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).