James McPartland, Ph.D., discusses current limitations in autism diagnosis and treatment, noting their reliance on behavioral observations despite the condition’s genetic and neurological underpinnings. He advocates integrating biomarkers as objective, measurable biological indicators that revolutionize clinical practice. The speaker details ongoing research into the N170 biomarker, its connection to social behavior and development, and potential for measuring intervention efficacy. McPartland outlines the collaborative work of the Autism Biomarkers Consortium for Clinical Trials (ABC-CT) and its implications for autism diagnosis and care.

Handouts are online HERE

In this webinar:

5:00 – The need for biomarkers in autism
11:00 – Practical considerations for clinical practice
13:30 – EEGs as a promising biomarker technology
20:00 – The N170 biomarker
28:00 – N170: social and behavioral development
34:00 – Confounds and responses to behavioral interventions
42:00 – Autism Biomarkers Consortium for Clinical Trials (ABC-CT)
47:00 – ABC-CT progress and impact
54:00 – Implications for clinical practice

The need for biomarkers in autism

highlighting that these methods have remained largely unchanged since the initial descriptions in 1943 (5:00). Although today we understand that autism is rooted in genetics and the brain, there are no biological assays—meaning no tests to aid in diagnosis, guide treatment selection, or measure intervention effectiveness. He explains that this lack of biological criteria impedes our ability to provide optimal care (8:17).

The presenter details various categories of biomarkers as defined by the FDA, each serving a distinct “context of use” or purpose:

• Diagnostic biomarkers are used to identify the condition.
• Susceptibility or risk markers can indicate the likelihood of developing autism.
• Pharmacodynamic or response biomarkers show changes in response to treatment.
• Prognostic biomarkers help to predict the course of development.
• Predictive biomarkers estimate response to specific treatments.
• Stratification biomarkers are used to subgroup the highly heterogeneous autistic population meaningfully. This last category, McPartland suggested, represents the “lowest hanging fruit” for immediate impact in autism.

Practical considerations for biomarker adoption in clinical settings include viability across the diverse autism population, cost-effectiveness, and accessibility (11:00).

EEG as a promising biomarker technology

McPartland presents various methods for measuring brain activity in autism, including electroencephalography (EEG), fMRI, PET scans, and eye tracking. He highlights EEG as an ideal technology that detects electrical activity produced by brain cells from the scalp. This technology offers many advantages, especially that it is non-invasive, movement tolerant, widely applicable, cost-effective, and accessible, as they are widely available in hospitals. EEGs have also been used effectively to understand social communicative development, supporting their use in autism research (13:18). The speaker notes the wide variability in clinical presentations of autism and the challenges this presents in using biomarkers for diagnostic purposes (18:00). 

The N170 biomarker and its Implications

The speaker describes the N170, an event-related potential (ERP) component measured by EEG. The N170 is a negative electrical spike that occurs around 170 milliseconds after seeing a human face. This indicates the brain’s rapid recognition of a face as a face, making it highly relevant to social communication (20:00). McPartland outlines a 2004 study that compared the N170 in autistic and allistic (non-autistic) adolescents and adults. Preliminary findings show that autistic participants exhibited a slower N170 response, which was replicated in a younger cohort. These findings, the speaker asserts, suggest a difference at the fundamental stages of face perception (22:30). Further research showed that the N170 latency correlated directly with impaired facial recognition abilities in autistic participants, providing crucial evidence that the N170 is not simply a brain anomaly, but a biomarker associated with a clinically relevant social function.

Event-related brain potentials reveal anomalies in temporal processing of faces in autism spectrum disorder (McPartland et al., 2004)

A biomarker for social behavior and development

To determine if the N170 response is meaningfully tied to social behavior, subsequent research by McPartland and colleagues compared brain responses to faces (social), letters (non-social, an autistic strength), and houses (control). Results indicated the N170 latency is specific to social stimuli, where similar slowness was not observed in response to letters (28:00). The only difference for letters was a tendency for autistic individuals to involve more of the right hemisphere, typically associated with faces (a difference in lateralization). These findings, the presenter asserts, confirm the N170 biomarker’s specificity to the social domain in autism, rather than a general indicator of slower sensory processing. As the findings were replicated in a younger cohort, this study also provides evidence of the N170 biomarker’s relevance to development (32:00). 

Atypical neural specialization for social percepts in autism spectrum disorder (McPartland et al., 2011)

Confounds and responses to behavioral interventions

McPartland briefly touches on research addressing confounds, such as eye gaze patterns. A 2021 study indicated that the N170 differences persisted even when eye gaze was experimentally controlled. This suggests that the brain difference is fundamental and not simply a consequence of where someone is looking, thus strengthening the validity of the N170 as a robust measure of underlying neural processes (34:00). Studies also show that the N170 biomarker may be sensitive to changes in clinical status following behavioral interventions. The speaker explains that this suggests potential for N170 to serve as a response biomarker, capable of measuring the effectiveness of therapeutic interventions (36:00). 

The N170 event-related potential reflects delayed neural response to faces when visual attention is directed to the eyes in youths with ASD (Parker et al., 2021)

Brief Report: Preliminary Evidence of the N170 as a Biomarker of Response to Treatment in Autism Spectrum Disorder (Kala et al., 2021)

Social attention: a possible early indicator of efficacy in autism clinical trials (Dawson et al., 2012)

Brain mechanisms of plasticity in response to treatments for core deficits in autism (Ventola et al., 2013)

Large-scale autism biomarkers consortium for clinical trials 

The presenter outlines the Large-Scale Autism Biomarkers Consortium Study, or the Autism Biomarkers Consortium for Clinical Trials (ABC-CT), a monumental effort to bridge the gap between scientific discovery and clinical application in autism. The overarching goal of the ABC-CT is to accelerate the development of effective treatments for social impairment in autism by identifying, developing, and validating a set of reliable, objective, and quantitative measures that can serve as biomarkers (42:00). McPartland notes the rationale for this multicenter research study, highlighting its potential for bridging the research-to-clinic gap. 

The main study is longitudinal, tracking participants between 6 and 11 years old across multiple time points to evaluate candidate biomarkers’ stability and sensitivity to change. A battery of measures was collected, including clinician and caregiver assessments, biospecimens (DNA samples), and lab-based measures like EEG, eye tracking, and behavior observations (45:00). The ABC-CT specifically investigates well-evidenced candidate biomarkers, such as N170. Candidate biomarkers must meet several criteria, including feasibility and construct validity.

Identifying Age Based Maturation in the ERP Response to Faces in Children With Autism: Implications for Developing Biomarkers for Use in Clinical Trials (Webb et al., 2022)

The Autism Biomarkers Consortium for Clinical Trials: Initial Evaluation of a Battery of Candidate EEG Biomarkers (Webb et al., 2023)

Progress and impact

To date, the ABC-CT reports high levels of successful data acquisition and acceptance of the N170 latency in upright human faces. Therefore, the FDA views the N170 as a promising stratification (subgrouping) biomarker for clinical trials. An eye-tracking biomarker has also been submitted, the Oculomotor Index of Orienting to Human Faces (47:00). In 2020, ABC-CT was renewed for a follow-up study to evaluate long-term stability, sensitivity to change, and longitudinal predictive value in the original cohort. Data collection occurred from May 2021 to August 2022, and a confirmation study was completed in March 2025. A feasibility study was also launched in August of 2024 (49:00). McPartland underscores the importance of increasing inclusivity in neuroscience studies, specifically of autistic people with intellectual disabilities. He presents N170 latency replication studies in this group (50:30). 

Implications for clinical practice

The speaker reiterates the potential impact of ABC-CT as a collaborative effort to develop objective tools that can address the heterogeneity of autism, improve the design and efficiency of clinical trials, and ultimately lead to more personalized and effective treatments for autistic individuals. He reiterates that rigorous study of biomarkers like the N170 holds immense implications for improving clinical understanding and care for autistic individuals via subgrouping, measurements of treatment effectiveness, earlier identification, and enhanced clinical trials (54:00). The presenter asserts that a biomarker’s utility is a “moving target,” evaluated for its purpose in a particular situation. The ongoing research into the N170 and other biomarkers represents a critical step towards a future where objective biological measures significantly enhance clinical understanding and intervention for autism. McPartland provides thanks and acknowledgments before the Q&A (55:02). 

Originally published November 4, 2024

Laurie A. Vismara, Ph.D., BCBA-D, R.B.A. (Ont.) details caregiver strategies for building infant social interaction. She introduces the Infant Start Manual, an extension of the Early Start Denver Model. The speaker outlines contemporary research on early-in-life autism support strategies and underscores the need for collaborative, family-centered support tactics for parents and caregivers. Vismara details six (6) infant learning characteristics, how they present, and what caregivers can do to foster these social communication skills during infancy.

Handouts are online HERE (.zip)

In this webinar: 

1:30 – Introduction
6:10 – Infant Start Manual
11:40 – Research findings
17:55 – Infant learning characteristics
20:25 – Attentional flexibility
27:10 – Object use
33:00 – Prelinguistic development
36:55 – Combined, pragmatic communication
42:25 – Speech
47:30 – Social attention and engagement
54:20 – Tips for baby learning
58:00 – Resources

Introduction

Vismara introduces the Infant Start Manual, an extension of the Early Start Denver Model that focuses on infant social interactions (1:30). She highlights the need for innovative, flexible approaches to support strategies that match the needs, goals, and priorities of parents and caregivers. The speaker outlines specific language used in the talk and notes presentation learning objectives (4:20). The vision of the Infant Start Manual is to facilitate families in their caregiving journey with interactive early-in-life topics aimed at infant social communication vulnerabilities (6:10). The manual provides supports through family-centered coaching and does not substitute for a diagnosis, fix, or cure. The Infant Start Model can be accessed freely at esdmonline.com/about in the Encouraging Infant Communication and Play Manual (Rogers & Vismara). Free access is for personal use only and should not be posted online. 

What the research says

Research on the efficacy of early-in-life autism supports is still emerging. Studies show mixed results across high (10-15 hrs/week) and low-intensity (0.5 – 3 hrs/week) supports delivered by parents and clinicians. All studies aim to teach caregiving skills for social and joint engagement through play and other activities. Results show improvements in caregiving domains, infant proximal behaviors, and interactive styles for caregivers. In contrast, infant autism domains show minimal changes (13:00). One pilot study of the Infant Start has been published. Overall, caregivers shared positive perspectives on content and delivery techniques, and infant vulnerability around social characteristics diminished over time compared to families that did not participate (11:40). 

Vismara posits that the cumulative benefits of small initial changes in interaction become more apparent over time, meaning the full impact of these interventions cannot yet be measured. However, these studies provide an opportunity to understand the acceptability of these support mechanisms from diverse caregiver perspectives, which will allow for the creation of individualized, culturally aligned support systems (15:00). 

Autism Treatment in the First Year of Life: A Pilot Study of Infant Start, a Parent-Implemented Intervention for Symptomatic Infants (Rogers et al., 2014)

Autistic and autism community perspectives on infant and family support in the first two years of life: Findings from a community consultation survey (Bent et al., 2024)

A Developmental Social Neuroscience Perspective on Infant Autism Interventions (Dawson et al., 2023)

Feasibility and acceptability of a caregiver-mediated early support program, delivered online, for infants at elevated familial likelihood for autism: A feasibility randomized controlled trial (Meera et al., 2024)

The Earlier, the Better? An In-Depth Interview Study on the Ethics of Early Detection with Parents of Children at an Elevated Likelihood for Autism (Jan-Vanaken et al., 2023)

Preemptive interventions for infants and toddlers with a high likelihood for autism: A systematic review and meta-analysis (Hampton et al., 2021)

Efficacy of very early interventions on neurodevelopmental outcomes for infants and toddlers at increased likelihood of or diagnosed with autism: A systematic review and meta-analysis (McGlade et al., 2023)

Parent-Mediated Interventions for Infants under 24 Months at Risk for Autism Spectrum Disorder: A Systematic Review of Randomized Controlled Trials (Law et al., 2022)

Infant learning characteristics

The presenter emphasizes that babies come ready to explore, learn, and respond just as caregivers have intuitive skills to nurture, love, and protect. Both shape and influence the others’ actions, resulting in a communication “dance” of synchronized rhythmic steps and movements. No matter who is leading, both sides must be aware of the other and sensitive to each other’s movements and what they mean (17:55). When there is a “misstep,” the dance may stop, and working to regain that attention and engagement is where the Early Start Manual comes in. Vismara outlines six (6) infant learning characteristics, how they present in infants, and how to strengthen caregiver-baby social interactions. 

Attentional flexibility

Attentional flexibility refers to babies’ ability to pay attention to people, objects, and experiences that they find interesting, even when there are distractions. Babies do this when they pick up and explore objects, move from one toy to the next, watch people doing things, or shift focus when something new is introduced (20:25). Attentional flexibility helps babies gather information, build on their learning experiences, and filter out distractions to stay on task.

The speaker provides play recommendations for supporting attentional flexibility (23:15). She suggests offering toys or materials that encourage exploration and interactive play. Holding pieces to your face and naming or commenting about them before you give them to the baby can help them orient your voice to your face. She suggests helping babies complete goals, adding playful sounds, hand gestures, or body movements to help call attention back to you, and giving babies choices for which objects they want to play with (25:15). 

Object use

Babies are naturally curious about objects, and playing with them allows them to explore, try out new behaviors, express ideas, and receive feedback. Vismara explains that babies develop play through patterns of repeated actions, called schemas, which are constantly changing and overlapping (27:10). During play, she suggests taking turns with the same object(s) to create a back-and-forth dance (baby does, you do – repeat) which encourages skill adoption and lets babies know that you are part of the activity/conversation. It is also helpful to be face-to-face during play, to set up toys and spaces in visually clear ways that are not over-stimulating, and to follow babies’ play before leading with your own (29:10). The speaker provides a resource table with play schema definitions and ideas (31:20).

Prelinguistic development

Vismara explains that baby communication begins with crying, which lets caregivers know the baby is hungry, uncomfortable, or upset. This quickly develops into “talking bodies” communication, where babies use their facial expressions, bodies, actions, and gestures to communicate in many different ways (33:00). We chat back to babies when we name and describe things, hold or point to things, copy sounds and expressions, and add silly sounds, hand gestures, or movements to play routines.

To support prelinguistic development, the speaker again suggests playing face-to-face with the baby and talking about everything. Describe what you are doing as you go through routines and make your own “talking body” stand out to babies with playful sounds and exaggerated facial expressions and movements (34:15). It is important to give babies time to respond and for you to put words to their communication methods (35:35). The presenter shows a resource table with activity ideas for strengthening talking bodies.

 Combined, pragmatic communication

Bundling communication behaviors is a critical step in the infant communication journey. Combining face, body, and voice makes communication more efficient and complex, allowing for information retention, skill expansion, and relationship building. Caregivers naturally encourage bundling when we model back-and-forth conversations with babies. For example, when a baby smiles and makes a sound, a caregiver can comment on what excited them (36:55). Vismara suggests giving babies time to think of what they want to say and going with the communication methods that the baby is naturally using. The speaker suggests starting with any two bundles (voice and body, face and voice, etc.) and being flexible, remembering you can always try again later. She warns against holding out for specific communication bundles and underscores working with the babies’ strengths (39:30). The speaker provides another resource table on encouraging bundling through daily routines. 

Speech

Baby babbling is a universal aspect of early speech development that unfolds across several stages and serves as a stepping stone to first words. Babbling is a continuous string of sounds that express babies’ emotions and desire to talk. They often mimic words and language qualities such as tone, pitch, and accent (42:25). To support speech development, Vismara suggests talking to babies and then pausing for them to listen and respond. It’s essential to interpret the intent of what babies say and to keep the conversation going as long as they wish to babble. Helpful tips include mouthing games, imitating baby, and using props to encourage sounds (44:45). The presenter shares a resource table with activities that encourage baby sounds and potential props that you can use to promote conversations. 

Social attention and engagement

Human beings are born with the need and desire to connect with the people around us. We can see this social-emotional connection through babies’ visual attention to people, desire for attention, and drive to start interactions (47:30). Early signs of the social-emotional journey include turning toward familiar faces, turning away when overwhelmed, following faces or objects, and responding to people in their environment. Caregivers can foster this bonding by pausing an activity to cue a response from the baby. For example, hold out a toy, show the play action, and wait for baby to say, “I like this, do it again” (reach, look, smile, move body, make sound) (50:00). 

The speaker reminds viewers to respect babies’ preferences and that it may take some detective work to find their comfort zone inside activities. It helps to repeat your actions when babies are watching and read their cues for what makes them smile. Tips for finding their comfort zone include changing movement pace, adjusting voice and animation, and moving closer or backing up until they feel comfortable. Once you find their smile, create a predictable routine for setting up the fun and pausing for a reaction from the baby before continuing the action (51:15). 

Tips for baby learning

The presenter reminds viewers to build on babies’ interests and experiences with other ideas only after you can follow their play patterns. Building on their preferences can include showing different ways to play with their favorite toys, increasing interaction, and introducing new toys and songs they may like. She notes that not all ideas are winners on the first try but that bridging their interests with new ideas will create understanding and participation through which their skills can expand. Vismara underscores the importance of active play and urges caregivers to expand their involvement in baby interactions (54:20). 

The speaker reiterates that the Infant Start Model is about supporting families with programs and resources that build off of dynamic, authentic early learning experiences. She asserts the need for researchers, families, and clinicians to think deeply about what early autism care looks like and to listen intently to what families are telling us to create collaborative, culturally-informed strategies (55:41). Vismara provides links to manuals, videos, and other resources as well as an Infant Start workshop for professionals (58:55).

Originally posted on October 8, 2024

Megan MacDonald, Ph.D., and Megan McClelland, Ph.D., discuss emerging research on the relationship between motor skill development and executive function in autism. They define motor skills and executive function, discuss their long-term impact on health and academic success, and consider their co-development during childhood and adolescence. The speakers highlight the importance of EF and motor skills in daily life, underscoring their malleability and potential for learning and building new skills. MacDonald and McClelland consider the effect of cultural contexts on skill development before the Q&A session. 

Handouts are online HERE

In this webinar: 

2:45 – Presentation overview
3:20 – Executive function
6:25 – Motor Skills
9:20 – Motor skills and autism
13:30 – Motor skill development
19:00 – Executive function and motor skills
22:20 – Cultural contexts
28:13 – Conclusions
30:20 – Q&A

Executive function

McClelland defines executive function (EF) as cognitive processes/skills like attention, working memory, and inhibitory control that lay the foundation for self-regulation (3:20). She explains that EF skills have emerged as a key indicator for a variety of short and long-term behaviors, including social competence in children and academic success throughout adolescents and early adulthood (4:55). She notes a recent publication that found that children with strong attention/persistence at age four had nearly 50% greater odds of completing university by age 25, underscoring long-term health, educational, and employment outcomes associated with EF (6:10). 

• Self-regulation in childhood as a predictor of future outcomes: A meta-analytic review. (Robson et al., 2020)
• Predictors of early growth in academic achievement: the head-toes-knees-shoulders task (McClelland et al., 2013)

Motor skills

MacDonald discusses gross motor skills, highlighting their importance for everyday activities like walking, running, lifting, and throwing. Both EF and motor skills are foundational for learning and early development, and children begin to have a core set of motor skills between seven and eleven years old (6:25). The speaker relates motor skills to a mountain of motor development, where each level builds on top of the last (8:15). Fine motor skills involve coordinating perceptions with motor movements (visuomotor integration), allowing us to control handwriting, buttoning shirts, drawing, and coloring in the lines. Visuomotor integration has also been linked to children’s academic success (8:25). 

Motor skills in autism

The presenter briefly reviews key autistic characteristics and prevalence rates, highlighting the role of gross motor skills in social and physical development during childhood (9:20). She cites references to motor skill deficits dating back to the first descriptions of autism. MacDonald explains that early delays in motor development milestones (i.e., walking and crawling) are common in autistic children and that parents/caregivers may start noticing delays between fourteen and twenty-four months (10:50). Motor skills used by school-aged children are different and center around social interactions and sports (12:48). 

• Developing together: The role of executive function and motor skills in children’s early academic lives. (McClelland & Cameron, 2019)
• Fine motor skills and early comprehension of the world: two new school readiness indicators (Grissmer et al., 2010)
• Fine Motor Skills and Executive Function Both Contribute to Kindergarten Achievement (Cameron et al., 2012)

Motor skill development

MacDonald outlines one of her publications on gross motor development across the first three years of life in autistic children (13:30). Gross motor skill development for autistic children showed consistent delays at each check-up, with children being nearly one year behind expected development targets by age three (14:15). Similarly findings for fine motor skill development in autistic children was slightly more than one year behind expected development by age three (15:15). Another study on school-aged children found that kids between ten and twelve years old were chronologically about half their age in terms of motor skill development (16:05). Therefore, the speaker asserts that gross and fine motor skill development in autism is delayed in autism from early life at least through childhood. 

Despite these delays, the speaker remains hopeful and iterates to viewers that motor skills are incredibly malleable and can be taught and improved with time (17:15). McClelland cites accumulating research suggesting that EF skills are a vital buffer/protective factor for children experiencing early risk factors. She reiterates that EF and motor skills are highly malleable and that we can do a lot to improve these aspects of development in autistic children (18:00). 

• Motor skills of toddlers with autism spectrum disorders (Lloyd et al., 2013)
• Fundamental Movement Skills and Autism Spectrum Disorders (Staples & Reid, 2010)
• School Readiness and Self-Regulation: A Developmental Psychobiological Approach (Blair & Raver, 2015)
• Relations between early family risk, children’s behavioral regulation, and academic achievement (Sektnan et al., 2010)
• Developing together: The role of executive function and motor skills in children’s early academic lives. (McClelland & Cameron, 2019)

Executive function and motor skills

McClelland discusses contemporary evidence suggesting the co-development of EF and motor skills in young children. She explains that some brain regions are involved in both EF and motor skill development, and certain EF traits (e.g., memory and inhibitory control) are related to fine motor skills (19:00). McDonald notes that, until very recently, motor skills and EF were studied independently. She highlights contemporary collaborative research and its importance in understanding autism (20:50). 

• Behavioral self-regulation and executive function both predict visuomotor skills and early academic achievement (Becker et al., 2014)
• Visuomotor integration and inhibitory control compensate for each other in school readiness (Cameron et al., 2015)
• Exercise improves executive function and achievement and alters brain activation in overweight children: A randomized, controlled trial (Davis et al., 2010)
• Prefrontal cortex development and development of cognitive functions (Diamond, 2000)
• Relations of Preschoolers’ Visual-Motor and Object Manipulation Skills With Executive Function and Social Behavior (MacDonald et al., 2016)

Cultural contexts

McDonald states that fine motor skills are associated with cognitive flexibility but that much of the research is based in the U.S. or other Western countries (22:20). A recent study found that Chinese children exhibit better fine motor skill performance while U.S. students have better object control skills (23:00). Similarly, gross motor skills of pre-schoolers in China were significantly lower than pre-schoolers in the U.S. Such findings, the speaker asserts, suggest that different cultures emphasize different skill sets in early life (fine vs gross) (24:15). 

The speaker outlines a recent survey study on EF and motor skills in autistic children from Taiwan and the U.S. (24:45). She outlines data collection and analysis methods, including regressions and covariates (26:10). Results show that motor skills are associated with EF across countries. Further, the relationship between EF and motor skills is culturally comparable, with significant and positive correlations of magnitude in both countries. She asserts that this preliminary evidence suggests the relationship between EF and motor skills is not specific to region or culture (27:15). 

• Association between motor skills and executive function of children with autism spectrum disorder in Taiwan and the United States (Sung et al., 2024)

Conclusions

McClellan reviews the presentation, highlighting that EF and motor skills contribute to healthy development and academic success for children from diverse backgrounds. She reiterates the malleability of these skills and their importance for daily activities. The speaker considers how improvements in EF may impact motor skills and vice versa (28:13). She gives thanks and acknowledgments before the Q&A, where the presenters discuss the impact of COVID on EF, intervention options, monitoring EF supports in daily life, the importance of routine and planning, and much more (30:20). 

Originally published on May 7, 2024

Dr. Jennifer Frankovich reviews what we know about the underlying mechanisms, trajectories, and symptoms of Pediatric Acute Neuropsychiatric Syndrome (PANS). She discusses the role of the Basal Ganglia in PANS symptoms and cites contemporary research that highlights this connection. Frankovich touches on the disruption of the blood-brain barrier and auto-antibody regulation in PANS. 

Playback of Dr. Mondal’s presentation and Dr. Hussein’s presentation will be available at a future date.

In this webinar:

1:30 – PANS/PANDAS overview
3:10 – Underlying mechanisms
5:00 – Common symptoms
7:55 – PANS trajectories
9:35 – Basal Ganglia
13:12 – Antibodies and the blood-brain barrier

PANS/PANDAS

Frankovich outlines Pediatric Acute Neuropsychiatric Syndrome (PANS) and Pediatric Acute Neuropsychiatric Disorders Associated with Strep (PANDAS), underscoring the different triggers attributed to both conditions (1:30). Classification criteria for both PANS and PANDAS include a sudden onset of obsessive-compulsive disorder or eating restrictions along with at least two other co-occurring conditions like anxiety, sensory dysregulation, motor abnormalities, developmental regression, and deterioration of cognitive functioning (2:15). 

Underlying mechanisms

Children with PANS-related symptoms often have some form of immune predisposition. The speaker explains that PANS occurs after a person gets an infection, which, due to predisposition, causes a systemic inflammatory response. She notes that these inflammatory responses may lead to Basal Ganglia inflammation, altered neuronal signaling, microbial activation, and more (3:10). 

Common symptoms

The presenter describes classic PANS experiences, such as swelling in the knees, hips, and heel bones, back pain and inflammation, and evidence of psoriasis. She explains that children who experience their first case between the ages of five and ten will likely have arthritis by the time they are 14 (5:00). Frankovich highlights our bodies’ abilities to self-regulate inflammation, noting that in many cases, PANS symptoms are resolved on their own (7:15). 

PANS Trajectories

There are generally four different trajectories for PANS:

• Relapsing and remitting – returning to the same baseline
• Relapsing and remitting – worsening baseline across time
• Primary persistent – no return to baseline, remains in chronic episode
• Secondary persistent – multiple episodes with increasing baseline until it reaches a chronic episode. 

Frankovich asserts that the primary and secondary persistent trajectories are likely more related to autoimmune predispositions than the others. Therefore, she continues, these trajectories require the most intense treatments and assessments (7:55). 

Basal Ganglia

The Basal Ganglia (BG), a group of nuclei located beneath the cerebral cortex, has an inhibitory influence on motor and behavior systems. The speaker notes that inflammation, autoantibodies, and injury can disrupt the BG, affecting movements, mood, emotion, behavior, procedural learning, and cognition (9:35). 

Frankovich briefly presents four brain imaging studies suggesting BG inflammation in PANS. She also discusses three studies indicating that patients experience abnormal movements during REM sleep cycles. These REM movements predict Parkinson’s in adults, making this a critical area of research and care (10:25). 

Other physical signs of BG disruption include specific tongue and mouth movements. For example, a positive glabellar tap reflex is present in children with PANS, though it should disappear after infancy. Other abnormal tongue movements, like milkmaid grip, are discussed (11:55). The speaker notes that between 80 and 92% of patients in her clinic exhibit at least one sign of BG disruption (12:25).

19% of autistic youth also have a positive glabellar tap, and 27% have milkmaid grip tongue movements. The presenter, therefore, asserts that these BG signs are not unique to PANS and should be investigated carefully across groups (12:38). 

Antibodies and the blood-brain barrier

PANS autoantibodies target interneurons and have been found in healthy kids and children with PANDAS. Frankovich explains that if these antibodies are causing problems in the body, it is because they are crossing the blood-brain barrier (BBB) (13:12). She highlights that current research suggests local disruptions to the BBB are associated with PANS symptoms (13:50).

Originally published on June 14, 2024

Dr. Waizbard-Bartov discusses changes in autism symptoms across childhood. She outlines the Autism Phenome Project and study methods for her recent work. The speaker presents findings on the frequency, patterns, and predictors of symptom severity changes across childhood periods in autism. Waizbard-Bartov touches on the intersection of autism symptom severity, assigned sex, and environmental factors. She summarizes presentation findings and considers pathways of future research before the Q&A. 

In this webinar:

1:09 – What is autism
5:00 – Social communication and RRB
12:06 – Autism Phenome Project
16:15 – Study methods
20:15 – Changes in autism symptom severity across childhood
24:30 – Variations in patterns of symptom change
28:05 – Predictors of changes in severity across childhood
30:25 – Sex differences
35:40 – Adaptive function
37:44 – Parental characteristics
39:10 – Co-occurring mental health conditions

What is autism?

Waizbard-Bartov describes autism as a neurodevelopmental condition broadly defined by difficulties with social communication and restrictive, repetitive behaviors (RRB) (1:09). For an autism assessment, social communication differences are subcategorized into social-emotional behavior, atypical nonverbal social behavior, and difficulty creating and maintaining relationships (2:15). RRBs are also subcategorized into stereotyped repetitive speech or actions, excessive adherence to non-functional routines, restricted/fixated interests, and atypical sensory behaviors (4:19). The speaker draws on her time working with preschoolers to illustrate how core characteristics/symptoms of autism range in presentation and severity across individuals and time (8:00). 

Study methods

The presenter outlines the Autism Phenome Project (APP), a longitudinal study of nearly 700 autistic and non-autistic children across five time points from early childhood to early adulthood (12:06). At each time point, the ongoing study assesses blood, MRI, language development, memory and attention, co-occurring conditions, and parental perspectives (14:20). Waizbard-Bartov describes her recent work on autism symptom trajectories across early (ages 3 – 6) and middle childhood (ages 6 – 11.5) (11:25). Her team used APP data for 183 autistic children (30% female-presenting) at three times points: between ages two and three and a half (2 – 3.5 yrs), between ages four and six (4 – 6 yrs), and again between ages nine and twelve (9 – 12 yrs) (16:15). Researchers used the calibrated severity scores from the Autism Diagnostic Observation Schedule (ADOS) to track symptom severity across time and domains (17:50). 

Q1: How common is change in autism symptom severity across childhood?

Researchers divided children into three groups based on observed changes in symptom severity across the first two time points (early childhood; ages 3 – 6). Of these, 54% remained stable, 29% significantly decreased, and 17% increased dramatically in symptom severity across early childhood (20:15). Correlations between behaviors and brain development were also found, where children with increased severity had slower white matter development compared to those with decreased symptom severity (21:43). When assessments were extended to the third time point (middle childhood; around age 11.5), the same three groups were identified, where 49% remained stable, 27% experienced a consistent decrease, and 24% experienced a consistent increase in symptom severity (22:55). Waizbard-Bartov reiterates that about half of children in the study demonstrated changes in severity across early and middle childhood, suggesting that such changes may be expected in autism (24:00). 

Q2: Do patterns of change vary across periods of childhood?

To understand patterns of symptom change across time, the presenter and her team compared early childhood severity changes to those of middle childhood (third time point). Results showed increases in symptom severity are equally as likely to occur during early and middle childhood, while decreases in severity are significantly more likely to occur in early childhood only (24:30). Researchers also found that more than 60% of the sample showed different patterns of change across childhood periods (i.e., decreased in early childhood and stable or increasing during middle childhood) (25:35). Waizbard-Bartov summarizes these findings, asserting that patterns of severity change across periods of childhood in autism (27:15). 

Q3: What predicts directional changes in symptoms across childhood?

To assess predictors of symptom severity changes in autism, Waizbard-Bartov and her team assessed related variables:

Cognitive ability/IQ

Results showed that children with decreased severity in early childhood had a higher IQ at the first two time points and exhibited IQ gains over time (28:45). Comparatively, those with increased severity had lower IQ at both time points that remained stable across time (29:30). The speaker asserts that these findings suggest a strong association between cognitive abilities and symptom severity during early development. 

Sex differences

Researchers found in female-presenting participants, symptom severity is likely to decrease or remain stable. However, for male-presenting participants, increases and decreases in severity are equally likely to occur (30:25). Further, calibrated severity scores revealed that female-presenting individuals show significant severity decreases in total symptoms and, more specifically, in RRB, especially during middle school. Conversely, male-presenting individuals show stable total symptom and RRB scores across childhood (31:15). The presenter discusses sex-compared changes across ADOS items, highlighting the stark trajectory differences between sex groups (33:15).  

Adaptive function

Adaptive functioning is meaningful for everyday life, and all three groups had the same score at age one. However, by age six, those experiencing decreases in severity had significantly higher adaptive functioning than those with increasing severity (35:40). The presenter explains how those with increasing severity did not necessarily lose adaptive function skills but that their rate of progress steadily slowed over time (36:40). 

Parental characteristics

Waizbard-Bartov and her team also found that fathers and mothers of children with decreasing severity were generally older and more educated. Contrastingly, parents of children with increasing severity were younger and less educated. The speaker notes the intersectionality of education and socioeconomic status and its impact on resource accessibility and self-advocacy (37:44). 

Co-occurring mental health conditions

Results showed a significant correlation between aspects of mental health and autism symptom domains (39:10). For example, 21% of participants had significant increases in both the severity of social communication issues and anxiety as they entered elementary school. ADHD levels also rose across middle school, and by age eleven, 84% of participants met the clinical requirements for an anxiety disorder (41:25). In female-presenting participants, improvements in RRB overtime ran parallel to increases in anxiety, where 94% had clinical anxiety disorders by age eleven (44:26). Waizbard-Bartov and her team found no evidence that initial symptom severity can predict changes across childhood, meaning everyone has the same potential for change (45:05). The presenter summarizes the findings for question three, noting that severity changes are correlated with assigned sex, IQ, adaptive functioning, parental characteristics, and mental health conditions (46:45).

• Trajectories of Autism Symptom Severity Change During Early Childhood (Waizbard-Bartov et al., 2021)
• Identifying autism symptom severity trajectories across childhood (Waizbard-Bartov et al., 2022)
• Changes in the severity of autism symptom domains are related to mental health challenges during middle childhood (Waizbard-Bartov et al., 2023)

Conclusion

Waizbard-Bartov summarizes research findings, underscoring that the severity of autism symptoms can change substantially across childhood periods and that patterns of change are not linear. She highlights that a child’s characteristics and environment can predict directional changes and that children have the potential for different severity trajectories regardless of their initial levels (47:55). 

She outlines future research directions, including how severity is affected during adolescence and how interactions between symptom severity and other characteristics play out over time (49:15). The presenter highlights current research around the impact of sex on symptom severity, underscoring the potential effects of camouflaging specific to female-presenting individuals (51:10). The speaker provides thanks and acknowledgments before the Q&A (53:25). 

Originally published on May 15, 2024

Tyler McFayden, Ph.D., considers the intersection of white matter development and language in autism. She discusses language modality and considers how white matter may help predict autism diagnosis and inform interventions. The speaker details an ongoing longitudinal investigation into the relationship between white matter and language development across diagnosis-likelihood groups. Preliminary results suggest a unique relationship between language and white matter in high-likelihood groups that is evident starting between 6 and 12 months postnatal. McFayden discusses study results and considers their implications in autism diagnosis, interventions, and future research. 

Handouts are online HERE 

In this webinar:

1:00 – Introduction, thanks, disclaimers, and QR codes
2:25 – Language, background, and study approach
5:00 – Language development in siblings of autistic children
8:09 – White matter in autism and language
13:30 – Study: White matter and language development across time
15:00 – Methods and analysis
19:35 – Significant relationships
23:15 – Differences in expressive and receptive languages
25:40 – Summary and discussion
30:45 – Future directions
34:12 – Q &A

Language modality and development

Studying language development in individuals who do not communicate verbally can help us understand how language modality (the way language is conveyed) relates to the brain and vice versa (4:15). Language delays are one of the earliest emerging endophenotypes (biomarker subsets) of autism. McFayden explains that standard models of language development employ the “wait and see” method, which is strictly behavior-based. Sometimes, practitioners wait too long and miss opportunities for the most effective early interventions (5:36). By investigating non-verbal language modality across time, the speaker continues, we can discover where language phenotypes begin to diverge from the neurotypical population and, therefore, find the most effective timing for early interventions (6:00). 

Siblings of autistic children (Sib-ASD) are 3 to 4 times more likely to experience language delays than siblings of non-autistic children. Similarly, 20 – 30% of Sib-ASD receive an autism diagnosis (7:00). Therefore, younger siblings of children with autism are considered a high likelihood (HL) group. In McFayden’s ongoing study, her team observes white matter development across time for groups at high likelihood (HL) and low likelihood (LL) for autism diagnosis (2:25). The speaker emphasizes that white matter measurements allow a mechanistic approach to language development, which may be visible earlier than behavioral presentations (7:25). 

White matter

McFayden describes white matter as the portion of the brain that is myelinated or enclosed in myelin. This fatty lipid-protein gives white matter its color (8:09). These myeline sheaths act as electrical insulators that support signal transduction in the brain and, as such, the formation of functional circuits. Therefore, white matter is critical to understanding how information is communicated throughout the brain (8:25). White matter is measured using an MRI to record fractional anisotropy (FA), an index of the rapid maturation of fiber pathways in the brain. Higher FA relates to increased age, myelination, and fiber maturation (9:15). 

White matter in autism

One of the earliest differences documented in autism is atypical organization of white matter, in some cases evident by six months post-natal (10:30). Behavioral implications of white matter organization discussed in the literature include social cognition, behavioral control, repetitive behaviors, and sensory interests. However, no work to date has mapped white matter development to language despite these occurring at similar times (11:40). 

White matter in language

White matter has been linked to expressive and receptive language in the neurotypical population, specifically in the arcuate fasciculus and corpus callosum. Generally, higher axon insulation (myelination) corresponds to higher language scores (12:10). McFayden reiterates the importance of evaluating the relationship between white matter and language to understand the pathophysiology and inform effective and timely language interventions for autism (13:00). 

Study: White matter and language development over time

The speaker outlines her teams’ ongoing study to evaluate longitudinal relationships between white matter microstructure in infants at HL (high likelihood) and LL (low likelihood) for autism at 6, 12, and 24 months. Specifically, they aim to answer two questions (13:30): 

• Which white matter structures are related to expressive and receptive language in the first two years postnatal?
• Do relationships between white matter and language over time differ as a function of the likelihood group? If so, when?

Data and analysis

Study data are gathered from the Infant Brain Imaging Study (IBIS), an ongoing (15 years and counting) investigation that follows younger siblings of autistic and neurotypical children at 6, 12, and 24 months (14:00). The speaker and her team separated 461 participants into three groups (15:00):

• HL-ASD: received a diagnosis at 24 months
• HL-Neg: did not receive a diagnosis
• LL-Neg: siblings of neurotypical children 

Researchers used the Mullen Scales of Early Learning to derive values for expressive and receptive language (EL/RL). They recorded FA values from nine common brain connection pathways to measure white matter volume (16:15). The speaker outlines analytical methods, highlighting linear mixed-effect modeling, piecewise linear estimates, and autoregressive correlation structure (17:10). White matter and language were considered main effects and used to assess how changes in white matter and language impact certain groups compared to others (18:25). 

Results 

Analysis revealed a unique significant relationship between language and the arcuate fasciculus (19:35). McFayden presents a data plot where participant FA values are plotted against expressive language measures at each time scale. Preliminary findings show a positive relationship between FA and expressive language in the HL-ASD group. This relationship diverges from the HL-Neg group at twelve months and the LL-Neg group at six months (20:53). The relationship between white matter and expressive language is steeply positive in the HL-ASD group compared to HL-Neg and LL-Neg groups (22:47). In a model of predictive language scores based on collected data, the HL-ASD group also breaks away in the third FA percentile (23:15).

Researchers did not find any significant interactions between FA values and receptive language for any group (24:06). However, group trends for receptive language exhibit the same patterns observed in the expressive language plots. The speaker asserts that, although the relationship did not reach statistical significance, the similarities in receptive and expressive language trends compared to white matter warrant further attention (25:00). 

Summary and discussion

McFayden reiterates the significant relationship between white matter and language in autism and notes the differences between expressive and receptive language (25:40). A robust positive relationship between language and white matter volume emerges in autism between six and twelve months postnatal and remains through the two-year mark. She explains that HL-ASD infants with the smallest gains in FA values exhibit the least gains in language over time and overall lowest language scores by 24 months. These findings underscore the importance of appropriately timed interventions (26:40). Because findings were specific to the right arcuate (expressive language), we can conclude that expressive and receptive language (left arcuate) do not have the same tractography (27:40). The speaker mentions evidence suggesting that left acuate lateralization does not happen to the same extent in autism, which could reflect right-dominant language processing (29:12). 

Future directions and key takeaways

Future investigations should use a smaller time window (e.g., 18 months) and larger HL-ASD samples to more fully understand the temporal aspect of this relationship. The speaker also suggests consistently observing brain-behavior relationships through school age (30:45). Finally, HL-ASD groups could be further parsed out according to language trajectory to reveal more subtle differences in the language-white matter endophenotype (31:28). 

McFayden reemphasizes that the development of white matter in infants who later receive an autism diagnosis is linked to lower expressive and receptive language in the first two years of life. These specific language relationships emerge as early as twelve months, which is well before diagnoses are generally made (32:20). The speaker asserts that these findings support the idea that the “wait and see” method is not a good approach. She notes that this is a new field of study and suggests incorporating these data into future observation and diagnosis methods to ensure proper intervention timing (33:00). 

McFayden provides thanks and acknowledgments before the Q&A, where she discusses preterm babies (40:30), ongoing studies and where to sign up (42:00), potential subgroups and language trajectories (47:00), and much more. 

Dr. Rajesh Kana considers the intersection of language comprehension, neuroplasticity, and autism interventions. He discusses contemporary research illustrating distinct patterns in autistic brain activity and underscores the importance of neuroplasticity in everyday life. The speaker examines brain connectivity and its relation to language comprehension and behavior. Kana details an ongoing reading intervention study and analyzes preliminary data that show significant improvements in reading and language comprehension for autistic participants. He provides thanks and acknowledgments before the Q&A.

Handouts are online HERE

In this webinar: 

0:45 – Conflict of interest
1:25 – Overview
2:10 – Introduction
5:30 – Descriptions of language and communication in autism
7:50 – Joint attention
10:30 – Difficult components of reading comprehension
13:35 – Brain, language & reading comprehension in autism
15:30 – Brain activity in autism
21:05 – Theory of mind and language comprehension
23:26 – Targeting brain plasticity in autism
25:05 – History and significance of neuroplasticity
30:25 – Neuroplasticity and reading comprehension
35:18 – Using reading intervention to change the brain
39:55 – Ongoing study: imagery reading intervention for language comprehension in autism
41:58 – Preliminary data trends
46:30 – Brain level changes and connectivity
51:40 – Summary and acknowledgments
54:05 – Q&A

Descriptions of language and communication in autism

Kana describes language and communication difficulties in autism and how they translate into social differences in everyday life (2:55). Descriptors of autistic communication within the literature include early language delays, atypical features (e.g., echolalia and jargon), failure to acquire spoken language across the lifespan, and discourse processing difficulties (5:30). The speaker lists conditions that commonly co-occur with language processing difficulties, emphasizing that these conditions often exacerbate obstacles to language comprehension (6:50). 

The presenter outlines a study that found that word comprehension plays a significant role in joint attention and that joint attention is critical to early language development (7:50). Two other studies suggest autistic children have strong decoding skills (11:30) but low language comprehension (9:15). Many components of reading comprehension, Kana explains, can be difficult for autistic children. Therefore, understanding how co-occurring conditions contribute to language and communication challenges is critical to implementing successful interventions (10:30). 

Brain, language, & reading comprehension in autism

Multiple studies on brain response to auditory word stimulations report higher activation of the right brain hemisphere (Wenicke’s area) in autistic groups compared to non-autistic controls (13:50) These findings, Kana continues, are supported by a recent meta-analysis, which also found lower activity in the left brain hemisphere (Broca’s area) of autistic participants compared to their non-autistic counterparts (18:10). The speaker asserts that these data show a distinct pattern of brain activity in autistic individuals.  

Sentence imagery findings indicate that autistic participants also have higher visual-spatial activation compared to controls, further evidencing that autistic people tend to be visually oriented (19:15). Kana cites a study that showed significant overlap in Theory of Mind and pragmatic (social) language processes between autistic and non-autistic groups. The study also found reduced activity in social cognitive networks in autistic groups compared to controls (21:05). These patterns in autistic brain activity can inform our general understanding and assessments of language and reading comprehension in autism (22:12). 

Targeting neuroplasticity in autism

The speaker uses the invention and development of reading to illustrate the human brain’s plasticity and capacity to learn (22:37). He reviews early brain plasticity studies (25:05) and explains how the discovery that new neurons can appear in the adult brain massively shifted our understanding of neuroplasticity (27:40). Kana describes differences in the brain structures of London taxi drivers (28:44) and meditative monks (29:50), highlighting the importance of brain plasticity in our everyday lives. Two studies on brain imagery and reading interventions in dyslexic children found changes in reading comprehension (30:25) and increased grey matter volume in participants following reading comprehension interventions (31:49). Kana ponders the creation and adoption of language, underlining the human brain’s capacity for change and how that can apply to evidence-based interventions (33:05). 

Using reading intervention to change the brain

The presenter describes an ongoing reading comprehension intervention trial, detailing study procedures (35:40), assessment protocol (38:00), and the Visualizing and Verbalizing for Language Comprehension and Thinking (V/V) Intervention Program (39:55). Preliminary data show significant improvement in comprehension skills for autistic participants (41:58) and that social cognition (43:40) and verbal working memory (45:15) may predict language comprehension. 

Kana discusses regional brain interactions (brain connectivity) and their use as a measure of changes in brain activity (47:55). Data from the ongoing reading comprehension study show (to this point) significant increases in local (51:00) and regional (49:00) brain connectivity, specifically between Broca’s and Wernicke’s areas. He notes that changes in connectivity are correlated to behavior function and advances in comprehension (49:40). These preliminary findings, the presenter asserts, provide an exciting peek at the potential of reading interventions in autism. 

The speaker summarizes initial data findings, highlighting the significant increases in comprehension following a reading intervention and how those changes are related to brain connectivity and neuroplasticity (51:40). He reiterates the parameters of the ongoing trial and provides information for interested participants (52:30) before the Q&A (54:05). 

Karen Heffler, MD, takes viewers on a comprehensive exploration of the relationship between early-life screen time exposure and autism risk. She delves into the intricate interplay of genetics, environmental factors, and development outcomes. The presentation highlights critical findings about screen time, social engagement, and autism symptoms. Heffler considers the potential consequences of screen media on young children’s development and discusses promising new intervention studies.

Printable handouts of the slides (pdf) are online HERE

A list of references from the talk (pdf) are online HERE

In this webinar: 

0:00 – Presenter introduction
3:40 – Background and positive developmental predictors
7:30 – Screen time and social experiences
9:20 – Video and television learning (Video deficit)
11:55 – Developmental outcome associated with early TV/Screen media viewing
13:25 – Brain differences in autism
16:48 – Brain plasticity, social development, and screen time
18:34 – Autism risk factors
21:35 – Study 1 – Association of early-life social and digital media experiences with the development of ASD-like symptoms
26:50 – Study strengths and limitations
28:20 – Literature review on early-life screen time and autism association studies
29:56 – Study 2 – Screen media and social intervention in autism: a 6-month pilot study
36:30 – Study strengths and limitations
37:54 – Literature review on intervention studies
39:12 – Case reports of interest
41:46 – Drivers and mechanisms of association of early-life screen time with autism
43:53 – Summary and next steps
45:45 – Q & A

Background

Autism risk is affected by both genetics and modifiable environmental factors such as verbal stimulation, parental responsiveness, and parental involvement in play (3:42). These factors have been linked to positive developmental and social outcomes, emphasizing the importance of early nurturing experiences (5:25). Conversely, Heffler explains, screen media is related to diminished parental responsiveness, hindered language development, and less toy play, all of which may contribute to adverse developmental outcomes (7:30). 

As defined in this presentation, screen time encompasses TV, video, gaming, mobile apps with viewing on tablets or smartphones, and some electronic toys. The presenter highlights that video chatting with family and friends is social and considered differently (20:55). Research indicates that children do not learn well from screens as pre-recorded videos are not socially responsive to the child’s actions. Some developmental outcomes associated with early TV/screen media viewing include language delay, attention problems, executive function difficulties, and disorganization in the white matter of the brain (11:55). Joint attention, or when a child looks back and forth between an adult’s eyes and an object of interest, does predict learning (9:20). 

Brain development and plasticity

The presenter discusses differences in autistic brains and underscores the high correlation between superior auditory and visual processing abilities and autism. She notes, however, that this does not necessarily convey good overall brain function (13:20). Some abilities have been linked to autism symptom severity, and other developmental differences predict whole brain overgrowth and/or autism development (15:16). 

Brain plasticity is how the brain responds to one’s experiences, and brain connections are formed based on those experiences and responses. Social factors like eyes, voices, and smiles naturally react to young children and promote the development of social brain pathways. Contrastingly, non-social factors, like screens and electronics, likely promote highly sensory-oriented brain connectivity due to their lack of natural social features (16:48).  

Risk factors and recommendations

Heffler outlines a study that found early-life social experiences and early-life screen time are two important risk factors for autism. The study found that infants with autism traits who were exposed to both screen time and social training from their parents are less likely to develop autism (18:34). She explains that high-screen viewing is concerning in early life because electronic media distracts the child from people and distracts the parents/caregivers from the child, both of which may directly affect brain connectivity and attention mechanisms in autism. Heffler and her colleagues recommend no screen viewing before 18-24 months of age and no more than one hour of screen time per day through age five (20:20).

Drexel Studies

Study 1: Association of early-life social and digital media experiences with the development of ASD-like symptoms 

Using parental report data from the National Children’s Study, researchers investigated the association between TV/DVD exposure and social experiences on autism symptoms. Findings suggested that higher screen time (4 or more hours per day) correlated with an increased risk of autism symptoms at 12 months and two years of age and that autistic children reported more screen time (21:35). Researchers also found that when parents play with the child less than daily at 12 months, there was an 8.9% increase in autism symptoms. Screen exposure at 12 months was also associated with a 4.2% increase (24:15).

Study 2: A literature review on early-life screentime and autism association studies

Heffler defines the parameters of the literature review and discusses critical findings: Greater daily screentime was associated with autism diagnosis (9 studies), autism symptoms (7 studies), and symptom severity (4 studies). Similarly, earlier first screen-viewing was associated with autism diagnosis and symptoms, and less parent-child interaction was also associated with autism risk and severity (28:20). 

Study 3 – Screen media and social intervention in autism: a 6-month pilot study

In collaboration with Lori Frome, Dr. Heffler developed a parent training program to reduce screen time and enhance social engagement, particularly for young children with autism and high media exposure (29:56). The program was implemented over six months and included instruction on digital media and child development, parent-child interactions, and poor screen learning. Parents received weekly, 1-hour, in-home support and were encouraged to involve children in family routines like laundry and cleaning to support social and learning development (32:05). 

Results showed a significant reduction in screen time, from 5.6 hours per day to five minutes a day, over six months. There was also a 23% reduction in core autism symptoms and a 19% increase in adaptive behavior function (did not quite reach significance) (34:20). Parental stress declined by 37% (large effect size), and positive feedback indicated awareness and improvements in social and developmental outcomes. Heffler notes that changes in child behavior began almost immediately, which made it easier for parents to cut screen time and interact with their children (36:00). 

Study 4: A literature review on intervention studies

This review considered six studies, five of which included parent training on social engagement and screen time reduction; these five studies pointed to a correlation between reducing screen time to less than one hour per day and a statistically significant decrease in autism symptoms. Similarly, more than one hour of screen time per day negatively affected therapeutic outcomes. Overall, improvement correlated with screen time reduction, parent stress was significantly improved, and in one study, EEG patterns also improved (37:54). 

The speaker discusses the strengths and limitations of each study.

Case reports and summary

The speaker outlines three case reports that further demonstrate the positive impact of minimizing screentime during early development, including one where autism symptoms entirely resolved in 4 months after eliminating screens (39:12). Heffler reiterates the potential impact of reduced screentime on autism symptom development and lists driving factors and mechanisms of this association (41:46). The speaker highlights the difference between association and causation, noting that all studies presented show associations. 

She summarizes the presentation by reiterating the main findings and suggestions:

• More screen time in the first year of life is associated with the development of autism and autism symptoms. 
• Early-life parent-child social engagement is associated with a decreased risk of autism development.
• Interventions, including screen time reduction and parental support for social engagement, are associated with a rapid decrease in autism symptoms in children with high screen viewing.

Researchers assert the need for randomized controlled trials, measures of gene-environment interactions, and community-based parent education studies in the future (44:58). Heffler also urges viewers to help raise awareness of these findings among parents of young children and across healthcare providers, government agencies, and everyone in between. She provides thanks before the Q&A (45:45). 

Giacomo Vivanti, Ph.D., dives into the complexities of establishing evidence-based autism interventions. He describes the ever-changing state of intervention literature, noting that autistic individuals still experience health inequalities. The speaker discusses four aspects of the interplay between research and practice and details studies demonstrating each assertion. Vivanti calls for universal outcome metrics based on well-being and considers how behavioral therapies can impact core autism symptoms (i.e., communication). The presenter asserts the usefulness of public health frameworks in assessing disparities between research and practice. He summarizes the presentation before the Q&A.

Take the knowledge quiz for this webinar HERE

In this webinar: 

1:15 – Individual evidence-based care
2:50 – Landscape of autism early interventions
4:55 – Health inequities for autistic individuals
6:45 – Study: Early onset dementia in autism
9:00 – Interplay of science and information dissemination
11:40 – Issue 1: The gap between research and practice
13:30 – Study: Treatment fidelity impact on skill acquisition
16:23 – Community implementation
19:00 – Community-partnered participatory research (CPPR)|
20:02 – Issue 2: Disagreements on what counts as evidence
24:15 – Study: Behavior analysts’ knowledge on naturalistic strategies
27:10 – Issue 3: Disagreements on conceptual categories
28:15 – Study: Meta-analysis of ABA treatments
29:55 – U.S. state insurance coverage
32:47 – Issue 4: Disagreement on intervention goals
37:05 – Complexity of well-being and autism symptoms
40:00 – Study: Consensus on target mechanisms
42:53 – Study: Inclusive vs autism-specific classroom learning opportunities
46:05 – Conclusions
48:20 – Q&A

Introduction

More research on autism early interventions has been published in the last three years than over the previous four decades combined (2:50). Vivanti explains that while the quality of early interventions has increased substantially, our knowledge is still rapidly evolving (4:05). Despite this, autistic individuals continue to experience health inequalities that lead to preventable adverse outcomes associated with mental and physical health, self-realization and self-determination, community participation, and educational opportunities (4:55). The presenter cites a study that found the chance of early onset dementia (Alzheimer’s) in autistic individuals is double that of the non-autistic population. He posits that this is due to the continued lack of cognitive, social, and educational support for autistic adults (6:45). Vivanti specifies four complexities of establishing evidence-based interventions:

# 1 – The gap between research and practice

The speaker recalls that his mother was blamed for his brother’s autism diagnosis in the 1990s, even though “cold parenting” had been disproven in the 1960s (9:00). Therefore, he continues, the first main issue in establishing evidence-based interventions is the gap between research and practice (11:40). The speaker explains how intervention procedures in community settings are often different from the procedures used in clinical trials (11:40). Vivanti details a study that assessed the association between outcomes of the Early Start Denver Model (ESDM) and intervention fidelity (adherence) to clinical implementation. Researchers found a lot of implementation variation not only across practitioners but also across patients of each practitioner (13:30). Children with the best outcomes were treated by therapists implementing interventions at higher fidelity (15:35). The presenter considers feasibility and philosophical commitments as reasons for such variations in community implementation (16:23). He underscores that procedures should be flexible to the individuals but rigorous as well. The speaker highlights community-partnered participatory research (CPPR) (19:00). 

# 2 – Disagreements about what counts as evidence

Vivanit explains that practitioners rely on information from different agencies and reviews that analyze the literature. However, each agency/review adheres to its own guidelines and criteria for what qualifies as evidenced-based (e.g., randomized trial vs. single subject design) (20:02). Different agencies therefore come to different conclusions about evidence-based interventions. Such confusion around facts makes clinicians and caregivers hesitant to adopt new interventions, despite documented effectiveness (23:05). The speaker outlines a survey study of 901 board-certified behavior analysts’ knowledge of naturalistic developmental behavioral interventions (NDBI). Although significant evidence supports NDBI efficacy, few participants believed these practices were effective or appropriate (24:15). 

# 3 – Disagreements on conceptual categories

Review agencies generally classify interventions according to conceptual categories like ABA, NDBI, CBT, etc. Vivanti explains that, again, arbitrary criteria for each category result in different recommendations for the evidence-based status of interventions across agencies (27:10). He outlines a meta-analysis of ABA-based interventions that found ABA therapies in various categories in several meta-analyses and reviews (28:15). Most states in the U.S., he continues, have an insurance coverage mandate for autism that specifically mentions ABA. Although providers are often willing to incorporate new evidence-based practices like the ESDM, insurance companies frequently refuse treatments if they are not identified as ABA (29:55). Therefore, many natural-based models are not provided, thus highlighting the extremely practical consequences of conceptual disagreements (31:50). 

# 4 – Disagreements on intervention goals

Vivanti asserts that “any consensus of what to do must be based on a consensus of why we are doing it.” He remembers thinking that the goals for his brother’s interventions ought to be based on safety and support in daily environments and activities (32:47). However, intervention goals are highly debated, which leads patients/users to assume interventions prioritize conformity and compliance at the expense of neurodiversity and individualized care. The speaker asserts that this lack of a universal metric for “successful outcomes” keeps service providers from being able to specifically communicate what they want to achieve (34:00). He underscores using language and concepts based on freedom from distress, community participation, and well-being across the lifetime (37:05). Vivanti and his colleagues tried to generate a consensus on legitimate aspects of effective interventions that are respectful to the diversity and uniqueness of children but also rigorously empower individuals. Researchers found that interventions informed by this model emphasize agency, learning through positive interactions, engagement in novel and familiar schemas, and an interplay of comfort and challenge (40:00). Another study found that autistic children with more social attention had better outcomes in inclusive classrooms compared to autism-specific classrooms (42:53). Such findings underscore the importance of rigorous individualized interventions.

Conclusion

The speaker emphasizes that knowledge of evidence-based interventions is advancing at an unprecedented pace. However, autistic people still experience healthcare differences. This is due to the complex interplay of research and practice. Key factors include disagreements about what counts as evidence, behavioral interventions, and intervention goals. These and other factors impacting implementation and adoption must be scientifically addressed and researched. Vivanti highlights that a public health framework can help examine these factors and contextualize challenges and opportunities associated with autism in the broader context of research and practice (46:05). The speaker provides thanks and resources before the Q&A, where he discusses recommended interventions, various treatment concepts, how behavioral education programs impact autism neural structure, and much more (48:20).