robots in Autism Therapy: How Informative are Standard HRI Metrics. Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction; Portland, Oregon, USA. 2696480: ACM; 2015. p. 335-42. https://doi.org/10.1145/2696454.2696480
97. Diehl JJ, Crowell CR, Villano M, Wier K, Tang K, Riek LD. Clinical Applications of Robots in Autism Spectrum Disorder Diagnosis and Treatment. In: Comprehensive Guide to Autism. Patel VB, Preedy VR, Martin CR, editors. New York, NY: Springer New York; 2014. p. 411-22. https://dx.doi.org/10.1007/978-1-4614-4788-7_14
98. Kim ES, Paul R, Shic F, Scassellati B. Bridging the Research Gap: Making HRI Useful to Individuals with Autism. Journal of Human-Robot Interaction. 2012;1(1):26-54. http://humanrobotinteraction.org/journal/index.php/HRI/article/view/25
99. Brugha TS, Doos L, Tempier A, Einfeld S, Howlin P. Outcome measures in intervention trials for adults with autism spectrum disorders; a systematic review of assessments of core autism features and associated emotional and behavioural problems. Int J Methods Psychiatr Res. 2015 Jun;24(2):99-115. [PMID: 26077193]
100. McConachie H, Parr JR, Glod M, Hanratty J, Livingstone N, Oono IP, Robalino S, Baird G, Beresford B, Charman T, Garland D, Green J, Gringras P, Jones G, Law J, Le Couteur AS, Macdonald G, McColl EM, Morris C, Rodgers J, Simonoff E, Terwee CB, Williams K. Systematic review of tools to measure outcomes for young children with autism spectrum disorder. Health Technol Assess. 2015 Jun;19(41):1-506. [PMID: 26065374]
101. Ashwood KL, Buitelaar J, Murphy D, Spooren W, Charman T. European clinical network: autism spectrum disorder assessments and patient characterisation. Eur Child Adolesc Psychiatry. 2015 Aug;24(8):985-95. [PMID: 25471824]
102. Payakachat N, Tilford JM, Kovacs E, Kuhlthau K. Autism spectrum disorders: a review of measures for clinical, health services and cost-effectiveness applications. Expert Rev Pharmacoecon Outcomes Res. 2012 Aug;12(4):485-503. [PMID: 22971035]
103. Esler AN, Bal VH, Guthrie W, Wetherby A, Ellis Weismer S, Lord C. The Autism Diagnostic Observation Schedule, Toddler Module: Standardized Severity Scores. J Autism Dev Disord. 2015 Sep;45(9):2704- 20. [PMID: 25832801]
104. Grzadzinski R, Carr T, Colombi C, McGuire K, Dufek S, Pickles A, Lord C. Measuring Changes in Social Communication Behaviors: Preliminary Development of the Brief Observation of Social Communication Change (BOSCC). J Autism Dev Disord. 2016 Jul;46(7):2464-79. [PMID: 27062034]
105. Parker KJ, Oztan O, Libove RA, Sumiyoshi RD, Jackson LP, Karhson DS, Summers JE, Hinman KE, Motonaga KS, Phillips JM, Carson DS, Garner JP, Hardan AY. Intranasal oxytocin treatment for social deficits and biomarkers of response in children with autism. Proc Natl Acad Sci U S A. 2017;114(30): 8119-24. [PMID: 28696286]
106. Dawson G, Bernier R, Ring RH. Social attention: a possible early indicator of efficacy in autism clinical trials. J Neurodev Disord. 2012 May 17;4(1):11. [PMID: 22958480]
107. Jeste SS, Frohlich J, Loo SK. Electrophysiological biomarkers of diagnosis and outcome in neurodevelopmental disorders. Curr Opin Neurol. 2015 Apr;28(2):110-6. [PMID: 25710286]
108. Philip RC, Dauvermann MR, Whalley HC, Baynham K, Lawrie SM, Stanfield AC. A systematic review and meta-analysis of the fMRI investigation of autism spectrum disorders. Neurosci Biobehav Rev. 2012 Feb;36(2):901-42. [PMID: 22101112]
109. Rane P, Cochran D, Hodge SM, Haselgrove C, Kennedy DN, Frazier JA. Connectivity in Autism: A Review of MRI Connectivity Studies. Harv Rev Psychiatry. 2015 Jul-Aug;23(4):223-44. [PMID: 26146755]
110. Stavropoulos KK-M. Using neuroscience as an outcome measure for behavioral interventions in Autism spectrum disorders (ASD): A review. Research in Autism Spectrum Disorders. 2017;35: 62-73. https://doi.org/10.1016/j.rasd.2017.01.001
111. Calderoni S, Billeci L, Narzisi A, Brambilla P, Retico A, Muratori F. Rehabilitative Interventions and Brain Plasticity in Autism Spectrum Disorders: Focus on MRI-Based Studies. Front Neurosci. 2016 Mar 31;10:139. [PMID: 27065795]
112. Adams ZW, McClure EA, Gray KM, Danielson CK, Treiber FA, Ruggiero KJ. Mobile devices for the remote acquisition of physiological and behavioral biomarkers in psychiatric clinical research. J Psychiatr Res. 2017 Feb;85:1-14. [PMID: 27814455]
113. Nicolaidis C, Raymaker D, McDonald K, Dern S, Ashkenazy E, Boisclair C, Robertson S, Baggs A. Collaboration strategies in nontraditional community-based participatory research partnerships: lessons from an academic−community partnership with autistic self-advocates. Prog Community Health Partnersh. 2011 Summer;5(2):143-50. [PMID: 21623016]